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1

Assumptions to Annual Energy Outlook - Energy Information Administration  

U.S. Energy Information Administration (EIA) Indexed Site

Assumptions to AEO2013 Assumptions to AEO2013 Release Date: May 14, 2013 | Next Release Date: May 2014 | full report Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 2013 [1] (AEO2013), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are the most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports [2]. The National Energy Modeling System Projections in the AEO2013 are generated using the NEMS, developed and maintained by the Office of Energy Analysis of the U.S. Energy Information Administration (EIA). In addition to its use in developing the Annual

2

Assumptions to Annual Energy Outlook - Energy Information Administration  

Gasoline and Diesel Fuel Update (EIA)

Assumptions to AEO2012 Assumptions to AEO2012 Release Date: August 2, 2012 | Next Release Date: August 2013 | Full report Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 2012 [1] (AEO2012), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are the most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports [2]. The National Energy Modeling System The projections in AEO2012 are generated using the NEMS, developed and maintained by the Office of Energy Analysis (OEA) of the U.S. Energy Information Administration (EIA). In addition to its use in developing the

3

Assumptions  

Gasoline and Diesel Fuel Update (EIA)

to the to the Annual Energy Outlook 1998 December 1997 Energy Information Administration Office of Integrated Analysis and Forecasting U.S. Department of Energy Washington, DC 20585 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Macroeconomic Activity Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 International Energy Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Household Expenditures Module. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Residential Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Commercial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Industrial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 Transportation Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Electricity Market Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 Oil and Gas Supply Module

4

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

6 6 Assumptions to the Annual Energy Outlook 2006 This report presents major assumptions of NEMS that are used to generate the projections in the AEO2006. Contents (Complete Report) Download complete Report. Need help, contact the National Energy Information Center at 202-586-8800. Introduction Introduction Section to the Assumptions to the Annual Energy Outlook 2006 Report. Need help, contact the National Energy Information Center at 202-586-8800. Introduction Section to the Assumptions to the Annual Energy Outlook 2006 Report. Need help, contact the National Energy Information Center at 202-586-8800. Macroeconomic Activity Module Macroeconomic Activity Module Section to the Assumptions to the Annual Energy Outlook 2006 Report. Need help, contact the National Energy Information Center at 202-586-8800.

5

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction Assumptions to the Annual Energy Outlook 2006 Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 20061 (AEO2006), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview3, which is updated once every few years. The National Energy Modeling System

6

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Electricity Market Module Electricity Market Module Assumptions to the Annual Energy Outlook 2006 The NEMS Electricity Market Module (EMM) represents the capacity planning, dispatching, and pricing of electricity. It is composed of four submodules—electricity capacity planning, electricity fuel dispatching, load and demand electricity, and electricity finance and pricing. It includes nonutility capacity and generation, and electricity transmission and trade. A detailed description of the EMM is provided in the EIA publication, Electricity Market Module of the National Energy Modeling System 2006, DOE/EIA- M068(2006). Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are assumptions about the operations of the electricity sector and the costs of various options in each of the EMM submodules. This section describes the model parameters and assumptions used in EMM. It includes a discussion of legislation and regulations that are incorporated in EMM as well as information about the climate change action plan. The various electricity and technology cases are also described.

7

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, imports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2006, DOE/EIA-M060(2006) (Washington, DC, 2006). Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, imports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2006, DOE/EIA-M060(2006) (Washington, DC, 2006). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Forty separate supply curves are developed for each of 14 supply regions, nine coal types (unique combinations of thermal grade and sulfur content), and two mine types (underground and surface). Supply curves are constructed using an econometric formulation that relates the minemouth prices of coal for the supply regions and coal types to a set of independent variables. The independent variables include: capacity utilization of mines, mining capacity, labor productivity, the user cost of capital of mining equipment, and the cost of factor inputs (labor and fuel).

8

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module Residential Demand Module Assumptions to the Annual Energy Outlook 2006 Figure 5. United States Census Divisions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Residential Demand Module forecasts future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimate of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the “unit energy consumption” by appliance (or UEC—in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment

9

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Oil and Gas Supply Module Oil and Gas Supply Module Assumptions to the Annual Energy Outlook 2006 Figure 7. Oil and Gas Supply Model Regions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Oil and Gas Supply Module (OGSM) constitutes a comprehensive framework with which to analyze oil and gas supply on a regional basis (Figure 7). A detailed description of the OGSM is provided in the EIA publication, Model Documentation Report: The Oil and Gas Supply Module (OGSM), DOE/EIA-M063(2006), (Washington, DC, 2006). The OGSM provides crude oil and natural gas short-term supply parameters to both the Natural Gas Transmission and Distribution Module and the Petroleum Market Module. The OGSM simulates the activity of numerous firms that produce oil and natural

10

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module (RFM) provides natural resources supply and technology input information for forecasts of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has seven submodules representing various renewable energy sources, biomass, geothermal, conventional hydroelectricity, landfill gas, solar thermal, solar photovoltaics, and wind108. Renewable Fuels Module (RFM) provides natural resources supply and technology input information for forecasts of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has seven submodules representing various renewable energy sources, biomass, geothermal, conventional hydroelectricity, landfill gas, solar thermal, solar photovoltaics, and wind108. Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as water, wind, and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration, from hydroelectric power, which was one of the first electric generation technologies, to newer power systems using biomass, geothermal, LFG, solar, and wind energy. In some cases, they require technological innovation to become cost effective or have inherent characteristics, such as intermittency, which make their penetration into the electricity grid dependent upon new methods for integration within utility system plans or upon the availability of low-cost energy storage systems.

11

Transportation radiological risk assessment for the programmatic environmental impact statement: An overview of methodologies, assumptions, and input parameters  

SciTech Connect (OSTI)

The U.S. Department of Energy is considering a broad range of alternatives for the future configuration of radioactive waste management at its network of facilities. Because the transportation of radioactive waste is an integral component of the management alternatives being considered, the estimated human health risks associated with both routine and accident transportation conditions must be assessed to allow a complete appraisal of the alternatives. This paper provides an overview of the technical approach being used to assess the radiological risks from the transportation of radioactive wastes. The approach presented employs the RADTRAN 4 computer code to estimate the collective population risk during routine and accident transportation conditions. Supplemental analyses are conducted using the RISKIND computer code to address areas of specific concern to individuals or population subgroups. RISKIND is used for estimating routine doses to maximally exposed individuals and for assessing the consequences of the most severe credible transportation accidents. The transportation risk assessment is designed to ensure -- through uniform and judicious selection of models, data, and assumptions -- that relative comparisons of risk among the various alternatives are meaningful. This is accomplished by uniformly applying common input parameters and assumptions to each waste type for all alternatives. The approach presented can be applied to all radioactive waste types and provides a consistent and comprehensive evaluation of transportation-related risk.

Monette, F.; Biwer, B.; LePoire, D.; Chen, S.Y.

1994-02-01T23:59:59.000Z

12

Assumption Parish, Louisiana: Energy Resources | Open Energy Information  

Open Energy Info (EERE)

Assumption Parish, Louisiana: Energy Resources Assumption Parish, Louisiana: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 29.9232544°, -91.09694° Loading map... {"minzoom":false,"mappingservice":"googlemaps3","type":"ROADMAP","zoom":14,"types":["ROADMAP","SATELLITE","HYBRID","TERRAIN"],"geoservice":"google","maxzoom":false,"width":"600px","height":"350px","centre":false,"title":"","label":"","icon":"","visitedicon":"","lines":[],"polygons":[],"circles":[],"rectangles":[],"copycoords":false,"static":false,"wmsoverlay":"","layers":[],"controls":["pan","zoom","type","scale","streetview"],"zoomstyle":"DEFAULT","typestyle":"DEFAULT","autoinfowindows":false,"kml":[],"gkml":[],"fusiontables":[],"resizable":false,"tilt":0,"kmlrezoom":false,"poi":true,"imageoverlays":[],"markercluster":false,"searchmarkers":"","locations":[{"text":"","title":"","link":null,"lat":29.9232544,"lon":-91.09694,"alt":0,"address":"","icon":"","group":"","inlineLabel":"","visitedicon":""}]}

13

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Industrial Demand Module Industrial Demand Module Assumptions to the Annual Energy Outlook 2006 The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 12 manufacturing and 6 nonmanufacturing industries. The manufacturing industries are further subdivided into the energy-intensive manufacturing industries and nonenergy-intensive manufacturing industries. The manufacturing industries are modeled through the use of a detailed process flow or end use accounting procedure, whereas the nonmanufacturing industries are modeled with substantially less detail (Table 17). The Industrial Demand Module forecasts energy consumption at the four Census region level (see Figure 5); energy consumption at the Census Division level is estimated by allocating the Census region forecast using the SEDS27 data.

14

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

International Energy Module International Energy Module Assumptions to the Annual Energy Outlook 2006 The International Energy Module determines changes in the world oil price and the supply prices of crude oils and petroleum products for import to the United States in response to changes in U.S. import requirements. A market clearing method is used to determine the price at which worldwide demand for oil is equal to the worldwide supply. The module determines new values for oil production and demand for regions outside the United States, along with a new world oil price that balances supply and demand in the international oil market. A detailed description of the International Energy Module is provided in the EIA publication, Model Documentation Report: The International Energy Module of the National Energy Modeling System, DOE/EIA-M071(06), (Washington, DC, February 2006).

15

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module Assumptions to the Annual Energy Outlook 2006 The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions (see Figure 5) and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption isthe sum of energy use in eight transport modes: light-duty vehicles (cars and light trucks), commercial light trucks (8,501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), freight and passenger aircraft, freight rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption.

16

Energy Information Administration (EIA) - Assumptions to the Annual Energy  

Gasoline and Diesel Fuel Update (EIA)

Commercial Demand Module Commercial Demand Module Assumptions to the Annual Energy Outlook 2006 The NEMS Commercial Sector Demand Module generates forecasts of commercial sector energy demand through 2030. The definition of the commercial sector is consistent with EIA’s State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial. Since most of commercial energy consumption occurs in buildings, the commercial module relies on the data from the EIA Commercial Buildings Energy Consumption Survey (CBECS) for characterizing the commercial sector activity mix as well as the equipment stock and fuels consumed to provide end use services.14

17

Energy Input Output Calculator | Open Energy Information  

Open Energy Info (EERE)

Input Output Calculator Input Output Calculator Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Energy Input-Output Calculator Agency/Company /Organization: Department of Energy Sector: Energy Focus Area: Energy Efficiency Resource Type: Online calculator User Interface: Website Website: www2.eere.energy.gov/analysis/iocalc/Default.aspx Web Application Link: www2.eere.energy.gov/analysis/iocalc/Default.aspx OpenEI Keyword(s): Energy Efficiency and Renewable Energy (EERE) Tools Language: English References: EERE Energy Input-Output Calculator[1] The Energy Input-Output Calculator (IO Calculator) allows users to estimate the economic development impacts from investments in alternate electricity generating technologies. About the Calculator The Energy Input-Output Calculator (IO Calculator) allows users to estimate

18

V-192: Symantec Security Information Manager Input Validation Flaws Permit  

Broader source: Energy.gov (indexed) [DOE]

92: Symantec Security Information Manager Input Validation Flaws 92: Symantec Security Information Manager Input Validation Flaws Permit Cross-Site Scripting, SQL Injection, and Information Disclosure Attacks V-192: Symantec Security Information Manager Input Validation Flaws Permit Cross-Site Scripting, SQL Injection, and Information Disclosure Attacks July 4, 2013 - 6:00am Addthis PROBLEM: Several vulnerabilities were reported in Symantec Security Information Manager PLATFORM: Symantec Security Information Manager Appliance Version 4.7.x and 4.8.0 ABSTRACT: Symantec was notified of multiple security issues impacting the SSIM management console REFERENCE LINKS: SecurityTracker Alert ID: 1028727 Symantec Security Advisory SYM13-006 CVE-2013-1613 CVE-2013-1614 CVE-2013-1615 IMPACT ASSESSMENT: Medium DISCUSSION: The console does not properly filter HTML code from user-supplied input

19

Residential Sector End-Use Forecasting with EPRI-REEPS 2.1: Summary Input Assumptions and Results  

E-Print Network [OSTI]

Consumption and Expenditures 1992. Energy Information Administration, U.S.92). April. US DOE. 1995c. Residential Energy ConsumptionConsumption and Expenditures 1993. EIA, Energy Information Administration, U.S.

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

20

AEO Assumptions  

Gasoline and Diesel Fuel Update (EIA)

for the for the Annual Energy Outlook 1997 December 1996 Energy Information Administration Office of Integrated Analysis and Forecasting U.S. Department of Energy Washington, DC 20585 Energy Information Administration/Assumptions for the Annual Energy Outlook 1997 Contents Page Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Macroeconomic Activity Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 International Energy Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Household Expenditures Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Residential Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Commercial Demand Module . . . . . . . . . . . . . . . . . .

Note: This page contains sample records for the topic "inform input assumptions" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


21

US Nuclear Regulatory Commission Input to DOE Request for Information Smart  

Broader source: Energy.gov (indexed) [DOE]

US Nuclear Regulatory Commission Input to DOE Request for US Nuclear Regulatory Commission Input to DOE Request for Information Smart Grid Implementation Input US Nuclear Regulatory Commission Input to DOE Request for Information Smart Grid Implementation Input US Nuclear Regulatory Commission Input to DOE Request for Information Smart Grid Implementation Input. Comments relevant to the following two sections of the RFI: "Long Term Issues: Managing a Grid with High Penetration of New Technologies" and "Reliability and Cyber-Security," US Nuclear Regulatory Commission Input to DOE Request for Information Smart Grid Implementation Input More Documents & Publications Comments of DRSG to DOE Smart Grid RFI: Addressing Policy and Logistical Challenges Reply Comments of Entergy Services, Inc. Progress Energy draft regarding Smart Grid RFI: Addressing Policy and

22

Annual Energy Outlook 96 Assumptions  

Gasoline and Diesel Fuel Update (EIA)

for for the Annual Energy Outlook 1996 January 1996 Energy Information Administration Office of Integrated Analysis and Forecasting U.S. Department of Energy Washington, DC 20585 Introduction This paper presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 1996 (AEO96). In this context, assumptions include general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports listed in the Appendix. 1 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview. The National Energy Modeling System The projections

23

Summary of Input to DOE Request for Information DE-FOA-0000225  

Broader source: Energy.gov [DOE]

Presentation on Sumary of Input to DOE Request for Information DE-FOA-0000225 - U.S. DOE Fuel Cells Technology Program

24

OECD Input-Output Tables | Open Energy Information  

Open Energy Info (EERE)

OECD Input-Output Tables OECD Input-Output Tables Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Input-Output Tables Agency/Company /Organization: Organisation for Economic Co-Operation and Development Topics: Co-benefits assessment, Market analysis, Co-benefits assessment, Pathways analysis Resource Type: Dataset Website: www.oecd.org/document/3/0,3343,en_2649_34445_38071427_1_1_1_1,00.html Country: Sweden, Finland, Japan, South Korea, Argentina, Australia, China, Israel, United Kingdom, Portugal, Romania, Greece, Poland, Slovakia, Chile, India, Canada, New Zealand, United States, Denmark, Norway, Spain, Austria, Italy, Netherlands, Ireland, France, Belgium, Brazil, Czech Republic, Estonia, Germany, Hungary, Luxembourg, Mexico, Slovenia, South Africa, Turkey, Indonesia, Switzerland, Taiwan, Russia

25

Time-lag of record inputs to the international nuclear information system bibliographic database  

Science Journals Connector (OSTI)

This paper discusses the timeliness in inputting bibliographical records to international databases with a case study of the international nuclear information system bibliographic database from the inception (1970) to the year 2008. The authors have attempted to calculate the overall and average inputting time-lag of the database. The time-lags of inputting countries and international organisations are analysed separately. The study also tries to identify the nature of inputs that are responsible for this delayed response.

E.R. Prakasan; Nita Bhaskar; K. Bhanumurthy

2011-01-01T23:59:59.000Z

26

Assumptions to the Annual Energy Outlook 2013  

U.S. Energy Information Administration (EIA) Indexed Site

Introduction Introduction This page inTenTionally lefT blank 3 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2013 Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 2013 [1] (AEO2013), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are the most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports [2]. The National Energy Modeling System Projections in the AEO2013 are generated using the NEMS, developed and maintained by the Office of Energy Analysis of the U.S.

27

DEPARTMENT OF ENERGY SOLICITS PUBLIC INPUT TO INFORM DEVELOPMENT OF A  

Broader source: Energy.gov (indexed) [DOE]

DEPARTMENT OF ENERGY SOLICITS PUBLIC INPUT TO INFORM DEVELOPMENT OF DEPARTMENT OF ENERGY SOLICITS PUBLIC INPUT TO INFORM DEVELOPMENT OF A PREFERRED ALTERNATIVE FOR DISPOSAL OF GREATER-THAN-CLASS C WASTE DEPARTMENT OF ENERGY SOLICITS PUBLIC INPUT TO INFORM DEVELOPMENT OF A PREFERRED ALTERNATIVE FOR DISPOSAL OF GREATER-THAN-CLASS C WASTE March 1, 2011 - 12:00pm Addthis During the months of April and May, 2011 the Department of Energy's Office of Environmental Management will be holding nine public hearings on the Draft Environmental Impact Statement (EIS) for the Disposal of Greater-Than-Class C (GTCC) Low-Level Radioactive Waste and GTCC-Like Waste. Hearings will be held at the each of the sites being considered for disposal of GTCC wastes and in Washington, DC. DOE does not have a preferred alternative at this time. These hearings will

28

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

States. States. OGSM encompasses domestic crude oil and natural gas supply by both conventional and nonconventional recovery techniques. Nonconventional recovery includes unconventional gas recovery from low permeability formations of sandstone and shale, and coalbeds. Energy Information Administration/Assumptions to the Annual Energy Outlook 2007 93 Figure 7. Oil and Gas Supply Model Regions Source: Energy Information Administration, Office of Integrated Analysis and Forecasting. Report #:DOE/EIA-0554(2007) Release date: April 2007 Next release date: March 2008 Primary inputs for the module are varied. One set of key assumptions concerns estimates of domestic technically recoverable oil and gas resources. Other factors affecting the projection include the assumed

29

Assumptions to the Annual Energy Outlook 2000 - Household Expenditures  

Gasoline and Diesel Fuel Update (EIA)

Key Assumptions Key Assumptions The historical input data used to develop the HEM version for the AEO2000 consists of recent household survey responses, aggregated to the desired level of detail. Two surveys performed by the Energy Information Administration are included in the AEO2000 HEM database, and together these input data are used to develop a set of baseline household consumption profiles for the direct fuel expenditure analysis. These surveys are the 1997 Residential Energy Consumption Survey (RECS) and the 1991 Residential Transportation Energy Consumption Survey (RTECS). HEM uses the consumption forecast by NEMS for the residential and transportation sectors as inputs to the disaggregation algorithm that results in the direct fuel expenditure analysis. Household end-use and personal transportation service consumption are obtained by HEM from the NEMS Residential and Transportation Demand Modules. Household disposable income is adjusted with forecasts of total disposable income from the NEMS Macroeconomic Activity Module.

30

Assumptions to the Annual Energy Outlook - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction Assumption to the Annual Energy Outlook Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 20041 (AEO2004), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview3, which is updated once every two years. The National Energy Modeling System The projections in the AEO2004 were produced with the National Energy Modeling System. NEMS is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the midterm time period and perform policy analyses requested by decisionmakers in the U.S. Congress, the Administration, including DOE Program Offices, and other government agencies.

31

Summary of Input to DOE Request for Information DE-FOA-0000225  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

& & Renewable Energy Summary of Input to DOE Request for Information DE FOA 0000225 DE-FOA-0000225 Greg Kleen Greg Kleen Golden Golden Field Field O Office ffice Golden Golden Field Field Office Office US DOE Fuel Cells T US DOE Fuel Cells Technology echnology Program Program Lakewood, Colorado Lakewood, Colorado March 16, 2010 March 16, 2010 83 ses o 6 o a at o s RFI Summary Release Date: 12/29/2009 Release Date: 12/29/2009 Closing Date: 1/29/2010 Purpose: Obtain Feedback from the Fuel Cell Community Purpose: Obtain Feedback from the Fuel Cell Community Planned Funding Opportunity Announcement Tentative Release: Summer 2010 Tentative Release: Summer 2010 Tentative Awards Made: Fiscal Year 2011 183 responses from 61 org ganizations espo Responses covered: Most fuel cell types and applications

32

Evaluation of Indian input to the international nuclear information system database  

Science Journals Connector (OSTI)

The study is aimed at analysing the INIS bibliographic records of publications in India during the period 2000-2008. The analysis includes the inputting trend, time-lag, contributing journals, country collaboration, content analysis through the classification and keywords. India has a total number of 14,697 records input to the database with an yearly average of 1631 records. The timeliness of input is very noteworthy as 29.15% of all articles are input in the same publication year, 52.57% articles are of only one year delay in inputting. Pramana, Journal of Medical Physics, Radiation Protection and Environment are found as the most contributed Indian journals. Scientists from USA, Germany, Japan, etc., are the main contributors. Nuclear physics and radiation physics, specific nuclear reactors and associated plants, particle accelerators, inorganic, organic, physical and analytical chemistry, etc., are main areas of the Indian input.

Anil Kumar; E.R. Prakasan; Sandeep Kadam; Nita Bhaskar

2011-01-01T23:59:59.000Z

33

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 20031 (AEO2003), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview.3 The National Energy Modeling System The projections in the AEO2003 were produced with the National Energy Modeling System. NEMS is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the midterm time period and perform policy analyses requested by decisionmakers and analysts in the U.S. Congress, the Department of Energy’s Office of Policy and International Affairs, other DOE offices, and other government agencies.

34

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Household Expenditures Module Household Expenditures Module The Household Expenditures Module (HEM) constructs household energy expenditure profiles using historical survey data on household income, population and demographic characteristics, and consumption and expenditures for fuels for various end-uses. These data are combined with NEMS forecasts of household disposable income, fuel consumption, and fuel expenditures by end-use and household type. The HEM disaggregation algorithm uses these combined results to forecast household fuel consumption and expenditures by income quintile and Census Division. Key Assumptions The historical input data used to develop the HEM version for the AEO2003 consists of recent household survey responses, aggregated to the desired level of detail. Two surveys performed by the Energy Information Administration are included in the AEO2003 HEM database, and together these input data are used to develop a set of baseline household consumption profiles for the direct fuel expenditure analysis. These surveys are the 1997 Residential Energy Consumption Survey (RECS) and the 1991 Residential Transportation Energy Consumption Survey (RTECS).

35

EIA - Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

7 7 Assumptions to the Annual Energy Outlook 2007 This report summarizes the major assumptions used in the NEMS to generate the AEO2007 projections. Contents (Complete Report) Download complete Report. Need help, contact the National Energy Information Center at 202-586-8800. Introduction Introduction Section to the Assumptions to the Annual Energy Outlook 2007 Report. Need help, contact the National Energy Information Center at 202-586-8800. Introduction Section to the Assumptions to the Annual Energy Outlook 2007 Report. Need help, contact the National Energy Information Center at 202-586-8800. Macroeconomic Activity Module Macroeconomic Activity Module Section to the Assumptions to the Annual Energy Outlook 2007 Report. Need help, contact the National Energy Information Center at 202-586-8800.

36

Assumptions to the Annual Energy Outlook - Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module Assumption to the Annual Energy Outlook Petroleum Market Module Figure 8. Petroleum Administration for Defense Districts. Having problems, call our National Energy Information Center at 202-586-8800 for help. The NEMS Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohols, ethers, and bioesters natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. The PMM contains a linear programming representation of U.S. refining

37

Key Assumptions Policy Issues  

E-Print Network [OSTI]

11/13/2014 1 Key Assumptions and Policy Issues RAAC Steering Committee November 17, 2014 Portland Supply Limitations 8 Withi h B l i8. Within-hour Balancing 9. Capacity and Energy Values for Wind/Solar t b it d d li d· Thermal: must be sited and licensed · Wind/solar: must be sited and licensed · EE

38

Assumptions to the Annual Energy Outlook 2001 - Household Expenditures  

Gasoline and Diesel Fuel Update (EIA)

Completed Copy in PDF Format Completed Copy in PDF Format Related Links Annual Energy Outlook2001 Supplemental Data to the AEO2001 NEMS Conference To Forecasting Home Page EIA Homepage Household Expenditures Module Key Assumptions The historical input data used to develop the HEM version for the AEO2001 consists of recent household survey responses, aggregated to the desired level of detail. Two surveys performed by the Energy Information Administration are included in the AEO2001 HEM database, and together these input data are used to develop a set of baseline household consumption profiles for the direct fuel expenditure analysis. These surveys are the 1997 Residential Energy Consumption Survey (RECS) and the 1991 Residential Transportation Energy Consumption Survey (RTECS). HEM uses the consumption forecast by NEMS for the residential and

39

Summary of Input to DOE Request for Information DE-PS36-08GO38002 (Presentation)  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Input to Input to DOE Request for Information DE-PS36-08GO38002 David Peterson Golden Field Office Department of Energy Fuel Cell Pre-Solicitation Workshop January 23, 2008 2 Purpose * Release date: 11/20/07; Close date: 1/14/08 * Obtain feedback from the fuel cell community. * Planned Funding Opportunity Announcement for RD&D of fuel cell technologies for automotive, stationary, portable power and early market applications * Tentative Release: Spring '08 * Tentative Awards Made: FY09 3 Response Areas * Technical topic areas * Catalysts (durability and reduced loading) and supports. * Catalyst layer. * Water management. * Membrane electrode assembly (MEA) optimization. * Accelerated durability testing. * Balance of plant component development. * Impurity effects.

40

EIA - Assumptions to the Annual Energy Outlook 2010 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction Assumptions to the Annual Energy Outlook 2010 Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 2010 [1] (AEO2010), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are the most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports [2]. The National Energy Modeling System The projections in the AEO2010 were produced with the NEMS, which is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the long term and perform policy analyses requested by decisionmakers in the White House, U.S. Congress, offices within the Department of Energy, including DOE Program Offices, and other government agencies. The Annual Energy Outlook (AEO) projections are also used by analysts and planners in other government agencies and outside organizations.

Note: This page contains sample records for the topic "inform input assumptions" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


41

EIA - Assumptions to the Annual Energy Outlook 2008 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction Assumptions to the Annual Energy Outlook 2008 Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 20081 (AEO2008), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are the most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 The National Energy Modeling System The projections in the AEO2008 were produced with the NEMS, which is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the long term and perform policy analyses requested by decisionmakers in the White House, U.S. Congress, offices within the Department of Energy, including DOE Program Offices, and other government agencies. The AEO projections are also used by analysts and planners in other government agencies and outside organizations.

42

EIA - Assumptions to the Annual Energy Outlook 2009 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction Assumptions to the Annual Energy Outlook 2009 Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 2009 (AEO2009),1 including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are the most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 The National Energy Modeling System The projections in the AEO2009 were produced with the NEMS, which is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the long term and perform policy analyses requested by decisionmakers in the White House, U.S. Congress, offices within the Department of Energy, including DOE Program Offices, and other government agencies. The Annual Energy Outlook (AEO) projections are also used by analysts and planners in other government agencies and outside organizations.

43

EIA - Assumptions to the Annual Energy Outlook 2008 - Petroleum Market  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module Assumptions to the Annual Energy Outlook 2008 Petroleum Market Module Figure 9. Petroleum Administration for Defense Districts. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Petroleum Market Module (PMM) projects petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, unfinished oil imports, other refinery inputs (including alcohols, ethers, bioesters, corn, biomass, and coal), natural gas plant liquids production, and refinery processing gain. In addition, the PMM projects capacity expansion and fuel consumption at domestic refineries. The PMM contains a linear programming (LP) representation of U.S. refining

44

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

7 7 1 (AEO2007), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are the most significant to formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports. 2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview 3 , which is updated once every few years. The National Energy Modeling System The projections in the AEO2007 were produced with the National Energy Modeling System. NEMS is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the long term and

45

EIA - Assumptions to the Annual Energy Outlook 2009 - Renewable Fuels  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module Renewable Fuels Module Assumptions to the Annual Energy Outlook 2009 Renewable Fuels Module The NEMS Renewable Fuels Module (RFM) provides natural resources supply and technology input information for projections of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has seven submodules representing various renewable energy sources, biomass, geothermal, conventional hydroelectricity, landfill gas, solar thermal, solar photovoltaics, and wind1. Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as water, wind, and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration, from hydroelectric power, which was one of the first electric generation technologies, to newer power systems using biomass, geothermal, LFG, solar, and wind energy.

46

Assumptions to the Annual Energy Outlook 2013  

U.S. Energy Information Administration (EIA) Indexed Site

Assumptions to the Annual Assumptions to the Annual Energy Outlook 2013 May 2013 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of approval by any other officer or employee of the United States Government. The views in this report therefore should not be construed as representing those of the Department of Energy or other Federal agencies. Table of Contents Introduction .................................................................................................................................................. 3

47

Assumptions  

Gasoline and Diesel Fuel Update (EIA)

1 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Macroeconomic Activity Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 International Energy Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Household Expenditures Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Residential Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Commercial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 Industrial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Transportation Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 Electricity Market Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 Oil and Gas Supply Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 Natural Gas Transmission and Distribution Module . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 Petroleum Market Module. . . . . . . . . . . . .

48

Assumptions to the Annual Energy Outlook 2000 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Introduction Introduction This paper presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 20001 (AEO2000), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview.3 The National Energy Modeling System The projections in the AEO2000 were produced with the National Energy Modeling System. NEMS is developed and maintained by the Office of Integrated Analysis and Forecasting of the Energy Information Administration (EIA) to provide projections of domestic energy-economy markets in the midterm time period and perform policy analyses requested by decisionmakers and analysts in the U.S. Congress, the Department of Energy’s Office of Policy, other DOE offices, and other government agencies.

49

Section 25: Future State Assumptions  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

the Compliance Certification Application (CCA), Chapter 6.0, Section 6.2 and Appendices SCR and MASS (U.S. DOE 1996). Many of these future state assumptions were derived from the...

50

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module The NEMS Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohol and ethers, natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. The PMM contains a linear programming representation of refining activities in three U.S. regions. This representation provides the marginal costs of production for a number of traditional and new petroleum products. The linear programming results are used to determine end-use product prices for each Census Division using the assumptions and methods described below.106

51

Assumptions to the Annual Energy Outlook 2013  

U.S. Energy Information Administration (EIA) Indexed Site

Energy Module Energy Module This page inTenTionally lefT blank 21 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2013 International Energy Module The LFMM International Energy Module (IEM) simulates the interaction between U.S. and global petroleum markets. It uses assumptions of economic growth and expectations of future U.S. and world crude-like liquids production and consumption to estimate the effects of changes in U.S. liquid fuels markets on the international petroleum market. For each year of the forecast, the LFMM IEM computes BRENT and WTI prices, provides a supply curve of world crude-like liquids, and generates a worldwide oil supply- demand balance with regional detail. The IEM also provides, for each year of the projection period, endogenous and

52

NGNP: High Temperature Gas-Cooled Reactor Key Definitions, Plant Capabilities, and Assumptions  

SciTech Connect (OSTI)

This document is intended to provide a Next Generation Nuclear Plant (NGNP) Project tool in which to collect and identify key definitions, plant capabilities, and inputs and assumptions to be used in ongoing efforts related to the licensing and deployment of a high temperature gas-cooled reactor (HTGR). These definitions, capabilities, and assumptions are extracted from a number of sources, including NGNP Project documents such as licensing related white papers [References 1-11] and previously issued requirement documents [References 13-15]. Also included is information agreed upon by the NGNP Regulatory Affairs group's Licensing Working Group and Configuration Council. The NGNP Project approach to licensing an HTGR plant via a combined license (COL) is defined within the referenced white papers and reference [12], and is not duplicated here.

Phillip Mills

2012-02-01T23:59:59.000Z

53

EIA - Assumptions to the Annual Energy Outlook 2010  

Gasoline and Diesel Fuel Update (EIA)

Assumptions to the Annual Energy Outlook 2010 This report summarizes the major assumptions used in the NEMS to generate the AEO2010 projections. Introduction Macroeconomic Activity Module International Energy Module Residential Demand Module Commercial Demand Module Industrial Demand Module Transportation Demand Module Electricity Market Module Oil and Gas Supply Module Natural Gas Transmission and Distribution Module Petroleum Market Module Coal Market Module Renewable Fuels Module PDF (GIF) Appendix A: Handling of Federal and Selected State Legislation and Regulation In the Annual Energy Outlook Past Assumptions Editions Download the Report Assumptions to the Annual Energy Outlook 2010 Report Cover. Need help, contact the National Energy Information Center at 202-586-8800.

54

MONITORED GEOLOGIC REPOSITORY LIFE CYCLE COST ESTIMATE ASSUMPTIONS DOCUMENT  

SciTech Connect (OSTI)

The purpose of this assumptions document is to provide general scope, strategy, technical basis, schedule and cost assumptions for the Monitored Geologic Repository (MGR) life cycle cost (LCC) estimate and schedule update incorporating information from the Viability Assessment (VA) , License Application Design Selection (LADS), 1999 Update to the Total System Life Cycle Cost (TSLCC) estimate and from other related and updated information. This document is intended to generally follow the assumptions outlined in the previous MGR cost estimates and as further prescribed by DOE guidance.

R.E. Sweeney

2001-02-08T23:59:59.000Z

55

Quantum water-filling solution for the capacity of Gaussian information channels  

E-Print Network [OSTI]

additive noise under the physical assumption of a finite input energy including the energy of classical signal (modulation) and the energy spent on squeezing the quantum states carrying information. Multiple finding the optimal distribution of the input energy between the channels. Above a certain input energy

Cerf, Nicolas

56

Assumptions to the Annual Energy Outlook - Renewable Fuels Module  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module Renewable Fuels Module Assumption to the Annual Energy Outlook Renewable Fuels Module The NEMS Renewable Fuels Module (RFM) provides natural resources supply and technology input information for forecasts of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has five submodules representing various renewable energy sources, biomass, geothermal, landfill gas, solar, and wind; a sixth renewable, conventional hydroelectric power, is represented in the Electricity Market Module (EMM).109 Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as wind and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration, from hydroelectric power, which was an original source of electricity generation, to newer power systems using biomass, geothermal, LFG, solar, and wind energy. In some cases, they require technological innovation to become cost effective or have inherent characteristics, such as intermittency, which make their penetration into the electricity grid dependent upon new methods for integration within utility system plans or upon low-cost energy storage.

57

EIA - Assumptions to the Annual Energy Outlook 2008 - Renewable Fuels  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module Renewable Fuels Module Assumptions to the Annual Energy Outlook 2008 Renewable Fuels Module The NEMS Renewable Fuels Module (RFM) provides natural resources supply and technology input information for projections of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has seven submodules representing various renewable energy sources, biomass, geothermal, conventional hydroelectricity, landfill gas, solar thermal, solar photovoltaics, and wind1. Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as water, wind, and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration, from hydroelectric power, which was one of the first electric generation technologies, to newer power systems using biomass, geothermal, LFG, solar, and wind energy. In some cases, they require technological innovation to become cost effective or have inherent characteristics, such as intermittency, which make their penetration into the electricity grid dependent upon new methods for integration within utility system plans or upon the availability of low-cost energy storage systems.

58

Assumptions to the Annual Energy Outlook 1999 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

link.gif (1946 bytes) link.gif (1946 bytes) bullet1.gif (843 bytes) Assumptions to the AEO99 bullet1.gif (843 bytes) Supplemental Tables to the AEO99 bullet1.gif (843 bytes) To Forecasting Home Page bullet1.gif (843 bytes) EIA Homepage introduction.gif (4117 bytes) This paper presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 19991 (AEO99), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview.3

59

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module Coal Market Module The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2003, DOE/EIA-M060(2003) (Washington, DC, January 2003). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Separate supply curves are developed for each of 11 supply regions and 12 coal types (unique combinations of thermal grade, sulfur content, and mine type). The modeling approach used to construct regional coal supply curves addresses the relationship between the minemouth price of coal and corresponding levels of capacity utilization of mines, mining capacity, labor productivity, and the cost of factor inputs (mining equipment, mine labor, and fuel requirements).

60

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Electricity Market Module Electricity Market Module The NEMS Electricity Market Module (EMM) represents the capacity planning, dispatching, and pricing of electricity. It is composed of four submodules—electricity capacity planning, electricity fuel dispatching, load and demand-side management, and electricity finance and pricing. It includes nonutility capacity and generation, and electricity transmission and trade. A detailed description of the EMM is provided in the EIA publication, Electricity Market Module of the National Energy Modeling System 2003, DOE/EIA-M068(2003) April 2003. Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are assumptions about the operations of the electricity sector and the costs of various options in each of the EMM submodules. This section describes the model parameters and assumptions used in EMM. It includes a discussion of legislation and regulations that are incorporated in EMM as well as information about the climate change action plan. The various electricity and technology cases are also described.

