Sample records for total propagated uncertainty

  1. Propagation of nuclear data uncertainties for ELECTRA burn-up calculations

    E-Print Network [OSTI]

    H. Sjöstrand; E. Alhassan; J. Duan; C. Gustavsson; A. Koning; S. Pomp; D. Rochman; M. Österlund

    2013-04-08T23:59:59.000Z

    The European Lead-Cooled Training Reactor (ELECTRA) has been proposed as a training reactor for fast systems within the Swedish nuclear program. It is a low-power fast reactor cooled by pure liquid lead. In this work, we propagate the uncertainties in Pu-239 transport data to uncertainties in the fuel inventory of ELECTRA during the reactor life using the Total Monte Carlo approach (TMC). Within the TENDL project the nuclear models input parameters were randomized within their uncertainties and 740 Pu-239 nuclear data libraries were generated. These libraries are used as inputs to reactor codes, in our case SERPENT, to perform uncertainty analysis of nuclear reactor inventory during burn-up. The uncertainty in the inventory determines uncertainties in: the long-term radio-toxicity, the decay heat, the evolution of reactivity parameters, gas pressure and volatile fission product content. In this work, a methodology called fast TMC is utilized, which reduces the overall calculation time. The uncertainty in the long-term radiotoxicity, decay heat, gas pressure and volatile fission products were found to be insignificant. However, the uncertainty of some minor actinides were observed to be rather large and therefore their impact on multiple recycling should be investigated further. It was also found that, criticality benchmarks can be used to reduce inventory uncertainties due to nuclear data. Further studies are needed to include fission yield uncertainties, more isotopes, and a larger set of benchmarks.

  2. Uncertainty Quantification and Propagation in Nuclear Density Functional Theory

    E-Print Network [OSTI]

    N. Schunck; J. D. McDonnell; D. Higdon; J. Sarich; S. M. Wild

    2015-03-19T23:59:59.000Z

    Nuclear density functional theory (DFT) is one of the main theoretical tools used to study the properties of heavy and superheavy elements, or to describe the structure of nuclei far from stability. While on-going efforts seek to better root nuclear DFT in the theory of nuclear forces [see Duguet et al., this issue], energy functionals remain semi-phenomenological constructions that depend on a set of parameters adjusted to experimental data in finite nuclei. In this paper, we review recent efforts to quantify the related uncertainties, and propagate them to model predictions. In particular, we cover the topics of parameter estimation for inverse problems, statistical analysis of model uncertainties and Bayesian inference methods. Illustrative examples are taken from the literature.

  3. Propagation of nuclear data uncertainties for ELECTRA burn-up calculations

    E-Print Network [OSTI]

    ostrand, H; Duan, J; Gustavsson, C; Koning, A; Pomp, S; Rochman, D; Osterlund, M

    2013-01-01T23:59:59.000Z

    The European Lead-Cooled Training Reactor (ELECTRA) has been proposed as a training reactor for fast systems within the Swedish nuclear program. It is a low-power fast reactor cooled by pure liquid lead. In this work, we propagate the uncertainties in Pu-239 transport data to uncertainties in the fuel inventory of ELECTRA during the reactor life using the Total Monte Carlo approach (TMC). Within the TENDL project the nuclear models input parameters were randomized within their uncertainties and 740 Pu-239 nuclear data libraries were generated. These libraries are used as inputs to reactor codes, in our case SERPENT, to perform uncertainty analysis of nuclear reactor inventory during burn-up. The uncertainty in the inventory determines uncertainties in: the long-term radio-toxicity, the decay heat, the evolution of reactivity parameters, gas pressure and volatile fission product content. In this work, a methodology called fast TMC is utilized, which reduces the overall calculation time. The uncertainty in the ...

  4. TOTAL MEASUREMENT UNCERTAINTY IN HOLDUP MEASUREMENTS AT THE PLUTONIUM FINISHING PLANT (PFP)

    SciTech Connect (OSTI)

    KEELE, B.D.

    2007-07-05T23:59:59.000Z

    An approach to determine the total measurement uncertainty (TMU) associated with Generalized Geometry Holdup (GGH) [1,2,3] measurements was developed and implemented in 2004 and 2005 [4]. This paper describes a condensed version of the TMU calculational model, including recent developments. Recent modifications to the TMU calculation model include a change in the attenuation uncertainty, clarifying the definition of the forward background uncertainty, reducing conservatism in the random uncertainty by selecting either a propagation of counting statistics or the standard deviation of the mean, and considering uncertainty in the width and height as a part of the self attenuation uncertainty. In addition, a detection limit is calculated for point sources using equations derived from summary equations contained in Chapter 20 of MARLAP [5]. The Defense Nuclear Facilities Safety Board (DNFSB) Recommendation 2007-1 to the Secretary of Energy identified a lack of requirements and a lack of standardization for performing measurements across the U.S. Department of Energy (DOE) complex. The DNFSB also recommended that guidance be developed for a consistent application of uncertainty values. As such, the recent modifications to the TMU calculational model described in this paper have not yet been implemented. The Plutonium Finishing Plant (PFP) is continuing to perform uncertainty calculations as per Reference 4. Publication at this time is so that these concepts can be considered in developing a consensus methodology across the complex.

  5. analytical uncertainty propagation: Topics by E-print Network

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

    uncertainties in typical oil and gas projects: 1. The oil price, 2. The investments (capex) and operating. The oil and gas reserves and production profiles, 5. The production...

  6. Nonlinear Propagation of Orbit Uncertainty Using Non-Intrusive Polynomial Chaos

    E-Print Network [OSTI]

    Born, George

    Nonlinear Propagation of Orbit Uncertainty Using Non-Intrusive Polynomial Chaos Brandon A. Jones1. Results presented in this paper use non-intrusive, i.e., sampling-based, methods in combination the use of polynomial chaos expansions (PCEs) for the non- linear, non-Gaussian propagation of orbit state

  7. AN OPTIMIZATION APPROACH TO UNCERTAINTY PROPAGATION IN BOUNDARY LOAD FLOW

    E-Print Network [OSTI]

    Stankoviæ, Aleksandar

    .g., SCADA) and network parame- ters. The method is potentially applicable to large-scale power systems) possible errors in measurements (e.g., SCADA). Topological uncertainties are linked with the fidelity. On the other hand, errors in network parameters and in SCADA measurements tend to be smaller in size

  8. Propagation of uncertainties in the nuclear DFT models

    E-Print Network [OSTI]

    Markus Kortelainen

    2014-09-04T23:59:59.000Z

    Parameters of the nuclear density functional theory (DFT) models are usually adjusted to experimental data. As a result they carry certain theoretical error, which, as a consequence, carries out to the predicted quantities. In this work we address the propagation of theoretical error, within the nuclear DFT models, from the model parameters to the predicted observables. In particularly, the focus is set on the Skyrme energy density functional models.

  9. Stochastic sampling method with MCNPX for nuclear data uncertainty propagation in criticality safety applications

    SciTech Connect (OSTI)

    Zhu, T.; Vasiliev, A.; Wieselquist, W.; Ferroukhi, H. [Paul Scherrer Institut, 5232 Villigen (Switzerland)

    2012-07-01T23:59:59.000Z

    In the domain of criticality safety, the efficient propagation of uncertainty in nuclear data to uncertainty in k{sub eff} is an important area of current research. In this paper, a method based on stochastic sampling is presented for uncertainty propagation in MCNPX calculations. To that aim, the nuclear data (i.e. cross sections) are assumed to have a multivariate normal distribution and simple random sampling is performed following this presumed probability distribution. A verification of the developed stochastic sampling procedure with MCNPX is then conducted using the {sup 239}Pu Jezebel experiment as well as the PB-2 BWR and TMI-1 PWR pin cell models from the Uncertainty Analysis in Modeling (UAM) exercises. For the Jezebel case, it is found that the developed stochastic sampling approach predicts similar k{sub eff} uncertainties compared to conventional sensitivity and uncertainty methods. For the UAM models, slightly lower uncertainties are obtained when comparing to existing preliminary results. Further details of these verification studies are discussed and directions for future work are outlined. (authors)

  10. Comparison of nuclear data uncertainty propagation methodologies for PWR burn-up simulations

    E-Print Network [OSTI]

    Diez, Carlos Javier; Hoefer, Axel; Porsch, Dieter; Cabellos, Oscar

    2014-01-01T23:59:59.000Z

    Several methodologies using different levels of approximations have been developed for propagating nuclear data uncertainties in nuclear burn-up simulations. Most methods fall into the two broad classes of Monte Carlo approaches, which are exact apart from statistical uncertainties but require additional computation time, and first order perturbation theory approaches, which are efficient for not too large numbers of considered response functions but only applicable for sufficiently small nuclear data uncertainties. Some methods neglect isotopic composition uncertainties induced by the depletion steps of the simulations, others neglect neutron flux uncertainties, and the accuracy of a given approximation is often very hard to quantify. In order to get a better sense of the impact of different approximations, this work aims to compare results obtained based on different approximate methodologies with an exact method, namely the NUDUNA Monte Carlo based approach developed by AREVA GmbH. In addition, the impact ...

  11. Propagating Uncertainty in Solar Panel Performance for Life Cycle Modeling in Early Stage Design

    E-Print Network [OSTI]

    Yang, Maria

    Propagating Uncertainty in Solar Panel Performance for Life Cycle Modeling in Early Stage Design. This work is conducted in the context of an amorphous photovoltaic (PV) panel, using data gathered from the National Solar Radiation Database, as well as realistic data collected from an experimental hardware setup

  12. A non-intrusive stochastic Galerkin approach for modeling uncertainty propagation in

    E-Print Network [OSTI]

    Zabaras, Nicholas J.

    A non-intrusive stochastic Galerkin approach for modeling uncertainty propagation in deformation or significant additions. In this paper we present an approach called Non-Intrusive Stochastic Galerkin (NISG-Carlo based stochastic analysis of de- formation process although relatively non-intrusive and trivial

  13. Comparison of nuclear data uncertainty propagation methodologies for PWR burn-up simulations

    E-Print Network [OSTI]

    Carlos Javier Diez; Oliver Buss; Axel Hoefer; Dieter Porsch; Oscar Cabellos

    2014-11-04T23:59:59.000Z

    Several methodologies using different levels of approximations have been developed for propagating nuclear data uncertainties in nuclear burn-up simulations. Most methods fall into the two broad classes of Monte Carlo approaches, which are exact apart from statistical uncertainties but require additional computation time, and first order perturbation theory approaches, which are efficient for not too large numbers of considered response functions but only applicable for sufficiently small nuclear data uncertainties. Some methods neglect isotopic composition uncertainties induced by the depletion steps of the simulations, others neglect neutron flux uncertainties, and the accuracy of a given approximation is often very hard to quantify. In order to get a better sense of the impact of different approximations, this work aims to compare results obtained based on different approximate methodologies with an exact method, namely the NUDUNA Monte Carlo based approach developed by AREVA GmbH. In addition, the impact of different covariance data is studied by comparing two of the presently most complete nuclear data covariance libraries (ENDF/B-VII.1 and SCALE 6.0), which reveals a high dependency of the uncertainty estimates on the source of covariance data. The burn-up benchmark Exercise I-1b proposed by the OECD expert group "Benchmarks for Uncertainty Analysis in Modeling (UAM) for the Design, Operation and Safety Analysis of LWRs" is studied as an example application. The burn-up simulations are performed with the SCALE 6.0 tool suite.

  14. Monte Carlo solution for uncertainty propagation in particle transport with a stochastic Galerkin method

    SciTech Connect (OSTI)

    Franke, B. C. [Sandia National Laboratories, Albuquerque, NM 87185 (United States); Prinja, A. K. [Department of Chemical and Nuclear Engineering, University of New Mexico, Albuquerque, NM 87131 (United States)

    2013-07-01T23:59:59.000Z

    The stochastic Galerkin method (SGM) is an intrusive technique for propagating data uncertainty in physical models. The method reduces the random model to a system of coupled deterministic equations for the moments of stochastic spectral expansions of result quantities. We investigate solving these equations using the Monte Carlo technique. We compare the efficiency with brute-force Monte Carlo evaluation of uncertainty, the non-intrusive stochastic collocation method (SCM), and an intrusive Monte Carlo implementation of the stochastic collocation method. We also describe the stability limitations of our SGM implementation. (authors)

  15. User's guide for ALEX: uncertainty propagation from raw data to final results for ORELA transmission measurements

    SciTech Connect (OSTI)

    Larson, N.M.

    1984-02-01T23:59:59.000Z

    This report describes a computer code (ALEX) developed to assist in AnaLysis of EXperimental data at the Oak Ridge Electron Linear Accelerator (ORELA). Reduction of data from raw numbers (counts per channel) to physically meaningful quantities (such as cross sections) is in itself a complicated procedure; propagation of experimental uncertainties through that reduction procedure has in the past been viewed as even more difficult - if not impossible. The purpose of the code ALEX is to correctly propagate all experimental uncertainties through the entire reduction procedure, yielding the complete covariance matrix for the reduced data, while requiring little additional input from the eperimentalist beyond that which is required for the data reduction itself. This report describes ALEX in detail, with special attention given to the case of transmission measurements (the code itself is applicable, with few changes, to any type of data). Application to the natural iron measurements of D.C. Larson et al. is described in some detail.

  16. Total Measurement Uncertainty (TMU) for Nondestructive Assay of Transuranic (TRU) Waste at the WRAP Facility

    SciTech Connect (OSTI)

    WILLS, C.E.

    2000-02-24T23:59:59.000Z

    The Waste Receiving and Processing (WRAP) facility, located on the Hanford Site in southeast Washington, is a key link in the certification of Hanford's transuranic (TRU) waste for shipment to the Waste Isolation Pilot Plant (WIPP). Waste characterization is one of the vital functions performed at WRAP, and nondestructive assay (NDA) measurements of TRU waste containers is one of two required methods used for waste characterization (Reference 1). Various programs exist to ensure the validity of waste characterization data; all of these cite the need for clearly defined knowledge of uncertainty, associated with any measurements taken. All measurements have an inherent uncertainty associated with them. The combined effect of all uncertainties associated with a measurement is referred to as the Total Measurement Uncertainty (TMU). The NDA measurement uncertainties can be numerous and complex. In addition to system-induced measurement uncertainty, other factors contribute to the TMU, each associated with a particular measurement. The NDA measurements at WRAP are based on processes (radioactive decay and induced fission) which are statistical in nature. As a result, the proper statistical summation of the various uncertainty components is essential. This report examines the contributing factors to NDA measurement uncertainty at WRAP. The significance of each factor on the TMU is analyzed, and a final method is given for determining the TMU for NDA measurements at WRAP. As more data becomes available, and WRAP gains in operational experience, this report will be reviewed semi-annually and updated as necessary. This report also includes the data flow paths for the analytical process in the radiometric determinations.

  17. A stochastic particle-mesh scheme for uncertainty propagation in vortical flows

    SciTech Connect (OSTI)

    Le Maitre, Olivier P. [Laboratoire de Mecanique et d'Energetique d'Evry and LIMSI-CNRS, Universite d'Evry Val d'Essonne, 40, rue du Pelvoux, CE 1455, 91020 Evry Cedex (France)], E-mail: olm@iup.univ-evry.fr; Knio, Omar M. [Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218-2686 (United States)], E-mail: knio@jhu.edu

    2007-09-10T23:59:59.000Z

    A new mesh-particle scheme is constructed for uncertainty propagation in vortical flow. The scheme is based on the incorporation of polynomial chaos (PC) expansions into a Lagrangian particle approximation of the Navier-Stokes equations. The main idea of the method is to use a unique set of particles to transport the stochastic modes of the solution. The particles are transported by the mean velocity field, while their stochastic strengths are updated to account for diffusive and convective effects induced by the coupling between stochastic modes. An integral treatment is used for the evaluation of the coupled stochastic terms, following the framework of the particle strength exchange (PSE) methods, which yields a conservative algorithm. It is also shown that it is possible to apply solution algorithms used in deterministic setting, including particle-mesh techniques and particle remeshing. Thus, the method combines the advantages of particles discretizations with the efficiency of PC representations. Validation of the method on uncertain diffusion and convection problems is first performed. An example is then presented of natural convection of a hot patch of fluid in infinite domain, and the computations are used to illustrate the effectiveness of the approach for both large number of particles and high-order PC expansions.

  18. Estimating and decomposing total uncertainty for survey-based abundance estimates of Norwegian spring-spawning herring

    E-Print Network [OSTI]

    Aldrin, Magne

    the Norwegian coast, feed in the Norwegian Sea and adjacent areas, and overwinter in various partsEstimating and decomposing total uncertainty for survey-based abundance estimates of Norwegian uncertainty for survey-based abundance estimates of Norwegian spring-spawning herring. ­ ICES Journal

  19. Uncertainties propagation in the framework of a Rod Ejection Accident modeling based on a multi-physics approach

    SciTech Connect (OSTI)

    Le Pallec, J. C.; Crouzet, N.; Bergeaud, V.; Delavaud, C. [CEA/DEN/DM2S, CEA/Saclay, 91191 Gif sur Yvette Cedex (France)

    2012-07-01T23:59:59.000Z

    The control of uncertainties in the field of reactor physics and their propagation in best-estimate modeling are a major issue in safety analysis. In this framework, the CEA develops a methodology to perform multi-physics simulations including uncertainties analysis. The present paper aims to present and apply this methodology for the analysis of an accidental situation such as REA (Rod Ejection Accident). This accident is characterized by a strong interaction between the different areas of the reactor physics (neutronic, fuel thermal and thermal hydraulic). The modeling is performed with CRONOS2 code. The uncertainties analysis has been conducted with the URANIE platform developed by the CEA: For each identified response from the modeling (output) and considering a set of key parameters with their uncertainties (input), a surrogate model in the form of a neural network has been produced. The set of neural networks is then used to carry out a sensitivity analysis which consists on a global variance analysis with the determination of the Sobol indices for all responses. The sensitivity indices are obtained for the input parameters by an approach based on the use of polynomial chaos. The present exercise helped to develop a methodological flow scheme, to consolidate the use of URANIE tool in the framework of parallel calculations. Finally, the use of polynomial chaos allowed computing high order sensitivity indices and thus highlighting and classifying the influence of identified uncertainties on each response of the analysis (single and interaction effects). (authors)

  20. An algorithm for U-Pb isotope dilution data reduction and uncertainty propagation

    E-Print Network [OSTI]

    McLean, Noah Morgan

    High-precision U-Pb geochronology by isotope dilution-thermal ionization mass spectrometry is integral to a variety of Earth science disciplines, but its ultimate resolving power is quantified by the uncertainties of ...

  1. An algorithm for U-Pb isotope dilution data reduction and uncertainty propagation

    E-Print Network [OSTI]

    McLean, Noah M.; Bowring, J.F.; Bowring, S.A.

    2011-06-01T23:59:59.000Z

    High-precision U-Pb geochronology by isotope dilution-thermal ionization mass spectrometry is integral to a variety of Earth science disciplines, but its ultimate resolving power is quantified by the uncertainties of ...

  2. Error propagation equations for estimating the uncertainty in high-speed wind tunnel test results

    SciTech Connect (OSTI)

    Clark, E.L.

    1994-07-01T23:59:59.000Z

    Error propagation equations, based on the Taylor series model, are derived for the nondimensional ratios and coefficients most often encountered in high-speed wind tunnel testing. These include pressure ratio and coefficient, static force and moment coefficients, dynamic stability coefficients, and calibration Mach number. The error equations contain partial derivatives, denoted as sensitivity coefficients, which define the influence of free-steam Mach number, M{infinity}, on various aerodynamic ratios. To facilitate use of the error equations, sensitivity coefficients are derived and evaluated for five fundamental aerodynamic ratios which relate free-steam test conditions to a reference condition.

  3. Total Measurement Uncertainty (TMU) for Nondestructive Assay of Transuranic (TRU) Waste at the WRAP Facility

    SciTech Connect (OSTI)

    WILLS, C.E.

    2000-01-06T23:59:59.000Z

    This report examines the contributing factors to NDA measurement uncertainty at WRAP The significance of each factor on the TMU is analyzed and a final method is given for determining the TMU for NDA measurements at WRAP. As more data becomes available and WRAP gains in operational experience this report will be reviewed semi annually and updated as necessary.

  4. Proposal for the utilization of the total cross section covariances and its correlations with channel reactions for sensitivity and uncertainty analysis

    SciTech Connect (OSTI)

    Sabouri, P.; Bidaud, A. [Labratoire de Physique Subatomique et de Cosmologie, CNRS-IN2P3/UJF/INPG, Grenoble (France); Dabiran, S.; Buijs, A. [Dept. of Engineering Physics, McMaster Univ., Hamilton, ON (Canada)

    2012-07-01T23:59:59.000Z

    An alternate method for the estimation of the global uncertainty on criticality, using the total cross section and its covariances, is proposed. Application of the method with currently available covariance data leads to an unrealistically large prediction of the global uncertainty on criticality. New covariances for total cross section and individual reactions are proposed. Analysis with the proposed covariance matrices is found to result in a global uncertainty for criticality consistent with the traditional method. Recommendations are made to evaluators for providing total cross section covariances. (authors)

  5. Total Measurement Uncertainty (TMU) for Nondestructive Assay of Transuranic (TRU) Waste at the WRAP Facility

    SciTech Connect (OSTI)

    CANTALOUB, M.G.

    2000-10-20T23:59:59.000Z

    At the WRAP facility, there are two identical imaging passive/active neutron (IPAN) assay systems and two identical gamma energy assay (GEA) systems. Currently, only the GEA systems are used to characterize waste, therefore, only the GEA systems are addressed in this document. This document contains the limiting factors relating to the waste drum analysis for shipments destined for WIPP. The TMU document provides the uncertainty basis in the NDA analysis of waste containers at the WRAP facility. The defined limitations for the current analysis scheme are as follows: (1) The WRAP waste stream debris is from the Hanford Plutonium Finishing Plant's process lines, primarily combustible materials. (2) Plutonium analysis range is from the minimum detectable concentration (MDC), Reference 6, to 200 grams (g). (3) The GEA system calibration density ranges from 0.013 g/cc to 1.6 g/cc. (4) PDP Plutonium drum densities were evaluated from 0.065 g/cc to 0.305 g/cc. (5) PDP Plutonium source weights ranged from 0.030 g to 318 g, in both empty and combustibles matrix drums. (6) The GEA system design density correction mass absorption coefficient table (MAC) is Lucite, a material representative of combustible waste. (7) Drums with material not fitting the debris waste criteria are targeted for additional calculations, reviews, and potential re-analysis using a calibration suited for the waste type.

  6. Total Measurement Uncertainty (TMU) for Nondestructive Assay of Transuranic (TRU) Waste at the WRAP Facility

    SciTech Connect (OSTI)

    CANTALOUB, M.G.

    2000-05-22T23:59:59.000Z

    At the WRAP facility, there are two identical imaging passive/active neutron (IPAN) assay systems and two identical gamma energy assay (GEA) systems. Currently, only the GEA systems are used to characterize waste, therefore, only the GEA systems are addressed in this document. This document contains the limiting factors relating to the waste drum analysis for shipments destined for WIPP. The TMU document provides the uncertainty basis in the NDA analysis of waste containers at the WRAP facility. The defined limitations for the current analysis scheme are as follows: The WRAP waste stream debris is from the Hanford Plutonium Finishing Plant's process lines, primarily combustible materials. Plutonium analysis range is from the minimum detectable concentration (MDC), Reference 6, to 160 grams (8). The GEA system calibration density ranges from 0.013 g/cc to 1.6 g/cc. PDP Plutonium drum densities were evaluated from 0.065 g/cc to 0.305 gkc. PDP Plutonium source weights ranged from 0.030 g to 3 18 g, in both empty and combustibles matrix drums. The GEA system design density correction macroscopic absorption cross section table (MAC) is Lucite, a material representative of combustible waste. Drums with material not fitting the debris waste criteria are targeted for additional calculations, reviews, and potential re-analysis using a calibration suited for the waste type.

  7. THE PROPAGATION OF UNCERTAINTIES IN STELLAR POPULATION SYNTHESIS MODELING. II. THE CHALLENGE OF COMPARING GALAXY EVOLUTION MODELS TO OBSERVATIONS

    SciTech Connect (OSTI)

    Conroy, Charlie; Gunn, James E. [Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08544 (United States); White, Martin [Departments of Physics and Astronomy, University of California Berkeley, CA 94720 (United States)

    2010-01-01T23:59:59.000Z

    Models for the formation and evolution of galaxies readily predict physical properties such as star formation rates, metal-enrichment histories, and, increasingly, gas and dust content of synthetic galaxies. Such predictions are frequently compared to the spectral energy distributions of observed galaxies via the stellar population synthesis (SPS) technique. Substantial uncertainties in SPS exist, and yet their relevance to the task of comparing galaxy evolution models to observations has received little attention. In the present work, we begin to address this issue by investigating the importance of uncertainties in stellar evolution, the initial stellar mass function (IMF), and dust and interstellar medium (ISM) properties on the translation from models to observations. We demonstrate that these uncertainties translate into substantial uncertainties in the ultraviolet, optical, and near-infrared colors of synthetic galaxies. Aspects that carry significant uncertainties include the logarithmic slope of the IMF above 1 M{sub sun}, dust attenuation law, molecular cloud disruption timescale, clumpiness of the ISM, fraction of unobscured starlight, and treatment of advanced stages of stellar evolution including blue stragglers, the horizontal branch, and the thermally pulsating asymptotic giant branch. The interpretation of the resulting uncertainties in the derived colors is highly non-trivial because many of the uncertainties are likely systematic, and possibly correlated with the physical properties of galaxies. We therefore urge caution when comparing models to observations.

  8. Methodology for characterizing modeling and discretization uncertainties in computational simulation

    SciTech Connect (OSTI)

    ALVIN,KENNETH F.; OBERKAMPF,WILLIAM L.; RUTHERFORD,BRIAN M.; DIEGERT,KATHLEEN V.

    2000-03-01T23:59:59.000Z

    This research effort focuses on methodology for quantifying the effects of model uncertainty and discretization error on computational modeling and simulation. The work is directed towards developing methodologies which treat model form assumptions within an overall framework for uncertainty quantification, for the purpose of developing estimates of total prediction uncertainty. The present effort consists of work in three areas: framework development for sources of uncertainty and error in the modeling and simulation process which impact model structure; model uncertainty assessment and propagation through Bayesian inference methods; and discretization error estimation within the context of non-deterministic analysis.

  9. Error propagation equations and tables for estimating the uncertainty in high-speed wind tunnel test results

    SciTech Connect (OSTI)

    Clark, E.L.

    1993-08-01T23:59:59.000Z

    Error propagation equations, based on the Taylor series model, are derived for the nondimensional ratios and coefficients most often encountered in high-speed wind tunnel testing. These include pressure ratio and coefficient, static force and moment coefficients, dynamic stability coefficients, calibration Mach number and Reynolds number. The error equations contain partial derivatives, denoted as sensitivity coefficients, which define the influence of free-stream Mach number, M{infinity}, on various aerodynamic ratios. To facilitate use of the error equations, sensitivity coefficients are derived and evaluated for nine fundamental aerodynamic ratios, most of which relate free-stream test conditions (pressure, temperature, density or velocity) to a reference condition. Tables of the ratios, R, absolute sensitivity coefficients, {partial_derivative}R/{partial_derivative}M{infinity}, and relative sensitivity coefficients, (M{infinity}/R) ({partial_derivative}R/{partial_derivative}M{infinity}), are provided as functions of M{infinity}.

  10. Direct Aerosol Forcing Uncertainty

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

    Mccomiskey, Allison

    Understanding sources of uncertainty in aerosol direct radiative forcing (DRF), the difference in a given radiative flux component with and without aerosol, is essential to quantifying changes in Earth's radiation budget. We examine the uncertainty in DRF due to measurement uncertainty in the quantities on which it depends: aerosol optical depth, single scattering albedo, asymmetry parameter, solar geometry, and surface albedo. Direct radiative forcing at the top of the atmosphere and at the surface as well as sensitivities, the changes in DRF in response to unit changes in individual aerosol or surface properties, are calculated at three locations representing distinct aerosol types and radiative environments. The uncertainty in DRF associated with a given property is computed as the product of the sensitivity and typical measurement uncertainty in the respective aerosol or surface property. Sensitivity and uncertainty values permit estimation of total uncertainty in calculated DRF and identification of properties that most limit accuracy in estimating forcing. Total uncertainties in modeled local diurnally averaged forcing range from 0.2 to 1.3 W m-2 (42 to 20%) depending on location (from tropical to polar sites), solar zenith angle, surface reflectance, aerosol type, and aerosol optical depth. The largest contributor to total uncertainty in DRF is usually single scattering albedo; however decreasing measurement uncertainties for any property would increase accuracy in DRF. Comparison of two radiative transfer models suggests the contribution of modeling error is small compared to the total uncertainty although comparable to uncertainty arising from some individual properties.

  11. Assessment and Propagation of Model Uncertainty

    E-Print Network [OSTI]

    David Draper

    2011-01-01T23:59:59.000Z

    1982). Outlook for World Oil Prices. Washington DC: U. S.run. But, in view of the oil price example, which is worse—Case N-2524-RC, of Oil Prices. Santa Monica, CA: RAND.

  12. Propagation of Uncertainty in Chemically Activated Systems

    E-Print Network [OSTI]

    Androulakis, Ioannis (Yannis)

    in internal combustion engines. A distinguishing feature of the low-temperature chemistry of many molecules

  13. Assessor Training Measurement Uncertainty

    E-Print Network [OSTI]

    NVLAP Assessor Training Measurement Uncertainty #12;Assessor Training 2009: Measurement Uncertainty Training 2009: Measurement Uncertainty 3 Measurement Uncertainty ·Calibration and testing labs performing Training 2009: Measurement Uncertainty 4 Measurement Uncertainty ·When the nature of the test precludes

  14. Uncertainty, investment, and industry evolution

    E-Print Network [OSTI]

    Caballero, Ricardo J.

    1992-01-01T23:59:59.000Z

    We study the effects of aggregate and idiosyncratic uncertainty on the entry of firms, total investment, and prices in a competitive industry with irreversible investment. We first use standard dynamic programming methods ...

