National Library of Energy BETA

Sample records for derived estimates total

  1. 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...

  2. 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.

  3. Derived Annual Estimates of Manufacturing Energy Consumption...

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

    > Derived Annual Estimates - Executive Summary Derived Annual Estimates of Manufacturing Energy Consumption, 1974-1988 Figure showing Derived Estimates Executive Summary This...

  4. 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

  5. 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 ...

  6. 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

  7. 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 ...

  8. Derivative-free optimization for parameter estimation in computational...

    Office of Scientific and Technical Information (OSTI)

    nuclear physics Citation Details In-Document Search Title: Derivative-free optimization for parameter estimation in computational nuclear physics Authors: Wild, S ; ...

  9. Derivative-free optimization for parameter estimation in computational...

    Office of Scientific and Technical Information (OSTI)

    Journal Article: Derivative-free optimization for parameter estimation in computational nuclear physics Citation Details ... RADIATION PHYSICS; 97 MATHEMATICS, COMPUTING, AND ...

  10. Derived annual estimates of manufacturing energy consumption, 1974--1988

    SciTech Connect (OSTI)

    Not Available

    1992-08-05

    This report presents a complete series of annual estimates of purchased energy used by the manufacturing sector of the US economy, for the years 1974 to 1988. These estimates interpolate over gaps in the actual data collections, by deriving estimates for the missing years 1982--1984 and 1986--1987. For the purposes of this report, ``purchased`` energy is energy brought from offsite for use at manufacturing establishments, whether the energy is purchased from an energy vendor or procured from some other source. The actual data on purchased energy comes from two sources, the US Department of Commerce Bureau of the Census`s Annual Survey of Manufactures (ASM) and EIA`s Manufacturing Energy Consumption Survey (MECS). The ASM provides annual estimates for the years 1974 to 1981. However, in 1982 (and subsequent years) the scope of the ASM energy data was reduced to collect only electricity consumption and expenditures and total expenditures for other purchased energy. In 1985, EIA initiated the triennial MECS collecting complete energy data. The series equivalent to the ASM is referred to in the MECS as ``offsite-produced fuels.``

  11. Uncertainty estimates for derivatives and intercepts

    SciTech Connect (OSTI)

    Clark, E.L.

    1994-09-01

    Straight line least squares fits of experimental data are widely used in the analysis of test results to provide derivatives and intercepts. A method for evaluating the uncertainty in these parameters is described. The method utilizes conventional least squares results and is applicable to experiments where the independent variable is controlled, but not necessarily free of error. A Monte Carlo verification of the method is given.

  12. 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.

  13. 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 ...

  14. 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 ...

  15. 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 ...

  16. 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 ...

  17. 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

  18. 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

  19. 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

  20. 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

  1. 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. Derived annual estimates of manufacturing energy consumption, 1974--1988. [Contains glossary

    SciTech Connect (OSTI)

    Not Available

    1992-08-05

    This report presents a complete series of annual estimates of purchased energy used by the manufacturing sector of the US economy, for the years 1974 to 1988. These estimates interpolate over gaps in the actual data collections, by deriving estimates for the missing years 1982--1984 and 1986--1987. For the purposes of this report, purchased'' energy is energy brought from offsite for use at manufacturing establishments, whether the energy is purchased from an energy vendor or procured from some other source. The actual data on purchased energy comes from two sources, the US Department of Commerce Bureau of the Census's Annual Survey of Manufactures (ASM) and EIA's Manufacturing Energy Consumption Survey (MECS). The ASM provides annual estimates for the years 1974 to 1981. However, in 1982 (and subsequent years) the scope of the ASM energy data was reduced to collect only electricity consumption and expenditures and total expenditures for other purchased energy. In 1985, EIA initiated the triennial MECS collecting complete energy data. The series equivalent to the ASM is referred to in the MECS as offsite-produced fuels.''

  9. Superconvergence of the derivative patch recovery technique and a posteriorii error estimation

    SciTech Connect (OSTI)

    Zhang, Z.; Zhu, J.Z.

    1995-12-31

    The derivative patch recovery technique developed by Zienkiewicz and Zhu for the finite element method is analyzed. It is shown that, for one dimensional problems and two dimensional problems using tensor product elements, the patch recovery technique yields superconvergence recovery for the derivatives. Consequently, the error estimator based on the recovered derivative is asymptotically exact.

  10. Total Ozone Mapping Spectrometer (TOMS) Derived Data, Global Earth Coverage (GEC) from NASA's Earth Probe Satellite

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    This is data from an external datastream processed through the ARM External Data Center (XDC) at Brookhaven National Laboratory. The XDC identifies sources and acquires data, called "external data", to augment the data being generated within the ARM program. The external data acquired are usually converted from native format to either netCDF or HDF formats. The GEC collection contains global data derived from the Total Ozone Mapping Spectrometer (TOMS) instrument on the Earth Probe satellite, consisting of daily values of aerosol index, ozone and reflectivity remapped into a regular 1x1.25 deg grid. Data are available from July 25, 1996 - December 31, 2005, but have been updated or replaced as of September 2007. See the explanation on the ARM web site at http://www.arm.gov/xds/static/toms.stm and the information at the NASA/TOMS web site: http://toms.gsfc.nasa.gov/ (Registration required)

  11. 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 1.4–146.3 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 (372.2–541.0) × 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 (0.003–68.5) × 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

  12. Steady state estimation of soil organic carbon using satellite-derived canopy leaf area index

    SciTech Connect (OSTI)

    Fang, Yilin; Liu, Chongxuan; Huang, Maoyi; Li, Hongyi; Leung, Lai-Yung R.