Note: This page contains sample records for the topic "inform input assumptions" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


61

Assumptions to the Annual Energy Outlook - Contacts  

Gasoline and Diesel Fuel Update (EIA)

Contacts Contacts Assumption to the Annual Energy Outlook Contacts Specific questions about the information in this report may be directed to: Introduction Paul D. Holtberg 202/586-1284 Macroeconomic Activity Module Ronald F. Earley Yvonne Taylor 202/586-1398 202/586-1398 International Energy Module G. Daniel Butler 202/586-9503 Household Expenditures Module/ Residential Demand Module John H. Cymbalsky 202/586-4815 Commercial Demand Module Erin E. Boedecker 202/586-4791 Industrial Demand Module T. Crawford Honeycutt 202/586-1420 Transportation Demand Module John D. Maples 202/586-1757 Electricity Market Module Laura Martin 202/586-1494 Oil and Gas Supply Module/Natural Gas Transmission and Distribution Module Joseph Benneche 202/586-6132 Petroleum Market Module Bill Brown 202/586-8181

62

Assumptions to the Annual Energy Outlook 2013  

U.S. Energy Information Administration (EIA) Indexed Site

Demand Module Demand Module This page inTenTionally lefT blank 27 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2013 Residential Demand Module The NEMS Residential Demand Module projects future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimate of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the "unit energy consumption" (UEC) by appliance (in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new units, retires existing

63

Assumptions to the Annual Energy Outlook 2013  

U.S. Energy Information Administration (EIA) Indexed Site

Industrial Demand Module Industrial Demand Module This page inTenTionally lefT blank 53 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2013 Industrial Demand Module The NEMS Industrial Demand Module (IDM) estimates energy consumption by energy source (fuels and feedstocks) for 15 manufacturing and 6 non-manufacturing industries. The manufacturing industries are subdivided further into the energy- intensive manufacturing industries and non-energy-intensive manufacturing industries (Table 6.1). The manufacturing industries are modeled through the use of a detailed process-flow or end-use accounting procedure. The non-manufacturing industries are modeled with less detail because processes are simpler and there is less available data. The petroleum refining

64

Assumptions to the Annual Energy Outlook 2013  

U.S. Energy Information Administration (EIA) Indexed Site

Macroeconomic Activity Module Macroeconomic Activity Module This page inTenTionally lefT blank 17 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2013 Macroeconomic Activity Module The Macroeconomic Activity Module (MAM) represents interactions between the U.S. economy and energy markets. The rate of growth of the economy, measured by the growth in gross domestic product (GDP), is a key determinant of growth in the demand for energy. Associated economic factors, such as interest rates and disposable income, strongly influence various elements of the supply and demand for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in economic growth resulting from increasing energy prices, are also reflected

65

Assumptions to the Annual Energy Outlook 2013  

U.S. Energy Information Administration (EIA) Indexed Site

Demand Module Demand Module This page inTenTionally lefT blank 39 U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2013 Commercial Demand Module The NEMS Commercial Sector Demand Module generates projections of commercial sector energy demand through 2040. The definition of the commercial sector is consistent with EIA's State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial.

66

EIA-Assumptions to the Annual Energy Outlook - Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module Assumptions to the Annual Energy Outlook 2007 Petroleum Market Module Figure 9. Petroleum Administration for Defense Districts. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, unfinished oil imports, other refinery inputs (including alcohols, ethers, and bioesters), natural gas plant liquids production, and refinery processing gain. In addition, the PMM projects capacity expansion and fuel consumption at domestic refineries. The PMM contains a linear programming (LP) representation of U.S. refining

67

Assumptions to the Annual Energy Outlook 2002 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 20021 (AEO2002), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview.3 The National Energy Modeling System The projections in the AEO2002 were produced with the National Energy Modeling System. NEMS is developed and maintained by the Office of

68

Assumptions to the Annual Energy Outlook 2001 - Introduction  

Gasoline and Diesel Fuel Update (EIA)

Outlook2001 Outlook2001 Supplemental Data to the AEO2001 NEMS Conference To Forecasting Home Page EIA Homepage Introduction This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook 20011 (AEO2001), including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are most significant in formulating the model results. Detailed documentation of the modeling system is available in a series of documentation reports.2 A synopsis of NEMS, the model components, and the interrelationships of the modules is presented in The National Energy Modeling System: An Overview.3 The National Energy Modeling System The projections in the AEO2001 were produced with the National Energy

69

Assumptions to the Annual Energy Outlook 2000 - Errata  

Gasoline and Diesel Fuel Update (EIA)

Assumptions to the Annual Energy Outlook 2000 Assumptions to the Annual Energy Outlook 2000 as of 4/4/2000 1. On table 20 "the fractional fuel efficiency change for 4-Speed Automatic" should be .045 instead of .030. On table 20 "the fractional fuel efficiency change for 5-Speed Automatic" should be .065 instead of .045. (Change made on 3/6/2000) 2. Table 28 should be labeled: "Alternative-Fuel Vehicle Attribute Inputs for Compact Cars for Two Stage Logit Model". (Change made on 3/6/2000) 3. The capital costs in Table 29 should read 1998 dollars not 1988 dollars. (Change made on 3/6/2000) 4. Table 37 changed the label "Year Available" to "First Year Completed." Changed the second sentence of Footnote 1 to read "these estimates are costs of new projects

70

EIA - Assumptions to the Annual Energy Outlook 2009 - Petroleum Market  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module Assumptions to the Annual Energy Outlook 2009 Petroleum Market Module Figure 9., Petroleum Administration for Defense Districts. Need help, contact the National Energy Information Center at 202-586-8800. Table 11.1. Petroleum Product Categories. Need help, contact the National Energy Information Center at 202-586-8800. printer-friendly version Table 11.2. Year Round Gasoline Specifications by Petroleum Administration for Defense Districts. Need help, contact the National Energy Information Center at 202-586-8800. printer-friendly version Table 11.3. Gasolline Sulfur Content Assumptions, by Region and Gasoline Type, Parts per Million (PPM). Need help, contact the National Energy Information Center at 202-586-8800. printer-friendly version

71

Refiner Crude Oil Inputs  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Day) Refiner Percent Operable Utilization Net Inputs (Refiner and Blender) of Motor Gasoline Blending Comp Net Inputs (Refiner and Blender) of RBOB Blending Components Net...

72

Climate Action Planning Tool Formulas and Assumptions  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

CLIMATE ACTION PLANNING TOOL FORMULAS AND ASSUMPTIONS Climate Action Planning Tool Formulas and Assumptions The Climate Action Planning Tool calculations use the following formulas and assumptions to generate the business-as-usual scenario and the greenhouse gas emissions reduction goals for the technology options. Business-as-Usual Scenario All Scope 1 (gas, oil, coal, fleet, and electricity) and Scope 2 calculations increase at a rate equal to the building growth rate. Scope 3 calculations (commuters and business travel) increase at a rate equal to the population growth rate. Assumptions New buildings will consume energy at the same rate (energy use intensity) as existing campus buildings. Fleet operations will be proportional to total building area.

73

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module Renewable Fuels Module The NEMS Renewable Fuels Module (RFM) provides natural resources supply and technology input information for forecasts of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has five submodules representing various renewable energy sources, biomass, geothermal, landfill gas, solar, and wind; a sixth renewable, conventional hydroelectric power, is represented in the Electricity Market Module (EMM).119 Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as wind and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration, from hydroelectric power, which was an original source of electricity generation, to newer power systems using biomass, geothermal, LFG, solar, and wind energy. In some cases, they require technological innovation to become cost effective or have inherent characteristics, such as intermittency, which make their penetration into the electricity grid dependent upon new methods for integration within utility system plans or upon low-cost energy storage.

74

Automatic input rectification  

E-Print Network [OSTI]

We present a novel technique, automatic input rectification, and a prototype implementation, SOAP. SOAP learns a set of constraints characterizing typical inputs that an application is highly likely to process correctly. ...

Long, Fan

75

Automatic Input Rectification  

E-Print Network [OSTI]

We present a novel technique, automatic input rectification, and a prototype implementation called SOAP. SOAP learns a set of constraints characterizing typical inputs that an application is highly likely to process ...

Long, Fan

2011-10-03T23:59:59.000Z

76

USDA, Departments of Energy and Navy Seek Input from Industry to Advance  

Broader source: Energy.gov (indexed) [DOE]

USDA, Departments of Energy and Navy Seek Input from Industry to USDA, Departments of Energy and Navy Seek Input from Industry to Advance Biofuels for Military and Commercial Transportation USDA, Departments of Energy and Navy Seek Input from Industry to Advance Biofuels for Military and Commercial Transportation August 30, 2011 - 12:23pm Addthis WASHINGTON, Aug. 30, 2011 -Secretary of Agriculture Tom Vilsack, Secretary of Energy Steven Chu, and Secretary of the Navy Ray Mabus today announced the next step in the creation of a public-private partnership to develop drop-in advanced biofuels. The Secretaries issued a Request for Information (RFI) laying out the Administration's goals, assumptions, and tools and requesting from industry specific ideas for how to leverage private capital markets to establish a commercially viable drop-in biofuels

77

TART input manual  

SciTech Connect (OSTI)

The TART code is a Monte Carlo neutron/photon transport code that is only on the CRAY computer. All the input cards for the TART code are listed, and definitions for all input parameters are given. The execution and limitations of the code are described, and input for two sample problems are given. (WHK)

Kimlinger, J.R.; Plechaty, E.F.

1982-04-01T23:59:59.000Z

78

Assumptions to the Annual Energy Outlook 2008  

Gasoline and Diesel Fuel Update (EIA)

8) 8) Release date: June 2008 Next release date: March 2009 Assumptions to the Annual Energy Outlook 2008 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Macroeconomic Activity Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 International Energy Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Residential Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Commercial Demand Module. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Industrial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Transportation Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 Electricity Market Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 Oil and Gas Supply Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 Natural Gas Transmission and Distribution Module. . . . . . . . . . . . . . . . . . . . . . 113 Petroleum Market Module

79

Preliminary Assumptions for Natural Gas Peaking  

E-Print Network [OSTI]

Preliminary Assumptions for Natural Gas Peaking Technologies Gillian Charles and Steve Simmons GRAC, Reciprocating Engines Next steps 2 #12;Definitions Baseload Energy: power generated (or conserved) across a period of time to serve system demands for electricity Peaking Capacity: capability of power generating

80

Preliminary Assumptions for Natural Gas Peaking  

E-Print Network [OSTI]

Preliminary Assumptions for Natural Gas Peaking Technologies Gillian Charles GRAC 2/27/14 #12;Today Vernon, WA PSE Klamath Generation Peakers June 2002 (2) 54 MW P&W FT8 Twin- pac 95 MW Klamath, OR IPP; winter-only PPA w/ PSE Dave Gates Generating Station Jan 2011 (3) P&W SWIFTPAC 150 MW Anaconda, MT North

Note: This page contains sample records for the topic "inform input assumptions" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


81

Empirically Revisiting the Test Independence Assumption  

E-Print Network [OSTI]

Empirically Revisiting the Test Independence Assumption Sai Zhang, Darioush Jalali, Jochen Wuttke}@cs.washington.edu ABSTRACT In a test suite, all the test cases should be independent: no test should affect any other test's result, and running the tests in any order should produce the same test results. Techniques such as test

Ernst, Michael

82

Assumptions to the Annual Energy Outlook 1999 - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

coal.gif (4423 bytes) coal.gif (4423 bytes) The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Model Documentation: Coal Market Module of the National Energy Modeling System, DOE/EIA-MO60. Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Separate supply curves are developed for each of 11 supply regions, and 12 coal types (unique combinations of thermal grade, sulfur content, and mine type). The modeling approach used to construct regional coal supply curves addresses the relationship between the minemouth price of coal and corresponding levels of coal production, labor productivity, and the cost of factor inputs (mining equipment, mine labor, and fuel requirements).

83

Assumptions to the Annual Energy Outlook 2001 - Transportation Demand  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars, light trucks, industry sport utility vehicles and vans), commercial light trucks (8501-10,000 lbs), freight trucks (>10,000 lbs), freight and passenger airplanes, freight rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption. Key Assumptions Macroeconomic Sector Inputs

84

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

7, DOE/EIA-M060(2007) (Washington, 7, DOE/EIA-M060(2007) (Washington, DC, 2007). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Forty separate supply curves are developed for each of 14 supply regions, nine coal types (unique combinations of thermal grade and sulfur content), and two mine types (underground and surface). Supply curves are constructed using an econometric formulation that relates the minemouth prices of coal for the supply regions and coal types to a set of independent variables. The independent variables include: capacity utilization of mines, mining capacity, labor productivity, the user cost of capital of mining equipment, and the cost of factor inputs (labor and fuel).

85

Assumptions to the Annual Energy Outlook 1999 - Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

petroleum.gif (4999 bytes) petroleum.gif (4999 bytes) The NEMS Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohol and ethers, natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. The PMM contains a linear programming representation of refining activities in three U.S. regions. This representation provides the marginal costs of production for a number of traditional and new petroleum products. The linear programming results are used to determine end-use product prices for each Census Division using the assumptions and methods described below. 75

86

EIA - Assumptions to the Annual Energy Outlook 2010 - Petroleum Market  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module Assumptions to the Annual Energy Outlook 2010 Petroleum Market Module The NEMS Petroleum Market Module (PMM) projects petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, unfinished oil imports, other refinery inputs (including alcohols, ethers, bioesters, corn, biomass, and coal), natural gas plant liquids production, and refinery processing gain. In addition, the PMM projects capacity expansion and fuel consumption at domestic refineries. Figure 9. Petroleum Administration for Defense Districts. The PMM contains a linear programming (LP) representation of U.S. refining activities in the five Petroleum Area Defense Districts (PADDs) (Figure 9),

87

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

7, DOE/EIA- 7, DOE/EIA- M068(2007). Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are assumptions about the operations of the electricity sector and the costs of various options in each of the EMM submodules. This section describes the model parameters and assumptions used in EMM. It includes a discussion of legislation and regulations that are incorporated in EMM as well as information about the climate change action plan. The various electricity and technology cases are also described. EMM Regions The supply regions used in EMM are based on the North American Electric Reliability Council regions and

88

INGEN: A COBRA-NC input generator user's manual  

SciTech Connect (OSTI)

The INGEN (INput GENerator) computer program has been developed as a preprocessor to simplify input generation for the COBRA-NC computer program. INGEN uses several empirical correlations and geometric assumptions to simplify the data input requirements for the COBRA-NC computer code. The simplified input scheme is obtained at the expense of much flexibility provided by COBRA-NC. For more complex problems requiring additional flexibility however, INGEN may be used to provide a skeletal input file to which the more detailed input may be added. This report describes the input requirements for INGEN and describes the algorithms and correlations used to generate the COBRA-NC input. 9 refs., 3 figs., 6 tabs.

Wheeler, C.L.; Dodge, R.E.

1986-12-01T23:59:59.000Z

89

Assumptions to the Annual Energy Outlook - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module Coal Market Module Assumption to the Annual Energy Outlook Coal Market Module The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, imports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2004, DOE/EIA-M060(2004) (Washington, DC, 2004). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Separate supply curves are developed for each of 11 supply regions and 12 coal types (unique combinations of thermal grade, sulfur content, and mine type). The modeling approach used to construct regional coal supply curves addresses the relationship between the minemouth price of coal and corresponding levels of capacity utilization of mines, mining capacity, labor productivity, and the cost of factor inputs (mining equipment, mine labor, and fuel requirements).

90

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Assumptions to the Annual Energy Outlook 2004 Assumptions to the Annual Energy Outlook 2004 143 Appendix A: Handling of Federal and Selected State Legislation and Regulation in the Annual Energy Outlook Legislation Brief Description AEO Handling Basis Residential Sector A. National Appliance Energy Conservation Act of 1987 Requires Secretary of Energy to set minimum efficiency standards for 10 appliance categories a. Room Air Conditioners Current standard of 8.82 EER Federal Register Notice of Final Rulemaking, b. Other Air Conditioners (<5.4 tons) Current standard 10 SEER for central air conditioner and heat pumps, increasing to 12 SEER in 2006. Federal Register Notice of Final Rulemaking, c. Water Heaters Electric: Current standard .86 EF, incr easing to .90 EF in 2004. Gas: Curren

91

Assumptions to the Annual Energy Outlook 2000 - Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohol and ethers, natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohol and ethers, natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. The PMM contains a linear programming representation of refining activities in three U.S. regions. This representation provides the marginal costs of production for a number of traditional and new petroleum products. The linear programming results are used to determine end-use product prices for each Census Division using the assumptions and methods described below.100

92

EIA - Assumptions to the Annual Energy Outlook 2008 - Electricity Market  

Gasoline and Diesel Fuel Update (EIA)

Electricity Market Module Electricity Market Module Assumptions to the Annual Energy Outlook 2008 Electricity Market Module The NEMS Electricity Market Module (EMM) represents the capacity planning, dispatching, and pricing of electricity. It is composed of four submodules—electricity capacity planning, electricity fuel dispatching, load and demand electricity, and electricity finance and pricing. It includes nonutility capacity and generation, and electricity transmission and trade. A detailed description of the EMM is provided in the EIA publication, Electricity Market Module of the National Energy Modeling System 2008, DOE/EIA-M068(2008). Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are assumptions about the operations of the electricity sector and the costs of various options in each of the EMM submodules. This section describes the model parameters and assumptions used in EMM. It includes a discussion of legislation and regulations that are incorporated in EMM as well as information about the climate change action plan. The various electricity and technology cases are also described.

93

Assumptions to the Annual Energy Outlook - Electricity Market Module  

Gasoline and Diesel Fuel Update (EIA)

Electricity Market Module Electricity Market Module Assumption to the Annual Energy Outlook Electricity Market Module The NEMS Electricity Market Module (EMM) represents the capacity planning, dispatching, and pricing of electricity. It is composed of four submodules—electricity capacity planning, electricity fuel dispatching, load and demand-side management, and electricity finance and pricing. It includes nonutility capacity and generation, and electricity transmission and trade. A detailed description of the EMM is provided in the EIA publication, Electricity Market Module of the National Energy Modeling System 2004, DOE/EIA- M068(2004). Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are assumptions about the operations of the electricity sector and the costs of various options in each of the EMM submodules. This section describes the model parameters and assumptions used in EMM. It includes a discussion of legislation and regulations that are incorporated in EMM as well as information about the climate change action plan. The various electricity and technology cases are also described.

94

EIA - Assumptions to the Annual Energy Outlook 2010 - International Energy  

Gasoline and Diesel Fuel Update (EIA)

International Energy Module International Energy Module Assumptions to the Annual Energy Outlook 2010 International Energy Module Figure 2. World Oil Prices in Three Cases, 1995-2035 Figure 2. World Oil Prices in three Cases, 1995-2035 (2008 dollars per barrel). Need help, contact the National Energy Information Center at 202-586-8800. figure data Figure 3. OPEC Total Liquids Production in the Reference Case, 1980-2035 Figure 3. OPEC Total Liquids Production in the Reference Case, 1995-2030 (million barrels per day). Need help, contact the National Energy Information Center at 202-586-8800. figure data Figure 4. Non-OPEC Total Liquids Production in the Reference Case, 1980-2035 Figure 4. Non-OPEC Total Liquids Production in the Reference Case, 1995-2030 (million barrels per day). Need help, contact the National Energy Information Center at 202-586-8800.

95

EIA - Assumptions to the Annual Energy Outlook 2009 - International Energy  

Gasoline and Diesel Fuel Update (EIA)

International Energy Module International Energy Module Assumptions to the Annual Energy Outlook 2009 International Energy Module Figure 2. World Oil Prices in three Cases, 1995-2030 (2006 dollars per barrel). Need help, contact the National Energy Information Center at 202-586-8800. figure data Figure 3. OPEC Total Liquids Production in the Reference Case, 1995-2030 (million barrels per day). Need help, contact the National Energy Information Center at 202-586-8800. figure data Figure 4. Non-OPEC Total Liquids Production in the Reference Case, 1995-2030 (million barrels per day). Need help, contact the National Energy Information Center at 202-586-8800. figure data The International Energy Module (IEM) performs two tasks in all NEMS runs. First, the module reads exogenously global and U.S.A. petroleum liquids

96

Assumptions to the Annual Energy Outlook - International Energy Module  

Gasoline and Diesel Fuel Update (EIA)

International Energy Module International Energy Module Assumption to the Annual Energy Outlook International Energy Module Figure 2. World Oil Prices in three Cases, 1970-2025. Having problems, call our National Energy Information Center at 202-586-8800 for help. Figure Data Figure 3. OPEC Oil Production in the Reference Case, 1970-2025. Having problems, call our National Energy Information Center at 202-586-8800 for help. Figure Data Figure 4. Non-OPEC Production in the Reference Case, 1970-2025. Having problems, call our National Energy Information Center at 202-586-8800 for help. Figure Data Table 4. Worldwide Oil Reserves as of January 1, 2002 (Billion Barrels) Printer Friendly Version Region Proved Oil Reserves Western Hemisphere 313.6 Western‘Europe 18.1 Asia-Pacific 38.7

97

Assumptions to the Annual Energy Outlook - Household Expenditures Module  

Gasoline and Diesel Fuel Update (EIA)

Household Expenditures Module Household Expenditures Module Assumption to the Annual Energy Outlook Household Expenditures Module Figure 5. United States Census Divisions. Having problems, call our National Energy Information Center at 202-586-8800 for help. The Household Expenditures Module (HEM) constructs household energy expenditure profiles using historical survey data on household income, population and demographic characteristics, and consumption and expenditures for fuels for various end-uses. These data are combined with NEMS forecasts of household disposable income, fuel consumption, and fuel expenditures by end-use and household type. The HEM disaggregation algorithm uses these combined results to forecast household fuel consumption and expenditures by income quintile and Census Division (see

98

Diversion assumptions for high-powered research reactors  

SciTech Connect (OSTI)

This study deals with diversion assumptions for high-powered research reactors -- specifically, MTR fuel; pool- or tank-type research reactors with light-water moderator; and water, beryllium, or graphite reflectors, and which have a power level of 25 MW(t) or more. The objective is to provide assistance to the IAEA in documentation of criteria and inspection observables related to undeclared plutonium production in the reactors described above, including: criteria for undeclared plutonium production, necessary design information for implementation of these criteria, verification guidelines including neutron physics and heat transfer, and safeguards measures to facilitate the detection of undeclared plutonium production at large research reactors.

Binford, F.T.

1984-01-01T23:59:59.000Z

99

Assumptions to the Annual Energy Outlook  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Transmission and Distribution Module Natural Gas Transmission and Distribution Module The NEMS Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each forecast year. These are derived by solving for the market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and distribution network that links them. In addition, natural gas flow patterns are a function of the pattern in the previous year, coupled with the relative prices of gas supply options as translated to the represented market “hubs.” The major assumptions used within the NGTDM are grouped into five general categories. They relate to (1) the classification of demand into core and noncore transportation service classes, (2) the pricing of transmission and distribution services, (3) pipeline and storage capacity expansion and utilization, and (4) the implementation of recent regulatory reform. A complete listing of NGTDM assumptions and in-depth methodology descriptions are presented in Model Documentation: Natural Gas Transmission and Distribution Model of the National Energy Modeling System, Model Documentation 2003, DOE/EIA- M062(2003) (Washington, DC, January 2003).

100

Assumptions to the Annual Energy Outlook 2013  

Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

and U.S. Energy Information Administration, The Cost and Performance of Distributed Wind Turbines, 2010-35 Final Report, ICF International, August 2010. 43 U.S. Energy Information...

Note: This page contains sample records for the topic "inform input assumptions" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


101

Manufacturing Energy and Carbon Footprint Definitions and Assumptions, October 2012  

Broader source: Energy.gov [DOE]

Definitions of parameters and table of assumptions for the Manufacturing Energy and Carbon Footprint

102

Assumptions to Annual Energy Outlook - Energy Information Administrati...  

Gasoline and Diesel Fuel Update (EIA)

Rule (CAIR), which was reinstated as binding legislation after the Cross- State Air Pollution Rule (CSAPR) 4 was vacated on August 21, 2012; updated handling of the...

103

Assumptions to Annual Energy Outlook - Energy Information Administrati...  

Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

by the U.S. Environmental Protection Agency (EPA) in December 2011; the Cross-State Air Pollution Rule (CSAPR) 4 as finalized by the EPA in July 2011; the new fuel...

104

Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and  

Alternative Fuels and Advanced Vehicles Data Center [Office of Energy Efficiency and Renewable Energy (EERE)]

Tools Tools Printable Version Share this resource Send a link to Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and Methodology to someone by E-mail Share Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and Methodology on Facebook Tweet about Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and Methodology on Twitter Bookmark Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and Methodology on Google Bookmark Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and Methodology on Delicious Rank Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and Methodology on Digg Find More places to share Alternative Fuels Data Center: Vehicle Cost Calculator Assumptions and Methodology on AddThis.com...

105

EIA-Assumptions to the Annual Energy Outlook - Renewable Fuels Module  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module Renewable Fuels Module Assumptions to the Annual Energy Outlook 2007 Renewable Fuels Module The NEMS Renewable Fuels Module (RFM) provides natural resources supply and technology input information for forecasts of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has seven submodules representing various renewable energy sources, biomass, geothermal, conventional hydroelectricity, landfill gas, solar thermal, solar photovoltaics, and wind.112 Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as water, wind, and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration, from hydroelectric power, which was one of the first electric generation technologies, to newer power systems using biomass, geothermal, LFG, solar, and wind energy. In some cases, they require technological innovation to become cost effective or have inherent characteristics, such as intermittency, which make their penetration into the electricity grid dependent upon new methods for integration within utility system plans or upon the availability of low-cost energy storage systems.

106

EIA - Assumptions to the Annual Energy Outlook 2009 - Industrial Demand  

Gasoline and Diesel Fuel Update (EIA)

Industrial Demand Module Industrial Demand Module Assumptions to the Annual Energy Outlook 2009 Industrial Demand Module Table 6.1. Industry Categories. Need help, contact the National Energy Information Center at 202-586-8800. printer-friendly version Table 6.2.Retirement Rates. Need help, contact the National Energy Information Center at 202-586-8800. printer-friendly version The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 15 manufacturing and 6 nonmanufacturing industries. The manufacturing industries are further subdivided into the energy-intensive manufacturing industries and nonenergy-intensive manufacturing industries (Table 6.1). The manufacturing industries are modeled through the use of a detailed process flow or end use accounting

107

Assumptions to the Annual Energy Outlook 2013  

Gasoline and Diesel Fuel Update (EIA)

for EIA (SENTECH Incorporated, 2010). Wind: The Cost and Performance of Distributed Wind Turbines, 2010-35 (ICF International, 2010). 31 U.S. Energy Information Administration |...

108

Assumptions to the Annual Energy Outlook 2000 - Transportation Demand  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module estimates energy consumption across the nine Census Divisions and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars, light trucks, industry sport utility vehicles and vans), commercial light trucks (8501-10,000 lbs), freight trucks (>10,000 lbs), freight and passenger airplanes, freight rail, freight shipping, mass transit, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption. Transportation Demand Module estimates energy consumption across the nine Census Divisions and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars, light trucks, industry sport utility vehicles and vans), commercial light trucks (8501-10,000 lbs), freight trucks (>10,000 lbs), freight and passenger airplanes, freight rail, freight shipping, mass transit, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption. Key Assumptions Macroeconomic Sector Inputs

109

Assumptions to the Annual Energy Outlook 2000 - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2000, DOE/EIA-M060(2000) January 2000. The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2000, DOE/EIA-M060(2000) January 2000. Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Separate supply curves are developed for each of 11 supply regions, and 12 coal types (unique combinations of thermal grade, sulfur content, and mine type). The modeling approach used to construct regional coal supply curves addresses the relationship between the minemouth price of coal and corresponding levels of coal production, labor productivity, and the cost of factor inputs (mining equipment, mine labor, and fuel requirements).

110

2010 Manufacturing Energy and Carbon Footprints: Definitions and Assumptions  

Broader source: Energy.gov [DOE]

This 13-page document provides definitions and assumptions used in the Manufacturing Energy and Carbon Footprints (MECS 2010)

111

V-139: Cisco Network Admission Control Input Validation Flaw...  

Broader source: Energy.gov (indexed) [DOE]

Sensitive Information U-270:Trend Micro Control Manager Input Validation Flaw in Ad Hoc Query Module Lets Remote Users Inject SQL Commands U-015: CiscoWorks Common...

112

EIA - Assumptions to the Annual Energy Outlook 2009 - Electricity Market  

Gasoline and Diesel Fuel Update (EIA)

Electricity Market Module Electricity Market Module Assumptions to the Annual Energy Outlook 2009 Electricity Market Module figure 6. Electricity Market Model Supply Regions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Electricity Market Module (EMM) represents the capacity planning, dispatching, and pricing of electricity. It is composed of four submodules—electricity capacity planning, electricity fuel dispatching, load and demand electricity, and electricity finance and pricing. It includes nonutility capacity and generation, and electricity transmission and trade. A detailed description of the EMM is provided in the EIA publication, Electricity Market Module of the National Energy Modeling System 2009, DOE/EIA-M068(2009). Based on fuel prices and electricity demands provided by the other modules

113

EIA - Assumptions to the Annual Energy Outlook 2008 - Natural Gas  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Transmission and Distribution Module Natural Gas Transmission and Distribution Module Assumptions to the Annual Energy Outlook 2008 Natural Gas Transmission and Distribution Module Figure 8. Natural Gas Transmission and Distribution Model Regions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each projection year. These are derived by solving for the market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and distribution

114

EIA - Assumptions to the Annual Energy Outlook 2009 - Natural Gas  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Transmission and Distribution Module Natural Gas Transmission and Distribution Module Assumptions to the Annual Energy Outlook 2009 Natural Gas Transmission and Distribution Module Figure 8. Natural Gas Transmission and distribution Model Regions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each projection year. These are derived by solving for the market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and distribution

115

EIA - Assumptions to the Annual Energy Outlook 2010 - Residential Demand  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module Residential Demand Module Assumptions to the Annual Energy Outlook 2010 Residential Demand Module Figure 5. United States Census Divisions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Residential Demand Module projects future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimate of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the "unit energy consumption" by appliance (or UEC-in million Btu per household per year). The projection process adds new housing units to the stock,

116

Assumptions to the Annual Energy Outlook - Natural Gas Transmission and  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Transmission and Distribution Module Natural Gas Transmission and Distribution Module Assumption to the Annual Energy Outlook Natural Gas Transmission and Distribution Module Figure 8. Natural Gas Transmission and Distribution Model Regions. Having problems, call our National Energy Information Center at 202-586-8800 for help. The NEMS Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each forecast year. These are derived by solving for the market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and distribution

117

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

clothes drying, ceiling fans, coffee makers, spas, home security clothes drying, ceiling fans, coffee makers, spas, home security systems, microwave ovens, set-top boxes, home audio equipment, rechargeable electronics, and VCR/DVDs. In addition to the major equipment-driven end-uses, the average energy consumption per household is projected for other electric and nonelectric appliances. The module's output includes number Energy Information Administration/Assumptions to the Annual Energy Outlook 2007 19 Pacific East South Central South Atlantic Middle Atlantic New England West South Central West North Central East North Central Mountain AK WA MT WY ID NV UT CO AZ NM TX OK IA KS MO IL IN KY TN MS AL FL GA SC NC WV PA NJ MD DE NY CT VT ME RI MA NH VA WI MI OH NE SD MN ND AR LA OR CA HI Middle Atlantic New England East North Central West North Central Pacific West South Central East South Central

118

EIA - Assumptions to the Annual Energy Outlook 2010 - Natural Gas  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Transmission and Distribution Module Natural Gas Transmission and Distribution Module Assumptions to the Annual Energy Outlook 2010 Natural Gas Transmission and Distribution Module Figure 8. Natural Gas Transmission and distribution Model Regions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each projection year. These are derived by solving for the market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and

119

NUPlans Budgeting Grant Input View  

E-Print Network [OSTI]

NUPlans Budgeting Grant Input View FMS704 NUPlansGrantInputViewV2 Last updated 4/7/2014 - rb © 2014 Northwestern University FMS704 NUPlans Contributor Budgeting 1 of 5 NUPlans Grant Input View NUPlans enables schools and units with grant projects to input grant expense estimates per project for the next fiscal

Shull, Kenneth R.

120

Assumptions to the Annual Energy Outlook 2002 - Renewable Fuels Module  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module Renewable Fuels Module The NEMS Renewable Fuels Module (RFM) provides natural resources supply and technology input information for forecasts of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has five submodules representing various renewable energy sources, biomass, geothermal, landfill gas, solar, and wind; a sixth renewable, conventional hydroelectric power, is represented in the Electricity Market Module (EMM).117 Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as wind and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration,

Note: This page contains sample records for the topic "inform input assumptions" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


121

Assumptions to the Annual Energy Outlook 2001 - Renewable Fuels Module  

Gasoline and Diesel Fuel Update (EIA)

Renewable Fuels Module Renewable Fuels Module The NEMS Renewable Fuels Module (RFM) provides natural resources supply and technology input information for forecasts of new central-station U.S. electricity generating capacity using renewable energy resources. The RFM has five submodules representing various renewable energy sources, biomass, geothermal, landfill gas, solar, and wind; a sixth renewable, conventional hydroelectric power, is represented in the Electricity Market Module (EMM).112 Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass materials, are fuels in the conventional sense of the word, while others, such as wind and solar radiation, are energy sources that do not involve the production or consumption of a fuel. Renewable technologies cover the gamut of commercial market penetration,

122

Prioritization Tool Measurement Input Form  

Broader source: Energy.gov [DOE]

BTO encourages stakeholders to recommend updates and improvements to the Prioritization Tool by using the below Measure Input Form.

123

Assumption-Commitment Support for CSP Model Checking  

E-Print Network [OSTI]

AVoCS 2006 Assumption-Commitment Support for CSP Model Checking Nick Moffat1 Systems Assurance using CSP. In our formulation, an assumption-commitment style property of a process SYS takes the form-Guarantee, CSP, Model Checking, Compositional Reasoning 1 Introduction The principle of compositional program

Paris-Sud XI, Université de

124

Residential Sector End-Use Forecasting with EPRI-REEPS 2.1: Summary Input Assumptions and Results  

E-Print Network [OSTI]

LPG Furnace Oil Furnace Electric Heat Pump Gas BoilerOil Boiler Electric Room Heater Gas Room Heater Wood Stove (Electric Heat Pump Gas Boiler Oil Boiler Electric Room Gas

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

125

Residential Sector End-Use Forecasting with EPRI-REEPS 2.1: Summary Input Assumptions and Results  

E-Print Network [OSTI]

Richard E. Brown, James W. Hanford, Alan H . Sanstad, andFrancis X . , James W. Hanford, Richard E. Brown, Alan H.place for these end-uses (Hanford et al. 1994, Hwang et al.

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

126

Residential Sector End-Use Forecasting with EPRI-REEPS 2.1: Summary Input Assumptions and Results  

E-Print Network [OSTI]

and the size of refrigerators and freezers; for all otherwhile water heating, refrigerator, and freezer end-uses showas projected by REEPS. Refrigerator and freezer percentage

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

127

RESIDENTIAL SECTOR END-USE FORECASTING WITH EPRI-REEPS 2.1: SUMMARY INPUT ASSUMPTIONS AND RESULTS  

E-Print Network [OSTI]

-76SF00098. #12;#12;i ABSTRACT This paper describes current and projected future energy use by end energy intensity per household of the residential sector is declining, and the electricity intensity per. Sanstad, and Leslie Shown Energy Analysis Program Energy and Environment Division Ernest Orlando Lawrence

128

Residential Sector End-Use Forecasting with EPRI-REEPS 2.1: Summary Input Assumptions and Results  

E-Print Network [OSTI]

Description Prices for oil, gas, electricity, liquidElectric Electric Electric Gas Oil Electric ElectricElectric Gas Electric Gas Oil Electric Electric Gas Oil

Koomey, Jonathan G.

2010-01-01T23:59:59.000Z

129

Opportunities for Public Input Into DOE Projects  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Opportunities for Public Input Into DOE Projects Opportunities for Public Input Into DOE Projects There are currently several DOE-proposed activities that citizens can comment on in the near future. Here is a summary of each, as well as a description of how to provide your input into the project: Hanford Draft Closure and Waste Management Environmental Impact Statement Idahoans might be interested in this document because one of the proposed actions involves sending a small amount of radioactive waste (approximately 5 cubic meters of special reactor components) to the Idaho Nuclear Technology and Engineering Center on DOE's Idaho Site for treatment. Here is a link to more information about the document: http://www.hanford.gov . A public hearing on the draft EIS will be held in Boise on Tuesday, Feb. 2 at the Owyhee Plaza Hotel. It begins at 6 p.m.

130

EIA - Assumptions to the Annual Energy Outlook 2009  

Gasoline and Diesel Fuel Update (EIA)

Assumptions to the Annual Energy Outlook 2009 The Early Release for next year's Annual Energy Outlook will be presented at the John Hopkins Kenney Auditorium on December 14th This report summarizes the major assumptions used in the NEMS to generate the AEO2009 projections. Introduction Macroeconomic Activity Module International Energy Module Residential Demand Module Commercial Demand Module Industrial Demand Module Transportation Demand Module Electricity Market Module Oil and Gas Supply Module Natural Gas Transmission and Distribution Module Petroleum Market Module Coal Market Module Renewable Fuels Module PDF (GIF) Appendix A: Handling of Federal and Selected State Legislation and Regulation In the Annual Energy Outlook Past Assumptions Editions

131

Notes 01. The fundamental assumptions and equations of lubrication theory  

E-Print Network [OSTI]

The fundamental assumption in Lubrication Theory. Derivation of thin film flow equations from Navier-Stokes equations. Importance of fluid inertia effects in thin film flows. Some fluid physical properties...

San Andres, Luis

2009-01-01T23:59:59.000Z

132

Idaho National Engineering Laboratory installation roadmap assumptions document. Revision 1  

SciTech Connect (OSTI)

This document is a composite of roadmap assumptions developed for the Idaho National Engineering Laboratory (INEL) by the US Department of Energy Idaho Field Office and subcontractor personnel as a key element in the implementation of the Roadmap Methodology for the INEL Site. The development and identification of these assumptions in an important factor in planning basis development and establishes the planning baseline for all subsequent roadmap analysis at the INEL.

Not Available

1993-05-01T23:59:59.000Z

133

Research Input Form  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

HighlightsSubmit HighlightsSubmit Form Submit a New Research Highlight Sort Highlights Submitter Title Research Area Working Group Submission Date DOE Progress Reports Notable Research Findings for 2001-2006 Biological and Environmental Research Abstracts Database Research Highlights Summaries Research Highlight Submittal Form Tell us about your research! This form is designed to collect summary information about working group research results. If you have any questions or comments, please contact the administrators. Journal or Book Reference(s) (if applicable): Look Up Your reference from the Publications Database. Limit two references. If you have not submitted the references, please Add it now. Area of Research: Radiation Processes Cloud Distributions/Characterizations Surface Properties General Circulation and Single Column

134

Link: exploiting the web of data to generate test inputs  

Science Journals Connector (OSTI)

Applications that process complex data, such as maps, personal data, book information, travel data, etc., are becoming extremely common. Testing such applications is hard, because they require realistic and coherent test inputs that are expensive to ... Keywords: System testing, Web of data, realistic test input

Leonardo Mariani; Mauro Pezz; Oliviero Riganelli; Mauro Santoro

2014-07-01T23:59:59.000Z

135

,"U.S. Blender Net Input"  

U.S. Energy Information Administration (EIA) Indexed Site

Blender Net Input of Residuum (Thousand Barrels)","U.S. Blender Net Input of Gasoline Blending Components (Thousand Barrels)","U.S. Blender Net Input of Reformulated...