  15. Quantifying uncertainty in LCA-modelling of waste management systems

    SciTech Connect (OSTI)

    Clavreul, Julie, E-mail: julc@env.dtu.dk [Department of Environmental Engineering, Technical University of Denmark, Miljoevej, Building 113, DK-2800 Kongens Lyngby (Denmark); Guyonnet, Dominique [BRGM, ENAG BRGM-School, BP 6009, 3 Avenue C. Guillemin, 45060 Orleans Cedex (France); Christensen, Thomas H. [Department of Environmental Engineering, Technical University of Denmark, Miljoevej, Building 113, DK-2800 Kongens Lyngby (Denmark)

    2012-12-15T23:59:59.000Z

    Highlights: Black-Right-Pointing-Pointer Uncertainty in LCA-modelling of waste management is significant. Black-Right-Pointing-Pointer Model, scenario and parameter uncertainties contribute. Black-Right-Pointing-Pointer Sequential procedure for quantifying uncertainty is proposed. Black-Right-Pointing-Pointer Application of procedure is illustrated by a case-study. - Abstract: Uncertainty analysis in LCA studies has been subject to major progress over the last years. In the context of waste management, various methods have been implemented but a systematic method for uncertainty analysis of waste-LCA studies is lacking. The objective of this paper is (1) to present the sources of uncertainty specifically inherent to waste-LCA studies, (2) to select and apply several methods for uncertainty analysis and (3) to develop a general framework for quantitative uncertainty assessment of LCA of waste management systems. The suggested method is a sequence of four steps combining the selected methods: (Step 1) a sensitivity analysis evaluating the sensitivities of the results with respect to the input uncertainties, (Step 2) an uncertainty propagation providing appropriate tools for representing uncertainties and calculating the overall uncertainty of the model results, (Step 3) an uncertainty contribution analysis quantifying the contribution of each parameter uncertainty to the final uncertainty and (Step 4) as a new approach, a combined sensitivity analysis providing a visualisation of the shift in the ranking of different options due to variations of selected key parameters. This tiered approach optimises the resources available to LCA practitioners by only propagating the most influential uncertainties.

  16. Quantitative Phenomena Identification and Ranking Table (QPIRT) for Bayesian uncertainty quantification

    SciTech Connect (OSTI)

    Yurko, J. P.; Buongiorno, J. [MIT, 77 Massachusetts Avenue, Cambridge, MA 02139 (United States)

    2012-07-01T23:59:59.000Z

    Propagating parameter uncertainty for a nuclear reactor system code is a challenging problem due to often non-linear system response to the numerous parameters involved and lengthy computational times; issues that compound when a statistical sampling procedure is adopted, since the code must be run many times. The number of parameters sampled must therefore be limited to as few as possible that still accurately characterize the uncertainty in the system response. A Quantitative Phenomena Identification and Ranking Table (QPIRT) was developed to accomplish this goal. The QPIRT consists of two steps: a 'Top-Down' step focusing on identifying the dominant physical phenomena controlling the system response, and a 'Bottom-Up' step which focuses on determining the correlations from those key physical phenomena that significantly contribute to the response uncertainty. The Top-Down step evaluates phenomena using the governing equations of the system code at nominal parameter values, providing a 'fast' screening step. The Bottom-Up step then analyzes the correlations and models for the phenomena identified from the Top-Down step to find which parameters to sample. The QPIRT, through the Top-Down and Bottom-Up steps thus provides a systematic approach to determining the limited set of physically relevant parameters that influence the uncertainty of the system response. This strategy was demonstrated through an application to the RELAP5-based analysis of a PWR Total Loss of main Feedwater Flow (TLOFW) accident, also known as feed and bleed' scenario, . Ultimately, this work is the first component in a larger task of building a calibrated uncertainty propagation framework. The QPIRT is an essential piece because the uncertainty of those selected parameters will be calibrated to data from both Separate and Integral Effect Tests (SETs and IETs). Therefore the system response uncertainty will incorporate the knowledge gained from the database of past large IETs. (authors)

  17. Uncertainty analysis

    SciTech Connect (OSTI)

    Thomas, R.E.

    1982-03-01T23:59:59.000Z

    An evaluation is made of the suitability of analytical and statistical sampling methods for making uncertainty analyses. The adjoint method is found to be well-suited for obtaining sensitivity coefficients for computer programs involving large numbers of equations and input parameters. For this purpose the Latin Hypercube Sampling method is found to be inferior to conventional experimental designs. The Latin hypercube method can be used to estimate output probability density functions, but requires supplementary rank transformations followed by stepwise regression to obtain uncertainty information on individual input parameters. A simple Cork and Bottle problem is used to illustrate the efficiency of the adjoint method relative to certain statistical sampling methods. For linear models of the form Ax=b it is shown that a complete adjoint sensitivity analysis can be made without formulating and solving the adjoint problem. This can be done either by using a special type of statistical sampling or by reformulating the primal problem and using suitable linear programming software.

  18. Experimental uncertainty estimation and statistics for data having interval uncertainty.

    SciTech Connect (OSTI)

    Kreinovich, Vladik (Applied Biomathematics, Setauket, New York); Oberkampf, William Louis (Applied Biomathematics, Setauket, New York); Ginzburg, Lev (Applied Biomathematics, Setauket, New York); Ferson, Scott (Applied Biomathematics, Setauket, New York); Hajagos, Janos (Applied Biomathematics, Setauket, New York)

    2007-05-01T23:59:59.000Z

    This report addresses the characterization of measurements that include epistemic uncertainties in the form of intervals. It reviews the application of basic descriptive statistics to data sets which contain intervals rather than exclusively point estimates. It describes algorithms to compute various means, the median and other percentiles, variance, interquartile range, moments, confidence limits, and other important statistics and summarizes the computability of these statistics as a function of sample size and characteristics of the intervals in the data (degree of overlap, size and regularity of widths, etc.). It also reviews the prospects for analyzing such data sets with the methods of inferential statistics such as outlier detection and regressions. The report explores the tradeoff between measurement precision and sample size in statistical results that are sensitive to both. It also argues that an approach based on interval statistics could be a reasonable alternative to current standard methods for evaluating, expressing and propagating measurement uncertainties.

  19. A surrogate-based uncertainty quantification with quantifiable errors

    SciTech Connect (OSTI)

    Bang, Y.; Abdel-Khalik, H. S. [North Carolina State Univ., Raleigh, NC 27695 (United States)

    2012-07-01T23:59:59.000Z

    Surrogate models are often employed to reduce the computational cost required to complete uncertainty quantification, where one is interested in propagating input parameters uncertainties throughout a complex engineering model to estimate responses uncertainties. An improved surrogate construction approach is introduced here which places a premium on reducing the associated computational cost. Unlike existing methods where the surrogate is constructed first, then employed to propagate uncertainties, the new approach combines both sensitivity and uncertainty information to render further reduction in the computational cost. Mathematically, the reduction is described by a range finding algorithm that identifies a subspace in the parameters space, whereby parameters uncertainties orthogonal to the subspace contribute negligible amount to the propagated uncertainties. Moreover, the error resulting from the reduction can be upper-bounded. The new approach is demonstrated using a realistic nuclear assembly model and compared to existing methods in terms of computational cost and accuracy of uncertainties. Although we believe the algorithm is general, it will be applied here for linear-based surrogates and Gaussian parameters uncertainties. The generalization to nonlinear models will be detailed in a separate article. (authors)

  20. Applying uncertainty quantification to multiphase flow computational fluid dynamics

    SciTech Connect (OSTI)

    Gel, A.; Garg, R.; Tong, C.; Shahnam, M.; Guenther, C.

    2013-07-01T23:59:59.000Z

    Multiphase computational fluid dynamics plays a major role in design and optimization of fossil fuel based reactors. There is a growing interest in accounting for the influence of uncertainties associated with physical systems to increase the reliability of computational simulation based engineering analysis. The U.S. Department of Energy's National Energy Technology Laboratory (NETL) has recently undertaken an initiative to characterize uncertainties associated with computer simulation of reacting multiphase flows encountered in energy producing systems such as a coal gasifier. The current work presents the preliminary results in applying non-intrusive parametric uncertainty quantification and propagation techniques with NETL's open-source multiphase computational fluid dynamics software MFIX. For this purpose an open-source uncertainty quantification toolkit, PSUADE developed at the Lawrence Livermore National Laboratory (LLNL) has been interfaced with MFIX software. In this study, the sources of uncertainty associated with numerical approximation and model form have been neglected, and only the model input parametric uncertainty with forward propagation has been investigated by constructing a surrogate model based on data-fitted response surface for a multiphase flow demonstration problem. Monte Carlo simulation was employed for forward propagation of the aleatory type input uncertainties. Several insights gained based on the outcome of these simulations are presented such as how inadequate characterization of uncertainties can affect the reliability of the prediction results. Also a global sensitivity study using Sobol' indices was performed to better understand the contribution of input parameters to the variability observed in response variable.

  1. RUMINATIONS ON NDA MEASUREMENT UNCERTAINTY COMPARED TO DA UNCERTAINTY

    SciTech Connect (OSTI)

    Salaymeh, S.; Ashley, W.; Jeffcoat, R.

    2010-06-17T23:59:59.000Z

    It is difficult to overestimate the importance that physical measurements performed with nondestructive assay instruments play throughout the nuclear fuel cycle. They underpin decision making in many areas and support: criticality safety, radiation protection, process control, safeguards, facility compliance, and waste measurements. No physical measurement is complete or indeed meaningful, without a defensible and appropriate accompanying statement of uncertainties and how they combine to define the confidence in the results. The uncertainty budget should also be broken down in sufficient detail suitable for subsequent uses to which the nondestructive assay (NDA) results will be applied. Creating an uncertainty budget and estimating the total measurement uncertainty can often be an involved process, especially for non routine situations. This is because data interpretation often involves complex algorithms and logic combined in a highly intertwined way. The methods often call on a multitude of input data subject to human oversight. These characteristics can be confusing and pose a barrier to developing and understanding between experts and data consumers. ASTM subcommittee C26-10 recognized this problem in the context of how to summarize and express precision and bias performance across the range of standards and guides it maintains. In order to create a unified approach consistent with modern practice and embracing the continuous improvement philosophy a consensus arose to prepare a procedure covering the estimation and reporting of uncertainties in non destructive assay of nuclear materials. This paper outlines the needs analysis, objectives and on-going development efforts. In addition to emphasizing some of the unique challenges and opportunities facing the NDA community we hope this article will encourage dialog and sharing of best practice and furthermore motivate developers to revisit the treatment of measurement uncertainty.

  2. Uncertainty Analysis for Photovoltaic Degradation Rates (Poster)

    SciTech Connect (OSTI)

    Jordan, D.; Kurtz, S.; Hansen, C.

    2014-04-01T23:59:59.000Z

    Dependable and predictable energy production is the key to the long-term success of the PV industry. PV systems show over the lifetime of their exposure a gradual decline that depends on many different factors such as module technology, module type, mounting configuration, climate etc. When degradation rates are determined from continuous data the statistical uncertainty is easily calculated from the regression coefficients. However, total uncertainty that includes measurement uncertainty and instrumentation drift is far more difficult to determine. A Monte Carlo simulation approach was chosen to investigate a comprehensive uncertainty analysis. The most important effect for degradation rates is to avoid instrumentation that changes over time in the field. For instance, a drifting irradiance sensor, which can be achieved through regular calibration, can lead to a substantially erroneous degradation rates. However, the accuracy of the irradiance sensor has negligible impact on degradation rate uncertainty emphasizing that precision (relative accuracy) is more important than absolute accuracy.

  3. Capturing Data Uncertainty in High-Volume Stream Processing

    E-Print Network [OSTI]

    Diao, Yanlei; Liu, Anna; Peng, Liping; Sutton, Charles; Tran, Thanh; Zink, Michael

    2009-01-01T23:59:59.000Z

    We present the design and development of a data stream system that captures data uncertainty from data collection to query processing to final result generation. Our system focuses on data that is naturally modeled as continuous random variables. For such data, our system employs an approach grounded in probability and statistical theory to capture data uncertainty and integrates this approach into high-volume stream processing. The first component of our system captures uncertainty of raw data streams from sensing devices. Since such raw streams can be highly noisy and may not carry sufficient information for query processing, our system employs probabilistic models of the data generation process and stream-speed inference to transform raw data into a desired format with an uncertainty metric. The second component captures uncertainty as data propagates through query operators. To efficiently quantify result uncertainty of a query operator, we explore a variety of techniques based on probability and statisti...

  4. Uncertainties in Gapped Graphene

    E-Print Network [OSTI]

    Eylee Jung; Kwang S. Kim; DaeKil Park

    2012-03-20T23:59:59.000Z

    Motivated by graphene-based quantum computer we examine the time-dependence of the position-momentum and position-velocity uncertainties in the monolayer gapped graphene. The effect of the energy gap to the uncertainties is shown to appear via the Compton-like wavelength $\\lambda_c$. The uncertainties in the graphene are mainly contributed by two phenomena, spreading and zitterbewegung. While the former determines the uncertainties in the long-range of time, the latter gives the highly oscillation to the uncertainties in the short-range of time. The uncertainties in the graphene are compared with the corresponding values for the usual free Hamiltonian $\\hat{H}_{free} = (p_1^2 + p_2^2) / 2 M$. It is shown that the uncertainties can be under control within the quantum mechanical law if one can choose the gap parameter $\\lambda_c$ freely.

  5. Uncertainty Propagation for Quality Assurance in Reinforcement Learning

    E-Print Network [OSTI]

    Schneegass and Steffen Udluft are with the Learning Sys- tems Department of the Siemens AG, Corporate Technology, Informa- tion & Communications; email: daniel.schneegass.ext@siemens.com, stef- fen.udluft@siemens

  6. Quantitative Phenomena Identification and Ranking Table (QPIRT) for Bayesian Uncertainty Quantification

    E-Print Network [OSTI]

    Yurko, Joseph P.

    Propagating parameter uncertainty for a nuclear reactor system code is a very challenging problem. Numerous parameters influence the system response in complicated and often non-linear fashions, in addition to sometimes ...

  7. Measurement uncertainty of adsorption testing of desiccant materials

    SciTech Connect (OSTI)

    Bingham, C E; Pesaran, A A

    1988-12-01T23:59:59.000Z

    The technique of measurement uncertainty analysis as described in the current ANSI/ASME standard is applied to the testing of desiccant materials in SERI`s Sorption Test Facility. This paper estimates the elemental precision and systematic errors in these tests and propagates them separately to obtain the resulting uncertainty of the test parameters, including relative humidity ({plus_minus}.03) and sorption capacity ({plus_minus}.002 g/g). Errors generated by instrument calibration, data acquisition, and data reduction are considered. Measurement parameters that would improve the uncertainty of the results are identified. Using the uncertainty in the moisture capacity of a desiccant, the design engineer can estimate the uncertainty in performance of a dehumidifier for desiccant cooling systems with confidence. 6 refs., 2 figs., 8 tabs.

  8. PEBBED Uncertainty and Sensitivity Analysis of the CRP-5 PBMR DLOFC Transient Benchmark with the SUSA Code

    SciTech Connect (OSTI)

    Gerhard Strydom

    2011-01-01T23:59:59.000Z

    The need for a defendable and systematic uncertainty and sensitivity approach that conforms to the Code Scaling, Applicability, and Uncertainty (CSAU) process, and that could be used for a wide variety of software codes, was defined in 2008. The GRS (Gesellschaft für Anlagen und Reaktorsicherheit) company of Germany has developed one type of CSAU approach that is particularly well suited for legacy coupled core analysis codes, and a trial version of their commercial software product SUSA (Software for Uncertainty and Sensitivity Analyses) was acquired on May 12, 2010. This report summarized the results of the initial investigations performed with SUSA, utilizing a typical High Temperature Reactor benchmark (the IAEA CRP-5 PBMR 400MW Exercise 2) and the PEBBED-THERMIX suite of codes. The following steps were performed as part of the uncertainty and sensitivity analysis: 1. Eight PEBBED-THERMIX model input parameters were selected for inclusion in the uncertainty study: the total reactor power, inlet gas temperature, decay heat, and the specific heat capability and thermal conductivity of the fuel, pebble bed and reflector graphite. 2. The input parameters variations and probability density functions were specified, and a total of 800 PEBBED-THERMIX model calculations were performed, divided into 4 sets of 100 and 2 sets of 200 Steady State and Depressurized Loss of Forced Cooling (DLOFC) transient calculations each. 3. The steady state and DLOFC maximum fuel temperature, as well as the daily pebble fuel load rate data, were supplied to SUSA as model output parameters of interest. The 6 data sets were statistically analyzed to determine the 5% and 95% percentile values for each of the 3 output parameters with a 95% confidence level, and typical statistical indictors were also generated (e.g. Kendall, Pearson and Spearman coefficients). 4. A SUSA sensitivity study was performed to obtain correlation data between the input and output parameters, and to identify the primary contributors to the output data uncertainties. It was found that the uncertainties in the decay heat, pebble bed and reflector thermal conductivities were responsible for the bulk of the propagated uncertainty in the DLOFC maximum fuel temperature. It was also determined that the two standard deviation (2s) uncertainty on the maximum fuel temperature was between ±58oC (3.6%) and ±76oC (4.7%) on a mean value of 1604 oC. These values mostly depended on the selection of the distributions types, and not on the number of model calculations above the required Wilks criteria (a (95%,95%) statement would usually require 93 model runs).

  9. Assessing Uncertainty in Simulation Based Maritime Risk Assessment

    E-Print Network [OSTI]

    van Dorp, Johan René

    , such as nuclear powered vessels [6], vessels transporting liquefied natural gas [7] and offshore oil #12;2 and gas the decision-makers unsure whether the evidence was sufficient to assess specific risks and benefits. The first techniques to propagate uncertainty throughout the analysis. The conclusions drawn in the original study

  10. Environmental Modeling: Coping with Uncertainty

    E-Print Network [OSTI]

    Politècnica de Catalunya, Universitat

    ­ Statistical heterogeneity ­ Complex correlation structures · Classical models: ­ Additive noise ­ UniEnvironmental Modeling: Coping with Uncertainty Daniel M. Tartakovsky (dmt@lanl.gov) Theoretical of environmental processes 2. Parametric uncertainty 3. Current approaches to uncertainty quantification 4. Random

  11. Uncertainty Analysis Economic Evaluations

    E-Print Network [OSTI]

    Bhulai, Sandjai

    uncertainties in typical oil and gas projects: 1. The oil price, 2. The investments (capex) and operating expenses (opex) for the project, 3. The number of wells and associated capex to recover the reserves, 4

  12. Trajectories without quantum uncertainties

    E-Print Network [OSTI]

    Eugene S. Polzik; Klemens Hammerer

    2014-05-13T23:59:59.000Z

    A common knowledge suggests that trajectories of particles in quantum mechanics always have quantum uncertainties. These quantum uncertainties set by the Heisenberg uncertainty principle limit precision of measurements of fields and forces, and ultimately give rise to the standard quantum limit in metrology. With the rapid developments of sensitivity of measurements these limits have been approached in various types of measurements including measurements of fields and acceleration. Here we show that a quantum trajectory of one system measured relatively to the other "reference system" with an effective negative mass can be quantum uncertainty--free. The method crucially relies on the generation of an Einstein-Podolsky-Rosen entangled state of two objects, one of which has an effective negative mass. From a practical perspective these ideas open the way towards force and acceleration measurements at new levels of sensitivity far below the standard quantum limit.

  13. Uncertainty and error in computational simulations

    SciTech Connect (OSTI)

    Oberkampf, W.L.; Diegert, K.V.; Alvin, K.F.; Rutherford, B.M.

    1997-10-01T23:59:59.000Z

    The present paper addresses the question: ``What are the general classes of uncertainty and error sources in complex, computational simulations?`` This is the first step of a two step process to develop a general methodology for quantitatively estimating the global modeling and simulation uncertainty in computational modeling and simulation. The second step is to develop a general mathematical procedure for representing, combining and propagating all of the individual sources through the simulation. The authors develop a comprehensive view of the general phases of modeling and simulation. The phases proposed are: conceptual modeling of the physical system, mathematical modeling of the system, discretization of the mathematical model, computer programming of the discrete model, numerical solution of the model, and interpretation of the results. This new view is built upon combining phases recognized in the disciplines of operations research and numerical solution methods for partial differential equations. The characteristics and activities of each of these phases is discussed in general, but examples are given for the fields of computational fluid dynamics and heat transfer. They argue that a clear distinction should be made between uncertainty and error that can arise in each of these phases. The present definitions for uncertainty and error are inadequate and. therefore, they propose comprehensive definitions for these terms. Specific classes of uncertainty and error sources are then defined that can occur in each phase of modeling and simulation. The numerical sources of error considered apply regardless of whether the discretization procedure is based on finite elements, finite volumes, or finite differences. To better explain the broad types of sources of uncertainty and error, and the utility of their categorization, they discuss a coupled-physics example simulation.

  14. TOTAL M F Total M F Total M F Total M F Total M F Total M F Total M F Total M F Total M F Total M F Total M F Total M F Total Spring 2010

    E-Print Network [OSTI]

    Hayes, Jane E.

    202 51 *total new freshmen 684: 636 Lexington campus, 48 Paducah campus MS Total 216 12 5 17 2 0 2 40 248 247 648 45 210 14 *total new freshmen 647: 595 Lexington campus, 52 Paducah campus MS Total 192 14

  15. Fuzzy-probabilistic calculations of water-balance uncertainty

    SciTech Connect (OSTI)

    Faybishenko, B.

    2009-10-01T23:59:59.000Z

    Hydrogeological systems are often characterized by imprecise, vague, inconsistent, incomplete, or subjective information, which may limit the application of conventional stochastic methods in predicting hydrogeologic conditions and associated uncertainty. Instead, redictions and uncertainty analysis can be made using uncertain input parameters expressed as probability boxes, intervals, and fuzzy numbers. The objective of this paper is to present the theory for, and a case study as an application of, the fuzzyprobabilistic approach, ombining probability and possibility theory for simulating soil water balance and assessing associated uncertainty in the components of a simple waterbalance equation. The application of this approach is demonstrated using calculations with the RAMAS Risk Calc code, to ssess the propagation of uncertainty in calculating potential evapotranspiration, actual evapotranspiration, and infiltration-in a case study at the Hanford site, Washington, USA. Propagation of uncertainty into the results of water-balance calculations was evaluated by hanging he types of models of uncertainty incorporated into various input parameters. The results of these fuzzy-probabilistic calculations are compared to the conventional Monte Carlo simulation approach and estimates from field observations at the Hanford site.

  16. Some methods of estimating uncertainty in accident reconstruction

    E-Print Network [OSTI]

    Milan Batista

    2011-07-20T23:59:59.000Z

    In the paper four methods for estimating uncertainty in accident reconstruction are discussed: total differential method, extreme values method, Gauss statistical method, and Monte Carlo simulation method. The methods are described and the program solutions are given.

  17. Optimal Uncertainty Quantification

    E-Print Network [OSTI]

    Owhadi, Houman; Sullivan, Timothy John; McKerns, Mike; Ortiz, Michael

    2010-01-01T23:59:59.000Z

    We propose a rigorous framework for Uncertainty Quantification (UQ) in which the UQ objectives and the assumptions/information set are brought to the forefront. This framework, which we call \\emph{Optimal Uncertainty Quantification} (OUQ), is based on the observation that, given a set of assumptions and information about the problem, there exist optimal bounds on uncertainties: these are obtained as extreme values of well-defined optimization problems corresponding to extremizing probabilities of failure, or of deviations, subject to the constraints imposed by the scenarios compatible with the assumptions and information. In particular, this framework does not implicitly impose inappropriate assumptions, nor does it repudiate relevant information. Although OUQ optimization problems are extremely large, we show that under general conditions, they have finite-dimensional reductions. As an application, we develop \\emph{Optimal Concentration Inequalities} (OCI) of Hoeffding and McDiarmid type. Surprisingly, contr...

  18. Uncertainty and calibration analysis

    SciTech Connect (OSTI)

    Coutts, D.A.

    1991-03-01T23:59:59.000Z

    All measurements contain some deviation from the true value which is being measured. In the common vernacular this deviation between the true value and the measured value is called an inaccuracy, an error, or a mistake. Since all measurements contain errors, it is necessary to accept that there is a limit to how accurate a measurement can be. The undertainty interval combined with the confidence level, is one measure of the accuracy for a measurement or value. Without a statement of uncertainty (or a similar parameter) it is not possible to evaluate if the accuracy of the measurement, or data, is appropriate. The preparation of technical reports, calibration evaluations, and design calculations should consider the accuracy of measurements and data being used. There are many methods to accomplish this. This report provides a consistent method for the handling of measurement tolerances, calibration evaluations and uncertainty calculations. The SRS Quality Assurance (QA) Program requires that the uncertainty of technical data and instrument calibrations be acknowledged and estimated. The QA Program makes some specific technical requirements related to the subject but does not provide a philosophy or method on how uncertainty should be estimated. This report was prepared to provide a technical basis to support the calculation of uncertainties and the calibration of measurement and test equipment for any activity within the Experimental Thermal-Hydraulics (ETH) Group. The methods proposed in this report provide a graded approach for estimating the uncertainty of measurements, data, and calibrations. The method is based on the national consensus standard, ANSI/ASME PTC 19.1.

  19. Optimization Under Generalized Uncertainty

    E-Print Network [OSTI]

    Lodwick, Weldon

    11 Optimization Under Generalized Uncertainty Optimization Modeling Math 4794/5794: Spring 2013 Weldon A. Lodwick Weldon.Lodwick@ucdenver.edu 2/14/2013 Optimization Modeling - Spring 2013 #12 in the context of optimization problems. The theoretical frame-work for these notes is interval analysis. From

  20. Cost uncertainty for different levels of technology maturity

    SciTech Connect (OSTI)

    DeMuth, S.F. [Los Alamos National Lab., NM (United States); Franklin, A.L. [Pacific Northwest National Lab., Richland, WA (United States)

    1996-08-07T23:59:59.000Z

    It is difficult at best to apply a single methodology for estimating cost uncertainties related to technologies of differing maturity. While highly mature technologies may have significant performance and manufacturing cost data available, less well developed technologies may be defined in only conceptual terms. Regardless of the degree of technical maturity, often a cost estimate relating to application of the technology may be required to justify continued funding for development. Yet, a cost estimate without its associated uncertainty lacks the information required to assess the economic risk. For this reason, it is important for the developer to provide some type of uncertainty along with a cost estimate. This study demonstrates how different methodologies for estimating uncertainties can be applied to cost estimates for technologies of different maturities. For a less well developed technology an uncertainty analysis of the cost estimate can be based on a sensitivity analysis; whereas, an uncertainty analysis of the cost estimate for a well developed technology can be based on an error propagation technique from classical statistics. It was decided to demonstrate these uncertainty estimation techniques with (1) an investigation of the additional cost of remediation due to beyond baseline, nearly complete, waste heel retrieval from underground storage tanks (USTs) at Hanford; and (2) the cost related to the use of crystalline silico-titanate (CST) rather than the baseline CS100 ion exchange resin for cesium separation from UST waste at Hanford.

  1. Uncertainty Quantification of Calculated Temperatures for the AGR-1 Experiment

    SciTech Connect (OSTI)

    Binh T. Pham; Jeffrey J. Einerson; Grant L. Hawkes

    2012-04-01T23:59:59.000Z

    This report documents an effort to quantify the uncertainty of the calculated temperature data for the first Advanced Gas Reactor (AGR-1) fuel irradiation experiment conducted in the INL's Advanced Test Reactor (ATR) in support of the Next Generation Nuclear Plant (NGNP) R&D program. Recognizing uncertainties inherent in physics and thermal simulations of the AGR-1 test, the results of the numerical simulations can be used in combination with the statistical analysis methods to improve qualification of measured data. Additionally, the temperature simulation data for AGR tests can be used for validation of the fuel transport and fuel performance simulation models. The crucial roles of the calculated fuel temperatures in ensuring achievement of the AGR experimental program objectives require accurate determination of the model temperature uncertainties. The report is organized into three chapters. Chapter 1 introduces the AGR Fuel Development and Qualification program and provides overviews of AGR-1 measured data, AGR-1 test configuration and test procedure, and thermal simulation. Chapters 2 describes the uncertainty quantification procedure for temperature simulation data of the AGR-1 experiment, namely, (i) identify and quantify uncertainty sources; (ii) perform sensitivity analysis for several thermal test conditions; (iii) use uncertainty propagation to quantify overall response temperature uncertainty. A set of issues associated with modeling uncertainties resulting from the expert assessments are identified. This also includes the experimental design to estimate the main effects and interactions of the important thermal model parameters. Chapter 3 presents the overall uncertainty results for the six AGR-1 capsules. This includes uncertainties for the daily volume-average and peak fuel temperatures, daily average temperatures at TC locations, and time-average volume-average and time-average peak fuel temperatures.

  2. Dike Propagation Near Drifts

    SciTech Connect (OSTI)

    NA

    2002-03-04T23:59:59.000Z

    The purpose of this Analysis and Model Report (AMR) supporting the Site Recommendation/License Application (SR/LA) for the Yucca Mountain Project is the development of elementary analyses of the interactions of a hypothetical dike with a repository drift (i.e., tunnel) and with the drift contents at the potential Yucca Mountain repository. This effort is intended to support the analysis of disruptive events for Total System Performance Assessment (TSPA). This AMR supports the Process Model Report (PMR) on disruptive events (CRWMS M&O 2000a). This purpose is documented in the development plan (DP) ''Coordinate Modeling of Dike Propagation Near Drifts Consequences for TSPA-SR/LA'' (CRWMS M&O 2000b). Evaluation of that Development Plan and the work to be conducted to prepare Interim Change Notice (ICN) 1 of this report, which now includes the design option of ''Open'' drifts, indicated that no revision to that DP was needed. These analyses are intended to provide reasonable bounds for a number of expected effects: (1) Temperature changes to the waste package from exposure to magma; (2) The gas flow available to degrade waste containers during the intrusion; (3) Movement of the waste package as it is displaced by the gas, pyroclasts and magma from the intruding dike (the number of packages damaged); (4) Movement of the backfill (Backfill is treated here as a design option); (5) The nature of the mechanics of the dike/drift interaction. These analyses serve two objectives: to provide preliminary analyses needed to support evaluation of the consequences of an intrusive event and to provide a basis for addressing some of the concerns of the Nuclear Regulatory Commission (NRC) expressed in the Igneous Activity Issue Resolution Status Report.