    2014-12-02

    Soil organic carbon (SOC) plays a key role in the global carbon cycle that is important for decadal-to-century climate prediction. Estimation of soil organic carbon stock using model-based methods typically requires spin-up (time marching transient simulation) of the carbon-nitrogen (CN) models by performing hundreds to thousands years long simulations until the carbon-nitrogen pools reach dynamic steady-state. This has become a bottleneck for global modeling and analysis, especially when testing new physical and/or chemical mechanisms and evaluating parameter sensitivity. Here we report a new numerical approach to estimate global soil carbon stock that can avoid the long term spin-up of the CN model. The approach uses canopy leaf area index (LAI) from satellite data and takes advantage of a reaction-based biogeochemical module NGBGC (Next Generation BioGeoChemical Module) that was recently developed and incorporated in version 4 of the Community Land Model (CLM4). Although NGBGC uses the same CN mechanisms as used in CLM4CN, it can be easily configured to run prognostic or steady state simulations. In this approach, monthly LAI from the multi-year Moderate Resolution Imaging Spectroradiometer (MODIS) data was used to calculate potential annual average gross primary production (GPP) and leaf carbon for the period of the atmospheric forcing. The calculated potential annual average GPP and leaf C are then used by NGBGC to calculate the steady-state distributions of carbon and nitrogen in different vegetation and soil pools by solving the steady-state reaction-network in NGBGC using the Newton-Raphson method. The new approach was applied at point and global scales and compared with SOC derived from long spin-up by running NGBGC in prognostic mode, and SOC from the empirical data of the Harmonized World Soil Database (HWSD). The steady-state solution is comparable to the spin-up value when the MODIS LAI is close to the LAI from the spin-up solution, and largely

  13. Steady state estimation of soil organic carbon using satellite-derived canopy leaf area index

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

    Fang, Yilin; Liu, Chongxuan; Huang, Maoyi; Li, Hongyi; Leung, Lai-Yung R.

    2014-12-02

    Soil organic carbon (SOC) plays a key role in the global carbon cycle that is important for decadal-to-century climate prediction. Estimation of soil organic carbon stock using model-based methods typically requires spin-up (time marching transient simulation) of the carbon-nitrogen (CN) models by performing hundreds to thousands years long simulations until the carbon-nitrogen pools reach dynamic steady-state. This has become a bottleneck for global modeling and analysis, especially when testing new physical and/or chemical mechanisms and evaluating parameter sensitivity. Here we report a new numerical approach to estimate global soil carbon stock that can avoid the long term spin-up of themore » CN model. The approach uses canopy leaf area index (LAI) from satellite data and takes advantage of a reaction-based biogeochemical module NGBGC (Next Generation BioGeoChemical Module) that was recently developed and incorporated in version 4 of the Community Land Model (CLM4). Although NGBGC uses the same CN mechanisms as used in CLM4CN, it can be easily configured to run prognostic or steady state simulations. In this approach, monthly LAI from the multi-year Moderate Resolution Imaging Spectroradiometer (MODIS) data was used to calculate potential annual average gross primary production (GPP) and leaf carbon for the period of the atmospheric forcing. The calculated potential annual average GPP and leaf C are then used by NGBGC to calculate the steady-state distributions of carbon and nitrogen in different vegetation and soil pools by solving the steady-state reaction-network in NGBGC using the Newton-Raphson method. The new approach was applied at point and global scales and compared with SOC derived from long spin-up by running NGBGC in prognostic mode, and SOC from the empirical data of the Harmonized World Soil Database (HWSD). The steady-state solution is comparable to the spin-up value when the MODIS LAI is close to the LAI from the spin-up solution, and largely

  14. 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.

  15. Adjusting lidar-derived digital terrain models in coastal marshes based on estimated aboveground biomass density

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

    Medeiros, Stephen; Hagen, Scott; Weishampel, John; Angelo, James

    2015-03-25

    Digital elevation models (DEMs) derived from airborne lidar are traditionally unreliable in coastal salt marshes due to the inability of the laser to penetrate the dense grasses and reach the underlying soil. To that end, we present a novel processing methodology that uses ASTER Band 2 (visible red), an interferometric SAR (IfSAR) digital surface model, and lidar-derived canopy height to classify biomass density using both a three-class scheme (high, medium and low) and a two-class scheme (high and low). Elevation adjustments associated with these classes using both median and quartile approaches were applied to adjust lidar-derived elevation values closer tomore » true bare earth elevation. The performance of the method was tested on 229 elevation points in the lower Apalachicola River Marsh. The two-class quartile-based adjusted DEM produced the best results, reducing the RMS error in elevation from 0.65 m to 0.40 m, a 38% improvement. The raw mean errors for the lidar DEM and the adjusted DEM were 0.61 ± 0.24 m and 0.32 ± 0.24 m, respectively, thereby reducing the high bias by approximately 49%.« less

  16. Adjusting lidar-derived digital terrain models in coastal marshes based on estimated aboveground biomass density

    SciTech Connect (OSTI)