136

Integrating surprisal and uncertain-input models in online sentence comprehension: formal techniques and empirical results  

Science Journals Connector (OSTI)

A system making optimal use of available information in incremental language comprehension might be expected to use linguistic knowledge together with current input to revise beliefs about previous input. Under some circumstances, such an error-correction ...

Roger Levy

2011-06-01T23:59:59.000Z

137

EIA - Assumptions to the Annual Energy Outlook 2009 - Oil and Gas Supply  

Gasoline and Diesel Fuel Update (EIA)

Oil and Gas Supply Module Oil and Gas Supply Module Assumptions to the Annual Energy Outlook 2009 Oil and Gas Supply Module Figure 7. Oil and Gas Supply Model Regions. Need help, contact the National Energy Information Center at 202-586-8800. Table 9.1. Crude Oil Technically Recoverable Resources. Need help, contact the Naitonal Energy Information Center at 202-586-8800. printer-friendly version Table 9.2. Natural Gas Technically Recoverable Resources. Need help, contact the National Energy Information Center at 202-586-8800. Table 9.2. Continued printer-friendly version Table 9.3. Assumed Size and Initial Production year of Major Announced Deepwater Discoveries. Need help, contact the National Energy Information Center at 202-586-8800. printer-friendly version Table 9.4. Assumed Annual Rates of Technological Progress for Conventional Crude Oil and Natural Gas Sources. Need help, contact the National Energy Information Center at 202-586-8800.

138

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

7) 7) Release date: April 2007 Next release date: March 2008 Assumptions to the Annual Energy Outlook 2007 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Macroeconomic Activity Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 International Energy Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Residential Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Commercial Demand Module. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Industrial Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Transportation Demand Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Electricity Market Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Oil and Gas Supply Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 Natural Gas Transmission and Distribution Module. . . . . . . . . . . . . . . . . . . . . . 107 Petroleum Market Module

139

COMPARING ALASKA'S OIL PRODUCTION TAXES: INCENTIVES AND ASSUMPTIONS1  

E-Print Network [OSTI]

1 COMPARING ALASKA'S OIL PRODUCTION TAXES: INCENTIVES AND ASSUMPTIONS1 Matthew Berman In a recent analysis comparing the current oil production tax, More Alaska Production Act (MAPA, also known as SB 21 oil prices, production rates, and costs. He noted that comparative revenues are highly sensitive

Pantaleone, Jim

140

Reasoning by Assumption: Formalisation and Analysis of Human Reasoning Traces  

E-Print Network [OSTI]

for the traces acquired in experiments undertaken. 1 Introduction Practical reasoning processes are often not limited to single reasoning steps, but extend to traces or trajectories of a number of interrelated by assumption'. This (non-deductive) practical reasoning pattern in- volves a number of interrelated reasoning

Treur, Jan

Note: This page contains sample records for the topic "inform input assumptions" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


141

Paducah DUF6 Conversion Final EIS - Chapter 4: Environmental Impact Assessment Approach, Assumptions, and Methodology  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Paducah DUF Paducah DUF 6 Conversion Final EIS 4 ENVIRONMENTAL IMPACT ASSESSMENT APPROACH, ASSUMPTIONS, AND METHODOLOGY This EIS evaluates potential impacts on human health and the natural environment from building and operating a DUF 6 conversion facility at three alternative locations at the Paducah site and for a no action alternative. These impacts might be positive, in that they would improve conditions in the human or natural environment, or negative, in that they would cause a decline in those conditions. This chapter provides an overview of the methods used to estimate the potential impacts associated with the EIS alternatives, summarizes the major assumptions that formed the basis of the evaluation, and provides some background information on human health

142

EIA-Assumptions to the Annual Energy Outlook - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module Coal Market Module Assumptions to the Annual Energy Outlook 2007 Coal Market Module The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, imports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2007, DOE/EIA-M060(2007) (Washington, DC, 2007). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Forty separate supply curves are developed for each of 14 supply regions, nine coal types (unique combinations of thermal grade and sulfur content), and two mine types (underground and surface). Supply curves are constructed using an econometric formulation that relates the minemouth prices of coal for the supply regions and coal types to a set of independent variables. The independent variables include: capacity utilization of mines, mining capacity, labor productivity, the user cost of capital of mining equipment, and the cost of factor inputs (labor and fuel).

143

EIA - Assumptions to the Annual Energy Outlook 2010 - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module Coal Market Module Assumptions to the Annual Energy Outlook 2010 Coal Market Module The NEMS Coal Market Module (CMM) provides projections of U.S. coal production, consumption, exports, imports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2010, DOE/EIA-M060(2010) (Washington, DC, 2010). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the projection. Forty separate supply curves are developed for each of 14 supply regions, nine coal types (unique combinations of thermal grade and sulfur content), and two mine types (underground and surface). Supply curves are constructed using an econometric formulation that relates the minemouth prices of coal for the supply regions and coal types to a set of independent variables. The independent variables include: capacity utilization of mines, mining capacity, labor productivity, the user cost of capital of mining equipment, the cost of factor inputs (labor and fuel), and other mine supply costs.

144

EIA - Assumptions to the Annual Energy Outlook 2008 - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module Coal Market Module Assumptions to the Annual Energy Outlook 2008 Coal Market Module The NEMS Coal Market Module (CMM) provides projections of U.S. coal production, consumption, exports, imports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2008, DOE/EIA-M060(2008) (Washington, DC, 2008). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the projection. Forty separate supply curves are developed for each of 14 supply regions, nine coal types (unique combinations of thermal grade and sulfur content), and two mine types (underground and surface). Supply curves are constructed using an econometric formulation that relates the minemouth prices of coal for the supply regions and coal types to a set of independent variables. The independent variables include: capacity utilization of mines, mining capacity, labor productivity, the user cost of capital of mining equipment, and the cost of factor inputs (labor and fuel).

145

PROJECT MANGEMENT PLAN EXAMPLES Policy & Operational Decisions, Assumptions  

Broader source: Energy.gov (indexed) [DOE]

Policy & Operational Decisions, Assumptions Policy & Operational Decisions, Assumptions and Strategies Examples 1 & 2 Example 1 1.0 Summary The 322-M Metallurgical Laboratory is currently categorized as a Radiological Facility. It is inactive with no future DOE mission. In May of 1998 it was ranked Number 45 in the Inactive Facilities Risk Ranking database which the Facilities Decommissioning Division maintains. A short-term surveillance and maintenance program is in-place while the facility awaits final deactivation. Completion of the end points described in this deactivation project plan will place the 322-M facility into an End State that can be described as "cold and dark". The facility will be made passively safe requiring minimal surveillance and no scheduled maintenance.

146

Cost and Performance Assumptions for Modeling Electricity Generation Technologies  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Cost and Performance Cost and Performance Assumptions for Modeling Electricity Generation Technologies Rick Tidball, Joel Bluestein, Nick Rodriguez, and Stu Knoke ICF International Fairfax, Virginia Subcontract Report NREL/SR-6A20-48595 November 2010 NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC. National Renewable Energy Laboratory 1617 Cole Boulevard Golden, Colorado 80401 303-275-3000 * www.nrel.gov Contract No. DE-AC36-08GO28308 Cost and Performance Assumptions for Modeling Electricity Generation Technologies Rick Tidball, Joel Bluestein, Nick Rodriguez, and Stu Knoke ICF International Fairfax, Virginia NREL Technical Monitor: Jordan Macknick

147

Intermediate inputs and economic productivity  

Science Journals Connector (OSTI)

...US sectoral-level production functions. Both the...316) and plastics and rubber-(326). The relationship...coefficients of the production function sum to a quantity...inputs were used in the production process. 16 This estimate...products 326 plastics and rubber products 327 non-metallic...

2013-01-01T23:59:59.000Z

148

PADD 3 Weekly Inputs & Utilization  

U.S. Energy Information Administration (EIA) Indexed Site

Utilization 97.4 95.3 94.8 94.9 95.9 92.2 2010-2015 Refiner and Blender Net Inputs Motor Gasoline Blending Components -2,174 -2,008 -2,012 -2,095 -2,214 -2,291 2004-2015 RBOB -283...

149

Resources Abstracts Input Transaction Form  

E-Print Network [OSTI]

#12;Resources Abstracts Input Transaction Form 4. Title 5. Report Date 6.Urban Aquaculture Covered The University of the District of Columbia 12. Sponsoring Organization Water Resources Research of the rainbow trout (Salmo gairdneri) in a closed recycling water system in an urban environment is described

District of Columbia, University of the

150

Charged-Particle Thermonuclear Reaction Rates: III. Nuclear Physics Input  

E-Print Network [OSTI]

The nuclear physics input used to compute the Monte Carlo reaction rates and probability density functions that are tabulated in the second paper of this series (Paper II) is presented. Specifically, we publish the input files to the Monte Carlo reaction rate code RatesMC, which is based on the formalism presented in the first paper of this series (Paper I). This data base contains overwhelmingly experimental nuclear physics information. The survey of literature for this review was concluded in November 2009.

Christian Iliadis; Richard Longland; Art Champagne; Alain Coc

2010-04-23T23:59:59.000Z

151

EIA-Assumptions to the Annual Energy Outlook - Electricity Market Module  

Gasoline and Diesel Fuel Update (EIA)

Electricity Market Module Electricity Market Module Assumptions to the Annual Energy Outlook 2007 Electricity Market Module The NEMS Electricity Market Module (EMM) represents the capacity planning, dispatching, and pricing of electricity. It is composed of four submodules-electricity capacity planning, electricity fuel dispatching, load and demand electricity, and electricity finance and pricing. It includes nonutility capacity and generation, and electricity transmission and trade. A detailed description of the EMM is provided in the EIA publication, Electricity Market Module of the National Energy Modeling System 2007, DOE/EIA- M068(2007). Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are assumptions about the operations of the electricity sector and the costs of various options in each of the EMM submodules. This section describes the model parameters and assumptions used in EMM. It includes a discussion of legislation and regulations that are incorporated in EMM as well as information about the climate change action plan. The various electricity and technology cases are also described.

152

Total electron and proton energy input during auroral substorms: Remote sensing with IMAGE-FUV  

E-Print Network [OSTI]

, it is found that the most critical factor is the assumption made on the energy of the auroral protonsTotal electron and proton energy input during auroral substorms: Remote sensing with IMAGE-FUV B and proton energy fluxes. The proton energy flux is derived from the Lyman a measurements on the basis

California at Berkeley, University of

153

Diversion assumptions for high-powered research reactors. ISPO C-50 Phase 1  

SciTech Connect (OSTI)

This study deals with diversion assumptions for high-powered research reactors -- specifically, MTR fuel; pool- or tank-type research reactors with light-water moderator; and water, beryllium, or graphite reflectors, and which have a power level of 25 MW(t) or more. The objective is to provide assistance to the IAEA in documentation of criteria and inspection observables related to undeclared plutonium production in the reactors described above, including: criteria for undeclared plutonium production, necessary design information for implementation of these criteria, verification guidelines including neutron physics and heat transfer, and safeguards measures to facilitate the detection of undeclared plutonium production at large research reactors.

Binford, F.T.

1984-01-01T23:59:59.000Z

154

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

unfinished oil imports, other refinery inputs (including alcohols, unfinished oil imports, other refinery inputs (including alcohols, ethers, and bioesters), natural gas plant liquids production, and refinery processing gain. In addition, the PMM projects capacity expansion and fuel consumption at domestic refineries. The PMM contains a linear programming (LP) representation of U.S. refining activities in the five Petroleum Area Defense Districts (PADDs) (Figure 9). The LP model is created by aggregating individual refineries within a PADD into one representative refinery, and linking all five PADD's via crude and product transit links. This representation provides the marginal costs of production for a number of conventional and new petroleum products. In order to interact with other NEMS modules with different regional representations,

155

Assumptions to the Annual Energy Outlook 2000 - Electricity Market Demand  

Gasoline and Diesel Fuel Update (EIA)

Electricity Market Module (EMM) represents the planning, operations, and pricing of electricity in the United States. It is composed of four primary submodules—electricity capacity planning, electricity fuel dispatching, load and demand-side management, and electricity finance and pricing. In addition, nonutility generation and supply and electricity transmission and trade are represented in the planning and dispatching submodules. Electricity Market Module (EMM) represents the planning, operations, and pricing of electricity in the United States. It is composed of four primary submodules—electricity capacity planning, electricity fuel dispatching, load and demand-side management, and electricity finance and pricing. In addition, nonutility generation and supply and electricity transmission and trade are represented in the planning and dispatching submodules. Based on fuel prices and electricity demands provided by the other modules of the NEMS, the EMM determines the most economical way to supply electricity, within environmental and operational constraints. There are assumptions about the operations of the electricity sector and the costs of various options in each of the EMM submodules. The major assumptions are summarized below.

156

Assumptions to the Annual Energy Outlook 1999 - Natural Gas Transmission  

Gasoline and Diesel Fuel Update (EIA)

The NEMS Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each forecast year. These are derived by obtaining market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and distribution network that links them. In addition, natural gas flow patterns are a function of the pattern in the previous year, coupled with the relative prices of gas supply options as translated to the represented market “hubs.” The major assumptions used within the NGTDM are grouped into five general categories. They relate to (1) the classification of demand into core and noncore transportation service classes, (2) the pricing of transmission and distribution services, (3) pipeline and storage capacity expansion and utilization, (4) the implementation of recent regulatory reform, and (5) the implementation of provisions of the Climate Change Action Plan (CCAP). A complete listing of NGTDM assumptions and in-depth methodology descriptions are presented in Model Documentation Report: Natural Gas Transmission and Distribution Model of the National Energy Modeling System, DOE/EIA-MO62/1, January 1999.

157

Assumptions to the Annual Energy Outlook 2000 - Natural Gas Transmission  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each forecast year. These are derived by solving for the market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and distribution network that links them. In addition, natural gas flow patterns are a function of the pattern in the previous year, coupled with the relative prices of gas supply options as translated to the represented market “hubs.” The major assumptions used within the NGTDM are grouped into five general categories. They relate to (1) the classification of demand into core and noncore transportation service classes, (2) the pricing of transmission and distribution services, (3) pipeline and storage capacity expansion and utilization, (4) the implementation of recent regulatory reform, and (5) the implementation of provisions of the Climate Change Action Plan (CCAP). A complete listing of NGTDM assumptions and in-depth methodology descriptions are presented in Model Documentation: Natural Gas Transmission and Distribution Model of the National Energy Modeling System, Model Documentation 2000, DOE/EIA-M062(2000), January 2000.

158

The contour method cutting assumption: error minimization and correction  

SciTech Connect (OSTI)

The recently developed contour method can measure 2-D, cross-sectional residual-stress map. A part is cut in two using a precise and low-stress cutting technique such as electric discharge machining. The contours of the new surfaces created by the cut, which will not be flat if residual stresses are relaxed by the cutting, are then measured and used to calculate the original residual stresses. The precise nature of the assumption about the cut is presented theoretically and is evaluated experimentally. Simply assuming a flat cut is overly restrictive and misleading. The critical assumption is that the width of the cut, when measured in the original, undeformed configuration of the body is constant. Stresses at the cut tip during cutting cause the material to deform, which causes errors. The effect of such cutting errors on the measured stresses is presented. The important parameters are quantified. Experimental procedures for minimizing these errors are presented. An iterative finite element procedure to correct for the errors is also presented. The correction procedure is demonstrated on experimental data from a steel beam that was plastically bent to put in a known profile of residual stresses.

Prime, Michael B [Los Alamos National Laboratory; Kastengren, Alan L [ANL

2010-01-01T23:59:59.000Z

159

DOE Seeks Industry Input on Nickel Disposition Strategy | Department of  

Broader source: Energy.gov (indexed) [DOE]

DOE Seeks Industry Input on Nickel Disposition Strategy DOE Seeks Industry Input on Nickel Disposition Strategy DOE Seeks Industry Input on Nickel Disposition Strategy March 23, 2012 - 12:00pm Addthis WASHINGTON, D.C. - The Energy Department's prime contractor, Fluor-B&W Portsmouth (FBP), managing the Portsmouth Gaseous Diffusion Plant (GDP), issued a request for Expressions of Interest (EOI) seeking industry input to support the development of an acquisition strategy for potential disposition of DOE nickel. The EOI requests technical, financial, and product market information to review the feasibility of technologies capable of decontaminating the nickel to a level indistinguishable from what is commercially available, such that it could be safely recycled and reused. The EOI scope is for 6,400 tons of nickel to be recovered from the uranium enrichment process

160

Input to the 2012-2021 Strategic Plan  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Related Federal Climate Efforts Related Federal Climate Efforts Input to the 2012-2021 Strategic Plan Print E-mail Engaging Stakeholders The USGCRP is dedicated to engaging stakeholders in strategic planning efforts. Our community outreach activities created a dialogue with our stakeholders through various communication channels, such as opportunities for interagency collaboration, town hall meetings, public presentations and listening sessions. These channels alongside our 60 day public comment period enabled the program to incorporate stakeholder input int the process of drafting this decadal plan. In addition, we welcome input - particularly on the future direction of USGCRP and on the climate information you need and use. Please send your comments to input@usgcrp.gov. Listening Sessions

Note: This page contains sample records for the topic "inform input assumptions" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


161

DOE Seeks Industry Input on Nickel Disposition Strategy | Department of  

Broader source: Energy.gov (indexed) [DOE]

Industry Input on Nickel Disposition Strategy Industry Input on Nickel Disposition Strategy DOE Seeks Industry Input on Nickel Disposition Strategy March 23, 2012 - 12:00pm Addthis WASHINGTON, D.C. - The Energy Department's prime contractor, Fluor-B&W Portsmouth (FBP), managing the Portsmouth Gaseous Diffusion Plant (GDP), issued a request for Expressions of Interest (EOI) seeking industry input to support the development of an acquisition strategy for potential disposition of DOE nickel. The EOI requests technical, financial, and product market information to review the feasibility of technologies capable of decontaminating the nickel to a level indistinguishable from what is commercially available, such that it could be safely recycled and reused. The EOI scope is for 6,400 tons of nickel to be recovered from the uranium enrichment process

162

Assumptions to the Annual Energy Outlook 1999 - Table 1  

Gasoline and Diesel Fuel Update (EIA)

Summary of AEO99 Cases Summary of AEO99 Cases Case Name Description Integration mode Reference Baseline economic growth, world oil price, and technology assumptions Fully Integrated Low Economic Growth Gross Domestic product grows at an average annual rate of 1.5 percent, compared to the reference case growth of 2.1 percent. Fully Integrated High Economic Growth Gross domestic product grows at an average annual rate of 2.6 percent, compared to the reference case growth of 2.1 percent. Fully Integrated Low World Oil Price World oil prices are $14.57 per barrel in 2020, compared to $22.73 per barrel in the reference case. Partially Integrated High World Oil Price World oil prices are $29.35 per barrel in 2020, compared to $22.73 per barrel in the reference case. Partially Integrated Residential: 1999 Technology

163

Assumptions to the Annual Energy Outlook - Transportation Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module Assumption to the Annual Energy Outlook Transportation Demand Module The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions (see Figure 5) and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars, light trucks, sport utility vehicles and vans), commercial light trucks (8,501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), freight and passenger airplanes, freight rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption.

164

EIA-Assumptions to the Annual Energy Outlook - Macroeconomic Activity  

Gasoline and Diesel Fuel Update (EIA)

Macroeconomic Activity Module Macroeconomic Activity Module Assumptions to the Annual Energy Outlook 2007 Macroeconomic Activity Module The Macroeconomic Activity Module (MAM) represents the interaction between the U.S. economy as a whole and energy markets. The rate of growth of the economy, measured by the growth in gross domestic product (GDP) is a key determinant of the growth in demand for energy. Associated economic factors, such as interest rates and disposable income, strongly influence various elements of the supply and demand for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in economic growth resulting from increasing energy prices, are also reflected in this module. A detailed description of the MAM is provided in the EIA publication, Model Documentation Report: Macroeconomic Activity Module (MAM) of the National Energy Modeling System, DOE/EIA-M065(2007), (Washington, DC, January 2007).

165

EIA - Assumptions to the Annual Energy Outlook 2008 - Transportation Demand  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module Assumptions to the Annual Energy Outlook 2008 Transportation Demand Module The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions (see Figure 5) and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars and light trucks), commercial light trucks (8,501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), freight and passenger aircraft, freight rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption.

166

EIA - Assumptions to the Annual Energy Outlook 2009 - Macroeconomic  

Gasoline and Diesel Fuel Update (EIA)

Macroeconomic Activity Module Macroeconomic Activity Module Assumptions to the Annual Energy Outlook 2010 Macroeconomic Activity Module The Macroeconomic Activity Module (MAM) represents the interaction between the U.S. economy as a whole and energy markets. The rate of growth of the economy, measured by the growth in gross domestic product (GDP) is a key determinant of the growth in demand for energy. Associated economic factors, such as interest rates and disposable income, strongly influence various elements of the supply and demand for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in economic growth resulting from increasing energy prices, are also reflected in this module. A detailed description of the MAM is provided in the EIA publication, Model Document>ation Report: Macroeconomic Activity Module (MAM) of the National Energy Modeling System, DOE/EIA-M065(2009), (Washington, DC, January 2009).

167

Assumptions to the Annual Energy Outlook 2001 - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module Coal Market Module The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2001, DOE/EIA-M060(2001) January 2001. Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Separate supply curves are developed for each of 11 supply regions, and 12 coal types (unique combinations of thermal grade, sulfur content, and mine type). The modeling approach used to construct regional coal supply curves

168

Assumptions to the Annual Energy Outlook 2007 Report  

Gasoline and Diesel Fuel Update (EIA)

7), 7), (Washington, DC, January 2007). Key Assumptions The output of the U.S. economy, measured by GDP, is expected to increase by 2.9 percent between 2005 and 2030 in the reference case. Two key factors help explain the growth in GDP: the growth rate of nonfarm employment and the rate of productivity change associated with employment. As Table 3 indicates, for the Reference Case GDP growth slows down in each of the periods identified, from 3.0 percent between 2005 and 2010, to 2.9 percent between 2010 and 2020, to 2.8 percent in the between 2020 and 2030. In the near term from 2005 through 2010, the growth in nonfarm employment is low at 1.2 percent compared with 2.4 percent in the second half of the 1990s, while the economy is expected to experiencing relatively strong

169

EIA - Assumptions to the Annual Energy Outlook 2008 - Industrial Demand  

Gasoline and Diesel Fuel Update (EIA)

Industrial Demand Module Industrial Demand Module Assumptions to the Annual Energy Outlook 2008 Industrial Demand Module The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 21 manufacturing and 6 nonmanufacturing industries. The manufacturing industries are further subdivided into the energy-intensive manufacturing industries and nonenergy-intensive manufacturing industries. The manufacturing industries are modeled through the use of a detailed process flow or end use accounting procedure, whereas the nonmanufacturing industries are modeled with substantially less detail (Table 17). The Industrial Demand Module projects energy consumption at the four Census region level (see Figure 5); energy consumption at the Census Division level is estimated by allocating the Census region projection using the SEDS1 data.

170

EIA - Assumptions to the Annual Energy Outlook 2008 - Macroeconomic  

Gasoline and Diesel Fuel Update (EIA)

Macroeconomic Activity Module Macroeconomic Activity Module Assumptions to the Annual Energy Outlook 2008 Macroeconomic Activity Module The Macroeconomic Activity Module (MAM) represents the interaction between the U.S. economy as a whole and energy markets. The rate of growth of the economy, measured by the growth in gross domestic product (GDP) is a key determinant of the growth in demand for energy. Associated economic factors, such as interest rates and disposable income, strongly influence various elements of the supply and demand for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in economic growth resulting from increasing energy prices, are also reflected in this module. A detailed description of the MAM is provided in the EIA publication, Model Documentation Report: Macroeconomic Activity Module (MAM) of the National Energy Modeling System, DOE/EIA-M065(2007), (Washington, DC, January 2007).

171

EIA - Assumptions to the Annual Energy Outlook 2009 - Macroeconomic  

Gasoline and Diesel Fuel Update (EIA)

Macroeconomic Activity Module Macroeconomic Activity Module Assumptions to the Annual Energy Outlook 2009 Macroeconomic Activity Module The Macroeconomic Activity Module (MAM) represents the interaction between the U.S. economy as a whole and energy markets. The rate of growth of the economy, measured by the growth in gross domestic product (GDP) is a key determinant of the growth in demand for energy. Associated economic factors, such as interest rates and disposable income, strongly influence various elements of the supply and demand for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in economic growth resulting from increasing energy prices, are also reflected in this module. A detailed description of the MAM is provided in the EIA publication, Model Documentation Report: Macroeconomic Activity Module (MAM) of the National Energy Modeling System, DOE/EIA-M065(2008), (Washington, DC, January 2008).

172

EIA - Assumptions to the Annual Energy Outlook 2008 - Commercial Demand  

Gasoline and Diesel Fuel Update (EIA)

Commercial Demand Module Commercial Demand Module Assumptions to the Annual Energy Outlook 2008 Commercial Demand Module The NEMS Commercial Sector Demand Module generates projections of commercial sector energy demand through 2030. The definition of the commercial sector is consistent with EIA’s State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial. Since most of commercial energy consumption occurs in buildings, the commercial module relies on the data from the EIA Commercial Buildings Energy Consumption Survey (CBECS) for characterizing the commercial sector activity mix as well as the equipment stock and fuels consumed to provide end use services.1

173

Assumptions to the Annual Energy Outlook - Industrial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Industrial Demand Module Industrial Demand Module Assumption to the Annual Energy Outlook Industrial Demand Module Table 17. Industry Categories Printer Friendly Version Energy-Intensive Manufacturing Nonenergy-Intensive Manufacturing Nonmanufacturing Industries Food and Kindred Products (NAICS 311) Metals-Based Durables (NAICS 332-336) Agricultural Production -Crops (NAICS 111) Paper and Allied Products (NAICS 322) Balance of Manufacturing (all remaining manufacturing NAICS) Other Agriculture Including Livestock (NAICS112- 115) Bulk Chemicals (NAICS 32B) Coal Mining (NAICS 2121) Glass and Glass Products (NAICS 3272) Oil and Gas Extraction (NAICS 211) Hydraulic Cement (NAICS 32731) Metal and Other Nonmetallic Mining (NAICS 2122- 2123) Blast Furnaces and Basic Steel (NAICS 331111) Construction (NAICS233-235)

174

EIA - Assumptions to the Annual Energy Outlook 2009 - Commercial Demand  

Gasoline and Diesel Fuel Update (EIA)

Commercial Demand Module Commercial Demand Module Assumptions to the Annual Energy Outlook 2009 Commercial Demand Module The NEMS Commercial Sector Demand Module generates projections of commercial sector energy demand through 2030. The definition of the commercial sector is consistent with EIA’s State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial. Since most of commercial energy consumption occurs in buildings, the commercial module relies on the data from the EIA Commercial Buildings Energy Consumption Survey (CBECS) for characterizing the commercial sector activity mix as well as the equipment stock and fuels consumed to provide end use services.1

175

EIA - Assumptions to the Annual Energy Outlook 2009 - Transportation Demand  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module Assumptions to the Annual Energy Outlook 2009 Transportation Demand Module The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions (see Figure 5) and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars and light trucks), commercial light trucks (8,501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), freight and passenger aircraft, freight, rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption.

176

EIA - Assumptions to the Annual Energy Outlook 2010 - Commercial Demand  

Gasoline and Diesel Fuel Update (EIA)

Commercial Demand Module Commercial Demand Module Assumptions to the Annual Energy Outlook 2009 Commercial Demand Module The NEMS Commercial Sector Demand Module generates projections of commercial sector energy demand through 2035. The definition of the commercial sector is consistent with EIA’s State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial. Since most of commercial energy consumption occurs in buildings, the commercial module relies on the data from the EIA Commercial Buildings Energy Consumption Survey (CBECS) for characterizing the commercial sector activity mix as well as the equipment stock and fuels consumed to provide end use services [1].

177

Assumptions to the Annual Energy Outlook 2002 - Coal Market Module  

Gasoline and Diesel Fuel Update (EIA)

Coal Market Module Coal Market Module The NEMS Coal Market Module (CMM) provides forecasts of U.S. coal production, consumption, exports, distribution, and prices. The CMM comprises three functional areas: coal production, coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, Coal Market Module of the National Energy Modeling System 2002, DOE/EIA-M060(2002) (Washington, DC, January 2002). Key Assumptions Coal Production The coal production submodule of the CMM generates a different set of supply curves for the CMM for each year of the forecast. Separate supply curves are developed for each of 11 supply regions and 12 coal types (unique combinations of thermal grade, sulfur content, and mine type). The modeling approach used to construct regional coal supply curves

178

Assumptions to the Annual Energy Outlook 2001 - Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module The NEMS Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohol and ethers, natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. The PMM contains a linear programming representation of refining activities in three U.S. regions. This representation provides the marginal costs of production for a number of traditional and new petroleum products. The linear programming results are used to determine end-use product prices for

179

Assumptions to the Annual Energy Outlook 2002 - Petroleum Market Module  

Gasoline and Diesel Fuel Update (EIA)

Petroleum Market Module Petroleum Market Module The NEMS Petroleum Market Module (PMM) forecasts petroleum product prices and sources of supply for meeting petroleum product demand. The sources of supply include crude oil (both domestic and imported), petroleum product imports, other refinery inputs including alcohol and ethers, natural gas plant liquids production, and refinery processing gain. In addition, the PMM estimates capacity expansion and fuel consumption of domestic refineries. The PMM contains a linear programming representation of refining activities in three U.S. regions. This representation provides the marginal costs of production for a number of traditional and new petroleum products. The linear programming results are used to determine end-use product prices for

180

Assumptions to the Annual Energy Outlook - Residential Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module Residential Demand Module Assumption to the Annual Energy Outlook Residential Demand Module The NEMS Residential Demand Module forecasts future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimates of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the “unit energy consumption” by appliance (or UEC—in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new units, retires existing housing units, and retires and replaces appliances. The primary exogenous drivers for the module are housing starts by type (single-family, multifamily and mobile homes) and Census Division and prices for each energy source for each of the nine Census Divisions (see Figure 5). The Residential Demand Module also requires projections of available equipment and their installed costs over the forecast horizon. Over time, equipment efficiency tends to increase because of general technological advances and also because of Federal and/or state efficiency standards. As energy prices and available equipment changes over the forecast horizon, the module includes projected changes to the type and efficiency of equipment purchased as well as projected changes in the usage intensity of the equipment stock.

Note: This page contains sample records for the topic "inform input assumptions" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


181

EIA - Assumptions to the Annual Energy Outlook 2009 - Residential Demand  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module Residential Demand Module Assumptions to the Annual Energy Outlook 2009 Residential Demand Module The NEMS Residential Demand Module projects future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimate of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the “unit energy consumption” by appliance (or UEC—in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new units, retires existing housing units, and retires and replaces appliances. The primary exogenous drivers for the module are housing starts by type (single-family, multifamily and mobile homes) and Census Division and prices for each energy source for each of the nine Census Divisions (see Figure 5). The Residential Demand Module also requires projections of available equipment and their installed costs over the projection horizon. Over time, equipment efficiency tends to increase because of general technological advances and also because of Federal and/or state efficiency standards. As energy prices and available equipment changes over the projection horizon, the module includes projected changes to the type and efficiency of equipment purchased as well as projected changes in the usage intensity of the equipment stock.

182

EIA - Assumptions to the Annual Energy Outlook 2008 - International Energy  

Gasoline and Diesel Fuel Update (EIA)

International Energy Module International Energy Module Assumptions to the Annual Energy Outlook 2008 International Energy Module The International Energy Module (IEM) performs two tasks in all NEMS runs. First, the module reads exogenously global and U.S.A. petroleum liquids supply and demand curves (1 curve per year; 2008-2030; approximated, isoelastic fit to previous NEMS results). These quantities are not modeled directly in NEMS. Previous versions of the IEM adjusted these quantities after reading in initial values. In an attempt to more closely integrate the AEO2008 with IEO2007 and the STEO some functionality was removed from IEM while a new algorithm was implemented. Based on the difference between U.S. total petroleum liquids production (consumption) and the expected U.S. total liquids production (consumption) at the current WTI price, curves for global petroleum liquids consumption (production) were adjusted for each year. According to previous operations, a new WTI price path was generated. An exogenous oil supply module, Generate World Oil Balances (GWOB), was also used in IEM to provide annual regional (country) level production detail for conventional and unconventional liquids.

183

Code input alternatives John C. Wright  

E-Print Network [OSTI]

Code input alternatives John C. Wright John Wright Oct 2009 ­ CSWIM Workshop@ORNL Extensible markup

Wright, John C.

184

A Simplified Universal Relation Assumption and Its Properties  

E-Print Network [OSTI]

--normal forms; schema and subschema; H.3.3. [Infor- mation Storage and Retrieval]: Information Search a real world that could possibly be of interest to a database designer. Lien especially [15, 161 has

Fagin, Ron

185

We have developed a software system that takes standard electro-cardiogram (ECG) input and interprets this input along with user-  

E-Print Network [OSTI]

a software system that takes standard electro- cardiogram (ECG) input and interprets this input along months 30 patients were monitored using a digital ECG system and this information was used to test that T wave inversions are sometimes seen on normal ECGs. Control ECGs of normal hearts were also taken

O'Sullivan, Carol

186

Modeling the cardiovascular system using a nonlinear additive autoregressive model with exogenous input  

Science Journals Connector (OSTI)

The parameters of heart rate variability and blood pressure variability have proved to be useful analytical tools in cardiovascular physics and medicine. Model-based analysis of these variabilities additionally leads to new prognostic information about mechanisms behind regulations in the cardiovascular system. In this paper, we analyze the complex interaction between heart rate, systolic blood pressure, and respiration by nonparametric fitted nonlinear additive autoregressive models with external inputs. Therefore, we consider measurements of healthy persons and patients suffering from obstructive sleep apnea syndrome (OSAS), with and without hypertension. It is shown that the proposed nonlinear models are capable of describing short-term fluctuations in heart rate as well as systolic blood pressure significantly better than similar linear ones, which confirms the assumption of nonlinear controlled heart rate and blood pressure. Furthermore, the comparison of the nonlinear and linear approaches reveals that the heart rate and blood pressure variability in healthy subjects is caused by a higher level of noise as well as nonlinearity than in patients suffering from OSAS. The residue analysis points at a further source of heart rate and blood pressure variability in healthy subjects, in addition to heart rate, systolic blood pressure, and respiration. Comparison of the nonlinear models within and among the different groups of subjects suggests the ability to discriminate the cohorts that could lead to a stratification of hypertension risk in OSAS patients.

M. Riedl; A. Suhrbier; H. Malberg; T. Penzel; G. Bretthauer; J. Kurths; N. Wessel

2008-07-24T23:59:59.000Z

187

Assumptions to the Annual Energy Outlook 1999 - Footnotes  

Gasoline and Diesel Fuel Update (EIA)

footnote.gif (3505 bytes) footnote.gif (3505 bytes) [1] Energy Information Administration, Annual Energy Outlook 1999 (AEO99), DOE/EIA-0383(99), (Washington, DC, December 1998). [2] NEMS documentation reports are available on the EIA CD-ROM and the EIA Homepage (http://www.eia.gov/bookshelf.html). For ordering information on the CD-ROM, contact STAT-USA's toll free order number: 1-800-STAT-USA or by calling (202) 482-1986. [3] Energy Information Administration, The National Energy Modeling System: An Overview 1998, DOE/EIA-0581(98), (Washington, DC, February 1998). [4] The underlying macroeconomic growth cases use DRI/McGraw-Hill’s August 1998 T250898 and February TO250298 and TP250298. [5] EIA, International Energy Outlook 1998, DOE/EIA-0484(98) (Washington DC, April 1998).

188

Assumptions to the Annual Energy Outlook 2001 - Footnotes  

Gasoline and Diesel Fuel Update (EIA)

Feedback Feedback Related Links Annual Energy Outlook2001 Supplemental Data to the AEO2001 NEMS Conference To Forecasting Home Page EIA Homepage FOOTNOTES [1] Energy Information Administration, Annual Energy Outlook 2001 (AEO2001), DOE/EIA-0383(2001), (Washington, DC, December 2000). [2] NEMS documentation reports are available on the EIA CD-ROM and the EIA Homepage (http://www.eia.gov/bookshelf.html). For ordering information on the CD-ROM, contact STAT-USA's toll free order number: 1-800-STAT-USA or by calling (202) 482-1986. [3] Energy Information Administration, The National Energy Modeling System: An Overview 2000, DOE/EIA-0581(2000), (Washington, DC, March 2000). [4] The underlying macroeconomic growth cases use Standard and Poor’s DRI February 2000 T250200 and February TO250299 and TP250299.