  3. Extended Forward Sensitivity Analysis for Uncertainty Quantification

    SciTech Connect (OSTI)

    Haihua Zhao; Vincent A. Mousseau

    2011-09-01T23:59:59.000Z

    Verification and validation (V&V) are playing more important roles to quantify uncertainties and realize high fidelity simulations in engineering system analyses, such as transients happened in a complex nuclear reactor system. Traditional V&V in the reactor system analysis focused more on the validation part or did not differentiate verification and validation. The traditional approach to uncertainty quantification is based on a 'black box' approach. The simulation tool is treated as an unknown signal generator, a distribution of inputs according to assumed probability density functions is sent in and the distribution of the outputs is measured and correlated back to the original input distribution. The 'black box' method mixes numerical errors with all other uncertainties. It is also not efficient to perform sensitivity analysis. Contrary to the 'black box' method, a more efficient sensitivity approach can take advantage of intimate knowledge of the simulation code. In these types of approaches equations for the propagation of uncertainty are constructed and the sensitivities are directly solved for as variables in the simulation. This paper presents the forward sensitivity analysis as a method to help uncertainty qualification. By including time step and potentially spatial step as special sensitivity parameters, the forward sensitivity method is extended as one method to quantify numerical errors. Note that by integrating local truncation errors over the whole system through the forward sensitivity analysis process, the generated time step and spatial step sensitivity information reflect global numerical errors. The discretization errors can be systematically compared against uncertainties due to other physical parameters. This extension makes the forward sensitivity method a much more powerful tool to help uncertainty qualification. By knowing the relative sensitivity of time and space steps with other interested physical parameters, the simulation is allowed to run at optimized time and space steps without affecting the confidence of the physical parameter sensitivity results. The time and space steps forward sensitivity analysis method can also replace the traditional time step and grid convergence study with much less computational cost. Several well defined benchmark problems with manufactured solutions are utilized to demonstrate the extended forward sensitivity analysis method. All the physical solutions, parameter sensitivity solutions, even the time step sensitivity in one case, have analytical forms, which allows the verification to be performed in the strictest sense.

  4. Wave Packets Propagation in Quantum Gravity

    E-Print Network [OSTI]

    Kourosh Nozari; S. H. Mehdipour

    2005-07-03T23:59:59.000Z

    Wave packet broadening in usual quantum mechanics is a consequence of dispersion behavior of the medium which the wave propagates in it. In this paper, we consider the problem of wave packet broadening in the framework of Generalized Uncertainty Principle(GUP) of quantum gravity. New dispersion relations are derived in the context of GUP and it has been shown that there exists a gravitational induced dispersion which leads to more broadening of the wave packets. As a result of these dispersion relations, a generalized Klein-Gordon equation is obtained and its interpretation is given.

  5. Nuclear data uncertainties by the PWR MOX/UO{sub 2} core rod ejection benchmark

    SciTech Connect (OSTI)

    Pasichnyk, I.; Klein, M.; Velkov, K.; Zwermann, W.; Pautz, A. [Boltzmannstr. 14, D-85748 Garching b. Muenchen (Germany)

    2012-07-01T23:59:59.000Z

    Rod ejection transient of the OECD/NEA and U.S. NRC PWR MOX/UO{sub 2} core benchmark is considered under the influence of nuclear data uncertainties. Using the GRS uncertainty and sensitivity software package XSUSA the propagation of the uncertainties in nuclear data up to the transient calculations are considered. A statistically representative set of transient calculations is analyzed and both integral as well as local output quantities are compared with the benchmark results of different participants. It is shown that the uncertainties in nuclear data play a crucial role in the interpretation of the results of the simulation. (authors)

  6. Probabilistic accident consequence uncertainty analysis: Dispersion and deposition uncertainty assessment, main report

    SciTech Connect (OSTI)

    Harper, F.T.; Young, M.L.; Miller, L.A. [Sandia National Labs., Albuquerque, NM (United States); Hora, S.C. [Univ. of Hawaii, Hilo, HI (United States); Lui, C.H. [Nuclear Regulatory Commission, Washington, DC (United States); Goossens, L.H.J.; Cooke, R.M. [Delft Univ. of Technology (Netherlands); Paesler-Sauer, J. [Research Center, Karlsruhe (Germany); Helton, J.C. [and others

    1995-01-01T23:59:59.000Z

    The development of two new probabilistic accident consequence codes, MACCS and COSYMA, was completed in 1990. These codes estimate the risks presented by nuclear installations based on postulated frequencies and magnitudes of potential accidents. In 1991, the US Nuclear Regulatory Commission (NRC) and the Commission of the European Communities (CEC) began a joint uncertainty analysis of the two codes. The ultimate objective of the joint effort was to develop credible and traceable uncertainty distributions for the input variables of the codes. Expert elicitation was identified as the best technology available for developing a library of uncertainty distributions for the selected consequence parameters. The study was formulated jointly and was limited to the current code models and to physical quantities that could be measured in experiments. Experts developed their distributions independently. To validate the distributions generated for the wet deposition input variables, samples were taken from these distributions and propagated through the wet deposition code model. Resulting distributions closely replicated the aggregated elicited wet deposition distributions. To validate the distributions generated for the dispersion code input variables, samples from the distributions and propagated through the Gaussian plume model (GPM) implemented in the MACCS and COSYMA codes. Project teams from the NRC and CEC cooperated successfully to develop and implement a unified process for the elaboration of uncertainty distributions on consequence code input parameters. Formal expert judgment elicitation proved valuable for synthesizing the best available information. Distributions on measurable atmospheric dispersion and deposition parameters were successfully elicited from experts involved in the many phenomenological areas of consequence analysis. This volume is the first of a three-volume document describing the project.

  7. Propagation of Ornamental Plants.

    E-Print Network [OSTI]

    DeWerth, A. F.

    1955-01-01T23:59:59.000Z

    Propagation of Ornamental Plants I A. I?. DEWERTH, Head Department of Floriculture and Landscape Architecture Texas A. & M. College System THE MULTIPLICATION of ornamental plants is After sterilizing, firm the soil to within 1; receiving more...

  8. Job Matching and Propagation

    E-Print Network [OSTI]

    Ramey, Garey; Fujita, Shigeru

    2006-01-01T23:59:59.000Z

    the labor force who want a job. Monthly Labor Review Cogley,G. , Watson, J. , June 2000. Job destruction and propagationJ. , June 2004. Gross job ?ows over the past two business

  9. Uncertainty Principle Respects Locality

    E-Print Network [OSTI]

    Dongsheng Wang

    2015-04-19T23:59:59.000Z

    The notion of nonlocality implicitly implies there might be some kind of spooky action at a distance in nature, however, the validity of quantum mechanics has been well tested up to now. In this work it is argued that the notion of nonlocality is physically improper, the basic principle of locality in nature is well respected by quantum mechanics, namely, the uncertainty principle. We show that the quantum bound on the Clauser, Horne, Shimony, and Holt (CHSH) inequality can be recovered from the uncertainty relation in a multipartite setting. We further argue that the super-quantum correlation demonstrated by the nonlocal box is not physically comparable with the quantum one. The origin of the quantum structure of nature still remains to be explained, some post-quantum theory which is more complete in some sense than quantum mechanics is possible and might not necessarily be a hidden variable theory.

  10. Sandia National Laboratories: Uncertainty Analysis

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

    Experimental Testing Phenomenological Modeling Risk and Safety Assessment Cyber-Based Vulnerability Assessments Uncertainty Analysis Transportation Safety Fire Science Human...

  11. Calibration Under Uncertainty.

    SciTech Connect (OSTI)

    Swiler, Laura Painton; Trucano, Timothy Guy

    2005-03-01T23:59:59.000Z

    This report is a white paper summarizing the literature and different approaches to the problem of calibrating computer model parameters in the face of model uncertainty. Model calibration is often formulated as finding the parameters that minimize the squared difference between the model-computed data (the predicted data) and the actual experimental data. This approach does not allow for explicit treatment of uncertainty or error in the model itself: the model is considered the %22true%22 deterministic representation of reality. While this approach does have utility, it is far from an accurate mathematical treatment of the true model calibration problem in which both the computed data and experimental data have error bars. This year, we examined methods to perform calibration accounting for the error in both the computer model and the data, as well as improving our understanding of its meaning for model predictability. We call this approach Calibration under Uncertainty (CUU). This talk presents our current thinking on CUU. We outline some current approaches in the literature, and discuss the Bayesian approach to CUU in detail.

  12. The impact of uncertainty and risk measures

    E-Print Network [OSTI]

    Jo, Soojin; Jo, Soojin

    2012-01-01T23:59:59.000Z

    First, it recovers historical oil price uncertainty seriesmore reliable historical oil price uncertainty series as itAs a result, the historical oil price uncertainty series is

  13. Electoral Competition, Political Uncertainty and Policy Insulation

    E-Print Network [OSTI]

    de Figueiredo, Rui J. P. Jr.

    2001-01-01T23:59:59.000Z

    Uncertainty and Policy Insulation Horn, Murray. 1995. TheUncertainty and Policy Insulation United States Congress.UNCERTAINTY AND POLICY INSULATION Rui J. P. de Figueiredo,

  14. Sandia Energy - Uncertainty Analysis

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of ScienceandMesa del Sol Home DistributionTransportation Safety Home Stationary PowerUncertainty

  15. A sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory.

    SciTech Connect (OSTI)

    Johnson, J. D. (Prostat, Mesa, AZ); Oberkampf, William Louis; Helton, Jon Craig (Arizona State University, Tempe, AZ); Storlie, Curtis B. (North Carolina State University, Raleigh, NC)

    2006-10-01T23:59:59.000Z

    Evidence theory provides an alternative to probability theory for the representation of epistemic uncertainty in model predictions that derives from epistemic uncertainty in model inputs, where the descriptor epistemic is used to indicate uncertainty that derives from a lack of knowledge with respect to the appropriate values to use for various inputs to the model. The potential benefit, and hence appeal, of evidence theory is that it allows a less restrictive specification of uncertainty than is possible within the axiomatic structure on which probability theory is based. Unfortunately, the propagation of an evidence theory representation for uncertainty through a model is more computationally demanding than the propagation of a probabilistic representation for uncertainty, with this difficulty constituting a serious obstacle to the use of evidence theory in the representation of uncertainty in predictions obtained from computationally intensive models. This presentation describes and illustrates a sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory. Preliminary trials indicate that the presented strategy can be used to propagate uncertainty representations based on evidence theory in analysis situations where naive sampling-based (i.e., unsophisticated Monte Carlo) procedures are impracticable due to computational cost.

  16. Interaction of loading pattern and nuclear data uncertainties in reactor core calculations

    SciTech Connect (OSTI)

    Klein, M.; Gallner, L.; Krzykacz-Hausmann, B.; Pautz, A.; Velkov, K.; Zwermann, W. [Gesellschaft fuer Anlagen- und Reaktorsicherheit GRS MbH, Boltzmannstr. 14, D- 85748 Garching b. Muenchen (Germany)

    2012-07-01T23:59:59.000Z

    Along with best-estimate calculations for design and safety analysis, understanding uncertainties is important to determine appropriate design margins. In this framework, nuclear data uncertainties and their propagation to full core calculations are a critical issue. To deal with this task, different error propagation techniques, deterministic and stochastic are currently developed to evaluate the uncertainties in the output quantities. Among these is the sampling based uncertainty and sensitivity software XSUSA which is able to quantify the influence of nuclear data covariance on reactor core calculations. In the present work, this software is used to investigate systematically the uncertainties in the power distributions of two PWR core loadings specified in the OECD UAM-Benchmark suite. With help of a statistical sensitivity analysis, the main contributors to the uncertainty are determined. Using this information a method is studied with which loading patterns of reactor cores can be optimized with regard to minimizing power distribution uncertainties. It is shown that this technique is able to halve the calculation uncertainties of a MOX/UOX core configuration. (authors)

  17. Uncertainty relation in Schwarzschild spacetime

    E-Print Network [OSTI]

    Jun Feng; Yao-Zhong Zhang; Mark D. Gould; Heng Fan

    2015-02-27T23:59:59.000Z

    We explore the entropic uncertainty relation in the curved background outside a Schwarzschild black hole, and find that Hawking radiation introduces a nontrivial modification on the uncertainty bound for particular observer, therefore it could be witnessed by proper uncertainty game experimentally. We first investigate an uncertainty game between a free falling observer and his static partner holding a quantum memory initially entangled with the quantum system to be measured. Due to the information loss from Hawking decoherence, we find an inevitable increase of the uncertainty on the outcome of measurements in the view of static observer, which is dependent on the mass of the black hole, the distance of observer from event horizon, and the mode frequency of quantum memory. To illustrate the generality of this paradigm, we relate the entropic uncertainty bound with other uncertainty probe, e.g., time-energy uncertainty. In an alternative game between two static players, we show that quantum information of qubit can be transferred to quantum memory through a bath of fluctuating quantum fields outside the black hole. For a particular choice of initial state, we show that the Hawking decoherence cannot counteract entanglement generation after the dynamical evolution of system, which triggers an effectively reduced uncertainty bound that violates the intrinsic limit $-\\log_2c$. Numerically estimation for a proper choice of initial state shows that our result is comparable with possible real experiments. Finally, a discussion on the black hole firewall paradox in the context of entropic uncertainty relation is given.

  18. Statistical uncertainties of a chiral interaction at next-to-next-to leading order

    E-Print Network [OSTI]

    A. Ekström; B. D. Carlsson; K. A. Wendt; C. Forssén; M. Hjorth-Jensen; R. Machleidt; S. M. Wild

    2014-06-30T23:59:59.000Z

    We have quantified the statistical uncertainties of the low-energy coupling-constants (LECs) of an optimized nucleon-nucleon (NN) interaction from chiral effective field theory ($\\chi$EFT) at next-to-next-to-leading order (NNLO). In addition, we have propagated the impact of the uncertainties of the LECs to two-nucleon scattering phase shifts, effective range parameters, and deuteron observables.

  19. 1997-2001 by M. Kostic Ch.5: Uncertainty/Error Analysis

    E-Print Network [OSTI]

    Kostic, Milivoje M.

    1 ©1997-2001 by M. Kostic Ch.5: Uncertainty/Error Analysis · Introduction · Bias and Precision Summation/Propagation (Expanded Combined Uncertainty) · Problem 5-30 ©1997-2001 by M. Kostic Ch.5) at corresponding Probability (%P) Remember: u = d%P = t,%PS (@ %P); z=t=d/S #12;2 ©1997-2001 by M. Kostic Bias

  20. Predicting System Performance with Uncertainty

    E-Print Network [OSTI]

    Yan, B.; Malkawi, A.

    2012-01-01T23:59:59.000Z

    on uncertainty in input values for predictions. The input values associated with predictions can come from estimations or measurements corrupted with noise. Therefore, it is more reasonable to assign probability distributions over their domains of plausible... increases, the number of simulations required increases significantly. The time cost limits the extension of uncertainty analysis. Current studies have not covered uncertainty related to system controls in operations. Measurements in system operations...

  1. Utility Maximization under Uncertainty

    E-Print Network [OSTI]

    Li, Jian

    2010-01-01T23:59:59.000Z

    Motivated by several search and optimization problems over uncertain datasets, we study the stochastic versions of a broad class of combinatorial problems where either the existences or the weights of the elements in the input dataset are uncertain. The class of problems that we study includes shortest paths, minimum weight spanning trees, and minimum weight matchings over probabilistic graphs; top-k queries over probabilistic datasets; and other combinatorial problems like knapsack. By noticing that the expected value is inadequate in capturing different types of risk-averse or risk-prone behaviors, we consider a more general objective which is to maximize the expected utility of the solution for some given utility function. For weight uncertainty model, we show that we can obtain a polynomial time approximation algorithm with additive error eps for any eps>0, if there is a pseudopolynomial time algorithm for the exact version of the problem. Our result generalizes several prior works on stochastic shortest ...

  2. Determining the Uncertainty Associated with Retrospective Air Sampling for Optimization Purposes

    SciTech Connect (OSTI)

    Hadlock, D.J.

    2003-10-03T23:59:59.000Z

    NUREG 1400 contains an acceptable methodology for determining the uncertainty associated with retrospective air sampling. The method is a fairly simple one in which both the systemic and random uncertainties, usually expressed as a percent error, are propagated using the square root of the sum of the squares. Historically, many people involved in air sampling have focused on the statistical counting error as the deciding factor of overall uncertainty in retrospective air sampling. This paper looks at not only the counting error but also other errors associated with the performance of retrospective air sampling.

  3. Probabilistic accident consequence uncertainty analysis: Dispersion and deposition uncertainty assessment, appendices A and B

    SciTech Connect (OSTI)

    Harper, F.T.; Young, M.L.; Miller, L.A. [Sandia National Labs., Albuquerque, NM (United States); Hora, S.C. [Univ. of Hawaii, Hilo, HI (United States); Lui, C.H. [Nuclear Regulatory Commission, Washington, DC (United States); Goossens, L.H.J.; Cooke, R.M. [Delft Univ. of Technology (Netherlands); Paesler-Sauer, J. [Research Center, Karlsruhe (Germany); Helton, J.C. [and others

    1995-01-01T23:59:59.000Z

    The development of two new probabilistic accident consequence codes, MACCS and COSYMA, completed in 1990, estimate the risks presented by nuclear installations based on postulated frequencies and magnitudes of potential accidents. In 1991, the US Nuclear Regulatory Commission (NRC) and the Commission of the European Communities (CEC) began a joint uncertainty analysis of the two codes. The objective was to develop credible and traceable uncertainty distributions for the input variables of the codes. Expert elicitation, developed independently, was identified as the best technology available for developing a library of uncertainty distributions for the selected consequence parameters. The study was formulated jointly and was limited to the current code models and to physical quantities that could be measured in experiments. To validate the distributions generated for the wet deposition input variables, samples were taken from these distributions and propagated through the wet deposition code model along with the Gaussian plume model (GPM) implemented in the MACCS and COSYMA codes. Resulting distributions closely replicated the aggregated elicited wet deposition distributions. Project teams from the NRC and CEC cooperated successfully to develop and implement a unified process for the elaboration of uncertainty distributions on consequence code input parameters. Formal expert judgment elicitation proved valuable for synthesizing the best available information. Distributions on measurable atmospheric dispersion and deposition parameters were successfully elicited from experts involved in the many phenomenological areas of consequence analysis. This volume is the second of a three-volume document describing the project and contains two appendices describing the rationales for the dispersion and deposition data along with short biographies of the 16 experts who participated in the project.

  4. Propagation of seismic waves through liquefied soils

    E-Print Network [OSTI]

    Taiebat, Mahdi; Jeremic, Boris; Dafalias, Yannis; Kaynia, Amir; Cheng, Zhao

    2010-01-01T23:59:59.000Z

    the mechanisms of wave propagation and ARTICLE IN PRESS M.Numerical analysis Wave propagation Earthquake Liquefactionenergy during any wave propagation. This paper summarizes

  5. Uncertainty and sensitivity analysis for photovoltaic system modeling.

    SciTech Connect (OSTI)

    Hansen, Clifford W.; Pohl, Andrew Phillip; Jordan, Dirk [National Center for Photovoltaics, National Renewable Energy Laboratory, Golden, CO] [National Center for Photovoltaics, National Renewable Energy Laboratory, Golden, CO

    2013-12-01T23:59:59.000Z

    We report an uncertainty and sensitivity analysis for modeling DC energy from photovoltaic systems. We consider two systems, each comprised of a single module using either crystalline silicon or CdTe cells, and located either at Albuquerque, NM, or Golden, CO. Output from a PV system is predicted by a sequence of models. Uncertainty in the output of each model is quantified by empirical distributions of each model's residuals. We sample these distributions to propagate uncertainty through the sequence of models to obtain an empirical distribution for each PV system's output. We considered models that: (1) translate measured global horizontal, direct and global diffuse irradiance to plane-of-array irradiance; (2) estimate effective irradiance from plane-of-array irradiance; (3) predict cell temperature; and (4) estimate DC voltage, current and power. We found that the uncertainty in PV system output to be relatively small, on the order of 1% for daily energy. Four alternative models were considered for the POA irradiance modeling step; we did not find the choice of one of these models to be of great significance. However, we observed that the POA irradiance model introduced a bias of upwards of 5% of daily energy which translates directly to a systematic difference in predicted energy. Sensitivity analyses relate uncertainty in the PV system output to uncertainty arising from each model. We found that the residuals arising from the POA irradiance and the effective irradiance models to be the dominant contributors to residuals for daily energy, for either technology or location considered. This analysis indicates that efforts to reduce the uncertainty in PV system output should focus on improvements to the POA and effective irradiance models.

  6. Gas Explosion Characterization, Wave Propagation

    E-Print Network [OSTI]

    s & Dt^boooo^j Risø-R-525 Gas Explosion Characterization, Wave Propagation (Small-Scale Experiments EXPLOSION CHARACTERIZATION, WAVE PROPAGATION (Small-Scale Experiments) G.C. Larsen Abstract. A number characteristics 14 3.5. Characteristics of the primary pressure wave 21 3.6. Pressure propagation over a hard

  7. TRITIUM UNCERTAINTY ANALYSIS FOR SURFACE WATER SAMPLES AT THE SAVANNAH RIVER SITE

    SciTech Connect (OSTI)

    Atkinson, R.

    2012-07-31T23:59:59.000Z

    Radiochemical analyses of surface water samples, in the framework of Environmental Monitoring, have associated uncertainties for the radioisotopic results reported. These uncertainty analyses pertain to the tritium results from surface water samples collected at five locations on the Savannah River near the U.S. Department of Energy's Savannah River Site (SRS). Uncertainties can result from the field-sampling routine, can be incurred during transport due to the physical properties of the sample, from equipment limitations, and from the measurement instrumentation used. The uncertainty reported by the SRS in their Annual Site Environmental Report currently considers only the counting uncertainty in the measurements, which is the standard reporting protocol for radioanalytical chemistry results. The focus of this work is to provide an overview of all uncertainty components associated with SRS tritium measurements, estimate the total uncertainty according to ISO 17025, and to propose additional experiments to verify some of the estimated uncertainties. The main uncertainty components discovered and investigated in this paper are tritium absorption or desorption in the sample container, HTO/H{sub 2}O isotopic effect during distillation, pipette volume, and tritium standard uncertainty. The goal is to quantify these uncertainties and to establish a combined uncertainty in order to increase the scientific depth of the SRS Annual Site Environmental Report.

  8. Oil and Gas Production Optimization; Lost Potential due to Uncertainty

    E-Print Network [OSTI]

    Johansen, Tor Arne

    Oil and Gas Production Optimization; Lost Potential due to Uncertainty Steinar M. Elgsaeter Olav.ntnu.no) Abstract: The information content in measurements of offshore oil and gas production is often low, and when in the context of offshore oil and gas fields, can be considered the total output of production wells, a mass

  9. Uncertainties and Ambiguities in Percentiles and how to Avoid Them

    E-Print Network [OSTI]

    Schreiber, Michael

    2012-01-01T23:59:59.000Z

    The recently proposed fractional scoring scheme is used to attribute publications to percentile rank classes. It is shown that in this way uncertainties and ambiguities in the evaluation of percentile ranks do not occur. Using the fractional scoring the total score of all papers exactly reproduces the theoretical value.

  10. The impact of uncertainty and risk measures

    E-Print Network [OSTI]

    Jo, Soojin; Jo, Soojin

    2012-01-01T23:59:59.000Z

    uncertainty on investment: Evidence from TEXAS oil drilling.investment decisions to changes in uncertainty using Texas oiloil price uncertainty deters var- ious types of real economic activities, such as output production, investment,

  11. Uncertainty in emissions projections for climate models

    E-Print Network [OSTI]

    Webster, Mort David.; Babiker, Mustafa H.M.; Mayer, Monika.; Reilly, John M.; Harnisch, Jochen.; Hyman, Robert C.; Sarofim, Marcus C.; Wang, Chien.

    Future global climate projections are subject to large uncertainties. Major sources of this uncertainty are projections of anthropogenic emissions. We evaluate the uncertainty in future anthropogenic emissions using a ...

  12. Pecan Propagation in Texas.

    E-Print Network [OSTI]

    Swallow, A. P.

    1924-01-01T23:59:59.000Z

    PECAN PROPAGATION IN TEXAS I SOIL AND CLIMATIC REQ:UIREMENTS The ideal condition for pecan production is to have the roots of the tree in perpetual, moderate moisture and the top in constant sunshine. Good pecan land should be fertile, deep, loose... it should be much deeper. Shallow soils cannot be relied upon to pro­ duce regular crops. Tight land prevents the growth of an extended root system, and is too uneven in its moisture content. The wood growing period of a pecan tree extends froInt the opening...

  13. Structural model uncertainty in stochastic simulation

    SciTech Connect (OSTI)

    McKay, M.D.; Morrison, J.D. [Los Alamos National Lab., NM (United States). Technology and Safety Assessment Div.

    1997-09-01T23:59:59.000Z

    Prediction uncertainty in stochastic simulation models can be described by a hierarchy of components: stochastic variability at the lowest level, input and parameter uncertainty at a higher level, and structural model uncertainty at the top. It is argued that a usual paradigm for analysis of input uncertainty is not suitable for application to structural model uncertainty. An approach more likely to produce an acceptable methodology for analyzing structural model uncertainty is one that uses characteristics specific to the particular family of models.

  14. Harvesting a renewable resource under uncertainty

    E-Print Network [OSTI]

    Saphores, Jean-Daniel M

    2003-01-01T23:59:59.000Z

    Consider a valuable renewable resource whose biomass X2003. “Harvesting a renewable resource under uncertainty,”Harvesting a Renewable Resource under Uncertainty 1 (with

  15. Reducing Petroleum Despendence in California: Uncertainties About...

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

    Petroleum Despendence in California: Uncertainties About Light-Duty Diesel Reducing Petroleum Despendence in California: Uncertainties About Light-Duty Diesel 2002 DEER Conference...

  16. Total Cross Sections for Neutron Scattering

    E-Print Network [OSTI]

    C. R. Chinn; Ch. Elster; R. M. Thaler; S. P. Weppner

    1994-10-19T23:59:59.000Z

    Measurements of neutron total cross-sections are both extensive and extremely accurate. Although they place a strong constraint on theoretically constructed models, there are relatively few comparisons of predictions with experiment. The total cross-sections for neutron scattering from $^{16}$O and $^{40}$Ca are calculated as a function of energy from $50-700$~MeV laboratory energy with a microscopic first order optical potential derived within the framework of the Watson expansion. Although these results are already in qualitative agreement with the data, the inclusion of medium corrections to the propagator is essential to correctly predict the energy dependence given by the experiment.

  17. Uncertainty quantification for large-scale ocean circulation predictions.

    SciTech Connect (OSTI)

    Safta, Cosmin; Debusschere, Bert J.; Najm, Habib N.; Sargsyan, Khachik

    2010-09-01T23:59:59.000Z

    Uncertainty quantificatio in climate models is challenged by the sparsity of the available climate data due to the high computational cost of the model runs. Another feature that prevents classical uncertainty analyses from being easily applicable is the bifurcative behavior in the climate data with respect to certain parameters. A typical example is the Meridional Overturning Circulation in the Atlantic Ocean. The maximum overturning stream function exhibits discontinuity across a curve in the space of two uncertain parameters, namely climate sensitivity and CO{sub 2} forcing. We develop a methodology that performs uncertainty quantificatio in the presence of limited data that have discontinuous character. Our approach is two-fold. First we detect the discontinuity location with a Bayesian inference, thus obtaining a probabilistic representation of the discontinuity curve location in presence of arbitrarily distributed input parameter values. Furthermore, we developed a spectral approach that relies on Polynomial Chaos (PC) expansions on each sides of the discontinuity curve leading to an averaged-PC representation of the forward model that allows efficient uncertainty quantification and propagation. The methodology is tested on synthetic examples of discontinuous data with adjustable sharpness and structure.

  18. The impact of uncertainty and risk measures

    E-Print Network [OSTI]

    Jo, Soojin; Jo, Soojin

    2012-01-01T23:59:59.000Z

    historical uncertainty series by incorporating a realized volatility series from daily oil price data

  19. Uncertainty Estimate for the Outdoor Calibration of Solar Pyranometers: A Metrologist Perspective

    SciTech Connect (OSTI)

    Reda, I.; Myers, D.; Stoffel, T.

    2008-12-01T23:59:59.000Z

    Pyranometers are used outdoors to measure solar irradiance. By design, this type of radiometer can measure the; total hemispheric (global) or diffuse (sky) irradiance when the detector is unshaded or shaded from the sun disk, respectively. These measurements are used in a variety of applications including solar energy conversion, atmospheric studies, agriculture, and materials science. Proper calibration of pyranometers is essential to ensure measurement quality. This paper describes a step-by-step method for calculating and reporting the uncertainty of the calibration, using the guidelines of the ISO 'Guide to the Expression of Uncertainty in Measurement' or GUM, that is applied to the pyranometer; calibration procedures used at the National Renewable Energy Laboratory (NREL). The NREL technique; characterizes a responsivity function of a pyranometer as a function of the zenith angle, as well as reporting a single; calibration responsivity value for a zenith angle of 45 ..deg... The uncertainty analysis shows that a lower uncertainty can be achieved by using the response function of a pyranometer determined as a function of zenith angle, in lieu of just using; the average value at 45..deg... By presenting the contribution of each uncertainty source to the total uncertainty; users will be able to troubleshoot and improve their calibration process. The uncertainty analysis method can also be used to determine the uncertainty of different calibration techniques and applications, such as deriving the uncertainty of field measurements.

  20. IAEA CRP on HTGR Uncertainty Analysis: Benchmark Definition and Test Cases

    SciTech Connect (OSTI)

    Gerhard Strydom; Frederik Reitsma; Hans Gougar; Bismark Tyobeka; Kostadin Ivanov

    2012-11-01T23:59:59.000Z

    Uncertainty and sensitivity studies are essential elements of the reactor simulation code verification and validation process. Although several international uncertainty quantification activities have been launched in recent years in the LWR, BWR and VVER domains (e.g. the OECD/NEA BEMUSE program [1], from which the current OECD/NEA LWR Uncertainty Analysis in Modelling (UAM) benchmark [2] effort was derived), the systematic propagation of uncertainties in cross-section, manufacturing and model parameters for High Temperature Reactor (HTGR) designs has not been attempted yet. This paper summarises the scope, objectives and exercise definitions of the IAEA Coordinated Research Project (CRP) on HTGR UAM [3]. Note that no results will be included here, as the HTGR UAM benchmark was only launched formally in April 2012, and the specification is currently still under development.

  1. Image Compression by Back Propagation

    E-Print Network [OSTI]

    Cottrell, Garrison W.

    CHAPTER 9 Image Compression by Back Propagation: An Example of Extensional Programming* GARRISON W the case with the computatiolls associated with basic cognitive pro- cesses such as vision and audition techniques. The technique we employ is known as back propagation. developed by l1umelhart, Hinton

  2. Uncertainty and Sensitivity Analyses Plan

    SciTech Connect (OSTI)

    Simpson, J.C.; Ramsdell, J.V. Jr.

