National Library of Energy BETA

Sample records for type total estimated

  1. Types of Cost Estimates

    Broader source: Directives, Delegations, and Requirements [Office of Management (MA)]

    1997-03-28

    The chapter describes the estimates required on government-managed projects for both general construction and environmental management.

  2. Cell Total Activity Final Estimate.xls

    Office of Legacy Management (LM)

    WSSRAP Cell Total Activity Final Estimate (calculated September 2002, Fleming) (Waste streams & occupied cell volumes from spreadsheet titled "cell waste volumes-8.23.02 with macros.xls") Waste Stream a Volume (cy) Mass (g) 2 Radiological Profile 3 Nuclide Activity (Ci) 4 Total % of Total U-238 U-234 U-235 Th-228 Th-230 Th-232 Ra-226 Ra-228 Rn-222 5 Activity if > 1% Raffinate Pits Work Zone (Ci) Raffinate processed through CSS Plant 1 159990 1.49E+11 Raffinate 6.12E+01 6.12E+01

  3. Total Estimated Contract Cost: Performance Period Total Fee Paid

    Office of Environmental Management (EM)

    Performance Period Total Fee Paid FY2008 $134,832 FY2009 $142,578 FY2010 $299,878 FY2011 $169,878 Cumulative Fee Paid $747,166 Contract Period: September 2007 - October 2012 $31,885,815 C/P/E Environmental Services, LLC DE-AM09-05SR22405/DE-AT30-07CC60011/SL14 Contractor: Contract Number: Contract Type: Cost Plus Award Fee $357,223 $597,797 $894,699 EM Contractor Fee Site: Stanford Linear Accelerator Center (SLAC) Contract Name: SLAC Environmental Remediation December 2012 $1,516,646 Fee

  4. Estimation of Anisotoropy from Total Cross Section and Optical...

    Office of Scientific and Technical Information (OSTI)

    Conference: Estimation of Anisotoropy from Total Cross Section and Optical Model Citation Details In-Document Search Title: Estimation of Anisotoropy from Total Cross Section and ...

  5. Contractor: Contract Number: Contract Type: Total Estimated

    Office of Environmental Management (EM)

    886,608 Computer Sciences Corporation DE-AC06-04RL14383 895,358 899,230 907,583 Cost Plus Award Fee 134,100,336 8,221,404 Fee Available Contract Period: Fee Information...

  6. Performance Measure Unit Lifecycle Total Estimate Pre-2016 Lifecycle...

    Office of Environmental Management (EM)

    Measure Unit Lifecycle Total Estimate Pre-2016 Lifecycle Values 2016 Target 2017 Target Pu packaged for long-term disposition Number of Containers 5,089 5,089 5,089 5,089 eU ...

  7. ,"Crude Oil and Petroleum Products Total Stocks Stocks by Type...

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

    Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Crude Oil and Petroleum Products Total Stocks Stocks by Type",6,"Monthly","82015","1151956"...

  8. Estimation of Anisotoropy from Total Cross Section and Optical...

    Office of Scientific and Technical Information (OSTI)

    Resource Type: Conference Resource Relation: Conference: Working Party on Evaluation Cooperation ; 2013-05-22 - 2013-05-24 ; Paris, France Research Org: Los Alamos National ...

  9. Total Estimated Contract Cost: Contract Option Period: Maximum...

    Office of Environmental Management (EM)

    LLC Contract Number: DE-AC30-11CC40015 Contract Type: Cost Plus Award Fee EM Contractor Fee December 2015 Site: Portsmouth Paducah Project Office Contract Name: Operation of DUF6

  10. Total Estimated Contract Cost: Contract Option Period: Maximum...

    Office of Environmental Management (EM)

    & Wilcox Conversion Services, LLC Contract Number: DE-AC30-11CC40015 Contract Type: Cost Plus Award Fee EM Contractor Fee September 2015 Site: Portsmouth Paducah Project Office...

  11. Total Estimated Contract Cost: Contract Option Period: Performance

    Office of Environmental Management (EM)

    Contractor: Bechtel National Inc. Contract Number: DE-AC27-01RV14136 Contract Type: Cost Plus Award Fee NA Maximum Fee 599,588,540 Fee Available 102,622,325 10,868,785,789...

  12. Total Estimated Contract Cost: Contract Option Period: Maximum Fee

    Office of Environmental Management (EM)

    Definition and Scope Answer/Comment 1 What significant policy challenges are likely to remain unaddressed if we employ Title XIII's definition? The following points are not referencedd in EISA 1301. ・Power provider should also control the output fluctuation of renewable resources. ・The end user should have the choice of which form of power storage to be used. Certain types of energy conservation and storage could work better in different applications (e.g. not only electricity power but also

  13. Total Estimated Contract Price: Contract Option Periods: Performance

    Office of Environmental Management (EM)

    Price: Contract Option Periods: Performance Period Fee Earned Base Period "A" $0 Base Period "B" Option 1 Option 2 Option 3 Cumulative Fee $0 EM Contractor Fee June 2015 Site: Office of River Protection, Richland, WA Contract Name: Hanford 222-S Laboratory Analysis and Testing Services Contractor: Wastren Advantage, Inc Contract Number: DE-EM0003722 Contract Type: Hybrid Contract with Award Fee Fee Available $44,562,457 Base Contract Period: November 21, 2016 to September 20,

  14. ,"Crude Oil and Petroleum Products Total Stocks Stocks by Type...

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

    Data for" ,"Data 1","Crude Oil and Petroleum Products Total Stocks Stocks ... AM" "Back to Contents","Data 1: Crude Oil and Petroleum Products Total Stocks Stocks ...

  15. Total

    Gasoline and Diesel Fuel Update (EIA)

    Product: Total Crude Oil Liquefied Petroleum Gases PropanePropylene Normal ButaneButylene Other Liquids Oxygenates Fuel Ethanol MTBE Other Oxygenates Biomass-based Diesel Other ...

  16. Total

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

    Product: Total Crude Oil Liquefied Petroleum Gases PropanePropylene Normal ButaneButylene Other Liquids Oxygenates Fuel Ethanol MTBE Other Oxygenates Biomass-based Diesel Fuel ...

  17. Total..........................................................

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

    ... Type of Renter-Occupied Housing Unit Housing Units (millions) Single-Family Units ... At Home Behavior Home Used for Business Yes......

  18. Total..........................................................

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

    ... Type of Owner-Occupied Housing Unit U.S. Housing Units (millions) Single-Family Units ... At Home Behavior Home Used for Business Yes......

  19. Total..........................................................

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

    ... Housing Characteristics Tables Single-Family Units Detached Type of Housing Unit Table ... At Home Behavior Home Used for Business Yes......

  20. Total..........................................................

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

    0.9 Q Q Q Heat Pump......7.7 0.3 Q Q Steam or Hot Water System......Census Division Total West Energy Information Administration ...

  1. Total..........................................................

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

    0.9 Q Q Q Heat Pump......6.2 3.8 2.4 Steam or Hot Water System......Census Division Total Northeast Energy Information ...

  2. Total..........................................................................

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

    . 111.1 20.6 15.1 5.5 Floorspace (Square Feet) Total Floorspace 1 Fewer than 500................................................... 3.2 0.9 0.5 0.4 500 to 999........................................................... 23.8 4.6 3.6 1.1 1,000 to 1,499..................................................... 20.8 2.8 2.2 0.6 1,500 to 1,999..................................................... 15.4 1.9 1.4 0.5 2,000 to 2,499..................................................... 12.2 2.3 1.7 0.5 2,500 to

  3. Total..........................................................................

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

    5.6 17.7 7.9 Floorspace (Square Feet) Total Floorspace 1 Fewer than 500................................................... 3.2 0.5 0.3 Q 500 to 999........................................................... 23.8 3.9 2.4 1.5 1,000 to 1,499..................................................... 20.8 4.4 3.2 1.2 1,500 to 1,999..................................................... 15.4 3.5 2.4 1.1 2,000 to 2,499..................................................... 12.2 3.2 2.1 1.1 2,500 to

  4. Total..........................................................................

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

    0.7 21.7 6.9 12.1 Floorspace (Square Feet) Total Floorspace 1 Fewer than 500................................................... 3.2 0.9 0.6 Q Q 500 to 999........................................................... 23.8 9.0 4.2 1.5 3.2 1,000 to 1,499..................................................... 20.8 8.6 4.7 1.5 2.5 1,500 to 1,999..................................................... 15.4 6.0 2.9 1.2 1.9 2,000 to 2,499..................................................... 12.2 4.1 2.1 0.7

  5. Total.............................................................................

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

    Do Not Have Cooling Equipment............................... 17.8 4.0 2.1 1.4 10.3 Have Cooling Equipment............................................ 93.3 16.5 23.5 39.3 13.9 Use Cooling Equipment............................................. 91.4 16.3 23.4 38.9 12.9 Have Equipment But Do Not Use it............................ 1.9 0.3 Q 0.5 1.0 Type of Air-Conditioning Equipment 1, 2 Central System........................................................ 65.9 6.0 17.3 32.1 10.5 Without a Heat

  6. Total.............................................................................

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

    Do Not Have Cooling Equipment............................... 17.8 2.1 1.8 0.3 Have Cooling Equipment............................................ 93.3 23.5 16.0 7.5 Use Cooling Equipment............................................. 91.4 23.4 15.9 7.5 Have Equipment But Do Not Use it............................ 1.9 Q Q Q Type of Air-Conditioning Equipment 1, 2 Central System........................................................ 65.9 17.3 11.3 6.0 Without a Heat

  7. Total.............................................................................

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

    Do Not Have Cooling Equipment............................... 17.8 1.4 0.8 0.2 0.3 Have Cooling Equipment............................................ 93.3 39.3 20.9 6.7 11.8 Use Cooling Equipment............................................. 91.4 38.9 20.7 6.6 11.7 Have Equipment But Do Not Use it............................ 1.9 0.5 Q Q Q Type of Air-Conditioning Equipment 1, 2 Central System........................................................ 65.9 32.1 17.6 5.2 9.3 Without a Heat

  8. Total.............................................................................

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

    Do Not Have Cooling Equipment............................... 17.8 10.3 3.1 7.3 Have Cooling Equipment............................................ 93.3 13.9 4.5 9.4 Use Cooling Equipment............................................. 91.4 12.9 4.3 8.5 Have Equipment But Do Not Use it............................ 1.9 1.0 Q 0.8 Type of Air-Conditioning Equipment 1, 2 Central System........................................................ 65.9 10.5 3.9 6.5 Without a Heat

  9. Total.............................................................................

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

    Do Not Have Cooling Equipment............................... 17.8 8.5 2.7 2.6 4.0 Have Cooling Equipment............................................ 93.3 38.6 16.2 20.1 18.4 Use Cooling Equipment............................................. 91.4 37.8 15.9 19.8 18.0 Have Equipment But Do Not Use it............................ 1.9 0.9 0.3 0.3 0.4 Type of Air-Conditioning Equipment 1, 2 Central System........................................................ 65.9 25.8 10.9 16.6 12.5 Without a Heat

  10. Total....................................................................................

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

    25.6 40.7 24.2 Personal Computers Do Not Use a Personal Computer.................................. 35.5 6.9 8.1 14.2 6.4 Use a Personal Computer.............................................. 75.6 13.7 17.5 26.6 17.8 Most-Used Personal Computer Type of PC Desk-top Model......................................................... 58.6 10.4 14.1 20.5 13.7 Laptop Model............................................................. 16.9 3.3 3.4 6.1 4.1 Hours Turned on Per Week Less than 2

  11. Total....................................................................................

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

    5.6 17.7 7.9 Personal Computers Do Not Use a Personal Computer.................................. 35.5 8.1 5.6 2.5 Use a Personal Computer.............................................. 75.6 17.5 12.1 5.4 Most-Used Personal Computer Type of PC Desk-top Model......................................................... 58.6 14.1 10.0 4.0 Laptop Model............................................................. 16.9 3.4 2.1 1.3 Hours Turned on Per Week Less than 2

  12. Total....................................................................................

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

    Personal Computers Do Not Use a Personal Computer.................................. 35.5 14.2 7.2 2.8 4.2 Use a Personal Computer.............................................. 75.6 26.6 14.5 4.1 7.9 Most-Used Personal Computer Type of PC Desk-top Model......................................................... 58.6 20.5 11.0 3.4 6.1 Laptop Model............................................................. 16.9 6.1 3.5 0.7 1.9 Hours Turned on Per Week Less than 2

  13. Total....................................................................................

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

    4.2 7.6 16.6 Personal Computers Do Not Use a Personal Computer.................................. 35.5 6.4 2.2 4.2 Use a Personal Computer.............................................. 75.6 17.8 5.3 12.5 Most-Used Personal Computer Type of PC Desk-top Model......................................................... 58.6 13.7 4.2 9.5 Laptop Model............................................................. 16.9 4.1 1.1 3.0 Hours Turned on Per Week Less than 2

  14. Total....................................................................................

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

    111.1 47.1 19.0 22.7 22.3 Personal Computers Do Not Use a Personal Computer.................................. 35.5 16.9 6.5 4.6 7.6 Use a Personal Computer.............................................. 75.6 30.3 12.5 18.1 14.7 Most-Used Personal Computer Type of PC Desk-top Model......................................................... 58.6 22.9 9.8 14.1 11.9 Laptop Model............................................................. 16.9 7.4 2.7 4.0 2.9 Hours Turned on Per Week Less than 2

  15. Total.........................................................................................

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

    ..... 111.1 7.1 7.0 8.0 12.1 Personal Computers Do Not Use a Personal Computer...................................... 35.5 3.0 2.0 2.7 3.1 Use a Personal Computer.................................................. 75.6 4.2 5.0 5.3 9.0 Most-Used Personal Computer Type of PC Desk-top Model............................................................. 58.6 3.2 3.9 4.0 6.7 Laptop Model................................................................. 16.9 1.0 1.1 1.3 2.4 Hours Turned on Per Week Less

  16. Total................................................

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

    .. 111.1 86.6 2,522 1,970 1,310 1,812 1,475 821 1,055 944 554 Total Floorspace (Square Feet) Fewer than 500............................. 3.2 0.9 261 336 162 Q Q Q 334 260 Q 500 to 999.................................... 23.8 9.4 670 683 320 705 666 274 811 721 363 1,000 to 1,499.............................. 20.8 15.0 1,121 1,083 622 1,129 1,052 535 1,228 1,090 676 1,500 to 1,999.............................. 15.4 14.4 1,574 1,450 945 1,628 1,327 629 1,712 1,489 808 2,000 to

  17. Total..........................................................

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

    .. 111.1 24.5 1,090 902 341 872 780 441 Total Floorspace (Square Feet) Fewer than 500...................................... 3.1 2.3 403 360 165 366 348 93 500 to 999.............................................. 22.2 14.4 763 660 277 730 646 303 1,000 to 1,499........................................ 19.1 5.8 1,223 1,130 496 1,187 1,086 696 1,500 to 1,999........................................ 14.4 1.0 1,700 1,422 412 1,698 1,544 1,348 2,000 to 2,499........................................ 12.7

  18. Total...................................................................

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

    Floorspace (Square Feet) Total Floorspace 1 Fewer than 500............................................ 3.2 0.4 Q 0.6 1.7 0.4 500 to 999................................................... 23.8 4.8 1.4 4.2 10.2 3.2 1,000 to 1,499............................................. 20.8 10.6 1.8 1.8 4.0 2.6 1,500 to 1,999............................................. 15.4 12.4 1.5 0.5 0.5 0.4 2,000 to 2,499............................................. 12.2 10.7 1.0 0.2 Q Q 2,500 to

  19. Total.........................................................................

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

    Floorspace (Square Feet) Total Floorspace 2 Fewer than 500.................................................. 3.2 Q 0.8 0.9 0.8 0.5 500 to 999.......................................................... 23.8 1.5 5.4 5.5 6.1 5.3 1,000 to 1,499.................................................... 20.8 1.4 4.0 5.2 5.0 5.2 1,500 to 1,999.................................................... 15.4 1.4 3.1 3.5 3.6 3.8 2,000 to 2,499.................................................... 12.2 1.4 3.2 3.0 2.3 2.3