    Medeiros, Stephen; Hagen, Scott; Weishampel, John; Angelo, James

    2015-03-25

    Digital elevation models (DEMs) derived from airborne lidar are traditionally unreliable in coastal salt marshes due to the inability of the laser to penetrate the dense grasses and reach the underlying soil. To that end, we present a novel processing methodology that uses ASTER Band 2 (visible red), an interferometric SAR (IfSAR) digital surface model, and lidar-derived canopy height to classify biomass density using both a three-class scheme (high, medium and low) and a two-class scheme (high and low). Elevation adjustments associated with these classes using both median and quartile approaches were applied to adjust lidar-derived elevation values closer to true bare earth elevation. The performance of the method was tested on 229 elevation points in the lower Apalachicola River Marsh. The two-class quartile-based adjusted DEM produced the best results, reducing the RMS error in elevation from 0.65 m to 0.40 m, a 38% improvement. The raw mean errors for the lidar DEM and the adjusted DEM were 0.61 ± 0.24 m and 0.32 ± 0.24 m, respectively, thereby reducing the high bias by approximately 49%.

  17. Worldwide estimates and bibliography of net primary productivity derived from pre-1982 publications

    SciTech Connect (OSTI)

    Esser, G.; Lieth, H.F.H.; Scurlock, J.M.O.; Olson, R.J.

    1997-10-01

    An extensive compilation of more than 700 field estimates of net primary productivity of natural and agricultural ecosystems worldwide was synthesized in Germany in the 1970s and early 1980s. Although the Osnabrueck data set has not been updated since the 1980s, it represents a wealth of information for use in model development and validation. This report documents the development of this data set, its contents, and its recent availability on the Internet from the Oak Ridge National Laboratory Distributed Active Archive Center for Biogeochemical Dynamics. Caution is advised in using these data, which necessarily include assumptions and conversions that may not be universally applicable to all sites.

  18. Derived Annual Estimates

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

    robert.adler@eia.doe.gov Robert Adler Survey Manager Phone: 202-586-1134 Fax: (202) 586-0018 thomas.lorenz@eia.doe.gov Thomas Lorenz Operations Research Analyst Phone: 202-586-3442...

  19. 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.

  20. 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

  1. 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 -

  2. 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

  3. 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

  4. 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

  5. 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 ...

  6. 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...

  7. 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...

  8. 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.

  9. 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...

  10. 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 ...

  11. ,"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...

  12. 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.

  13. 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...

  14. ,"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...

  15. ,"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...

  16. 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...

  17. 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*...

  18. ,"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...

  19. ,"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...

  20. ,"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"...

  1. 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.

  2. 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 ...

  3. 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

  4. 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 ...

  5. 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...

  6. 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...

  7. 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 ...

  8. 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

  9. 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:

  10. 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,

  11. 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,

  12. 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. ...

  13. 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...

  14. 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

  15. 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).

  16. 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.

  17. 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

  18. 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

  19. 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.

  20. 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

  1. 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...

  2. Scaling properties of proton-nucleus total reaction cross sections

    SciTech Connect (OSTI)

    Abu-Ibrahim, Badawy; Kohama, Akihisa

    2010-05-15

    We study the scaling properties of proton-nucleus total reaction cross sections for stable nuclei and propose an approximate expression in proportion to Z{sup 2/3}sigma{sub pp}{sup total}+N{sup 2/3}sigma{sub pn}{sup total}. Based on this expression, we can derive a relation that enables us to predict a total reaction cross section for any stable nucleus within 10% uncertainty at most, using the empirical value of the total reaction cross section of a given nucleus.

  3. Table A32. Total Consumption of Offsite-Produced Energy for...

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

    Consumption of Offsite-Produced Energy for Heat, Power, and" " Electricity Generation by ... The derived estimates presented" "in this table represent the consumption of energy ...

  4. 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

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

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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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

  1. 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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

  1. 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

  2. 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

  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 3.7 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.....................................................

  4. 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

  5. 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

  6. 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 ...

  7. 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 ...

  8. 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 ...

  9. 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 ...

  10. 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 ...

  11. 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 ...

  12. 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 ...

  13. 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 ...

  14. 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 ...

  15. 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 ...

  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

    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

  19. 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

  20. 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......

  1. 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......

  2. 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......

  3. 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 ...

  4. 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 ...

  5. 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......

  6. 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 ...

  7. 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 ...

  8. 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 ...

  9. 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 ...

  10. 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 ...

  11. 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

  12. 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.

  13. Manufacturing Consumption of Energy 1994 - Derived measures of...

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

    eialogo Calculation of MECS Energy Measures Reported energy values were used to construct several derived values, which, in turn, were used to prepare the estimates appearing in...

  14. 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.

  15. 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.

  16. Total least squares for anomalous change detection

    SciTech Connect (OSTI)

    Theiler, James P; Matsekh, Anna M

    2010-01-01

    A family of difference-based anomalous change detection algorithms is derived from a total least squares (TLSQ) framework. This provides an alternative to the well-known chronochrome algorithm, which is derived from ordinary least squares. In both cases, the most anomalous changes are identified with the pixels that exhibit the largest residuals with respect to the regression of the two images against each other. The family of TLSQ-based anomalous change detectors is shown to be equivalent to the subspace RX formulation for straight anomaly detection, but applied to the stacked space. However, this family is not invariant to linear coordinate transforms. On the other hand, whitened TLSQ is coordinate invariant, and furthermore it is shown to be equivalent to the optimized covariance equalization algorithm. What whitened TLSQ offers, in addition to connecting with a common language the derivations of two of the most popular anomalous change detection algorithms - chronochrome and covariance equalization - is a generalization of these algorithms with the potential for better performance.