    1993-04-01T23:59:59.000Z

    Hanford Environmental Dose Reconstruction (HEDR) Project staff are developing mathematical models to be used to estimate the radiation dose that individuals may have received as a result of emissions since 1944 from the US Department of Energy's (DOE) Hanford Site near Richland, Washington. An uncertainty and sensitivity analyses plan is essential to understand and interpret the predictions from these mathematical models. This is especially true in the case of the HEDR models where the values of many parameters are unknown. This plan gives a thorough documentation of the uncertainty and hierarchical sensitivity analysis methods recommended for use on all HEDR mathematical models. The documentation includes both technical definitions and examples. In addition, an extensive demonstration of the uncertainty and sensitivity analysis process is provided using actual results from the Hanford Environmental Dose Reconstruction Integrated Codes (HEDRIC). This demonstration shows how the approaches used in the recommended plan can be adapted for all dose predictions in the HEDR Project.

  3. Total Light Management

    Broader source: Energy.gov [DOE]

    Presentation covers total light management, and is given at the Spring 2010 Federal Utility Partnership Working Group (FUPWG) meeting in Providence, Rhode Island.

  4. Total Space Heat-

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (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...

  5. 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...

  6. Fuel cycle cost uncertainty from nuclear fuel cycle comparison

    SciTech Connect (OSTI)

    Li, J.; McNelis, D. [Institute for the Environment, University of North Carolina, Chapel Hill (United States); Yim, M.S. [Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology (Korea, Republic of)

    2013-07-01T23:59:59.000Z

    This paper examined the uncertainty in fuel cycle cost (FCC) calculation by considering both model and parameter uncertainty. Four different fuel cycle options were compared in the analysis including the once-through cycle (OT), the DUPIC cycle, the MOX cycle and a closed fuel cycle with fast reactors (FR). The model uncertainty was addressed by using three different FCC modeling approaches with and without the time value of money consideration. The relative ratios of FCC in comparison to OT did not change much by using different modeling approaches. This observation was consistent with the results of the sensitivity study for the discount rate. Two different sets of data with uncertainty range of unit costs were used to address the parameter uncertainty of the FCC calculation. The sensitivity study showed that the dominating contributor to the total variance of FCC is the uranium price. In general, the FCC of OT was found to be the lowest followed by FR, MOX, and DUPIC. But depending on the uranium price, the FR cycle was found to have lower FCC over OT. The reprocessing cost was also found to have a major impact on FCC.

  7. 4, 507532, 2004 Emission uncertainty

    E-Print Network [OSTI]

    Boyer, Edmond

    and Physics Discussions Impact of different emission inventories on simulated tropospheric ozone over China The importance of emission inventory uncertainty on the simulation of summertime tro- pospheric Ozone over China has been analyzed using a regional chemical transport model. Three independent emissions inventories

  8. Impact of orifice metering uncertainties

    SciTech Connect (OSTI)

    Stuart, J.W. (Pacific Gas and Electric Co., San Francisco, CA (USA))

    1990-12-01T23:59:59.000Z

    In a recent utility study, attributed 38% of its unaccounted-for UAF gas to orifice metering uncertainty biasing caused by straightening vanes. How this was determined and how this applied to the company's orifice meters is described. Almost all (97%) of the company's UAF gas was found to be attributed to identifiable accounting procedures, measurement problems, theft and leakage.

  9. Uncertainty quantification and error analysis

    SciTech Connect (OSTI)

    Higdon, Dave M [Los Alamos National Laboratory; Anderson, Mark C [Los Alamos National Laboratory; Habib, Salman [Los Alamos National Laboratory; Klein, Richard [Los Alamos National Laboratory; Berliner, Mark [OHIO STATE UNIV.; Covey, Curt [LLNL; Ghattas, Omar [UNIV OF TEXAS; Graziani, Carlo [UNIV OF CHICAGO; Seager, Mark [LLNL; Sefcik, Joseph [LLNL; Stark, Philip [UC/BERKELEY; Stewart, James [SNL

    2010-01-01T23:59:59.000Z

    UQ studies all sources of error and uncertainty, including: systematic and stochastic measurement error; ignorance; limitations of theoretical models; limitations of numerical representations of those models; limitations on the accuracy and reliability of computations, approximations, and algorithms; and human error. A more precise definition for UQ is suggested below.

  10. 5, 45074543, 2005 Uncertainty analysis

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    parameterization, the representation of the in-cloud updraft velocity, the relationship between effective radius10 of the study of aerosol effects on the global climate, uncertainty in the estimation of the indirect aerosol the aerosol chemical and physical properties and cloud microphysics. Aerosols influence cloud radiative

  11. A Two-Step Approach to Uncertainty Quantification of Core Simulators

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

    Yankov, Artem; Collins, Benjamin; Klein, Markus; Jessee, Matthew A.; Zwermann, Winfried; Velkov, Kiril; Pautz, Andreas; Downar, Thomas

    2012-01-01T23:59:59.000Z

    For the multiple sources of error introduced into the standard computational regime for simulating reactor cores, rigorous uncertainty analysis methods are available primarily to quantify the effects of cross section uncertainties. Two methods for propagating cross section uncertainties through core simulators are the XSUSA statistical approach and the “two-step” method. The XSUSA approach, which is based on the SUSA code package, is fundamentally a stochastic sampling method. Alternatively, the two-step method utilizes generalized perturbation theory in the first step and stochastic sampling in the second step. The consistency of these two methods in quantifying uncertainties in the multiplication factor andmore »in the core power distribution was examined in the framework of phase I-3 of the OECD Uncertainty Analysis in Modeling benchmark. With the Three Mile Island Unit 1 core as a base model for analysis, the XSUSA and two-step methods were applied with certain limitations, and the results were compared to those produced by other stochastic sampling-based codes. Based on the uncertainty analysis results, conclusions were drawn as to the method that is currently more viable for computing uncertainties in burnup and transient calculations.« less

  12. Sound propagation around underwater seamounts

    E-Print Network [OSTI]

    Sikora, Joseph J., III

    2009-01-01T23:59:59.000Z

    In the ocean, low frequency acoustic waves propagate with low attenuation and cylindrical spreading loss over long-ranges, making them an effective tool for underwater source localization, tomography, and communications. ...

  13. Reconstruction of nonlinear wave propagation

    DOE Patents [OSTI]

    Fleischer, Jason W; Barsi, Christopher; Wan, Wenjie

    2013-04-23T23:59:59.000Z

    Disclosed are systems and methods for characterizing a nonlinear propagation environment by numerically propagating a measured output waveform resulting from a known input waveform. The numerical propagation reconstructs the input waveform, and in the process, the nonlinear environment is characterized. In certain embodiments, knowledge of the characterized nonlinear environment facilitates determination of an unknown input based on a measured output. Similarly, knowledge of the characterized nonlinear environment also facilitates formation of a desired output based on a configurable input. In both situations, the input thus characterized and the output thus obtained include features that would normally be lost in linear propagations. Such features can include evanescent waves and peripheral waves, such that an image thus obtained are inherently wide-angle, farfield form of microscopy.

  14. Photon propagator for axion electrodynamics

    SciTech Connect (OSTI)

    Itin, Yakov [Institute of Mathematics, Hebrew University of Jerusalem, Givat Ram, Jerusalem, 91904 (Israel) and Jerusalem College of Technology, P.O.B. 16031, Jerusalem, 91160 (Israel)

    2007-10-15T23:59:59.000Z

    The axion modified electrodynamics is usually used as a model for description of possible violation of Lorentz invariance in field theory. The low-energy manifestation of Lorentz violation can hopefully be observed in experiments with electromagnetic waves. It justifies the importance of studying how a small axion addition can modify the wave propagation. Although a constant axion does not contribute to the dispersion relation at all, even a slowly varying axion field destroys the light cone structure. In this paper, we study the wave propagation in the axion modified electrodynamics in the framework of the premetric approach. In addition to the modified dispersion relation, we derive the axion generalization of the photon propagator in Feynman and Landau gauge. Our consideration is free of the usual restriction to the constant gradient axion field. It is remarkable that the axion modified propagator is Hermitian. Consequently, the dissipation effects are absent even in the phenomenological model considered here.

  15. The impact of uncertainty and risk measures

    E-Print Network [OSTI]

    Jo, Soojin; Jo, Soojin

    2012-01-01T23:59:59.000Z

    xi Chapter 1 The Effects of Oil Price Uncertainty on theLIST OF FIGURES Figure Figure Figure Figure Figure Oil priceOil price uncertainty with and without realized

  16. Total Synthesis of (?)-Himandrine

    E-Print Network [OSTI]

    Movassaghi, Mohammad

    We describe the first total synthesis of (?)-himandrine, a member of the class II galbulimima alkaloids. Noteworthy features of this chemistry include a diastereoselective Diels?Alder reaction in the rapid synthesis of the ...

  17. Quantifying uncertainty from material inhomogeneity.

    SciTech Connect (OSTI)

    Battaile, Corbett Chandler; Emery, John M.; Brewer, Luke N.; Boyce, Brad Lee

    2009-09-01T23:59:59.000Z

    Most engineering materials are inherently inhomogeneous in their processing, internal structure, properties, and performance. Their properties are therefore statistical rather than deterministic. These inhomogeneities manifest across multiple length and time scales, leading to variabilities, i.e. statistical distributions, that are necessary to accurately describe each stage in the process-structure-properties hierarchy, and are ultimately the primary source of uncertainty in performance of the material and component. When localized events are responsible for component failure, or when component dimensions are on the order of microstructural features, this uncertainty is particularly important. For ultra-high reliability applications, the uncertainty is compounded by a lack of data describing the extremely rare events. Hands-on testing alone cannot supply sufficient data for this purpose. To date, there is no robust or coherent method to quantify this uncertainty so that it can be used in a predictive manner at the component length scale. The research presented in this report begins to address this lack of capability through a systematic study of the effects of microstructure on the strain concentration at a hole. To achieve the strain concentration, small circular holes (approximately 100 {micro}m in diameter) were machined into brass tensile specimens using a femto-second laser. The brass was annealed at 450 C, 600 C, and 800 C to produce three hole-to-grain size ratios of approximately 7, 1, and 1/7. Electron backscatter diffraction experiments were used to guide the construction of digital microstructures for finite element simulations of uniaxial tension. Digital image correlation experiments were used to qualitatively validate the numerical simulations. The simulations were performed iteratively to generate statistics describing the distribution of plastic strain at the hole in varying microstructural environments. In both the experiments and simulations, the deformation behavior was found to depend strongly on the character of the nearby microstructure.

  18. Quantification of Geometric Uncertainties in Single Cell Cavities for BESSY VSR Using Polynomial Chaos

    E-Print Network [OSTI]

    Heller, J; Schmidt, C; van Rienen, U

    2014-01-01T23:59:59.000Z

    The electromagnetic properties of SRF cavities are mostly determined by their shape. Due to fabrication tolerances, tuning and limited resolution of measurement systems, the exact shape remains uncertain. In order to make assessments for the real life behaviour it is important to quantify how these geometrical uncertainties propagate through the mathematical system and influence certain electromagnetic properties, like the resonant frequencies of the structure’s eigenmodes. This can be done by using non-intrusive straightforward methods like Monte Carlo (MC) simulations. However, such simulations require a large number of deterministic problem solutions to obtain a sufficient accuracy. In order to avoid this scaling behaviour, the so-called generalized polynomial chaos (gPC) expansion is used. This technique allows for the relatively fast computation of uncertainty propagation for few uncertain parameters in the case of computationally expensive deterministic models. In this paper we use the gPC expansion t...

  19. Phase-dependent propagation in a two-level system with intermediate states

    SciTech Connect (OSTI)

    Sharypov, A. V.; Eilam, A.; Wilson-Gordon, A. D.; Friedmann, H. [Department of Chemistry, Bar-Ilan University, Ramat Gan IL-52900 (Israel)

    2010-01-15T23:59:59.000Z

    We study the phase-dependent propagation of a strong, resonant pump and two weak symmetrically detuned fields in a two-level system with population decay through a cascade of intermediate levels. As this system forms a closed loop, the propagation is phase-dependent. For an initial total phase PHI=0, there is constructive interference between the two weak fields, leading to parametric amplification on propagation. When PHI=pi, destructive interference occurs, leading to absorption of the weak fields on propagation. When the weak fields are initially equal in intensity, and PHI=0,pi, PHI remains constant on propagation. For other initial phases, PHI changes on propagation. Dramatic phase changes from pi to 0 can occur when the weak fields are initially unequal in intensity and PHI=pi.

  20. Quantum mechanical time contradicts the uncertainty principle

    E-Print Network [OSTI]

    Hitoshi Kitada

    1999-11-17T23:59:59.000Z

    The a priori time in conventional quantum mechanics is shown to contradict the uncertainty principle. A possible solution is given.

  1. APPROACHES TO EVALUATE WATER QUALITY MODEL PARAMETER UNCERTAINTY FOR ADAPTIVE TMDL IMPLEMENTATION1

    E-Print Network [OSTI]

    is particularly handy for that task. (KEY TERMS: total maximum daily load; water quality model; ecological quality management and decisions such as total maximum daily load (TMDL) determinations (NRC 2001). ModelsAPPROACHES TO EVALUATE WATER QUALITY MODEL PARAMETER UNCERTAINTY FOR ADAPTIVE TMDL IMPLEMENTATION1

  2. Theoretical Uncertainties in Inflationary Predictions

    E-Print Network [OSTI]

    William H. Kinney; Antonio Riotto

    2006-03-09T23:59:59.000Z

    With present and future observations becoming of higher and higher quality, it is timely and necessary to investigate the most significant theoretical uncertainties in the predictions of inflation. We show that our ignorance of the entire history of the Universe, including the physics of reheating after inflation, translates to considerable errors in observationally relevant parameters. Using the inflationary flow formalism, we estimate that for a spectral index $n$ and tensor/scalar ratio $r$ in the region favored by current observational constraints, the theoretical errors are of order $\\Delta n / | n - 1| \\sim 0.1 - 1$ and $\\Delta r /r \\sim 0.1 - 1$. These errors represent the dominant theoretical uncertainties in the predictions of inflation, and are generically of the order of or larger than the projected uncertainties in future precision measurements of the Cosmic Microwave Background. We also show that the lowest-order classification of models into small field, large field, and hybrid breaks down when higher order corrections to the dynamics are included. Models can flow from one region to another.

  3. Probabilistic accident consequence uncertainty analysis: Dispersion and deposition uncertainty assessment. Volume 3, Appendices C, D, E, F, and G

    SciTech Connect (OSTI)

    Harper, F.T.; Young, M.L.; Miller, L.A. [Sandia National Labs., Albuquerque, NM (United States)] [and others

    1995-01-01T23:59:59.000Z

    The development of two new probabilistic accident consequence codes, MACCS and COSYMA, completed in 1990, estimate the risks presented by nuclear installations based on postulated frequencies and magnitudes of potential accidents. In 1991, the US Nuclear Regulatory Commission (NRC) and the Commission of the European Communities (CEC) began a joint uncertainty analysis of the two codes. The objective was to develop credible and traceable uncertainty distributions for the input variables of the codes. Expert elicitation, developed independently, was identified as the best technology available for developing a library of uncertainty distributions for the selected consequence parameters. The study was formulated jointly and was limited to the current code models and to physical quantities that could be measured in experiments. To validate the distributions generated for the wet deposition input variables, samples were taken from these distributions and propagated through the wet deposition code model along with the Gaussian plume model (GPM) implemented in the MACCS and COSYMA codes. Resulting distributions closely replicated the aggregated elicited wet deposition distributions. Project teams from the NRC and CEC cooperated successfully to develop and implement a unified process for the elaboration of uncertainty distributions on consequence code input parameters. Formal expert judgment elicitation proved valuable for synthesizing the best available information. Distributions on measurable atmospheric dispersion and deposition parameters were successfully elicited from experts involved in the many phenomenological areas of consequence analysis. This volume is the third of a three-volume document describing the project and contains descriptions of the probability assessment principles; the expert identification and selection process; the weighting methods used; the inverse modeling methods; case structures; and summaries of the consequence codes.

  4. Entropic uncertainty relations for multiple measurements

    E-Print Network [OSTI]

    Shang Liu; Liang-Zhu Mu; Heng Fan

    2014-11-23T23:59:59.000Z

    We present the entropic uncertainty relations for multiple measurement settings in quantum mechanics. Those uncertainty relations are obtained for both cases with and without the presence of quantum memory. They take concise forms which can be proven in a unified method and easy to calculate. Our results recover the well known entropic uncertainty relations for two observables, which show the uncertainties about the outcomes of two incompatible measurements. Those uncertainty relations are applicable in both foundations of quantum theory and the security of many quantum cryptographic protocols.

  5. Total Energy Monitor

    SciTech Connect (OSTI)

    Friedrich, S

    2008-08-11T23:59:59.000Z

    The total energy monitor (TE) is a thermal sensor that determines the total energy of each FEL pulse based on the temperature rise induced in a silicon wafer upon absorption of the FEL. The TE provides a destructive measurement of the FEL pulse energy in real-time on a pulse-by-pulse basis. As a thermal detector, the TE is expected to suffer least from ultra-fast non-linear effects and to be easy to calibrate. It will therefore primarily be used to cross-calibrate other detectors such as the Gas Detector or the Direct Imager during LCLS commissioning. This document describes the design of the TE and summarizes the considerations and calculations that have led to it. This document summarizes the physics behind the operation of the Total Energy Monitor at LCLS and derives associated engineering specifications.

  6. OPTIMAL DESIGN OF HYBRID ELECTRIC FUEL CELL VEHICLES UNDER UNCERTAINTY AND ENTERPRISE CONSIDERATIONS

    E-Print Network [OSTI]

    Jeongwoo Han; Panos Papalambros

    System research on Hybrid Electric Fuel Cell Vehicles (HEFCV) explores the tradeoffs among safety, fuel economy, acceleration, and other vehicle attributes. In addition to engineering considerations, inclusion of business aspects is important in a preliminary vehicle design optimization study. For a new technology, such as fuel cells, it is also important to include uncertainties stemming from manufacturing variability to market response to fuel price fluctuations. This paper applies a decomposition-based multidisciplinary design optimization strategy to an HEFCV. Uncertainty propagated throughout the system is accounted for in a computationally efficient manner. The latter is achieved with a new coordination strategy based on sequential linearizations. The hierarchically partitioned HEFCV design model includes enterprise, powertrain, fuel cell, and battery subsystem models. In addition to engineering uncertainties, the model takes into account uncertain behavior by consumers, and the expected maximum profit is calculated using probabilistic consumer preferences while satisfying engineering feasibility constraints. 1

  7. Survey of sampling-based methods for uncertainty and sensitivity analysis.

    SciTech Connect (OSTI)

    Johnson, Jay Dean; Helton, Jon Craig; Sallaberry, Cedric J. PhD. (.; .); Storlie, Curt B. (Colorado State University, Fort Collins, CO)

    2006-06-01T23:59:59.000Z

    Sampling-based methods for uncertainty and sensitivity analysis are reviewed. The following topics are considered: (1) Definition of probability distributions to characterize epistemic uncertainty in analysis inputs, (2) Generation of samples from uncertain analysis inputs, (3) Propagation of sampled inputs through an analysis, (4) Presentation of uncertainty analysis results, and (5) Determination of sensitivity analysis results. Special attention is given to the determination of sensitivity analysis results, with brief descriptions and illustrations given for the following procedures/techniques: examination of scatterplots, correlation analysis, regression analysis, partial correlation analysis, rank transformations, statistical tests for patterns based on gridding, entropy tests for patterns based on gridding, nonparametric regression analysis, squared rank differences/rank correlation coefficient test, two dimensional Kolmogorov-Smirnov test, tests for patterns based on distance measures, top down coefficient of concordance, and variance decomposition.

  8. A Framework for Optimization and Quantification of Uncertainty and Sensitivity for Developing Carbon Capture Systems

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

    Eslick, John C; Ng, Brenda; Gao, Qianwen; Tong, Charles H.; Sahinidis, Nikolaos V.; Miller, David C.

    2014-01-01T23:59:59.000Z

    Under the auspices of the U.S. Department of Energy’s Carbon Capture Simulation Initiative (CCSI), a Framework for Optimization and Quantification of Uncertainty and Sensitivity (FOQUS) has been developed. This tool enables carbon capture systems to be rapidly synthesized and rigorously optimized, in an environment that accounts for and propagates uncertainties in parameters and models. FOQUS currently enables (1) the development of surrogate algebraic models utilizing the ALAMO algorithm, which can be used for superstructure optimization to identify optimal process configurations, (2) simulation-based optimization utilizing derivative free optimization (DFO) algorithms with detailed black-box process models, and (3) rigorous uncertainty quantification throughmore »PSUADE. FOQUS utilizes another CCSI technology, the Turbine Science Gateway, to manage the thousands of simulated runs necessary for optimization and UQ. This computational framework has been demonstrated for the design and analysis of a solid sorbent based carbon capture system.« less

  9. A framework for optimization and quantification of uncertainty and sensitivity for developing carbon capture systems

    SciTech Connect (OSTI)

    John C Eslick, John C; Ng, Brenda Ng; Gao, Qianwen; Tong, Charles H.; Sahinidis, Nikolaos V.; Miller, David C.

    2014-01-01T23:59:59.000Z

    Under the auspices of the U.S. Department of Energy’s Carbon Capture Simulation Initiative (CCSI), a Framework for Optimization and Quantification of Uncertainty and Sensitivity (FOQUS) has been developed. This tool enables carbon capture systems to be rapidly synthesized and rigorously optimized, in an environment that accounts for and propagates uncertainties in parameters and models. FOQUS currently enables (1) the development of surrogate algebraic models utilizing the ALAMO algorithm, which can be used for superstructure optimization to identify optimal process configurations, (2) simulation-based optimization utilizing derivative free optimization (DFO) algorithms with detailed black-box process models, and (3) rigorous uncertainty quantification through PSUADE. FOQUS utilizes another CCSI technology, the Turbine Science Gateway, to manage the thousands of simulated runs necessary for optimization and UQ. This computational framework has been demonstrated for the design and analysis of a solid sorbent based carbon capture system.

  10. Uncertainty quantification of a radionuclide release model using an adaptive spectral technique

    SciTech Connect (OSTI)

    Gilli, L.; Hoogwerf, C.; Lathouwers, D.; Kloosterman, J. L. [Delft University of Technology, Faculty of Applied Sciences Radiation Science and Technology, Department Nuclear Energy and Radiation Applications, Mekelweg 15, 2629 JB Delft (Netherlands)

    2013-07-01T23:59:59.000Z

    In this paper we present the application of a non-intrusive spectral techniques we recently developed for the evaluation of the uncertainties associated with a radionuclide migration problem. Spectral techniques can be used to reconstruct stochastic quantities of interest by means of a Fourier-like expansion. Their application to uncertainty propagation problems can be performed by evaluating a set of realizations which are chosen adaptively, in this work the main details about how this is done are presented. The uncertainty quantification problem we are going to deal with was first solved in a recent work where the authors used a spectral technique based on an intrusive approach. In this paper we are going to reproduce the results of this reference work, compare them and discuss the main numerical aspects. (authors)

  11. Total Precipitable Water

    SciTech Connect (OSTI)

    None

    2012-01-01T23:59:59.000Z

    The simulation was performed on 64K cores of Intrepid, running at 0.25 simulated-years-per-day and taking 25 million core-hours. This is the first simulation using both the CAM5 physics and the highly scalable spectral element dynamical core. The animation of Total Precipitable Water clearly shows hurricanes developing in the Atlantic and Pacific.

  12. 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

    2009-09-25T23:59:59.000Z

    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.

  13. Uncertainty Propagation in Hypersonic Flight Dynamics and Comparison of Different Methods

    E-Print Network [OSTI]

    Prabhakar, Avinash

    2010-01-16T23:59:59.000Z

    due to initial condition, ballistic coefficient, lift over drag ratio and atmospheric density. We compute the statistics using the continuous linearization (CL) approach. This approach computes the jacobian of the perturbational variables about...

  14. Quantifying galactic propagation uncertainty in WIMP dark matter search with AMS01 Z=-1 spectrum

    E-Print Network [OSTI]

    Xiao, Sa, Ph. D. Massachusetts Institute of Technology

    2009-01-01T23:59:59.000Z

    A search for a WIMP dark matter annihilation signal is carried out in the AMS01 negatively charged (Z=-I) particle spectrum, following a set of supersymmetric benchmark scenarios in the mSUGRA framework. The result is ...

  15. Propagators for Noncommutative Field Theories

    E-Print Network [OSTI]

    R. Gurau; V. Rivasseau; F. Vignes-Tourneret

    2006-02-06T23:59:59.000Z

    In this paper we provide exact expressions for propagators of noncommutative Bosonic or Fermionic field theories after adding terms of the Grosse-Wulkenhaar type in order to ensure Langmann-Szabo covariance. We emphasize the new Fermionic case and we give in particular all necessary bounds for the multiscale analysis and renormalization of the noncommutative Gross-Neveu model.

  16. Cumulative theoretical uncertainties in lithium depletion boundary age

    E-Print Network [OSTI]

    Tognelli, Emanuele; Degl'Innocenti, Scilla

    2015-01-01T23:59:59.000Z

    We performed a detailed analysis of the main theoretical uncertainties affecting the age at the lithium depletion boundary (LDB). To do that we computed almost 12000 pre-main sequence models with mass in the range [0.06, 0.4] M_sun by varying input physics (nuclear reaction cross-sections, plasma electron screening, outer boundary conditions, equation of state, and radiative opacity), initial chemical elements abundances (total metallicity, helium and deuterium abundances, and heavy elements mixture), and convection efficiency (mixing length parameter, alpha_ML). As a first step, we studied the effect of varying these quantities individually within their extreme values. Then, we analysed the impact of simultaneously perturbing the main input/parameters without an a priori assumption of independence. Such an approach allowed us to build for the first time the cumulative error stripe, which defines the edges of the maximum uncertainty region in the theoretical LDB age. We found that the cumulative error stripe ...

  17. CALiPER Exploratory Study: Accounting for Uncertainty in Lumen Measurements

    SciTech Connect (OSTI)

    Bergman, Rolf; Paget, Maria L.; Richman, Eric E.

    2011-03-31T23:59:59.000Z

    With a well-defined and shared understanding of uncertainty in lumen measurements, testing laboratories can better evaluate their processes, contributing to greater consistency and credibility of lighting testing a key component of the U.S. Department of Energy (DOE) Commercially Available LED Product Evaluation and Reporting (CALiPER) program. Reliable lighting testing is a crucial underlying factor contributing toward the success of many energy-efficient lighting efforts, such as the DOE GATEWAY demonstrations, Lighting Facts Label, ENERGY STAR® energy efficient lighting programs, and many others. Uncertainty in measurements is inherent to all testing methodologies, including photometric and other lighting-related testing. Uncertainty exists for all equipment, processes, and systems of measurement in individual as well as combined ways. A major issue with testing and the resulting accuracy of the tests is the uncertainty of the complete process. Individual equipment uncertainties are typically identified, but their relative value in practice and their combined value with other equipment and processes in the same test are elusive concepts, particularly for complex types of testing such as photometry. The total combined uncertainty of a measurement result is important for repeatable and comparative measurements for light emitting diode (LED) products in comparison with other technologies as well as competing products. This study provides a detailed and step-by-step method for determining uncertainty in lumen measurements, working closely with related standards efforts and key industry experts. This report uses the structure proposed in the Guide to Uncertainty Measurements (GUM) for evaluating and expressing uncertainty in measurements. The steps of the procedure are described and a spreadsheet format adapted for integrating sphere and goniophotometric uncertainty measurements is provided for entering parameters, ordering the information, calculating intermediate values and, finally, obtaining expanded uncertainties. Using this basis and examining each step of the photometric measurement and calibration methods, mathematical uncertainty models are developed. Determination of estimated values of input variables is discussed. Guidance is provided for the evaluation of the standard uncertainties of each input estimate, covariances associated with input estimates and the calculation of the result measurements. With this basis, the combined uncertainty of the measurement results and finally, the expanded uncertainty can be determined.

  18. Incorporating Forecast Uncertainty in Utility Control Center

    SciTech Connect (OSTI)

    Makarov, Yuri V.; Etingov, Pavel V.; Ma, Jian

    2014-07-09T23:59:59.000Z

    Uncertainties in forecasting the output of intermittent resources such as wind and solar generation, as well as system loads are not adequately reflected in existing industry-grade tools used for transmission system management, generation commitment, dispatch and market operation. There are other sources of uncertainty such as uninstructed deviations of conventional generators from their dispatch set points, generator forced outages and failures to start up, load drops, losses of major transmission facilities and frequency variation. These uncertainties can cause deviations from the system balance, which sometimes require inefficient and costly last minute solutions in the near real-time timeframe. This Chapter considers sources of uncertainty and variability, overall system uncertainty model, a possible plan for transition from deterministic to probabilistic methods in planning and operations, and two examples of uncertainty-based fools for grid operations.This chapter is based on work conducted at the Pacific Northwest National Laboratory (PNNL)

  19. Uncertainty Quantification Techniques for Sensor Calibration...

    Office of Scientific and Technical Information (OSTI)

    Uncertainty Quantification Techniques for Sensor Calibration Monitoring in Nuclear Power Plants Re-direct Destination: This report describes research towards the development of...

  20. The impact of uncertainty and risk measures

    E-Print Network [OSTI]

    Jo, Soojin; Jo, Soojin

    2012-01-01T23:59:59.000Z

    peak, and finds that this nonlinear transformation of the oiland oil price growth rates. As seen in the above illustration, uncertainty is at its peak

  1. High-level waste qualification: Managing uncertainty

    SciTech Connect (OSTI)

    Pulsipher, B.A. [Pacific Northwest Lab., Richland, WA (United States)

    1993-12-31T23:59:59.000Z

    Qualification of high-level waste implies specifications driven by risk against which performance can be assessed. The inherent uncertainties should be addressed in the specifications and statistical methods should be employed to appropriately manage these uncertainties. Uncertainties exist whenever measurements are obtained, sampling is employed, or processes are affected by systematic or random perturbations. This paper presents the approach and statistical methods currently employed by Pacific Northwest Laboratory (PNL) and West Valley Nuclear Services (WVNS) to characterize, minimize, and control uncertainties pertinent to a waste-form acceptance specification concerned with product consistency.