  20. Total..........................................................................

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

    25.6 40.7 24.2 Floorspace (Square Feet) Total Floorspace 1 Fewer than 500................................................... 3.2 0.9 0.5 0.9 1.0 500 to 999........................................................... 23.8 4.6 3.9 9.0 6.3 1,000 to 1,499..................................................... 20.8 2.8 4.4 8.6 5.0 1,500 to 1,999..................................................... 15.4 1.9 3.5 6.0 4.0 2,000 to 2,499..................................................... 12.2 2.3 3.2 4.1

  1. Total..........................................................................

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

    7.1 7.0 8.0 12.1 Floorspace (Square Feet) Total Floorspace 1 Fewer than 500................................................... 3.2 0.4 Q Q 0.5 500 to 999........................................................... 23.8 2.5 1.5 2.1 3.7 1,000 to 1,499..................................................... 20.8 1.1 2.0 1.5 2.5 1,500 to 1,999..................................................... 15.4 0.5 1.2 1.2 1.9 2,000 to 2,499..................................................... 12.2 0.7 0.5 0.8 1.4

  2. Total..................................................................

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

    . 111.1 14.7 7.4 12.5 12.5 18.9 18.6 17.3 9.2 Do Not Have Cooling Equipment..................... 17.8 3.9 1.8 2.2 2.1 3.1 2.6 1.7 0.4 Have Cooling Equipment................................. 93.3 10.8 5.6 10.3 10.4 15.8 16.0 15.6 8.8 Use Cooling Equipment.................................. 91.4 10.6 5.5 10.3 10.3 15.3 15.7 15.3 8.6 Have Equipment But Do Not Use it................. 1.9 Q Q Q Q 0.6 0.4 0.3 Q Type of Air-Conditioning Equipment 1, 2 Central

  3. Total...........................................................

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

    14.7 7.4 12.5 12.5 18.9 18.6 17.3 9.2 Floorspace (Square Feet) Total Floorspace 1 Fewer than 500.................................... 3.2 0.7 Q 0.3 0.3 0.7 0.6 0.3 Q 500 to 999........................................... 23.8 2.7 1.4 2.2 2.8 5.5 5.1 3.0 1.1 1,000 to 1,499..................................... 20.8 2.3 1.4 2.4 2.5 3.5 3.5 3.6 1.6 1,500 to 1,999..................................... 15.4 1.8 1.4 2.2 2.0 2.4 2.4 2.1 1.2 2,000 to 2,499..................................... 12.2 1.4 0.9

  4. Estimating the releasable source term for Type B packages

    SciTech Connect (OSTI)

    Anderson, B.L.; Carlson, R.W.; Osgood, N.

    1995-11-01

    The release rate criteria for Type B packages designed to transport radioactive materials is given in Title 10 of the Code of Federal Regulations (10 CFR 71). Before the maximum allowable volumetric leakage rate that corresponds to the regulatory release rate can be calculated, estimation of the releasable source term activity density (concentration of releasable radioactive material) is required. This work provides methods for estimating the releasable source term for packages holding various contents types. The contents types considered include: (1) radioactive liquids; (2) radioactive gases; (3) radioactive powders and dispersible solids; (4) non-dispersible radioactive solids and (5) irradiated nuclear fuel rods. The numbers given, especially as related to the source term for packages transporting irradiated fuel rods, are preliminary and are subject to change upon development of improved methods and/or upon review of additional experimental data.

  5. Estimating Total Energy Consumption and Emissions of China's Commercial and Office Buildings

    SciTech Connect (OSTI)

    Fridley, David; Fridley, David G.; Zheng, Nina; Zhou, Nan

    2008-03-01

    Buildings represent an increasingly important component of China's total energy consumption mix. However, accurately assessing the total volume of energy consumed in buildings is difficult owing to deficiencies in China's statistical collection system and a lack of national surveys. Official statistics suggest that buildings account for about 19% of China's total energy consumption, while others estimate the proportion at 23%, rising to 30% over the next few years. In addition to operational energy, buildings embody the energy used in the in the mining, extraction, harvesting, processing, manufacturing and transport of building materials as well as the energy used in the construction and decommissioning of buildings. This embodied energy, along with a building's operational energy, constitutes the building's life-cycle energy and emissions footprint. This report first provides a review of international studies on commercial building life-cycle energy use from which data are derived to develop an assessment of Chinese commercial building life-cycle energy use, then examines in detail two cases for the development of office building operational energy consumption to 2020. Finally, the energy and emissions implications of the two cases are presented.

  6. Cancer risk estimates from radiation therapy for heterotopic ossification prophylaxis after total hip arthroplasty

    SciTech Connect (OSTI)

    Mazonakis, Michalis; Berris, Theoharris; Damilakis, John; Lyraraki, Efrossyni

    2013-10-15

    Purpose: Heterotopic ossification (HO) is a frequent complication following total hip arthroplasty. This study was conducted to calculate the radiation dose to organs-at-risk and estimate the probability of cancer induction from radiotherapy for HO prophylaxis.Methods: Hip irradiation for HO with a 6 MV photon beam was simulated with the aid of a Monte Carlo model. A realistic humanoid phantom representing an average adult patient was implemented in Monte Carlo environment for dosimetric calculations. The average out-of-field radiation dose to stomach, liver, lung, prostate, bladder, thyroid, breast, uterus, and ovary was calculated. The organ-equivalent-dose to colon, that was partly included within the treatment field, was also determined. Organ dose calculations were carried out using three different field sizes. The dependence of organ doses upon the block insertion into primary beam for shielding colon and prosthesis was investigated. The lifetime attributable risk for cancer development was estimated using organ, age, and gender-specific risk coefficients.Results: For a typical target dose of 7 Gy, organ doses varied from 1.0 to 741.1 mGy by the field dimensions and organ location relative to the field edge. Blocked field irradiations resulted in a dose range of 1.4146.3 mGy. The most probable detriment from open field treatment of male patients was colon cancer with a high risk of 564.3 10{sup ?5} to 837.4 10{sup ?5} depending upon the organ dose magnitude and the patient's age. The corresponding colon cancer risk for female patients was (372.2541.0) 10{sup ?5}. The probability of bladder cancer development was more than 113.7 10{sup ?5} and 110.3 10{sup ?5} for males and females, respectively. The cancer risk range to other individual organs was reduced to (0.00368.5) 10{sup ?5}.Conclusions: The risk for cancer induction from radiation therapy for HO prophylaxis after total hip arthroplasty varies considerably by the treatment parameters

  7. Estimation of total cloud cover from solar radiation observations at Lake Rotorua, New Zealand

    SciTech Connect (OSTI)

    Luo, Liancong; Hamilton, David; Han, Boping

    2010-03-15

    The DYRESM-CAEDYM model is a valuable tool for simulating water temperature for biochemical studies in aquatic ecosystem. The model requires inputs of surface short-wave radiation and long-wave radiation or total cloud cover fraction (TC). Long-wave radiation is often not measured directly so a method to determine TC from commonly measured short-wave solar irradiance (E{sub 0}) and theoretical short-wave solar irradiance under a clear sky (E{sub c}) has broad application. A more than 17-year (15 November 1991 to 20 February 2009) hourly solar irradiance data set was used to estimate the peak solar irradiance for each ordinal date over one year, which was assumed to be representative of solar irradiance in the absence of cloud. Comparison between these daily observed values and the modelled clear-sky solar radiation over one year was in close agreement (Pearson correlation coefficient, r = 0.995 and root mean squared error, RMSE = 12.54 W m{sup -2}). The downloaded hourly cloudiness measurements from 15 November 1991 to 20 February 2009 was used to calculate the daily values for this period and then the calculated daily values over the 17 years were used to calculate the average values for each ordinal date over one year. A regression equation between (1 - E{sub 0}/E{sub c}) and TC produced a correlation coefficient value of 0.99 (p > 0.01, n = 71). The validation of this cloud cover estimation model was conducted with observed short-wave solar radiation and TC at two sites. Values of TC derived from the model at the Lake Rotorua site gave a reasonable prediction of the observed values (RMSE = 0.10, r = 0.86, p > 0.01, n = 61). The model was also tested at Queenstown (South Island of New Zealand) and it provided satisfactory results compared to the measurements (RMSE = 0.16, r = 0.67, p > 0.01, n = 61). Therefore the model's good performance and broad applicability will contribute to the DYRESM-CAEDYM accuracy of water temperature simulation when long-wave radiation

  8. Estimating Source Terms for Diverse Spent Nuclear Fuel Types

    SciTech Connect (OSTI)

    Brett Carlsen; Layne Pincock

    2004-11-01

    The U.S. Department of Energy (DOE) National Spent Nuclear Fuel Program is responsible for developing a defensible methodology for determining the radionuclide inventory for the DOE spent nuclear fuel (SNF) to be dispositioned at the proposed Monitored Geologic Repository at the Yucca Mountain Site. SNF owned by DOE includes diverse fuels from various experimental, research, and production reactors. These fuels currently reside at several DOE sites, universities, and foreign research reactor sites. Safe storage, transportation, and ultimate disposal of these fuels will require radiological source terms as inputs to safety analyses that support design and licensing of the necessary equipment and facilities. This paper summarizes the methodology developed for estimating radionuclide inventories associated with DOE-owned SNF. The results will support development of design and administrative controls to manage radiological risks and may later be used to demonstrate conformance with repository acceptance criteria.

  9. An estimation of the total atmospheric pollution in the city of Thessaloniki using solar energy data

    SciTech Connect (OSTI)

    Sahsamanoglou, H.S.; Makrogiannis, T.I.; Meletis, H. )

    1991-01-01

    The atmospheric mass over the city of Thessaloniki is characterized by a generally increased pollution due to solid particles in the lower atmosphere. This conclusion has been reached after a comparison between values of total solar radiation, taken in the city center during clear sky days, and values predicted by the model of D.F. Heermann et al. for corresponding days. Pollution varies between a minimum value which is constant over the year and independent of weather situations (pollution background), and a maximum value. The minimum pollution causes an attenuation of solar radiation about 15%, compared to the values given by the above model. The atmospheric pollution in the city, during a usual day with clear sky, causes an attenuation varying between 10% in the summer and 20% in the winter, when compared to the constant background of the pollution. During the most unfavorable days with clear sky, the percentages are 30% in the summer and 40% in the winter.

  10. Types of Possible Survey Errors in Estimates Published in the Weekly Natural Gas Storage Report

    Reports and Publications (EIA)

    2016-01-01

    This document lists types of potential errors in EIA estimates published in the WNGSR. Survey errors are an unavoidable aspect of data collection. Error is inherent in all collected data, regardless of the source of the data and the care and competence of data collectors. The type and extent of error depends on the type and characteristics of the survey.

  11. Types of Possible Survey Errors in Estimates Published in the Weekly Natural Gas Storage Report

    Weekly Natural Gas Storage Report (EIA)

    U.S. Energy Information Administration | Types of Possible Survey Errors in Estimates Published in the Weekly Natural Gas Storage Report 1 February 2016 Types of Possible Survey Errors in Estimates Published in the Weekly Natural Gas Storage Report The U.S. Energy Information Administration (EIA) collects and publishes natural gas storage information on a monthly and weekly basis. The Form EIA-191, Monthly Underground Natural Gas Storage Report, is a census survey that collects field-level

  12. THE SLOAN LENS ACS SURVEY. X. STELLAR, DYNAMICAL, AND TOTAL MASS CORRELATIONS OF MASSIVE EARLY-TYPE GALAXIES

    SciTech Connect (OSTI)

    Auger, M. W.; Treu, T.; Marshall, P. J.; Bolton, A. S.; Gavazzi, R.; Koopmans, L. V. E.; Moustakas, L. A.

    2010-11-20

    We use stellar masses, surface photometry, strong-lensing masses, and stellar velocity dispersions ({sigma}{sub e/2}) to investigate empirical correlations for the definitive sample of 73 early-type galaxies (ETGs) that are strong gravitational lenses from the SLACS survey. The traditional correlations (fundamental plane (FP) and its projections) are consistent with those found for non-lens galaxies, supporting the thesis that SLACS lens galaxies are representative of massive ETGs (dimensional mass M{sub dim} = 10{sup 11}-10{sup 12} M{sub sun}). The addition of high-precision strong-lensing estimates of the total mass allows us to gain further insights into their internal structure: (1) the average slope of the total mass-density profile ({rho}{sub tot}{proportional_to}r{sup -}{gamma}') is ({gamma}') = 2.078 {+-} 0.027 with an intrinsic scatter of 0.16 {+-} 0.02; (2) {gamma}' correlates with effective radius (r{sub e}) and central mass density, in the sense that denser galaxies have steeper profiles; (3) the dark matter (DM) fraction within r{sub e} /2 is a monotonically increasing function of galaxy mass and size (due to a mass-dependent central cold DM distribution or due to baryonic DM-stellar remnants or low-mass stars-if the initial mass function is non-universal and its normalization increases with mass); (4) the dimensional mass M{sub dim} {identical_to} 5r{sub e} {sigma}{sup 2}{sub e/2}/G is proportional to the total (lensing) mass M{sub r{sub e/2}}, and both increase more rapidly than stellar mass M{sub *} (M{sub *{proportional_to}}M{sub r{sub e/2}{sup 0.8}); (5) the mass plane (MP), obtained by replacing surface brightness with surface mass density in the FP, is found to be tighter and closer to the virial relation than the FP and the M{sub *}P, indicating that the scatter of those relations is dominated by stellar population effects; (6) we construct the fundamental hyper-plane by adding stellar masses to the MP and find the M{sub *} coefficient to be

  13. Final report (Grant No. DOE DE-FG02-97ER62366) [Retrieval of cloud fraction and type using broadband diffuse and total shortwave irradiance measurements

    SciTech Connect (OSTI)

    Clothiaux, Eugene

    2001-05-17

    The primary research effort supported by Grant No. DOE DEFG02-97ER62366 titled ''Retrieval of Cloud Fraction and Type Using Broadband Diffuse and Total Shortwave Irradiance Measurements'' was application of clear-sky identification and cloud fraction estimation algorithms developed by Charles N. Long and Thomas P. Ackerman to the downwelling total, direct and diffuse shortwave irradiance measurements made at all of the central, boundary, and extended facilities of the DOE Atmospheric Radiation Measurement (ARM) Program Southern Great Plains (SOP) site. Goals of the research were finalization and publication of the two algorithms in the peer-reviewed literature and operational application of them to all of aforementioned data streams from the ARM SGP site. The clear-sky identification algorithm was published as Long and Ackerman (2000) in the Journal of Geophysical Research, while a description of the cloud fraction estimation algorithm made it to the scientific literature as Long et al. (1999) in the Proceedings of the 10th American Meteorological Association Conference on Atmospheric Radiation held in Madison, Wisconsin. The cloud fraction estimation algorithm relies on empirical relationships between the outputs of the clear-sky identification algorithm and cloud fraction; as such, the cloud fraction estimation algorithm requires significant amounts of data both to properly develop the empirical relationships and to thoroughly test them. With this perspective in mind the major focus of our research efforts in the later half of the project became the operational implementation of the clear-sky identification algorithm on DOE ARM SGP data so that we could develop the data set necessary for final tuning of the cloud fraction estimation algorithm in research extending beyond the lifetime of the project.