  17. 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...

  18. 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

  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. Measurements and modeling of total solar irradiance in X-class solar flares

    SciTech Connect (OSTI)

    Moore, Christopher Samuel; Chamberlin, Phillip Clyde; Hock, Rachel

    2014-05-20

    The Total Irradiance Monitor (TIM) from NASA's SOlar Radiation and Climate Experiment can detect changes in the total solar irradiance (TSI) to a precision of 2 ppm, allowing observations of variations due to the largest X-class solar flares for the first time. Presented here is a robust algorithm for determining the radiative output in the TIM TSI measurements, in both the impulsive and gradual phases, for the four solar flares presented in Woods et al., as well as an additional flare measured on 2006 December 6. The radiative outputs for both phases of these five flares are then compared to the vacuum ultraviolet (VUV) irradiance output from the Flare Irradiance Spectral Model (FISM) in order to derive an empirical relationship between the FISM VUV model and the TIM TSI data output to estimate the TSI radiative output for eight other X-class flares. This model provides the basis for the bolometric energy estimates for the solar flares analyzed in the Emslie et al. study.

  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. 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 ...

  4. 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

  5. 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 ...

  6. 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

  7. 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

  8. 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.

  9. 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

  10. 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

  11. 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

  12. 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.

  13. Estimates of US biomass energy consumption 1992

    SciTech Connect (OSTI)

    Not Available

    1994-05-06

    This report is the seventh in a series of publications developed by the Energy Information Administration (EIA) to quantify the biomass-derived primary energy used by the US economy. It presents estimates of 1991 and 1992 consumption. The objective of this report is to provide updated estimates of biomass energy consumption for use by Congress, Federal and State agencies, biomass producers and end-use sectors, and the public at large.

  14. 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.

  15. Estimated United States Transportation Energy Use 2005

    SciTech Connect (OSTI)

    Smith, C A; Simon, A J; Belles, R D

    2011-11-09

    A flow chart depicting energy flow in the transportation sector of the United States economy in 2005 has been constructed from publicly available data and estimates of national energy use patterns. Approximately 31,000 trillion British Thermal Units (trBTUs) of energy were used throughout the United States in transportation activities. Vehicles used in these activities include automobiles, motorcycles, trucks, buses, airplanes, rail, and ships. The transportation sector is powered primarily by petroleum-derived fuels (gasoline, diesel and jet fuel). Biomass-derived fuels, electricity and natural gas-derived fuels are also used. The flow patterns represent a comprehensive systems view of energy used within the transportation sector.

  16. 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...

  17. 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...

  18. 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...

  19. 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...

  20. 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...

  1. ,"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)" ...

  2. ,"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" ...

  3. ,"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)" ...

  4. ,"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 ...

  5. ,"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 ...

  6. 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.

  7. Reference air kerma and kerma-area product as estimators of peak skin dose for fluoroscopically guided interventions

    SciTech Connect (OSTI)

    Kwon, Deukwoo; Little, Mark P.; Miller, Donald L.

    2011-07-15

    Purpose: To determine more accurate regression formulas for estimating peak skin dose (PSD) from reference air kerma (RAK) or kerma-area product (KAP). Methods: After grouping of the data from 21 procedures into 13 clinically similar groups, assessments were made of optimal clustering using the Bayesian information criterion to obtain the optimal linear regressions of (log-transformed) PSD vs RAK, PSD vs KAP, and PSD vs RAK and KAP. Results: Three clusters of clinical groups were optimal in regression of PSD vs RAK, seven clusters of clinical groups were optimal in regression of PSD vs KAP, and six clusters of clinical groups were optimal in regression of PSD vs RAK and KAP. Prediction of PSD using both RAK and KAP is significantly better than prediction of PSD with either RAK or KAP alone. The regression of PSD vs RAK provided better predictions of PSD than the regression of PSD vs KAP. The partial-pooling (clustered) method yields smaller mean squared errors compared with the complete-pooling method.Conclusion: PSD distributions for interventional radiology procedures are log-normal. Estimates of PSD derived from RAK and KAP jointly are most accurate, followed closely by estimates derived from RAK alone. Estimates of PSD derived from KAP alone are the least accurate. Using a stochastic search approach, it is possible to cluster together certain dissimilar types of procedures to minimize the total error sum of squares.

  8. 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

  9. " 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 ...

  10. 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.

  11. 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.

  12. 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

  13. 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...

  14. 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.

  15. Population Estimates for Chum Salmon Spawning in the Mainstem Columbia River, 2002 Technical Report.

    SciTech Connect (OSTI)

    Rawding, Dan; Hillson, Todd D.