  2. TotalView Training

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of ScienceandMesa del SolStrengthening a solidSynthesisAppliances » Top InnovativeTopoisomeraseTotalView

  3. A stochastic approach to estimate the uncertainty of dose mapping caused by uncertainties in b-spline registration

    SciTech Connect (OSTI)

    Hub, Martina; Thieke, Christian; Kessler, Marc L.; Karger, Christian P. [Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), 69120 Heidelberg (Germany); Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany and Department of Radiation Oncology, University Clinic Heidelberg, 69120 Heidelberg (Germany); Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan 48109 (United States); Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), 69120 Heidelberg (Germany)

    2012-04-15T23:59:59.000Z

    Purpose: In fractionated radiation therapy, image guidance with daily tomographic imaging becomes more and more clinical routine. In principle, this allows for daily computation of the delivered dose and for accumulation of these daily dose distributions to determine the actually delivered total dose to the patient. However, uncertainties in the mapping of the images can translate into errors of the accumulated total dose, depending on the dose gradient. In this work, an approach to estimate the uncertainty of mapping between medical images is proposed that identifies areas bearing a significant risk of inaccurate dose accumulation. Methods: This method accounts for the geometric uncertainty of image registration and the heterogeneity of the dose distribution, which is to be mapped. Its performance is demonstrated in context of dose mapping based on b-spline registration. It is based on evaluation of the sensitivity of dose mapping to variations of the b-spline coefficients combined with evaluation of the sensitivity of the registration metric with respect to the variations of the coefficients. It was evaluated based on patient data that was deformed based on a breathing model, where the ground truth of the deformation, and hence the actual true dose mapping error, is known. Results: The proposed approach has the potential to distinguish areas of the image where dose mapping is likely to be accurate from other areas of the same image, where a larger uncertainty must be expected. Conclusions: An approach to identify areas where dose mapping is likely to be inaccurate was developed and implemented. This method was tested for dose mapping, but it may be applied in context of other mapping tasks as well.

  4. Nonlinear Saturation of Vertically Propagating Rossby Waves

    E-Print Network [OSTI]

    Giannitsis, Constantine

    The interaction between vertical Rossby wave propagation and wave breaking is studied in the idealized context of a beta-plane channel model. Considering the problem of propagation through a uniform zonal flow in an ...

  5. Propagation Plane waves -High order Modes

    E-Print Network [OSTI]

    Berlin,Technische Universität

    1 Propagation · Plane waves - High order Modes y x a ky = n a One wave: p(x,y,t)=p0 cos(k y)e-jk x e j t vy(y,t)= 0 ; y=0,a xy } Propagation · Plane waves - High order Modes x n a p(x,y,t)=pn cos( y;4 Propagation · Circular duct ­ Helical waves (spiralling waves) kc=m/a kz kH Projection: Propagation #12

  6. Wave Propagation in Fractured Poroelastic Media

    E-Print Network [OSTI]

    Seismic wave propagation through fractures and cracks is an important subject in exploration and production geophysics, earthquake seismology and mining.

  7. Adaptive polynomial chaos techniques for uncertainty quantification of a gas cooled fast reactor transient

    SciTech Connect (OSTI)

    Perko, Z. [Section Physics of Nuclear Reactors, Department of Radiation, Radionuclides and Reactors, TU Delft, Mekelweg 15, 2629 JB, Delft (Netherlands); Gilli, L.; Lathouwers, D.; Kloosterman, J. L. [Section Physics of Nuclear Reactors, Department of Radiation, Radionuclides and Reactors, Delft University of Technology, Mekelweg 15, 2629 JB, Delft (Netherlands)

    2013-07-01T23:59:59.000Z

    Uncertainty quantification plays an increasingly important role in the nuclear community, especially with the rise of Best Estimate Plus Uncertainty methodologies. Sensitivity analysis, surrogate models, Monte Carlo sampling and several other techniques can be used to propagate input uncertainties. In recent years however polynomial chaos expansion has become a popular alternative providing high accuracy at affordable computational cost. This paper presents such polynomial chaos (PC) methods using adaptive sparse grids and adaptive basis set construction, together with an application to a Gas Cooled Fast Reactor transient. Comparison is made between a new sparse grid algorithm and the traditionally used technique proposed by Gerstner. An adaptive basis construction method is also introduced and is proved to be advantageous both from an accuracy and a computational point of view. As a demonstration the uncertainty quantification of a 50% loss of flow transient in the GFR2400 Gas Cooled Fast Reactor design was performed using the CATHARE code system. The results are compared to direct Monte Carlo sampling and show the superior convergence and high accuracy of the polynomial chaos expansion. Since PC techniques are easy to implement, they can offer an attractive alternative to traditional techniques for the uncertainty quantification of large scale problems. (authors)

  8. A High-Performance Embedded Hybrid Methodology for Uncertainty Quantification With Applications

    SciTech Connect (OSTI)

    Iaccarino, Gianluca

    2014-04-01T23:59:59.000Z

    Multiphysics processes modeled by a system of unsteady di#11;erential equations are natu- rally suited for partitioned (modular) solution strategies. We consider such a model where probabilistic uncertainties are present in each module of the system and represented as a set of random input parameters. A straightforward approach in quantifying uncertainties in the predicted solution would be to sample all the input parameters into a single set, and treat the full system as a black-box. Although this method is easily parallelizable and requires minimal modi#12;cations to deterministic solver, it is blind to the modular structure of the underlying multiphysical model. On the other hand, using spectral representations polynomial chaos expansions (PCE) can provide richer structural information regarding the dynamics of these uncertainties as they propagate from the inputs to the predicted output, but can be prohibitively expensive to implement in the high-dimensional global space of un- certain parameters. Therefore, we investigated hybrid methodologies wherein each module has the exibility of using sampling or PCE based methods of capturing local uncertainties while maintaining accuracy in the global uncertainty analysis. For the latter case, we use a conditional PCE model which mitigates the curse of dimension associated with intru- sive Galerkin or semi-intrusive Pseudospectral methods. After formalizing the theoretical framework, we demonstrate our proposed method using a numerical viscous ow simulation and benchmark the performance against a solely Monte-Carlo method and solely spectral method.

  9. Coupled Parabolic Equations for Wave Propagation

    E-Print Network [OSTI]

    Zhao, Hongkai

    Coupled Parabolic Equations for Wave Propagation Kai Huang, Knut Solna and Hongkai Zhao #3; April simulation of wave propagation over long distances. The coupled parabolic equations are derived from a two algorithms are important in order to understand wave propagation in complex media. Resolving the wavelength

  10. Solitary waves propagating over variable Roger Grimshaw

    E-Print Network [OSTI]

    Solitary waves propagating over variable topography Roger Grimshaw Loughborough University waves that can propagate steadily over long distances. They were first observed by Russell in 1837 in a now famous report [26] on his observations of a solitary wave propagating along a Scottish canal

  11. Two-dimensional cross-section and SED uncertainty analysis for the Fusion Engineering Device (FED)

    SciTech Connect (OSTI)

    Embrechts, M.J.; Urban, W.T.; Dudziak, D.J.

    1982-01-01T23:59:59.000Z

    The theory of two-dimensional cross-section and secondary-energy-distribution (SED) sensitivity was implemented by developing a two-dimensional sensitivity and uncertainty analysis code, SENSIT-2D. Analyses of the Fusion Engineering Design (FED) conceptual inboard shield indicate that, although the calculated uncertainties in the 2-D model are of the same order of magnitude as those resulting from the 1-D model, there might be severe differences. The more complex the geometry, the more compulsory a 2-D analysis becomes. Specific results show that the uncertainty for the integral heating of the toroidal field (TF) coil for the FED is 114.6%. The main contributors to the cross-section uncertainty are chromium and iron. Contributions to the total uncertainty were smaller for nickel, copper, hydrogen and carbon. All analyses were performed with the Los Alamos 42-group cross-section library generated from ENDF/B-V data, and the COVFILS covariance matrix library. The large uncertainties due to chromium result mainly from large convariances for the chromium total and elastic scattering cross sections.

  12. Wave propagation in axion electrodynamics

    E-Print Network [OSTI]

    Yakov Itin

    2007-06-20T23:59:59.000Z

    In this paper, the axion contribution to the electromagnetic wave propagation is studied. First we show how the axion electrodynamics model can be embedded into a premetric formalism of Maxwell electrodynamics. In this formalism, the axion field is not an arbitrary added Chern-Simon term of the Lagrangian, but emerges in a natural way as an irreducible part of a general constitutive tensor.We show that in order to represent the axion contribution to the wave propagation it is necessary to go beyond the geometric approximation, which is usually used in the premetric formalism. We derive a covariant dispersion relation for the axion modified electrodynamics. The wave propagation in this model is studied for an axion field with timelike, spacelike and null derivative covectors. The birefringence effect emerges in all these classes as a signal of Lorentz violation. This effect is however completely different from the ordinary birefringence appearing in classical optics and in premetric electrodynamics. The axion field does not simple double the ordinary light cone structure. In fact, it modifies the global topological structure of light cones surfaces. In CFJ-electrodynamics, such a modification results in violation of causality. In addition, the optical metrics in axion electrodynamics are not pseudo-Riemannian. In fact, for all types of the axion field, they are even non-Finslerian.

  13. Wave Propagation in Lipid Monolayers

    E-Print Network [OSTI]

    J. Griesbauer; A. Wixforth; M. F. Schneider

    2010-05-26T23:59:59.000Z

    Sound waves are excited on lipid monolayers using a set of planar electrodes aligned in parallel with the excitable medium. By measuring the frequency dependent change in the lateral pressure we are able to extract the sound velocity for the entire monolayer phase diagram. We demonstrate that this velocity can also be directly derived from the lipid monolayer compressibility and consequently displays a minimum in the phase transition regime. This minimum decreases from v0=170m/s for one component lipid monolayers down to vm=50m/s for lipid mixtures. No significant attenuation can be detected confirming an adiabatic phenomenon. Finally our data propose a relative lateral density oscillation of \\Delta\\rho/\\rho ~ 2% implying a change in all area dependent physical properties. Order of magnitude estimates from static couplings therefore predict propagating changes in surface potential of 1-50mV, 1 unit in pH (electrochemical potential) and 0.01{\\deg}K in temperature and fall within the same order of magnitude as physical changes measured during nerve pulse propagation. These results therefore strongly support the idea of propagating adiabatic sound waves along nerves as first thoroughly described by Kaufmann in 1989 and recently by Heimburg and Jackson, but claimed by Wilke already in 1912.

  14. Parallelisation of Wave Propagation Algorithms for Odour Propagation in Multi-Agent Systems

    E-Print Network [OSTI]

    Vialle, Stéphane

    Parallelisation of Wave Propagation Algorithms for Odour Propagation in Multi-Agent Systems Eugen-agent systems is based on the wave propagation model. This article discusses some sequential (recursive is introduced. Keywords: parallel algorithms, wave propagation model, multi-agent systems. 1 Introduction

  15. BUDVYTIS et al.: LABEL PROPAGATION 1 Label propagation in complex video

    E-Print Network [OSTI]

    Kim, Tae-Kyun

    Propagation (PGP) Proposed Hybrid Model (PHM) Occlusion-aware labelling (Classifier injection off ) ProlongedBUDVYTIS et al.: LABEL PROPAGATION 1 Label propagation in complex video sequences using semi graphical model for label propagation in lengthy and complex video sequences. Given hand-labelled start

  16. Sensitivity and uncertainty in the effective delayed neutron fraction ({beta}{sub eff})

    SciTech Connect (OSTI)

    Kodeli, I. I. [Jozef Stefan Inst., Jamova 39, Ljubljana (Slovenia)

    2012-07-01T23:59:59.000Z

    Precise knowledge of effective delayed neutron fraction ({beta}{sub eff}) and of the corresponding uncertainty is important for reactor safety analysis. The interest in developing the methodology for estimating the uncertainty in {beta}{sub eff} was expressed in the scope of the UAM project of the OECD/NEA. A novel approach for the calculation of the nuclear data sensitivity and uncertainty of the effective delayed neutron fraction is proposed, based on the linear perturbation theory. The method allows the detailed analysis of components of {beta}{sub eff} uncertainty. The procedure was implemented in the SUSD3D sensitivity and uncertainty code applied to several fast neutron benchmark experiments from the ICSBEP and IRPhE databases. According to the JENDL-4 covariance matrices and taking into account the uncertainty in the cross sections and in the prompt and delayed fission spectra the total uncertainty in {beta}eff was found to be of the order of {approx}2 to {approx}3.5 % for the studied fast experiments. (authors)

  17. Recursive Total Least Squares: An Alternative to the Discrete Kalman Filter

    E-Print Network [OSTI]

    Boley, Daniel

    Recursive Total Least Squares: An Alternative to the Discrete Kalman Filter Daniel L. Boley The discrete Kalman lter, which is becoming a common tool for reducing uncertainty in robot navigation, su ers total least squares estimator (RTLS) as an alternative to the Kalman lter, and compare

  18. Estimating the uncertainty in underresolved nonlinear dynamics

    SciTech Connect (OSTI)

    Chorin, Alelxandre; Hald, Ole

    2013-06-12T23:59:59.000Z

    The Mori-Zwanzig formalism of statistical mechanics is used to estimate the uncertainty caused by underresolution in the solution of a nonlinear dynamical system. A general approach is outlined and applied to a simple example. The noise term that describes the uncertainty turns out to be neither Markovian nor Gaussian. It is argued that this is the general situation.

  19. Neutron total cross section measurements of gold and tantalum at the nELBE photoneutron source

    E-Print Network [OSTI]

    Roland Hannaske; Zoltan Elekes; Roland Beyer; Arnd Junghans; Daniel Bemmerer; Evert Birgersson; Anna Ferrari; Eckart Grosse; Mathias Kempe; Toni Kögler; Michele Marta; Ralph Massarczyk; Andrija Matic; Georg Schramm; Ronald Schwengner; Andreas Wagner

    2013-11-05T23:59:59.000Z

    Neutron total cross sections of $^{197}$Au and $^\\text{nat}$Ta have been measured at the nELBE photoneutron source in the energy range from 0.1 - 10 MeV with a statistical uncertainty of up to 2 % and a total systematic uncertainty of 1 %. This facility is optimized for the fast neutron energy range and combines an excellent time structure of the neutron pulses (electron bunch width 5 ps) with a short flight path of 7 m. Because of the low instantaneous neutron flux transmission measurements of neutron total cross sections are possible, that exhibit very different beam and background conditions than found at other neutron sources.

  20. A flexible uncertainty quantification method for linearly coupled multi-physics systems

    SciTech Connect (OSTI)

    Chen, Xiao, E-mail: chen73@llnl.gov; Ng, Brenda; Sun, Yunwei; Tong, Charles, E-mail: tong10@llnl.gov

    2013-09-01T23:59:59.000Z

    Highlights: •We propose a “modularly hybrid” UQ methodology suitable for independent development of module-based multi-physics simulation. •Our algorithmic framework allows for each module to have its own UQ method (either intrusive or non-intrusive). •Information from each module is combined systematically to propagate “global uncertainty”. •Our proposed approach can allow for easy swapping of new methods for any modules without the need to address incompatibilities. •We demonstrate the proposed framework on a practical application involving a multi-species reactive transport model. -- Abstract: This paper presents a novel approach to building an integrated uncertainty quantification (UQ) methodology suitable for modern-day component-based approach for multi-physics simulation development. Our “hybrid” UQ methodology supports independent development of the most suitable UQ method, intrusive or non-intrusive, for each physics module by providing an algorithmic framework to couple these “stochastic” modules for propagating “global” uncertainties. We address algorithmic and computational issues associated with the construction of this hybrid framework. We demonstrate the utility of such a framework on a practical application involving a linearly coupled multi-species reactive transport model.

  1. Numerical uncertainty in computational engineering and physics

    SciTech Connect (OSTI)

    Hemez, Francois M [Los Alamos National Laboratory

    2009-01-01T23:59:59.000Z

    Obtaining a solution that approximates ordinary or partial differential equations on a computational mesh or grid does not necessarily mean that the solution is accurate or even 'correct'. Unfortunately assessing the quality of discrete solutions by questioning the role played by spatial and temporal discretizations generally comes as a distant third to test-analysis comparison and model calibration. This publication is contributed to raise awareness of the fact that discrete solutions introduce numerical uncertainty. This uncertainty may, in some cases, overwhelm in complexity and magnitude other sources of uncertainty that include experimental variability, parametric uncertainty and modeling assumptions. The concepts of consistency, convergence and truncation error are overviewed to explain the articulation between the exact solution of continuous equations, the solution of modified equations and discrete solutions computed by a code. The current state-of-the-practice of code and solution verification activities is discussed. An example in the discipline of hydro-dynamics illustrates the significant effect that meshing can have on the quality of code predictions. A simple method is proposed to derive bounds of solution uncertainty in cases where the exact solution of the continuous equations, or its modified equations, is unknown. It is argued that numerical uncertainty originating from mesh discretization should always be quantified and accounted for in the overall uncertainty 'budget' that supports decision-making for applications in computational physics and engineering.

  2. Uncertainty quantification approaches for advanced reactor analyses.

    SciTech Connect (OSTI)

    Briggs, L. L.; Nuclear Engineering Division

    2009-03-24T23:59:59.000Z

    The original approach to nuclear reactor design or safety analyses was to make very conservative modeling assumptions so as to ensure meeting the required safety margins. Traditional regulation, as established by the U. S. Nuclear Regulatory Commission required conservatisms which have subsequently been shown to be excessive. The commission has therefore moved away from excessively conservative evaluations and has determined best-estimate calculations to be an acceptable alternative to conservative models, provided the best-estimate results are accompanied by an uncertainty evaluation which can demonstrate that, when a set of analysis cases which statistically account for uncertainties of all types are generated, there is a 95% probability that at least 95% of the cases meet the safety margins. To date, nearly all published work addressing uncertainty evaluations of nuclear power plant calculations has focused on light water reactors and on large-break loss-of-coolant accident (LBLOCA) analyses. However, there is nothing in the uncertainty evaluation methodologies that is limited to a specific type of reactor or to specific types of plant scenarios. These same methodologies can be equally well applied to analyses for high-temperature gas-cooled reactors and to liquid metal reactors, and they can be applied to steady-state calculations, operational transients, or severe accident scenarios. This report reviews and compares both statistical and deterministic uncertainty evaluation approaches. Recommendations are given for selection of an uncertainty methodology and for considerations to be factored into the process of evaluating uncertainties for advanced reactor best-estimate analyses.

  3. Fractional revivals through Rényi uncertainty relations

    E-Print Network [OSTI]

    Elvira Romera; Francisco de los Santos

    2014-09-19T23:59:59.000Z

    We show that the R\\'enyi uncertainty relations give a good description of the dynamical behavior of wave packets and constitute a sound approach to revival phenomena by analyzing three model systems: the simple harmonic oscillator, the infinite square well, and the quantum bouncer. We prove the usefulness of entropic uncertainty relations as a tool for identifying fractional revivals by providing a comparison in different contexts with the usual Heisenberg uncertainty relation and with the common approach in terms of the autocorrelation function.

  4. Uncertainty Quantification on Prompt Fission Neutrons Spectra

    SciTech Connect (OSTI)

    Talou, P. [T-16, Nuclear Physics Group, Los Alamos National Laboratory, NM 87545 (United States)], E-mail: talou@lanl.gov; Madland, D.G.; Kawano, T. [T-16, Nuclear Physics Group, Los Alamos National Laboratory, NM 87545 (United States)

    2008-12-15T23:59:59.000Z

    Uncertainties in the evaluated prompt fission neutrons spectra present in ENDF/B-VII.0 are assessed in the framework of the Los Alamos model. The methodology used to quantify the uncertainties on an evaluated spectrum is introduced. We also briefly review the Los Alamos model and single out the parameters that have the largest influence on the calculated results. Using a Kalman filter, experimental data and uncertainties are introduced to constrain model parameters, and construct an evaluated covariance matrix for the prompt neutrons spectrum. Preliminary results are shown in the case of neutron-induced fission of {sup 235}U from thermal up to 15 MeV incident energies.

  5. DAMAGE ASSESSMENT OF COMPOSITE PLATE STRUCTURES WITH UNCERTAINTY

    E-Print Network [OSTI]

    Boyer, Edmond

    DAMAGE ASSESSMENT OF COMPOSITE PLATE STRUCTURES WITH UNCERTAINTY Chandrashekhar M.* , Ranjan Uncertainties associated with a structural model and measured vibration data may lead to unreliable damage that material uncertainties in composite structures cause considerable problem in damage assessment which can

  6. Error Detection and Recovery for Robot Motion Planning with Uncertainty

    E-Print Network [OSTI]

    Donald, Bruce Randall

    1987-07-01T23:59:59.000Z

    Robots must plan and execute tasks in the presence of uncertainty. Uncertainty arises from sensing errors, control errors, and uncertainty in the geometry of the environment. The last, which is called model error, has ...

  7. Uncertainty in climate change policy analysis

    E-Print Network [OSTI]

    Jacoby, Henry D.; Prinn, Ronald G.

    Achieving agreement about whether and how to control greenhouse gas emissions would be difficult enough even if the consequences were fully known. Unfortunately, choices must be made in the face of great uncertainty, about ...

  8. Methods for Composing Tradeoff Studies under Uncertainty

    E-Print Network [OSTI]

    Bily, Christopher

    2012-10-19T23:59:59.000Z

    for the power train. This level of knowledge reuse is in keeping with good systems engineering practice. However, existing procedures for generating tradeoff studies under uncertainty involve assumptions that preclude engineers from composing them in a...

  9. Uncertainty Quantification in ocean state estimation

    E-Print Network [OSTI]

    Kalmikov, Alexander G

    2013-01-01T23:59:59.000Z

    Quantifying uncertainty and error bounds is a key outstanding challenge in ocean state estimation and climate research. It is particularly difficult due to the large dimensionality of this nonlinear estimation problem and ...

  10. A note on competitive investment under uncertainty

    E-Print Network [OSTI]

    Pindyck, Robert S.

    1991-01-01T23:59:59.000Z

    This paper clarifies how uncertainty affects irreversible investment in a competitive market equilibrium. With free entry, irreversibility affects the distribution of future prices, and thereby creates an opportunity cost ...

  11. Handling uncertainty in DEX methodology Martin Znidarsic

    E-Print Network [OSTI]

    Bohanec, Marko

    URPDM2010 1 Handling uncertainty in DEX methodology Martin Znidarsic Jozef Stefan Institute, Jamova cesta 39, martin.znidarsic@ijs.si Marko Bohanec Jozef Stefan Institute, Jamova cesta 39, marko

  12. Estimating uncertainties in integrated reservoir studies

    E-Print Network [OSTI]

    Zhang, Guohong

    2004-09-30T23:59:59.000Z

    To make sound investment decisions, decision makers need accurate estimates of the uncertainties present in forecasts of reservoir performance. In this work I propose a method, the integrated mismatch method, that incorporates the misfit...

  13. Multifidelity methods for multidisciplinary design under uncertainty

    E-Print Network [OSTI]

    Christensen, Daniel Erik

    2012-01-01T23:59:59.000Z

    For computational design and analysis tasks, scientists and engineers often have available many different simulation models. The output of each model has an associated uncertainty that is a result of the modeling process. ...

  14. Sandia Energy - Computational Fluid Dynamics & Large-Scale Uncertainty...

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

    & Large-Scale Uncertainty Quantification for Wind Energy Home Highlights - HPC Computational Fluid Dynamics & Large-Scale Uncertainty Quantification for Wind Energy Previous Next...

  15. Uncertainty analysis of multi-rate kinetics of uranium desorption...

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

    Uncertainty analysis of multi-rate kinetics of uranium desorption from sediments. Uncertainty analysis of multi-rate kinetics of uranium desorption from sediments. Abstract: A...

  16. avt-147 computational uncertainty: Topics by E-print Network

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

    Summary: models of uncertainties, the construction of the stochastic models using the maximum entropy principleStochastic modeling of uncertainties in computational structural...

  17. advanced lmrs uncertainties: Topics by E-print Network

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

    Summary: models of uncertainties, the construction of the stochastic models using the maximum entropy principleStochastic modeling of uncertainties in computational structural...

  18. Excited States in Staggered Meson Propagators

    E-Print Network [OSTI]

    MILC Collaboration; C. Bernard; T. Burch; C. DeTar; Steven Gottlieb; E. B. Gregory; U. M. Heller; J. Osborn; R. Sugar; D. Toussaint

    2003-09-16T23:59:59.000Z

    We report on preliminary results from multi-particle fits to meson propagators with three flavors of light dynamical quarks. We are able to measure excited states in propagators with pion quantum numbers, which we interpret as the pion 2S state, and is evidence of locality of the action. In the a_0 (0^{++}) propagators we find evidence for excited states which are probably the expected decay channels, pi+eta and K+Kbar.

  19. Propagation Plane waves -High order Modes

    E-Print Network [OSTI]

    Berlin,Technische Universität

    1 Propagation · Plane waves - High order Modes y x a One wave: p(x,y,t)=p0 cos(k y)e-jk x e j t vy(y,t)= 0 ; y=0,a xy } ky = n a Propagation · Plane waves - High order Modes x n a p(x,y,t)=pn cos( y + - +- + + - +- + - + + +- - - (m,n) #12;4 Propagation · Circular duct ­ Helical waves (spiralling waves) kc=m/a kz k

  20. MUJERES TOTAL BIOLOGIA 16 27

    E-Print Network [OSTI]

    Autonoma de Madrid, Universidad

    , PLASTICA Y VISUAL 2 2 EDUCACION FISICA, DEPORTE Y MOTRICIDAD HUMANA 1 1 6 11 TOTAL CIENCIAS Nº DE TESIS

  1. MUJERES ( * ) TOTAL BIOLOGA 16 22

    E-Print Network [OSTI]

    Autonoma de Madrid, Universidad

    , DEPORTE Y MOTRICIDAD HUMANA 0 4 TOTAL FORMACIÓN DE PROFESORADO Y EDUCACIÓN 0 6 ANATOMÍA PATOLÓGICA 2 5

  2. Programmatic methods for addressing contaminated volume uncertainties.

    SciTech Connect (OSTI)

    DURHAM, L.A.; JOHNSON, R.L.; RIEMAN, C.R.; SPECTOR, H.L.; Environmental Science Division; U.S. ARMY CORPS OF ENGINEERS BUFFALO DISTRICT

    2007-01-01T23:59:59.000Z

    Accurate estimates of the volumes of contaminated soils or sediments are critical to effective program planning and to successfully designing and implementing remedial actions. Unfortunately, data available to support the preremedial design are often sparse and insufficient for accurately estimating contaminated soil volumes, resulting in significant uncertainty associated with these volume estimates. The uncertainty in the soil volume estimates significantly contributes to the uncertainty in the overall project cost estimates, especially since excavation and off-site disposal are the primary cost items in soil remedial action projects. The Army Corps of Engineers Buffalo District's experience has been that historical contaminated soil volume estimates developed under the Formerly Utilized Sites Remedial Action Program (FUSRAP) often underestimated the actual volume of subsurface contaminated soils requiring excavation during the course of a remedial activity. In response, the Buffalo District has adopted a variety of programmatic methods for addressing contaminated volume uncertainties. These include developing final status survey protocols prior to remedial design, explicitly estimating the uncertainty associated with volume estimates, investing in predesign data collection to reduce volume uncertainties, and incorporating dynamic work strategies and real-time analytics in predesign characterization and remediation activities. This paper describes some of these experiences in greater detail, drawing from the knowledge gained at Ashland1, Ashland2, Linde, and Rattlesnake Creek. In the case of Rattlesnake Creek, these approaches provided the Buffalo District with an accurate predesign contaminated volume estimate and resulted in one of the first successful FUSRAP fixed-price remediation contracts for the Buffalo District.

  3. The Total RNA Story Introduction

    E-Print Network [OSTI]

    Goldman, Steven A.

    The Total RNA Story Introduction Assessing RNA sample quality as a routine part of the gene about RNA sample quality. Data from a high quality total RNA preparation Although a wide variety RNA data interpretation and identify features from total RNA electropherograms that reveal information

  4. Shock propagation and neutrino oscillation in supernova

    E-Print Network [OSTI]

    K. Takahashi; K. Sato; H. E. Dalhed; J. R. Wilson

    2003-02-26T23:59:59.000Z

    The effect of the shock propagation on neutrino oscillation in supernova is studied paying attention to evolution of average energy of $\

  5. Wave Propagation in Fractured Poroelastic Media

    E-Print Network [OSTI]

    2014-06-22T23:59:59.000Z

    Wave Propagation in Fractured. Poroelastic Media. WCCM, Barcelona, Spain, July 2014. Juan E. Santos,. 1. 1. Instituto del Gas y del Petr´oleo (IGPUBA), UBA,

  6. Light propagation and Imaging in Indefinite Metamaterials

    E-Print Network [OSTI]

    Yao, Jie

    2010-01-01T23:59:59.000Z

    photolithography by polarized light,” Applied PhysicsZhang, “Imaging visible light using anisotropic metamaterialcross-sectional review of the light propagation of TE mode (

  7. Wireless@Virginia Tech Antennas and Propagation

    E-Print Network [OSTI]

    Beex, A. A. "Louis"

    cutting- edge research at the intersection of engineering, science, and medicine. Please visit www and form factor requirements. The statistical nature of electromagnetic wave propagation combined

  8. Uncertainty analysis on reactivity and discharged inventory for a pressurized water reactor fuel assembly due to {sup 235,238}U nuclear data uncertainties

    SciTech Connect (OSTI)

    Da Cruz, D. F.; Rochman, D.; Koning, A. J. [Nuclear Research and Consultancy Group NRG, Westerduinweg 3, 1755 ZG Petten (Netherlands)

    2012-07-01T23:59:59.000Z

    This paper discusses the uncertainty analysis on reactivity and inventory for a typical PWR fuel element as a result of uncertainties in {sup 235,238}U nuclear data. A typical Westinghouse 3-loop fuel assembly fuelled with UO{sub 2} fuel with 4.8% enrichment has been selected. The Total Monte-Carlo method has been applied using the deterministic transport code DRAGON. This code allows the generation of the few-groups nuclear data libraries by directly using data contained in the nuclear data evaluation files. The nuclear data used in this study is from the JEFF3.1 evaluation, and the nuclear data files for {sup 238}U and {sup 235}U (randomized for the generation of the various DRAGON libraries) are taken from the nuclear data library TENDL. The total uncertainty (obtained by randomizing all {sup 238}U and {sup 235}U nuclear data in the ENDF files) on the reactor parameters has been split into different components (different nuclear reaction channels). Results show that the TMC method in combination with a deterministic transport code constitutes a powerful tool for performing uncertainty and sensitivity analysis of reactor physics parameters. (authors)

  9. Estimating uncertainty of inference for validation

    SciTech Connect (OSTI)

    Booker, Jane M [Los Alamos National Laboratory; Langenbrunner, James R [Los Alamos National Laboratory; Hemez, Francois M [Los Alamos National Laboratory; Ross, Timothy J [UNM

    2010-09-30T23:59:59.000Z

    We present a validation process based upon the concept that validation is an inference-making activity. This has always been true, but the association has not been as important before as it is now. Previously, theory had been confirmed by more data, and predictions were possible based on data. The process today is to infer from theory to code and from code to prediction, making the role of prediction somewhat automatic, and a machine function. Validation is defined as determining the degree to which a model and code is an accurate representation of experimental test data. Imbedded in validation is the intention to use the computer code to predict. To predict is to accept the conclusion that an observable final state will manifest; therefore, prediction is an inference whose goodness relies on the validity of the code. Quantifying the uncertainty of a prediction amounts to quantifying the uncertainty of validation, and this involves the characterization of uncertainties inherent in theory/models/codes and the corresponding data. An introduction to inference making and its associated uncertainty is provided as a foundation for the validation problem. A mathematical construction for estimating the uncertainty in the validation inference is then presented, including a possibility distribution constructed to represent the inference uncertainty for validation under uncertainty. The estimation of inference uncertainty for validation is illustrated using data and calculations from Inertial Confinement Fusion (ICF). The ICF measurements of neutron yield and ion temperature were obtained for direct-drive inertial fusion capsules at the Omega laser facility. The glass capsules, containing the fusion gas, were systematically selected with the intent of establishing a reproducible baseline of high-yield 10{sup 13}-10{sup 14} neutron output. The deuterium-tritium ratio in these experiments was varied to study its influence upon yield. This paper on validation inference is the first in a series of inference uncertainty estimations. While the methods demonstrated are primarily statistical, these do not preclude the use of nonprobabilistic methods for uncertainty characterization. The methods presented permit accurate determinations for validation and eventual prediction. It is a goal that these methods establish a standard against which best practice may evolve for determining degree of validation.