  14. Using Geothermal Play Types as an Analogue for Estimating Potential Resource Size

    SciTech Connect (OSTI)

    Terry, Rachel; Young, Katherine

    2015-09-02

    Blind geothermal systems are becoming increasingly common as more geothermal fields are developed. Geothermal development is known to have high risk in the early stages of a project development because reservoir characteristics are relatively unknown until wells are drilled. Play types (or occurrence models) categorize potential geothermal fields into groups based on geologic characteristics. To aid in lowering exploration risk, these groups' reservoir characteristics can be used as analogues in new site exploration. The play type schemes used in this paper were Moeck and Beardsmore play types (Moeck et al. 2014) and Brophy occurrence models (Brophy et al. 2011). Operating geothermal fields throughout the world were classified based on their associated play type, and then reservoir characteristics data were catalogued. The distributions of these characteristics were plotted in histograms to develop probability density functions for each individual characteristic. The probability density functions can be used as input analogues in Monte Carlo estimations of resource potential for similar play types in early exploration phases. A spreadsheet model was created to estimate resource potential in undeveloped fields. The user can choose to input their own values for each reservoir characteristic or choose to use the probability distribution functions provided from the selected play type. This paper also addresses the United States Geological Survey's 1978 and 2008 assessment of geothermal resources by comparing their estimated values to reported values from post-site development. Information from the collected data was used in the comparison for thirty developed sites in the United States. No significant trends or suggestions for methodologies could be made by the comparison.

  15. Types of Possible Survey Errors in Estimates Published in the Weekly

    Weekly Natural Gas Storage Report (EIA)

    Natural Gas Storage Report Types of Possible Survey Errors in Estimates Published in the Weekly Natural Gas Storage Report Release date: March 1, 2016 The U.S. Energy Information Administration (EIA) collects and publishes natural gas storage information on a monthly and weekly basis. The Form EIA-191, Monthly Underground Natural Gas Storage Report, is a census survey that collects field-level information from all underground natural gas storage operators in the United States known to EIA.

  16. Table 8.6a Estimated Consumption of Combustible Fuels for Useful Thermal Output at Combined-Heat-and-Power Plants: Total (All Sectors), 1989-2011 (Sum of Tables 8.6b and 8.6c)

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

    a Estimated Consumption of Combustible Fuels for Useful Thermal Output at Combined-Heat-and-Power Plants: Total (All Sectors), 1989-2011 (Sum of Tables 8.6b and 8.6c) Year Coal 1 Petroleum Natural Gas 6 Other Gases 7 Biomass Other 10 Distillate Fuel Oil 2 Residual Fuel Oil 3 Other Liquids 4 Petroleum Coke 5 Total 5 Wood 8 Waste 9 Short Tons Barrels Short Tons Barrels Thousand Cubic Feet Billion Btu Billion Btu Billion Btu 1989 16,509,639 1,410,151 16,356,550 353,000 247,409 19,356,746

  17. Total Imports

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

    Data Series: Imports - Total Imports - Crude Oil Imports - Crude Oil, Commercial Imports - by SPR Imports - into SPR by Others Imports - Total Products Imports - Total Motor Gasoline Imports - Finished Motor Gasoline Imports - Reformulated Gasoline Imports - Reformulated Gasoline Blended w/ Fuel Ethanol Imports - Other Reformulated Gasoline Imports - Conventional Gasoline Imports - Conv. Gasoline Blended w/ Fuel Ethanol Imports - Conv. Gasoline Blended w/ Fuel Ethanol, Ed55 & < Imports -

  18. Country Total

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

    Country Total Percent of U.S. total Canada 61,078 1% China 3,323,297 57% Germany 154,800 3% Japan 12,593 0% India 47,192 1% South Korea 251,105 4% All Others 2,008,612 34% Total 5,858,677 100% Table 7 . Photovoltaic module import shipments by country, 2014 (peak kilowatts) Note: All Others includes Cambodia, Czech Republic, Hong Kong, Malaysia, Mexico, Netherlands, Philippines, Singapore, Taiwan and Turkey Source: U.S. Energy Information Administration, Form EIA-63B, 'Annual Photovoltaic

  19. State Total

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

    State Total Percent of U.S. total Alabama 482 0.0% Alaska 81 0.0% Arizona 194,476 3.3% Arkansas 336 0.0% California 3,163,120 53.0% Colorado 47,240 0.8% Connecticut 50,745 0.9% Delaware 6,600 0.1% District of Columbia 751 0.0% Florida 18,593 0.3% Georgia 47,660 0.8% Hawaii 78,329 1.3% Illinois 5,795 0.1% Indiana 37,016 0.6% Iowa 14,281 0.2% Kansas 1,809 0.0% Kentucky 520 0.0% Louisiana 12,147 0.2% Maine 1,296 0.0% Maryland 63,077 1.1% Massachusetts 157,415 2.6% Michigan 4,210 0.1% Minnesota

  20. Summary Max Total Units

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Summary Max Total Units *If All Splits, No Rack Units **If Only FW, AC Splits 1000 52 28 28 2000 87 59 35 3000 61 33 15 4000 61 33 15 Totals 261 153 93 ***Costs $1,957,500.00 $1,147,500.00 $697,500.00 Notes: added several refrigerants removed bins from analysis removed R-22 from list 1000lb, no Glycol, CO2 or ammonia Seawater R-404A only * includes seawater units ** no seawater units included *** Costs = (total units) X (estimate of $7500 per unit) 1000lb, air cooled split systems, fresh water

  1. Notices Total Estimated Number of Annual

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    ... 27 Dumont Street, Thursday, September 26, 2013, 5-8 p.m. Requests to speak at one or more public scoping meeting(s) should be received at the address for Brian Mills indicated ...

  2. Total Estimated Contract Cost: Performance Period

    Office of Environmental Management (EM)

    FY2012 Fee Information Minimum Fee Maximum Fee September 2015 Contract Number: Cost Plus Incentive Fee Contractor: 3,264,909,094 Contract Period: EM Contractor Fee s Idaho...

  3. Effects of core type, placement, and width on the estimated interstrand coupling properties of QXF-type Nb3Sn Rutherford cables

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Collings, E. W.; Sumption, M. D.; Majoros, M.; Wang, X.; Dietderich, D. R.

    2015-01-12

    The coupling magnetization of a Rutherford cable is inversely proportional to an effective interstrand contact resistance Reff , a function of the crossing-strand resistance Rc, and the adjacent strand resistance Ra. In cored cables, Reff continuously varies with W, the core width expressed as percent interstrand cover. For a series of un-heat-treated stabrite-coated NbTi LHC-inner cables with stainless-steel (SS, insulating) cores, Reff (W) decreased smoothly as W decreased from 100%, whereas for a set of research-wound SS-cored Nb3Sn cables, Reff plummeted abruptly and remained low over most of the range. The difference is due to the controlling influence of Rcmore » - 2.5 μΩ for the stabrite/NbTi and 0.26 μΩ for Nb3Sn. The experimental behavior was replicated in the Reff (W)’s calculated by the program CUDI, which (using the basic parameters of the QXF cable) went on to show in terms of decreasing W that: 1) in QXF-type Nb3Sn cables (Rc = 0.26 μΩ), Reff dropped even more suddenly when the SS core, instead of being centered, was offset to one edge of the cable; 2) Reff decreased more gradually in cables with higher Rc’s; and 3) a suitable Reff for a Nb3Sn cable can be achieved by inserting a suitably resistive core rather than an insulating (SS) one.« less

  4. Estimation of benchmark dose as the threshold levels of urinary cadmium, based on excretion of total protein, {beta} {sub 2}-microglobulin, and N-acetyl-{beta}-D-glucosaminidase in cadmium nonpolluted regions in Japan

    SciTech Connect (OSTI)

    Kobayashi, Etsuko . E-mail: ekoba@faculty.chiba-u.jp; Suwazono, Yasushi; Uetani, Mirei; Inaba, Takeya; Oishi, Mitsuhiro; Kido, Teruhiko; Nishijo, Muneko; Nakagawa, Hideaki; Nogawa, Koji

    2006-07-15

    Previously, we investigated the association between urinary cadmium (Cd) concentration and indicators of renal dysfunction, including total protein, {beta} {sub 2}-microglobulin ({beta} {sub 2}-MG), and N-acetyl-{beta}-D-glucosaminidase (NAG). In 2778 inhabitants {>=}50 years of age (1114 men, 1664 women) in three different Cd nonpolluted areas in Japan, we showed that a dose-response relationship existed between renal effects and Cd exposure in the general environment without any known Cd pollution. However, we could not estimate the threshold levels of urinary Cd at that time. In the present study, we estimated the threshold levels of urinary Cd as the benchmark dose low (BMDL) using the benchmark dose (BMD) approach. Urinary Cd excretion was divided into 10 categories, and an abnormality rate was calculated for each. Cut-off values for urinary substances were defined as corresponding to the 84% and 95% upper limit values of the target population who have not smoked. Then we calculated the BMD and BMDL using a log-logistic model. The values of BMD and BMDL for all urinary substances could be calculated. The BMDL for the 84% cut-off value of {beta} {sub 2}-MG, setting an abnormal value at 5%, was 2.4 {mu}g/g creatinine (cr) in men and 3.3 {mu}g/g cr in women. In conclusion, the present study demonstrated that the threshold level of urinary Cd could be estimated in people living in the general environment without any known Cd-pollution in Japan, and the value was inferred to be almost the same as that in Belgium, Sweden, and China.

  5. Check Estimates and Independent Costs

    Broader source: Directives, Delegations, and Requirements [Office of Management (MA)]

    1997-03-28

    Check estimates and independent cost estimates (ICEs) are tools that can be used to validate a cost estimate. Estimate validation entails an objective review of the estimate to ensure that estimate criteria and requirements have been met and well documented, defensible estimate has been developed. This chapter describes check estimates and their procedures and various types of independent cost estimates.

  6. Derived Annual Estimates

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

    74-1988 For Methodology Concerning the Derived Estimates Total Consumption of Offsite-Produced Energy for Heat and Power by Industry Group, 1974-1988 Total Energy *** Electricity...

  7. THE INFLUENCE OF DARK MATTER HALOS ON DYNAMICAL ESTIMATES OF BLACK HOLE MASS: 10 NEW MEASUREMENTS FOR HIGH-{sigma} EARLY-TYPE GALAXIES

    SciTech Connect (OSTI)

    Rusli, S. P.; Thomas, J.; Saglia, R. P.; Fabricius, M.; Erwin, P.; Bender, R.; Nowak, N.; Lee, C. H.; Riffeser, A.; Sharp, R.

    2013-09-15

    Adaptive optics assisted SINFONI observations of the central regions of 10 early-type galaxies are presented. Based primarily on the SINFONI kinematics, 10 black hole (BH) masses occupying the high-mass regime of the M{sub BH}-{sigma} relation are derived using three-integral Schwarzschild models. The effect of dark matter (DM) inclusion on the BH mass is explored. The omission of a DM halo in the model results in a higher stellar mass-to-light ratio, especially when extensive kinematic data are used in the model. However, when the diameter of the sphere of influence-computed using the BH mass derived without a dark halo-is at least 10 times the point-spread function FWHM during the observations, it is safe to exclude a DM component in the dynamical modeling, i.e., the change in BH mass is negligible. When the spatial resolution is marginal, restricting the mass-to-light ratio to the right value returns the correct M{sub BH} although a dark halo is not present in the model. Compared to the M{sub BH}-{sigma} and M{sub BH}-L relations of McConnell et al., the 10 BHs are all more massive than expected from the luminosities and 7 BH masses are higher than expected from the stellar velocity dispersions of the host bulges. Using new fitted relations, which include the 10 galaxies, we find that the space density of the most massive BHs (M{sub BH} {approx}> 10{sup 9} M{sub Sun }) estimated from the M{sub BH}-L relation is higher than the estimate based on the M{sub BH}-{sigma} relation and the latter is higher than model predictions based on quasar counts, each by about an order of magnitude.

  8. THE AKARI 2.5-5.0 ?m SPECTRAL ATLAS OF TYPE-1 ACTIVE GALACTIC NUCLEI: BLACK HOLE MASS ESTIMATOR, LINE RATIO, AND HOT DUST TEMPERATURE

    SciTech Connect (OSTI)

    Kim, Dohyeong; Im, Myungshin; Kim, Ji Hoon; Jun, Hyunsung David; Lee, Seong-Kook; Woo, Jong-Hak; Lee, Hyung Mok; Lee, Myung Gyoon; Nakagawa, Takao; Matsuhara, Hideo; Wada, Takehiko; Takagi, Toshinobu; Oyabu, Shinki; Ohyama, Youichi E-mail: mim@astro.snu.ac.kr

    2015-01-01

    We present 2.5-5.0?m spectra of 83 nearby (0.002 < z < 0.48) and bright (K < 14 mag) type-1 active galactic nuclei (AGNs) taken with the Infrared Camera on board AKARI. The 2.5-5.0?m spectral region contains emission lines such as Br? (2.63?m), Br? (4.05?m), and polycyclic aromatic hydrocarbons (3.3?m), which can be used for studying the black hole (BH) masses and star formation activity in the host galaxies of AGNs. The spectral region also suffers less dust extinction than in the ultra violet (UV) or optical wavelengths, which may provide an unobscured view of dusty AGNs. Our sample is selected from bright quasar surveys of Palomar-Green and SNUQSO, and AGNs with reverberation-mapped BH masses from Peterson etal. Using 11 AGNs with reliable detection of Brackett lines, we derive the Brackett-line-based BH mass estimators. We also find that the observed Brackett line ratios can be explained with the commonly adopted physical conditions of the broad line region. Moreover, we fit the hot and warm dust components of the dust torus by adding photometric data of SDSS, 2MASS, WISE, and ISO to the AKARI spectra, finding hot and warm dust temperatures of ?1100 K and ?220 K, respectively, rather than the commonly cited hot dust temperature of 1500 K.

  9. Table A39. Total Expenditures for Purchased Electricity and Steam

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

    9. Total Expenditures for Purchased Electricity and Steam" " by Type of Supplier, Census Region, Census Division, and" " Economic Characteristics of the Establishment, 1994" " (Estimates in Million Dollars)" ," Electricity",," Steam" ,,,,,"RSE" ,"Utility","Nonutility","Utility","Nonutility","Row" "Economic

  10. Barge Truck Total

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

    Barge Truck Total delivered cost per short ton Shipments with transportation rates over total shipments Total delivered cost per short ton Shipments with transportation rates over...

  11. Weekly Coal Production Estimation Methodology

    Gasoline and Diesel Fuel Update (EIA)

    Weekly Coal Production Estimation Methodology Step 1 (Estimate total amount of weekly U.S. coal production) U.S. coal production for the current week is estimated using a ratio ...

  12. ,"Total Natural Gas Consumption

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

    Gas Consumption (billion cubic feet)",,,,,"Natural Gas Energy Intensity (cubic feetsquare foot)" ,"Total ","Space Heating","Water Heating","Cook- ing","Other","Total ","Space...

  13. Estimating Methods

    Broader source: Directives, Delegations, and Requirements [Office of Management (MA)]

    1997-03-28

    Based on the project's scope, the purpose of the estimate, and the availability of estimating resources, the estimator can choose one or a combination of techniques when estimating an activity or project. Estimating methods, estimating indirect and direct costs, and other estimating considerations are discussed in this chapter.

  14. Types of Radiation Exposure

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

    External Irradiation Contamination Incorporation Biological Effects of Acute, Total Body Irradiation Managing Radiation Emergencies Procedure Demonstration Types of radiation ...

  15. Cost-effectiveness of controlling emissions for various alternative-fuel vehicle types, with vehicle and fuel price subsidies estimated on the basis of monetary values of emission reductions

    SciTech Connect (OSTI)

    Wang, M.Q.