189

EIA-Assumptions to the Annual Energy Outlook - Oil and Gas Supply Module  

Gasoline and Diesel Fuel Update (EIA)

Oil and Gas Supply Module Oil and Gas Supply Module Assumptions to the Annual Energy Outlook 2007 Oil and Gas Supply Module Figure 7. Oil and Gas Supply Model Regions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Oil and Gas Supply Module (OGSM) constitutes a comprehensive framework with which to analyze oil and gas supply on a regional basis (Figure 7). A detailed description of the OGSM is provided in the EIA publication, Model Documentation Report: The Oil and Gas Supply Module (OGSM), DOE/EIA-M063(2006), (Washington, DC, 2006). The OGSM provides crude oil and natural gas short-term supply parameters to both the Natural Gas Transmission and Distribution Module and the Petroleum Market Module. The OGSM simulates the activity of numerous firms that produce oil and natural

190

EIA - Assumptions to the Annual Energy Outlook 2008 - Oil and Gas Supply  

Gasoline and Diesel Fuel Update (EIA)

Oil and Gas Supply Module Oil and Gas Supply Module Assumptions to the Annual Energy Outlook 2008 Oil and Gas Supply Module Figure 7. Oil and Gas Supply Module. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Oil and Gas Supply Module (OGSM) constitutes a comprehensive framework with which to analyze oil and gas supply on a regional basis (Figure 7). A detailed description of the OGSM is provided in the EIA publication, Model Documentation Report: The Oil and Gas Supply Module (OGSM), DOE/EIA-M063(2007), (Washington, DC, 2007). The OGSM provides crude oil and natural gas short-term supply parameters to both the Natural Gas Transmission and Distribution Module and the Petroleum Market Module. The OGSM simulates the activity of numerous firms that produce oil and natural

191

EIA-Assumptions to the Annual Energy Outlook - Residential Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Residential Demand Module Residential Demand Module Assumptions to the Annual Energy Outlook 2007 Residential Demand Module Figure 5. United States Census Divisions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Residential Demand Module forecasts future residential sector energy requirements based on projections of the number of households and the stock, efficiency, and intensity of use of energy-consuming equipment. The Residential Demand Module projections begin with a base year estimate of the housing stock, the types and numbers of energy-consuming appliances servicing the stock, and the "unit energy consumption" by appliance (or UEC-in million Btu per household per year). The projection process adds new housing units to the stock, determines the equipment installed in new

192

EIA-Assumptions to the Annual Energy Outlook - National Gas Transmission  

Gasoline and Diesel Fuel Update (EIA)

Natural Gas Transmission and Distribution Module Natural Gas Transmission and Distribution Module Assumptions to the Annual Energy Outlook 2007 National Gas Transmission and Distribution Module Figure 8. Natural Gas Transmission and Distribution Model Regions. Need help, contact the National Energy Information Center at 202-586-8800. The NEMS Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas production, wellhead and border prices, end-use prices, and flows of natural gas through the regional interstate network, for both a peak (December through March) and off peak period during each forecast year. These are derived by solving for the market equilibrium across the three main components of the natural gas market: the supply component, the demand component, and the transmission and distribution

193

Assumptions to the Annual Energy Outlook - Oil and Gas Supply Module  

Gasoline and Diesel Fuel Update (EIA)

Oil and Gas Supply Module Oil and Gas Supply Module Assumption to the Annual Energy Outlook Oil and Gas Supply Module Figure 7. Oil and Gas Supply Model Regions. Having problems, call our National Energy Information Center at 202-586-8800 for help. Table 50. Crude Oil Technically Recoverable Resources (Billion barrels) Printer Friendly Version Crude Oil Resource Category As of January 1, 2002 Undiscovered 56.02 Onshore 19.33 Northeast 1.47 Gulf Coast 4.76 Midcontinent 1.12 Southwest 3.25 Rocky Moutain 5.73 West Coast 3.00 Offshore 36.69 Deep (>200 meter W.D.) 35.01 Shallow (0-200 meter W.D.) 1.69 Inferred Reserves 49.14 Onshore 37.78 Northeast 0.79 Gulf Coast 0.80 Midcontinent 3.73 Southwest 14.61 Rocky Mountain 9.91 West Coast 7.94

194

Assumptions to the Annual Energy Outlook 2000 - Footnote  

Gasoline and Diesel Fuel Update (EIA)

[1] Energy Information Administration, Annual Energy Outlook 2000 (AEO2000), DOE/EIA-0383(2000), (Washington, DC, December 1999). [1] Energy Information Administration, Annual Energy Outlook 2000 (AEO2000), DOE/EIA-0383(2000), (Washington, DC, December 1999). [2] NEMS documentation reports are available on the EIA CD-ROM and the EIA Homepage (http://www.eia.gov/bookshelf.html). For ordering information on the CD-ROM, contact STAT-USA's toll free order number: 1-800-STAT-USA or by calling (202) 482-1986. [3] Energy Information Administration, The National Energy Modeling System: An Overview 1998, DOE/EIA-0581(98), (Washington, DC, February 1998). [4] The underlying macroeconomic growth cases use Standard and Poor’s DRI August 1999 T250899 and February TO250299 and TP250299. [5] PennWell Publishing Co., International Petroleum Encyclopedia, (Tulsa, OK, 1999). [6] EIA, EIA Model Documentation: World Oil Refining Logistics Demand Model, “WORLD” Reference Manual, DOE/EIA-M058, (Washington, DC, March 1994).

195

On the self-similarity assumption in dynamic models for large eddy simulations  

E-Print Network [OSTI]

that the present formulation of the DP is usually incompatible with its under- lying self-similarity assumption SSAOn the self-similarity assumption in dynamic models for large eddy simulations Daniele Carati eddy simulations and their underlying self-similarity assumption is discussed. The interpretation

Van Den Eijnden, Eric

196

Comparison of risk-dominant scenario assumptions for several TRU waste facilities in the DOE complex  

SciTech Connect (OSTI)

In order to gain a risk management perspective, the DOE Rocky Flats Field Office (RFFO) initiated a survey of other DOE sites regarding risks from potential accidents associated with transuranic (TRU) storage and/or processing facilities. Recently-approved authorization basis documents at the Rocky Flats Environmental Technology Site (RFETS) have been based on the DOE Standard 3011 risk assessment methodology with three qualitative estimates of frequency of occurrence and quantitative estimates of radiological consequences to the collocated worker and the public binned into three severity levels. Risk Class 1 and 2 events after application of controls to prevent or mitigate the accident are designated as risk-dominant scenarios. Accident Evaluation Guidelines for selection of Technical Safety Requirements (TSRs) are based on the frequency and consequence bin assignments to identify controls that can be credited to reduce risk to Risk Class 3 or 4, or that are credited for Risk Class 1 and 2 scenarios that cannot be further reduced. This methodology resulted in several risk-dominant scenarios for either the collocated worker or the public that warranted consideration on whether additional controls should be implemented. RFFO requested the survey because of these high estimates of risks that are primarily due to design characteristics of RFETS TRU waste facilities (i.e., Butler-type buildings without a ventilation and filtration system, and a relatively short distance to the Site boundary). Accident analysis methodologies and key assumptions are being compared for the DOE sites responding to the survey. This includes type of accidents that are risk dominant (e.g., drum explosion, material handling breach, fires, natural phenomena, external events, etc.), source term evaluation (e.g., radionuclide material-at-risk, chemical and physical form, damage ratio, airborne release fraction, respirable fraction, leakpath factors), dispersion analysis (e.g., meteorological assumptions, distance to receptors, plume meander, deposition, and other factors affecting the calculated {chi}/Q), dose assessments (specific activities, inhalation dose conversion factors, breathing rates), designated frequency of occurrence, and risk assignment per the DOE Standard 3011 methodology. Information from the sites is being recorded on a spreadsheet to facilitate comparisons. The first response from Westinghouse Safety Management Solutions for the Savannah River Site (SRS) also provided a detailed analysis of the major differences in methods and assumptions between RFETS and SRS, which forms much of the basis for this paper. Other sites responding to the survey include the Idaho National Engineering and Environmental Laboratory (INEEL), Hanford, and the Los Alamos National Laboratory (LANL).

Foppe, T.L. [Foppe and Associates, Inc., Golden, CO (United States); Marx, D.R. [Westinghouse Safety Management Solutions, Inc., Aiken, SC (United States)

1999-06-01T23:59:59.000Z

197

Assumptions to the Annual Energy Outlook 1999 - Acronyms  

Gasoline and Diesel Fuel Update (EIA)

acronyms.gif (3143 bytes) acronyms.gif (3143 bytes) AEO Annual Energy Outlook AEO98 Annual Energy Outlook 1998 AEO99 Annual Energy Outlook 1999 AFV AFV Alternative-Fuel Vehicle AGA American Gas Association ANGTS Alaskan Natural Gas Transportation System BEA Bureau of Economic Analysis BSC Boiler/Steam/Cogeneration BTU British Thermal Unit CAAA90 Clean Air Act Amendments of 1990 CBECS Commercial Buildings Energy Consumption Surveys CCAP Climate Change Action Plan CDD Cooling Degree-Days CNG Compressed natural gas DOE U.S. Department of Energy DRB Demonstrated Reserve Base DRI Data Resources, Inc./McGraw Hill EER Energy Efficiency Ratio EIA Energy Information Administration EIS Environmental Impact Statement EPA U.S. Environmental Protection Agency EPACT Energy Policy Act of 1992 EWG Exempt Wholesale Generator FAA Federal Aviation Administration

198

Code design for multiple-input multiple-output broadcast channels  

E-Print Network [OSTI]

Recent information theoretical results indicate that dirty-paper coding (DPC) achieves the entire capacity region of the Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC). This thesis presents practical code designs for Gaussian...

Uppal, Momin Ayub

2009-06-02T23:59:59.000Z

199

Oak Ridge's EM Program Seeks Public Input on Cleanup | Department of  

Broader source: Energy.gov (indexed) [DOE]

Seeks Public Input on Cleanup Seeks Public Input on Cleanup Oak Ridge's EM Program Seeks Public Input on Cleanup April 25, 2013 - 12:00pm Addthis Oak Ridge’s EM leadership informed members of the public about projects and goals and answered questions during a public workshop this week. Oak Ridge's EM leadership informed members of the public about projects and goals and answered questions during a public workshop this week. Local residents and other stakeholders listen to Oak Ridge's EM senior leadership in a public workshop to learn about EM and provide input about future mission work. Local residents and other stakeholders listen to Oak Ridge's EM senior leadership in a public workshop to learn about EM and provide input about future mission work. Oak Ridge EM Manager Mark Whitney addresses participants on EM’s mission and priorities.

200

Oak Ridge's EM Program Seeks Public Input on Cleanup | Department of  

Broader source: Energy.gov (indexed) [DOE]

Oak Ridge's EM Program Seeks Public Input on Cleanup Oak Ridge's EM Program Seeks Public Input on Cleanup Oak Ridge's EM Program Seeks Public Input on Cleanup April 25, 2013 - 12:00pm Addthis Oak Ridge’s EM leadership informed members of the public about projects and goals and answered questions during a public workshop this week. Oak Ridge's EM leadership informed members of the public about projects and goals and answered questions during a public workshop this week. Local residents and other stakeholders listen to Oak Ridge's EM senior leadership in a public workshop to learn about EM and provide input about future mission work. Local residents and other stakeholders listen to Oak Ridge's EM senior leadership in a public workshop to learn about EM and provide input about future mission work. Oak Ridge EM Manager Mark Whitney addresses participants on EM’s mission and priorities.

Note: This page contains sample records for the topic "inform input assumptions" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


201

National Climate Assessment: Available Technical Inputs  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Available Technical Inputs Print E-mail Available Technical Inputs Print E-mail Technical inputs for the 2013 National Climate Assessment were due March 1, 2012. Please note that these reports were submitted independently to the National Climate Assessment for consideration and have not been reviewed by the National Climate Assessment Development and Advisory Committee. Links to agency-sponsored reports will be posted here as they are made available. Sectors National Climate Assessment Health Sector Literature Review and Bibliography. Technical Input for the Interagency Climate Change and Human Health Group, September 2012. Overview Bibliography Bibliography User's Guide Search Strategy and Results Walthall et al. 2012. Climate Change and Agriculture in the United States: Effects and Adaptation. USDA Technical Bulletin 1935. Washington, DC. 186 pages. | Report FAQs

202

Evaluation of boolean formulas with restricted inputs  

E-Print Network [OSTI]

In this thesis, I will investigate the running time of quantum algorithms for evaluating boolean functions when the input is promised to satisfy certain conditions. The two quantum algorithms considered in this paper are ...

Zhan, Bohua

2010-01-01T23:59:59.000Z

203

Generation of RTL verification input stimulus  

E-Print Network [OSTI]

This thesis presents an approach for generating input stimulus for verification of register-transfer level (RTL) design of VLSI circuits. RTL design is often subjected to a significant verification effort due to errors introduced during manual...

Selvarathinam, Anand Manivannan

2012-06-07T23:59:59.000Z

204

Information input for multi-stage stochastic programs  

Science Journals Connector (OSTI)

......worst-case approach to risk management are described...Operations Research & Management Science). New York...semidefinite programming approach to optimal moment...Man- agement Science: Stochastic...Applications to Risk Management. Princeton......

Jana Cerbkov

2010-04-01T23:59:59.000Z

205

US Nuclear Regulatory Commission Input to DOE Request for Information...  

Broader source: Energy.gov (indexed) [DOE]

Comments relevant to the following two sections of the RFI: "Long Term Issues: Managing a Grid with High Penetration of New Technologies" and "Reliability and Cyber-Security," US...

206

OIKOS 101: 499504, 2003 Do seedlings in gaps interact? A field test of assumptions in ESS  

E-Print Network [OSTI]

OIKOS 101: 499­504, 2003 Do seedlings in gaps interact? A field test of assumptions in ESS seed seedlings in gaps interact? A field test of assumptions in ESS seed size models. ­ Oikos 101: 499­504. ESS for the occupancy of `safe sites' or vegetation gaps. If mortality rates are high and/or frequency-independent, ESS

Silvertown, Jonathan

207

Granular Matter 4(3) (2002) How good is the equipartition assumption for the transport  

E-Print Network [OSTI]

Granular Matter 4(3) (2002) How good is the equipartition assumption for the transport properties of a granular mixture? Meheboob Alam (1) , Stefan Luding (1;2) ? Abstract Kinetic-theory, with the assumption of equipar- tition of granular energy, suggests that the pressure and viscosity of a granular mixture vary

Luding, Stefan

208

Impact of assumption of log-normal distribution on monthly rainfall estimation from TMI  

E-Print Network [OSTI]

The log-normal assumption for the distribution of the rain rates used for the estimation of monthly rain totals proposed in Wilheit et al 1991 was examined. Since the log-normal assumption was originally used for the SSM/I, it is now necessary to re...

Lee, Dong Heon

2012-06-07T23:59:59.000Z

209

A new scenario framework for climate change research: The concept of Shared Climate Policy Assumptions  

SciTech Connect (OSTI)

The paper presents the concept of shared climate policy assumptions as an important element of the new scenario framework. Shared climate policy assumptions capture key climate policy dimensions such as the type and scale of mitigation and adaptation measures. They are not specified in the socio-economic reference pathways, and therefore introduce an important third dimension to the scenario matrix architecture. Climate policy assumptions will have to be made in any climate policy scenario, and can have a significant impact on the scenario description. We conclude that a meaningful set of shared climate policy assumptions is useful for grouping individual climate policy analyses and facilitating their comparison. Shared climate policy assumptions should be designed to be policy relevant, and as a set to be broad enough to allow a comprehensive exploration of the climate change scenario space.

Kriegler, Elmar; Edmonds, James A.; Hallegatte, Stephane; Ebi, Kristie L.; Kram, Tom; Riahi, Keywan; Winkler, Harald; Van Vuuren, Detlef

2014-04-01T23:59:59.000Z

210

Documentation of Calculation Methodology, Input Data, and Infrastructure  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Documentation of Calculation Methodology, Input Data, and Infrastructure Documentation of Calculation Methodology, Input Data, and Infrastructure for the Home Energy Saver Web Site Title Documentation of Calculation Methodology, Input Data, and Infrastructure for the Home Energy Saver Web Site Publication Type Report LBNL Report Number LBNL-51938 Year of Publication 2005 Authors Pinckard, Margaret J., Richard E. Brown, Evan Mills, James D. Lutz, Mithra M. Moezzi, Celina S. Atkinson, Christopher A. Bolduc, Gregory K. Homan, and Katie Coughlin Document Number LBNL-51938 Pagination 108 Date Published July 13 Publisher Lawrence Berkeley National Laboratory City Berkeley Abstract The Home Energy Saver (HES, http://HomeEnergySaver.lbl.gov) is an interactive web site designed to help residential consumers make decisions about energy use in their homes. This report describes the underlying methods and data for estimating energy consumption. Using engineering models, the site estimates energy consumption for six major categories (end uses); heating, cooling, water heating, major appliances, lighting, and miscellaneous equipment. The approach taken by the Home Energy Saver is to provide users with initial results based on a minimum of user input, allowing progressively greater control in specifying the characteristics of the house and energy consuming appliances. Outputs include energy consumption (by fuel and end use), energy-related emissions (carbon dioxide), energy bills (total and by fuel and end use), and energy saving recommendations. Real-world electricity tariffs are used for many locations, making the bill estimates even more accurate. Where information about the house is not available from the user, default values are used based on end-use surveys and engineering studies. An extensive body of qualitative decision-support information augments the analytical results.

211

Moldy Assumptions  

E-Print Network [OSTI]

sustainability movements. 2 Despite these noble intentions, using human responsibility as a base for architecture

Heully, Gustave Paul

2012-01-01T23:59:59.000Z

212

Total Blender Net Input of Petroleum Products  

U.S. Energy Information Administration (EIA) Indexed Site

Input Input Product: Total Input Natural Gas Plant Liquids and Liquefied Refinery Gases Pentanes Plus Liquid Petroleum Gases Normal Butane Isobutane Other Liquids Oxygenates/Renewables Methyl Tertiary Butyl Ether (MTBE) Renewable Fuels (incl. Fuel Ethanol) Fuel Ethanol Renewable Diesel Fuel Other Renewable Fuels Unfinished Oils (net) Unfinished Oils, Naphthas and Lighter Unfinished Oils, Kerosene and Light Gas Oils Unfinished Oils, Heavy Gas Oils Residuum Motor Gasoline Blending Components (MGBC) (net) MGBC - Reformulated MGBC - Reformulated - RBOB MGBC - Reformulated, RBOB for Blending w/ Alcohol MGBC - Reformulated, RBOB for Blending w/ Ether MGBC - Reformulated, GTAB MGBC - Conventional MGBC - Conventional, CBOB MGBC - Conventional, GTAB MGBC - Other Conventional Period-Unit: Monthly-Thousand Barrels Monthly-Thousand Barrels per Day Annual-Thousand Barrels Annual-Thousand Barrels per Day

213

Characterization of industrial process waste heat and input heat streams  

SciTech Connect (OSTI)

The nature and extent of industrial waste heat associated with the manufacturing sector of the US economy are identified. Industry energy information is reviewed and the energy content in waste heat streams emanating from 108 energy-intensive industrial processes is estimated. Generic types of process equipment are identified and the energy content in gaseous, liquid, and steam waste streams emanating from this equipment is evaluated. Matchups between the energy content of waste heat streams and candidate uses are identified. The resultant matrix identifies 256 source/sink (waste heat/candidate input heat) temperature combinations. (MHR)

Wilfert, G.L.; Huber, H.B.; Dodge, R.E.; Garrett-Price, B.A.; Fassbender, L.L.; Griffin, E.A.; Brown, D.R.; Moore, N.L.

1984-05-01T23:59:59.000Z

214

CBE UFAD cost analysis tool: Life cycle cost model, issues and assumptions  

E-Print Network [OSTI]

Building Maintenance and Repair Cost Reference. WhitestoneJ. Wallis and H. Lin. 2008. CBE UFAD Cost Analysis Tool:UFAD First Cost Model, Issues and Assumptions. Center for

Webster, Tom; Benedek, Corinne; Bauman, Fred

2008-01-01T23:59:59.000Z

215

Microwave Properties of Ice-Phase Hydrometeors for Radar and Radiometers: Sensitivity to Model Assumptions  

Science Journals Connector (OSTI)

A simplified framework is presented for assessing the qualitative sensitivities of computed microwave properties, satellite brightness temperatures, and radar reflectivities to assumptions concerning the physical properties of ice-phase ...

Benjamin T. Johnson; Grant W. Petty; Gail Skofronick-Jackson

2012-12-01T23:59:59.000Z

216

Behavioral Assumptions Underlying California Residential Sector Energy Efficiency Programs (2009 CIEE Report)  

Broader source: Energy.gov [DOE]

This paper examines the behavioral assumptions that underlie Californias residential sector energy efficiency programs and recommends improvements that will help to advance the states ambitious greenhouse gas reduction goals.

217

Length measurement of a moving rod by a single observer without assumptions concerning its magnitude  

E-Print Network [OSTI]

We extend the results presented by Weinstein concerning the measurement of the length of a moving rod by a single observer, without making assumptions concerning the distance between the moving rod and the observer who measures its length.

Bernhard Rothenstein; Ioan Damian

2005-07-03T23:59:59.000Z

218

Assumptions about the U.S., the EU, NATO, and their Impact on the Transatlantic Agenda  

Science Journals Connector (OSTI)

I propose in this paper to discuss, from an American perspective, the assumptions and assertions that influence the way that I look at foreign policy events at the end of this decade. I will conclude with a fe...

Stanley Sloan

2000-01-01T23:59:59.000Z

219

DOE Seeks Public Input on an Integrated, Interagency Pre-Application  

Broader source: Energy.gov (indexed) [DOE]

Seeks Public Input on an Integrated, Interagency Seeks Public Input on an Integrated, Interagency Pre-Application Process for Transmission Authorizations DOE Seeks Public Input on an Integrated, Interagency Pre-Application Process for Transmission Authorizations August 29, 2013 - 9:09am Addthis A Request for Information (RFI) seeking public input for a draft Integrated, Interagency Pre-application (IIP) Process was published in the Federal Register on August 29, 2013. The Federal Register Notice is available now for downloading. Comments must be received on or before September 30, 2013. As comments are received, they will be posted online. The proposed IIP Process is intended to improve interagency and intergovernmental coordination focused on ensuring that project proponents develop and submit accurate and complete information early in the project

220

DOE Seeks Public Input on an Integrated, Interagency Pre-Application  

Broader source: Energy.gov (indexed) [DOE]

DOE Seeks Public Input on an Integrated, Interagency DOE Seeks Public Input on an Integrated, Interagency Pre-Application Process for Transmission Authorizations DOE Seeks Public Input on an Integrated, Interagency Pre-Application Process for Transmission Authorizations August 29, 2013 - 9:09am Addthis A Request for Information (RFI) seeking public input for a draft Integrated, Interagency Pre-application (IIP) Process was published in the Federal Register on August 29, 2013. The Federal Register Notice is available now for downloading. Comments must be received on or before September 30, 2013. As comments are received, they will be posted online. The proposed IIP Process is intended to improve interagency and intergovernmental coordination focused on ensuring that project proponents develop and submit accurate and complete information early in the project

Note: This page contains sample records for the topic "inform input assumptions" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


221

Watfactory Virtual Manufacturing Process Varying Inputs  

E-Print Network [OSTI]

with the virtual process: · Allows quick exploration (i.e. during a short course) of process improvement ideasWatfactory Virtual Manufacturing Process Machine 1 Machine 2 Machine 3 Stream 1 Machine B Stream 2 Inputs Can be Set by Stream z19, ..., z24 The Watfactory virtual process simulates a manufacturing

Zhu, Mu

222

Automatic interpretation of loosely encoded input  

Science Journals Connector (OSTI)

Knowledge-based systems are often brittle when given unanticipated input, i.e. assertions or queries that misalign with the ontology of the knowledge base. We call such misalignments ''loose speak''. We found that loose speak occurs frequently in interactions ... Keywords: Knowledge based systems, Metonymy, Noun compound, Question answering

James Fan; Ken Barker; Bruce Porter

2009-02-01T23:59:59.000Z

223

U.S. Weekly Inputs & Utilization  

U.S. Energy Information Administration (EIA) Indexed Site

Utilization 95.4 93.5 93.5 94.4 93.9 91 1990-2015 Refiner and Blender Net Inputs Motor Gasoline Blending Components 8 234 445 192 -558 -219 2004-2015 RBOB 167 330 371 103 9 261...

224

Contractive Systems with Inputs Eduardo D. Sontag  

E-Print Network [OSTI]

Contractive Systems with Inputs Eduardo D. Sontag Dedicated to Y. Yamamoto on the occasion of his 60th birthday Abstract. Contraction theory provides an elegant way of analyzing the behaviors-contained introduction to some basic results, with a focus on contractions with respect to non-Euclidean metrics. 1

Sontag, Eduardo

225

Green Computing input for better outcomes  

E-Print Network [OSTI]

Journal Profile: Udi Dahan Green Maturity Model for Virtualization Profiling Energy Usage for Efficient suggests that tracking energy consumption at every level will become the factor of success for greenGreen Computing input for better outcomes Learn the discipline, pursue the art, and contribute

Amir, Yair

226

Input to Priorities Panel August 7, 2012  

E-Print Network [OSTI]

Input to Priorities Panel August 7, 2012 Jeff Freidberg MIT 1 #12;The Emperor of Fusion has · Comparison (1 GW overnight cost) · Coal $ 3B · Gas $ 1B · Nuclear $ 4B · Wind $ 2B · Solar-T $ 3B · ITER $25B

227

Dale Meade regarding input international collaboration panel  

E-Print Network [OSTI]

Dale Meade regarding input international collaboration panel 1 message Saskia to add 2 comments to this discussion: 1. This regards not only international collaborations, but also national collaborations. We need to decide on using 1 video conferencing system. One of the main

228

,"Maryland Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Maryland Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_smd_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_smd_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:52 AM"

229

,"New Jersey Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","New Jersey Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_snj_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_snj_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:54 AM"

230

,"Hawaii Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Hawaii Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_shi_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_shi_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:51 AM"

231

,"Rhode Island Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Rhode Island Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sri_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sri_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:55 AM"

232

,"Louisiana Natural Gas Input Supplemental Fuels (Million Cubic Feet)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (Million Cubic Feet)" Input Supplemental Fuels (Million Cubic Feet)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Louisiana Natural Gas Input Supplemental Fuels (Million Cubic Feet)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","nga_epg0_ovi_sla_mmcfa.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/nga_epg0_ovi_sla_mmcfa.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov"

233

,"North Carolina Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","North Carolina Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_snc_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_snc_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:53 AM"

234

,"Alaska Natural Gas Input Supplemental Fuels (Million Cubic Feet)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (Million Cubic Feet)" Input Supplemental Fuels (Million Cubic Feet)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Alaska Natural Gas Input Supplemental Fuels (Million Cubic Feet)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na_epg0_ovi_sak_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na_epg0_ovi_sak_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov"

235

,"Connecticut Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Connecticut Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sct_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sct_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:50 AM"

236

,"Minnesota Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Minnesota Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_smn_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_smn_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:53 AM"

237

,"New Mexico Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","New Mexico Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_snm_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_snm_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:54 AM"

238

,"Wyoming Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Wyoming Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_swy_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_swy_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:57 AM"

239

,"Washington Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Washington Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_swa_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_swa_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:57 AM"

240

,"Wisconsin Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Wisconsin Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_swi_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_swi_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:57 AM"

Note: This page contains sample records for the topic "inform input assumptions" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


241

,"New Hampshire Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","New Hampshire Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_snh_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_snh_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:54 AM"

242

,"Kentucky Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Kentucky Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sky_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sky_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:51 AM"

243

,"Tennessee Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Tennessee Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_stn_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_stn_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:56 AM"

244

,"Indiana Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Indiana Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sin_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sin_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:51 AM"

245

,"Michigan Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Michigan Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_smi_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_smi_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:52 AM"

246

,"Virginia Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Virginia Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sva_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sva_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:57 AM"

247

,"Georgia Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Georgia Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sga_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sga_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:50 AM"

248

,"South Dakota Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","South Dakota Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_ssd_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_ssd_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:56 AM"

249

,"Nebraska Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Nebraska Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sne_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sne_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:53 AM"

250

,"Delaware Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Delaware Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sde_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sde_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:50 AM"

251

,"Arkansas Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Arkansas Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sar_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sar_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:49 AM"

252

,"Illinois Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Illinois Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sil_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sil_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:51 AM"

253

,"Catalytic Reforming Downstream Processing of Fresh Feed Input"  

U.S. Energy Information Administration (EIA) Indexed Site

Catalytic Reforming Downstream Processing of Fresh Feed Input" Catalytic Reforming Downstream Processing of Fresh Feed Input" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Catalytic Reforming Downstream Processing of Fresh Feed Input",16,"Monthly","9/2013","1/15/2010" ,"Release Date:","11/27/2013" ,"Next Release Date:","Last Week of December 2013" ,"Excel File Name:","pet_pnp_dwns_a_(na)_ydr_mbblpd_m.xls" ,"Available from Web Page:","http://www.eia.gov/dnav/pet/pet_pnp_dwns_a_(na)_ydr_mbblpd_m.htm" ,"Source:","Energy Information Administration"

254

,"Pennsylvania Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Pennsylvania Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_spa_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_spa_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:55 AM"

255

,"Iowa Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Iowa Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sia_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sia_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:51 AM"

256

,"Alabama Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Alabama Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sal_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sal_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:49 AM"

257

,"North Dakota Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","North Dakota Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_snd_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_snd_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:53 AM"

258

,"South Carolina Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","South Carolina Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_ssc_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_ssc_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:56 AM"

259

,"Massachusetts Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Massachusetts Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sma_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sma_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:52 AM"

260

,"Nevada Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Nevada Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_snv_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_snv_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:54 AM"

Note: This page contains sample records for the topic "inform input assumptions" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


261

,"Texas Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Texas Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_stx_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_stx_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:56 AM"

262

,"U.S. Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","U.S. Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","n9090us2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/n9090us2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:57:08 AM"

263

,"Colorado Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Colorado Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sco_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sco_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:49 AM"

264

,"Oregon Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Oregon Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sor_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sor_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:55 AM"

265

,"Florida Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Florida Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sfl_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sfl_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:50 AM"

266

,"Vermont Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Vermont Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_svt_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_svt_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:57 AM"

267

,"Maine Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (MMcf)" Input Supplemental Fuels (MMcf)" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Maine Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2012 ,"Release Date:","12/12/2013" ,"Next Release Date:","1/7/2014" ,"Excel File Name:","na1400_sme_2a.xls" ,"Available from Web Page:","http://tonto.eia.gov/dnav/ng/hist/na1400_sme_2a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.doe.gov" ,,"(202) 586-8800",,,"12/19/2013 6:58:52 AM"

268

T-623: HP Business Availability Center Input Validation Hole...  

Broader source: Energy.gov (indexed) [DOE]

Business Availability Center Input Validation Hole Permits Cross-Site Scripting Attacks T-623: HP Business Availability Center Input Validation Hole Permits Cross-Site Scripting...

269

Tribal Leaders Provide White House with Input on Bolstering Climate...  

Energy Savers [EERE]

Tribal Leaders Provide White House with Input on Bolstering Climate Resilience Tribal Leaders Provide White House with Input on Bolstering Climate Resilience January 7, 2015 -...

270

U-252: Barracuda Web Filter Input Validation Flaws Permit Cross...  

Broader source: Energy.gov (indexed) [DOE]

2: Barracuda Web Filter Input Validation Flaws Permit Cross-Site Scripting Attacks U-252: Barracuda Web Filter Input Validation Flaws Permit Cross-Site Scripting Attacks September...

271

U-219: Symantec Web Gateway Input Validation Flaws Lets Remote...  

Broader source: Energy.gov (indexed) [DOE]

9: Symantec Web Gateway Input Validation Flaws Lets Remote Users Inject SQL Commands, Execute Arbitrary Commands, and Change User Passwords U-219: Symantec Web Gateway Input...

272

Application of computer voice input/output  

SciTech Connect (OSTI)

The advent of microprocessors and other large-scale integration (LSI) circuits is making voice input and output for computers and instruments practical; specialized LSI chips for speech processing are appearing on the market. Voice can be used to input data or to issue instrument commands; this allows the operator to engage in other tasks, move about, and to use standard data entry systems. Voice synthesizers can generate audible, easily understood instructions. Using voice characteristics, a control system can verify speaker identity for security purposes. Two simple voice-controlled systems have been designed at Los Alamos for nuclear safeguards applicaations. Each can easily be expanded as time allows. The first system is for instrument control that accepts voice commands and issues audible operator prompts. The second system is for access control. The speaker's voice is used to verify his identity and to actuate external devices.

Ford, W.; Shirk, D.G.

1981-01-01T23:59:59.000Z

273

Generalized Input-Output Inequality Systems  

SciTech Connect (OSTI)

In this paper two types of generalized Leontief input-output inequality systems are introduced. The minimax properties for a class of functions associated with the inequalities are studied. Sufficient and necessary conditions for the inequality systems to have solutions are obtained in terms of the minimax value. Stability analysis for the solution set is provided in terms of upper semi-continuity and hemi-continuity of set-valued maps.

Liu Yingfan [Department of Mathematics, Nanjing University of Post and Telecommunications, Nanjing 210009 (China)], E-mail: yingfanliu@hotmail.com; Zhang Qinghong [Department of Mathematics and Computer Science, Northern Michigan University, Marquette, MI 49855 (United States)], E-mail: qzhang@nmu.edu

2006-09-15T23:59:59.000Z

274

Annual Energy Outlook 2001-Appendix G: Major Assumptions for the Forecasts  

Gasoline and Diesel Fuel Update (EIA)

Forecasts Forecasts Summary of the AEO2001 Cases/ Scenarios - Appendix Table G1 bullet1.gif (843 bytes) Model Results (Formats - PDF, ZIP) - Appendix Tables - Reference Case - 1998 to 2020 bullet1.gif (843 bytes) Download Report - Entire AEO2001 (PDF) - AEO2001 by Chapters (PDF) bullet1.gif (843 bytes) Acronyms bullet1.gif (843 bytes) Contacts Related Links bullet1.gif (843 bytes) Assumptions to the AEO2001 bullet1.gif (843 bytes) Supplemental Data to the AEO2001 (Only available on the Web) - Regional and more detailed AEO 2001 Reference Case Results - 1998, 2000 to 2020 bullet1.gif (843 bytes) NEMS Conference bullet1.gif (843 bytes) Forecast Homepage bullet1.gif (843 bytes) EIA Homepage Appendix G Major Assumptions for the Forecasts Component Modules Major Assumptions for the Annual Energy Outlook 2001

275

Sensitivity of Rooftop PV Projections in the SunShot Vision Study to Market Assumptions  

SciTech Connect (OSTI)

The SunShot Vision Study explored the potential growth of solar markets if solar prices decreased by about 75% from 2010 to 2020. The SolarDS model was used to simulate rooftop PV demand for this study, based on several PV market assumptions--future electricity rates, customer access to financing, and others--in addition to the SunShot PV price projections. This paper finds that modeled PV demand is highly sensitive to several non-price market assumptions, particularly PV financing parameters.

Drury, E.; Denholm, P.; Margolis, R.

2013-01-01T23:59:59.000Z

276

DOE Seeks Additional Input on Next Generation Nuclear Plant | Department of  

Broader source: Energy.gov (indexed) [DOE]

Seeks Additional Input on Next Generation Nuclear Plant Seeks Additional Input on Next Generation Nuclear Plant DOE Seeks Additional Input on Next Generation Nuclear Plant April 17, 2008 - 10:49am Addthis WASHINGTON, DC -The U.S. Department of Energy (DOE) today announced it is seeking public and industry input on how to best achieve the goals and meet the requirements for the Next Generation Nuclear Plant (NGNP) demonstration project work at DOE's Idaho National Laboratory. DOE today issued a Request for Information and Expressions of Interest from prospective participants and interested parties on utilizing cutting-edge high temperature gas reactor technology in the effort to reduce greenhouse gas emissions by enabling nuclear energy to replace fossil fuels used by industry for process heat. "This is an opportunity to advance the development of safe, reliable, and

277

Campus Recreation at Sonoma State University RELEASE OF LIABILITY -PROMISE NOT TO SUE ASSUMPTION OF  

E-Print Network [OSTI]

Campus Recreation at Sonoma State University RELEASE OF LIABILITY - PROMISE NOT TO SUE ASSUMPTION OF RISK - AGREEMENT TO PAY CLAIMS PERMISSION TO USE VISUAL LIKENESS Activities: a) USE OF SSU RECREATION RECREATION PROGRAMS. Effective Locations and Time Periods: a) RECREATION CENTER: DURING HOURS OF OPERATION

Ravikumar, B.

278

Cognitive Assessment Models with Few Assumptions, and Connections with Nonparametric IRT  

E-Print Network [OSTI]

Cognitive Assessment Models with Few Assumptions, and Connections with Nonparametric IRT Brian of the monotonicity conditions discussed in Section 4. #12;Abstract In recent years, as cognitive theories of learning" on student achievement relative to theory-driven lists of examinee skills, beliefs and other cognitive

Junker, Brian

279

Draft -F. Nicoud 1 About the zero Mach number assumption in  

E-Print Network [OSTI]

Draft - F. Nicoud 1 About the zero Mach number assumption in the calculation of thermoacoustic as the the flame forcing ('Rayleigh') term. Besides, the net effect of the non zero Mach number terms the frequency of oscillation and growth rate are modified when the Mach number is not zero. It is demonstrated

Nicoud, Franck

280

Models of transcription factor binding: Sensitivity of activation functions to model assumptions  

E-Print Network [OSTI]

on statistical physics, a Markov-chain model and a computational simulation. Comparison of these models suggests for cooperativity. The simulation model suggests that direct interactions between TFs are unlikely to be the main in this contribution, the assumption of the cell being a well stirred reactor makes a qualitative difference

Kent, University of

Note: This page contains sample records for the topic "inform input assumptions" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


281

Assumptions and Criteria for Performing a Feasability Study of the Conversion of the High Flux Isotope Reactor Core to Use Low-Enriched Uranium Fuel  

SciTech Connect (OSTI)

A computational study will be initiated during fiscal year 2006 to examine the feasibility of converting the High Flux Isotope Reactor from highly enriched uranium fuel to low-enriched uranium. The study will be limited to steady-state, nominal operation, reactor physics and thermal-hydraulic analyses of a uranium-molybdenum alloy that would be substituted for the current fuel powder--U{sub 3}O{sub 8} mixed with aluminum. The purposes of this document are to (1) define the scope of studies to be conducted, (2) define the methodologies to be used to conduct the studies, (3) define the assumptions that serve as input to the methodologies, (4) provide an efficient means for communication with the Department of Energy and American research reactor operators, and (5) expedite review and commentary by those parties.

Primm, R.T., III; Ellis, R.J.; Gehin, J.C.; Moses, D.L.; Binder, J.L.; Xoubi, N. (U. of Cincinnati)

2006-02-01T23:59:59.000Z

282

Introduction The bioenergy industry is pursuing low-input crops to be  

E-Print Network [OSTI]

1 Introduction The bioenergy industry is pursuing low-input crops to be grown on marginal lands the unintentional introduction and spread of potentially invasive species. Background Information The bioenergy- generation bioenergy crops are grown specifically for biomass pro- duction. Therefore, bioenergy crops

Liskiewicz, Maciej

283

On the Wind Power Input to the Ocean General Circulation  

E-Print Network [OSTI]

The wind power input to the ocean general circulation is usually calculated from the time-averaged wind products. Here, this wind power input is reexamined using available observations, focusing on the role of the synoptically ...

Zhai, Xiaoming

284

,"U.S. Refinery Crude Oil Input Qualities"  

U.S. Energy Information Administration (EIA) Indexed Site

Sulfur Content (Weighted Average) of Crude Oil Input to Refineries (Percent)","U.S. API Gravity (Weighted Average) of Crude Oil Input to Refineries (Degrees)" 31062,0.88,32.64...

285

,"U.S. Refinery Crude Oil Input Qualities"  

U.S. Energy Information Administration (EIA) Indexed Site

Sulfur Content (Weighted Average) of Crude Oil Input to Refineries (Percent)","U.S. API Gravity (Weighted Average) of Crude Oil Input to Refineries (Degrees)" 31228,0.91,32.46...