  10. Beam Propagation Method Using a [(p -1)/p] Pade Approximant of the Propagator

    E-Print Network [OSTI]

    Lu, Ya Yan

    propagation method (BPM) is developed based on a direct approximation to the propagator using the [(p - 1)/p of the BPM. 1 Introduction The beam propagation method (BPM)1­4 is widely used in numerical simulation, the governing equation is a scalar Helmholtz equation. The BPM relies on approximating the Helmholtz equation

  11. Removing Propagation Redundant Constraints in Redundant Modeling

    E-Print Network [OSTI]

    Stuckey, Peter J.

    propagation redundant constraints in redundant modeling can speed up search by several order of magnitudes but not least, the choice of variables and the associated domains should lead to a smaller search space than search with various degrees of constraint propagation for pruning the search space. One common technique

  12. Propagation testing multi-cell batteries.

    SciTech Connect (OSTI)

    Orendorff, Christopher J.; Lamb, Joshua; Steele, Leigh Anna Marie; Spangler, Scott Wilmer

    2014-10-01T23:59:59.000Z

    Propagation of single point or single cell failures in multi-cell batteries is a significant concern as batteries increase in scale for a variety of civilian and military applications. This report describes the procedure for testing failure propagation along with some representative test results to highlight the potential outcomes for different battery types and designs.

  13. INCORPORATING UNCERTAINTY INTO DAM SAFETY RISK Sanjay S. Chauhan1

    E-Print Network [OSTI]

    Chauhan, Sanjay S.

    on the topic of uncertainty in quantitative risk and policy analysis the reader is referred to Morgan to incorporating input uncertainties into risk analysis model. Input uncertainties are captured by using for uncertainty analysis in dam safety risk assessment, and demonstrates some useful formats for presenting

  14. Bounded Uncertainty Roadmaps for Path Planning Leonidas J. Guibas1

    E-Print Network [OSTI]

    Guibas, Leonidas J.

    Bounded Uncertainty Roadmaps for Path Planning Leonidas J. Guibas1 , David Hsu2 , Hanna Kurniawati2 uncertainty during planning. We in- troduce the notion of a bounded uncertainty roadmap (BURM) and use, and it is not much slower than classic probabilistic roadmap planning algorithms, which ignore uncertainty

  15. Uncertainty and sampling issues in tank characterization

    SciTech Connect (OSTI)

    Liebetrau, A.M.; Pulsipher, B.A.; Kashporenko, D.M. [and others

    1997-06-01T23:59:59.000Z

    A defensible characterization strategy must recognize that uncertainties are inherent in any measurement or estimate of interest and must employ statistical methods for quantifying and managing those uncertainties. Estimates of risk and therefore key decisions must incorporate knowledge about uncertainty. This report focuses statistical methods that should be employed to ensure confident decision making and appropriate management of uncertainty. Sampling is a major source of uncertainty that deserves special consideration in the tank characterization strategy. The question of whether sampling will ever provide the reliable information needed to resolve safety issues is explored. The issue of sample representativeness must be resolved before sample information is reliable. Representativeness is a relative term but can be defined in terms of bias and precision. Currently, precision can be quantified and managed through an effective sampling and statistical analysis program. Quantifying bias is more difficult and is not being addressed under the current sampling strategies. Bias could be bounded by (1) employing new sampling methods that can obtain samples from other areas in the tanks, (2) putting in new risers on some worst case tanks and comparing the results from existing risers with new risers, or (3) sampling tanks through risers under which no disturbance or activity has previously occurred. With some bound on bias and estimates of precision, various sampling strategies could be determined and shown to be either cost-effective or infeasible.

  16. Stochastic methods for uncertainty quantification in radiation transport

    SciTech Connect (OSTI)

    Fichtl, Erin D [Los Alamos National Laboratory; Prinja, Anil K [Los Alamos National Laboratory; Warsa, James S [Los Alamos National Laboratory

    2009-01-01T23:59:59.000Z

    The use of generalized polynomial chaos (gPC) expansions is investigated for uncertainty quantification in radiation transport. The gPC represents second-order random processes in terms of an expansion of orthogonal polynomials of random variables and is used to represent the uncertain input(s) and unknown(s). We assume a single uncertain input-the total macroscopic cross section-although this does not represent a limitation of the approaches considered here. Two solution methods are examined: The Stochastic Finite Element Method (SFEM) and the Stochastic Collocation Method (SCM). The SFEM entails taking Galerkin projections onto the orthogonal basis, which, for fixed source problems, yields a linear system of fully -coupled equations for the PC coefficients of the unknown. For k-eigenvalue calculations, the SFEM system is non-linear and a Newton-Krylov method is employed to solve it. The SCM utilizes a suitable quadrature rule to compute the moments or PC coefficients of the unknown(s), thus the SCM solution involves a series of independent deterministic transport solutions. The accuracy and efficiency of the two methods are compared and contrasted. The PC coefficients are used to compute the moments and probability density functions of the unknown(s), which are shown to be accurate by comparing with Monte Carlo results. Our work demonstrates that stochastic spectral expansions are a viable alternative to sampling-based uncertainty quantification techniques since both provide a complete characterization of the distribution of the flux and the k-eigenvalue. Furthermore, it is demonstrated that, unlike perturbation methods, SFEM and SCM can handle large parameter uncertainty.

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

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

    Q 0.4 3 or More Units... 5.4 0.3 Q Q Central Air-Conditioning Usage Air-Conditioned Floorspace (Square Feet)...

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

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

    ... 1.9 1.1 Q Q 0.3 Q Do Not Use Central Air-Conditioning... 45.2 24.6 3.6 5.0 8.8 3.2 Use a Programmable...

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

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

    Q 0.6 3 or More Units... 5.4 3.8 2.9 0.4 Q N 0.2 Central Air-Conditioning Usage Air-Conditioned Floorspace (Square Feet)...

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

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

    1.3 Q 3 or More Units... 5.4 1.6 0.8 Q 0.3 0.3 Q Central Air-Conditioning Usage Air-Conditioned Floorspace (Square Feet)...

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

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

    3 or More Units... 5.4 2.4 1.4 0.7 0.9 Central Air-Conditioning Usage Air-Conditioned Floorspace (Square Feet)...

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

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

    3 or More Units... 5.4 2.3 1.7 0.6 Central Air-Conditioning Usage Air-Conditioned Floorspace (Square Feet)...

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

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

    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 System......

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

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

    3 or More Units... 5.4 2.1 0.9 0.2 1.0 Central Air-Conditioning Usage Air-Conditioned Floorspace (Square Feet)...

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

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

    30.3 Have Equipment But Do Not Use it... 1.9 0.5 0.6 0.4 Q Q 0.5 0.8 Type of Air-Conditioning Equipment 1, 2 Central System......

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

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

    0.3 3 or More Units... 5.4 0.7 0.5 Q Central Air-Conditioning Usage Air-Conditioned Floorspace (Square Feet)...

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

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

    3 or More Units... 5.4 2.3 0.7 2.1 0.3 Central Air-Conditioning Usage Air-Conditioned Floorspace (Square Feet)...

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

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

    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......

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

    Gasoline and Diesel Fuel Update (EIA)

    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......

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

    Gasoline and Diesel Fuel Update (EIA)

    Personal Computers Do Not Use a Personal Computer... 35.5 14.2 7.2 2.8 4.2 Use a Personal Computer... 75.6...

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

    Gasoline and Diesel Fuel Update (EIA)

    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......

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

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

    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......

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

    Gasoline and Diesel Fuel Update (EIA)

    ..... 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......

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

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

    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......

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

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

    1.3 0.8 0.5 Once a Day... 19.2 4.6 3.0 1.6 Between Once a Day and Once a Week... 32.0 8.9 6.3 2.6 Once a...

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

    Gasoline and Diesel Fuel Update (EIA)

    AppliancesTools.... 56.2 11.6 3.3 8.2 Other Appliances Used Auto BlockEngineBattery Heater... 0.8 0.2 Q 0.1 Hot Tub or Spa......

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

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

    Tools... 56.2 20.5 10.8 3.6 6.1 Other Appliances Used Auto BlockEngineBattery Heater... 0.8 N N N N Hot Tub or Spa......

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

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

    Tools... 56.2 27.2 10.6 9.3 9.2 Other Appliances Used Auto BlockEngineBattery Heater... 0.8 Q Q Q 0.4 Hot Tub or Spa......

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

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

    AppliancesTools.... 56.2 12.2 9.4 2.8 Other Appliances Used Auto BlockEngineBattery Heater... 0.8 Q Q Q Hot Tub or Spa......

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

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

    1.3 3.8 Table HC7.10 Home Appliances Usage Indicators by Household Income, 2005 Below Poverty Line Eligible for Federal Assistance 1 40,000 to 59,999 60,000 to 79,999 80,000...

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.1 86.6 2,720

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.1 86.6 2,720..

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.1 86.6 2,720..

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.1 86.6

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.1 86.6Q Table

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.1 86.6Q TableQ

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.1 86.6Q

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.1 86.6Q26.7

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.1

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.1

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.126.7 28.8 20.6

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.126.7 28.8

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.126.7 28.8,171

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.126.7

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.126.70.7 21.7

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.126.70.7

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.126.70.747.1

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.126.70.747.1Do

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.126.70.747.1Do

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.7 7.4 12.5 12.5

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.7 7.4 12.5

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.7 7.4 12.578.1

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.7 7.4

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.7 7.4. 111.1 14.7

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.7 7.4. 111.1

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.7 7.4. 111.115.2

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.7 7.4.

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.7

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.72,033 1,618

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.72,033 1,61814.7

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.72,033

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.72,0335.6 17.7

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.72,0335.6 17.74.2

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.72,0335.6

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.72,0335.615.1 5.5

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.72,0335.615.1

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.72,0335.615.10.7

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1 Do Not Have

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1 Do Not Have7.1

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1 Do Not

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1 Do Not25.6 40.7

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1 Do Not25.6

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1 Do Not25.65.6

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1 Do

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1 Do4.2 7.6 16.6

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1 Do4.2 7.6

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1 Do4.2 7.67.1

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1 Do4.2 7.67.10.6

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1 Do4.2

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1 Do4.24.2 7.6

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1 Do4.24.2

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1 Do4.24.2Cooking

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1Do Not Have

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1Do Not HaveDo

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1Do Not HaveDoDo

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1Do Not

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1Do NotDo Not

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1Do NotDo Not

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1Do NotDo Not20.6

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1Do NotDo

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1Do NotDo7.1 19.0

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1Do NotDo7.1

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1Do NotDo7.1...

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1Do

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1DoCooking

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1DoCooking25.6

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1DoCooking25.65.6

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.04.2 7.6 16.6 Personal

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.04.2 7.6 16.6 Personal

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.04.2 7.6 16.6

  15. Total

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5 Tables July 1996 Energy Information Administration Office ofthroughYear JanYear Jan Feb Mar Apr May(MillionFeet)July 23,

  16. Total

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5 Tables July 1996 Energy Information Administration Office ofthroughYear JanYear Jan Feb Mar Apr May(MillionFeet)July 23,Product:

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.1 86.6 2,720 1,970

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.1 86.6 2,720

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.1 86.6 2,720 111.1

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.1 86.6 2,720

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.1 86.6 2,720Q Table

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.1 86.6 2,720Q

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.1 86.6 2,720Q14.7

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.1 86.6

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.1

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.1

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.126.7 28.8 20.6

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.126.7 28.8 20.6,171

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.126.7 28.8

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.126.7 28.820.6 25.6

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.126.7 28.820.6

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.126.7 28.820.626.7

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.126.7

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.126.747.1 19.0 22.7

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.126.747.1 19.0 22.7

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.126.747.1 19.0

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.126.747.1 19.014.7

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.126.747.1

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.126.747.178.1 64.1

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.126.747.178.1

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.126.747.178.1.

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.8 1.0 1.2 3.3 1.9

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.8 1.0 1.2 3.3

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.8 1.0 1.2 3.3Type

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.8 1.0 1.2

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.8 1.0 1.214.7 7.4

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.8 1.0 1.214.7

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.8 1.0 1.214.75.6

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.8 1.0

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.8 1.025.6 40.7

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.8 1.025.6

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.8 1.025.65.6 17.7

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.8 1.025.65.6

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.8 1.025.65.64.2

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.8

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.87.1 19.0 22.7

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.87.1 19.0

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.87.1 19.025.6

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.87.1 19.025.6.

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.87.1 19.025.6.5.6

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.87.1

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.87.14.2 7.6 16.6

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.87.14.2 7.6

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.87.14.2 7.67.1

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.87.14.2 7.67.10.6

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.87.14.2

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.87.14.24.2 7.6

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.87.14.24.2 7.6Do

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.87.14.24.2

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.87.14.24.2Cooking

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do Not Have Cooling

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do Not Have

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do Not HaveDo Not

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do Not HaveDo NotDo

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do Not HaveDo

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do Not HaveDo0.7

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do Not HaveDo0.7

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do Not HaveDo0.77.1

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do Not

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do Not7.1 7.0 8.0

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do Not7.1 7.0

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do Not7.1 7.05.6

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do Not7.1

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do Not7.1Personal

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do Not7.1Personal4.2

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do 111.1 47.1 19.0

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

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do 111.1 47.1

  11. ISO/GUM UNCERTAINTIES AND CIAAW (UNCERTAINTY TREATMENT FOR RECOMMENDED ATOMIC WEIGHTS AND ISOTOPIC ABUNDANCES)

    SciTech Connect (OSTI)

    HOLDEN,N.E.

    2007-07-23T23:59:59.000Z

    The International Organization for Standardization (ISO) has published a Guide to the expression of Uncertainty in Measurement (GUM). The IUPAC Commission on Isotopic Abundance and Atomic Weight (CIAAW) began attaching uncertainty limits to their recommended values about forty years ago. CIAAW's method for determining and assigning uncertainties has evolved over time. We trace this evolution to their present method and their effort to incorporate the basic ISO/GUM procedures into evaluations of these uncertainties. We discuss some dilemma the CIAAW faces in their present method and whether it is consistent with the application of the ISO/GUM rules. We discuss the attempt to incorporate variations in measured isotope ratios, due to natural fractionation, into the ISO/GUM system. We make some observations about the inconsistent treatment in the incorporation of natural variations into recommended data and uncertainties. A recommendation for expressing atomic weight values using a tabulated range of values for various chemical elements is discussed.

  12. Programmatic methods for addressing contaminated volume uncertainties

    SciTech Connect (OSTI)

    Rieman, C.R.; Spector, H.L. [U.S. Army Corps of Engineers Buffalo District, Buffalo, NY (United States); Durham, L.A.; Johnson, R.L. [Argonne National Laboratory, Environmental Science Div., IL (United States)

    2007-07-01T23:59:59.000Z

    Accurate estimates of the volumes of contaminated soils or sediments are critical to effective program planning and to successfully designing and implementing remedial actions. Unfortunately, data available to support the pre-remedial design are often sparse and insufficient for accurately estimating contaminated soil volumes, resulting in significant uncertainty associated with these volume estimates. The uncertainty in the soil volume estimates significantly contributes to the uncertainty in the overall project cost estimates, especially since excavation and off-site disposal are the primary cost items in soil remedial action projects. The U.S. Army Corps of Engineers Buffalo District's experience has been that historical contaminated soil volume estimates developed under the Formerly Utilized Sites Remedial Action Program (FUSRAP) often underestimated the actual volume of subsurface contaminated soils requiring excavation during the course of a remedial activity. In response, the Buffalo District has adopted a variety of programmatic methods for addressing contaminated volume uncertainties. These include developing final status survey protocols prior to remedial design, explicitly estimating the uncertainty associated with volume estimates, investing in pre-design data collection to reduce volume uncertainties, and incorporating dynamic work strategies and real-time analytics in pre-design characterization and remediation activities. This paper describes some of these experiences in greater detail, drawing from the knowledge gained at Ashland 1, Ashland 2, Linde, and Rattlesnake Creek. In the case of Rattlesnake Creek, these approaches provided the Buffalo District with an accurate pre-design contaminated volume estimate and resulted in one of the first successful FUSRAP fixed-price remediation contracts for the Buffalo District. (authors)

  13. Topological Aspects of Wave Propagation

    E-Print Network [OSTI]

    Carlos Valero

    2014-06-13T23:59:59.000Z

    In the context of wave propagation on a manifold X, the characteristic functions are real valued functions on cotangent bundle of X that specify the allowable phase velocities of the waves. For certain classes of differential operators (e.g Maxwell's Equations) the associated characteristic functions have singularities. These singularities account for phenomena like conical refraction and the transformation of longitudinal waves into transversal ones (or viceversa). For a specific class of differential operators on surface, we prove that the singularities of the characteristic functions can be accounted from purely topological considerations. We also prove that there is a natural way to desingularsize the characteristic functions, and observe that this fact and Morse Theory establishes a specific connection between singularities and critical points of these functions. The relation between characteristic functions and differential operators is obtained through what is known as the symbol of the operator. We establish a connection between these symbols and holomorphic vector fields, which will provide us with symbols whose characteristic functions have interesting singularity sets.

  14. Study of correlation of PDF uncertainty in single top and top pair production at the LHC

    E-Print Network [OSTI]

    The ATLAS collaboration

    2015-01-01T23:59:59.000Z

    The incomplete knowledge of parton distribution functions is an important source of systematic uncertainty for top-quark measurements, including top-quark pair and single top-quark production cross sections, as well as for analyses that have a large background from these processes. The correlation of the parton-distribution-function uncertainty is studied for top-quark pair production and single top-quark production in association with a W boson, in final states with two reconstructed leptons. Four types of correlation are studied: between total production cross-sections, between cross-section and acceptance correction, between the two processes for common selection requirements, and between different jet multiplicity requirements. The uncertainty correlation is evaluated for several sets of parton distribution functions using simulated samples of top-quark pair and single top-quark events.

  15. Is the Heisenberg uncertainty relation really violated?

    E-Print Network [OSTI]

    Masao Kitano

    2008-03-31T23:59:59.000Z

    It has been pointed out that for some types of measurement the Heisenberg uncertainty relation seems to be violated. In order to save the situation a new uncertainty relation was proposed by Ozawa. Here we introduce revised definitions of error and disturbance taking into account the gain associated with generalized measurement interactions. With these new definitions, the validity of the Heisenberg inequality is recovered for continuous linear measurement interactions. We also examine the changes in distribution functions caused by the general measurement interaction and clarify the physical meanings of infinitely large errors and disturbances.

  16. Incorporating uncertainties into risk assessment with an application to the exploratory studies facilities at Yucca Mountain

    SciTech Connect (OSTI)

    Fathauer, P.M.

    1995-08-01T23:59:59.000Z

    A methodology that incorporates variability and reducible sources of uncertainty into the probabilistic and consequence components of risk was developed. The method was applied to the north tunnel of the Exploratory Studies Facility at Yucca Mountain in Nevada. In this assessment, variability and reducible sources of uncertainty were characterized and propagated through the risk assessment models using a Monte Carlo based software package. The results were then manipulated into risk curves at the 5% and 95% confidence levels for both the variability and overall uncertainty analyses, thus distinguishing between variability and reducible sources of uncertainty. In the Yucca Mountain application, the designation of the north tunnel as an item important to public safety, as defined by 10 CFR 60, was determined. Specifically, the annual frequency of a rock fall breaching a waste package causing an off-site dose of 500 mrem (5x10{sup -3} Sv) was calculated. The annual frequency, taking variability into account, ranged from 1.9x10{sup -9} per year at the 5% confidence level to 2.5x10{sup -9} per year at the 95% confidence level. The frequency range after including all uncertainty was 9.5x10{sup -10} to 1.8x10{sup -8} per year. The maximum observable frequency, at the 100% confidence level, was 4.9x10{sup -8} per year. This is below the 10{sup -6} per year frequency criteria of 10 CFR 60. Therefore, based on this work, the north tunnel does not fall under the items important to public safety designation for the event studied.

  17. Reactor Neutrino Flux Uncertainty Suppression on Multiple Detector Experiments

    E-Print Network [OSTI]

    Cucoanes, Andi; Cabrera, Anatael; Fallot, Muriel; Onillon, Anthony; Obolensky, Michel; Yermia, Frederic

    2015-01-01T23:59:59.000Z

    This publication provides a coherent treatment for the reactor neutrino flux uncertainties suppression, specially focussed on the latest $\\theta_{13}$ measurement. The treatment starts with single detector in single reactor site, most relevant for all reactor experiments beyond $\\theta_{13}$. We demonstrate there is no trivial error cancellation, thus the flux systematic error can remain dominant even after the adoption of multi-detector configurations. However, three mechanisms for flux error suppression have been identified and calculated in the context of Double Chooz, Daya Bay and RENO sites. Our analysis computes the error {\\it suppression fraction} using simplified scenarios to maximise relative comparison among experiments. We have validated the only mechanism exploited so far by experiments to improve the precision of the published $\\theta_{13}$. The other two newly identified mechanisms could lead to total error flux cancellation under specific conditions and are expected to have major implications o...

  18. Uncertainty Analysis for a Virtual Flow Meter Using an Air-Handling Unit Chilled Water Valve

    SciTech Connect (OSTI)

    Song, Li; Wang, Gang; Brambley, Michael R.

    2013-04-28T23:59:59.000Z

    A virtual water flow meter is developed that uses the chilled water control valve on an air-handling unit as a measurement device. The flow rate of water through the valve is calculated using the differential pressure across the valve and its associated coil, the valve command, and an empirically determined valve characteristic curve. Thus, the probability of error in the measurements is significantly greater than for conventionally manufactured flow meters. In this paper, mathematical models are developed and used to conduct uncertainty analysis for the virtual flow meter, and the results from the virtual meter are compared to measurements made with an ultrasonic flow meter. Theoretical uncertainty analysis shows that the total uncertainty in flow rates from the virtual flow meter is 1.46% with 95% confidence; comparison of virtual flow meter results with measurements from an ultrasonic flow meter yielded anuncertainty of 1.46% with 99% confidence. The comparable results from the theoretical uncertainty analysis and empirical comparison with the ultrasonic flow meter corroborate each other, and tend to validate the approach to computationally estimating uncertainty for virtual sensors introduced in this study.

  19. Modeling of Uncertainty in Wind Energy Forecast

    E-Print Network [OSTI]

    regression and splines are combined to model the prediction error from Tunø Knob wind power plant. This data of the thesis is quantile regression and splines in the context of wind power modeling. Lyngby, February 2006Modeling of Uncertainty in Wind Energy Forecast Jan Kloppenborg Møller Kongens Lyngby 2006 IMM-2006

  20. Bayesian Environmetrics: Uncertainty and Sensitivity Analysis

    E-Print Network [OSTI]

    Draper, David

    (joint work with Bruno Mendes) Department of Applied Mathematics and Statistics University of California and analysis of variance tools provide sensitivity analysis insight. -- Model uncertainty audit shows, as an energy source, has been employed in the United States and Europe for more than 50 years, and yet

  1. Stochastic Reduced Basis Methods for Uncertainty Quantification

    E-Print Network [OSTI]

    Sóbester, András

    Turbine Blades In general, stochastic analysis (using SRBM) of any physical system involves two main steps the variability in the performance of a turbine blade in the presence of uncertainty. These blades operate variability in material properties and boundary condi- tions. Given a numerical solution of the set of SPDEs

  2. Model Uncertainty in Discrete Event Systems \\Lambda

    E-Print Network [OSTI]

    Garg, Vijay

    that a correct model of the system to be controlled was avail­ able. A goal of this wok is to provide be controllably distin­ guished. We use the finite state machine model with controllable and uncontrollable events to control systems in the presence of uncertainty in the model of the system and environment in which

  3. IJCAI-05 Workshop Reasoning with Uncertainty in

    E-Print Network [OSTI]

    Pineau, Joelle

    Problems via Spatio-Temporal Belief State Clustering [p.17] X. Li, W.K. Cheung, and J. Liu Dept Uncertainty [p. 70] P. Fabiani and F. Teichteil-K¨onigsbuch ONERA/DCSC, Toulouse, France iii #12;iv #12;IJCAI, Canada) Stergios Roumeliotis (University of Minnesota, USA) Nicholas Roy (MIT, USA) Alessandro Saffiotti

  4. Robot Motion Planning with Uncertainty The Challenge

    E-Print Network [OSTI]

    Whitton, Mary C.

    Roadmap (SMR), a new motion planning framework that explicitly considers uncertainty in robot motion approach. Our framework builds on the highly successful approach used in Probabilistic Roadmaps (PRMs of discrete states is selected in the state space, and a roadmap is built that represents their collision

  5. Dynamic Scheduling of Maintenance Activities Under Uncertainties

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    of the required treatment. There are mainly two types of maintenance activities: the preventive maintenance, whoseDynamic Scheduling of Maintenance Activities Under Uncertainties Fran¸cois Marmier, Christophe in maintenance services field where the different practical knowledges or skills are their working tools. We

  6. Applying Calibration to Improve Uncertainty Assessment

    E-Print Network [OSTI]

    Fondren, Mark Edward

    2013-08-02T23:59:59.000Z

    that uncertainty can be assessed more reliably through look-backs and calibration, i.e., comparing actual results to probabilistic predictions over time. While many recognize the importance of look-backs, calibration is seldom practiced in industry. I believe a...

  7. anisotropic ultrasound propagation: Topics by E-print Network

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

    that were performed on wave propagation in a randomly generated anisotropic used for the propagation of waves in geophysical media are not compatible with the surface recordings...

  8. action potential propagation: Topics by E-print Network

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

    for action potential propagation in excitable cells CERN Preprints Summary: Speed of propagation of small-amplitude pressure waves through the cytoplasmic interior of...

  9. anomalous ultrasound propagation: Topics by E-print Network

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

    The equations of fluid dynamics developed in paper I are applied to the study of the propagation of ultrasound waves. There is good agreement between the predicted propagation...

  10. anisotropic propagation model: Topics by E-print Network

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

    that were performed on wave propagation in a randomly generated anisotropic used for the propagation of waves in geophysical media are not compatible with the surface recordings...

  11. A Model for Analyzing Components of Uncertainty Encountered in {sup 3}H-Standard Efficiency Tracing in 4{pi}{beta} Liquid Scintillation Counting

    SciTech Connect (OSTI)

    Brian E. Zimmerman; R. Colle

    2000-11-12T23:59:59.000Z

    Over the past decade, uniform conventions for assessing and reporting measurement uncertainties have been adopted by nearly every international metrological organization, as well as by many scientific and engineering associations and principal laboratories. This uncertainty approach is available as guidelines published by the International Organization for Standardization (ISO) and is used by the National Institute of Standards and Technology (NIST) for the dissemination of all of its standards, calibrations, and measurement results. One of the most widely used techniques for the radioactivity standardizations at NIST is liquid scintillation (LS) spectrometry, mainly through the use of a {sup 3}H-standard efficiency tracing technique that has come to be known as the CIEMAT/NIST method. Although the method is relatively simple in concept and implementation, correct analysis of the uncertainties involved in applying the method using ISO guidelines is not. An initial requirement for a proper uncertainty analysis is the development of a model that explicitly specifies the relationship between the different input and output variables involved in the measurement that lead to an uncertainty in the final certified activity. The approach taken in this analysis is based on the fact that use of black-box computer codes as an integral part of the calculation of a final value makes a formal mathematical expression of the measurement model difficult, if not impossible. Therefore, many of the uncertainty components were estimated by propagating the uncertainty from each of the respective components through the data reduction equations using a spreadsheet.

  12. Mass inequality for the quark propagator

    E-Print Network [OSTI]

    Dean Lee; Richard Thomson

    2005-06-09T23:59:59.000Z

    We show that for any gauge-fixing scheme with positive semi-definite functional integral measure, the inverse correlation length of the quark propagator is bounded below by one-half the pion mass.

  13. Shock wave propagation in vibrofluidized granular materials

    E-Print Network [OSTI]

    Kai Huang; Guoqing Miao; Peng Zhang; Yi Yun; Rongjue Wei

    2005-11-29T23:59:59.000Z

    Shock wave formation and propagation in two-dimensional granular materials under vertical vibration are studied by digital high speed photography. The steepen density and temperature wave fronts form near the plate as granular layer collides with vibrating plate and propagate upward through the layer. The temperature front is always in the transition region between the upward and downward granular flows. The effects of driving parameters and particle number on the shock are also explored.