    1993-12-31

    Emission-control cost-effectiveness is estimated for ten alternative-fuel vehicle (AFV) types (i.e., vehicles fueled with reformulated gasoline, M85 flexible-fuel vehicles [FFVs], M100 FFVs, dedicated M85 vehicles, dedicated M100 vehicles, E85 FFVS, dual-fuel liquefied petroleum gas vehicles, dual-fuel compressed natural gas vehicles [CNGVs], dedicated CNGVs, and electric vehicles [EVs]). Given the assumptions made, CNGVs are found to be most cost-effective in controlling emissions and E85 FFVs to be least cost-effective, with the other vehicle types falling between these two. AFV cost-effectiveness is further calculated for various cases representing changes in costs of vehicles and fuels, AFV emission reductions, and baseline gasoline vehicle emissions, among other factors. Changes in these parameters can change cost-effectiveness dramatically. However, the rank of the ten AFV types according to their cost-effectiveness remains essentially unchanged. Based on assumed dollars-per-ton emission values and estimated AFV emission reductions, the per-vehicle monetary value of emission reductions is calculated for each AFV type. Calculated emission reduction values ranged from as little as $500 to as much as $40,000 per vehicle, depending on AFV type, dollar-per-ton emission values, and baseline gasoline vehicle emissions. Among the ten vehicle types, vehicles fueled with reformulated gasoline have the lowest per-vehicle value, while EVs have the highest per-vehicle value, reflecting the magnitude of emission reductions by these vehicle types. To translate the calculated per-vehicle emission reduction values to individual AFV users, AFV fuel or vehicle price subsidies are designed to be equal to AFV emission reduction values. The subsidies designed in this way are substantial. In fact, providing the subsidies to AFVs would change most AFV types from net cost increases to net cost decreases, relative to conventional gasoline vehicles.

  16. Total Energy Facilities Biomass Facility | Open Energy Information

    Open Energy Info (EERE)

    Energy Facilities Biomass Facility Jump to: navigation, search Name Total Energy Facilities Biomass Facility Facility Total Energy Facilities Sector Biomass Facility Type...

  17. Total Space Heat-

    Gasoline and Diesel Fuel Update (EIA)

    Commercial Buildings Energy Consumption Survey: Energy End-Use Consumption Tables Total Space Heat- ing Cool- ing Venti- lation Water Heat- ing Light- ing Cook- ing Refrig- eration...

  18. ,"Total Fuel Oil Expenditures

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

    A. Fuel Oil Expenditures by Census Region for All Buildings, 2003" ,"Total Fuel Oil Expenditures (million dollars)",,,,"Fuel Oil Expenditures (dollars)" ,,,,,"per Gallon",,,,"per...

  19. ,"Total Fuel Oil Consumption

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

    A. Fuel Oil Consumption (gallons) and Energy Intensities by End Use for All Buildings, 2003" ,"Total Fuel Oil Consumption (million gallons)",,,,,"Fuel Oil Energy Intensity...

  20. Total Space Heat-

    Gasoline and Diesel Fuel Update (EIA)

    Revised: December, 2008 Total Space Heat- ing Cool- ing Venti- lation Water Heat- ing Light- ing Cook- ing Refrig- eration Office Equip- ment Com- puters Other All Buildings...

  1. Total Space Heat-

    Gasoline and Diesel Fuel Update (EIA)

    Released: September, 2008 Total Space Heat- ing Cool- ing Venti- lation Water Heat- ing Light- ing Cook- ing Refrig- eration Office Equip- ment Com- puters Other All Buildings*...

  2. ,"Total Fuel Oil Expenditures

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

    . Fuel Oil Expenditures by Census Region for Non-Mall Buildings, 2003" ,"Total Fuel Oil Expenditures (million dollars)",,,,"Fuel Oil Expenditures (dollars)" ,,,,,"per...

  3. ,"Total Fuel Oil Consumption

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

    0. Fuel Oil Consumption (gallons) and Energy Intensities by End Use for Non-Mall Buildings, 2003" ,"Total Fuel Oil Consumption (million gallons)",,,,,"Fuel Oil Energy Intensity...

  4. ,"Total Fuel Oil Expenditures

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

    4. Fuel Oil Expenditures by Census Region, 1999" ,"Total Fuel Oil Expenditures (million dollars)",,,,"Fuel Oil Expenditures (dollars)" ,,,,,"per Gallon",,,,"per Square Foot"...

  5. Parallel Total Energy

    Energy Science and Technology Software Center (OSTI)

    2004-10-21

    This is a total energy electronic structure code using Local Density Approximation (LDA) of the density funtional theory. It uses the plane wave as the wave function basis set. It can sue both the norm conserving pseudopotentials and the ultra soft pseudopotentials. It can relax the atomic positions according to the total energy. It is a parallel code using MP1.

  6. U.S. Total Exports

    Gasoline and Diesel Fuel Update (EIA)

    Total to Chile Sabine Pass, LA Total to China Kenai, AK Sabine Pass, LA Total to Egypt ... Sabine Pass, LA Total to Russia Total to South Korea Freeport, TX Sabine Pass, LA Total ...

  7. Site Performance Measure Unit Lifecycle Total Estimate Pre-2016...

    Office of Environmental Management (EM)

    Number of Release Sites 443 443 443 443 Brookhaven National Laboratory Nuclear Facility Completions Number of Facilities 1 1 2 2 Brookhaven National Laboratory Radioactive Facility ...

  8. Total Estimated Contract Cost: Contract Option Period: Performance

    Office of Environmental Management (EM)

    Performance Period Fee Earned FY2000 thru 2008 $102,622,325 FY2009 $12,259,719 FY2010 $35,789,418 FY2011 $24,126,240 FY2012 $24,995,209 FY2013 $6,340,762 FY2014 $16,285,867 FY2015 $35,931,000 $8,595,000 FY2016 $20,891,000 $9,310,000 FY2017 $24,849,000 FY2018 $99,100,000 FY2019 $129,700,000 Cumulative Fee $240,324,540 $595,298,540 $12,259,719 $35,789,418 $38,554,240 $41,785,209 $16,698,762 $37,117,867 Maximum Fee $595,298,540 Fee Available $102,622,325 $10,921,302,346 Completion Contract:

  9. Estimating Radiation Risk from Total Effective Dose Equivalent...

    National Nuclear Security Administration (NNSA)

    0 Tc 0.37 No. 1Tj 5.25 0 TD 0.098 Tc 0 Tw (No.) Tj 15.755 -13.5 TD 0.3to Tw ( ) Tj 5.25 0 TD F1 10.5 Tf -0.1925 Tc 0 5 Report 0 1Tj 5.25 0 TD 0.098 Tc 0 Tw 8No. 7 0 TD -0.IRRPC,

  10. Estimating Radiation Risk from Total Effective Dose Equivalent...

    National Nuclear Security Administration (NNSA)

    of Dose to the Public, DOE Report DOEEH-0070, July 1988. DOE, 1988b. Internal Dose Conversion Factors for Calculation of Dose to the Public, DOE Report DOEEH-0071, July 1988. ...

  11. Total Space Heat-

    Gasoline and Diesel Fuel Update (EIA)

    Survey: Energy End-Use Consumption Tables Total Space Heat- ing Cool- ing Venti- lation Water Heat- ing Light- ing Cook- ing Refrig- eration Office Equip- ment Com- puters Other...

  12. ARM - Measurement - Total carbon

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

    carbon ARM Data Discovery Browse Data Comments? We would love to hear from you! Send us a note below or call us at 1-888-ARM-DATA. Send Measurement : Total carbon The total concentration of carbon in all its organic and non-organic forms. Categories Atmospheric Carbon, Aerosols Instruments The above measurement is considered scientifically relevant for the following instruments. Refer to the datastream (netcdf) file headers of each instrument for a list of all available measurements, including

  13. Project Functions and Activities Definitions for Total Project Cost

    Broader source: Directives, Delegations, and Requirements [Office of Management (MA)]

    1997-03-28

    This chapter provides guidelines developed to define the obvious disparity of opinions and practices with regard to what exactly is included in total estimated cost (TEC) and total project cost (TPC).

  14. Independent Cost Estimate (ICE)

    Broader source: Energy.gov [DOE]

    Independent Cost Estimate (ICE). On August 8-12, the Office of Project Management Oversight and Assessments (PM) will conduct an ICE on the NNSA Albuquerque Complex Project (NACP) at Albuquerque, NM. This estimate will support the Critical Decision (CD) for establishing the performance baseline and approval to start construction (CD-2/3). This project is at CD-1, with a total project cost range of $183M to $251M.

  15. Total DOE/NNSA

    National Nuclear Security Administration (NNSA)

    8 Actuals 2009 Actuals 2010 Actuals 2011 Actuals 2012 Actuals 2013 Actuals 2014 Actuals 2015 Actuals Total DOE/NNSA 4,385 4,151 4,240 4,862 5,154 5,476 7,170 7,593 Total non-NNSA 3,925 4,017 4,005 3,821 3,875 3,974 3,826 3765 Total Facility 8,310 8,168 8,245 8,683 9,029 9,450 10,996 11,358 non-NNSA includes DOE offices and Strategic Parternship Projects (SPP) employees NNSA M&O Employee Reporting

  16. Estimation of economic parameters of U.S. hydropower resources

    SciTech Connect (OSTI)

    Hall, Douglas G.; Hunt, Richard T.; Reeves, Kelly S.; Carroll, Greg R.

    2003-06-01

    Tools for estimating the cost of developing and operating and maintaining hydropower resources in the form of regression curves were developed based on historical plant data. Development costs that were addressed included: licensing, construction, and five types of environmental mitigation. It was found that the data for each type of cost correlated well with plant capacity. A tool for estimating the annual and monthly electric generation of hydropower resources was also developed. Additional tools were developed to estimate the cost of upgrading a turbine or a generator. The development and operation and maintenance cost estimating tools, and the generation estimating tool were applied to 2,155 U.S. hydropower sites representing a total potential capacity of 43,036 MW. The sites included totally undeveloped sites, dams without a hydroelectric plant, and hydroelectric plants that could be expanded to achieve greater capacity. Site characteristics and estimated costs and generation for each site were assembled in a database in Excel format that is also included within the EERE Library under the title, “Estimation of Economic Parameters of U.S. Hydropower Resources - INL Hydropower Resource Economics Database.”

  17. Property:Estimated End Date | Open Energy Information

    Open Energy Info (EERE)

    Estimated End Date Jump to: navigation, search Property Name Estimated End Date Property Type String Pages using the property "Estimated End Date" Showing 4 pages using this...

  18. "Table A38. Total Expenditures for Purchased Electricity, Steam, and Natural Gas"

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

    8. Total Expenditures for Purchased Electricity, Steam, and Natural Gas" " by Type of Supplier, Census Region, Census Division, Industry Group," " and Selected Industries, 1994" " (Estimates in Million Dollars)" ,," Electricity",," Steam" ,,,,,,"RSE" "SIC",,"Utility","Nonutility","Utility","Nonutility","Row" "Code(a)","Industry Group and

  19. "Table A46. Total Expenditures for Purchased Electricity, Steam, and Natural"

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

    6. Total Expenditures for Purchased Electricity, Steam, and Natural" " Gas by Type of Supplier, Census Region, Industry Group, and Selected Industries," 1991 " (Estimates in Million Dollars)" ,," Electricity",," Steam",," Natural Gas" ,,"-","-----------","-","-----------","-","------------","-","RSE"

  20. Table A41. Total Inputs of Energy for Heat, Power, and Electricity

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

    A41. Total Inputs of Energy for Heat, Power, and Electricity" " Generation by Census Region, Industry Group, Selected Industries, and Type of" " Energy Management Program, 1991" " (Estimates in Trillion Btu)" ,,," Census Region",,,,"RSE" "SIC","Industry Groups",," -------------------------------------------",,,,"Row" "Code(a)","and

  1. Table A50. Total Inputs of Energy for Heat, Power, and Electricity Generatio

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

    A50. Total Inputs of Energy for Heat, Power, and Electricity Generation" " by Census Region, Industry Group, Selected Industries, and Type of" " Energy-Management Program, 1994" " (Estimates in Trillion Btu)" ,,,," Census Region",,,"RSE" "SIC",,,,,,,"Row" "Code(a)","Industry Group and

  2. TOTAL WORKFORCE Males

    National Nuclear Security Administration (NNSA)

    76 Females Male Female Male Female Male Female Male Female Male Female 27 24 86 134 65 24 192 171 1189 423 PAY PLAN SES 96 EX 4 EJ/EK 60 EN 05 39 EN 04 159 EN 03 21 EN 00 8 NN (Engineering) 398 NQ (Prof/Tech/Admin) 1165 NU (Tech/Admin Support) 54 NV (Nuc Mat Courier) 325 GS 15 3 GS 14 1 GS 13 1 GS 10 1 Total includes 2318 permanent and 17 temporary employees. DIVERSITY 2335 1559 66.8% American Indian Alaska Native African American Asian American Pacific Islander Hispanic White 33.2% National

  3. Physisorption and Chemisorption Methods for Evaluating the Total...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Physisorption and Chemisorption Methods for Evaluating the Total Surface Area and Active Surface Area of Two Types of Carbon Materials Physisorption and Chemisorption Methods for ...

  4. Crude Oil and Petroleum Products Total Stocks Stocks by Type

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

    Product: Crude Oil and Petroleum Products Crude Oil All Oils (Excluding Crude Oil) Pentanes Plus Liquefied Petroleum Gases Ethane/Ethylene Propane/Propylene Normal Butane/Butylene Isobutane/Butylene Other Hydrocarbons Oxygenates (excluding Fuel Ethanol) MTBE Other Oxygenates Renewables (including Fuel Ethanol) Fuel Ethanol Renewable Diesel Fuel Other Renewable Fuels Unfinished Oils Unfinished Oils, Naphthas & Lighter Unfinished Oils, Kerosene & Light Gas Unfinished Oils, Heavy Gas Oils

  5. Estimate of federal relighting potential and demand for efficient lighting products

    SciTech Connect (OSTI)

    Shankle, S.A.; Dirks, J.A.; Elliott, D.B.; Richman, E.E.; Grover, S.E.

    1993-11-01

    The increasing level of electric utility rebates for energy-efficient lighting retrofits has recently prompted concern over the adequacy of the market supply of energy-efficient lighting products (Energy User News 1991). In support of the U.S. Department of Energy`s Federal Energy Management Program, Pacific Northwest Laboratory (PNL) has developed an estimate of the total potential for energy-efficient lighting retrofits in federally owned buildings. This estimate can be used to address the issue of the impact of federal relighting projects on the supply of energy-efficient lighting products. The estimate was developed in 1992, using 1991 data. Any investments in energy-efficient lighting products that occurred in 1992 will reduce the potential estimated here. This analysis proceeds by estimating the existing stock of lighting fixtures in federally owned buildings. The lighting technology screening matrix is then used to determine the minimum life-cycle cost retrofit for each type of existing lighting fixture. Estimates of the existing stock are developed for (1) four types of fluorescent lighting fixtures (2-, 3-, and 4-lamp, F40 4-foot fixtures, and 2-lamp, F96 8-foot fixtures, all with standard magnetic ballasts); (2) one type of incandescent fixture (a 75-watt single bulb fixture); and (3) one type of exit sign (containing two 20-watt incandescent bulbs). Estimates of the existing stock of lighting fixtures in federally owned buildings, estimates of the total potential demand for energy-efficient lighting products if all cost-effective retrofits were undertaken immediately, and total potential annual energy savings (in MWh and dollars), the total investment required to obtain the energy savings and the present value of the efficiency investment, are presented.

  6. Supercooled liquid water Estimation Tool

    Energy Science and Technology Software Center (OSTI)

    2012-05-04

    The Cloud Supercooled liquid water Estimation Tool (SEET) is a user driven Graphical User Interface (GUI) that estimates cloud supercooled liquid water (SLW) content in terms of vertical column and total mass from Moderate resolution Imaging Supercooled liquid water Estimation Tool Spectroradiometer (MODIS) spatially derived cloud products and realistic vertical cloud parameterizations that are user defined. It also contains functions for post-processing of the resulting data in tabular and graphical form.

  7. Total-derivative supersymmetry breaking

    SciTech Connect (OSTI)

    Haba, Naoyuki; Uekusa, Nobuhiro

    2010-05-15

    On an interval compactification in supersymmetric theory, boundary conditions for bulk fields must be treated carefully. If they are taken arbitrarily following the requirement that a theory is supersymmetric, the conditions could give redundant constraints on the theory. We construct a supersymmetric action integral on an interval by introducing brane interactions with which total-derivative terms under the supersymmetry transformation become zero due to a cancellation. The variational principle leads equations of motion and also boundary conditions for bulk fields, which determine boundary values of bulk fields. By estimating mass spectrum, spontaneous supersymmetry breaking in this simple setup can be realized in a new framework. This supersymmetry breaking does not induce a massless R axion, which is favorable for phenomenology. It is worth noting that fermions in hyper-multiplet, gauge bosons, and the fifth-dimensional component of gauge bosons can have zero-modes (while the other components are all massive as Kaluza-Klein modes), which fits the gauge-Higgs unification scenarios.