286

EIA-Assumptions to the Annual Energy Outlook - International Energy Module  

Gasoline and Diesel Fuel Update (EIA)

International Energy Module International Energy Module Assumptions to the Annual Energy Outlook 2007 International Energy Module The International Energy Module (IEM) performs two tasks in all NEMS runs. First, the module reads exogenously derived supply curves, initial price paths and international regional supply and demand levels into NEMS. These quantities are not modeled directly in NEMS because NEMS is not an international model. Previous versions of the IEM adjusted these quantities after reading in initial values. In an attempt to more closely integrate the AEO2007 with the IEO2006 and the STEO some functionality was removed from the IEM. More analyst time was devoted to analyzing price relationships between marker crude oils and refined products. A new exogenous oil supply model, Generate World Oil Balances (GWOB), was also developed to incorporate actual investment occurring in the international oil market through 2015 and resource assumptions through 2030. The GWOB model provides annual country level oil production detail for eight conventional and unconventional oils.

287

How Sensitive is Processor Customization to the Workload's Input Datasets?  

E-Print Network [OSTI]

How Sensitive is Processor Customization to the Workload's Input Datasets? Maximilien Breughe Zheng though is to what extent processor customiza- tion is sensitive to the training workload's input datasets. Current practice is to consider a single or only a few input datasets per workload during the processor

Eeckhout, Lieven

288

FEAT Equations for CO, HC and NO. G. A. Bishop Last updated June 2011. ASSUMPTIONS  

E-Print Network [OSTI]

:H ratio is 2 and non-oxygenated. Applies to gasoline and diesel in general. Fuel is approximated make the math simpler we have chosen for the exhaust HC to be a multiple of the input HC are correct for diesel vehicles) and assume an 8cm path length. For a direct tailpipe comparison for diesel

Denver, University of

289

U.S. Blender Net Input  

U.S. Energy Information Administration (EIA) Indexed Site

2007 2008 2009 2010 2011 2012 View 2007 2008 2009 2010 2011 2012 View History Total Input 1,184,435 1,522,193 1,850,204 2,166,784 2,331,109 2,399,318 2005-2012 Natural Gas Plant Liquids and Liquefied Refinery Gases 3,445 5,686 6,538 7,810 10,663 2008-2012 Pentanes Plus 2,012 474 1,808 1,989 2,326 4,164 2005-2012 Liquid Petroleum Gases 2,971 3,878 4,549 5,484 6,499 2008-2012 Normal Butane 2,943 2,971 3,878 4,549 5,484 6,499 2005-2012 Isobutane 2005-2006 Other Liquids 1,518,748 1,844,518 2,160,246 2,323,299 2,388,655 2008-2012 Oxygenates/Renewables 234,047 274,974 286,837 295,004 2009-2012 Methyl Tertiary Butyl Ether (MTBE) 2005-2006 Renewable Fuels (incl. Fuel Ethanol) 234,047 274,974 286,837 295,004 2009-2012 Fuel Ethanol 131,810 182,772 232,677 273,107 281,507 287,433 2005-2012

290

U.S. Blender Net Input  

U.S. Energy Information Administration (EIA) Indexed Site

Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sep-13 View Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sep-13 View History Total Input 206,541 217,867 212,114 216,075 219,783 208,203 2005-2013 Natural Gas Plant Liquids and Liquefied Refinery Gases 891 352 376 196 383 1,397 2008-2013 Pentanes Plus 261 301 313 67 287 393 2005-2013 Liquid Petroleum Gases 630 51 63 129 96 1,004 2008-2013 Normal Butane 630 51 63 129 96 1,004 2005-2013 Isobutane 2005-2006 Other Liquids 205,650 217,515 211,738 215,879 219,400 206,806 2008-2013 Oxygenates/Renewables 25,156 26,576 26,253 26,905 27,788 25,795 2009-2013 Methyl Tertiary Butyl Ether (MTBE) 2005-2006 Renewable Fuels (incl. Fuel Ethanol) 25,156 26,576 26,253 26,905 27,788 25,795 2009-2013 Fuel Ethanol 24,163 25,526 24,804 25,491 25,970 24,116 2005-2013

291

Bases, Assumptions, and Results of the Flowsheet Calculations for the Decision Phase Salt Disposition Alternatives  

SciTech Connect (OSTI)

The High Level Waste (HLW) Salt Disposition Systems Engineering Team was formed on March 13, 1998, and chartered to identify options, evaluate alternatives, and recommend a selected alternative(s) for processing HLW salt to a permitted wasteform. This requirement arises because the existing In-Tank Precipitation process at the Savannah River Site, as currently configured, cannot simultaneously meet the HLW production and Authorization Basis safety requirements. This engineering study was performed in four phases. This document provides the technical bases, assumptions, and results of this engineering study.

Dimenna, R.A.; Jacobs, R.A.; Taylor, G.A.; Durate, O.E.; Paul, P.K.; Elder, H.H.; Pike, J.A.; Fowler, J.R.; Rutland, P.L.; Gregory, M.V.; Smith III, F.G.; Hang, T.; Subosits, S.G.; Campbell, S.G.

2001-03-26T23:59:59.000Z

292

DOE Seeks Further Public Input on How Best To Streamline Existing  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

Further Public Input on How Best To Streamline Existing Further Public Input on How Best To Streamline Existing Regulations DOE Seeks Further Public Input on How Best To Streamline Existing Regulations December 7, 2011 - 12:34pm Addthis The Department of Energy (DOE) has announced a further step to implementing the President's Executive Order on Improving Regulatory Review. The Executive Order directs federal agencies to review existing regulations and determine whether they are still necessary and crafted effectively to solve current problems. Engaging the public in an open, transparent process is a crucial step in DOE's regulatory review process. Because public comments in response to the Request for Information (RFI) issued in January were important in the development of DOE's plan for retrospective regulatory review, DOE issued a second RFI this week asking the public how

293

,"Sulfur Content, Weighted Average Refinery Crude Oil Input Qualities"  

U.S. Energy Information Administration (EIA) Indexed Site

Sulfur Content, Weighted Average Refinery Crude Oil Input Qualities" Sulfur Content, Weighted Average Refinery Crude Oil Input Qualities" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description","# Of Series","Frequency","Latest Data for" ,"Data 1","Sulfur Content, Weighted Average Refinery Crude Oil Input Qualities",16,"Monthly","9/2013","1/15/1985" ,"Release Date:","11/27/2013" ,"Next Release Date:","Last Week of December 2013" ,"Excel File Name:","pet_pnp_crq_a_epc0_ycs_pct_m.xls" ,"Available from Web Page:","http://www.eia.gov/dnav/pet/pet_pnp_crq_a_epc0_ycs_pct_m.htm" ,"Source:","Energy Information Administration"

294

Influence of thermal zone assumptions on DOE-2 energy use estimations of a commercial building  

E-Print Network [OSTI]

heating and cooling energy consumption and the simulated values was only 3%. These results were found based on monthly energy data, and several iterations were performed to adjust the input file when major inconsistencies were found between... and Atmospheric Administration's Typical Meteorological Year (TMY) file and then "tuned" again. The ECM's were then modeled and ranked according to their cost effectiveness. This report shows one way of matching simulated data to monitored data...

Hinchey, Sharon Beth

1991-01-01T23:59:59.000Z

295

Water Flows in the Spanish Economy: Agri-Food Sectors, Trade and Households Diets in an Input-Output Framework  

Science Journals Connector (OSTI)

Water Flows in the Spanish Economy: Agri-Food Sectors, Trade and Households Diets in an Input-Output Framework ... So although we use the information from a SAM, since we leave as exogenous accounts the household consumption and foreign trade; it is not a traditional SAM analysis, but more an extended input-output analysis. ... The countries concerned are France, Germany, Portugal, Italy, UK, Netherlands, U.S., Belgium, China, and Japan. ...

Ignacio Cazcarro; Rosa Duarte; Julio Snchez-Chliz

2012-05-21T23:59:59.000Z

296

,"U.S. Downstream Processing of Fresh Feed Input"  

U.S. Energy Information Administration (EIA) Indexed Site

Annual",2012,"6/30/1987" Annual",2012,"6/30/1987" ,"Release Date:","9/27/2013" ,"Next Release Date:","9/26/2014" ,"Excel File Name:","pet_pnp_dwns_dc_nus_mbblpd_a.xls" ,"Available from Web Page:","http://www.eia.gov/dnav/pet/pet_pnp_dwns_dc_nus_mbblpd_a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.gov" ,,"(202) 586-8800",,,"11/25/2013 11:17:28 AM" "Back to Contents","Data 1: U.S. Downstream Processing of Fresh Feed Input" "Sourcekey","M_NA_YDR_NUS_MBBLD","MCRCCUS2","MCRCHUS2","MCRDFUS2" "Date","U.S. Downstream Processing of Fresh Feed Input by Catalytic Reforming Units (Thousand Barrels per Day)","U.S. Downstream Processing of Fresh Feed Input by Catalytic Cracking Units (Thousand Barrels per Day)","U.S. Downstream Processing of Fresh Feed Input by Catalytic Hydrocracking Units (Thousand Barrels per Day)","U.S. Downstream Processing of Fresh Feed Input by Delayed and Fluid Coking Units (Thousand Barrels per Day)"

297

Assumptions to the Annual Energy Outlook 2001 - Table 3. Coal-Related  

Gasoline and Diesel Fuel Update (EIA)

Coal-Related Methane Assumptions Coal-Related Methane Assumptions Northern Appalachia Central Appalachia Southern Appalachia Eastern Interior Western Fraction of underground coal production at: Gassy mines 0.885 0.368 0.971 0.876 0.681 Nongassy mines 0.115 0.632 0.029 0.124 0.319 Production from mines with degasification systems (fraction of underground production) 0.541 0.074 0.810 0.067 0.056 Emission factors (kilograms methane per short ton of coal produced) Underground Mining Gassy mines 6.047 5.641 27.346 2.988 6.027 Nongassy mines 0.362 0.076 15.959 0.285 0.245 Degassified mines 4.085 37.724 22.025 0.310 0.000 Surface Mining 0.706 0.706 0.706 0.706 0.706 Post-Mining, underground-mined 1.505 1.505 1.505 1.505 1.505 Post-Mining, surface-mined 0.061 0.061 0.061 0.061 0.061 Methane recovery at active coal mines

298

Information erasure  

Science Journals Connector (OSTI)

Landauers principle states that in erasing one bit of information, on average, at least kBT ln(2) energy is dissipated into the environment (where kB is Boltzmanns constant and T is the temperature of the environment at which one erases). Here, Landauers principle is microscopically derived without direct reference to the second law of thermodynamics. This is done for a classical system with continuous space and time, with discrete space and time, and for a quantum system. The assumption made in all three cases is that during erasure the bit is in contact with a thermal reservoir.

Barbara Piechocinska

2000-05-17T23:59:59.000Z

299

Mercury/Waterfilling: Optimum Power Allocation with Arbitrary Input Constellations  

E-Print Network [OSTI]

Mercury/Waterfilling: Optimum Power Allocation with Arbitrary Input Constellations Angel Lozano gives the power allocation policy, referred to as mercury/waterfilling, that maximizes the sum mutual

Verdú, Sergio

300

,"New York Natural Gas Input Supplemental Fuels (MMcf)"  

U.S. Energy Information Administration (EIA) Indexed Site

Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","New York Natural Gas Input Supplemental Fuels (MMcf)",1,"Annual",2013 ,"Release Date:","1031...

Note: This page contains sample records for the topic "inform input assumptions" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


301

,"U.S. Blender Net Input"  

U.S. Energy Information Administration (EIA) Indexed Site

Monthly","9/2013","1/15/2005" Monthly","9/2013","1/15/2005" ,"Release Date:","11/27/2013" ,"Next Release Date:","Last Week of December 2013" ,"Excel File Name:","pet_pnp_inpt3_dc_nus_mbbl_m.xls" ,"Available from Web Page:","http://www.eia.gov/dnav/pet/pet_pnp_inpt3_dc_nus_mbbl_m.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.gov" ,,"(202) 586-8800",,,"11/25/2013 11:22:43 AM" "Back to Contents","Data 1: U.S. Blender Net Input" "Sourcekey","MTXRB_NUS_1","M_EPL0_YIB_NUS_MBBL","MPPRB_NUS_1","M_EPLL_YIB_NUS_MBBL","MBNRB_NUS_1","MBIRB_NUS_1","M_EPOL_YIB_NUS_MBBL","M_EPOOXR_YIB_NUS_MBBL","MMTRB_NUS_1","M_EPOOR_YIB_NUS_MBBL","MFERB_NUS_1","M_EPOORD_YIB_NUS_MBBL","M_EPOORO_YIB_NUS_MBBL","M_EPPU_YIB_NUS_MBBL","M_EPOUN_YIB_NUS_MBBL","M_EPOUK_YIB_NUS_MBBL","M_EPOUH_YIB_NUS_MBBL","M_EPOUR_YIB_NUS_MBBL","MBCRB_NUS_1","MO1RB_NUS_1","M_EPOBGRR_YIB_NUS_MBBL","MO3RB_NUS_1","MO4RB_NUS_1","MO2RB_NUS_1","MO5RB_NUS_1","MO6RB_NUS_1","MO7RB_NUS_1","MO9RB_NUS_1"

302

"Information-Friction" and its implications on minimum energy required for communication  

E-Print Network [OSTI]

Just as there are frictional losses associated with moving masses on a surface, what if there were frictional losses associated with moving information on a substrate? Indeed, many modes of communication suffer from such frictional losses. We propose to model these losses as proportional to "bit-meters," i.e., the product of mass of information (i.e., the number of bits) and the distance of information transport. We use this "information- friction" model to understand fundamental energy requirements on encoding and decoding in communication circuitry. First, for communication across a binary input AWGN channel, we arrive at fundamental limits on bit-meters (and thus energy consumption) for decoding implementations that have a predetermined input-independent length of messages. For encoding, we relax the fixed-length assumption and derive bounds for flexible-message- length implementations. Using these lower bounds we show that the total (transmit + encoding + decoding) energy-per-bit must diverge to infinity as the target error probability is lowered to zero. Further, the closer the communication rate is maintained to the channel capacity (as the target error-probability is lowered to zero), the faster the required decoding energy diverges to infinity.

Pulkit Grover

2014-09-01T23:59:59.000Z

303

EIA-Assumptions to the Annual Energy Outlook - Transportation Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Transportation Demand Module Transportation Demand Module Assumptions to the Annual Energy Outlook 2007 Transportation Demand Module The NEMS Transportation Demand Module estimates energy consumption across the nine Census Divisions (see Figure 5) and over ten fuel types. Each fuel type is modeled according to fuel-specific technology attributes applicable by transportation mode. Total transportation energy consumption isthe sum of energy use in eight transport modes: light-duty vehicles (cars and light trucks), commercial light trucks (8,501-10,000 lbs gross vehicle weight), freight trucks (>10,000 lbs gross vehicle weight), freight and passenger aircraft, freight rail, freight shipping, and miscellaneous transport such as mass transit. Light-duty vehicle fuel consumption is further subdivided into personal usage and commercial fleet consumption.

304

EIA-Assumptions to the Annual Energy Outlook - Commercial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Commercial Demand Module Commercial Demand Module Assumptions to the Annual Energy Outlook 2007 Commercial Demand Module The NEMS Commercial Sector Demand Module generates forecasts of commercial sector energy demand through 2030. The definition of the commercial sector is consistent with EIA's State Energy Data System (SEDS). That is, the commercial sector includes business establishments that are not engaged in transportation or in manufacturing or other types of industrial activity (e.g., agriculture, mining or construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, pumps, bridges, and public services are also included if the establishment operating them is considered commercial. Since most of commercial energy consumption occurs in buildings, the commercial module relies on the data from the EIA Commercial Buildings Energy Consumption Survey (CBECS) for characterizing the commercial sector activity mix as well as the equipment stock and fuels consumed to provide end use services.12

305

Assumptions to the Annual Energy Outlook 2000-Table 1. Summary of the  

Gasoline and Diesel Fuel Update (EIA)

0 Cases 0 Cases Case Name Description Integration mode Reference Baseline economic growth, world oil price, and technology assumptions Fully Integrated Low Economic Growth Gross Domestic product grows at an average annual rate of 1.7 percent, compared to the reference case growth of 2.2 percent. Fully Integrated High Economic Growth Gross domestic product grows at an average annual rate of 2.6 percent, compared to the reference case growth of 2.2 percent. Fully Integrated Low World Oil Price World oil prices are $14.90 per barrel in 2020, compared to $22.04 per barrel in the reference case. Fully Integrated High World Oil Price World oil prices are $28.04 per barrel in 2020, compared to $22.04 per barrel in the reference case. Fully Integrated Residential: 2000 Technology

306

Assumptions to the Annual Energy Outlook 2001 - Table 1. Summary of AEO2001  

Gasoline and Diesel Fuel Update (EIA)

1 Cases 1 Cases Case name Description Integration mode Reference Baseline economic growth, world oil price, and technology assumptions Fully integrated Low Economic Growth Gross domestic product grows at an average annual rate of 2.5 percent, compared to the reference case growth of 3.0 percent. Fully integrated High Economic Growth Gross domestic product grows at an average annual rate of 3.5 percent, compared to the reference case growth of 3.0 percent. Fully integrated Low World Oil Price World oil prices are $15.10 per barrel in 2020, compared to $22.41 per barrel in the reference case. Fully integrated High World Oil Price World oil prices are $28.42 per barrel in 2020, compared to $22.41 per barrel in the reference case. Fully integrated Residential: 2001 Technology

307

EIA - Assumptions to the Annual Energy Outlook 2010 - Oil and Gas Supply  

Gasoline and Diesel Fuel Update (EIA)

Oil and Gas Supply Module Oil and Gas Supply Module Assumptions to the Annual Energy Outlook 2010 Oil and Gas Supply Module Figure 8. Natural Gas Transmission and Distribution Model Regions. The NEMS Oil and Gas Supply Module (OGSM) constitutes a comprehensive framework with which to analyze oil and gas natural gas exploration and development on a regional basis (Figure 7). The OGSM is organized into 4 submodules: Onshore Lower 48 Oil and Gas Supply Submodule, Offshore Oil and Gas Supply Submodule, Oil Shale Supply submodule, and Alaska Oil and Gas Supply Submodule. A detailed description of the OGSM is provided in the EIA publication, Model Documentation Report: The Oil and Gas Supply Module (OGSM), DOE/EIA-M063(2010), (Washington, DC, 2010). The OGSM provides crude oil and natural gas short-term supply parameters to both the Natural

308

EIA-Assumptions to the Annual Energy Outlook - Industrial Demand Module  

Gasoline and Diesel Fuel Update (EIA)

Industrial Demand Module Industrial Demand Module Assumptions to the Annual Energy Outlook 2007 Industrial Demand Module The NEMS Industrial Demand Module estimates energy consumption by energy source (fuels and feedstocks) for 21 manufacturing and 6 nonmanufacturing industries. The manufacturing industries are further subdivided into the energy-intensive manufacturing industries and nonenergy-intensive manufacturing industries. The manufacturing industries are modeled through the use of a detailed process flow or end use accounting procedure, whereas the nonmanufacturing industries are modeled with substantially less detail (Table 17). The Industrial Demand Module forecasts energy consumption at the four Census region level (see Figure 5); energy consumption at the Census Division level is estimated by allocating the Census region forecast using the SEDS25 data.

309

Measuring Information Transfer  

Science Journals Connector (OSTI)

An information theoretic measure is derived that quantifies the statistical coherence between systems evolving in time. The standard time delayed mutual information fails to distinguish information that is actually exchanged from shared information due to common history and input signals. In our new approach, these influences are excluded by appropriate conditioning of transition probabilities. The resulting transfer entropy is able to distinguish effectively driving and responding elements and to detect asymmetry in the interaction of subsystems.

Thomas Schreiber

2000-07-10T23:59:59.000Z

310

Performance Measures For Input Shaping and Command Generation  

E-Print Network [OSTI]

Performance Measures For Input Shaping and Command Generation Kris Kozak Department of Precision performance measures for input shaping and command generation have appeared in the literature, but very rarely have these measures been critically evaluated or thoroughly discussed. In this paper we review

Singhose, William

311

Univariate input models for stochastic simulation , NM Steiger4  

E-Print Network [OSTI]

of the continuous univariate probabilistic input processes that drive discrete-event simulation experiments that accu- rately mimic the behaviour of the random input processes driving the system under study. Often the following interrelated difficulties arise in attempts to use standard distribution families for simulation

312

ANALOG-DIGITAL INPUT OUTPUT SYSTEM FOR APPLE CO  

E-Print Network [OSTI]

ADIOS ANALOG-DIGITAL INPUT OUTPUT SYSTEM FOR APPLE CO NATIONAL RADIO ASTRONOMY OBSERVATORY TABLES ADIOS - ANALOG-DIGITAL INPUT OUTPUT SYSTEM FOR APPLE COMPUTER TABLE FOR CONTENTS Page I Module and Apple Card (Photograph) Figure 3 Complete Apple/ADIOS System (Photograph) Figure 4 Analog

Groppi, Christopher

313

Soft-Input Soft-Output Sphere Decoding Christoph Studer  

E-Print Network [OSTI]

Soft-Input Soft-Output Sphere Decoding Christoph Studer Integrated Systems Laboratory ETH Zurich Laboratory ETH Zurich, 8092 Zurich, Switzerland Email: boelcskei@nari.ee.ethz.ch Abstract--Soft-input soft, 8092 Zurich, Switzerland Email: studer@iis.ee.ethz.ch Helmut Bölcskei Communication Technology

314

T-623: HP Business Availability Center Input Validation Hole Permits  

Broader source: Energy.gov (indexed) [DOE]

3: HP Business Availability Center Input Validation Hole 3: HP Business Availability Center Input Validation Hole Permits Cross-Site Scripting Attacks T-623: HP Business Availability Center Input Validation Hole Permits Cross-Site Scripting Attacks May 16, 2011 - 3:05pm Addthis PROBLEM: A vulnerability was reported in HP Business Availability Center. A remote user can conduct cross-site scripting attacks. PLATFORM: HP Business Availability Center software 8.06 and prior versions ABSTRACT: The software does not properly filter HTML code from user-supplied input before displaying the input. reference LINKS: SecurityTracker Alert ID:1025535 HP Knowledge Base CVE-2011-1856 Secunia ID: SA44569 HP Document ID:c02823184 | ESB-2011.0525 IMPACT ASSESSMENT: High Discussion: A remote user can cause arbitrary scripting code to be executed by the

315

Anonymous Fingerprinting as Secure as the Bilinear Die-Hellman Assumption  

E-Print Network [OSTI]

been invested into the design of methods that technically support the copyright protection of digital Kim, and Kwangjo Kim International Research center for Information Security (IRIS), Information appeared as techniques for copyright protection from symmetric #12;ngerprinting by Boneh and Shaw [3

Kim, Kwangjo

316

Wavelength meter having single mode fiber optics multiplexed inputs  

DOE Patents [OSTI]

A wavelength meter having a single mode fiber optics input is disclosed. The single mode fiber enables a plurality of laser beams to be multiplexed to form a multiplexed input to the wavelength meter. The wavelength meter can provide a determination of the wavelength of any one or all of the plurality of laser beams by suitable processing. Another aspect of the present invention is that one of the laser beams could be a known reference laser having a predetermined wavelength. Hence, the improved wavelength meter can provide an on-line calibration capability with the reference laser input as one of the plurality of laser beams.

Hackel, R.P.; Paris, R.D.; Feldman, M.

1993-02-23T23:59:59.000Z

317

Wavelength meter having single mode fiber optics multiplexed inputs  

DOE Patents [OSTI]

A wavelength meter having a single mode fiber optics input is disclosed. The single mode fiber enables a plurality of laser beams to be multiplexed to form a multiplexed input to the wavelength meter. The wavelength meter can provide a determination of the wavelength of any one or all of the plurality of laser beams by suitable processing. Another aspect of the present invention is that one of the laser beams could be a known reference laser having a predetermined wavelength. Hence, the improved wavelength meter can provide an on-line calibration capability with the reference laser input as one of the plurality of laser beams.

Hackel, Richard P. (Livermore, CA); Paris, Robert D. (San Ramon, CA); Feldman, Mark (Pleasanton, CA)

1993-01-01T23:59:59.000Z

318

50-year-old assumptions about strength muscled aside | Argonne National  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

C. David Williams with an X-ray diffraction apparatus used to measure lattice spacing of filaments in moth wing muscle samples. To view a larger version of the image, click on it. Credit: A. Kidder/University of Washington. C. David Williams with an X-ray diffraction apparatus used to measure lattice spacing of filaments in moth wing muscle samples. To view a larger version of the image, click on it. Credit: A. Kidder/University of Washington. C. David Williams with an X-ray diffraction apparatus used to measure lattice spacing of filaments in moth wing muscle samples. To view a larger version of the image, click on it. Credit: A. Kidder/University of Washington. To view a larger, downloadable version of the image, click on it. To view a larger, downloadable version of the image, click on it. 50-year-old assumptions about strength muscled aside July 11, 2013 Tweet EmailPrint LEMONT, Ill. - Doctors have a new way of thinking about how to treat heart and skeletal muscle diseases. Body builders have a new way of

319

Special relativity as the limit of an Aristotelian universal friction theory under Reye's assumption  

E-Print Network [OSTI]

This work explores a classical mechanical theory under two further assumptions: (a) there is a universal dry friction force (Aristotelian mechanics), and (b) the variation of the mass of a body due to wear is proportional to the work done by the friction force on the body (Reye's hypothesis). It is shown that mass depends on velocity as in Special Relativity, and that the velocity is constant for a particular characteristic value. In the limit of vanishing friction the theory satisfies a relativity principle as bodies do not decelerate and, therefore, the absolute frame becomes unobservable. However, the limit theory is not Newtonian mechanics, with its Galilei group symmetry, but rather Special Relativity. This result suggests to regard Special Relativity as the limit of a theory presenting universal friction and exchange of mass-energy with a reservoir (vacuum). Thus, quite surprisingly, Special Relativity follows from the absolute space (ether) concept and could have been discovered following studies of Aristotelian mechanics and friction. We end the work confronting the full theory with observations. It predicts the Hubble law through tired light, and hence it is incompatible with supernova light curves unless both mechanisms of tired light (locally) and universe expansion (non-locally) are at work. It also nicely accounts for some challenging numerical coincidences involving phenomena under low acceleration.

E. Minguzzi

2014-11-28T23:59:59.000Z

320

,"U.S. Refinery Net Input"  

U.S. Energy Information Administration (EIA) Indexed Site

3,"Monthly","9/2013","1/15/2005" 3,"Monthly","9/2013","1/15/2005" ,"Data 2","Alaskan Crude Oil Receipts",1,"Monthly","9/2013","1/15/1986" ,"Release Date:","11/27/2013" ,"Next Release Date:","Last Week of December 2013" ,"Excel File Name:","pet_pnp_inpt2_dc_nus_mbbl_m.xls" ,"Available from Web Page:","http://www.eia.gov/dnav/pet/pet_pnp_inpt2_dc_nus_mbbl_m.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.gov" ,,"(202) 586-8800",,,"11/25/2013 11:21:05 AM" "Back to Contents","Data 1: Refinery Net Input" "Sourcekey","MTTRO_NUS_1","MCRRO_NUS_1","MNGRO_NUS_1","MPPRO_NUS_1","MLPRO_NUS_1","MBNRO_NUS_1","MBIRO_NUS_1","MOLRO_NUS_1","MOHRO_NUS_1","M_EPOOOH_YIY_NUS_MBBL","M_EPOOXXFE_YIY_NUS_MBBL","MMTRO_NUS_1","MOORO_NUS_1","M_EPOOR_YIY_NUS_MBBL","MFERO_NUS_1","M_EPOORD_YIY_NUS_MBBL","M_EPOORO_YIY_NUS_MBBL","M_EPOOOXH_YIY_NUS_MBBL","MUORO_NUS_1","MNLRO_NUS_1","MKORO_NUS_1","MH1RO_NUS_1","MRURO_NUS_1","MBCRO_NUS_1","MO1RO_NUS_1","M_EPOBGRR_YIY_NUS_MBBL","MO3RO_NUS_1","MO4RO_NUS_1","MO5RO_NUS_1","MO6RO_NUS_1","MO7RO_NUS_1","MO9RO_NUS_1","MBARO_NUS_1"

Note: This page contains sample records for the topic "inform input assumptions" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


321

,"U.S. Refinery Net Input"  

U.S. Energy Information Administration (EIA) Indexed Site

2,"Annual",2012,"6/30/2005" 2,"Annual",2012,"6/30/2005" ,"Data 2","Alaskan Crude Oil Receipts",1,"Annual",2012,"6/30/1986" ,"Release Date:","9/27/2013" ,"Next Release Date:","9/26/2014" ,"Excel File Name:","pet_pnp_inpt2_dc_nus_mbbl_a.xls" ,"Available from Web Page:","http://www.eia.gov/dnav/pet/pet_pnp_inpt2_dc_nus_mbbl_a.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.gov" ,,"(202) 586-8800",,,"11/25/2013 11:21:04 AM" "Back to Contents","Data 1: Refinery Net Input" "Sourcekey","MTTRO_NUS_1","MCRRO_NUS_1","MNGRO_NUS_1","MPPRO_NUS_1","MLPRO_NUS_1","MBNRO_NUS_1","MBIRO_NUS_1","MOLRO_NUS_1","MOHRO_NUS_1","M_EPOOOH_YIY_NUS_MBBL","M_EPOOXXFE_YIY_NUS_MBBL","MMTRO_NUS_1","MOORO_NUS_1","M_EPOOR_YIY_NUS_MBBL","MFERO_NUS_1","M_EPOORD_YIY_NUS_MBBL","M_EPOOOXH_YIY_NUS_MBBL","MUORO_NUS_1","MNLRO_NUS_1","MKORO_NUS_1","MH1RO_NUS_1","MRURO_NUS_1","MBCRO_NUS_1","MO1RO_NUS_1","M_EPOBGRR_YIY_NUS_MBBL","MO3RO_NUS_1","MO4RO_NUS_1","MO5RO_NUS_1","MO6RO_NUS_1","MO7RO_NUS_1","MO9RO_NUS_1","MBARO_NUS_1"

322

Abandoned Uranium Mines Report to Congress: LM Wants Your Input |  

Broader source: Energy.gov (indexed) [DOE]

Abandoned Uranium Mines Report to Congress: LM Wants Your Input Abandoned Uranium Mines Report to Congress: LM Wants Your Input Abandoned Uranium Mines Report to Congress: LM Wants Your Input April 11, 2013 - 1:33pm Addthis C-SR-10 Uintah Mine, Colorado, LM Uranium Lease Tracts C-SR-10 Uintah Mine, Colorado, LM Uranium Lease Tracts What does this project do? Goal 4. Optimize the use of land and assets Abandoned Uranium Mines Report to Congress The U.S. Department of Energy (DOE) Office of Legacy Management (LM) is seeking stakeholder input on an abandoned uranium mines report to Congress. On January 2, 2013, President Obama signed into law the National Defense Authorization Act for Fiscal Year 2013, which requires the Secretary of Energy, in consultation with the Secretary of the U.S Department of the Interior (DOI) and the Administrator

323

Comparison of wind stress algorithms, datasets and oceanic power input  

E-Print Network [OSTI]

If the ocean is in a statistically steady state, energy balance is a strong constraint, suggesting that the energy input into the world ocean is dissipated simultaneously at the same rate. Energy conservation is one of the ...

Yuan, Shaoyu

2009-01-01T23:59:59.000Z

324

Operation of buck regulator with ultra-low input voltage  

E-Print Network [OSTI]

Based on the LTC3621 and LTC3624, the designed buck regulator proposed in this thesis aims to lower the allowed input voltage and increase efficiency compared to the original part without making significant changes to ...

Harris, Cory Angelo

2014-01-01T23:59:59.000Z

325

Data sheet acquired from Harris Semiconductor Buffered Inputs  

E-Print Network [OSTI]

1 Data sheet acquired from Harris Semiconductor SCHS121D Features · Buffered Inputs · Typical. The suffixes 96 and R denote tape and reel. The suffix T denotes a small-quantity reel of 250. CAUTION

Kretchmar, R. Matthew

326

Automatic testing of software with structurally complex inputs  

E-Print Network [OSTI]

Modern software pervasively uses structurally complex data such as linked data structures. The standard approach to generating test suites for such software, manual generation of the inputs in the suite, is tedious and ...

Marinov, Darko, 1976-

2005-01-01T23:59:59.000Z

327

Face Interface : a methodology for experimental learning of input modalities  

E-Print Network [OSTI]

This thesis demonstrates that creating a system with a visual representation of the face which mirrors the user's facial gestures appears to solve problems in teaching a user to use the new input affordances of face-based ...

Wetzel, Jon William

2007-01-01T23:59:59.000Z

328

T-693: Symantec Endpoint Protection Manager Input Validation Hole Permits  

Broader source: Energy.gov (indexed) [DOE]

3: Symantec Endpoint Protection Manager Input Validation Hole 3: Symantec Endpoint Protection Manager Input Validation Hole Permits Cross-Site Scripting and Cross-Site Request Forgery Attacks T-693: Symantec Endpoint Protection Manager Input Validation Hole Permits Cross-Site Scripting and Cross-Site Request Forgery Attacks August 15, 2011 - 3:42pm Addthis PROBLEM: Two vulnerabilities were reported in Symantec Endpoint Protection Manager. A remote user can conduct cross-site scripting attacks. A remote user can conduct cross-site request forgery attacks. PLATFORM: Version(s): 11.0 RU6(11.0.600x), 11.0 RU6-MP1(11.0.6100), 11.0 RU6-MP2(11.0.6200), 11.0 RU6-MP3(11.0.6300) ABSTRACT: Symantec Endpoint Protection Manager Input Validation Hole Permits Cross-Site Scripting and Cross-Site Request Forgery Attacks. reference LINKS:

329

FULL FUEL CYCLE ASSESSMENT WELL TO WHEELS ENERGY INPUTS,  

E-Print Network [OSTI]

, greenhouse gas (GHG) emissions, criteria pollutant emissions, air toxics emissions, and multimedia impacts on a full fuel cycle basis for alternative-fueled vehicles is important when assessing the overall control, and assumptions regarding feedstock sources and fuel production conversion efficiency

330

DSM of Newton type for solving operator equations F(u) = f with minimal smoothness assumptions on F  

Science Journals Connector (OSTI)

This paper is a review of the authors' results on the Dynamical Systems Method (DSM) for solving operator equation (*) F(u) = f. It is assumed that (*) is solvable. The novel feature of the results is the minimal assumption on the smoothness of F. It is assumed that F is continuously Frechet differentiable, but no smoothness assumptions on F?(u) are imposed. The DSM for solving equation (*) is developed. Under weak assumptions global existence of the solution u(t) is proved, the existence of u(?) is established, and the relation F(u(?)) = f is obtained. The DSM is developed for a stable solution of equation (*) when noisy data f? are given, ''f ? f?'' ? ?.

N.S. Hoang; A.G. Ramm

2010-01-01T23:59:59.000Z

331

Information to iteration : using information and communication technologies [ICT] in design for remote regions  

E-Print Network [OSTI]

Remote design comes with significant challenges. A major barrier to designing in remote regions is the lack of communication between designers and users. As a result, the lack of information flow leads to assumptions about ...

Griffith, Kenfield A. (Kenfield Allistair)

2012-01-01T23:59:59.000Z

332

The SCALE Verified, Archived Library of Inputs and Data - VALID  

SciTech Connect (OSTI)

The Verified, Archived Library of Inputs and Data (VALID) at ORNL contains high quality, independently reviewed models and results that improve confidence in analysis. VALID is developed and maintained according to a procedure of the SCALE quality assurance (QA) plan. This paper reviews the origins of the procedure and its intended purpose, the philosophy of the procedure, some highlights of its implementation, and the future of the procedure and associated VALID library. The original focus of the procedure was the generation of high-quality models that could be archived at ORNL and applied to many studies. The review process associated with model generation minimized the chances of errors in these archived models. Subsequently, the scope of the library and procedure was expanded to provide high quality, reviewed sensitivity data files for deployment through the International Handbook of Evaluated Criticality Safety Benchmark Experiments (IHECSBE). Sensitivity data files for approximately 400 such models are currently available. The VALID procedure and library continue fulfilling these multiple roles. The VALID procedure is based on the quality assurance principles of ISO 9001 and nuclear safety analysis. Some of these key concepts include: independent generation and review of information, generation and review by qualified individuals, use of appropriate references for design data and documentation, and retrievability of the models, results, and documentation associated with entries in the library. Some highlights of the detailed procedure are discussed to provide background on its implementation and to indicate limitations of data extracted from VALID for use by the broader community. Specifically, external users of data generated within VALID must take responsibility for ensuring that the files are used within the QA framework of their organization and that use is appropriate. The future plans for the VALID library include expansion to include additional experiments from the IHECSBE, to include experiments from areas beyond criticality safety, such as reactor physics and shielding, and to include application models. In the future, external SCALE users may also obtain qualification under the VALID procedure and be involved in expanding the library. The VALID library provides a pathway for the criticality safety community to leverage modeling and analysis expertise at ORNL.

Marshall, William BJ J [ORNL] [ORNL; Rearden, Bradley T [ORNL] [ORNL

2013-01-01T23:59:59.000Z

333

Older People With Dementia Cared for Mostly at Home Study challenges assumption that most patients die in nursing homes  

E-Print Network [OSTI]

Older People With Dementia Cared for Mostly at Home Study challenges assumption that most patients die in nursing homes -- Robert Preidt FRIDAY, May 11 (HealthDay News) -- Many elderly people with dementia live and die at home rather than in nursing homes, a new study has found. The findings challenge

Belogay, Eugene A.