    2003-11-15

    Accurate and precise population estimates of chum salmon (Oncorhynchus keta) spawning in the mainstem Columbia River are needed to provide a basis for informed water allocation decisions, to determine the status of chum salmon listed under the Endangered Species Act, and to evaluate the contribution of the Duncan Creek re-introduction program to mainstem spawners. Currently, mark-recapture experiments using the Jolly-Seber model provide the only framework for this type of estimation. In 2002, a study was initiated to estimate mainstem Columbia River chum salmon populations using seining data collected while capturing broodstock as part of the Duncan Creek re-introduction. The five assumptions of the Jolly-Seber model were examined using hypothesis testing within a statistical framework, including goodness of fit tests and secondary experiments. We used POPAN 6, an integrated computer system for the analysis of capture-recapture data, to obtain maximum likelihood estimates of standard model parameters, derived estimates, and their precision. A more parsimonious final model was selected using Akaike Information Criteria. Final chum salmon escapement estimates and (standard error) from seining data for the Ives Island, Multnomah, and I-205 sites are 3,179 (150), 1,269 (216), and 3,468 (180), respectively. The Ives Island estimate is likely lower than the total escapement because only the largest two of four spawning sites were sampled. The accuracy and precision of these estimates would improve if seining was conducted twice per week instead of weekly, and by incorporating carcass recoveries into the analysis. Population estimates derived from seining mark-recapture data were compared to those obtained using the current mainstem Columbia River salmon escapement methodologies. The Jolly-Seber population estimate from carcass tagging in the Ives Island area was 4,232 adults with a standard error of 79. This population estimate appears reasonable and precise but batch

  16. Estimates of U.S. Biomass Energy Consumption 1992

    Reports and Publications (EIA)

    1994-01-01

    This report is the seventh in a series of publications developed by the Energy Information Administration (EIA) to quantify the biomass derived primary energy used by the U.S. economy. It presents estimates of 1991 and 1992 consumption.

  17. 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 ...

  18. Estimating Uranium Partition Coefficients from Laboratory Adsorption Isotherms

    SciTech Connect (OSTI)

    Hull, L.C.; Grossman, C.; Fjeld, R.A.; Coates, J.T.; Elzerman, A.W.

    2002-05-10

    An estimated 330 metric tons of uranium have been buried in the radioactive waste Subsurface Disposal Area (SDA) at the Idaho National Engineering and Environmental Laboratory (INEEL). An assessment of uranium transport parameters is being performed to decrease the uncertainty in risk and dose predictions derived from computer simulations of uranium fate and transport to the underlying Snake River Plain Aquifer. Uranium adsorption isotherms have been measured in the laboratory and fit with a Freundlich isotherm. The Freundlich n parameter was statistically identical for 14 sediment samples. The Freundlich Kf for seven samples, where material properties have been measured, is correlated to sediment surface area. Based on these empirical observations, a model has been derived for adsorption of uranium on INEEL sedimentary materials using surface complexation theory. The model was then used to predict the range of adsorption conditions to be expected at the SDA. Adsorption in the deep vadose zone is predicted to be stronger than in near-surface sediments because the total dissolved carbonate decreases with depth.

  19. Estimating Uranium Partition Coefficients from Laboratory Adsorption Isotherms

    SciTech Connect (OSTI)

    Hull, Laurence Charles; Grossman, Christopher; Fjeld, R. A.; Coates, C.J.; Elzerman, A.

    2002-08-01

    An estimated 330 metric tons of uranium have been buried in the radioactive waste Subsurface Disposal Area (SDA) at the Idaho National Engineering and Environmental Laboratory (INEEL). An assessment of uranium transport parameters is being performed to decrease the uncertainty in risk and dose predictions derived from computer simulations of uranium fate and transport to the underlying Snake River Plain Aquifer. Uranium adsorption isotherms have been measured in the laboratory and fit with a Freundlich isotherm. The Freundlich n parameter was statistically identical for 14 sediment samples. The Freundlich Kf for seven samples, where material properties have been measured, is correlated to sediment surface area. Based on these empirical observations, a model has been derived for adsorption of uranium on INEEL sedimentary materials using surface complexation theory. The model was then used to predict the range of adsorption conditions to be expected at the SDA. Adsorption in the deep vadose zone is predicted to be stronger than in near-surface sediments because the total dissolved carbonate decreases with depth.

  20. Derivative expansion of the effective action

    SciTech Connect (OSTI)

    Cheyette, O.

    1987-04-01

    This paper describes some methods for calculating derivative terms in the one loop effective action for a quantum field theory. The functional approach and background field method are first used to derive the general form of the one loop determinant. Then the determinant is expanded in powers of derivatives of the background fields. The form of this expansion is described for the simple case of an interacting scalar field, and then for the more complicated problem of a non-abelian gauge field. Finally, the expansion is applied to the task of calculating Higgs mass dependent effects in the Glashow-Weinberg-Salam model, and all terms which grow with the Higgs mass M/sub H/ are found in the one loop approximation. The result of this calculation is used to find the dependence of the gauge boson mass ratio rho on M/sub H/, and also to estimate the size of corrections to W and Z scattering theorems.

  1. 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...

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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.

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

    Office of Legacy Management (LM)

  10. Estimate of the Geothermal Energy Resource in the Major Sedimentary Basins in the United States (Presentation)

    SciTech Connect (OSTI)

    Esposito, A.; Porro, C.; Augustine, C.; Roberts, B.