  8. Estimates of particulate mass in multi-canister overpacks

    SciTech Connect (OSTI)

    SLOUGHTER, J.P.

    1999-02-25

    High, best estimate, and low values are developed for particulate inventories within MCO baskets that have been loaded with freshly cleaned fuel assemblies and scrap. These per-basket estimates are then applied to all anticipated MCO payload configurations to identify which configurations are bounding for each type of particulate. Finally the resulting bounding and nominal values for residual particulates are combined with corresponding values [from other documents] for particulate that may be generated by corrosion of exposed uranium after the fuel has been cleaned. The resulting rounded nominal estimate for a typical MCO after 40 years of storage is 8 kg. The estimate for a bounding total particulate case MCO is that it may contain up to 64 kg of particulate after 40 years of storage.

  9. Estimates of Particulate Mass in Multi Canister Overpacks (MCO)

    SciTech Connect (OSTI)

    SLOUGHTER, J.P.

    2000-02-16

    High, best estimate, and low values are developed for particulate inventories within MCO baskets that have been loaded with freshly cleaned fuel assemblies and scrap. These per-basket estimates are then applied to all anticipated MCO payload configurations to identify which configurations are bounding for each type of particulate. Finally the resulting bounding and nominal values for residual particulates are combined with corresponding values [from other documents] for particulates that may be generated by corrosion of exposed uranium after the fuel has been cleaned. The resulting rounded nominal estimate for a typical MCO after 40 years of storage is 8 kg. The estimate for a bounding total particulate case MCO is that it may contain up to 64 kg of particulate after 40 years of storage.

  10. Determination of Total Petroleum Hydrocarbons (TPH) Using Total Carbon Analysis

    SciTech Connect (OSTI)

    Ekechukwu, A.A.

    2002-05-10

    Several methods have been proposed to replace the Freon(TM)-extraction method to determine total petroleum hydrocarbon (TPH) content. For reasons of cost, sensitivity, precision, or simplicity, none of the replacement methods are feasible for analysis of radioactive samples at our facility. We have developed a method to measure total petroleum hydrocarbon content in aqueous sample matrixes using total organic carbon (total carbon) determination. The total carbon content (TC1) of the sample is measured using a total organic carbon analyzer. The sample is then contacted with a small volume of non-pokar solvent to extract the total petroleum hydrocarbons. The total carbon content of the resultant aqueous phase of the extracted sample (TC2) is measured. Total petroleum hydrocarbon content is calculated (TPH = TC1-TC2). The resultant data are consistent with results obtained using Freon(TM) extraction followed by infrared absorbance.

  11. U.S. Total Exports

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

    Barbados Total To Brazil Freeport, TX Sabine Pass, LA Total to Canada Eastport, ID Calais, ME Detroit, MI Marysville, MI Port Huron, MI Crosby, ND Portal, ND Sault St. Marie, MI St. Clair, MI Noyes, MN Warroad, MN Babb, MT Havre, MT Port of Morgan, MT Sherwood, ND Pittsburg, NH Buffalo, NY Grand Island, NY Massena, NY Niagara Falls, NY Waddington, NY Sumas, WA Sweetgrass, MT Total to Chile Sabine Pass, LA Total to China Kenai, AK Sabine Pass, LA Total to Egypt Freeport, TX Total to India

  12. Examples of Cost Estimation Packages

    Broader source: Directives, Delegations, and Requirements [Office of Management (MA)]

    1997-03-28

    Estimates can be performed in a variety of ways. Some of these are for projects for an undefined scope, a conventional construction project, or where there is a level of effort required to complete the work. Examples of cost estimation packages for these types of projects are described in this appendix.

  13. Estimates of Savings Achievable from Irrigation Controller

    SciTech Connect (OSTI)

    Williams, Alison; Fuchs, Heidi; Whitehead, Camilla Dunham

    2014-03-28

    This paper performs a literature review and meta-analysis of water savings from several types of advanced irrigation controllers: rain sensors (RS), weather-based irrigation controllers (WBIC), and soil moisture sensors (SMS).The purpose of this work is to derive average water savings per controller type, based to the extent possible on all available data. After a preliminary data scrubbing, we utilized a series of analytical filters to develop our best estimate of average savings. We applied filters to remove data that might bias the sample such as data self-reported by manufacturers, data resulting from studies focusing on high-water users, or data presented in a non-comparable format such as based on total household water use instead of outdoor water use. Because the resulting number of studies was too small to be statistically significant when broken down by controller type, this paper represents a survey and synthesis of available data rather than a definitive statement regarding whether the estimated water savings are representative.

  14. Total Eolica | Open Energy Information

    Open Energy Info (EERE)

    Eolica Jump to: navigation, search Name: Total Eolica Place: Spain Product: Project developer References: Total Eolica1 This article is a stub. You can help OpenEI by expanding...

  15. Total..............................................

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

    111.1 86.6 2,720 1,970 1,310 1,941 1,475 821 1,059 944 554 Census Region and Division Northeast.................................... 20.6 13.9 3,224 2,173 836 2,219 1,619 583 903 830 Q New England.......................... 5.5 3.6 3,365 2,154 313 2,634 1,826 Q 951 940 Q Middle Atlantic........................ 15.1 10.3 3,167 2,181 1,049 2,188 1,603 582 Q Q Q Midwest...................................... 25.6 21.0 2,823 2,239 1,624 2,356 1,669 1,336 1,081 961 778 East North

  16. Total............................................................

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

  17. Total..............................................................

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

    ,171 1,618 1,031 845 630 401 Census Region and Division Northeast................................................... 20.6 2,334 1,664 562 911 649 220 New England.......................................... 5.5 2,472 1,680 265 1,057 719 113 Middle Atlantic........................................ 15.1 2,284 1,658 670 864 627 254 Midwest...................................................... 25.6 2,421 1,927 1,360 981 781 551 East North Central.................................. 17.7 2,483 1,926 1,269

  18. Total...............................................................

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

    20.6 25.6 40.7 24.2 Personal Computers Do Not Use a Personal Computer ........... 35.5 6.9 8.1 14.2 6.4 Use a Personal Computer......................... 75.6 13.7 17.5 26.6 17.8 Number of Desktop PCs 1.......................................................... 50.3 9.3 11.9 18.2 11.0 2.......................................................... 16.2 2.9 3.5 5.5 4.4 3 or More............................................. 9.0 1.5 2.1 2.9 2.5 Number of Laptop PCs

  19. Total...............................................................

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

    0.7 21.7 6.9 12.1 Personal Computers Do Not Use a Personal Computer ........... 35.5 14.2 7.2 2.8 4.2 Use a Personal Computer......................... 75.6 26.6 14.5 4.1 7.9 Number of Desktop PCs 1.......................................................... 50.3 18.2 10.0 2.9 5.3 2.......................................................... 16.2 5.5 3.0 0.7 1.8 3 or More............................................. 9.0 2.9 1.5 0.5 0.8 Number of Laptop PCs

  20. Total...............................................................

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

    26.7 28.8 20.6 13.1 22.0 16.6 38.6 Personal Computers Do Not Use a Personal Computer ........... 35.5 17.1 10.8 4.2 1.8 1.6 10.3 20.6 Use a Personal Computer......................... 75.6 9.6 18.0 16.4 11.3 20.3 6.4 17.9 Number of Desktop PCs 1.......................................................... 50.3 8.3 14.2 11.4 7.2 9.2 5.3 14.2 2.......................................................... 16.2 0.9 2.6 3.7 2.9 6.2 0.8 2.6 3 or More............................................. 9.0 0.4 1.2

  1. Total...............................................................

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

    47.1 19.0 22.7 22.3 Personal Computers Do Not Use a Personal Computer ........... 35.5 16.9 6.5 4.6 7.6 Use a Personal Computer......................... 75.6 30.3 12.5 18.1 14.7 Number of Desktop PCs 1.......................................................... 50.3 21.1 8.3 10.7 10.1 2.......................................................... 16.2 6.2 2.8 4.1 3.0 3 or More............................................. 9.0 2.9 1.4 3.2 1.6 Number of Laptop PCs

  2. Total.................................................................

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

    49.2 15.1 15.6 11.1 7.0 5.2 8.0 Have Cooling Equipment............................... 93.3 31.3 15.1 15.6 11.1 7.0 5.2 8.0 Use Cooling Equipment................................ 91.4 30.4 14.6 15.4 11.1 6.9 5.2 7.9 Have Equipment But Do Not Use it............... 1.9 1.0 0.5 Q Q Q Q Q Do Not Have Cooling Equipment................... 17.8 17.8 N N N N N N Air-Conditioning Equipment 1, 2 Central System............................................. 65.9 3.9 15.1 15.6 11.1 7.0 5.2 8.0 Without a Heat

  3. Total.................................................................

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

    14.7 7.4 12.5 12.5 18.9 18.6 17.3 9.2 Do Not Have Space Heating Equipment........ 1.2 N Q Q 0.2 0.4 0.2 0.2 Q Have Main Space Heating Equipment........... 109.8 14.7 7.4 12.4 12.2 18.5 18.3 17.1 9.2 Use Main Space Heating Equipment............. 109.1 14.6 7.3 12.4 12.2 18.2 18.2 17.1 9.1 Have Equipment But Do Not Use It............... 0.8 Q Q Q Q 0.3 Q N Q Main Heating Fuel and Equipment Natural Gas................................................... 58.2 9.2 4.9 7.8 7.1 8.8 8.4 7.8 4.2 Central

  4. Total...................................................................

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

    15.2 7.8 1.0 1.2 3.3 1.9 For Two Housing Units............................. 0.9 Q N Q 0.6 N Heat Pump.................................................. 9.2 7.4 0.3 Q 0.7 0.5 Portable Electric Heater............................... 1.6 0.8 Q Q Q 0.3 Other Equipment......................................... 1.9 0.7 Q Q 0.7 Q Fuel Oil........................................................... 7.7 5.5 0.4 0.8 0.9 0.2 Steam or Hot Water System........................ 4.7 2.9 Q 0.7 0.8 N For One Housing

  5. Total...................................................................

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

    Air-Conditioning Equipment 1, 2 Central System............................................... 65.9 47.5 4.0 2.8 7.9 3.7 Without a Heat Pump.................................. 53.5 37.8 3.4 2.2 7.0 3.1 With a Heat Pump....................................... 12.3 9.7 0.6 0.5 1.0 0.6 Window/Wall Units.......................................... 28.9 14.9 2.3 3.5 6.0 2.1 1 Unit........................................................... 14.5 6.6 1.0 1.6 4.2 1.2 2

  6. Total.......................................................................

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

    0.6 15.1 5.5 Personal Computers Do Not Use a Personal Computer ................... 35.5 6.9 5.3 1.6 Use a Personal Computer................................ 75.6 13.7 9.8 3.9 Number of Desktop PCs 1.................................................................. 50.3 9.3 6.8 2.5 2.................................................................. 16.2 2.9 1.9 1.0 3 or More..................................................... 9.0 1.5 1.1 0.4 Number of Laptop PCs

  7. Total.......................................................................

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

    5.6 17.7 7.9 Personal Computers Do Not Use a Personal Computer ................... 35.5 8.1 5.6 2.5 Use a Personal Computer................................ 75.6 17.5 12.1 5.4 Number of Desktop PCs 1.................................................................. 50.3 11.9 8.4 3.4 2.................................................................. 16.2 3.5 2.2 1.3 3 or More..................................................... 9.0 2.1 1.5 0.6 Number of Laptop PCs

  8. Total.......................................................................

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

    4.2 7.6 16.6 Personal Computers Do Not Use a Personal Computer ................... 35.5 6.4 2.2 4.2 Use a Personal Computer................................ 75.6 17.8 5.3 12.5 Number of Desktop PCs 1.................................................................. 50.3 11.0 3.4 7.6 2.................................................................. 16.2 4.4 1.3 3.1 3 or More..................................................... 9.0 2.5 0.7 1.8 Number of Laptop PCs

  9. Total........................................................................

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

    25.6 40.7 24.2 Do Not Have Space Heating Equipment............... 1.2 Q Q Q 0.7 Have Main Space Heating Equipment.................. 109.8 20.5 25.6 40.3 23.4 Use Main Space Heating Equipment.................... 109.1 20.5 25.6 40.1 22.9 Have Equipment But Do Not Use It...................... 0.8 N N Q 0.6 Main Heating Fuel and Equipment Natural Gas.......................................................... 58.2 11.4 18.4 13.6 14.7 Central Warm-Air Furnace................................ 44.7 6.1

  10. Total........................................................................

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

    5.6 17.7 7.9 Do Not Have Space Heating Equipment............... 1.2 Q Q N Have Main Space Heating Equipment.................. 109.8 25.6 17.7 7.9 Use Main Space Heating Equipment.................... 109.1 25.6 17.7 7.9 Have Equipment But Do Not Use It...................... 0.8 N N N Main Heating Fuel and Equipment Natural Gas.......................................................... 58.2 18.4 13.1 5.3 Central Warm-Air Furnace................................ 44.7 16.2 11.6 4.7 For One Housing

  11. Total........................................................................

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

    0.7 21.7 6.9 12.1 Do Not Have Space Heating Equipment............... 1.2 Q Q N Q Have Main Space Heating Equipment.................. 109.8 40.3 21.4 6.9 12.0 Use Main Space Heating Equipment.................... 109.1 40.1 21.2 6.9 12.0 Have Equipment But Do Not Use It...................... 0.8 Q Q N N Main Heating Fuel and Equipment Natural Gas.......................................................... 58.2 13.6 5.6 2.3 5.7 Central Warm-Air Furnace................................ 44.7 11.0 4.4

  12. Total........................................................................

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

    7.1 7.0 8.0 12.1 Do Not Have Space Heating Equipment............... 1.2 Q Q Q 0.2 Have Main Space Heating Equipment.................. 109.8 7.1 6.8 7.9 11.9 Use Main Space Heating Equipment.................... 109.1 7.1 6.6 7.9 11.4 Have Equipment But Do Not Use It...................... 0.8 N Q N 0.5 Main Heating Fuel and Equipment Natural Gas.......................................................... 58.2 3.8 0.4 3.8 8.4 Central Warm-Air Furnace................................ 44.7 1.8 Q 3.1 6.0

  13. Total...........................................................................

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

    0.6 15.1 5.5 Do Not Have Cooling Equipment............................. 17.8 4.0 2.4 1.7 Have Cooling Equipment.......................................... 93.3 16.5 12.8 3.8 Use Cooling Equipment........................................... 91.4 16.3 12.6 3.7 Have Equipment But Do Not Use it.......................... 1.9 0.3 Q Q Air-Conditioning Equipment 1, 2 Central System........................................................ 65.9 6.0 5.2 0.8 Without a Heat

  14. Total...........................................................................

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

    5.6 17.7 7.9 Do Not Have Cooling Equipment............................. 17.8 2.1 1.8 0.3 Have Cooling Equipment.......................................... 93.3 23.5 16.0 7.5 Use Cooling Equipment........................................... 91.4 23.4 15.9 7.5 Have Equipment But Do Not Use it.......................... 1.9 Q Q Q Air-Conditioning Equipment 1, 2 Central System........................................................ 65.9 17.3 11.3 6.0 Without a Heat

  15. Total...........................................................................

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

    4.2 7.6 16.6 Do Not Have Cooling Equipment............................. 17.8 10.3 3.1 7.3 Have Cooling Equipment.......................................... 93.3 13.9 4.5 9.4 Use Cooling Equipment........................................... 91.4 12.9 4.3 8.5 Have Equipment But Do Not Use it.......................... 1.9 1.0 Q 0.8 Air-Conditioning Equipment 1, 2 Central System........................................................ 65.9 10.5 3.9 6.5 Without a Heat