334

Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting with EPRI-REEPS 2.1  

E-Print Network [OSTI]

LBL-34045 UC-1600 Residential HVAC Data, Assumptions and Methodology for End-Use Forecasting-uses include Heating, Ventilation and Air Conditioning (HVAC). Our analysis uses the modeling framework provided by the HVAC module in the Residential End-Use Energy Planning System (REEPS), which was developed

335

Washington Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Washington Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 15 13 15 11 11 9 10 21 79 154 1990's 181 154 180 4 0 0 0 0 0 0 2000's 0 0 0 0 0 0 0 0 0 0 2010's 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Washington Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply & Disposition

336

Minnesota Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Minnesota Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 48 106 337 1 3 11 2 1 385 315 1990's 56 49 52 78 289 194 709 172 50 64 2000's 101 118 13 42 71 154 13 54 46 47 2010's 12 20 9 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Minnesota Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply &

337

District of Columbia Natural Gas Input Supplemental Fuels (Million Cubic  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) District of Columbia Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 2 1 46 0 0 0 0 0 0 0 1990's 0 0 0 0 0 0 0 0 0 0 2000's 0 0 0 0 0 0 0 0 0 0 2010's 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas District of Columbia Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply & Disposition)

338

Maryland Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Maryland Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 484 498 984 352 332 373 155 136 743 899 1990's 24 72 126 418 987 609 882 178 80 498 2000's 319 186 48 160 124 382 41 245 181 170 2010's 115 89 116 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Maryland Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply &

339

Iowa Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Iowa Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 57 64 68 23 53 45 44 40 34 82 1990's 81 46 45 84 123 96 301 137 17 12 2000's 44 39 23 143 30 31 46 40 27 3 2010's 2 1 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Iowa Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply & Disposition

340

Pennsylvania Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Pennsylvania Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 3,127 10,532 5,621 3,844 82 221 196 247 254 305 1990's 220 222 132 110 252 75 266 135 80 119 2000's 261 107 103 126 131 132 124 145 123 205 2010's 4 2 2 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Pennsylvania Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply &

Note: This page contains sample records for the topic "inform input assumptions" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


341

Possible Magmatic Input to the Dixie Valley Geothermal Field, and  

Open Energy Info (EERE)

Possible Magmatic Input to the Dixie Valley Geothermal Field, and Possible Magmatic Input to the Dixie Valley Geothermal Field, and Implications for District-Scale Resource Exploration, Inferred from Magnetotelluric (MT) Resistivity Surveying Jump to: navigation, search OpenEI Reference LibraryAdd to library Journal Article: Possible Magmatic Input to the Dixie Valley Geothermal Field, and Implications for District-Scale Resource Exploration, Inferred from Magnetotelluric (MT) Resistivity Surveying Abstract Magnetotelluric (MT) profiling in northwestern Nevadais used to test hypotheses on the main sources of heat andhydrothermal fluid for the Dixie Valley-Central NevadaSeismic Belt area. The transect reveals families of resistivitystructures commonly dominated by steeply-dipping features,some of which may be of key geothermal significance. Mostnotably, 2-D inversion

342

Missouri Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Missouri Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 65 60 2,129 1,278 326 351 1 1 2 1,875 1990's 0 0 0 0 371 4 785 719 40 207 2000's 972 31 62 1,056 917 15 78 66 6 10 2010's 18 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Missouri Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply &

343

Rhode Island Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Rhode Island Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 257 951 718 594 102 130 182 109 391 219 1990's 51 92 155 126 0 27 42 18 1 1 2000's 0 0 0 0 0 0 0 0 0 0 2010's 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Rhode Island Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply &

344

DOE Seeks Input On Addressing Contractor Pension and Medical Benefits  

Broader source: Energy.gov (indexed) [DOE]

Input On Addressing Contractor Pension and Medical Input On Addressing Contractor Pension and Medical Benefits Liabilities DOE Seeks Input On Addressing Contractor Pension and Medical Benefits Liabilities March 27, 2007 - 12:10pm Addthis WASHINGTON, DC - The U.S. Department of Energy (DOE) today announced in the Federal Register that it is seeking public comment on how to address the increasing costs and liabilities of contractor employee pension and medical benefits. Under the Department of Energy's unique Management and Operating and other site management contracts, DOE reimburses its contractors for allowable costs incurred in providing contractor employee pension and medical benefits to current employees and retirees. In FY2006, these costs reached approximately $1.1 billion - a more than 226 percent increase since FY2000 - and are expected to grow in future years.

345

Georgia Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Georgia Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 24 57 151 84 28 121 124 248 241 292 1990's 209 185 166 199 123 130 94 14 16 12 2000's 73 51 7 14 5 0 3 2 52 2010's 732 701 660 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Georgia Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply &

346

Delaware Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Delaware Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 55 135 56 20 13 12 9 0 2 18 1990's 4,410 4,262 3,665 3,597 3,032 1 1 2 0 0 2000's 6 0 0 7 17 0 W 5 2 2 2010's 1 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Delaware Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply & Disposition

347

South Dakota Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) South Dakota Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 9 24 50 1 0 0 0 0 10 16 1990's 10 3 10 9 61 37 87 30 4 5 2000's 13 5 3 57 5 4 0 1 0 0 2010's 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas South Dakota Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply & Disposition

348

New Hampshire Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) New Hampshire Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 774 720 582 328 681 509 362 464 492 592 1990's 205 128 96 154 160 90 147 102 103 111 2000's 180 86 66 58 91 84 92 9 0 0 2010's 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas New Hampshire Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply &

349

Formalization of computer input and output: the Hadley model  

Science Journals Connector (OSTI)

Current digital evidence acquisition tools are effective, but are tested rather than formally proven correct. We assert that the forensics community will benefit in evidentiary ways and the scientific community will benefit in practical ways by moving beyond simple testing of systems to a formal model. To this end, we present a hierarchical model of peripheral input to and output from von Neumann computers, patterned after the Open Systems Interconnection model of networking. The Hadley model categorizes all components of peripheral input and output in terms of data flow; with constructive aspects concentrated in the data flow between primary memory and the computer sides of peripherals' interfaces. The constructive domain of Hadley is eventually expandable to all areas of the I/O hierarchy, allowing for a full view of peripheral input and output and enhancing the forensics community's capabilities to analyze, obtain, and give evidentiary force to data.

Matthew Gerber; John Leeson

2004-01-01T23:59:59.000Z

350

Connecticut Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Connecticut Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 144 1,584 1,077 291 239 343 298 180 245 251 1990's 111 146 40 94 29 68 48 37 33 31 2000's 20 6 6 57 191 273 91 0 0 1 2010's 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Connecticut Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply &

351

South Carolina Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) South Carolina Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 74 184 63 73 62 87 31 22 191 201 1990's 17 47 26 34 154 62 178 10 0 18 2000's 63 6 3 15 2 86 75 0 2010's 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas South Carolina Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply &

352

Tennessee Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Tennessee Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 12 42 90 39 25 36 13 26 36 78 1990's 3 8 12 13 84 33 73 19 4 11 2000's 13 0 1 1 0 0 0 0 0 0 2010's 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Tennessee Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply & Disposition

353

Accepting Information with a Pinch of Salt: Handling Untrusted Information Sources  

Science Journals Connector (OSTI)

This paper describes on-going research developing a system to allow incident controllers and similar decision makers to augment official information input streams with information contributed by the wider publ...

Syed Sadiqur Rahman; Sadie Creese; Michael Goldsmith

2012-01-01T23:59:59.000Z

354

Table 3. U.S. Inputs to Biodiesel Production  

U.S. Energy Information Administration (EIA) Indexed Site

U.S. Inputs to Biodiesel Production U.S. Inputs to Biodiesel Production (million pounds) 2011 January 8 17 - W 150 W 14 11 February 26 13 - W 150 W 14 11 March 68 14 - W 190 W 19 27 April 88 20 - W 236 W 15 47 May 113 21 - W 264 W 16 36 June 75 34 - W 311 W 23 49 July 77 35 - W 367 W 26 64 August 84 37 W W 398 W 34 38 September 84 27 W W 430 W

355

Cyberspace Policy Review: Assuring a Trusted and Resilient Information...  

Broader source: Energy.gov (indexed) [DOE]

Information and Communications Infrastructure The review team of government cybersecurity experts engaged and received input from a broad cross-section of industry,...

356

FULL FUEL CYCLE ASSESSMENT WELL TO TANK ENERGY INPUTS,  

E-Print Network [OSTI]

FULL FUEL CYCLE ASSESSMENT WELL TO TANK ENERGY INPUTS, EMISSIONS, AND WATER IMPACTS Prepared For be divided into two parts: · Well-to-Tank (WTT) Feedstock extraction, transport, storage, processing, distribution, transport, and storage · Tank-to-Wheels (TTW) Refueling, consumption and evaporation The full

357

"Why Are Some Firms More Innovative? Knowledge Inputs, Knowledge Stocks,  

E-Print Network [OSTI]

"Why Are Some Firms More Innovative? Knowledge Inputs, Knowledge Stocks, and the Role of Global, Exporting, Knowledge and Technological Change Abstract Why do some firms create more knowledge than others stock of knowledge. But there is very little empirical evidence on production functions for new ideas

Sadoulet, Elisabeth

358

Fast RNA Structure Alignment for Crossing Input Rolf Backofena  

E-Print Network [OSTI]

is to predict for every input sequence the minimum free-energy non-crossing structure (in O(n3 ) time function. Since the structure of RNA is evolu- tionarily more conserved than its sequence, predicting a folding with minimal free energy [5, 6, 7, 8, 9]. Albeit this so-named thermodynamic approach is a success

Tsur, Dekel

359

Input to review of STFC UK Nuclear Physics Community  

E-Print Network [OSTI]

Input to review of STFC UK Nuclear Physics Community Introduction STFC covers essentially and project funding for Astronomy, Nuclear Physics, Particle Physics and Space Science Since STFC was formed programme. Grant funding Nuclear Physics grant funding was in EPSRC until 2007 and then moved to STFC

Crowther, Paul

360

Global sensitivity analysis of computer models with functional inputs  

E-Print Network [OSTI]

function. Lastly, the new methodology is applied to an industrial computer code that simulates the nuclear with scalar input variables. For example, in the nuclear engineering domain, global SA tools have been applied (Helton et al. [7]), environmental model of dose calculations (Iooss et al. [10]), reactor dosimetry

Boyer, Edmond

Note: This page contains sample records for the topic "inform input assumptions" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


361

Toward a Theory of Input Acceptance for Transactional Memories  

E-Print Network [OSTI]

-core architectures requires numerous events to be treated upon reception. In fact, the transactional code executed, experimental validation compares the presented TM designs in terms of input acceptance with realistic workloads database systems transac- tional events can be buffered on the server-side before treatment

Guerraoui, Rachid

362

The Matrix Converter Drive Performance Under Abnormal Input Voltage Conditions  

E-Print Network [OSTI]

that generates variable magnitude variable frequency output voltage from the ac utility line. It has high power voltage disturbance related performance issues of the MC drive. Since the MC is a direct frequencyThe Matrix Converter Drive Performance Under Abnormal Input Voltage Conditions Jun-Koo Kang

Hava, Ahmet

363

Assumptions to the Annual Energy Outlook 2001 - Table 4. Coefficients of  

Gasoline and Diesel Fuel Update (EIA)

Coefficients of Linear Equations for Natural Gas- and Coefficients of Linear Equations for Natural Gas- and Oil-Related Methane Emissions Emissions Sources Intercept Variable Name and Units Coefficient Variable Name and Units Coefficient Natural Gas -38.77 Time trend (calendar year) .02003 Dry gas production (thousand cubic feet .02186 Natural Gas Processing -0.9454 Natural gas liquids production (million barrels per day) .9350 Not applicable Natural Gas Transmission and Storage 2.503 Pipeline fuel use (thousand cubic feet) 1.249 Dry gas production (thousand cubic feet) -0.06614 Natural Gas Distribution -58.16 Time trend (calendar year) .0297 Natural gas consumption (quadrillion Btu) .0196 Oil production, Refining, and Transport 0.03190 Oil consumption (quadrillion Btu) .002764 Not applicable Source: Derived from data used in Energy Information Administration, Emissions of Greenhouse Gases in the United States 1999, DOE/EIA-0573(99), (Washington, DC, October 2000).

364

Quantum Information Causality  

Science Journals Connector (OSTI)

How much information can a transmitted physical system fundamentally communicate? We introduce the principle of quantum information causality, which states the maximum amount of quantum information that a quantum system can communicate as a function of its dimension, independently of any previously shared quantum physical resources. We present a new quantum information task, whose success probability is upper bounded by the new principle, and show that an optimal strategy to perform it combines the quantum teleportation and superdense coding protocols with a task that has classical inputs.

Damin Pitala-Garca

2013-05-22T23:59:59.000Z

365

Sensors as Information Transducers  

E-Print Network [OSTI]

This chapter reviews the mechanisms by which sensors gather information from the physical world and transform it into the electronic signals that are used in today's information and control systems. It introduces a new methodology for describing sensing mechanisms based on the process of information flow and applies it to the broad spectrum of sensors, instruments and data input devices in current use. We identify four distinct elemental transduction processes: energy conversion, energy dispersion, energy modulation and modulation of a material property. We posit that these four mechanisms form a complete set for describing information transduction in sensing systems.

J. David zook; Norbert Schroeder

2008-04-04T23:59:59.000Z

366

Int. J. Spray and Comb. Dynamics -Accepted for publication 1 About the zero Mach number assumption in  

E-Print Network [OSTI]

as much as the the flame forcing ('Rayleigh') term. Besides, the net effect of the non zero Mach numberInt. J. Spray and Comb. Dynamics - Accepted for publication 1 About the zero Mach number assumption in the calculation of thermoacoustic instabilities By F. N I C O U D1 AND K. W I E C Z O R E K1,2 1 University

Paris-Sud XI, Université de

367

Information: basic definitions Steven Lindell  

E-Print Network [OSTI]

. The act of informing or the condition of being informed; communication of knowledge: Safety instructions used as an input to a computer or communications system. 6. A numerical measure of the uncertainty of an experimental outcome. 7. Law. A formal accusation of a crime made by a public officer rather than by grand jury

Lindell, Steven

368

Total Refinery Net Input of Crude Oil and Petroleum Products  

U.S. Energy Information Administration (EIA) Indexed Site

Input Input Product: Total Crude Oil & Petroleum Products Crude Oil Natural Gas Plant Liquids Pentanes Plus Liquefied Petroleum Gases Normal Butane Isobutane Other Liquids Hydrogen/Oxygenates/Renewables/Other Hydrocarbons Hydrogen Oxygenates (excl. Fuel Ethanol) Methyl Tertiary Butyl Ether (MTBE) All Other Oxygenates Renewable Fuels (incl. Fuel Ethanol) Fuel Ethanol Renewable Diesel Fuel Other Renewable Fuels Other Hydrocarbons Unfinished Oils (net) Unfinished Oils, Naphthas and Lighter Unfinished Oils, Kerosene and Light Gas Oils Unfinished Oils, Heavy Gas Oils Residuum Motor Gasoline Blending Components (MGBC) (net) MGBC - Reformulated MGBC - Reformulated - RBOB MGBC - Reformulated, RBOB for Blending w/ Alcohol MGBC - Reformulated, RBOB for Blending w/ Ether MGBC - Conventional MGBC - CBOB MGBC - Conventional, GTAB MGBC - Other Conventional Aviation Gasoline Blending Components (net) Alaskan Crude Oil Receipts Period-Unit: Monthly-Thousand Barrels Monthly-Thousand Barrels per Day Annual-Thousand Barrels Annual-Thousand Barrels per Day

369

Refinery & Blenders Net Input of Crude Oil  

U.S. Energy Information Administration (EIA) Indexed Site

Input Input Product: Total Crude Oil & Petroleum Products Crude Oil Natural Gas Plant Liquids and Liquefied Refinery Gases Pentanes Plus Liquefied Petroleum Gases Ethane Normal Butane Isobutane Other Liquids Hydrogen/Oxygenates/Renewables/Other Hydrocarbons Hydrogen Oxygenates (excl. Fuel Ethanol) Methyl Tertiary Butyl Ether (MTBE) All Other Oxygenates Renewable Fuels (incl. Fuel Ethanol) Fuel Ethanol Renewable Diesel Fuel Other Renewable Fuels Other Hydrocarbons Unfinished Oils (net) Unfinished Oils, Naphthas and Lighter Unfinished Oils, Kerosene and Light Gas Oils Unfinished Oils, Heavy Gas Oils Residuum Motor Gasoline Blending Components (MGBC) (net) MGBC - Reformulated MGBC - Reformulated - RBOB MGBC - Reformulated, RBOB for Blending w/ Alcohol MGBC - Reformulated, RBOB for Blending w/ Ether MGBC - Reformulated, GTAB MGBC - Conventional MGBC - CBOB MGBC - Conventional, GTAB MGBC - Other Conventional Aviation Gasoline Blending Components (net) Period-Unit: Monthly-Thousand Barrels Monthly-Thousand Barrels per Day Annual-Thousand Barrels Annual-Thousand Barrels per Day

370

Environmental Transport Input Parameters for the Biosphere Model  

SciTech Connect (OSTI)

This analysis report is one of the technical reports documenting the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), a biosphere model supporting the total system performance assessment for the license application (TSPA-LA) for the geologic repository at Yucca Mountain. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows relationships among the reports developed for biosphere modeling and biosphere abstraction products for the TSPA-LA, as identified in the ''Technical Work Plan for Biosphere Modeling and Expert Support'' (BSC 2004 [DIRS 169573]) (TWP). This figure provides an understanding of how this report contributes to biosphere modeling in support of the license application (LA). This report is one of the five reports that develop input parameter values for the biosphere model. The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes the conceptual model and the mathematical model. The input parameter reports, shown to the right of the Biosphere Model Report in Figure 1-1, contain detailed description of the model input parameters. The output of this report is used as direct input in the ''Nominal Performance Biosphere Dose Conversion Factor Analysis'' and in the ''Disruptive Event Biosphere Dose Conversion Factor Analysis'' that calculate the values of biosphere dose conversion factors (BDCFs) for the groundwater and volcanic ash exposure scenarios, respectively. The purpose of this analysis was to develop biosphere model parameter values related to radionuclide transport and accumulation in the environment. These parameters support calculations of radionuclide concentrations in the environmental media (e.g., soil, crops, animal products, and air) resulting from a given radionuclide concentration at the source of contamination (i.e., either in groundwater or in volcanic ash). The analysis was performed in accordance with the TWP (BSC 2004 [DIRS 169573]).

M. Wasiolek

2004-09-10T23:59:59.000Z

371

Inhalation Exposure Input Parameters for the Biosphere Model  

SciTech Connect (OSTI)

This analysis is one of 10 reports that support the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN) biosphere model. The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes in detail the conceptual model as well as the mathematical model and its input parameters. This report documents development of input parameters for the biosphere model that are related to atmospheric mass loading and supports the use of the model to develop biosphere dose conversion factors (BDCFs). The biosphere model is one of a series of process models supporting the total system performance assessment (TSPA) for a Yucca Mountain repository. Inhalation Exposure Input Parameters for the Biosphere Model is one of five reports that develop input parameters for the biosphere model. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling, and the plan for development of the biosphere abstraction products for TSPA, as identified in the Technical Work Plan for Biosphere Modeling and Expert Support (BSC 2004 [DIRS 169573]). This analysis report defines and justifies values of mass loading for the biosphere model. Mass loading is the total mass concentration of resuspended particles (e.g., dust, ash) in a volume of air. Mass loading values are used in the air submodel of ERMYN to calculate concentrations of radionuclides in air inhaled by a receptor and concentrations in air surrounding crops. Concentrations in air to which the receptor is exposed are then used in the inhalation submodel to calculate the dose contribution to the receptor from inhalation of contaminated airborne particles. Concentrations in air surrounding plants are used in the plant submodel to calculate the concentrations of radionuclides in foodstuffs contributed from uptake by foliar interception.

K. Rautenstrauch

2004-09-10T23:59:59.000Z

372

New Jersey Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) New Jersey Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 9,574 11,504 9,786 9,896 8,616 13,421 12,099 13,774 14,846 14,539 1990's 9,962 14,789 14,362 14,950 7,737 7,291 6,778 6,464 9,082 5,761 2000's 8,296 12,330 3,526 473 530 435 175 379 489 454 2010's 457 392 139 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas New Jersey Supplemental Supplies of Natural Gas

373

Nebraska Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Nebraska Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 9 1,838 63 2,006 2,470 2,689 2,142 2,199 1,948 2,088 1990's 2,361 2,032 1,437 791 890 15 315 134 11 4 2000's 339 6 1 13 39 16 19 33 28 18 2010's 12 9 4 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Nebraska Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply &

374

Michigan Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Michigan Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 3 3,038 2,473 2,956 2,773 2,789 2,754 2,483 2,402 2,402 1990's 19,106 15,016 14,694 12,795 13,688 21,378 21,848 22,238 21,967 20,896 2000's 12,423 4,054 0 0 0 0 0 0 0 0 2010's 0 0 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Michigan Supplemental Supplies of Natural Gas

375

Colorado Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Colorado Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 9,868 9,133 8,877 7,927 9,137 8,934 8,095 8,612 10,322 9,190 1990's 15,379 6,778 7,158 8,456 8,168 7,170 6,787 6,314 5,292 4,526 2000's 4,772 5,625 5,771 5,409 5,308 5,285 6,149 6,869 6,258 7,527 2010's 5,148 4,268 4,412 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Colorado Supplemental Supplies of Natural Gas

376

Ohio Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Ohio Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 69,169 69,850 64,812 62,032 43,866 24,444 5,182 18 44 348 1990's 849 891 1,051 992 1,432 904 1,828 1,423 1,194 1,200 2000's 1,442 1,149 79 1,002 492 579 423 608 460 522 2010's 353 296 366 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Ohio Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply &

377

Hawaii Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Hawaii Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 3,190 2,993 2,899 2,775 2,449 2,655 2,630 2,461 2,801 2,844 1990's 2,817 2,725 2,711 2,705 2,831 2,793 2,761 2,617 2,715 2,752 2000's 2,769 2,689 2,602 2,602 2,626 2,606 2,613 2,683 2,559 2,447 2010's 2,472 2,467 2,510 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Hawaii Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply &

378

Massachusetts Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Massachusetts Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 15,366 21,828 17,586 10,732 6,545 3,668 2,379 1,404 876 692 1990's 317 120 105 61 154 420 426 147 68 134 2000's 26 16 137 324 80 46 51 15 13 10 2010's 0 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Massachusetts Supplemental Supplies of Natural Gas Supplies of Natural Gas Supplemental Fuels (Annual Supply &

379

Indiana Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Indiana Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 1,602 5,056 3,496 4,142 4,027 2,711 2,351 3,890 4,243 3,512 1990's 3,015 3,077 3,507 3,232 2,457 3,199 3,194 3,580 3,149 5,442 2000's 5,583 5,219 1,748 2,376 2,164 1,988 1,642 635 30 1 2010's 1 5 1 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Indiana Supplemental Supplies of Natural Gas

380

North Dakota Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) North Dakota Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 196 417 102 0 8,335 40,370 49,847 51,543 49,014 54,408 1990's 53,144 52,557 58,496 57,680 57,127 57,393 55,867 53,179 54,672 53,185 2000's 49,190 51,004 53,184 53,192 47,362 51,329 54,361 51,103 50,536 53,495 2010's 54,813 51,303 52,541 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas

Note: This page contains sample records for the topic "inform input assumptions" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


381

Illinois Natural Gas Input Supplemental Fuels (Million Cubic Feet)  

U.S. Energy Information Administration (EIA) Indexed Site

Input Supplemental Fuels (Million Cubic Feet) Input Supplemental Fuels (Million Cubic Feet) Illinois Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0 0 0 1970's 0 0 0 0 0 0 0 0 0 0 1980's 36,713 29,509 19,005 19,734 17,308 19,805 22,980 12,514 9,803 9,477 1990's 8,140 6,869 8,042 9,760 7,871 6,256 3,912 4,165 2,736 2,527 2000's 1,955 763 456 52 14 15 13 11 15 20 2010's 17 1 1 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 12/12/2013 Next Release Date: 1/7/2014 Referring Pages: Total Supplemental Supply of Natural Gas Illinois Supplemental Supplies of Natural Gas

382

Utility, informativity and protocols Robert van Rooy  

E-Print Network [OSTI]

Utility, informativity and protocols Robert van Rooy ILLC/University of Amsterdam R particular natural assumptions the utility of questions and answers reduces to their informativity, and that the ordering relation induced by utility sometimes even reduces to the logical relation of entailment

van Rooij, Robert

383

Form:NEPA Doc | Open Energy Information  

Open Energy Info (EERE)

GEOTHERMAL ENERGY Input the name of a NEPA Document below. If the document already exists, you will be able to edit its information. AddEdit a NEPA Document Retrieved from...

384

Form:SampleForm | Open Energy Information  

Open Energy Info (EERE)

SampleForm Jump to: navigation, search Input the name of a Test Page below. If the resource already exists, you will be able to edit its information. AddEdit a Test Page The text...

385

A comparative study of avionics control input methods  

E-Print Network [OSTI]

Standardization of avionics locations in early models of small general aviation aircraft was almost non-existent, due largely to limited panel space and lack of human engineering considerations. Aircraft were typically purchased with a limited avionics package... Major Subject: Industrial Engineering A COMPARATIVE STUDY OF AVIONICS CONTROL INPUT METHODS A Thesis by JOHN ROBERT BARBER, JR. Approved as to style and content by: C airman of C ittee Dr. R. Dale H ingson Co-ch i n Dr. Rodger J. Koppa Member...

Barber, John Robert

1984-01-01T23:59:59.000Z

386

Inhalation Exposure Input Parameters for the Biosphere Model  

SciTech Connect (OSTI)

This analysis is one of the technical reports that support the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), referred to in this report as the biosphere model. ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes in detail the conceptual model as well as the mathematical model and its input parameters. This report documents development of input parameters for the biosphere model that are related to atmospheric mass loading and supports the use of the model to develop biosphere dose conversion factors (BDCFs). The biosphere model is one of a series of process models supporting the total system performance assessment (TSPA) for a Yucca Mountain repository. ''Inhalation Exposure Input Parameters for the Biosphere Model'' is one of five reports that develop input parameters for the biosphere model. A graphical representation of the documentation hierarchy for the biosphere model is presented in Figure 1-1 (based on BSC 2006 [DIRS 176938]). This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling and how this analysis report contributes to biosphere modeling. This analysis report defines and justifies values of atmospheric mass loading for the biosphere model. Mass loading is the total mass concentration of resuspended particles (e.g., dust, ash) in a volume of air. Mass loading values are used in the air submodel of the biosphere model to calculate concentrations of radionuclides in air inhaled by a receptor and concentrations in air surrounding crops. Concentrations in air to which the receptor is exposed are then used in the inhalation submodel to calculate the dose contribution to the receptor from inhalation of contaminated airborne particles. Concentrations in air surrounding plants are used in the plant submodel to calculate the concentrations of radionuclides in foodstuffs contributed from uptake by foliar interception. This report is concerned primarily with the physical attributes of airborne particulate matter, such as the airborne concentrations of particles and their sizes. The conditions of receptor exposure (duration of exposure in various microenvironments), breathing rates, and dosimetry of inhaled particulates are discussed in more detail in ''Characteristics of the Receptor for the Biosphere Model'' (BSC 2005 [DIRS 172827]).

M. Wasiolek

2006-06-05T23:59:59.000Z

387

Residential oil burners with low input and two stages firing  

SciTech Connect (OSTI)

The residential oil burner market is currently dominated by the pressure-atomized, retention head burner. At low firing rates pressure atomizing nozzles suffer rapid fouling of the small internal passages, leading to bad spray patterns and poor combustion performance. To overcome the low input limitations of conventional burners, a low pressure air-atomized burner has been developed watch can operate at fining rates as low as 0.25 gallons of oil per hour (10 kW). In addition, the burner can be operated in a high/low fining rate mode. Field tests with this burner have been conducted at a fixed input rate of 0.35 gph (14 kW) with a side-wall vented boiler/water storage tank combination. At the test home, instrumentation was installed to measure fuel and energy flows and record trends in system temperatures. Laboratory efficiency testing with water heaters and boilers has been completed using standard single purpose and combined appliance test procedures. The tests quantify benefits due to low firing rates and other burner features. A two stage oil burner gains a strong advantage in rated efficiency while maintaining capacity for high domestic hot water and space heating loads.

Butcher, T.; Krajewski, R.; Leigh, R. [and others

1997-12-31T23:59:59.000Z

388

Heat transfer analysis in Stirling engine heat input system  

SciTech Connect (OSTI)

One of the major factor in commercialization of Stirling engine is mass productivity, and the heat input system including tubular heater is one of the obstacles to mass production because of its complexity in shape and difficulty in manufacturing, which resulted from using oxidation-resistant, low-creep alloys which are not easy to machine and weld. Therefore a heater heat exchanger which is very simple in shape and easy to make has been devised, and a burner system appropriate to this heater also has been developed. In this paper specially devised heat input system which includes a heater shell shaped like U-cup and a flame tube located in the heater shell is analyzed in point of heat transfer processes to find optimum heat transfer. To enhance the heat transfer from the flame tube to the heater shell wall, it is required that the flame tube diameter be enlarged as close to the heater shell diameter as possible, and the flame tube temperature be raised as high as possible. But the enlargement of the flame tube diameter should be restricted by the state of combustion affected by hydraulic resistance of combustion gas, and the boost of the flame tube temperature should be considered carefully in the aspects of the flame tube`s service life.

Chung, W.; Kim, S. [LG Electronics Inc., Seoul (Korea, Republic of). Living System Lab.

1995-12-31T23:59:59.000Z

389

,"U.S. Downstream Processing of Fresh Feed Input"  

U.S. Energy Information Administration (EIA) Indexed Site

Monthly","9/2013","1/15/1987" Monthly","9/2013","1/15/1987" ,"Release Date:","11/27/2013" ,"Next Release Date:","Last Week of December 2013" ,"Excel File Name:","pet_pnp_dwns_dc_nus_mbblpd_m.xls" ,"Available from Web Page:","http://www.eia.gov/dnav/pet/pet_pnp_dwns_dc_nus_mbblpd_m.htm" ,"Source:","Energy Information Administration" ,"For Help, Contact:","infoctr@eia.gov" ,,"(202) 586-8800",,,"11/25/2013 11:17:28 AM" "Back to Contents","Data 1: U.S. Downstream Processing of Fresh Feed Input" "Sourcekey","M_NA_YDR_NUS_MBBLD","MCRCCUS2","MCRCHUS2","MCRDFUS2"

390

Soil-related Input Parameters for the Biosphere Model  

SciTech Connect (OSTI)

This analysis is one of the technical reports containing documentation of the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN), a biosphere model supporting the Total System Performance Assessment (TSPA) for the geologic repository at Yucca Mountain. The biosphere model is one of a series of process models supporting the Total System Performance Assessment (TSPA) for the Yucca Mountain repository. A graphical representation of the documentation hierarchy for the ERMYN biosphere model is presented in Figure 1-1. This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling, and the plan for development of the biosphere abstraction products for TSPA, as identified in the ''Technical Work Plan: for Biosphere Modeling and Expert Support'' (BSC 2003 [163602]). It should be noted that some documents identified in Figure 1-1 may be under development at the time this report is issued and therefore not available. This figure is included to provide an understanding of how this analysis report contributes to biosphere modeling in support of the license application, and is not intended to imply that access to the listed documents is required to understand the contents of this report. This report, ''Soil Related Input Parameters for the Biosphere Model'', is one of the five analysis reports that develop input parameters for use in the ERMYN model. This report is the source documentation for the six biosphere parameters identified in Table 1-1. ''The Biosphere Model Report'' (BSC 2003 [160699]) describes in detail the conceptual model as well as the mathematical model and its input parameters. The purpose of this analysis was to develop the biosphere model parameters needed to evaluate doses from pathways associated with the accumulation and depletion of radionuclides in the soil. These parameters support the calculation of radionuclide concentrations in soil from on-going irrigation and ash deposition and, as a direct consequence, radionuclide concentration in resuspended particulate matter in the atmosphere. The analysis was performed in accordance with the technical work plan for the biosphere modeling and expert support (TWP) (BSC 2003 [163602]). This analysis revises the previous one titled ''Evaluate Soil/Radionuclide Removal by Erosion and Leaching'' (CRWMS M&O 2001 [152517]). In REV 00 of this report, the data generated were fixed (i.e., taking no account of uncertainty and variability) values. This revision incorporates uncertainty and variability into the values for the bulk density, elemental partition coefficients, average annual loss of soil from erosion, resuspension enhancement factor, and field capacity water content.

A. J. Smith

2003-07-02T23:59:59.000Z

391

Sensitivity of Utility-Scale Solar Deployment Projections in the SunShot Vision Study to Market and Performance Assumptions  

SciTech Connect (OSTI)

The SunShot Vision Study explored the potential growth of solar markets if solar prices decreased by about 75% from 2010 to 2020. The ReEDS model was used to simulate utility PV and CSP deployment for this present study, based on several market and performance assumptions - electricity demand, natural gas prices, coal retirements, cost and performance of non-solar renewable technologies, PV resource variability, distributed PV deployment, and solar market supply growth - in addition to the SunShot solar price projections. This study finds that utility-scale solar deployment is highly sensitive to solar prices. Other factors can have significant impacts, particularly electricity demand and natural gas prices.

Eurek, K.; Denholm, P.; Margolis, R.; Mowers, M.

2013-04-01T23:59:59.000Z

392

Soil-Related Input Parameters for the Biosphere Model  

SciTech Connect (OSTI)

This report presents one of the analyses that support the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN). The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes the details of the conceptual model as well as the mathematical model and the required input parameters. The biosphere model is one of a series of process models supporting the postclosure Total System Performance Assessment (TSPA) for the Yucca Mountain repository. A schematic representation of the documentation flow for the Biosphere input to TSPA is presented in Figure 1-1. This figure shows the evolutionary relationships among the products (i.e., analysis and model reports) developed for biosphere modeling, and the biosphere abstraction products for TSPA, as identified in the ''Technical Work Plan for Biosphere Modeling and Expert Support'' (TWP) (BSC 2004 [DIRS 169573]). This figure is included to provide an understanding of how this analysis report contributes to biosphere modeling in support of the license application, and is not intended to imply that access to the listed documents is required to understand the contents of this report. This report, ''Soil-Related Input Parameters for the Biosphere Model'', is one of the five analysis reports that develop input parameters for use in the ERMYN model. This report is the source documentation for the six biosphere parameters identified in Table 1-1. The purpose of this analysis was to develop the biosphere model parameters associated with the accumulation and depletion of radionuclides in the soil. These parameters support the calculation of radionuclide concentrations in soil from on-going irrigation or ash deposition and, as a direct consequence, radionuclide concentration in other environmental media that are affected by radionuclide concentrations in soil. The analysis was performed in accordance with the TWP (BSC 2004 [DIRS 169573]) where the governing procedure was defined as AP-SIII.9Q, ''Scientific Analyses''. This analysis revises the previous version with the same name (BSC 2003 [DIRS 161239]), which was itself a revision of one titled ''Evaluate Soil/Radionuclide Removal by Erosion and Leaching'' (CRWMS M&O 2001 [DIRS 152517]). In Revision 00 of this report, the data generated were fixed values (i.e., taking no account of uncertainty and variability). Revision 01 (BSC 2003 [DIRS 161239]) incorporated uncertainty and variability into the values for the bulk density, elemental partition coefficients, average annual loss of soil from erosion, resuspension enhancement factor, and field capacity water content. The current revision of this document improves the transparency and traceability of the products without changing the details of the analysis. This analysis report supports the treatment of six of the features, events, and processes (FEPs) applicable to the Yucca Mountain reference biosphere (DTN: MO0407SEPFEPLA.000 [DIRS 170760]). The use of the more recent FEP list in DTN: MO0407SEPFEPLA.000 [DIRS 170760] represents a deviation from the detail provided in the TWP (BSC 2004 [DIRS 169573]), which referenced a previous version of the FEP list. The parameters developed in this report support treatment of these six FEPs addressed in the biosphere model that are listed in Table 1-1. Inclusion and treatment of FEPs in the biosphere model is described in the ''Biosphere Model Report'' (BSC 2004 [DIRS 169460], Section 6.2).

A. J. Smith

2004-09-09T23:59:59.000Z

393

231A. Hernndez-Sols et al. / Annals of Nuclear Energy 57 (2013) 230245 Lattice calculations use nuclear libraries as input basis data,  

E-Print Network [OSTI]

#12;231A. Hernández-Solís et al. / Annals of Nuclear Energy 57 (2013) 230­245 Lattice calculations use nuclear libraries as input basis data, describing the properties of nuclei and the fundamental/or estimated values from nuclear physics models are the source of information of these libraries. Because

Demazière, Christophe

394

T-698: Adobe ColdFusion Input Validation Flaw in 'probe.cfm'...  

Broader source: Energy.gov (indexed) [DOE]

ColdFusion Input Validation Flaw in 'probe.cfm' Permits Cross-Site Scripting Attacks T-698: Adobe ColdFusion Input Validation Flaw in 'probe.cfm' Permits Cross-Site Scripting...

395

U-050: Adobe Flex SDK Input Validation Flaw Permits Cross-Site...  

Broader source: Energy.gov (indexed) [DOE]

Attacks U-050: Adobe Flex SDK Input Validation Flaw Permits Cross-Site Scripting Attacks December 2, 2011 - 5:24am Addthis PROBLEM: Adobe Flex SDK Input Validation Flaw Permits...

396

E-Print Network 3.0 - ac input power Sample Search Results  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

factor with high total harmonic... input cur- rent shape at near unity power factor. Advantages of the proposed topology are: no dc... as well as input supply variations. Matrix...

397

V-168: Splunk Web Input Validation Flaw Permits Cross-Site Scripting...  

Broader source: Energy.gov (indexed) [DOE]

8: Splunk Web Input Validation Flaw Permits Cross-Site Scripting Attacks V-168: Splunk Web Input Validation Flaw Permits Cross-Site Scripting Attacks May 31, 2013 - 6:00am Addthis...

398

V-124: Splunk Web Input Validation Flaw Permits Cross-Site Scripting...  

Broader source: Energy.gov (indexed) [DOE]

4: Splunk Web Input Validation Flaw Permits Cross-Site Scripting Attacks V-124: Splunk Web Input Validation Flaw Permits Cross-Site Scripting Attacks April 2, 2013 - 1:13am Addthis...

399

T-602: BlackBerry Enterprise Server Input Validation Flaw in...  

Broader source: Energy.gov (indexed) [DOE]

02: BlackBerry Enterprise Server Input Validation Flaw in BlackBerry Web Desktop Manager Permits Cross-Site Scripting Attacks T-602: BlackBerry Enterprise Server Input Validation...