  14. Uncertainty Quantification for Nuclear Density Functional Theory and Information Content of New Measurements

    E-Print Network [OSTI]

    J. D. McDonnell; N. Schunck; D. Higdon; J. Sarich; S. M. Wild; W. Nazarewicz

    2015-01-15T23:59:59.000Z

    Statistical tools of uncertainty quantification can be used to assess the information content of measured observables with respect to present-day theoretical models; to estimate model errors and thereby improve predictive capability; to extrapolate beyond the regions reached by experiment; and to provide meaningful input to applications and planned measurements. To showcase new opportunities offered by such tools, we make a rigorous analysis of theoretical statistical uncertainties in nuclear density functional theory using Bayesian inference methods. By considering the recent mass measurements from the Canadian Penning Trap at Argonne National Laboratory, we demonstrate how the Bayesian analysis and a direct least-squares optimization, combined with high-performance computing, can be used to assess the information content of the new data with respect to a model based on the Skyrme energy density functional approach. Employing the posterior probability distribution computed with a Gaussian process emulator, we apply the Bayesian framework to propagate theoretical statistical uncertainties in predictions of nuclear masses, two-neutron dripline, and fission barriers. Overall, we find that the new mass measurements do not impose a constraint that is strong enough to lead to significant changes in the model parameters. The example discussed in this study sets the stage for quantifying and maximizing the impact of new measurements with respect to current modeling and guiding future experimental efforts, thus enhancing the experiment-theory cycle in the scientific method.

  15. Representation of analysis results involving aleatory and epistemic uncertainty.

    SciTech Connect (OSTI)

    Johnson, Jay Dean (ProStat, Mesa, AZ); Helton, Jon Craig (Arizona State University, Tempe, AZ); Oberkampf, William Louis; Sallaberry, Cedric J.

    2008-08-01T23:59:59.000Z

    Procedures are described for the representation of results in analyses that involve both aleatory uncertainty and epistemic uncertainty, with aleatory uncertainty deriving from an inherent randomness in the behavior of the system under study and epistemic uncertainty deriving from a lack of knowledge about the appropriate values to use for quantities that are assumed to have fixed but poorly known values in the context of a specific study. Aleatory uncertainty is usually represented with probability and leads to cumulative distribution functions (CDFs) or complementary cumulative distribution functions (CCDFs) for analysis results of interest. Several mathematical structures are available for the representation of epistemic uncertainty, including interval analysis, possibility theory, evidence theory and probability theory. In the presence of epistemic uncertainty, there is not a single CDF or CCDF for a given analysis result. Rather, there is a family of CDFs and a corresponding family of CCDFs that derive from epistemic uncertainty and have an uncertainty structure that derives from the particular uncertainty structure (i.e., interval analysis, possibility theory, evidence theory, probability theory) used to represent epistemic uncertainty. Graphical formats for the representation of epistemic uncertainty in families of CDFs and CCDFs are investigated and presented for the indicated characterizations of epistemic uncertainty.

  16. Use of Forward Sensitivity Analysis Method to Improve Code Scaling, Applicability, and Uncertainty (CSAU) Methodology

    SciTech Connect (OSTI)

    Haihua Zhao; Vincent A. Mousseau; Nam T. Dinh

    2010-10-01T23:59:59.000Z

    Code Scaling, Applicability, and Uncertainty (CSAU) methodology was developed in late 1980s by US NRC to systematically quantify reactor simulation uncertainty. Basing on CSAU methodology, Best Estimate Plus Uncertainty (BEPU) methods have been developed and widely used for new reactor designs and existing LWRs power uprate. In spite of these successes, several aspects of CSAU have been criticized for further improvement: i.e., (1) subjective judgement in PIRT process; (2) high cost due to heavily relying large experimental database, needing many experts man-years work, and very high computational overhead; (3) mixing numerical errors with other uncertainties; (4) grid dependence and same numerical grids for both scaled experiments and real plants applications; (5) user effects; Although large amount of efforts have been used to improve CSAU methodology, the above issues still exist. With the effort to develop next generation safety analysis codes, new opportunities appear to take advantage of new numerical methods, better physical models, and modern uncertainty qualification methods. Forward sensitivity analysis (FSA) directly solves the PDEs for parameter sensitivities (defined as the differential of physical solution with respective to any constant parameter). When the parameter sensitivities are available in a new advanced system analysis code, CSAU could be significantly improved: (1) Quantifying numerical errors: New codes which are totally implicit and with higher order accuracy can run much faster with numerical errors quantified by FSA. (2) Quantitative PIRT (Q-PIRT) to reduce subjective judgement and improving efficiency: treat numerical errors as special sensitivities against other physical uncertainties; only parameters having large uncertainty effects on design criterions are considered. (3) Greatly reducing computational costs for uncertainty qualification by (a) choosing optimized time steps and spatial sizes; (b) using gradient information (sensitivity result) to reduce sampling number. (4) Allowing grid independence for scaled integral effect test (IET) simulation and real plant applications: (a) eliminate numerical uncertainty on scaling; (b) reduce experimental cost by allowing smaller scaled IET; (c) eliminate user effects. This paper will review the issues related to the current CSAU, introduce FSA, discuss a potential Q-PIRT process, and show simple examples to perform FSA. Finally, the general research direction and requirements to use FSA in a system analysis code will be discussed.

  17. A Framework for Modeling Uncertainty in Regional Climate Change

    E-Print Network [OSTI]

    Monier, Erwan

    In this study, we present a new modeling framework and a large ensemble of climate projections to investigate the uncertainty in regional climate change over the US associated with four dimensions of uncertainty. The sources ...

  18. Adaptive control of hypersonic vehicles in presence of actuation uncertainties

    E-Print Network [OSTI]

    Somanath, Amith

    2010-01-01T23:59:59.000Z

    The thesis develops a new class of adaptive controllers that guarantee global stability in presence of actuation uncertainties. Actuation uncertainties culminate to linear plants with a partially known input matrix B. ...

  19. Uncertainty analysis of climate change and policy response

    E-Print Network [OSTI]

    Webster, Mort David.; Forest, Chris Eliot.; Reilly, John M.; Babiker, Mustafa H.M.; Kicklighter, David W.; Mayer, Monika.; Prinn, Ronald G.; Sarofim, Marcus C.; Sokolov, Andrei P.; Stone, Peter H.; Wang, Chien.

    To aid climate policy decisions, accurate quantitative descriptions of the uncertainty in climate outcomes under various possible policies are needed. Here, we apply an earth systems model to describe the uncertainty in ...

  20. Uncertainties in Energy Consumption Introduced by Building Operations and

    E-Print Network [OSTI]

    Uncertainties in Energy Consumption Introduced by Building Operations and Weather for a Medium between predicted and actual building energy consumption can be attributed to uncertainties introduced in energy consumption due to actual weather and building operational practices, using a simulation

  1. The Time's Arrow within the Uncertainty Quantum

    E-Print Network [OSTI]

    Zhen Wang

    1998-06-22T23:59:59.000Z

    A generalized framework is developed which uses a set description instead of wavefunction to emphasize the role of the observer. Such a framework is found to be very effective in the study of the measurement problem and time's arrow. Measurement in classical and quantum theory is given a unified treatment. With the introduction of the concept of uncertainty quantum which is the basic unit of measurement, we show that the time's arrow within the uncertainty quantum is just opposite to the time's arrow in the observable reality. A special constant is discussed which explains our sensation of time and provides a permanent substrate for all change. It is shown that the whole spacetime connects together in a delicate structure.

  2. Aquatic manoeuvering with counter-propagating waves: a novel

    E-Print Network [OSTI]

    Lauder, George V.

    Aquatic manoeuvering with counter-propagating waves: a novel locomotive strategy Oscar M. Curet1 of these inward counter-propagating waves. In addition, we compare the flow structure and upward force generated by inward counter-propagating waves to standing waves, unidirectional waves, and outward counter-propagating

  3. Wave-Based Sound Propagation for VR Applications Ravish Mehra

    E-Print Network [OSTI]

    North Carolina at Chapel Hill, University of

    Wave-Based Sound Propagation for VR Applications Ravish Mehra University of North Carolina to state-of-the-art wave solvers, enabling real-time, wave-based sound propagation in scenes spanning propagation accurately, it is important to develop interactive wave-based propagation techniques. We present

  4. Spectral Representations of Uncertainty: Algorithms and Applications

    SciTech Connect (OSTI)

    George Em Karniadakis

    2005-04-24T23:59:59.000Z

    The objectives of this project were: (1) Develop a general algorithmic framework for stochastic ordinary and partial differential equations. (2) Set polynomial chaos method and its generalization on firm theoretical ground. (3) Quantify uncertainty in large-scale simulations involving CFD, MHD and microflows. The overall goal of this project was to provide DOE with an algorithmic capability that is more accurate and three to five orders of magnitude more efficient than the Monte Carlo simulation.

  5. Uncertainty estimates for derivatives and intercepts

    SciTech Connect (OSTI)

    Clark, E.L.

    1990-01-01T23:59:59.000Z

    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 7 refs., 2 tabs.

  6. Information-Disturbance theorem and Uncertainty Relation

    E-Print Network [OSTI]

    Takayuki Miyadera; Hideki Imai

    2007-07-31T23:59:59.000Z

    It has been shown that Information-Disturbance theorem can play an important role in security proof of quantum cryptography. The theorem is by itself interesting since it can be regarded as an information theoretic version of uncertainty principle. It, however, has been able to treat restricted situations. In this paper, the restriction on the source is abandoned, and a general information-disturbance theorem is obtained. The theorem relates information gain by Eve with information gain by Bob.

  7. Informatively optimal levels of confidence for mesurement uncertainty

    E-Print Network [OSTI]

    David Kisets

    2011-10-13T23:59:59.000Z

    Oct 13, 2011 ... netvision.net.il) ... "Informatively optimal combining, expanding, and establishing traceability in evaluating measurement uncertainties".

  8. The effects of incorporating dynamic data on estimates of uncertainty

    E-Print Network [OSTI]

    Mulla, Shahebaz Hisamuddin

    2004-09-30T23:59:59.000Z

    in production forecasts will help in assessing risk and making good economic decisions. This study investigates the effect of combining dynamic data with the uncertainty in static data to see the effect on estimates of uncertainty in production forecasting... combined with linear uncertainty analysis. The results were compared with the uncertainty predicted using only static data. We also investigated approaches for best selecting a smaller number of models from a larger set of realizations to be history...

  9. Generalized Uncertainty Principle: Approaches and Applications

    E-Print Network [OSTI]

    Abdel Nasser Tawfik; Abdel Magied Diab

    2014-11-23T23:59:59.000Z

    We review highlights from string theory, black hole physics and doubly special relativity and some "thought" experiments which were suggested to probe the shortest distance and/or the maximum momentum at the Planck scale. The models which are designed to implement the minimal length scale and/or the maximum momentum in different physical systems are analysed entered the literature as the Generalized Uncertainty Principle (GUP). We compare between them. The existence of a minimal length and a maximum momentum accuracy is preferred by various physical observations. Furthermore, assuming modified dispersion relation allows for a wide range of applications in estimating, for example, the inflationary parameters, Lorentz invariance violation, black hole thermodynamics, Saleker-Wigner inequalities, entropic nature of the gravitational laws, Friedmann equations, minimal time measurement and thermodynamics of the high-energy collisions. One of the higher-order GUP approaches gives predictions for the minimal length uncertainty. Another one predicts a maximum momentum and a minimal length uncertainty, simultaneously. An extensive comparison between the different GUP approaches is summarized. We also discuss the GUP impacts on the equivalence principles including the universality of the gravitational redshift and the free fall and law of reciprocal action and on the kinetic energy of composite system. The concern about the compatibility with the equivalence principles, the universality of gravitational redshift and the free fall and law of reciprocal action should be addressed. We conclude that the value of the GUP parameters remain a puzzle to be verified.

  10. Advances in total scattering analysis

    SciTech Connect (OSTI)

    Proffen, Thomas E [Los Alamos National Laboratory; Kim, Hyunjeong [Los Alamos National Laboratory

    2008-01-01T23:59:59.000Z

    In recent years the analysis of the total scattering pattern has become an invaluable tool to study disordered crystalline and nanocrystalline materials. Traditional crystallographic structure determination is based on Bragg intensities and yields the long range average atomic structure. By including diffuse scattering into the analysis, the local and medium range atomic structure can be unravelled. Here we give an overview of recent experimental advances, using X-rays as well as neutron scattering as well as current trends in modelling of total scattering data.

  11. Total Imports of Residual Fuel

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are nowTotal" (Percent) Type: Sulfur Content API GravityDakota" "Fuel, quality", 2013,Iowa"Dakota"YearProductionShaleInput Product: TotalCountry:

  12. Volume Rendering Data with Uncertainty Information Suzana Djurcilov

    E-Print Network [OSTI]

    Robinson, Allan R.

    Volume Rendering Data with Uncertainty Information Suzana Djurcilov , Kwansik Kim , Pierre F. J uncertainty information in direct volume rendering. The goal is to produce vol- ume rendered images the uncertainty information directly into the volume rendering equation. The second method involves post

  13. Uncertainty, Performance, and Model Dependency in Approximate Adaptive Nonlinear Control

    E-Print Network [OSTI]

    Szepesvari, Csaba

    Uncertainty, Performance, and Model Dependency in Approximate Adaptive Nonlinear Control M. French, and the performance of a class of approximate model based adaptive controllers is studied. An upper performance bound uncertainty model; control effort bounds require both L 2 and L 1 uncertainty models), and various structural

  14. Trio: A System for Data, Uncertainty, and Lineage

    E-Print Network [OSTI]

    Bejerano, Gill

    or resolve uncertainty #12;13 Our Goal Develop a new kind of database management system (DBMS) in which: 1. Data 2. Uncertainty 3. Lineage are all first-class interrelated concepts With all the "usual" DBMS a new kind of database management system (DBMS) in which: 1. Data 2. Uncertainty 3. Lineage are all

  15. Markov transitions and the propagation of chaos

    SciTech Connect (OSTI)

    Gottlieb, A.

    1998-12-01T23:59:59.000Z

    The propagation of chaos is a central concept of kinetic theory that serves to relate the equations of Boltzmann and Vlasov to the dynamics of many-particle systems. Propagation of chaos means that molecular chaos, i.e., the stochastic independence of two random particles in a many-particle system, persists in time, as the number of particles tends to infinity. We establish a necessary and sufficient condition for a family of general n-particle Markov processes to propagate chaos. This condition is expressed in terms of the Markov transition functions associated to the n-particle processes, and it amounts to saying that chaos of random initial states propagates if it propagates for pure initial states. Our proof of this result relies on the weak convergence approach to the study of chaos due to Sztitman and Tanaka. We assume that the space in which the particles live is homomorphic to a complete and separable metric space so that we may invoke Prohorov's theorem in our proof. We also s how that, if the particles can be in only finitely many states, then molecular chaos implies that the specific entropies in the n-particle distributions converge to the entropy of the limiting single-particle distribution.

  16. Page (Total 3) Philadelphia University

    E-Print Network [OSTI]

    Page (Total 3) Philadelphia University Faculty of Science Department of Biotechnology and Genetic be used in animals or plants. It can be also used in environmental monitoring, food processing ...etc are developed and marketed in kit format by biotechnology companies. The main source of information is web sites

  17. Double porosity modeling in elastic wave propagation for reservoir characterization

    SciTech Connect (OSTI)

    Berryman, J. G., LLNL

    1998-06-01T23:59:59.000Z

    Phenomenological equations for the poroelastic behavior of a double porosity medium have been formulated and the coefficients in these linear equations identified. The generalization from a single porosity model increases the number of independent coefficients from three to six for an isotropic applied stress. In a quasistatic analysis, the physical interpretations are based upon considerations of extremes in both spatial and temporal scales. The limit of very short times is the one most relevant for wave propagation, and in this case both matrix porosity and fractures behave in an undrained fashion. For the very long times more relevant for reservoir drawdown,the double porosity medium behaves as an equivalent single porosity medium At the macroscopic spatial level, the pertinent parameters (such as the total compressibility) may be determined by appropriate field tests. At the mesoscopic scale pertinent parameters of the rock matrix can be determined directly through laboratory measurements on core, and the compressibility can be measured for a single fracture. We show explicitly how to generalize the quasistatic results to incorporate wave propagation effects and how effects that are usually attributed to squirt flow under partially saturated conditions can be explained alternatively in terms of the double-porosity model. The result is therefore a theory that generalizes, but is completely consistent with, Biot`s theory of poroelasticity and is valid for analysis of elastic wave data from highly fractured reservoirs.

  18. The various manifestations of collisionless dissipation in wave propagation

    SciTech Connect (OSTI)

    Benisti, Didier; Morice, Olivier; Gremillet, Laurent [CEA, DAM, DIF, F-91297 Arpajon (France)

    2012-06-15T23:59:59.000Z

    The propagation of an electrostatic wave packet inside a collisionless and initially Maxwellian plasma is always dissipative because of the irreversible acceleration of the electrons by the wave. Then, in the linear regime, the wave packet is Landau damped, so that in the reference frame moving at the group velocity, the wave amplitude decays exponentially with time. In the nonlinear regime, once phase mixing has occurred and when the electron motion is nearly adiabatic, the damping rate is strongly reduced compared to the Landau one, so that the wave amplitude remains nearly constant along the characteristics. Yet, we show here that the electrons are still globally accelerated by the wave packet, and in one dimension, this leads to a non local amplitude dependence of the group velocity. As a result, a freely propagating wave packet would shrink, and therefore, so would its total energy. In more than one dimension, not only does the magnitude of the group velocity nonlinearly vary, but also its direction. In the weakly nonlinear regime, when the collisionless damping rate is still significant compared to its linear value, the group velocity is directed towards the outside of the wave packet and tends to increase its transverse extent, while the opposite is true once the wave is essentially undamped. The impact of the nonlinear variation of the group velocity on the transverse size of the wave packet is quantified, and compared to that induced by the self-focussing due to wave front bowing.

  19. Reactor Neutrino Flux Uncertainty Suppression on Multiple Detector Experiments

    E-Print Network [OSTI]

    Andi Cucoanes; Pau Novella; Anatael Cabrera; Muriel Fallot; Anthony Onillon; Michel Obolensky; Frederic Yermia

    2015-01-02T23:59:59.000Z

    This publication provides a coherent treatment for the reactor neutrino flux uncertainties suppression, specially focussed on the latest $\\theta_{13}$ measurement. The treatment starts with single detector in single reactor site, most relevant for all reactor experiments beyond $\\theta_{13}$. We demonstrate there is no trivial error cancellation, thus the flux systematic error can remain dominant even after the adoption of multi-detector configurations. However, three mechanisms for flux error suppression have been identified and calculated in the context of Double Chooz, Daya Bay and RENO sites. Our analysis computes the error {\\it suppression fraction} using simplified scenarios to maximise relative comparison among experiments. We have validated the only mechanism exploited so far by experiments to improve the precision of the published $\\theta_{13}$. The other two newly identified mechanisms could lead to total error flux cancellation under specific conditions and are expected to have major implications on the global $\\theta_{13}$ knowledge today. First, Double Chooz, in its final configuration, is the only experiment benefiting from a negligible reactor flux error due to a $\\sim$90\\% geometrical suppression. Second, Daya Bay and RENO could benefit from their partial geometrical cancellation, yielding a potential $\\sim$50\\% error suppression, thus significantly improving the global $\\theta_{13}$ precision today. And third, we illustrate the rationale behind further error suppression upon the exploitation of the inter-reactor error correlations, so far neglected. So, our publication is a key step forward in the context of high precision neutrino reactor experiments providing insight on the suppression of their intrinsic flux error uncertainty, thus affecting past and current experimental results, as well as the design of future experiments.

  20. Propagation of sound waves through a spatially homogeneous but smoothly time-dependent medium

    SciTech Connect (OSTI)

    Hayrapetyan, A.G., E-mail: armen@physi.uni-heidelberg.de [Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, D-69120 Heidelberg (Germany); Max-Planck-Institut für Kernphysik, Saupfercheckweg 1, D-69117 Heidelberg (Germany); Grigoryan, K.K.; Petrosyan, R.G. [Yerevan State University, 1 Alex Manoogian Str., 0025 Yerevan (Armenia)] [Yerevan State University, 1 Alex Manoogian Str., 0025 Yerevan (Armenia); Fritzsche, S. [Helmholtz-Institut Jena, Fröbelstieg 3, D-07743 Jena (Germany) [Helmholtz-Institut Jena, Fröbelstieg 3, D-07743 Jena (Germany); Theoretisch-Physikalisches Institut, Friedrich-Schiller-Universität Jena, Max-Wien-Platz 1, D-07743 Jena (Germany)

    2013-06-15T23:59:59.000Z

    The propagation of sound through a spatially homogeneous but non-stationary medium is investigated within the framework of fluid dynamics. For a non-vortical fluid, especially, a generalized wave equation is derived for the (scalar) potential of the fluid velocity distribution in dependence of the equilibrium mass density of the fluid and the sound wave velocity. A solution of this equation for a finite transition period ? is determined in terms of the hypergeometric function for a phenomenologically realistic, sigmoidal change of the mass density and sound wave velocity. Using this solution, it is shown that the energy flux of the sound wave is not conserved but increases always for the propagation through a non-stationary medium, independent of whether the equilibrium mass density is increased or decreased. It is found, moreover, that this amplification of the transmitted wave arises from an energy exchange with the medium and that its flux is equal to the (total) flux of the incident and the reflected wave. An interpretation of the reflected wave as a propagation of sound backward in time is given in close analogy to Feynman and Stueckelberg for the propagation of anti-particles. The reflection and transmission coefficients of sound propagating through a non-stationary medium is analyzed in more detail for hypersonic waves with transition periods ? between 15 and 200 ps as well as the transformation of infrasound waves in non-stationary oceans. -- Highlights: •Analytically exact study of sound propagation through a non-stationary medium. •Energy exchange between the non-stationary medium and the sound wave. •Transformation of hypersonic and ultrasound frequencies in non-stationary media. •Propagation of sound backward in time in close analogy to anti-particles. •Prediction of tsunamis both in spatially and temporally inhomogeneous oceans.

  1. The propagation of kinetic energy across scales in turbulent flows

    E-Print Network [OSTI]

    Cardesa, José I; Dong, Siwei; Jiménez, Javier

    2015-01-01T23:59:59.000Z

    A temporal study of energy transfer across length scales is performed in 3D numerical simulations of homogeneous shear flow and isotropic turbulence, at Reynolds numbers in the range $Re_{\\lambda}=107-384$. The average time taken by perturbations in the energy flux to travel between scales is measured and shown to be additive, as inferred from the agreement between the total travel time from a given scale to the smallest dissipative motions, and the time estimated from successive jumps through intermediate scales. Our data suggests that the propagation of disturbances in the energy flux is independent of the forcing and that it defines a `velocity' that determines the energy flux itself. These results support that the cascade is, on average, a scale-local process where energy is continuously transmitted from one scale to the next in order of decreasing size.

  2. A Bayesian approach to simultaneously quantify assignments and linguistic uncertainty

    SciTech Connect (OSTI)

    Chavez, Gregory M [Los Alamos National Laboratory; Booker, Jane M [BOOKER SCIENTIFIC FREDERICKSBURG; Ross, Timothy J [UNM

    2010-10-07T23:59:59.000Z

    Subject matter expert assessments can include both assignment and linguistic uncertainty. This paper examines assessments containing linguistic uncertainty associated with a qualitative description of a specific state of interest and the assignment uncertainty associated with assigning a qualitative value to that state. A Bayesian approach is examined to simultaneously quantify both assignment and linguistic uncertainty in the posterior probability. The approach is applied to a simplified damage assessment model involving both assignment and linguistic uncertainty. The utility of the approach and the conditions under which the approach is feasible are examined and identified.

  3. Uncertainty and sensitivity analysis of chronic exposure results with the MACCS reactor accident consequence model

    SciTech Connect (OSTI)

    Helton, J.C. [Arizona State Univ., Tempe, AZ (United States); Johnson, J.D.; Rollstin, J.A. [Gram, Inc., Albuquerque, NM (United States); Shiver, A.W.; Sprung, J.L. [Sandia National Labs., Albuquerque, NM (United States)

    1995-01-01T23:59:59.000Z

    Uncertainty and sensitivity analysis techniques based on Latin hypercube sampling, partial correlation analysis and stepwise regression analysis are used in an investigation with the MACCS model of the chronic exposure pathways associated with a severe accident at a nuclear power station. The primary purpose of this study is to provide guidance on the variables to be considered in future review work to reduce the uncertainty in the important variables used in the calculation of reactor accident consequences. The effects of 75 imprecisely known input variables on the following reactor accident consequences are studied: crop growing season dose, crop long-term dose, water ingestion dose, milk growing season dose, long-term groundshine dose, long-term inhalation dose, total food pathways dose, total ingestion pathways dose, total long-term pathways dose, total latent cancer fatalities, area-dependent cost, crop disposal cost, milk disposal cost, population-dependent cost, total economic cost, condemnation area, condemnation population, crop disposal area and milk disposal area. When the predicted variables are considered collectively, the following input variables were found to be the dominant contributors to uncertainty: dry deposition velocity, transfer of cesium from animal feed to milk, transfer of cesium from animal feed to meat, ground concentration of Cs-134 at which the disposal of milk products will be initiated, transfer of Sr-90 from soil to legumes, maximum allowable ground concentration of Sr-90 for production of crops, fraction of cesium entering surface water that is consumed in drinking water, groundshine shielding factor, scale factor defining resuspension, dose reduction associated with decontamination, and ground concentration of 1-131 at which disposal of crops will be initiated due to accidents that occur during the growing season.

  4. Resonant Propagation of Entangled Rhodium Mossbauer Gammas

    E-Print Network [OSTI]

    Yao Cheng; Zhongming Wang

    2006-10-19T23:59:59.000Z

    We report the resonant propagation of the long-lived Mossbauer gamma in the time-resolved Mossbauer spectroscopy. Recently, three entangled gammas emitted from the E3 rhodium Mossbauer transition has been proposed to interpret the extraordinary observations in the previous report. Further observation reported here is the dynamic beat of these entangled gammas at room temperature and 77K. Apparent beat anisotropy reveals their long-distance resonant propagation, which leads to suppressed Doppler shift of entangled photon transport in the Borrmann channel.

  5. Resonant Propagation of Entangled Rhodium Mossbauer Gammas

    E-Print Network [OSTI]

    Cheng, Y; Cheng, Yao; Wang, Zhongming

    2006-01-01T23:59:59.000Z

    We report the resonant propagation of the long-lived Mossbauer gamma in the time-resolved Mossbauer spectroscopy. Recently, three entangled gammas emitted from the E3 rhodium Mossbauer transition has been proposed to interpret the extraordinary observations in the previous report. Further observation reported here is the dynamic beat of these entangled gammas at room temperature and 77K. Apparent beat anisotropy reveals their long-distance resonant propagation, which leads to suppressed Doppler shift of entangled photon transport in the Borrmann channel.

  6. Propagation of polymer slugs through porous media

    SciTech Connect (OSTI)

    Lecourtier, J.; Chauveteau, G.

    1984-09-01T23:59:59.000Z

    This paper describes an experimental and theoretical study of the mechanisms governing polymer slug propagation through porous media. An analytical model taking into account the macromolecule exclusion from pore walls is proposed to predict rodlike polymer velocity in porous media and thus the spreading out of polydispersed polymer slugs. Under conditions where this wall exclusion is maximum, i.e. at low shear rates and polymer concentrations, the experiments show that xanthan propagation is effectively predicted by this model. At higher flow rates and polymer concentrations, the effects of hydrodynamic dispersion and viscous fingering are analyzed. A new fractionation method for determining molecular weight distribution of polymers used in EOR is proposed.

  7. Estimate of the theoretical uncertainty of the cross sections for nucleon knockout in neutral-current neutrino-oxygen interactions

    E-Print Network [OSTI]

    Ankowski, Artur M; Benhar, Omar; Caballero, Juan A; Giusti, Carlotta; González-Jiménez, Raúl; Megias, Guillermo D; Meucci, Andrea

    2015-01-01T23:59:59.000Z

    Free nucleons propagating in water are known to produce gamma rays, which form a background to the searches for diffuse supernova neutrinos and sterile neutrinos carried out with Cherenkov detectors. As a consequence, the process of nucleon knockout induced by neutral-current quasielastic interactions of atmospheric (anti)neutrinos with oxygen needs to be under control at the quantitative level in the background simulations of the ongoing and future experiments. In this paper, we provide a quantitative assessment of the uncertainty associated with the theoretical description of the nuclear cross sections, estimating it from the discrepancies between the predictions of different models.

  8. Uncertainty Budget Analysis for Dimensional Inspection Processes (U)

    SciTech Connect (OSTI)

    Valdez, Lucas M. [Los Alamos National Laboratory

    2012-07-26T23:59:59.000Z

    This paper is intended to provide guidance and describe how to prepare an uncertainty analysis of a dimensional inspection process through the utilization of an uncertainty budget analysis. The uncertainty analysis is stated in the same methodology as that of the ISO GUM standard for calibration and testing. There is a specific distinction between how Type A and Type B uncertainty analysis is used in a general and specific process. All theory and applications are utilized to represent both a generalized approach to estimating measurement uncertainty and how to report and present these estimations for dimensional measurements in a dimensional inspection process. The analysis of this uncertainty budget shows that a well-controlled dimensional inspection process produces a conservative process uncertainty, which can be attributed to the necessary assumptions in place for best possible results.

  9. Measurement uncertainty in surface flatness measurement

    E-Print Network [OSTI]

    H. L. Thang

    2011-11-29T23:59:59.000Z

    Flatness of a plate is a parameter has been put under consideration for long time. Factors influencing the accuracy of this parameter have been recognized and examined carefully but placed scatterringly. Beside that those reports have not been always in harmonization with Guide for expression of uncertainty measurement (GUM). Furthermore, mathematical equations describing clearly the flatness measurement have not been seen in those reports also. We have collected those influencing factors for systematic reference purpose, re-written the equation describing the profile measurement of the plate topography, and proposed an equation for flatness determination. An illustrative numerical example will be also shown.

  10. Thermal hydraulic limits analysis for the MIT Research Reactor low enrichment uranium core conversion using statistical propagation of parametric uncertainties

    E-Print Network [OSTI]

    Chiang, Keng-Yen

    2012-01-01T23:59:59.000Z

    The MIT Research Reactor (MITR) is evaluating the conversion from highly enriched uranium (HEU) to low enrichment uranium (LEU) fuel. In addition to the fuel element re-design from 15 to 18 plates per element, a reactor ...