  16. Total.............................................................................

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

    Cooking Appliances Frequency of Hot Meals Cooked 3 or More Times A Day......................................... 8.2 1.2 1.0 0.2 2 Times A Day...................................................... 24.6 4.0 2.7 1.2 Once a Day........................................................... 42.3 7.9 5.4 2.5 A Few Times Each Week...................................... 27.2 6.0 4.8 1.2 About Once a Week.............................................. 3.9 0.6 0.5 Q Less Than Once a

  17. Total.............................................................................

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

    Cooking Appliances Frequency of Hot Meals Cooked 3 or More Times A Day......................................... 8.2 1.4 1.0 0.4 2 Times A Day...................................................... 24.6 5.8 3.5 2.3 Once a Day........................................................... 42.3 10.7 7.8 2.9 A Few Times Each Week...................................... 27.2 5.6 4.0 1.6 About Once a Week.............................................. 3.9 0.9 0.6 0.3 Less Than Once a

  18. Total.............................................................................

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

    Cooking Appliances Frequency of Hot Meals Cooked 3 or More Times A Day......................................... 8.2 2.6 0.7 1.9 2 Times A Day...................................................... 24.6 6.6 2.0 4.6 Once a Day........................................................... 42.3 8.8 2.9 5.8 A Few Times Each Week...................................... 27.2 4.7 1.5 3.1 About Once a Week.............................................. 3.9 0.7 Q 0.6 Less Than Once a

  19. Total..............................................................................

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

    20.6 25.6 40.7 24.2 Do Not Have Cooling Equipment................................ 17.8 4.0 2.1 1.4 10.3 Have Cooling Equipment............................................. 93.3 16.5 23.5 39.3 13.9 Use Cooling Equipment.............................................. 91.4 16.3 23.4 38.9 12.9 Have Equipment But Do Not Use it............................. 1.9 0.3 Q 0.5 1.0 Air-Conditioning Equipment 1, 2 Central System........................................................... 65.9 6.0 17.3 32.1 10.5

  20. Total..............................................................................

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

    0.7 21.7 6.9 12.1 Do Not Have Cooling Equipment................................ 17.8 1.4 0.8 0.2 0.3 Have Cooling Equipment............................................. 93.3 39.3 20.9 6.7 11.8 Use Cooling Equipment.............................................. 91.4 38.9 20.7 6.6 11.7 Have Equipment But Do Not Use it............................. 1.9 0.5 Q Q Q Air-Conditioning Equipment 1, 2 Central System........................................................... 65.9 32.1 17.6 5.2 9.3 Without a

  1. Total..............................................................................

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

    111.1 7.1 7.0 8.0 12.1 Personal Computers Do Not Use a Personal Computer .......................... 35.5 3.0 2.0 2.7 3.1 Use a Personal Computer....................................... 75.6 4.2 5.0 5.3 9.0 Number of Desktop PCs 1......................................................................... 50.3 3.1 3.4 3.4 5.4 2......................................................................... 16.2 0.7 1.1 1.2 2.2 3 or More............................................................ 9.0 0.3

  2. Total..............................................................................

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

    7.1 19.0 22.7 22.3 Do Not Have Cooling Equipment................................ 17.8 8.5 2.7 2.6 4.0 Have Cooling Equipment............................................. 93.3 38.6 16.2 20.1 18.4 Use Cooling Equipment.............................................. 91.4 37.8 15.9 19.8 18.0 Have Equipment But Do Not Use it............................. 1.9 0.9 0.3 0.3 0.4 Air-Conditioning Equipment 1, 2 Central System........................................................... 65.9 25.8 10.9 16.6 12.5

  3. Total....................................................................................

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

    Cooking Appliances Frequency of Hot Meals Cooked 3 or More Times A Day................................................. 8.2 3.0 1.6 0.3 1.1 2 Times A Day.............................................................. 24.6 8.3 4.2 1.3 2.7 Once a Day................................................................... 42.3 15.0 8.1 2.7 4.2 A Few Times Each Week............................................. 27.2 10.9 6.0 1.8 3.1 About Once a Week..................................................... 3.9

  4. Total....................................................................................

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

    Cooking Appliances Frequency of Hot Meals Cooked 3 or More Times A Day................................................. 8.2 3.7 1.6 1.4 1.5 2 Times A Day.............................................................. 24.6 10.8 4.1 4.3 5.5 Once a Day................................................................... 42.3 17.0 7.2 8.7 9.3 A Few Times Each Week............................................. 27.2 11.4 4.7 6.4 4.8 About Once a Week.....................................................

  5. Total..........................................................

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

    ... Basements Basement in Single-Family Homes and Apartments in 2-4 Unit Buildings ... Attics Attic in Single-Family Homes and Apartments in 2-4 Unit Buildings ...

  6. Total

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

    ... Climate region 3 Very coldCold 31,898 30,469 28,057 28,228 21,019 30,542 25,067 Mixed-humid 27,873 26,716 24,044 26,365 21,026 27,096 22,812 Mixed-dryHot-dry 12,037 10,484 7,628 ...

  7. Total..........................................................

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

    Air-Conditioning Equipment 1, 2 Central System......Central Air-Conditioning...... 65.9 1.1 6.4 6.4 ...

  8. Total..........................................................

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

    Income Relative to Poverty Line Below 100 Percent......1.3 1.2 0.8 0.4 1. Below 150 percent of poverty line or 60 percent of median State ...

  9. Total..........................................................

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

    ... Below Poverty Line Eligible for Federal Assistance 1 80,000 or More 60,000 to 79,999 ... Below Poverty Line Eligible for Federal Assistance 1 80,000 or More 60,000 to 79,999 ...

  10. Total..........................................................

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

    ... Table HC7.4 Space Heating Characteristics by Household Income, 2005 Below Poverty Line ... Below Poverty Line Eligible for Federal Assistance 1 80,000 or More Space Heating ...

  11. Total..........................................................

    Gasoline and Diesel Fuel Update (EIA)

    ... Table HC7.7 Air-Conditioning Usage Indicators by Household Income, 2005 Below Poverty Line ... Table HC7.7 Air-Conditioning Usage Indicators by Household Income, 2005 Below Poverty Line ...

  12. Total..........................................................

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

    ... Living Space Characteristics Below Poverty Line Eligible for Federal Assistance 1 Million ... Living Space Characteristics Below Poverty Line Eligible for Federal Assistance 1 Million ...

  13. Total..........................................................

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

    ... Table HC7.12 Home Electronics Usage Indicators by Household Income, 2005 Below Poverty ... Table HC7.12 Home Electronics Usage Indicators by Household Income, 2005 Below Poverty ...

  14. Total..........................................................

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

    ... Table HC7.10 Home Appliances Usage Indicators by Household Income, 2005 Below Poverty Line ... Below Poverty Line Eligible for Federal Assistance 1 40,000 to 59,999 60,000 to 79,999 ...

  15. Total

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

    1,001 to 5,000 2,777 8,041 10,232 2.9 786 56 5,001 to 10,000 1,229 8,900 9,225 7.2 965 62 10,001 to 25,000 884 14,105 14,189 16.0 994 65 25,001 to 50,000 332 11,917 11,327 35.9 1,052 72 50,001 to 100,000 199 13,918 12,345 69.9 1,127 80 100,001 to 200,000 90 12,415 11,310 137.9 1,098 89 200,001 to 500,000 38 10,724 10,356 284.2 1,035 99 Over 500,000 8 7,074 9,196 885.0 769 117 Principal building activity Education 389 12,239 10,885 31.5 1,124 53 Food sales 177 1,252 1,172 7.1 1,067 121 Food

  16. Total

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

    1,001 to 5,000 2,777 8,041 10,232 2.9 786 56 5,001 to 10,000 1,229 8,900 9,225 7.2 965 62 10,001 to 25,000 884 14,105 14,189 16.0 994 65 25,001 to 50,000 332 11,917 11,327 35.9 1,052 72 50,001 to 100,000 199 13,918 12,345 69.9 1,127 80 100,001 to 200,000 90 12,415 11,310 137.9 1,098 89 200,001 to 500,000 38 10,724 10,356 284.2 1,035 99 Over 500,000 8 7,074 9,196 885.0 769 117 Principal building activity Education 389 12,239 10,885 31.5 1,124 53 Food sales 177 1,252 1,172 7.1 1,067 121 Food

  17. Total

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

    1,001 to 5,000 2,777 8,041 10,232 2.9 786 56 5,001 to 10,000 1,229 8,900 9,225 7.2 965 62 10,001 to 25,000 884 14,105 14,189 16.0 994 65 25,001 to 50,000 332 11,917 11,327 35.9 1,052 72 50,001 to 100,000 199 13,918 12,345 69.9 1,127 80 100,001 to 200,000 90 12,415 11,310 137.9 1,098 89 200,001 to 500,000 38 10,724 10,356 284.2 1,035 99 Over 500,000 8 7,074 9,196 885.0 769 117 Principal building activity Education 389 12,239 10,885 31.5 1,124 53 Food sales 177 1,252 1,172 7.1 1,067 121 Food

  18. Total

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

    Median square feet per building (thousand) Median square feet per worker Median operating hours per week Median age of buildings (years) All buildings 5,557 87,093 88,182 5.0 1,029 50 32 Building floorspace (square feet) 1,001 to 5,000 2,777 8,041 10,232 2.8 821 49 37 5,001 to 10,000 1,229 8,900 9,225 7.0 1,167 50 31 10,001 to 25,000 884 14,105 14,189 15.0 1,444 56 32 25,001 to 50,000 332 11,917 11,327 35.0 1,461 60 29 50,001 to 100,000 199 13,918 12,345 67.0 1,442 60 26 100,001 to 200,000 90

  19. Total..........................................................

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

    ... Housing Units (millions) UrbanRural Location (as Self-Reported) Living Space ... Housing Units (millions) UrbanRural Location (as Self-Reported) Living Space ...

  20. Total..........................................................

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

    ... Housing Units (millions) UrbanRural Location (as Self-Reported) City Town Suburbs Rural ... Housing Units (millions) UrbanRural Location (as Self-Reported) City Town Suburbs Rural ...

  1. Total..........................................................

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

    ... 41.8 2,603 2,199 1,654 941 795 598 1-Car Garage...... 9.5 2,064 1,664 1,039 775 624 390 2-Car Garage......

  2. Total..........................................................

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

    ... Average Square Feet per Apartment in a -- Apartments (millions) Major Outside Wall Construction Siding (Aluminum, Vinyl, Steel)...... 35.3 3.5 1,286 1,090 325 852 786 461 ...

  3. Total..........................................................

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

    Living Space Characteristics Detached Attached Energy Information Administration 2005 Residential Energy Consumption Survey: Preliminary Housing Characteristics Tables Table HC3.2 ...

  4. Total..........................................................

    Gasoline and Diesel Fuel Update (EIA)

    Table HC4.2 Living Space Characteristics by Renter-Occupied Housing Units, 2005 2 to 4 Units 5 or More Units Mobile Homes Energy Information Administration 2005 Residential Energy ...

  5. Total..........................................................

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

    ... Per Household Member Average Square Feet Energy Information Administration 2005 Residential Energy Consumption Survey: Preliminary Housing Characteristics Tables Table HC1.2.2 ...

  6. Total..........................................................

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

    ... 111.1 20.6 15.1 5.5 Do Not Have Cooling Equipment...... 17.8 4.0 2.4 1.7 Have Cooling Equipment...... 93.3 ...

  7. Physisorption and Chemisorption Methods for Evaluating the Total Surface

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Area and Active Surface Area of Two Types of Carbon Materials | Department of Energy Physisorption and Chemisorption Methods for Evaluating the Total Surface Area and Active Surface Area of Two Types of Carbon Materials Physisorption and Chemisorption Methods for Evaluating the Total Surface Area and Active Surface Area of Two Types of Carbon Materials TSA is a gross indicator of soot reactivity and does not always correlate well with the real reactivity. This research shows that a more

  8. Property:Number of Plants included in Capacity Estimate | Open...

    Open Energy Info (EERE)

    Plants included in Capacity Estimate Jump to: navigation, search Property Name Number of Plants included in Capacity Estimate Property Type Number Retrieved from "http:...

  9. Property:Number of Plants Included in Planned Estimate | Open...

    Open Energy Info (EERE)

    Number of Plants Included in Planned Estimate Jump to: navigation, search Property Name Number of Plants Included in Planned Estimate Property Type String Description Number of...

  10. Table A10. Total Inputs of Energy for Heat, Power, and Electricity Generatio

    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 Btu)" ,,,,,"Distillate",,,"Coal" ,,,,,"Fuel Oil",,,"(excluding",,"RSE" "SIC",,,"Net","Residual","and Diesel",,,"Coal Coke",,"Row" "Code(a)","End-Use

  11. Apparatus and method for quantitatively evaluating total fissile and total fertile nuclide content in samples

    DOE Patents [OSTI]

    Caldwell, John T.; Kunz, Walter E.; Cates, Michael R.; Franks, Larry A.

    1985-01-01

    Simultaneous photon and neutron interrogation of samples for the quantitative determination of total fissile nuclide and total fertile nuclide material present is made possible by the use of an electron accelerator. Prompt and delayed neutrons produced from resulting induced fissions are counted using a single detection system and allow the resolution of the contributions from each interrogating flux leading in turn to the quantitative determination sought. Detection limits for .sup.239 Pu are estimated to be about 3 mg using prompt fission neutrons and about 6 mg using delayed neutrons.

  12. Property:EstimatedCostLowUSD | Open Energy Information

    Open Energy Info (EERE)

    Name EstimatedCostLowUSD Property Type Quantity Description the low estimate of cost in USD Use this type to express a monetary value in US Dollars. The default unit is one...

  13. Property:EstimatedCostHighUSD | Open Energy Information

    Open Energy Info (EERE)

    Name EstimatedCostHighUSD Property Type Quantity Description the high estimate of cost in USD Use this type to express a monetary value in US Dollars. The default unit is one...

  14. Property:EstimatedCostMedianUSD | Open Energy Information

    Open Energy Info (EERE)

    Name EstimatedCostMedianUSD Property Type Quantity Description the median estimate of cost in USD Use this type to express a monetary value in US Dollars. The default unit is one...

  15. Characteristics RSE Column Factor: Total

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

    and 1994 Vehicle Characteristics RSE Column Factor: Total 1993 Family Income Below Poverty Line Eli- gible for Fed- eral Assist- ance 1 RSE Row Factor: Less than 5,000 5,000...

  16. Property:RenewableFuelStandard/Total | Open Energy Information

    Open Energy Info (EERE)

    Property Edit with form History Facebook icon Twitter icon Property:RenewableFuelStandardTotal Jump to: navigation, search This is a property of type Number. Pages using the...

  17. ARM - Measurement - Total cloud water

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

    cloud water ARM Data Discovery Browse Data Comments? We would love to hear from you! Send us a note below or call us at 1-888-ARM-DATA. Send Measurement : Total cloud water The total concentration (mass/vol) of ice and liquid water particles in a cloud; this includes condensed water content (CWC). Categories Cloud Properties Instruments The above measurement is considered scientifically relevant for the following instruments. Refer to the datastream (netcdf) file headers of each instrument for a

  18. Estimates of Green potentials. Applications

    SciTech Connect (OSTI)

    Danchenko, V I

    2003-02-28

    Optimal Cartan-type covers by hyperbolic discs of carriers of Green {alpha}-potentials are obtained in a simply connected domain in the complex plane and estimates of the potentials outside the carriers are presented. These results are applied to problems on the separation of singularities of analytic and harmonic functions. For instance, uniform and integral estimates in terms of Green capacities of components of meromorphic functions are obtained.

  19. Award Number: Federal Non-Federal Federal Non-Federal Total

    Office of Environmental Management (EM)

    B - Budget Categories Catalog of Federal Domestic Assistance Number Grant Program Function or Activity Estimated Unobligated Funds e. Supplies i. Total Direct Charges (sum of...