400

Environmental Transport Input Parameters for the Biosphere Model  

SciTech Connect (OSTI)

This analysis report is one of the technical reports documenting the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN), a biosphere model supporting the total system performance assessment (TSPA) for the geologic repository at Yucca Mountain. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows relationships among the reports developed for biosphere modeling and biosphere abstraction products for the TSPA, as identified in the ''Technical Work Plan: for Biosphere Modeling and Expert Support'' (TWP) (BSC 2003 [163602]). Some documents in Figure 1-1 may be under development and not available when this report is issued. This figure provides an understanding of how this report contributes to biosphere modeling in support of the license application (LA), but access to the listed documents is not required to understand the contents of this report. This report is one of the reports that develops input parameter values for the biosphere model. The ''Biosphere Model Report'' (BSC 2003 [160699]) describes the conceptual model, the mathematical model, and the input parameters. The purpose of this analysis is to develop biosphere model parameter values related to radionuclide transport and accumulation in the environment. These parameters support calculations of radionuclide concentrations in the environmental media (e.g., soil, crops, animal products, and air) resulting from a given radionuclide concentration at the source of contamination (i.e., either in groundwater or volcanic ash). The analysis was performed in accordance with the TWP (BSC 2003 [163602]). This analysis develops values of parameters associated with many features, events, and processes (FEPs) applicable to the reference biosphere (DTN: M00303SEPFEPS2.000 [162452]), which are addressed in the biosphere model (BSC 2003 [160699]). The treatment of these FEPs is described in BSC (2003 [160699], Section 6.2). Parameter values developed in this report, and the related FEPs, are listed in Table 1-1. The relationship between the parameters and FEPs was based on a comparison of the parameter definition and the FEP descriptions as presented in BSC (2003 [160699], Section 6.2). The parameter values developed in this report support the biosphere model and are reflected in the TSPA through the biosphere dose conversion factors (BDCFs). Biosphere modeling focuses on radionuclides screened for the TSPA-LA (BSC 2002 [160059]). The same list of radionuclides is used in this analysis (Section 6.1.4). The analysis considers two human exposure scenarios (groundwater and volcanic ash) and climate change (Section 6.1.5). This analysis combines and revises two previous reports, ''Transfer Coefficient Analysis'' (CRWMS M&O 2000 [152435]) and ''Environmental Transport Parameter Analysis'' (CRWMS M&O 2001 [152434]), because the new ERMYN biosphere model requires a redefined set of input parameters. The scope of this analysis includes providing a technical basis for the selection of radionuclide- and element-specific biosphere parameters (except for Kd) that are important for calculating BDCFs based on the available radionuclide inventory abstraction data. The environmental transport parameter values were developed specifically for use in the biosphere model and may not be appropriate for other applications.

M. A. Wasiolek

2003-06-27T23:59:59.000Z

Note: This page contains sample records for the topic "inform input assumptions" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


401

Mutual Entropy in Quantum Information and Information Genetics  

E-Print Network [OSTI]

After Shannon, entropy becomes a fundamental quantity to describe not only uncertainity or chaos of a system but also information carried by the system. Shannon's important discovery is to give a mathematical expression of the mutual entropy (information), information transmitted from an input system to an output system, by which communication processes could be analyzed on the stage of mathematical science. In this paper, first we review the quantum mutual entropy and discuss its uses in quantum information theory, and secondly we show how the classical mutual entropy can be used to analyze genomes, in particular, those of HIV.

Masanori Ohya

2004-06-30T23:59:59.000Z

402

Gross Input to Atmospheric Crude Oil Distillation Units  

U.S. Energy Information Administration (EIA) Indexed Site

Day) Day) Process: Gross Input to Atmospheric Crude Oil Dist. Units Operable Capacity (Calendar Day) Operating Capacity Idle Operable Capacity Operable Utilization Rate Period: Monthly Annual Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes Show Data By: Process Area Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sep-13 View History U.S. 15,283 15,709 16,327 16,490 16,306 16,162 1985-2013 PADD 1 1,134 1,188 1,178 1,142 1,122 1,130 1985-2013 East Coast 1,077 1,103 1,080 1,058 1,031 1,032 1985-2013 Appalachian No. 1 57 85 98 84 90 97 1985-2013 PADD 2 3,151 3,087 3,336 3,572 3,538 3,420 1985-2013 Ind., Ill. and Ky. 2,044 1,947 2,069 2,299 2,330 2,266 1985-2013

403

Interface module for transverse energy input to dye laser modules  

DOE Patents [OSTI]

An interface module (10) for transverse energy input to dye laser modules is provided particularly for the purpose of delivering enhancing transverse energy beams (36) in the form of illumination bar (54) to the lasing zone (18) of a dye laser device, in particular to a dye laser amplifier (12). The preferred interface module (10) includes an optical fiber array (30) having a plurality of optical fibers (38) arrayed in a co-planar fashion with their distal ends (44) receiving coherent laser energy from an enhancing laser source (46), and their proximal ends (4) delivered into a relay structure (3). The proximal ends (42) of the optical fibers (38) are arrayed so as to be coplanar and to be aimed generally at a common point. The transverse energy beam array (36) delivered from the optical fiber array (30) is acted upon by an optical element array (34) to produce an illumination bar (54) which has a cross section in the form of a elongated rectangle at the position of the lasing window (18). The illumination bar (54) is selected to have substantially uniform intensity throughout.

English, Jr., Ronald E. (Tracy, CA); Johnson, Steve A. (Tracy, CA)

1994-01-01T23:59:59.000Z

404

U-001:Symantec IM Manager Input Validation Flaws | Department of Energy  

Broader source: Energy.gov (indexed) [DOE]

U-001:Symantec IM Manager Input Validation Flaws U-001:Symantec IM Manager Input Validation Flaws U-001:Symantec IM Manager Input Validation Flaws October 3, 2011 - 12:45pm Addthis PROBLEM: Symantec IM Manager Input Validation Flaws Permit Cross-Site Scripting, SQL Injection, and Code Execution Attacks. PLATFORM: Version(s): prior to 8.4.18 ABSTRACT: Symantec IM Manager Input Validation Flaws Permit Cross-Site Scripting, SQL Injection, and Code Execution Attacks. reference LINKS: Security Advisory: SYM11-012 SecurityTracker Alert ID: 1026130 IMPACT ASSESSMENT: Medium Discussion: Several vulnerabilities were reported in Symantec IM Manager. A remote user can conduct cross-site scripting attacks. A remote user can inject SQL commands. Several scripts do not properly filter HTML code from user-supplied input before displaying the input [CVE-2011-0552]. A remote user can create a

405

Reclamation cost inputs for the resource allocation and mine costing model. Final working paper  

SciTech Connect (OSTI)

The purpose of this study is to improve estimates of surface mining reclamation cost components used as inputs to the Energy Information Administration's Resource Allocation and Mine Costing (RAMC) model. Costs ignored by the RAMC equations and input separately into the model on a regional basis were the focus of this study. Estimates of costs associated with the following reclamation activities were developed: valley fill construction, topsoil handling, runoff and diversion ditch construction and backfilling, sediment pond construction and backfilling, final pit backfilling and highwall reduction, revegetation, and permitting. For each activity, separate estimates were developed by cost component (initial capital, deferred capital, and annual operating), region (central Appalachia, northern Appalachia, the Midwest, and the West), and overburden ratio. For the first five activities, a ''composite mine'' approach was used. Basic engineering data on the quantity of material moved, and the distance over which it is moved, were obtained on a task-by-task basis for regional samples of actual mining operations. Mine permit applications filed with state and federal regulatory agencies were used as the source of these data. On the basis of the collected data, average material quantities and transportation distances were calculated for each region and reclamation task; these averages were used as the composite mine specifications assumed to be representative of the typical earthmoving requirements associated with each task in each region. Revegetation costs were estimated on the basis of published or publicly available data representing either the actual or estimated costs to state governments of revegetating abandoned mine sites. Permitting costs were developed on the basis of estimates of typical regional permitting costs solicited from engineering contractors providing permitting services to the coal industry. 11 tabs.

Not Available

1984-11-30T23:59:59.000Z

406

MULTIPLE-INPUT TRANSLINEAR ELEMENT NETWORKS Bradley A. Minch  

E-Print Network [OSTI]

in the construction of analog VLSI information processing systems. In the Nonlin- ear Circuits Handbook from Analog just quoted from the Nonlinear Circuits Handbook [1]. Despite these claims, these power-law circuits We describe a class of nonlinear circuits that accurately em- body product-of-power-law relationships

Diorio, Chris

407

Weather Forecast Data an Important Input into Building Management Systems  

E-Print Network [OSTI]

Lewis Poulin Implementation and Operational Services Section Canadian Meteorological Centre, Dorval, Qc National Prediction Operations Division ICEBO 2013, Montreal, Qc October 10 2013 Version 2013-09-27 Weather Forecast Data An Important... and weather information ? Numerical weather forecast production 101 ? From deterministic to probabilistic forecasts ? Some MSC weather forecast (NWP) datasets ? Finding the appropriate data for the appropriate forecast ? Preparing for probabilistic...

Poulin, L.

2013-01-01T23:59:59.000Z

408

Analyzing the impacts of sales tax on agricultural inputs  

E-Print Network [OSTI]

's sales tax files and sales by class of customer information obtained from a supplemental report of the 1987 Census of Agriculture on the purchases of machinery. The estimates were extrapolated based on Wharton Econometric's price index for machinery... obtained from the 1987 Census of Agriculture. The 1987 estimate was then extrapolated through 1996 using Wharton's indexes. Horse revenue was based on data on horse expenditures obtained from the Texas Agricultural Extension Service, while mule and other...

Lamar, Christina Helweg

2012-06-07T23:59:59.000Z

409

Information Relationships  

E-Print Network [OSTI]

INPUT: Profits soared at Boeing Co., easily topping forecasts on Wall Street, as their CEO Alan Mulally announced first quarter results. OUTPUT: Profits soared at [Company Boeing Co.], easily topping forecasts between Entities INPUT: Boeing is located in Seattle. Alan Mulally is the CEO. OUTPUT: f

Collins, Michael

410

Biotrans: Cost Optimization Model | Open Energy Information  

Open Energy Info (EERE)

Biotrans: Cost Optimization Model Biotrans: Cost Optimization Model Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Biotrans: Cost Optimization Model Focus Area: Ethanol Topics: Market Analysis Website: www.ecn.nl/units/ps/models-and-tools/biotrans/ Equivalent URI: cleanenergysolutions.org/content/biotrans-cost-optimization-model,http Language: English Policies: Deployment Programs DeploymentPrograms: Demonstration & Implementation BIOTRANS optimizes the biofuel supply chain allocation by finding the least-cost configuration of resources and trade to meet a specified biofuel demand in the European transportation sector. The user can constrain the optimization by inputting a number of economic and technological assumptions for a specific target year. References Retrieved from

411

V-124: Splunk Web Input Validation Flaw Permits Cross-Site Scripting  

Broader source: Energy.gov (indexed) [DOE]

4: Splunk Web Input Validation Flaw Permits Cross-Site 4: Splunk Web Input Validation Flaw Permits Cross-Site Scripting Attacks V-124: Splunk Web Input Validation Flaw Permits Cross-Site Scripting Attacks April 2, 2013 - 1:13am Addthis PROBLEM: Splunk Web Input Validation Flaw Permits Cross-Site Scripting Attacks PLATFORM: Version(s): 4.3.0 through 4.3.5 ABSTRACT: A vulnerability was reported in Splunk Web. REFERENCE LINKS: SecurityTracker Alert ID: 1028371 Splunk IMPACT ASSESSMENT: High DISCUSSION: Splunk Web does not properly filter HTML code from user-supplied input before displaying the input. A remote user can cause arbitrary scripting code to be executed by the target user's browser. The code will originate from the site running the Splunk Web software and will run in the security context of that site. As a result, the code will be able to access the

412

U-252: Barracuda Web Filter Input Validation Flaws Permit Cross-Site  

Broader source: Energy.gov (indexed) [DOE]

2: Barracuda Web Filter Input Validation Flaws Permit 2: Barracuda Web Filter Input Validation Flaws Permit Cross-Site Scripting Attacks U-252: Barracuda Web Filter Input Validation Flaws Permit Cross-Site Scripting Attacks September 6, 2012 - 6:00am Addthis PROBLEM: Barracuda Web Filter Input Validation Flaws Permit Cross-Site Scripting Attacks PLATFORM: Barracuda Web Filter 5.0.015 is vulnerable; other versions may also be affected. ABSTRACT: Barracuda Web Filter Authentication Module Multiple HTML Injection Vulnerabilities reference LINKS: Barracuda Networks Barracuda Networks Security ID: BNSEC-279/BNYF-5533 SecurityTracker Alert ID: 1027500 Bugtraq ID: 55394 seclists.org IMPACT ASSESSMENT: Medium Discussion: Two scripts not properly filter HTML code from user-supplied input before displaying the input. A remote user can cause arbitrary scripting code to

413

T-670: Skype Input Validation Flaw in 'mobile phone' Profile Entry Permits  

Broader source: Energy.gov (indexed) [DOE]

70: Skype Input Validation Flaw in 'mobile phone' Profile Entry 70: Skype Input Validation Flaw in 'mobile phone' Profile Entry Permits Cross-Site Scripting Attacks T-670: Skype Input Validation Flaw in 'mobile phone' Profile Entry Permits Cross-Site Scripting Attacks July 18, 2011 - 7:09am Addthis PROBLEM: A vulnerability was reported in Skype. A remote user can conduct cross-site scripting attacks. PLATFORM: 5.3.0.120 and prior versions ABSTRACT: The software does not properly filter HTML code from user-supplied input in the The "mobile phone" profile entry before displaying the input. reference LINKS: SecurityTracker Alert ID: 1025789 Skype Security Advisory KoreSecure News H Security ID: 1279864 IMPACT ASSESSMENT: High Discussion: Skype suffers from a persistent Cross-Site Scripting vulnerability due to a lack of input validation and output sanitization of the "mobile phone"

414

U-050: Adobe Flex SDK Input Validation Flaw Permits Cross-Site Scripting  

Broader source: Energy.gov (indexed) [DOE]

0: Adobe Flex SDK Input Validation Flaw Permits Cross-Site 0: Adobe Flex SDK Input Validation Flaw Permits Cross-Site Scripting Attacks U-050: Adobe Flex SDK Input Validation Flaw Permits Cross-Site Scripting Attacks December 2, 2011 - 5:24am Addthis PROBLEM: Adobe Flex SDK Input Validation Flaw Permits Cross-Site Scripting Attacks. PLATFORM: Adobe Flex SDK 4.5.1 and earlier 4.x versions for Windows, Macintosh and Linux Adobe Flex SDK 3.6 and earlier 3.x versions for Windows, Macintosh and Linux ABSTRACT: Flex applications created using the Flex SDK may not properly filter HTML code from user-supplied input before displaying the input. reference LINKS: Adobe Security Bulletin CVE-2011-2461 SecurityTracker Alert ID: 1026361 IMPACT ASSESSMENT: High Discussion: A remote user may be able to cause arbitrary scripting code to be executed

415

T-698: Adobe ColdFusion Input Validation Flaw in 'probe.cfm' Permits  

Broader source: Energy.gov (indexed) [DOE]

8: Adobe ColdFusion Input Validation Flaw in 'probe.cfm' 8: Adobe ColdFusion Input Validation Flaw in 'probe.cfm' Permits Cross-Site Scripting Attacks T-698: Adobe ColdFusion Input Validation Flaw in 'probe.cfm' Permits Cross-Site Scripting Attacks August 22, 2011 - 3:54pm Addthis PROBLEM: A vulnerability was reported in Adobe ColdFusion. A remote user can conduct cross-site scripting attacks. PLATFORM: Adobe ColdFusion 9.x ABSTRACT: Adobe ColdFusion Input Validation Flaw in 'probe.cfm' Permits Cross-Site Scripting Attacks. reference LINKS: Adobe Vulnerability Report Adobe Security Bulletin ColdFusion Support SecurityTracker Alert ID: 1025957 IMPACT ASSESSMENT: Medium Discussion: The 'probe.cfm' script does not properly filter HTML code from user-supplied input in the 'name' parameter before displaying the input. A remote user can create a specially crafted URL that, when loaded by a

416

T-670: Skype Input Validation Flaw in 'mobile phone' Profile Entry Permits  

Broader source: Energy.gov (indexed) [DOE]

0: Skype Input Validation Flaw in 'mobile phone' Profile Entry 0: Skype Input Validation Flaw in 'mobile phone' Profile Entry Permits Cross-Site Scripting Attacks T-670: Skype Input Validation Flaw in 'mobile phone' Profile Entry Permits Cross-Site Scripting Attacks July 18, 2011 - 7:09am Addthis PROBLEM: A vulnerability was reported in Skype. A remote user can conduct cross-site scripting attacks. PLATFORM: 5.3.0.120 and prior versions ABSTRACT: The software does not properly filter HTML code from user-supplied input in the The "mobile phone" profile entry before displaying the input. reference LINKS: SecurityTracker Alert ID: 1025789 Skype Security Advisory KoreSecure News H Security ID: 1279864 IMPACT ASSESSMENT: High Discussion: Skype suffers from a persistent Cross-Site Scripting vulnerability due to a lack of input validation and output sanitization of the "mobile phone"

417

U-132: Apache Wicket Input Validation Flaw in 'wicket:pageMapName'  

Broader source: Energy.gov (indexed) [DOE]

2: Apache Wicket Input Validation Flaw in 'wicket:pageMapName' 2: Apache Wicket Input Validation Flaw in 'wicket:pageMapName' Parameter Permits Cross-Site Scripting Attacks U-132: Apache Wicket Input Validation Flaw in 'wicket:pageMapName' Parameter Permits Cross-Site Scripting Attacks March 23, 2012 - 7:42am Addthis PROBLEM: Apache Wicket Input Validation Flaw in 'wicket:pageMapName' Parameter Permits Cross-Site Scripting Attacks PLATFORM: Apache Wicket 1.4.x ABSTRACT: A remote user can conduct cross-site scripting attacks. reference LINKS: Apache Wicket CVE-2012-0047 SecurityTracker Alert ID: 1026839 IMPACT ASSESSMENT: High Discussion: The software does not properly filter HTML code from user-supplied input in the 'wicket:pageMapName' request parameter before displaying the input. A remote user can cause arbitrary scripting code to be executed by the target

418

T-698: Adobe ColdFusion Input Validation Flaw in 'probe.cfm' Permits  

Broader source: Energy.gov (indexed) [DOE]

8: Adobe ColdFusion Input Validation Flaw in 'probe.cfm' 8: Adobe ColdFusion Input Validation Flaw in 'probe.cfm' Permits Cross-Site Scripting Attacks T-698: Adobe ColdFusion Input Validation Flaw in 'probe.cfm' Permits Cross-Site Scripting Attacks August 22, 2011 - 3:54pm Addthis PROBLEM: A vulnerability was reported in Adobe ColdFusion. A remote user can conduct cross-site scripting attacks. PLATFORM: Adobe ColdFusion 9.x ABSTRACT: Adobe ColdFusion Input Validation Flaw in 'probe.cfm' Permits Cross-Site Scripting Attacks. reference LINKS: Adobe Vulnerability Report Adobe Security Bulletin ColdFusion Support SecurityTracker Alert ID: 1025957 IMPACT ASSESSMENT: Medium Discussion: The 'probe.cfm' script does not properly filter HTML code from user-supplied input in the 'name' parameter before displaying the input. A remote user can create a specially crafted URL that, when loaded by a

419

Fossil energy use in conventional and low-external-input cropping systems.  

E-Print Network [OSTI]

??The production of fossil fuels will crest within the next decade and with reliance of modern conventional agriculture on fossil fuel energy inputs, food production (more)

Cruse, Michael James

2009-01-01T23:59:59.000Z

420

Combining frequency and time domain approaches to systems with multiple spike train input and output  

E-Print Network [OSTI]

between neuronal spike trains. Prog Biophys Mol Biol Vapnikto systems with multiple spike train input and output D. R.Keywords Multiple spike trains Neural coding Maximum

Brillinger, D. R.; Lindsay, K. A.; Rosenberg, J. R.

2009-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "inform input assumptions" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


421

E-Print Network 3.0 - alpha motoneurone input Sample Search Results  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

such as input resistance (Ri,), membrane time constant (T... Spinalization on Ankle Extensor Motoneurons II. Motoneuron Electrical Properties S. HOCHMAN AND D. A. Mc......

422

A CSP Timed Input-Output Relation and a Strategy for Mechanised Conformance Verification  

Science Journals Connector (OSTI)

Here we propose a timed input-output conformance relation (named CSPTIO) based on the process algebra CSP. In contrast to other relations, CSPTIO...

Gustavo Carvalho; Augusto Sampaio

2013-01-01T23:59:59.000Z

423

Factors Controlling the Input of Electrical Energy into a Fish in an ...  

Science Journals Connector (OSTI)

In order to determine the electrical energy - input into a fish, both voltage and resistance, as applied to the fish itself, should be known. Neither of these quantities...

1999-12-13T23:59:59.000Z

424

Table A10. Total Inputs of Energy for Heat, Power, and Electricity...  

U.S. Energy Information Administration (EIA) Indexed Site

0. Total Inputs of Energy for Heat, Power, and Electricity Generation" " by Fuel Type, Industry Group, Selected Industries, and End Use, 1994:" " Part 2" " (Estimates in Trillion...

425

FORMALIZATION OF INPUT AND OUTPUT IN MODERN OPERATING SYSTEMS: THE HADLEY MODEL.  

E-Print Network [OSTI]

??We present the Hadley model, a formal descriptive model of input and output for modern computer operating systems. Our model is intentionally inspired by the (more)

Gerber, Matthew

2005-01-01T23:59:59.000Z

426

Form:Energy Generation Facility | Open Energy Information  

Open Energy Info (EERE)

Facility Jump to: navigation, search Input the name of an Energy Generation Facility below. If the resource already exists, you will be able to edit its information. AddEdit an...

427

Generalized Mercury/Waterfilling for Multiple-Input Multiple-Output Channels  

E-Print Network [OSTI]

Generalized Mercury/Waterfilling for Multiple-Input Multiple-Output Channels Fernando P procedure that generalizes the mercury/waterfilling algorithm, previously proposed for parallel non-interfering chan- nels. In this generalization the mercury level accounts for the sub- optimal (non-Gaussian) input

Verdú, Sergio

428

April 3-4, 2007/ARR Engineering Input to System Code and  

E-Print Network [OSTI]

with providing input to system code - Assessing high-leverage engineering parameters to guide integrated trade 3-4, 2007/ARR 2 Schematic of ARIES Next Step Study as I Understand It (TBD) Design Requirements to Demonstrate Those System Code Development and Integration (ARIES-AT as starting point) Translating Input to

Raffray, A. René

429

Polar study of ionospheric ion outflow versus energy input Yihua Zheng,1  

E-Print Network [OSTI]

versus energy input is performed by using multi- instrument data (TIDE, EFI, MFI, HYDRA) from PolarPolar study of ionospheric ion outflow versus energy input Yihua Zheng,1 Thomas E. Moore,2 Forrest/6 Hz), the electron density, temperature, and the electron energy flux. The perturbation fields used

California at Berkeley, University of

430

Pinch-drag-flick vs. spatial input: rethinking zoom & pan on mobile displays  

Science Journals Connector (OSTI)

The multi-touch-based pinch to zoom, drag and flick to pan metaphor has gained wide popularity on mobile displays, where it is the paradigm of choice for navigating 2D documents. But is finger-based navigation really the gold standard' In this paper, ... Keywords: mobile displays, multi-touch input, spatial input, spatially aware displays, user study

Martin Spindler; Martin Schuessler; Marcel Martsch; Raimund Dachselt

2014-04-01T23:59:59.000Z

431

Feeling Music: Integration of Auditory and Tactile Inputs in Musical Meter Perception  

E-Print Network [OSTI]

Feeling Music: Integration of Auditory and Tactile Inputs in Musical Meter Perception Juan Huang1 are integrated in humans performing a musical meter recognition task. Subjects discriminated between two types coherent meter percepts, and 3) Simultaneously presented bimodal inputs where the two channels contained

Wang, Xiaoqin

432

Estimation of input energy in rocket-triggered lightning Vinod Jayakumar,1  

E-Print Network [OSTI]

the input power and energy, each per unit channel length and as a function of time, associated with return- lightning first stroke, based on the conversion of measured optical energy to total energy using energy., 2002] and measured current, I(t), at the channel base to estimate the input power per unit length, P

Florida, University of

433

Asynchronous Gate-Diffusion----Input (GDI) Circuits Arkadiy Morgenshtein, Michael Moreinis and Ran Ginosar  

E-Print Network [OSTI]

1 Asynchronous Gate-Diffusion----Input (GDI) Circuits Arkadiy Morgenshtein, Michael Moreinis, Israel [ran@ee.technion.ac.il] Abstract: Novel Gate-Diffusion Input (GDI) circuits are applied to asynchronous design. A variety of GDI implementations are compared with typical CMOS asynchronous circuits

Ginosar, Ran

434

Lean and Steering Motorcycle Dynamics Reconstruction : An Unknown Input HOSMO Approach  

E-Print Network [OSTI]

Lean and Steering Motorcycle Dynamics Reconstruction : An Unknown Input HOSMO Approach L. Nehaoua1. For this purpose, we consider a unknown input high order sliding mode observer (UIHOSMO). First, a motorcycle- flected by an important increase of motorcycle's fatalities. Recent statistics confirm this fact

Paris-Sud XI, Université de

435

Accepting information with a pinch of salt: handling untrusted information sources  

Science Journals Connector (OSTI)

This paper describes on-going research developing a system to allow incident controllers and similar decision makers to augment official information input streams with information contributed by the wider public (either explicitly submitted to them or ... Keywords: crowd-sourcing, incident management, mash-up, situational awareness, trust, uncertainty

Syed Sadiqur Rahman; Sadie Creese; Michael Goldsmith

2011-06-01T23:59:59.000Z

436

U-102: Cisco IronPort Encryption Appliance Input Validation Flaw Permits  

Broader source: Energy.gov (indexed) [DOE]

2: Cisco IronPort Encryption Appliance Input Validation Flaw 2: Cisco IronPort Encryption Appliance Input Validation Flaw Permits Cross-Site Scripting Attacks U-102: Cisco IronPort Encryption Appliance Input Validation Flaw Permits Cross-Site Scripting Attacks February 14, 2012 - 8:00am Addthis PROBLEM: A vulnerability was reported in Cisco IronPort Encryption Appliance. PLATFORM: Version(s): prior to 6.5.3 ABSTRACT: A remote user can conduct cross-site scripting reference LINKS: Vendor URL CVE-2012-0340 Security Tracker ID:1026669 IMPACT ASSESSMENT: Medium Discussion: The interface does not properly filter HTML code from user-supplied input before displaying the input. A remote user can create a specially crafted URL that, when loaded by a target user, will cause arbitrary scripting code to be executed by the target user's browser. The code will originate from

437

V-168: Splunk Web Input Validation Flaw Permits Cross-Site Scripting  

Broader source: Energy.gov (indexed) [DOE]

8: Splunk Web Input Validation Flaw Permits Cross-Site 8: Splunk Web Input Validation Flaw Permits Cross-Site Scripting Attacks V-168: Splunk Web Input Validation Flaw Permits Cross-Site Scripting Attacks May 31, 2013 - 6:00am Addthis PROBLEM: A vulnerability was reported in Splunk Web PLATFORM: Version(s) prior to 5.0.3 ABSTRACT: A reflected cross-site scripting vulnerability was identified in Splunk Web REFERENCE LINKS: SecurityTracker Alert ID: 1028605 Splunk Security Advisory SPL-59895 CVE-2012-6447 IMPACT ASSESSMENT: Medium DISCUSSION: The web interface does not properly filter HTML code from user-supplied input before displaying the input. A remote user can create a specially crafted URL that, when loaded by a target user, will cause arbitrary scripting code to be executed by the target user's browser. The code will

438

U-204: HP Network Node Manager i Input Validation Hole Permits Cross-Site  

Broader source: Energy.gov (indexed) [DOE]

4: HP Network Node Manager i Input Validation Hole Permits 4: HP Network Node Manager i Input Validation Hole Permits Cross-Site Scripting Attacks U-204: HP Network Node Manager i Input Validation Hole Permits Cross-Site Scripting Attacks July 3, 2012 - 7:00am Addthis PROBLEM: HP Network Node Manager i Input Validation Hole Permits Cross-Site Scripting Attacks PLATFORM: Version(s): 8.x, 9.0x, 9.1x ABSTRACT: Potential security vulnerabilities have been identified with HP Network Node Manager I (NNMi) for HP-UX, Linux, Solaris, and Windows. The vulnerabilities could be remotely exploited resulting in cross site scripting (XSS). reference LINKS: The Vendor's Advisory SecurityTracker Alert ID: 1027215 CVE-2012-2018 IMPACT ASSESSMENT: Medium Discussion: A vulnerability was reported in HP Network Node Manager i. The software does not properly filter HTML code from user-supplied input before

439

V-139: Cisco Network Admission Control Input Validation Flaw Lets Remote  

Broader source: Energy.gov (indexed) [DOE]

9: Cisco Network Admission Control Input Validation Flaw Lets 9: Cisco Network Admission Control Input Validation Flaw Lets Remote Users Inject SQL Commands V-139: Cisco Network Admission Control Input Validation Flaw Lets Remote Users Inject SQL Commands April 21, 2013 - 11:50pm Addthis PROBLEM: Cisco Network Admission Control Input Validation Flaw Lets Remote Users Inject SQL Commands PLATFORM: Cisco NAC Manager versions prior to 4.8.3.1 and 4.9.2 ABSTRACT: A vulnerability was reported in Cisco Network Admission Control. REFERENCE LINKS: SecurityTracker Alert ID: 1028451 Cisco Advisory ID: cisco-sa-20130417-nac CVE-2013-1177 IMPACT ASSESSMENT: High DISCUSSION: The Cisco Network Admission Control (NAC) Manager does not properly validate user-supplied input. A remote user can supply a specially crafted parameter value to execute SQL commands on the underlying database.

440

U-144:Juniper Secure Access Input Validation Flaw Permits Cross-Site  

Broader source: Energy.gov (indexed) [DOE]

4:Juniper Secure Access Input Validation Flaw Permits 4:Juniper Secure Access Input Validation Flaw Permits Cross-Site Scripting Attacks U-144:Juniper Secure Access Input Validation Flaw Permits Cross-Site Scripting Attacks April 10, 2012 - 7:30am Addthis PROBLEM: A vulnerability was reported in Juniper Secure Access/Instant Virtual Extranet (IVE). A remote user can conduct cross-site scripting attacks. PLATFORM: Version(s): prior to 7.0R9 and 7.1R ABSTRACT: The VPN management interface does not properly filter HTML code from user-supplied input before displaying the input. A remote user can cause arbitrary scripting code to be executed by the target user's browser. reference LINKS: Vendor URL SecurityTracker Alert ID: 1026893 IMPACT ASSESSMENT: High Discussion: The code will originate from the interface and will run in the security

Note: This page contains sample records for the topic "inform input assumptions" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


441

How are basement walls input in REScheck? | Building Energy Codes Program  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

basement walls input in REScheck? basement walls input in REScheck? After selecting a basement wall type, a basement wall illustration will appear with input boxes for the basement wall height, depth below grade, and depth of insulation. The illustration helps identify the dimensions being requested. You may enter basement wall dimensions directly into this illustration and select the OK button to have them transferred to the corresponding row in the table on the Envelope screen. If you prefer to enter the dimensions directly into the table on the Envelope screen, you can select Cancel to remove the illustration without entering dimensions. To view the basement wall illustration and inputs at a later time, click the right-mouse button anywhere on the basement row and select Edit Basement Inputs from the popup menu.

442

V-193: Barracuda SSL VPN Input Validation Hole Permits Cross-Site Scripting  

Broader source: Energy.gov (indexed) [DOE]

93: Barracuda SSL VPN Input Validation Hole Permits Cross-Site 93: Barracuda SSL VPN Input Validation Hole Permits Cross-Site Scripting Attacks V-193: Barracuda SSL VPN Input Validation Hole Permits Cross-Site Scripting Attacks July 5, 2013 - 6:00am Addthis PROBLEM: A vulnerability was reported in Barracuda SSL VPN PLATFORM: Version(s) prior to 2.3.3.216 ABSTRACT: Several scripts do not properly filter HTML code from user-supplied input before displaying the input via several parameters REFERENCE LINKS: SecurityTracker Alert ID: 1028736 Barracuda SSL VPN Release Notes Zero Science Lab IMPACT ASSESSMENT: Medium DISCUSSION: The code will originate from the Barracuda SSL VPN interface and will run in the security context of that site. As a result, the code will be able to access the target user's cookies (including authentication cookies), if

443

V-153: Symantec Brightmail Gateway Input Validation Flaw Permits Cross-Site  

Broader source: Energy.gov (indexed) [DOE]

3: Symantec Brightmail Gateway Input Validation Flaw Permits 3: Symantec Brightmail Gateway Input Validation Flaw Permits Cross-Site Scripting Attacks V-153: Symantec Brightmail Gateway Input Validation Flaw Permits Cross-Site Scripting Attacks May 10, 2013 - 6:00am Addthis PROBLEM: A vulnerability was reported in Symantec Brightmail Gateway PLATFORM: The vulnerabilities are reported in versions prior to 9.5.x ABSTRACT: Symantec's Brightmail Gateway management console is susceptible to stored cross-site scripting (XSS) issues found in some of the administrative interface pages. REFERENCE LINKS: Security Tracker Alert ID: 1028530 Symantec Security Advisory CVE-2013-1611 IMPACT ASSESSMENT: Medium DISCUSSION: The administrative interface does not properly filter HTML code from user-supplied input before displaying the input. A remote user can cause

444

U-255: Apache Wicket Input Validation Flaw Permits Cross-Site Scripting  

Broader source: Energy.gov (indexed) [DOE]

5: Apache Wicket Input Validation Flaw Permits Cross-Site 5: Apache Wicket Input Validation Flaw Permits Cross-Site Scripting Attacks U-255: Apache Wicket Input Validation Flaw Permits Cross-Site Scripting Attacks September 11, 2012 - 6:00am Addthis PROBLEM: Apache Wicket Input Validation Flaw Permits Cross-Site Scripting Attacks PLATFORM: Apache Software Foundation Apache Wicket 1.5.5 Apache Software Foundation Apache Wicket 1.5-RC5.1 Apache Software Foundation Apache Wicket 1.4.20 Apache Software Foundation Apache Wicket 1.4.18 Apache Software Foundation Apache Wicket 1.4.17 Apache Software Foundation Apache Wicket 1.4.16 ABSTRACT: A vulnerability was reported in Apache Wicket reference LINKS: Apache Wicket SecurityTracker Alert ID: 1027508 Bugtraq ID: 55445 CVE-2012-3373 IMPACT ASSESSMENT: Medium Discussion: The software does not properly filter HTML code from user-supplied input in

445

U-139: IBM Tivoli Directory Server Input Validation Flaw | Department of  

Broader source: Energy.gov (indexed) [DOE]

39: IBM Tivoli Directory Server Input Validation Flaw 39: IBM Tivoli Directory Server Input Validation Flaw U-139: IBM Tivoli Directory Server Input Validation Flaw April 3, 2012 - 7:00am Addthis PROBLEM: A vulnerability was reported in IBM Tivoli Directory Server. A remote user can conduct cross-site scripting attacks PLATFORM: Version(s): 6.2, 6.3 ABSTRACT: The Web Admin Tool does not properly filter HTML code from user-supplied input before displaying the input. Reference LINKS: Vendor Advisory Security Tracker ID 1026880 CVE-2012-0740 IMPACT ASSESSMENT: Medium Discussion: A remote user can create a specially crafted URL that, when loaded by a target user, will cause arbitrary scripting code to be executed by the target user's browser. The code will originate from the site running the IBM Tivoli Directory Server software and will run in the security context

446

V-229: IBM Lotus iNotes Input Validation Flaws Permit Cross-Site Scripting  

Broader source: Energy.gov (indexed) [DOE]

V-229: IBM Lotus iNotes Input Validation Flaws Permit Cross-Site V-229: IBM Lotus iNotes Input Validation Flaws Permit Cross-Site Scripting Attacks V-229: IBM Lotus iNotes Input Validation Flaws Permit Cross-Site Scripting Attacks August 28, 2013 - 6:00am Addthis PROBLEM: Several vulnerabilities were reported in IBM Lotus iNotes PLATFORM: IBM Lotus iNotes 8.5.x ABSTRACT: IBM Lotus iNotes 8.5.x contains four cross-site scripting vulnerabilities REFERENCE LINKS: Security Tracker Alert ID 1028954 IBM Security Bulletin 1647740 Seclist.org CVE-2013-0590 CVE-2013-0591 CVE-2013-0595 IMPACT ASSESSMENT: Medium DISCUSSION: The software does not properly filter HTML code from user-supplied input before displaying the input. A remote user can cause arbitrary scripting code to be executed by the target user's browser. The code will originate

447

U-204: HP Network Node Manager i Input Validation Hole Permits Cross-Site  

Broader source: Energy.gov (indexed) [DOE]

204: HP Network Node Manager i Input Validation Hole Permits 204: HP Network Node Manager i Input Validation Hole Permits Cross-Site Scripting Attacks U-204: HP Network Node Manager i Input Validation Hole Permits Cross-Site Scripting Attacks July 3, 2012 - 7:00am Addthis PROBLEM: HP Network Node Manager i Input Validation Hole Permits Cross-Site Scripting Attacks PLATFORM: Version(s): 8.x, 9.0x, 9.1x ABSTRACT: Potential security vulnerabilities have been identified with HP Network Node Manager I (NNMi) for HP-UX, Linux, Solaris, and Windows. The vulnerabilities could be remotely exploited resulting in cross site scripting (XSS). reference LINKS: The Vendor's Advisory SecurityTracker Alert ID: 1027215 CVE-2012-2018 IMPACT ASSESSMENT: Medium Discussion: A vulnerability was reported in HP Network Node Manager i. The software does not properly filter HTML code from user-supplied input before

448

DOE Seeking Input on Alternative Uses of Nickel Inventory | Department of  

Broader source: Energy.gov (indexed) [DOE]

Seeking Input on Alternative Uses of Nickel Inventory Seeking Input on Alternative Uses of Nickel Inventory DOE Seeking Input on Alternative Uses of Nickel Inventory March 9, 2007 - 10:28am Addthis WASHINGTON, DC - The U.S. Department of Energy (DOE) is seeking input from industry representatives on the safe disposition of approximately 15,300 tons of nickel scrap recovered from uranium enrichment process equipment at the Department's Oak Ridge, TN, and Paducah, KY, facilities. The Expression of Interest (EOI), released today, will assist in DOE's evaluation of restricted uses of its nickel material for controlled radiological applications. These restricted uses could include use in commercial nuclear power plants, DOE nuclear facilities, or by the U.S. Navy. The Department will solicit input through May 8, 2007.