    2012-09-01

    Because most sedimentary basins have been explored for oil and gas, well logs, temperatures at depth, and reservoir properties such as depth to basement and formation thickness are well known. The availability of this data reduces exploration risk and allows development of geologic exploration models for each basin. This study estimates the magnitude of recoverable geothermal energy from 15 major known U.S. sedimentary basins and ranks these basins relative to their potential. The total available thermal resource for each basin was estimated using the volumetric heat-in-place method originally proposed by (Muffler, 1979). A qualitative recovery factor was determined for each basin based on data on flow volume, hydrothermal recharge, and vertical and horizontal permeability. Total sedimentary thickness maps, stratigraphic columns, cross sections, and temperature gradient information was gathered for each basin from published articles, USGS reports, and state geological survey reports. When published data were insufficient, thermal gradients and reservoir properties were derived from oil and gas well logs obtained on oil and gas commission databases. Basin stratigraphy, structural history, and groundwater circulation patterns were studied in order to develop a model that estimates resource size, temperature distribution, and a probable quantitative recovery factor.

  11. Estimate of Geothermal Energy Resource in Major U.S. Sedimentary Basins (Presentation)

    SciTech Connect (OSTI)

    Porro, C.; Augustine, C.

    2012-04-01

    This study estimates the magnitude of geothermal energy from fifteen major known US sedimentary basins and ranks these basins relative to their potential. Because most sedimentary basins have been explored for oil and gas, well logs, temperatures at depth, and reservoir properties are known. This reduces exploration risk and allows development of geologic exploration models for each basin as well as a relative assessment of geologic risk elements for each play. The total available thermal resource for each basin was estimated using the volumetric heat-in-place method originally proposed by Muffler (USGS). Total sedimentary thickness maps, stratigraphic columns, cross sections, and temperature gradient Information were gathered for each basin from published articles, USGS reports, and state geological survey reports. When published data was insufficient, thermal gradients and reservoir properties were derived from oil and gas well logs obtained on oil and gas commission websites. Basin stratigraphy, structural history, and groundwater circulation patterns were studied in order to develop a model that estimates resource size and temperature distribution, and to qualitatively assess reservoir productivity.

  12. 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

  13. Pushing schedule derivation method

    SciTech Connect (OSTI)

    Henriquez, B.

    1996-12-31

    The development of a Pushing Schedule Derivation Method has allowed the company to sustain the maximum production rate at CSH`s Coke Oven Battery, in spite of having single set oven machinery with a high failure index as well as a heat top tendency. The stated method provides for scheduled downtime of up to two hours for machinery maintenance purposes, periods of empty ovens for decarbonization and production loss recovery capability, while observing lower limits and uniformity of coking time.

  14. AN OVERVIEW OF TOOL FOR RESPONSE ACTION COST ESTIMATING (TRACE)

    SciTech Connect (OSTI)

    FERRIES SR; KLINK KL; OSTAPKOWICZ B

    2012-01-30

    Tools and techniques that provide improved performance and reduced costs are important to government programs, particularly in current times. An opportunity for improvement was identified for preparation of cost estimates used to support the evaluation of response action alternatives. As a result, CH2M HILL Plateau Remediation Company has developed Tool for Response Action Cost Estimating (TRACE). TRACE is a multi-page Microsoft Excel{reg_sign} workbook developed to introduce efficiencies into the timely and consistent production of cost estimates for response action alternatives. This tool combines costs derived from extensive site-specific runs of commercially available remediation cost models with site-specific and estimator-researched and derived costs, providing the best estimating sources available. TRACE also provides for common quantity and key parameter links across multiple alternatives, maximizing ease of updating estimates and performing sensitivity analyses, and ensuring consistency.

  15. 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.

  16. Energy and materials flows in the production of olefins and their derivatives

    SciTech Connect (OSTI)

    Gaines, L.L.; Shen, S.Y.

    1980-08-01

    Production of olefins and their derivatives uses almost 3.5% of the oil and gas consumed annually in the United States. It is estimated that their production requires an input energy of 2 Q, which is 50% of the energy used in the production of all petrochemicals. Substantial amounts of this energy could be recovered through recycling. For example, recycling of a single plastic product, polyester soft drink bottles, could have recovered about 0.014 Q in 1979. (About 1.4 Q is used to produce plastic derivatives of olefins). Petrochemical processes use fuels as feedstocks, as well as for process energy, and a portion of this energy is not foregone and can be recovered through combustion of the products. The energy foregone in the production of ethylene is estimated to be 7800 Btu/lb. The energy foregone in plastics production ranges from 12,100 Btu/lb for the new linear low-density polyethylene to 77,200 Btu/lb for nylon 66, which is about 60% of the total energy input for that product. Further investigation of the following areas could yield both material and energy savings in the olefins industry: (1) recycling of petrochemical products to recover energy in addition to that recoverable through combustion, (2) impact of feedstock substitution on utilization of available national resources, and (3) effective use of the heat embodied in process steam. This steam accounts for a major fraction of the industry's energy input.