  20. Total aerosol effect: forcing or radiative flux perturbation?

    SciTech Connect (OSTI)

    Lohmann, Ulrike; Storelvmo, Trude; Jones, Andy; Rotstayn, Leon; Menon, Surabi; Quaas, Johannes; Ekman, Annica; Koch, Dorothy; Ruedy, Reto

    2009-09-25

    Uncertainties in aerosol forcings, especially those associated with clouds, contribute to a large extent to uncertainties in the total anthropogenic forcing. The interaction of aerosols with clouds and radiation introduces feedbacks which can affect the rate of rain formation. Traditionally these feedbacks were not included in estimates of total aerosol forcing. Here we argue that they should be included because these feedbacks act quickly compared with the time scale of global warming. We show that for different forcing agents (aerosols and greenhouse gases) the radiative forcings as traditionally defined agree rather well with estimates from a method, here referred to as radiative flux perturbations (RFP), that takes these fast feedbacks and interactions into account. Thus we propose replacing the direct and indirect aerosol forcing in the IPCC forcing chart with RFP estimates. This implies that it is better to evaluate the total anthropogenic aerosol effect as a whole.

  1. Cost estimate for muddy water palladium production facility at Mound

    SciTech Connect (OSTI)

    McAdams, R.K.

    1988-11-30

    An economic feasibility study was performed on the ''Muddy Water'' low-chlorine content palladium powder production process developed by Mound. The total capital investment and total operating costs (dollars per gram) were determined for production batch sizes of 1--10 kg in 1-kg increments. The report includes a brief description of the Muddy Water process, the process flow diagram, and material balances for the various production batch sizes. Two types of facilities were evaluated--one for production of new, ''virgin'' palladium powder, and one for recycling existing material. The total capital investment for virgin facilities ranged from $600,000 --$1.3 million for production batch sizes of 1--10 kg, respectively. The range for recycle facilities was $1--$2.3 million. The total operating cost for 100% acceptable powder production in the virgin facilities ranged from $23 per gram for a 1-kg production batch size to $8 per gram for a 10-kg batch size. Similarly for recycle facilities, the total operating cost ranged from $34 per gram to $5 per gram. The total operating cost versus product acceptability (ranging from 50%--100% acceptability) was also evaluated for both virgin and recycle facilities. Because production sizes studied vary widely and because scale-up factors are unknown for batch sizes greater than 1 kg, all costs are ''order-of-magnitude'' estimates. All costs reported are in 1987 dollars.

  2. CATEGORY Total Procurement Total Small Business Small Disadvantaged

    National Nuclear Security Administration (NNSA)

    CATEGORY Total Procurement Total Small Business Small Disadvantaged Business Woman Owned Small Business HubZone Small Business Veteran-Owned Small Business Service Disabled Veteran Owned Small Business FY 2013 Dollars Accomplished $1,049,087,940 $562,676,028 $136,485,766 $106,515,229 $12,080,258 $63,473,852 $28,080,960 FY 2013 % Accomplishment 54.40% 13.00% 10.20% 1.20% 6.60% 2.70% FY 2014 Dollars Accomplished $868,961,755 $443,711,175 $92,478,522 $88,633,031 $29,867,820 $43,719,452 $26,826,374

  3. Airborne aerosol in situ measurements during TCAP: A closure study of total scattering

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Kassianov, Evgueni; Sedlacek, Arthur; Berg, Larry K.; Pekour, Mikhail; Barnard, James; Chand, Duli; Flynn, Connor; Ovchinnikov, Mikhail; Schmid, Beat; Shilling, John; et al

    2015-07-31

    We present a framework for calculating the total scattering of both non-absorbing and absorbing aerosol at ambient conditions from aircraft data. Our framework is developed emphasizing the explicit use of chemical composition data for estimating the complex refractive index (RI) of particles, and thus obtaining improved ambient size spectra derived from Optical Particle Counter (OPC) measurements. The feasibility of our framework for improved calculations of total scattering is demonstrated using three types of data collected by the U.S. Department of Energy’s (DOE) aircraft during the Two-Column Aerosol Project (TCAP). Namely, these data types are: (1) size distributions measured by amore » suite of OPC’s; (2) chemical composition data measured by an Aerosol Mass Spectrometer and a Single Particle Soot Photometer; and (3) the dry total scattering coefficient measured by a integrating nephelometer and scattering enhancement factor measured with a humidification system. We demonstrate that good agreement (~10%) between the observed and calculated scattering can be obtained under ambient conditions (RH < 80%) by applying chemical composition data for the RI-based correction of the OPC-derived size spectra. We also demonstrate that ignoring the RI-based correction or using non-representative RI values can cause a substantial underestimation (~40%) or overestimation (~35%) of the calculated scattering, respectively.« less

  4. Airborne aerosol in situ measurements during TCAP: A closure study of total scattering

    SciTech Connect (OSTI)

    Kassianov, Evgueni; Sedlacek, Arthur; Berg, Larry K.; Pekour, Mikhail; Barnard, James; Chand, Duli; Flynn, Connor; Ovchinnikov, Mikhail; Schmid, Beat; Shilling, John; Tomlinson, Jason; Fast, Jerome

    2015-07-31

    We present a framework for calculating the total scattering of both non-absorbing and absorbing aerosol at ambient conditions from aircraft data. Our framework is developed emphasizing the explicit use of chemical composition data for estimating the complex refractive index (RI) of particles, and thus obtaining improved ambient size spectra derived from Optical Particle Counter (OPC) measurements. The feasibility of our framework for improved calculations of total scattering is demonstrated using three types of data collected by the U.S. Department of Energy’s (DOE) aircraft during the Two-Column Aerosol Project (TCAP). Namely, these data types are: (1) size distributions measured by a suite of OPC’s; (2) chemical composition data measured by an Aerosol Mass Spectrometer and a Single Particle Soot Photometer; and (3) the dry total scattering coefficient measured by a integrating nephelometer and scattering enhancement factor measured with a humidification system. We demonstrate that good agreement (~10%) between the observed and calculated scattering can be obtained under ambient conditions (RH < 80%) by applying chemical composition data for the RI-based correction of the OPC-derived size spectra. We also demonstrate that ignoring the RI-based correction or using non-representative RI values can cause a substantial underestimation (~40%) or overestimation (~35%) of the calculated scattering, respectively.

  5. Million Cu. Feet Percent of National Total

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

    0 New Hampshire - Natural Gas 2014 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle ...

  6. Total Number of Operable Refineries

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

    Data Series: Total Number of Operable Refineries Number of Operating Refineries Number of Idle Refineries Atmospheric Crude Oil Distillation Operable Capacity (B/CD) Atmospheric Crude Oil Distillation Operating Capacity (B/CD) Atmospheric Crude Oil Distillation Idle Capacity (B/CD) Atmospheric Crude Oil Distillation Operable Capacity (B/SD) Atmospheric Crude Oil Distillation Operating Capacity (B/SD) Atmospheric Crude Oil Distillation Idle Capacity (B/SD) Vacuum Distillation Downstream Charge

  7. Development of surface mine cost estimating equations

    SciTech Connect (OSTI)

    Not Available

    1980-09-26

    Cost estimating equations were developed to determine capital and operating costs for five surface coal mine models in Central Appalachia, Northern Appalachia, Mid-West, Far-West, and Campbell County, Wyoming. Engineering equations were used to estimate equipment costs for the stripping function and for the coal loading and hauling function for the base case mine and for several mines with different annual production levels and/or different overburden removal requirements. Deferred costs were then determined through application of the base case depreciation schedules, and direct labor costs were easily established once the equipment quantities (and, hence, manpower requirements) were determined. The data points were then fit with appropriate functional forms, and these were then multiplied by appropriate adjustment factors so that the resulting equations yielded the model mine costs for initial and deferred capital and annual operating cost. (The validity of this scaling process is based on the assumption that total initial and deferred capital costs are proportional to the initial and deferred costs for the primary equipment types that were considered and that annual operating cost is proportional to the direct labor costs that were determined based on primary equipment quantities.) Initial capital costs ranged from $3,910,470 in Central Appalachia to $49,296,785; deferred capital costs ranged from $3,220,000 in Central Appalachia to $30,735,000 in Campbell County, Wyoming; and annual operating costs ranged from $2,924,148 in Central Appalachia to $32,708,591 in Campbell County, Wyoming. (DMC)

  8. Design Storm for Total Retention.pdf

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

    Title: Design Storm for "Total Retention" under Individual Permit, Poster, Individual ... International. Environmental Programs Design Storm for "Total Retention" under ...

  9. U.S. Department of Energy Releases Revised Total System Life Cycle Cost

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Estimate and Fee Adequacy Report for Yucca Mountain Project | Department of Energy Revised Total System Life Cycle Cost Estimate and Fee Adequacy Report for Yucca Mountain Project U.S. Department of Energy Releases Revised Total System Life Cycle Cost Estimate and Fee Adequacy Report for Yucca Mountain Project August 5, 2008 - 2:40pm Addthis WASHINGTON, DC -The U.S. Department of Energy (DOE) today released a revised estimate of the total system life cycle cost for a repository at Yucca

  10. U.S. Total Imports

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

    St. Clair, MI International Falls, MN Noyes, MN Warroad, MN Babb, MT Havre, MT Port of Del Bonita, MT Port of Morgan, MT Sweetgrass, MT Whitlash, MT Portal, ND Sherwood, ND Pittsburg, NH Champlain, NY Grand Island, NY Massena, NY Niagara Falls, NY Waddington, NY Sumas, WA Highgate Springs, VT North Troy, VT U.S. Pipeline Total from Mexico Ogilby, CA Otay Mesa, CA Alamo, TX El Paso, TX Galvan Ranch, TX Hidalgo, TX McAllen, TX Penitas, TX LNG Imports from Algeria Cove Point, MD Everett, MA Lake

  11. Total quality management implementation guidelines

    SciTech Connect (OSTI)

    Not Available

    1993-12-01

    These Guidelines were designed by the Energy Quality Council to help managers and supervisors in the Department of Energy Complex bring Total Quality Management to their organizations. Because the Department is composed of a rich mixture of diverse organizations, each with its own distinctive culture and quality history, these Guidelines are intended to be adapted by users to meet the particular needs of their organizations. For example, for organizations that are well along on their quality journeys and may already have achieved quality results, these Guidelines will provide a consistent methodology and terminology reference to foster their alignment with the overall Energy quality initiative. For organizations that are just beginning their quality journeys, these Guidelines will serve as a startup manual on quality principles applied in the Energy context.

  12. Total Imports of Residual Fuel

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

    Jan-16 Feb-16 Mar-16 Apr-16 May-16 Jun-16 View History U.S. Total 9,010 5,030 8,596 6,340 4,707 8,092 1936-2016 PAD District 1 3,127 2,664 2,694 1,250 1,327 2,980 1981-2016 Connecticut 1995-2015 Delaware 280 1995-2016 Florida 858 649 800 200 531 499 1995-2016 Georgia 210 262 149 106 1995-2016 Maine 1995-2015 Maryland 84 1995-2016 Massachusetts 1995-2015 New Hampshire 1995-2015 New Jersey 1,283 843 1,073 734 355 1,984 1995-2016 New York 234 824 210 196 175 1995-2016 North Carolina 1995-2011

  13. Total Imports of Residual Fuel

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

    2010 2011 2012 2013 2014 2015 View History U.S. Total 133,646 119,888 93,672 82,173 63,294 68,265 1936-2015 PAD District 1 88,999 79,188 59,594 33,566 30,944 33,789 1981-2015 Connecticut 220 129 1995-2015 Delaware 748 1,704 510 1,604 2,479 1995-2015 Florida 15,713 11,654 10,589 8,331 5,055 7,013 1995-2015 Georgia 5,648 7,668 6,370 4,038 2,037 1,629 1995-2015 Maine 1,304 651 419 75 317 135 1995-2015 Maryland 3,638 1,779 1,238 433 938 539 1995-2015 Massachusetts 123 50 78 542 88 1995-2015 New

  14. Total Adjusted Sales of Kerosene

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

    End Use: Total Residential Commercial Industrial Farm All Other Period: Annual Download Series History Download Series History Definitions, Sources & Notes Definitions, Sources & Notes Show Data By: End Use Area 2009 2010 2011 2012 2013 2014 View History U.S. 269,010 305,508 187,656 81,102 79,674 137,928 1984-2014 East Coast (PADD 1) 198,762 237,397 142,189 63,075 61,327 106,995 1984-2014 New England (PADD 1A) 56,661 53,363 38,448 15,983 15,991 27,500 1984-2014 Connecticut 8,800 7,437

  15. Total quality management program planning

    SciTech Connect (OSTI)

    Thornton, P.T.; Spence, K.

    1994-05-01

    As government funding grows scarce, competition between the national laboratories is increasing dramatically. In this era of tougher competition, there is no for resistance to change. There must instead be a uniform commitment to improving the overall quality of our products (research and technology) and an increased focus on our customers` needs. There has been an ongoing effort to bring the principles of total quality management (TQM) to all Energy Systems employees to help them better prepare for future changes while responding to the pressures on federal budgets. The need exists for instituting a vigorous program of education and training to an understanding of the techniques needed to improve and initiate a change in organizational culture. The TQM facilitator is responsible for educating the work force on the benefits of self-managed work teams, designing a program of instruction for implementation, and thus getting TQM off the ground at the worker and first-line supervisory levels so that the benefits can flow back up. This program plan presents a conceptual model for TQM in the form of a hot air balloon. In this model, there are numerous factors which can individually and collectively impede the progress of TQM within the division and the Laboratory. When these factors are addressed and corrected, the benefits of TQM become more visible. As this occurs, it is hoped that workers and management alike will grasp the ``total quality`` concept as an acceptable agent for change and continual improvement. TQM can then rise to the occasion and take its rightful place as an integral and valid step in the Laboratory`s formula for survival.

  16. Estimating electron drift velocities in magnetron discharges...

    Office of Scientific and Technical Information (OSTI)

    Citation Details In-Document Search Title: Estimating ... OSTI Identifier: 1172974 Report Number(s): LBNL-5865E DOE Contract Number: DE-AC02-05CH11231 Resource Type: Journal ...

  17. State energy data report 1994: Consumption estimates

    SciTech Connect (OSTI)

    1996-10-01

    This document provides annual time series estimates of State-level energy consumption by major economic sector. The estimates are developed in the State Energy Data System (SEDS), operated by EIA. SEDS provides State energy consumption estimates to members of Congress, Federal and State agencies, and the general public, and provides the historical series needed for EIA`s energy models. Division is made for each energy type and end use sector. Nuclear electric power is included.

  18. Award Types

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

    Awards Team (505) 667-7824 Email Types of Awards The Awards Office, sponsored by the Technology Transfer Division and the Science and Technology Base Program Office, coordinates...

  19. Cost Estimation Package

    Broader source: Directives, Delegations, and Requirements [Office of Management (MA)]

    1997-03-28

    This chapter focuses on the components (or elements) of the cost estimation package and their documentation.

  20. Total Space Heating Water Heating Cook-

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

    Commercial Buildings Energy Consumption Survey: Energy End-Use Consumption Tables Total Space Heating Water Heating Cook- ing Other Total Space Heating Water Heating Cook- ing...

  1. Total Space Heating Water Heating Cook-

    Gasoline and Diesel Fuel Update (EIA)

    Released: September, 2008 Total Space Heating Water Heating Cook- ing Other Total Space Heating Water Heating Cook- ing Other All Buildings* ... 1,870 1,276...

  2. Total Space Heating Water Heating Cook-

    Gasoline and Diesel Fuel Update (EIA)

    Energy Consumption Survey: Energy End-Use Consumption Tables Total Space Heating Water Heating Cook- ing Other Total Space Heating Water Heating Cook- ing Other All...

  3. Total Space Heating Water Heating Cook-

    Gasoline and Diesel Fuel Update (EIA)

    Released: September, 2008 Total Space Heating Water Heating Cook- ing Other Total Space Heating Water Heating Cook- ing Other All Buildings* ... 1,602 1,397...