449

DOE Seeking Input on Alternative Uses of Nickel Inventory | Department of  

Broader source: Energy.gov (indexed) [DOE]

DOE Seeking Input on Alternative Uses of Nickel Inventory DOE Seeking Input on Alternative Uses of Nickel Inventory DOE Seeking Input on Alternative Uses of Nickel Inventory March 9, 2007 - 10:28am Addthis WASHINGTON, DC - The U.S. Department of Energy (DOE) is seeking input from industry representatives on the safe disposition of approximately 15,300 tons of nickel scrap recovered from uranium enrichment process equipment at the Department's Oak Ridge, TN, and Paducah, KY, facilities. The Expression of Interest (EOI), released today, will assist in DOE's evaluation of restricted uses of its nickel material for controlled radiological applications. These restricted uses could include use in commercial nuclear power plants, DOE nuclear facilities, or by the U.S. Navy. The Department will solicit input through May 8, 2007.

450

V-168: Splunk Web Input Validation Flaw Permits Cross-Site Scripting  

Broader source: Energy.gov (indexed) [DOE]

68: Splunk Web Input Validation Flaw Permits Cross-Site 68: Splunk Web Input Validation Flaw Permits Cross-Site Scripting Attacks V-168: Splunk Web Input Validation Flaw Permits Cross-Site Scripting Attacks May 31, 2013 - 6:00am Addthis PROBLEM: A vulnerability was reported in Splunk Web PLATFORM: Version(s) prior to 5.0.3 ABSTRACT: A reflected cross-site scripting vulnerability was identified in Splunk Web REFERENCE LINKS: SecurityTracker Alert ID: 1028605 Splunk Security Advisory SPL-59895 CVE-2012-6447 IMPACT ASSESSMENT: Medium DISCUSSION: The web interface does not properly filter HTML code from user-supplied input before displaying the input. A remote user can create a specially crafted URL that, when loaded by a target user, will cause arbitrary scripting code to be executed by the target user's browser. The code will

451

V-085: Cisco Unity Express Input Validation Hole Permits Cross-Site Request  

Broader source: Energy.gov (indexed) [DOE]

5: Cisco Unity Express Input Validation Hole Permits Cross-Site 5: Cisco Unity Express Input Validation Hole Permits Cross-Site Request Forgery Attacks V-085: Cisco Unity Express Input Validation Hole Permits Cross-Site Request Forgery Attacks February 6, 2013 - 1:06am Addthis PROBLEM: Cisco Unity Express Input Validation Hole Permits Cross-Site Request Forgery Attacks PLATFORM: Cisco Unity Express prior to 8.0 ABSTRACT: A vulnerability was reported in Cisco Unity Express. REFERENCE LINKS: Cisco Security Notice SecurityTracker Alert ID: 1028075 CVE-2013-1120 IMPACT ASSESSMENT: Medium DISCUSSION: Cisco Unity Express software prior to version 8.0 contains vulnerabilities that could allow an unauthenticated, remote attacker to conduct cross site request forgery attacks. The vulnerabilities are due to insufficient input validation. An attacker could exploit these vulnerabilities by

452

Title: Ontario Wind Resources Information Ontario Ministry of Natural Resources  

E-Print Network [OSTI]

information (monthly, yearly, extreme months and inter ­ annually) and wind rose information. All dataTitle: Ontario Wind Resources Information Data Creator / Copyright Owner: Ontario Ministry as an input to the Wind Resource Atlas, a web mapping application for helping users determine the feasibility

453

Information Cartography 1 Information Cartography  

E-Print Network [OSTI]

Information Cartography 1 Information Cartography · The use of Geographic Information Systems (GIS) to visualize non- geographic data · Utilizes Geographic Information Science to develop models and organize information--not an art form · Used to build information maps. Information maps consist of a landscape (base

Old, L. John

454

Information encoding in an oscillatory network  

Science Journals Connector (OSTI)

Information encoding in a globally coupled network is studied. When the network is in an oscillatory state, the network activities are dominated by the intrinsic oscillatory current and the stimulus is poorly encoded. However, when the amplitude of the input signal is large, the input can still be well read from the population rate and the temporal correlation between spike trains. The underlying reason is that there exists a competition between the intrinsic correlation caused by the oscillatory current and the external correlation caused by the input signal. With small input signal, the rate code performs better than the temporal correlation code. Our results provide insights into the effects of network dynamics on neuronal computations.

Sentao Wang and Changsong Zhou

2009-06-09T23:59:59.000Z

455

All-optical routing of single photons with multiple input and output ports by interferences  

E-Print Network [OSTI]

We propose a waveguide-cavity coupled system to achieve the routing of photons by the phases of other photons. Our router has four input ports and four output ports. The transport of the coherent-state photons injected through any input port can be controlled by the phases of the coherent-state photons injected through other input ports. This control can be achieved when the mean numbers of the routed and control photons are small enough and require no additional control fields. Therefore, the all-optical routing of photons can be achieved at the single-photon level.

Wei-Bin Yan; Bao Liu; Ling Zhou; Heng Fan

2014-09-23T23:59:59.000Z

456

7-117 The claim of a heat pump designer regarding the COP of the heat pump is to be evaluated. Assumptions The heat pump operates steadily.  

E-Print Network [OSTI]

7-47 7-117 The claim of a heat pump designer regarding the COP of the heat pump is to be evaluated. Assumptions The heat pump operates steadily. HP Wnet,in QH QL TL TH Analysis The maximum heat pump coefficient of performance would occur if the heat pump were completely reversible, 5.7 K026K300 K300 COP maxHP, LH H TT

Bahrami, Majid

457

Review of technical justification of assumptions and methods used by the Environmental Protection Agency for estimating risks avoided by implementing MCLs for radionuclides  

SciTech Connect (OSTI)

The Environmental Protection Agency (EPA) has proposed regulations for allowable levels of radioactive material in drinking water (40 CFR Part 141, 56 FR 33050, July 18, 1991). This review examined the assumptions and methods used by EPA in calculating risks that would be avoided by implementing the proposed Maximum Contaminant Levels for uranium, radium, and radon. Proposed limits on gross alpha and beta-gamma emitters were not included in this review.

Morris, S.C.; Rowe, M.D.; Holtzman, S.; Meinhold, A.F.

1992-11-01T23:59:59.000Z

458

U-219: Symantec Web Gateway Input Validation Flaws Lets Remote Users Inject  

Broader source: Energy.gov (indexed) [DOE]

19: Symantec Web Gateway Input Validation Flaws Lets Remote 19: Symantec Web Gateway Input Validation Flaws Lets Remote Users Inject SQL Commands, Execute Arbitrary Commands, and Change User Passwords U-219: Symantec Web Gateway Input Validation Flaws Lets Remote Users Inject SQL Commands, Execute Arbitrary Commands, and Change User Passwords July 24, 2012 - 7:00am Addthis PROBLEM: Symantec Web Gateway Input Validation Flaws Lets Remote Users Inject SQL Commands, Execute Arbitrary Commands, and Change User Passwords PLATFORM: Symantec Web Gateway 5.0.x.x ABSTRACT: Several vulnerabilities were reported in Symantec Web Gateway. REFERENCE LINKS: Security Advisories Relating to Symantec Products SecurityTracker Alert ID: 1027289 Bugtraq ID: 54424 Bugtraq ID: 54425 Bugtraq ID: 54426 Bugtraq ID: 54427 Bugtraq ID: 54429 Bugtraq ID: 54430

459

T-701: Citrix Access Gateway Enterprise Edition Input Validation Flaw in  

Broader source: Energy.gov (indexed) [DOE]

1: Citrix Access Gateway Enterprise Edition Input Validation 1: Citrix Access Gateway Enterprise Edition Input Validation Flaw in Logon Portal Permits Cross-Site Scripting Attacks T-701: Citrix Access Gateway Enterprise Edition Input Validation Flaw in Logon Portal Permits Cross-Site Scripting Attacks August 25, 2011 - 3:33pm Addthis PROBLEM: A vulnerability was reported in Citrix Access Gateway Enterprise Edition. A remote user can conduct cross-site scripting attacks. PLATFORM: Citrix Access Gateway Enterprise Edition 9.2-49.8 and prior. Citrix Access Gateway Enterprise Edition version 9.3 is not affected by this vulnerability. ABSTRACT: Citrix Access Gateway Enterprise Edition Input Validation Flaw in Logon Portal Permits Cross-Site Scripting Attacks. reference LINKS: SecurityTracker Alert ID: 1025973 Citrix Document ID: CTX129971

460

U-195: PHPlist Input Validation Flaws Permit Cross-Site Scripting and SQL  

Broader source: Energy.gov (indexed) [DOE]

5: PHPlist Input Validation Flaws Permit Cross-Site Scripting 5: PHPlist Input Validation Flaws Permit Cross-Site Scripting and SQL Injection Attacks U-195: PHPlist Input Validation Flaws Permit Cross-Site Scripting and SQL Injection Attacks June 20, 2012 - 7:00am Addthis PROBLEM: Two vulnerabilities were reported in PHPlist. A remote user can conduct cross-site scripting attacks. A remote authenticated user can inject SQL commands. PLATFORM: Version(s): prior to 2.10.18 ABSTRACT: The 'public_html/lists/admin' pages do not properly validate user-supplied input in the 'sortby' parameter [CVE-2012-2740]. A remote authenticated administrative user can supply a specially crafted parameter value to execute SQL commands on the underlying database. REFERENCE LINKS: Vendor Advisory Security Tracker ID 1027181 CVE-2012-2740, CVE-2012-2741

Note: This page contains sample records for the topic "inform input assumptions" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


461

T-546: Microsoft MHTML Input Validation Hole May Permit Cross-Site  

Broader source: Energy.gov (indexed) [DOE]

6: Microsoft MHTML Input Validation Hole May Permit Cross-Site 6: Microsoft MHTML Input Validation Hole May Permit Cross-Site Scripting Attacks Arbitrary Code T-546: Microsoft MHTML Input Validation Hole May Permit Cross-Site Scripting Attacks Arbitrary Code January 31, 2011 - 7:00am Addthis PROBLEM: Microsoft MHTML Input Validation Hole May Permit Cross-Site Scripting Attacks Arbitrary Code. PLATFORM: Microsoft 2003 SP2, Vista SP2, 2008 SP2, XP SP3, 7; and prior service packs ABSTRACT: A vulnerability was reported in Microsoft MHTML. A remote user can conduct cross-site scripting attacks. reference LINKS: Microsoft Security Advisory 2501696 Microsoft Support Security Tracker Alert CVE-2011-0096 IMPACT ASSESSMENT: Medium Discussion: The vulnerability exists due to the way MHTML interprets MIME-formatted requests for content blocks within a document. It is possible for this

462

U-238: HP Service Manager Input Validation Flaw Permits Cross-Site  

Broader source: Energy.gov (indexed) [DOE]

8: HP Service Manager Input Validation Flaw Permits Cross-Site 8: HP Service Manager Input Validation Flaw Permits Cross-Site Scripting Attacks U-238: HP Service Manager Input Validation Flaw Permits Cross-Site Scripting Attacks August 17, 2012 - 7:00am Addthis PROBLEM: HP Service Manager Input Validation Flaw Permits Cross-Site Scripting Attacks PLATFORM: Version(s): 7.11, 9.21, 9.30 ABSTRACT: Cross-site scripting (XSS) vulnerability in HP Service Manager Web Tier 7.11, 9.21, and 9.30, and HP Service Center Web Tier 6.28, allows remote attackers to inject arbitrary web script or HTML via unspecified vectors. REFERENCE LINKS: www2.hp.com http://www.securitytracker.com/id/1027399 CVE-2012-3251 IMPACT ASSESSMENT: Moderate Discussion: A vulnerability was reported in HP Service Manager. A remote user can conduct cross-site scripting attacks. The software does not properly filter

463

V-150: Apache VCL Input Validation Flaw Lets Remote Authenticated Users  

Broader source: Energy.gov (indexed) [DOE]

0: Apache VCL Input Validation Flaw Lets Remote Authenticated 0: Apache VCL Input Validation Flaw Lets Remote Authenticated Users Gain Elevated Privileges V-150: Apache VCL Input Validation Flaw Lets Remote Authenticated Users Gain Elevated Privileges May 7, 2013 - 12:01am Addthis PROBLEM: Apache VCL Input Validation Flaw Lets Remote Authenticated Users Gain Elevated Privileges PLATFORM: Apache VCL Versions: 2.1, 2.2, 2.2.1, 2.3, 2.3.1 ABSTRACT: A vulnerability was reported in Apache VCL. REFERENCE LINKS: Apache Securelist SecurityTracker Alert ID: 1028515 CVE-2013-0267 IMPACT ASSESSMENT: Medium DISCUSSION: A remote authenticated administrative user with minimal administrative privileges (i.e., nodeAdmin, manageGroup, resourceGrant, or userGrant) can send specially crafted data via the web interface or XMLRPC API to gain additional administrative privileges.

464

U-219: Symantec Web Gateway Input Validation Flaws Lets Remote Users Inject  

Broader source: Energy.gov (indexed) [DOE]

19: Symantec Web Gateway Input Validation Flaws Lets Remote 19: Symantec Web Gateway Input Validation Flaws Lets Remote Users Inject SQL Commands, Execute Arbitrary Commands, and Change User Passwords U-219: Symantec Web Gateway Input Validation Flaws Lets Remote Users Inject SQL Commands, Execute Arbitrary Commands, and Change User Passwords July 24, 2012 - 7:00am Addthis PROBLEM: Symantec Web Gateway Input Validation Flaws Lets Remote Users Inject SQL Commands, Execute Arbitrary Commands, and Change User Passwords PLATFORM: Symantec Web Gateway 5.0.x.x ABSTRACT: Several vulnerabilities were reported in Symantec Web Gateway. REFERENCE LINKS: Security Advisories Relating to Symantec Products SecurityTracker Alert ID: 1027289 Bugtraq ID: 54424 Bugtraq ID: 54425 Bugtraq ID: 54426 Bugtraq ID: 54427 Bugtraq ID: 54429 Bugtraq ID: 54430

465

U-229: HP Network Node Manager i Input Validation Flaw Permits Cross-Site  

Broader source: Energy.gov (indexed) [DOE]

9: HP Network Node Manager i Input Validation Flaw Permits 9: HP Network Node Manager i Input Validation Flaw Permits Cross-Site Scripting Attacks U-229: HP Network Node Manager i Input Validation Flaw Permits Cross-Site Scripting Attacks August 7, 2012 - 7:00am Addthis PROBLEM: HP Network Node Manager i Input Validation Flaw Permits Cross-Site Scripting Attacks PLATFORM: HP Network Node Manager I (NNMi) v8.x, v9.0x, v9.1x, v9.20 for HP-UX, Linux, Solaris, and Windows ABSTRACT: Potential security vulnerabilities have been identified with HP Network Node Manager i (NNMi) for HP-UX, Linux, Solaris, and Windows. The vulnerabilities could be remotely exploited resulting in cross site scripting (XSS). Reference LINKS: HP Document ID: c03405705 SecurityTracker Alert ID: 1027345 Bugtraq ID: 54815 CVE-2012-2022 IMPACT ASSESSMENT:

466

T-590: HP Diagnostics Input Validation Hole Permits Cross-Site Scripting  

Broader source: Energy.gov (indexed) [DOE]

0: HP Diagnostics Input Validation Hole Permits Cross-Site 0: HP Diagnostics Input Validation Hole Permits Cross-Site Scripting Attacks T-590: HP Diagnostics Input Validation Hole Permits Cross-Site Scripting Attacks March 29, 2011 - 3:05pm Addthis PROBLEM: HP Diagnostics Input Validation Hole Permits Cross-Site Scripting Attacks in ActiveSync Lets Remote Users Execute Arbitrary Code. PLATFORM: HP Diagnostics software: version(s) 7.5, 8.0 prior to 8.05.54.225 ABSTRACT: A potential security vulnerability has been identified in HP Diagnostics. The vulnerability could be exploited remotely resulting in cross site scripting (XSS). reference LINKS: HP Document ID: c02770512 SecurityTracker Alert ID: 1025255 CVE-2011-0892 Security Focus Document ID: c02770512 IMPACT ASSESSMENT: High Discussion: A vulnerability was reported in HP Diagnostics. A remote user can conduct

467

U-238: HP Service Manager Input Validation Flaw Permits Cross-Site  

Broader source: Energy.gov (indexed) [DOE]

38: HP Service Manager Input Validation Flaw Permits Cross-Site 38: HP Service Manager Input Validation Flaw Permits Cross-Site Scripting Attacks U-238: HP Service Manager Input Validation Flaw Permits Cross-Site Scripting Attacks August 17, 2012 - 7:00am Addthis PROBLEM: HP Service Manager Input Validation Flaw Permits Cross-Site Scripting Attacks PLATFORM: Version(s): 7.11, 9.21, 9.30 ABSTRACT: Cross-site scripting (XSS) vulnerability in HP Service Manager Web Tier 7.11, 9.21, and 9.30, and HP Service Center Web Tier 6.28, allows remote attackers to inject arbitrary web script or HTML via unspecified vectors. REFERENCE LINKS: www2.hp.com http://www.securitytracker.com/id/1027399 CVE-2012-3251 IMPACT ASSESSMENT: Moderate Discussion: A vulnerability was reported in HP Service Manager. A remote user can conduct cross-site scripting attacks. The software does not properly filter

468

T-546: Microsoft MHTML Input Validation Hole May Permit Cross-Site  

Broader source: Energy.gov (indexed) [DOE]

6: Microsoft MHTML Input Validation Hole May Permit Cross-Site 6: Microsoft MHTML Input Validation Hole May Permit Cross-Site Scripting Attacks Arbitrary Code T-546: Microsoft MHTML Input Validation Hole May Permit Cross-Site Scripting Attacks Arbitrary Code January 31, 2011 - 7:00am Addthis PROBLEM: Microsoft MHTML Input Validation Hole May Permit Cross-Site Scripting Attacks Arbitrary Code. PLATFORM: Microsoft 2003 SP2, Vista SP2, 2008 SP2, XP SP3, 7; and prior service packs ABSTRACT: A vulnerability was reported in Microsoft MHTML. A remote user can conduct cross-site scripting attacks. reference LINKS: Microsoft Security Advisory 2501696 Microsoft Support Security Tracker Alert CVE-2011-0096 IMPACT ASSESSMENT: Medium Discussion: The vulnerability exists due to the way MHTML interprets MIME-formatted requests for content blocks within a document. It is possible for this

469

V-034: RSA Adaptive Authentication (On-Premise) Input Validation Flaws  

Broader source: Energy.gov (indexed) [DOE]

4: RSA Adaptive Authentication (On-Premise) Input Validation 4: RSA Adaptive Authentication (On-Premise) Input Validation Flaws Permit Cross-Site Scripting Attacks V-034: RSA Adaptive Authentication (On-Premise) Input Validation Flaws Permit Cross-Site Scripting Attacks November 27, 2012 - 2:00am Addthis PROBLEM: RSA Adaptive Authentication (On-Premise) Input Validation Flaws Permit Cross-Site Scripting Attacks PLATFORM: RSA Adaptive Authentication (On-Premise) 6.x ABSTRACT: A vulnerability was reported in RSA Adaptive Authentication (On-Premise). REFERENCE LINKS: SecurityTracker Alert ID: 1027811 SecurityFocus Security Alert RSA Customer Support CVE-2012-4611 IMPACT ASSESSMENT: Medium DISCUSSION: A vulnerability was reported in RSA Adaptive Authentication (On-Premise). A remote user can conduct cross-site scripting attacks. The software does not

470

V-112: Microsoft SharePoint Input Validation Flaws Permit Cross-Site  

Broader source: Energy.gov (indexed) [DOE]

2: Microsoft SharePoint Input Validation Flaws Permit 2: Microsoft SharePoint Input Validation Flaws Permit Cross-Site Scripting and Denial of Service Attacks V-112: Microsoft SharePoint Input Validation Flaws Permit Cross-Site Scripting and Denial of Service Attacks March 15, 2013 - 6:00am Addthis PROBLEM: Several vulnerabilities were reported in Microsoft SharePoint PLATFORM: Microsoft SharePoint 2010 SP1 ABSTRACT: This security update resolves four reported vulnerabilities in Microsoft SharePoint and Microsoft SharePoint Foundation. REFERENCE LINKS: Security Tracker Alert ID 1028278 MS Security Bulletin MS13-024 CVE-2013-0080 CVE-2013-0083 CVE-2013-0084 CVE-2013-0085 IMPACT ASSESSMENT: High DISCUSSION: The security update addresses the vulnerabilities correcting the way that Microsoft SharePoint Server validates URLs and user input.

471

U-015: CiscoWorks Common Services Home Page Input Validation Flaw Lets  

Broader source: Energy.gov (indexed) [DOE]

15: CiscoWorks Common Services Home Page Input Validation Flaw 15: CiscoWorks Common Services Home Page Input Validation Flaw Lets Remote Users Execute Arbitrary Commands U-015: CiscoWorks Common Services Home Page Input Validation Flaw Lets Remote Users Execute Arbitrary Commands October 20, 2011 - 7:30am Addthis PROBLEM: CiscoWorks Common Services Home Page Input Validation Flaw Lets Remote Users Execute Arbitrary Commands. PLATFORM: CiscoWorks Common Services-based products prior to version 4.1 running on Microsoft Windows ABSTRACT: Successful exploitation of this vulnerability may allow an authenticated, remote attacker to execute arbitrary commands on the affected system with the privileges of a system administrator. reference LINKS: Cisco Security Advisory ID: cisco-sa-20111019-cs Cisco Security Advisories and Responses

472

T-722: IBM WebSphere Commerce Edition Input Validation Holes Permit  

Broader source: Energy.gov (indexed) [DOE]

2: IBM WebSphere Commerce Edition Input Validation Holes Permit 2: IBM WebSphere Commerce Edition Input Validation Holes Permit Cross-Site Scripting Attacks T-722: IBM WebSphere Commerce Edition Input Validation Holes Permit Cross-Site Scripting Attacks September 21, 2011 - 8:15am Addthis PROBLEM: IBM WebSphere Commerce Edition Input Validation Holes Permit Cross-Site Scripting Attacks. PLATFORM: WebSphere Commerce Edition V7.0 ABSTRACT: A remote user can access the target user's cookies (including authentication cookies), if any, associated with the site running the IBM WebSphere software, access data recently submitted by the target user via web form to the site, or take actions on the site acting as the target user. reference LINKS: IBM Recommended Fixes for WebSphere Commerce IBM Support SecurityTracker Alert ID: 1026074

473

V-112: Microsoft SharePoint Input Validation Flaws Permit Cross-Site  

Broader source: Energy.gov (indexed) [DOE]

2: Microsoft SharePoint Input Validation Flaws Permit 2: Microsoft SharePoint Input Validation Flaws Permit Cross-Site Scripting and Denial of Service Attacks V-112: Microsoft SharePoint Input Validation Flaws Permit Cross-Site Scripting and Denial of Service Attacks March 15, 2013 - 6:00am Addthis PROBLEM: Several vulnerabilities were reported in Microsoft SharePoint PLATFORM: Microsoft SharePoint 2010 SP1 ABSTRACT: This security update resolves four reported vulnerabilities in Microsoft SharePoint and Microsoft SharePoint Foundation. REFERENCE LINKS: Security Tracker Alert ID 1028278 MS Security Bulletin MS13-024 CVE-2013-0080 CVE-2013-0083 CVE-2013-0084 CVE-2013-0085 IMPACT ASSESSMENT: High DISCUSSION: The security update addresses the vulnerabilities correcting the way that Microsoft SharePoint Server validates URLs and user input.

474

T-590: HP Diagnostics Input Validation Hole Permits Cross-Site Scripting  

Broader source: Energy.gov (indexed) [DOE]

0: HP Diagnostics Input Validation Hole Permits Cross-Site 0: HP Diagnostics Input Validation Hole Permits Cross-Site Scripting Attacks T-590: HP Diagnostics Input Validation Hole Permits Cross-Site Scripting Attacks March 29, 2011 - 3:05pm Addthis PROBLEM: HP Diagnostics Input Validation Hole Permits Cross-Site Scripting Attacks in ActiveSync Lets Remote Users Execute Arbitrary Code. PLATFORM: HP Diagnostics software: version(s) 7.5, 8.0 prior to 8.05.54.225 ABSTRACT: A potential security vulnerability has been identified in HP Diagnostics. The vulnerability could be exploited remotely resulting in cross site scripting (XSS). reference LINKS: HP Document ID: c02770512 SecurityTracker Alert ID: 1025255 CVE-2011-0892 Security Focus Document ID: c02770512 IMPACT ASSESSMENT: High Discussion: A vulnerability was reported in HP Diagnostics. A remote user can conduct

475

BPO Inputs to ITER Design Review on Pellet Pacing, RMP and RWM Coils,  

E-Print Network [OSTI]

Is a Pressing Issue for ITER Loarte et al., Nuclear Fusion, ITER Physics Basis,Chapter 4 Recent results reducedBPO Inputs to ITER Design Review on Pellet Pacing, RMP and RWM Coils, and Disruption Mitigation

476

On the Patterns of Wind-Power Input to the Ocean Circulation  

E-Print Network [OSTI]

Pathways of wind-power input into the ocean general circulation are analyzed using Ekman theory. Direct rates of wind work can be calculated through the wind stress acting on the surface geostrophic flow. However, because ...

Roquet, Fabien

477

SEMILAR: A Semantic Similarity Toolkit For Assessing Students' Natural Language Inputs  

E-Print Network [OSTI]

SEMILAR: A Semantic Similarity Toolkit For Assessing Students' Natural Language Inputs Vasile Rus to understand students' natural language responses. Accurate assessment of students' responses enables, & Graesser, in press). There are at least two different types of natural language assessments

Rus, Vasile

478

A study of the effects of natural fertility, weather and productive inputs in Chinese agriculture  

E-Print Network [OSTI]

This paper presents an investigation of the relations in China between farm output, the natural fertility of agricultural land, and the use of anthropogenic farm inputs. The methodology is presented as a potential increment ...

Eckaus, Richard S.; Tso, Katherine Kit-Yan.

479

U-255: Apache Wicket Input Validation Flaw Permits Cross-Site...  

Broader source: Energy.gov (indexed) [DOE]

Input Validation Flaw Permits Cross-Site Scripting Attacks PLATFORM: Apache Software Foundation Apache Wicket 1.5.5 Apache Software Foundation Apache Wicket 1.5-RC5.1 Apache...

480

Estimating production functions with damage control inputs: an application to Korean vegetable production  

E-Print Network [OSTI]

This thesis focuses on the use of chemicals for pest control in Korean cucumber production. The empirical issue addressed is whether estimating crop production functions consistent with the economic theory of damage control inputs makes significant...

Park, Pil Ja

2002-01-01T23:59:59.000Z

Note: This page contains sample records for the topic "inform input assumptions" from the National Library of EnergyBeta (NLEBeta).
While these samples are representative of the content of NLEBeta,
they are not comprehensive nor are they the most current set.
We encourage you to perform a real-time search of NLEBeta
to obtain the most current and comprehensive results.


481

A Survey of Inputs to the North Sea Resulting from Oil and Gas Developments [and Discussion  

Science Journals Connector (OSTI)

...annual inputs from the offshore oil and gas exploration and...of fresh, unweathered oil rapidly enters otherwise uncontaminated offshore sediments, producing...remain little affected by offshore oil and gas developments...

1987-01-01T23:59:59.000Z

482

Weathering rates in catchments calculated by different methods and their relationship to acidic inputs  

Science Journals Connector (OSTI)

The sensitivity of catchments to acidification is often assessed by calculation of weathering rates for comparison of the rates of release of base cations with the measured acidic inputs. Methods of calculatio...

D. C. Bain; S. J. Langan

483

E-Print Network 3.0 - additional power input Sample Search Results  

Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

W to 50 W. A closed-system efficiency of 65% at 50 ... RF and wind power sources operating over a 10:1 input power range from 500 W to 50 W. The...

484

Lessons Learned in Optimizing Workers' and Worker Representatives' Input to Work Planning and Control  

Broader source: Energy.gov [DOE]

Slide Presentation by Tom McQuiston, Dr. P.H., United Steelworkers - Tony Mazzocchi Center for Health, Safety and Environmental Education. Lessons Learned in Optimizing Workers and Worker Representatives Input in Work Planning and Control.

485

Factors Controlling the Input of Electrical Energy into a Fish in an ...  

Science Journals Connector (OSTI)

of these conditions may cause changes in energy-input t,o the fish by ... errors during measurement owing to elcctrol- .... drop per unit length in the water immedi-.

1999-12-13T23:59:59.000Z

486

Stabilizability of the linear algebro-differential one-input control systems  

Science Journals Connector (OSTI)

Consideration was given to the controllable system of ordinary linear differential equations with the matrix at the derivative of the desired vector function that is identically degenerate in the domain of definition. For the one-input systems, the questions ...

A. A. Shcheglova

2010-09-01T23:59:59.000Z

487

Gate-diffusion input (GDI): a power-efficient method for digital combinatorial circuits  

Science Journals Connector (OSTI)

Gate diffusion input (GDI) - a new technique of low-power digital combinatorial circuit design - is described. This technique allows reducing power consumption, propagation delay, and area of digital circuits while maintaining low complexity of logic ...

A. Morgenshtein; A. Fish; I. A. Wagner

2002-10-01T23:59:59.000Z

488

A reduced-basis method for input-output uncertainty propagation in stochastic PDEs  

E-Print Network [OSTI]

Recently there has been a growing interest in quantifying the effects of random inputs in the solution of partial differential equations that arise in a number of areas, including fluid mechanics, elasticity, and wave ...

Vidal Codina, Ferran

2013-01-01T23:59:59.000Z

489

U-270:Trend Micro Control Manager Input Validation Flaw in Ad Hoc Query  

Broader source: Energy.gov (indexed) [DOE]

0:Trend Micro Control Manager Input Validation Flaw in Ad Hoc 0:Trend Micro Control Manager Input Validation Flaw in Ad Hoc Query Module Lets Remote Users Inject SQL Commands U-270:Trend Micro Control Manager Input Validation Flaw in Ad Hoc Query Module Lets Remote Users Inject SQL Commands September 28, 2012 - 6:00am Addthis PROBLEM: Trend Micro Control Manager Input Validation Flaw in Ad Hoc Query Module Lets Remote Users Inject SQL Commands PLATFORM: Control Manager - 3.0, 3.5, 5.0, 5.5, 6.0 ABSTRACT: Trend Micro has been notified of a potential product vulnerability in Control Manager. reference LINKS: Trend Micro Technical Support ID 1061043 SecurityTracker Alert ID: 1027584 Secunia Advisory SA50760 CVE-2012-2998 IMPACT ASSESSMENT: Medium Discussion: A vulnerability has been reported in Trend Micro Control Manager, which can

490

U.S. Crude Input Rising -- Still Need +1 MMB/D Through Mid-Summer  

Gasoline and Diesel Fuel Update (EIA)

5 5 Notes: Refineries in fourth quarter 1999 and first quarter 2000 were running at fairly low input rates compared to prior years, despite higher demand. U.S. refineries typically increase their crude inputs during the second quarter over the first quarter as they return from maintenance and turnaround schedules to ramp up for the high demand gasoline season. The year began with low refining margins and a low level of crude inputs in January and February. This created a lower base than last year from which to grow into the summer gasoline season, when inputs will need to peak at higher levels than in 1998 or 1999. The good news is that crude runs have been increasing strongly as expected during March the first quarter. Keep in mind that they still need an additional 1 million barrels per day of crude oil between now and mid

491

U-015: CiscoWorks Common Services Home Page Input Validation Flaw Lets  

Broader source: Energy.gov (indexed) [DOE]

5: CiscoWorks Common Services Home Page Input Validation Flaw 5: CiscoWorks Common Services Home Page Input Validation Flaw Lets Remote Users Execute Arbitrary Commands U-015: CiscoWorks Common Services Home Page Input Validation Flaw Lets Remote Users Execute Arbitrary Commands October 20, 2011 - 7:30am Addthis PROBLEM: CiscoWorks Common Services Home Page Input Validation Flaw Lets Remote Users Execute Arbitrary Commands. PLATFORM: CiscoWorks Common Services-based products prior to version 4.1 running on Microsoft Windows ABSTRACT: Successful exploitation of this vulnerability may allow an authenticated, remote attacker to execute arbitrary commands on the affected system with the privileges of a system administrator. reference LINKS: Cisco Security Advisory ID: cisco-sa-20111019-cs Cisco Security Advisories and Responses

492

Table A54. Number of Establishments by Total Inputs of Energy for Heat, Powe  

U.S. Energy Information Administration (EIA) Indexed Site

Number of Establishments by Total Inputs of Energy for Heat, Power, and Electricity Generation," Number of Establishments by Total Inputs of Energy for Heat, Power, and Electricity Generation," " by Industry Group, Selected Industries, and" " Presence of General Technologies, 1994: Part 2" ,," "," ",," "," ",," "," "," "," " ,,,,"Computer Control" ,," "," ","of Processes"," "," ",," "," ",," " ,," ","Computer Control","or Major",,,"One or More"," ","RSE" "SIC"," ",,"of Building","Energy-Using","Waste Heat"," Adjustable-Speed","General Technologies","None","Row"

493

Table A45. Total Inputs of Energy for Heat, Power, and Electricity Generation  

U.S. Energy Information Administration (EIA) Indexed Site

Total Inputs of Energy for Heat, Power, and Electricity Generation" Total Inputs of Energy for Heat, Power, and Electricity Generation" " by Enclosed Floorspace, Percent Conditioned Floorspace, and Presence of Computer" " Controls for Building Environment, 1991" " (Estimates in Trillion Btu)" ,,"Presence of Computer Controls" ,," for Buildings Environment",,"RSE" "Enclosed Floorspace and"," ","--------------","--------------","Row" "Percent Conditioned Floorspace","Total","Present","Not Present","Factors" " "," " "RSE Column Factors:",0.8,1.3,0.9 "ALL SQUARE FEET CATEGORIES" "Approximate Conditioned Floorspace"

494

Rethinking Pen Input Interaction: Enabling Freehand Sketching Through Improved Primitive Recognition  

E-Print Network [OSTI]

RETHINKING PEN INPUT INTERACTION: ENABLING FREEHAND SKETCHING THROUGH IMPROVED PRIMITIVE RECOGNITION A Dissertation by BRANDON CHASE PAULSON Submitted to the O ce of Graduate Studies of Texas A&M University in partial ful llment... of the requirements for the degree of DOCTOR OF PHILOSOPHY May 2010 Major Subject: Computer Science RETHINKING PEN INPUT INTERACTION: ENABLING FREEHAND SKETCHING THROUGH IMPROVED PRIMITIVE RECOGNITION A Dissertation by BRANDON CHASE PAULSON Submitted to the O...

Paulson, Brandon C.

2011-08-08T23:59:59.000Z

495

Energy Input per Unit Length High Accuracy Kinematic Metrology in Laser Material Processing  

Science Journals Connector (OSTI)

Laser material processes require constant energy input per unit length. Besides focal z-position, spot size, laser power and other process parameters, the relative travel speed (feed rate) of the laser spot on the work piece has the highest influence on the resulting energy input per unit length. In this paper a new metrology method is introduced, which enables users in industry and research to measure the real travel speed of the laser spot and the resulting contour of the trajectory.

Christoph Franz; Peter Abels; Raphael Rolser; Michael Becker

2011-01-01T23:59:59.000Z

496

TRIPLE 3-INPUT POSITIVE-NAND GATES SCHS317 NOVEMBER 2002  

E-Print Network [OSTI]

-SIDE MARKING PDIP ­ E Tube CD74AC10E CD74AC10E ­55°C to 125°C SOIC M Tube CD74AC10M AC10MSOIC ­ M Tape and Reel-maximum-rated conditions for extended periods may affect device reliability. NOTES: 1. The input and output voltage ratings may be exceeded if the input and output current ratings are observed. 2. The package thermal impedance

Kretchmar, R. Matthew

497

CD54AC08, CD74AC08 QUADRUPLE 2-INPUT POSITIVE-AND GATES  

E-Print Network [OSTI]

SOIC M Tube CD74AC08M AC08M­55°C to 125°C SOIC ­ M Tape and reel CD74AC08M96 AC08M CDIP ­ F Tube CD54AC-maximum-rated conditions for extended periods may affect device reliability. NOTES: 1. The input and output voltage ratings may be exceeded if the input and output current ratings are observed. 2. The package thermal impedance

Kretchmar, R. Matthew

498

CD54AC02, CD74AC02 QUADRUPLE 2-INPUT POSITIVE-NOR GATES  

E-Print Network [OSTI]

to 125°C SOIC ­ M Tape and reel CD74AC02M96 AC02M CDIP ­ F Tube CD54AC02F3A CD54AC02F3A Package drawings-maximum-rated conditions for extended periods may affect device reliability. NOTES: 1. The input and output voltage ratings may be exceeded if the input and output current ratings are observed. 2. The package thermal impedance

Kretchmar, R. Matthew

499

CD54AC32, CD74AC32 QUADRUPLE 2-INPUT POSITIVE-OR GATES  

E-Print Network [OSTI]

Tape and reel CD74AC32M96 AC32M CDIP ­ F Tube CD54AC32F3A CD54AC32F3A Package drawings, standard-maximum-rated conditions for extended periods may affect device reliability. NOTES: 1. The input and output voltage ratings may be exceeded if the input and output current ratings are observed. 2. The package thermal impedance

Kretchmar, R. Matthew

500

CD54ACT00, CD74ACT00 QUADRUPLE 2-INPUT POSITIVE-NAND GATES  

E-Print Network [OSTI]

CD74ACT00E 55°C to 125°C SOIC M Tube CD74ACT00M ACT00M­55°C to 125°C SOIC ­ M Tape and reel CD74ACT00-maximum-rated conditions for extended periods may affect device reliability. NOTES: 1. The input and output voltage ratings may be exceeded if the input and output current ratings are observed. 2. The package thermal impedance

Kretchmar, R. Matthew