  17. 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

  18. 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

  19. 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

  20. 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

  1. 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. Million Cu. Feet Percent of National Total

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

    8 Nevada - 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 S30. Summary statistics for natural gas - Nevada, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 0 0 0 R 4 4 Production (million cubic feet) Gross Withdrawals From Gas Wells 0 0 0 0 3 From Oil Wells

  15. Million Cu. Feet Percent of National Total

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

    4 New Mexico - 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 S33. Summary statistics for natural gas - New Mexico, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 44,748 32,302 28,206 R 27,073 27,957 Production (million cubic feet) Gross Withdrawals From

  16. Million Cu. Feet Percent of National Total

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

    6 New York - 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 S34. Summary statistics for natural gas - New York, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 6,736 6,157 7,176 R 6,902 7,119 Production (million cubic feet) Gross Withdrawals From Gas Wells

  17. Million Cu. Feet Percent of National Total

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

    2 Ohio - 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 S37. Summary statistics for natural gas - Ohio, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 34,931 46,717 35,104 R 32,664 32,967 Production (million cubic feet) Gross Withdrawals From Gas Wells

  18. Million Cu. Feet Percent of National Total

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

    4 Oklahoma - 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 S38. Summary statistics for natural gas - Oklahoma, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 44,000 41,238 40,000 39,776 40,070 Production (million cubic feet) Gross Withdrawals From Gas

  19. Million Cu. Feet Percent of National Total

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

    6 Oregon - 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 S39. Summary statistics for natural gas - Oregon, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 26 24 27 R 26 28 Production (million cubic feet) Gross Withdrawals From Gas Wells 1,407 1,344 770 770

  20. Million Cu. Feet Percent of National Total

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

    8 Pennsylvania - 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 S40. Summary statistics for natural gas - Pennsylvania, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 44,500 54,347 55,136 R 53,762 70,400 Production (million cubic feet) Gross Withdrawals

  1. Million Cu. Feet Percent of National Total

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

    6 Tennessee - 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 S44. Summary statistics for natural gas - Tennessee, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 230 210 212 R 1,089 1,024 Production (million cubic feet) Gross Withdrawals From Gas Wells 5,144

  2. Million Cu. Feet Percent of National Total

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

    8 Texas - 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 S45. Summary statistics for natural gas - Texas, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 95,014 100,966 96,617 97,618 98,279 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 Utah - 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 S46. Summary statistics for natural gas - Utah, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 6,075 6,469 6,900 R 7,030 7,275 Production (million cubic feet) Gross Withdrawals From Gas Wells 328,135

  4. Million Cu. Feet Percent of National Total

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

    4 Virginia - 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 S48. Summary statistics for natural gas - Virginia, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 7,470 7,903 7,843 R 7,956 7,961 Production (million cubic feet) Gross Withdrawals From Gas Wells

  5. Million Cu. Feet Percent of National Total

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

    8 West Virginia - 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 S50. Summary statistics for natural gas - West Virginia, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 52,498 56,813 50,700 R 54,920 60,000 Production (million cubic feet) Gross Withdrawals

  6. Cost Estimating Handbook for Environmental Restoration

    SciTech Connect (OSTI)

    1990-09-01

    Environmental restoration (ER) projects have presented the DOE and cost estimators with a number of properties that are not comparable to the normal estimating climate within DOE. These properties include: An entirely new set of specialized expressions and terminology. A higher than normal exposure to cost and schedule risk, as compared to most other DOE projects, due to changing regulations, public involvement, resource shortages, and scope of work. A higher than normal percentage of indirect costs to the total estimated cost due primarily to record keeping, special training, liability, and indemnification. More than one estimate for a project, particularly in the assessment phase, in order to provide input into the evaluation of alternatives for the cleanup action. While some aspects of existing guidance for cost estimators will be applicable to environmental restoration projects, some components of the present guidelines will have to be modified to reflect the unique elements of these projects. The purpose of this Handbook is to assist cost estimators in the preparation of environmental restoration estimates for Environmental Restoration and Waste Management (EM) projects undertaken by DOE. The DOE has, in recent years, seen a significant increase in the number, size, and frequency of environmental restoration projects that must be costed by the various DOE offices. The coming years will show the EM program to be the largest non-weapons program undertaken by DOE. These projects create new and unique estimating requirements since historical cost and estimating precedents are meager at best. It is anticipated that this Handbook will enhance the quality of cost data within DOE in several ways by providing: The basis for accurate, consistent, and traceable baselines. Sound methodologies, guidelines, and estimating formats. Sources of cost data/databases and estimating tools and techniques available at DOE cost professionals.

  7. Total-system performance assessment for Yucca Mountain - SNL second iteration (TSPA-1993); Volume 2

    SciTech Connect (OSTI)

    Wilson, M.L.; Barnard, R.W.; Barr, G.E.; Dockery, H.A.; Dunn, E.; Eaton, R.R.; Martinez, M.J.; Gauthier, J.H.; Guerin, D.C.; Lu, N.

    1994-04-01

    Sandia National Laboratories has completed the second iteration of the periodic total-system performance assessments (TSPA-93) for the Yucca Mountain Site Characterization Project (YMP). These analyses estimate the future behavior of a potential repository for high-level nuclear waste at the Yucca Mountain, Nevada, site under consideration by the Department of Energy. TSPA-93 builds upon previous efforts by emphasizing YMP concerns relating to site characterization, design, and regulatory compliance. Scenarios describing expected conditions (aqueous and gaseous transport of contaminants) and low-probability events (human-intrusion drilling and volcanic intrusion) are modeled. The hydrologic processes modeled include estimates of the perturbations to ambient conditions caused by heating of the repository resulting from radioactive decay of the waste. Hydrologic parameters and parameter probability distributions have been derived from available site data. Possible future climate changes are modeled by considering two separate groundwater infiltration conditions: {open_quotes}wet{close_quotes} with a mean flux of 10 mm/yr, and {open_quotes}dry{close_quotes} with a mean flux of 0.5 mm/yr. Two alternative waste-package designs and two alternative repository areal thermal power densities are investigated. One waste package is a thin-wall container emplaced in a vertical borehole, and the second is a container designed with corrosion-resistant and corrosion-allowance walls emplaced horizontally in the drift. Thermal power loadings of 57 kW/acre (the loading specified in the original repository conceptual design) and 114 kW/acre (a loading chosen to investigate effects of a {open_quotes}hot repository{close_quotes}) are considered. TSPA-93 incorporates significant new detailed process modeling, including two- and three-dimensional modeling of thermal effects, groundwater flow in the saturated-zone aquifers, and gas flow in the unsaturated zone.