  4. Total Space Heating Water Heating Cook-

    Gasoline and Diesel Fuel Update (EIA)

    Released: September, 2008 Total Space Heating Water Heating Cook- ing Other Total Space Heating Water Heating Cook- ing Other All Buildings ... 2,037...

  5. ,"West Virginia Natural Gas Total Consumption (MMcf)"

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

    Data for" ,"Data 1","West Virginia Natural Gas Total Consumption ... AM" "Back to Contents","Data 1: West Virginia Natural Gas Total Consumption (MMcf)" ...

  6. ,"Total Crude Oil and Petroleum Products Exports"

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

    Data for" ,"Data 1","Total Crude Oil and Petroleum Products ... "Back to Contents","Data 1: Total Crude Oil and Petroleum Products Exports" ...

  7. ,"New Mexico Natural Gas Total Consumption (MMcf)"

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

    Data for" ,"Data 1","New Mexico Natural Gas Total Consumption ... AM" "Back to Contents","Data 1: New Mexico Natural Gas Total Consumption (MMcf)" ...

  8. ,"North Dakota Natural Gas Total Consumption (MMcf)"

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

    Data for" ,"Data 1","North Dakota Natural Gas Total Consumption ... 9:10:34 AM" "Back to Contents","Data 1: North Dakota Natural Gas Total Consumption ...

  9. ,"North Carolina Natural Gas Total Consumption (MMcf)"

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

    Data for" ,"Data 1","North Carolina Natural Gas Total Consumption ... 9:10:33 AM" "Back to Contents","Data 1: North Carolina Natural Gas Total Consumption ...

  10. Using Utility Load Data to Estimate Demand for Space Cooling and Potential for Shiftable Loads

    SciTech Connect (OSTI)

    Denholm, P.; Ong, S.; Booten, C.

    2012-05-01

    This paper describes a simple method to estimate hourly cooling demand from historical utility load data. It compares total hourly demand to demand on cool days and compares these estimates of total cooling demand to previous regional and national estimates. Load profiles generated from this method may be used to estimate the potential for aggregated demand response or load shifting via cold storage.

  11. The Federal Highway Administration Gasohol Consumption Estimation Model

    SciTech Connect (OSTI)

    Hwang, HL

    2003-08-28

    The Federal Highway Administration (FHWA) is responsible for estimating the portion of Federal highway funds attributable to each State. The process involves use of State-reported data (gallons) and a set of estimation models when accurate State data is unavailable. To ensure that the distribution of funds is equitable, FHWA periodically reviews the estimation models. Estimation of the use of gasohol is difficult because of State differences in the definition of gasohol, inability of many States to separate and report gasohol usage from other fuel types, changes in fuel composition in nonattainment areas to address concerns over the use of certain fuel additives, and the lack of a valid State-level surrogate data set for gasohol use. Under the sponsorship of FHWA, Oak Ridge National Laboratory (ORNL) reviewed the regression-based gasohol estimation model that has been in use for several years. Based on an analytical assessment of that model and an extensive review of potential data sets, ORNL developed an improved rule-based model. The new model uses data from Internal Revenue Service, Energy Information Administration, Environmental Protection Agency, Department of Energy, ORNL, and FHWA sources. The model basically consists of three parts: (1) development of a controlled total of national gasohol usage, (2) determination of reliable State gasohol consumption data, and (3) estimation of gasohol usage for all other States. The new model will be employed for the 2004 attribution process. FHWA is currently soliciting comments and inputs from interested parties. Relevant data, as identified, will be pursued and refinements will be made by the research team if warranted.

  12. FY 2007 Total System Life Cycle Cost, Pub 2008 | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    FY 2007 Total System Life Cycle Cost, Pub 2008 FY 2007 Total System Life Cycle Cost, Pub 2008 The Analysis of the Total System Life Cycle Cost (TSLCC) of the Civilian Radioactive Waste Management Program presents the Office of Civilian Radioactive Waste Management's (OCRWM) May 2007 total system cost estimate for the disposal of the Nation's spent nuclear fuel (SNF) and high-level radioactive waste (HLW). The TSLCC analysis provides a basis for assessing the adequacy of the Nuclear Waste Fund

  13. " Level: National Data and Regional Totals...

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

    ... by" "petroleum refineries, rather than purchased ... ,,"Total United States" ,"RSE Column ... 324,"Petroleum and Coal ...

  14. Site: Contract Name: Contractor: Contract Number: Contract Type:

    Office of Environmental Management (EM)

    Contractor: Contract Number: Contract Type: Total Estimated Contract Cost: Contract Base Period: Contract Option Period: Minimum Fee Maximum Fee Performance Period Fee Available Fee Earned FY2011 $6,190,992 $5,779,687 FY2012 $16,380,944 $14,173,044 FY2013 $16,972,816 $12,693,413 FY2014 $15,520,007 $13,207,526 FY2015 $14,269,197 $10,503,998 FY2016 Base Period $24,350,863 March 29, 2016- September 30, 2017 $28,251,114 $6,823,811 October 1, 2017- September 30, 2018 $18,834,076 October 1, 2018-

  15. Apparatus and method for quantitatively evaluating total fissile and total fertile nuclide content in samples. [Patent application

    DOE Patents [OSTI]

    Caldwell, J.T.; Kunz, W.E.; Cates, M.R.; Franks, L.A.

    1982-07-07

    Simultaneous photon and neutron interrogation of samples for the quantitative determination of total fissile nuclide and total fertile nuclide material present is made possible by the use of an electron accelerator. Prompt and delayed neutrons produced from resulting induced fission are counted using a single detection system and allow the resolution of the contributions from each interrogating flux leading in turn to the quantitative determination sought. Detection limits for /sup 239/Pu are estimated to be about 3 mg using prompt fission neutrons and about 6 mg using delayed neutrons.

  16. Process Equipment Cost Estimation, Final Report

    SciTech Connect (OSTI)

    H.P. Loh; Jennifer Lyons; Charles W. White, III

    2002-01-01

    This report presents generic cost curves for several equipment types generated using ICARUS Process Evaluator. The curves give Purchased Equipment Cost as a function of a capacity variable. This work was performed to assist NETL engineers and scientists in performing rapid, order of magnitude level cost estimates or as an aid in evaluating the reasonableness of cost estimates submitted with proposed systems studies or proposals for new processes. The specific equipment types contained in this report were selected to represent a relatively comprehensive set of conventional chemical process equipment types.

  17. State Energy Production Estimates

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

    Energy Production Estimates 1960 Through 2012 2012 Summary Tables Table P1. Energy Production Estimates in Physical Units, 2012 Alabama 19,455 215,710 9,525 0 Alaska 2,052 351,259...

  18. BatPRO: Battery Manufacturing Cost Estimation | Argonne National...

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

    BatPRO: Battery Manufacturing Cost Estimation BatPRO models a stiff prismatic pouch-type cell battery pack with cells linked in series. BatPRO models a stiff prismatic pouch-type ...

  19. U.S. Department of Energy Releases Revised Total System Life...

    Energy Savers [EERE]

    U.S. Department of Energy Releases Revised Total System Life Cycle Cost Estimate and Fee Adequacy Report ... U.S. Department of Energy Awards Contracts for Waste Storage Canisters for ...

  20. Total Space Heating Water Heating Cook-

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

    Tables Total Space Heating Water Heating Cook- ing Other Total Space Heating Water Heating Cook- ing Other All Buildings* ... 634 578 46 1 Q 116.4 106.3...

  1. Million Cu. Feet Percent of National Total

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

    2 Alaska - Natural Gas 2014 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S2. Summary statistics for natural gas - Alaska, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 269 277 185 R 159 170 Production (million cubic feet) Gross Withdrawals From Gas Wells 127,417 112,268

  2. Million Cu. Feet Percent of National Total

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

    6 District of Columbia - Natural Gas 2014 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S9. Summary statistics for natural gas - District of Columbia, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells

  3. Million Cu. Feet Percent of National Total

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

    0 Indiana - Natural Gas 2014 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S16. Summary statistics for natural gas - Indiana, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 620 914 819 R 921 895 Production (million cubic feet) Gross Withdrawals From Gas Wells 6,802 9,075

  4. Million Cu. Feet Percent of National Total

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

    4 Massachusetts - Natural Gas 2014 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S23. Summary statistics for natural gas - Massachusetts, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0

  5. Million Cu. Feet Percent of National Total

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

    6 Nebraska - Natural Gas 2014 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S29. Summary statistics for natural gas - Nebraska, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 276 322 270 R 357 310 Production (million cubic feet) Gross Withdrawals From Gas Wells 2,092 1,854

  6. Million Cu. Feet Percent of National Total

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

    50 North Dakota - Natural Gas 2014 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S36. Summary statistics for natural gas - North Dakota, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 188 239 211 200 200 Production (million cubic feet) Gross Withdrawals From Gas Wells

  7. Total System Performance Assessment Peer Review Panel

    Broader source: Energy.gov [DOE]

    Total System Performance Assessment (TSPA) Peer Review Panel for predicting the performance of a repository at Yucca Mountain.

  8. Total Activity Estimation - Quarry Area. QY-500-501-1.06.

    Office of Legacy Management (LM)

  9. Type: Renewal

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    1 INCITE Awards Type: Renewal Title: -Ab Initio Dynamical Simulations for the Prediction of Bulk Properties‖ Principal Investigator: Theresa Windus, Iowa State University Co-Investigators: Brett Bode, Iowa State University Graham Fletcher, Argonne National Laboratory Mark Gordon, Iowa State University Monica Lamm, Iowa State University Michael Schmidt, Iowa State University Scientific Discipline: Chemistry: Physical INCITE Allocation: 10,000,000 processor hours Site: Argonne National

  10. Facility Type!

    Office of Legacy Management (LM)

    ITY: --&L~ ----------- srct-r~ -----------~------~------- if yee, date contacted ------------- cl Facility Type! i I 0 Theoretical Studies Cl Sample 84 Analysis ] Production 1 Diepasal/Storage 'YPE OF CONTRACT .--------------- 1 Prime J Subcontract&- 1 Purchase Order rl i '1 ! Other information (i.e., ---------~---~--~-------- :ontrait/Pirchaee Order # , I C -qXlJ- --~-------~~-------~~~~~~ I I ~~~---~~~~~~~T~~~ FONTRACTING PERIODi IWNERSHIP: ,I 1 AECIMED AECMED GOVT GOUT &NTtiAC+OR

  11. Reservoir Temperature Estimator

    Energy Science and Technology Software Center (OSTI)

    2014-12-08

    The Reservoir Temperature Estimator (RTEst) is a program that can be used to estimate deep geothermal reservoir temperature and chemical parameters such as CO2 fugacity based on the water chemistry of shallower, cooler reservoir fluids. This code uses the plugin features provided in The Geochemist’s Workbench (Bethke and Yeakel, 2011) and interfaces with the model-independent parameter estimation code Pest (Doherty, 2005) to provide for optimization of the estimated parameters based on the minimization of themore » weighted sum of squares of a set of saturation indexes from a user-provided mineral assemblage.« less

  12. Type here

    Office of Environmental Management (EM)

    Injury at the Savannah River National Laboratory | Department of Energy January 10, 2006, Flash Fire and Injury at the Savannah River National Laboratory Type B Accident Investigation of the January 10, 2006, Flash Fire and Injury at the Savannah River National Laboratory February 1, 2006 On January 10, 2006, at approximately 7:47 a.m., a first-line manager (FLM) at the Savannah River National Laboratory (SRNL) received first- and second-degree burns to his head, face, neck, and left hand

  13. Estimating Specialty Costs

    Broader source: Directives, Delegations, and Requirements [Office of Management (MA)]

    1997-03-28

    Specialty costs are those nonstandard, unusual costs that are not typically estimated. Costs for research and development (R&D) projects involving new technologies, costs associated with future regulations, and specialty equipment costs are examples of specialty costs. This chapter discusses those factors that are significant contributors to project specialty costs and methods of estimating costs for specialty projects.

  14. Million Cu. Feet Percent of National Total

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

    0 Alabama - Natural Gas 2014 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S1. Summary statistics for natural gas - Alabama, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 7,026 7,063 6,327 R 6,165 6,118 Production (million cubic feet) Gross Withdrawals From Gas Wells

  15. Million Cu. Feet Percent of National Total

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

    6 Arkansas - Natural Gas 2014 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S4. Summary statistics for natural gas - Arkansas, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 7,397 8,388 8,538 R 9,843 10,150 Production (million cubic feet) Gross Withdrawals From Gas Wells

  16. Million Cu. Feet Percent of National Total

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

    8 California - Natural Gas 2014 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S5. Summary statistics for natural gas - California, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 1,580 1,308 1,423 R 1,335 1,118 Production (million cubic feet) Gross Withdrawals From Gas

  17. Million Cu. Feet Percent of National Total

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

    0 Colorado - Natural Gas 2014 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S6. Summary statistics for natural gas - Colorado, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 28,813 30,101 32,000 R 32,468 38,346 Production (million cubic feet) Gross Withdrawals From Gas

  18. Million Cu. Feet Percent of National Total

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

    8 Florida - Natural Gas 2014 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S10. Summary statistics for natural gas - Florida, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 17,182 16,459 19,742

  19. Million Cu. Feet Percent of National Total

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

    0 Georgia - Natural Gas 2014 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S11. Summary statistics for natural gas - Georgia, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells

  20. Million Cu. Feet Percent of National Total

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

    6 Idaho - Natural Gas 2014 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S14. Summary statistics for natural gas - Idaho, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0

  1. Million Cu. Feet Percent of National Total

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

    8 Illinois - Natural Gas 2014 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S15. Summary statistics for natural gas - Illinois, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 50 40 40 R 34 36 Production (million cubic feet) Gross Withdrawals From Gas Wells E 1,697 2,114

  2. Million Cu. Feet Percent of National Total

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

    2 Iowa - Natural Gas 2014 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S17. Summary statistics for natural gas - Iowa, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0 0

  3. Million Cu. Feet Percent of National Total

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

    4 Kansas - Natural Gas 2014 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S18. Summary statistics for natural gas - Kansas, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 22,145 25,758 24,697 R 23,792 24,354 Production (million cubic feet) Gross Withdrawals From Gas Wells

  4. Million Cu. Feet Percent of National Total

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

    6 Kentucky - Natural Gas 2014 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S19. Summary statistics for natural gas - Kentucky, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 17,670 14,632 17,936 R 19,494 19,256 Production (million cubic feet) Gross Withdrawals From Gas

  5. Million Cu. Feet Percent of National Total

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

    8 Louisiana - Natural Gas 2014 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S20. Summary statistics for natural gas - Louisiana, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 19,137 21,235 19,792 R 19,528 19,251 Production (million cubic feet) Gross Withdrawals From Gas

  6. Million Cu. Feet Percent of National Total

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

    0 Maine - Natural Gas 2014 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S21. Summary statistics for natural gas - Maine, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 0 0 0 0 0 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 0 From Oil Wells 0 0

  7. Million Cu. Feet Percent of National Total

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

    6 Michigan - Natural Gas 2014 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S24. Summary statistics for natural gas - Michigan, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 10,100 11,100 10,900 R 10,550 10,500 Production (million cubic feet) Gross Withdrawals From Gas

  8. Million Cu. Feet Percent of National Total

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

    0 Mississippi - Natural Gas 2014 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S26. Summary statistics for natural gas - Mississippi, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 1,979 5,732 1,669 R 1,967 1,645 Production (million cubic feet) Gross Withdrawals From Gas

  9. Million Cu. Feet Percent of National Total

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

    2 Missouri - Natural Gas 2014 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S27. Summary statistics for natural gas - Missouri, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 0 53 100 R 26 28 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 R 8 8 From

  10. Million Cu. Feet Percent of National Total

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

    4 Montana - Natural Gas 2014 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S28. Summary statistics for natural gas - Montana, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 6,059 6,477 6,240 5,754 5,754 Production (million cubic feet) Gross Withdrawals From Gas Wells