  8. Total-system performance assessment for Yucca Mountain - SNL second iteration (TSPA-1993); Volume 1

    SciTech Connect (OSTI)

    Wilson, M.L.; Gauthier, J.H.; Barnard, R.W.; Barr, G.E.; Dockery, H.A.; Dunn, E.; Eaton, R.R.; Guerin, D.C.; Lu, N.; Martinez, M.J.

    1994-04-01

    Sandia National Laboratories has completed the second iteration of the periodic total-system performance assessments (TSPA-93) for the Yucca Mountain Site Characterization Project (YMP). These analyses estimate the future behavior of a potential repository for high-level nuclear waste at the Yucca Mountain, Nevada, site under consideration by the Department of Energy. TSPA-93 builds upon previous efforts by emphasizing YMP concerns relating to site characterization, design, and regulatory compliance. Scenarios describing expected conditions (aqueous and gaseous transport of contaminants) and low-probability events (human-intrusion drilling and volcanic intrusion) are modeled. The hydrologic processes modeled include estimates of the perturbations to ambient conditions caused by heating of the repository resulting from radioactive decay of the waste. Hydrologic parameters and parameter probability distributions have been derived from available site data. Possible future climate changes are modeled by considering two separate groundwater infiltration conditions: {open_quotes}wet{close_quotes} with a mean flux of 10 mm/yr, and {open_quotes}dry{close_quotes} with a mean flux of 0.5 mm/yr. Two alternative waste-package designs and two alternative repository areal thermal power densities are investigated. One waste package is a thin-wall container emplaced in a vertical borehole, and the second is a container designed with corrosion-resistant and corrosion-allowance walls emplaced horizontally in the drift. Thermal power loadings of 57 kW/acre (the loading specified in the original repository conceptual design) and 114 kW/acre (a loading chosen to investigate effects of a {open_quotes}hot repository{close_quotes}) are considered. TSPA-93 incorporates significant new detailed process modeling, including two- and three-dimensional modeling of thermal effects, groundwater flow in the saturated-zone aquifers, and gas flow in the unsaturated zone.

  9. Budget estimates. Fiscal year 1998

    SciTech Connect (OSTI)

    1997-02-01

    The U.S. Congress has determined that the safe use of nuclear materials for peaceful purposes is a legitimate and important national goal. It has entrusted the Nuclear Regulatory Commission (NRC) with the primary Federal responsibility for achieving that goal. The NRC`s mission, therefore, is to regulate the Nation`s civilian use of byproduct, source, and special nuclear materials to ensure adequate protection of public health and safety, to promote the common defense and security, and to protect the environment. The NRC`s FY 1998 budget requests new budget authority of $481,300,000 to be funded by two appropriations - one is the NRC`s Salaraies and Expenses appropriation for $476,500,000, and the other is NRC`s Office of Inspector General appropriation for $4,800,000. Of the funds appropriated to the NRC`s Salaries and Expenses, $17,000,000, shall be derived from the Nuclear Waste Fund and $2,000,000 shall be derived from general funds. The proposed FY 1998 appropriation legislation would also exempt the $2,000,000 for regulatory reviews and other assistance provided to the Department of Energy from the requirement that the NRC collect 100 percent of its budget from fees. The sums appropriated to the NRC`s Salaries and Expenses and NRC`s Office of Inspector General shall be reduced by the amount of revenues received during FY 1998 from licensing fees, inspection services, and other services and collections, so as to result in a final FY 1998 appropriation for the NRC of an estimated $19,000,000 - the amount appropriated from the Nuclear Waste Fund and from general funds. Revenues derived from enforcement actions shall be deposited to miscellaneous receipts of the Treasury.

  10. Building unbiased estimators from non-gaussian likelihoods with application to shear estimation

    SciTech Connect (OSTI)

    Madhavacheril, Mathew S.; McDonald, Patrick; Sehgal, Neelima; Slosar, Anze

    2015-01-15

    We develop a general framework for generating estimators of a given quantity which are unbiased to a given order in the difference between the true value of the underlying quantity and the fiducial position in theory space around which we expand the likelihood. We apply this formalism to rederive the optimal quadratic estimator and show how the replacement of the second derivative matrix with the Fisher matrix is a generic way of creating an unbiased estimator (assuming choice of the fiducial model is independent of data). Next we apply the approach to estimation of shear lensing, closely following the work of Bernstein and Armstrong (2014). Our first order estimator reduces to their estimator in the limit of zero shear, but it also naturally allows for the case of non-constant shear and the easy calculation of correlation functions or power spectra using standard methods. Both our first-order estimator and Bernstein and Armstrong’s estimator exhibit a bias which is quadratic in true shear. Our third-order estimator is, at least in the realm of the toy problem of Bernstein and Armstrong, unbiased to 0.1% in relative shear errors ?g/g for shears up to |g| = 0.2.