  11. Information Propagation in the Bitcoin Network

    E-Print Network [OSTI]

    Information Propagation in the Bitcoin Network Christian Decker ETH Zurich ­ Distributed Computing Group ­ www.disco.ethz.ch #12;What is Bitcoin? #12;What is Bitcoin? + #12;What is Bitcoin? + = #12;What 250 300 Price[USD] USD / Bitcoin exchange price 150$/BTC #12;What's it worth? Oct 2010 Feb 2011 Jun

  12. Distributed Kalman Filter via Gaussian Belief Propagation

    E-Print Network [OSTI]

    Dolev, Danny

    Distributed Kalman Filter via Gaussian Belief Propagation Danny Bickson IBM Haifa Research Lab interpretations. First, we show equivalence to computing one iteration of the Kalman filter. Second, we show that the Kalman filter is a special case of the Gaussian information bottleneck algorithm, when the weight

  13. Wave propagation in the magnetic sun

    E-Print Network [OSTI]

    T. Hartlep; M. S. Miesch; N. N. Mansour

    2008-05-03T23:59:59.000Z

    This paper reports on efforts to simulate wave propagation in the solar interior. Presented is work on extending a numerical code for constant entropy acoustic waves in the absence of magnetic fields to the case where magnetic fields are present. A set of linearized magnetohydrodynamic (MHD) perturbation equations has been derived and implemented.

  14. Wave propagation Remco Hartkamp (University of Twente)

    E-Print Network [OSTI]

    Entekhabi, Dara

    ) waves Sound: 20 Hz ­ 20 kHz Gas: P Liquid: P Plasma: P Solid: P & S #12;Stretched string example 1D wave Dispersion: Waves with different wavelengths propagate at different speeds 6 k c k k Shallow water: c gh mJ K material parameter (related to the strain saturation of the material) det FJ bulk modulus

  15. Detonation propagation in a high loss configuration

    SciTech Connect (OSTI)

    Jackson, Scott I [Los Alamos National Laboratory; Shepherd, Joseph E [CALTECH

    2009-01-01T23:59:59.000Z

    This work presents an experimental study of detonation wave propagation in tubes with inner diameters (ID) comparable to the mixture cell size. Propane-oxygen mixtures were used in two test section tubes with inner diameters of 1.27 mm and 6.35 mm. For both test sections, the initial pressure of stoichiometric mixtures was varied to determine the effect on detonation propagation. For the 6.35 mm tube, the equivalence ratio {phi} (where the mixture was {phi} C{sub 3}H{sub 8} + 50{sub 2}) was also varied. Detonations were found to propagate in mixtures with cell sizes as large as five times the diameter of the tube. However, under these conditions, significant losses were observed, resulting in wave propagation velocities as slow as 40% of the CJ velocity U{sub CJ}. A review of relevant literature is presented, followed by experimental details and data. Observed velocity deficits are predicted using models that account for boundary layer growth inside detonation waves.

  16. On the Vacuum Propagation of Gravitational Waves

    E-Print Network [OSTI]

    Xiao Liu

    2007-06-05T23:59:59.000Z

    We show that, for any local, causal quantum field theory which couples covariantly to gravity, and which admits Minkowski spacetime vacuum(a) invariant under the inhomogeneous proper orthochronous Lorentz group, plane gravitational waves propagating in such Minkowski vacuum(a) do not dissipate energy or momentum via quantum field theoretic effects.

  17. Uncertainty evaluation in transition temperature measurements

    SciTech Connect (OSTI)

    Brillaud, C. [Electricite de France, Avoine (France); Augendre, H. [Electricite de France, Clamart (France); Bethmont, M. [Electricite de France, Ecuelles (France)

    1996-12-31T23:59:59.000Z

    The pressure vessel surveillance program is mainly based on the transition temperature change assessment, a change which is induced by neutron irradiation. Uncertainties in Charpy test measurements are well known; however, the authors are less familiar with uncertainties due to general procedures governing experiments, which can be significant and therefore must be taken into account. In fact, procedures specify neither the number of specimens needed to obtain a transition curve, nor the choice of test temperatures, nor the fitting method for the transition curve. A study has been conducted to determine the influence of the experimental procedure on the accuracy of transition temperature determination, and the initial results are presented in this paper. Two EDF laboratories performed Charpy tests on the surveillance program reference metal, using 8, 16, 24, 32 and 64 specimens to evaluate how the number of specimens affects the transition temperature. The influence of the scatter of mechanical properties has also been studied at two levels of irradiation. The authors have evaluated the effect of different sampling strategies and investigated a new fitting method, which is based on a simultaneous fitting of all curves with common constraints on parameters.

  18. Uncertainty Analysis Technique for OMEGA Dante Measurements

    SciTech Connect (OSTI)

    May, M J; Widmann, K; Sorce, C; Park, H; Schneider, M

    2010-05-07T23:59:59.000Z

    The Dante is an 18 channel X-ray filtered diode array which records the spectrally and temporally resolved radiation flux from various targets (e.g. hohlraums, etc.) at X-ray energies between 50 eV to 10 keV. It is a main diagnostics installed on the OMEGA laser facility at the Laboratory for Laser Energetics, University of Rochester. The absolute flux is determined from the photometric calibration of the X-ray diodes, filters and mirrors and an unfold algorithm. Understanding the errors on this absolute measurement is critical for understanding hohlraum energetic physics. We present a new method for quantifying the uncertainties on the determined flux using a Monte-Carlo parameter variation technique. This technique combines the uncertainties in both the unfold algorithm and the error from the absolute calibration of each channel into a one sigma Gaussian error function. One thousand test voltage sets are created using these error functions and processed by the unfold algorithm to produce individual spectra and fluxes. Statistical methods are applied to the resultant set of fluxes to estimate error bars on the measurements.

  19. Uncertainty quantification in reacting flow modeling.

    SciTech Connect (OSTI)

    Le MaÒitre, Olivier P. (UniversitÔe d'Evry Val d'Essonne, Evry, France); Reagan, Matthew T.; Knio, Omar M. (Johns Hopkins University, Baltimore, MD); Ghanem, Roger Georges (Johns Hopkins University, Baltimore, MD); Najm, Habib N.

    2003-10-01T23:59:59.000Z

    Uncertainty quantification (UQ) in the computational modeling of physical systems is important for scientific investigation, engineering design, and model validation. In this work we develop techniques for UQ based on spectral and pseudo-spectral polynomial chaos (PC) expansions, and we apply these constructions in computations of reacting flow. We develop and compare both intrusive and non-intrusive spectral PC techniques. In the intrusive construction, the deterministic model equations are reformulated using Galerkin projection into a set of equations for the time evolution of the field variable PC expansion mode strengths. The mode strengths relate specific parametric uncertainties to their effects on model outputs. The non-intrusive construction uses sampling of many realizations of the original deterministic model, and projects the resulting statistics onto the PC modes, arriving at the PC expansions of the model outputs. We investigate and discuss the strengths and weaknesses of each approach, and identify their utility under different conditions. We also outline areas where ongoing and future research are needed to address challenges with both approaches.

  20. Total Adjusted Sales of Kerosene

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5 Tables July 1996 Energy Information Administration Office ofthroughYear JanYear Jan Feb Mar Apr May(MillionFeet)JulyEnd Use: Total

  1. U.S. Total Exports

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are nowTotal" (Percent) Type: Sulfur Content API GravityDakota" "Fuel, quality",Area: U.S. East Coast (PADD 1) New120,814 136,9322009 2010(Billion

  2. U.S. Total Exports

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are nowTotal" (Percent) Type: Sulfur Content API GravityDakota" "Fuel, quality",Area: U.S. East Coast (PADD 1) New120,814 136,9322009 2010(Billion120,814 136,932

  3. U.S. Total Imports

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are nowTotal" (Percent) Type: Sulfur Content API GravityDakota" "Fuel, quality",Area: U.S. East Coast (PADD 1) New120,814 136,9322009 2010(Billion120,814

  4. U.S. Total Imports

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are nowTotal" (Percent) Type: Sulfur Content API GravityDakota" "Fuel, quality",Area: U.S. East Coast (PADD 1) New120,814 136,9322009 2010(Billion120,814Pipeline

  5. U.S. Total Stocks

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are nowTotal" (Percent) Type: Sulfur Content API GravityDakota" "Fuel, quality",Area: U.S. East Coast (PADD 1) New120,814 136,9322009Feet)

  6. Construction and AvailabilityConstruction and Availability Uncertainty in the RegionalUncertainty in the Regional

    E-Print Network [OSTI]

    Page 1 Construction and AvailabilityConstruction and Availability Uncertainty in the Regional and Technology Availability Construction Costs Economic Retirement Variable Capacity for Existing Units #12;Page to construction power plants or to take other action May include policies for particular resources "Scenario

  7. Optimal investment and scheduling of distributed energy resources with uncertainty in electric vehicles driving schedules

    SciTech Connect (OSTI)

    Center for Energy and Innovative Technologies; NEC Laboratories America Inc.; Cardoso, Goncalo; Stadler, Michael; Bozchalui, Mohammed C.; Sharma, Ratnesh; Marnay, Chris; Barbosa-Povoa, Ana; Ferrao, Paulo

    2013-10-27T23:59:59.000Z

    The large scale penetration of electric vehicles (EVs) will introduce technical challenges to the distribution grid, but also carries the potential for vehicle-to-grid services. Namely, if available in large enough numbers, EVs can be used as a distributed energy resource (DER) and their presence can influence optimal DER investment and scheduling decisions in microgrids. In this work, a novel EV fleet aggregator model is introduced in a stochastic formulation of DER-CAM [1], an optimization tool used to address DER investment and scheduling problems. This is used to assess the impact of EV interconnections on optimal DER solutions considering uncertainty in EV driving schedules. Optimization results indicate that EVs can have a significant impact on DER investments, particularly if considering short payback periods. Furthermore, results suggest that uncertainty in driving schedules carries little significance to total energy costs, which is corroborated by results obtained using the stochastic formulation of the problem.

  8. Measurements of cosmic ray antiprotons with PAMELA and studies of propagation models

    E-Print Network [OSTI]

    Juan Wu

    2012-05-22T23:59:59.000Z

    Studying the acceleration and propagation mechanisms of Galactic cosmic rays can provide information regarding astrophysical sources, the properties of our Galaxy, and possible exotic sources such as dark matter. To understand cosmic ray acceleration and propagation mechanisms, accurate measurements of different cosmic ray elements over a wide energy range are needed. The PAMELA experiment is a satellite-borne apparatus which allows different cosmic ray species to be identified over background. Measurements of the cosmic ray antiproton flux and the antiproton-to-proton flux ratio from 1.5 GeV to 180 GeV are presented in this thesis. Compared to previous experiments, PAMELA extends the energy range of antiproton measurements and provides significantly higher statistics. The derived antiproton flux and antiproton-to-proton flux ratio are consistent with previous measurements and generally considered to be produced as secondary products when cosmic ray protons and helium nuclei interact with the interstellar medium. To constrain cosmic ray acceleration and propagation models, the antiproton data measured by PAMELA were further used together with the proton spectrum reported by PAMELA, as well as the B/C data provided by other experiments. Statistical tools were interfaced with the cosmic ray propagation package GALPROP to perform the constraining analyses. Diffusion models with a linear diffusion coefficient and modified diffusion models with a low energy dependence of the diffusion coefficient were studied in the $\\chi^{2}$ study. Uncertainties on the parameters and the goodness of fit of each model were given. Some models are further studied using the Bayesian inference. Posterior means and errors of the parameters base on our prior knowledge on them were obtained in the Bayesian framework. This method also allowed us to understand the correlation between parameters and compare models.

  9. Results for Phase I of the IAEA Coordinated Research Program on HTGR Uncertainties

    SciTech Connect (OSTI)

    Gerhard Strydom; Friederike Bostelmann

    2015-01-01T23:59:59.000Z

    The quantification of uncertainties in design and safety analysis of reactors is today not only broadly accepted, but in many cases became the preferred way to replace traditional conservative analysis for safety and licensing analysis. The use of a more fundamental methodology is also consistent with the reliable high fidelity physics models and robust, efficient, and accurate codes available today. To facilitate uncertainty analysis applications a comprehensive approach and methodology must be developed and applied. High Temperature Gas-cooled Reactors (HTGR) has its own peculiarities, coated particle design, large graphite quantities, different materials and high temperatures that also require other simulation requirements. The IAEA has therefore launched a Coordinated Research Project (CRP) on the HTGR Uncertainty Analysis in Modeling (UAM) in 2013 to study uncertainty propagation specifically in the HTGR analysis chain. Two benchmark problems are defined, with the prismatic design represented by the General Atomics (GA) MHTGR-350 and a 250 MW modular pebble bed design similar to the HTR-PM (INET, China). This report summarizes the contributions of the HTGR Methods Simulation group at Idaho National Laboratory (INL) up to this point of the CRP. The activities at INL have been focused so far on creating the problem specifications for the prismatic design, as well as providing reference solutions for the exercises defined for Phase I. An overview is provided of the HTGR UAM objectives and scope, and the detailed specifications for Exercises I-1, I-2, I-3 and I-4 are also included here for completeness. The main focus of the report is the compilation and discussion of reference results for Phase I (i.e. for input parameters at their nominal or best-estimate values), which is defined as the first step of the uncertainty quantification process. These reference results can be used by other CRP participants for comparison with other codes or their own reference results. The status on the Monte Carlo modeling of the experimental VHTRC facility is also discussed. Reference results were obtained for the neutronics stand-alone cases (Ex. I-1 and Ex. I-2) using the (relatively new) Monte Carlo code Serpent, and comparisons were performed with the more established Monte Carlo codes MCNP and KENO-VI. For the thermal-fluids stand-alone cases (Ex. I-3 and I-4) the commercial CFD code CFX was utilized to obtain reference results that can be compared with lower fidelity tools.

  10. Incorporating uncertainty in RADTRAN 6.0 input files.

    SciTech Connect (OSTI)

    Dennis, Matthew L.; Weiner, Ruth F.; Heames, Terence John (Alion Science and Technology)

    2010-02-01T23:59:59.000Z

    Uncertainty may be introduced into RADTRAN analyses by distributing input parameters. The MELCOR Uncertainty Engine (Gauntt and Erickson, 2004) has been adapted for use in RADTRAN to determine the parameter shape and minimum and maximum of the distribution, to sample on the distribution, and to create an appropriate RADTRAN batch file. Coupling input parameters is not possible in this initial application. It is recommended that the analyst be very familiar with RADTRAN and able to edit or create a RADTRAN input file using a text editor before implementing the RADTRAN Uncertainty Analysis Module. Installation of the MELCOR Uncertainty Engine is required for incorporation of uncertainty into RADTRAN. Gauntt and Erickson (2004) provides installation instructions as well as a description and user guide for the uncertainty engine.

  11. Survey and Evaluate Uncertainty Quantification Methodologies

    SciTech Connect (OSTI)

    Lin, Guang; Engel, David W.; Eslinger, Paul W.

    2012-02-01T23:59:59.000Z

    The Carbon Capture Simulation Initiative (CCSI) is a partnership among national laboratories, industry and academic institutions that will develop and deploy state-of-the-art computational modeling and simulation tools to accelerate the commercialization of carbon capture technologies from discovery to development, demonstration, and ultimately the widespread deployment to hundreds of power plants. The CCSI Toolset will provide end users in industry with a comprehensive, integrated suite of scientifically validated models with uncertainty quantification, optimization, risk analysis and decision making capabilities. The CCSI Toolset will incorporate commercial and open-source software currently in use by industry and will also develop new software tools as necessary to fill technology gaps identified during execution of the project. The CCSI Toolset will (1) enable promising concepts to be more quickly identified through rapid computational screening of devices and processes; (2) reduce the time to design and troubleshoot new devices and processes; (3) quantify the technical risk in taking technology from laboratory-scale to commercial-scale; and (4) stabilize deployment costs more quickly by replacing some of the physical operational tests with virtual power plant simulations. The goal of CCSI is to deliver a toolset that can simulate the scale-up of a broad set of new carbon capture technologies from laboratory scale to full commercial scale. To provide a framework around which the toolset can be developed and demonstrated, we will focus on three Industrial Challenge Problems (ICPs) related to carbon capture technologies relevant to U.S. pulverized coal (PC) power plants. Post combustion capture by solid sorbents is the technology focus of the initial ICP (referred to as ICP A). The goal of the uncertainty quantification (UQ) task (Task 6) is to provide a set of capabilities to the user community for the quantification of uncertainties associated with the carbon capture processes. As such, we will develop, as needed and beyond existing capabilities, a suite of robust and efficient computational tools for UQ to be integrated into a CCSI UQ software framework.

  12. 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...

  13. 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...

  14. 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...

  15. 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...

  16. Wave Propagation in Fractured Poroelastic Media - Department of ...

    E-Print Network [OSTI]

    robiel

    and sizes is essential since these factors control hydrocarbon production. ... saturation and fractal porosity (fractal frame properties). Wave Propagation in ...

  17. Propagation of nonlinear waves in waveguides and application to nondestructive stress measurement

    E-Print Network [OSTI]

    Nucera, Claudio

    2012-01-01T23:59:59.000Z

    of  ultrasonic  wave  propagation  to  identify defects in investigation of elastic wave  propagation in a cylinder.  Modeling  guided  wave  propagation with application to the 

  18. Low-frequency dilatational wave propagation through unsaturated porous media containing two immiscible fluids

    E-Print Network [OSTI]

    Lo, W.-C.

    2009-01-01T23:59:59.000Z

    1988, Bulk elastic wave propagation in partially saturated1986, Compressional wave propagation in liquid and/or gassaturation and seismic-wave propagation, Annu. Rev. Earth

  19. UNCERTAINTY EVALUATION OF AVAILABLE ENERGY AND POWER

    SciTech Connect (OSTI)

    Jon P. Christophersen; John L. Morrison

    2006-05-01T23:59:59.000Z

    The Idaho National Laboratory does extensive testing and evaluation of advanced technology batteries and ultracapacitors for applications in electric and hybrid vehicles. The testing is essentially acquiring time records of voltage, current and temperature from a variety of charge and discharge time profiles. From these three basic measured parameters, a complex assortment of derived parameters (resistance, power, etc.) is computed. Derived parameters are in many cases functions of multiple layers of other derived parameters that eventually work back to the three basic measured parameters. The purpose of this paper is to document the methodology used for the uncertainty analysis of the most complicated derived parameters broadly grouped as available energy and available power. This work is an analytical derivation. Future work will report the implementation of algorithms based upon this effort.

  20. Generalized uncertainty principle and black hole thermodynamics

    E-Print Network [OSTI]

    Sunandan Gangopadhyay; Abhijit Dutta; Anirban Saha

    2014-01-08T23:59:59.000Z

    We study the Schwarzschild and Reissner-Nordstr\\"{o}m black hole thermodynamics using the simplest form of the generalized uncertainty principle (GUP) proposed in the literature. The expressions for the mass-temperature relation, heat capacity and entropy are obtained in both cases from which the critical and remnant masses are computed. Our results are exact and reveal that these masses are identical and larger than the so called singular mass for which the thermodynamics quantities become ill-defined. The expression for the entropy reveals the well known area theorem in terms of the horizon area in both cases upto leading order corrections from GUP. The area theorem written in terms of a new variable which can be interpreted as the reduced horizon area arises only when the computation is carried out to the next higher order correction from GUP.

  1. accident consequence uncertainty: Topics by E-print Network

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

    uncertainties in typical oil and gas projects: 1. The oil price, 2. The investments (capex) and operating. The oil and gas reserves and production profiles, 5. The production...

  2. assessing spatial uncertainty: Topics by E-print Network

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

    uncertainties in typical oil and gas projects: 1. The oil price, 2. The investments (capex) and operating. The oil and gas reserves and production profiles, 5. The production...

  3. Special Issue on Integrated Uncertainty Management for Decision Making

    E-Print Network [OSTI]

    under soft constraints #12;· Application: ­ Ranking and recommendation systems ­ Supply chain management and decision making. Topics include but are not limited to: · Methodology: ­ Uncertainty formalisms: Bayesian

  4. Worst-case-expectation approach to optimization under uncertainty

    E-Print Network [OSTI]

    Alexander Shapiro

    2012-10-30T23:59:59.000Z

    Oct 30, 2012 ... Worst-case-expectation approach to optimization under uncertainty ... approximation, risk neutral and risk averse approaches, case studies.

  5. Optimization Online - The impact of wind uncertainty on the strategic ...

    E-Print Network [OSTI]

    Pedro Crespo Del Granado

    2015-01-14T23:59:59.000Z

    Jan 14, 2015 ... Abstract: The intermittent nature of wind energy generation has introduced a new degree of uncertainty to the tactical planning of energy ...

  6. Measurement uncertainty analysis techniques applied to PV performance measurements

    SciTech Connect (OSTI)

    Wells, C.

    1992-10-01T23:59:59.000Z

    The purpose of this presentation is to provide a brief introduction to measurement uncertainty analysis, outline how it is done, and illustrate uncertainty analysis with examples drawn from the PV field, with particular emphasis toward its use in PV performance measurements. The uncertainty information we know and state concerning a PV performance measurement or a module test result determines, to a significant extent, the value and quality of that result. What is measurement uncertainty analysis It is an outgrowth of what has commonly been called error analysis. But uncertainty analysis, a more recent development, gives greater insight into measurement processes and tests, experiments, or calibration results. Uncertainty analysis gives us an estimate of the I interval about a measured value or an experiment's final result within which we believe the true value of that quantity will lie. Why should we take the time to perform an uncertainty analysis A rigorous measurement uncertainty analysis: Increases the credibility and value of research results; allows comparisons of results from different labs; helps improve experiment design and identifies where changes are needed to achieve stated objectives (through use of the pre-test analysis); plays a significant role in validating measurements and experimental results, and in demonstrating (through the post-test analysis) that valid data have been acquired; reduces the risk of making erroneous decisions; demonstrates quality assurance and quality control measures have been accomplished; define Valid Data as data having known and documented paths of: Origin, including theory; measurements; traceability to measurement standards; computations; uncertainty analysis of results.

  7. DRAFT REPORT HIERARCHY OF METHODS TO CHARACTERIZE UNCERTAINTY

    E-Print Network [OSTI]

    Frey, H. Christopher

    ...................................................................................................... 5 1.2.3 Risk, Cost, Uncertainty and Decisions ........................................................... 20 1.4.3 Other Quantitative Methods.............................................................................................. 24 1.4.5 Sensitivity Analysis

  8. Nonmarket Valuation under Preference Uncertainty: Econometric Models and Estimation

    E-Print Network [OSTI]

    Hanemann, W. Michael; Kristrom, Bengt; Li, Chuan-Zhong

    1996-01-01T23:59:59.000Z

    3 The EconometricUNCERTAINTY: ECONOMETRIC MODELS AND ESTIMATION bY W. MichaelSection 3 introduces ihe econometric model. Section 4

  9. Characterizing and responding to uncertainty in climate change

    E-Print Network [OSTI]

    Lemoine, Derek Mark

    2011-01-01T23:59:59.000Z

    Irreversible abatement investment under cost uncertainties:about other firms’ investment cost. Third, if the regulatorIn addition, while making investment costs heterogeneous

  10. Characterizing Uncertainty for Regional Climate Change Mitigation and Adaptation Decisions

    SciTech Connect (OSTI)

    Unwin, Stephen D.; Moss, Richard H.; Rice, Jennie S.; Scott, Michael J.

    2011-09-30T23:59:59.000Z

    This white paper describes the results of new research to develop an uncertainty characterization process to help address the challenges of regional climate change mitigation and adaptation decisions.

  11. Which Models Matter: Uncertainty and Sensitivity Analysis for

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

    Models Matter: Uncertainty and Sensitivity Analysis for Photovoltaic Power Systems Clifford W. Hansen and Andrew Pohl Sandia National Laboratories, Albuquerque, NM, 87185-1033, USA...

  12. The Top Mass: Interpretation and Theoretical Uncertainties

    E-Print Network [OSTI]

    André H. Hoang

    2014-12-11T23:59:59.000Z

    Currently the most precise LHC measurements of the top quark mass are determinations of the top quark mass parameter of Monte-Carlo (MC) event generators reaching uncertainties of well below $1$ GeV. However, there is an additional theoretical problem when using the MC top mass $m_t^{\\rm MC}$ as an input for theoretical predictions, because a rigorous relation of $m_t^{\\rm MC}$ to a renormalized field theory mass is, at the very strict level, absent. In this talk I show how - nevertheless - some concrete statements on $m_t^{\\rm MC}$ can be deduced assuming that the MC generator behaves like a rigorous first principles QCD calculator for the observables that are used for the analyses. I give simple conceptual arguments showing that in this context $m_t^{\\rm MC}$ can be interpreted like the mass of a heavy-light top meson, and that there is a conversion relation to field theory top quark masses that requires a non-perturbative input. The situation is in analogy to B physics where a similar relation exists between experimental B meson masses and field theory bottom masses. The relation gives a prescription how to use $m_t^{\\rm MC}$ as an input for theoretical predictions in perturbative QCD. The outcome is that at this time an additional uncertainty of about $1$ GeV has to be accounted for. I discuss limitations of the arguments I give and possible ways to test them, or even to improve the current situation.

  13. Propagation of gravitational waves in multimetric gravity

    E-Print Network [OSTI]

    Manuel Hohmann

    2012-04-22T23:59:59.000Z

    We discuss the propagation of gravitational waves in a recently discussed class of theories containing N >= 2 metric tensors and a corresponding number of standard model copies. Using the formalism of gauge-invariant linear perturbation theory we show that all gravitational waves propagate at the speed of light. We then employ the Newman-Penrose formalism to show that two to six polarizations of gravitational waves may exist, depending on the parameters entering the equations of motion. This corresponds to E(2) representations N_2, N_3, III_5 and II_6. We finally apply our general discussion to a recently presented concrete multimetric gravity model and show that it is of class N_2, i.e., it allows only two tensor polarizations, as it is the case for general relativity. Our results provide the theoretical background for tests of multimetric gravity theories using the upcoming gravitational wave experiments.

  14. Method and apparatus for charged particle propagation

    DOE Patents [OSTI]

    Hershcovitch, A.

    1996-11-26T23:59:59.000Z

    A method and apparatus are provided for propagating charged particles from a vacuum to a higher pressure region. A generator includes an evacuated chamber having a gun for discharging a beam of charged particles such as an electron beam or ion beam. The beam is discharged through a beam exit in the chamber into a higher pressure region. A plasma interface is disposed at the beam exit and includes a plasma channel for bounding a plasma maintainable between a cathode and an anode disposed at opposite ends thereof. The plasma channel is coaxially aligned with the beam exit for propagating the beam from the chamber, through the plasma, and into the higher pressure region. The plasma is effective for pumping down the beam exit for preventing pressure increase in the chamber and provides magnetic focusing of the beam discharged into the higher pressure region 24. 7 figs.

  15. Exact identity for nonlinear wave propagation Duncan Ralph,1

    E-Print Network [OSTI]

    California at Santa Cruz, University of

    Exact identity for nonlinear wave propagation Duncan Ralph,1 Onuttom Narayan,1 and Richard The propagation of waves in nonlinear media is of great importance in a variety of fields, from seismology. Despite their im- portance, exact results for nonlinear wave propagation are rare. Although the existence

  16. Propagation Analysis of Electromagnetic Waves: Application to Auroral Kilometric Radiation

    E-Print Network [OSTI]

    Santolik, Ondrej

    12 Propagation Analysis of Electromagnetic Waves: Application to Auroral Kilometric Radiation, containing waves which simultaneously propagate in different directions and/or wave modes the concept emission is found to propagate predominantly in the R-X mode with wave energy distributed in relatively

  17. Propagation of nonlinearly generated harmonic spin waves in microscopic stripes

    E-Print Network [OSTI]

    Otani, Yoshichika

    Propagation of nonlinearly generated harmonic spin waves in microscopic stripes O. Rousseau,1 M on the experimental study of the propagation of nonlinearly generated harmonic spin waves in microscopic CoFeB stripes wave propagation. VC 2014 AIP Publishing LLC. [http://dx.doi.org/10.1063/1.4864480] In recent years

  18. Matching of asymptotic expansions for the wave propagation in media

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    Matching of asymptotic expansions for the wave propagation in media with thin slot S-SAM Matching of asymptotic expansions for the wave propagation in media with thin slot ­ p.1/38 inria-00528070 of asymptotic expansions for the wave propagation in media with thin slot ­ p.2/38 inria-00528070,version1-21Oct

  19. Sound wave propagation in weakly polydisperse granular materials

    E-Print Network [OSTI]

    Luding, Stefan

    Sound wave propagation in weakly polydisperse granular materials O. Mouraille, S. Luding NSM/DCT/TUDelft, Julianalaan 136, 2628 BL Delft, Netherlands Abstract Dynamic simulations of wave propagation are performed. A small perturbation is created on one side of a static packing and its propagation, for both P- and S-waves

  20. Gravity waves excited by jets: Propagation versus generation R. Plougonven

    E-Print Network [OSTI]

    Plougonven, Riwal

    Gravity waves excited by jets: Propagation versus generation R. Plougonven School of Mathematics imposed by the generation mechanism. In proceeding so, effects due to the propagation of the waves through simulations demonstrate that the propagation of inertia-gravity waves through horizontal deformation

  1. Propagation of elastic waves through a lattice of cylindrical cavities

    E-Print Network [OSTI]

    Propagation of elastic waves through a lattice of cylindrical cavities By S. Guo & P. Mc asymptotic homogenization to obtain low-frequency approximations to elastic wave propagation through periodic follows that of McIver (2007) who investigates acoustic-wave propagation through a lattice of rigid

  2. FINITE VOLUME SCHEMES FOR DISPERSIVE WAVE PROPAGATION AND RUNUP

    E-Print Network [OSTI]

    Boyer, Edmond

    FINITE VOLUME SCHEMES FOR DISPERSIVE WAVE PROPAGATION AND RUNUP DENYS DUTYKH , THEODOROS KATSAOUNIS to bidirectional nonlinear, dispersive wave propagation in one space dimension. Special emphasis is given require the computation of the wave generation [DD07, KDD07], propagation [TG97], interaction with solid

  3. Wave propagation in highly inhomogeneous thin films: exactly solvable models

    E-Print Network [OSTI]

    Boyer, Edmond

    Wave propagation in highly inhomogeneous thin films: exactly solvable models Guillaume Petite(1 of wave propagation in some inhomogeneous thin films with highly space- dependent dielectric constant will show that depending on the type of space dependence, an incident wave can either propagate or tunnel

  4. Shock wave propagation in composites and active Vinamra Agrawal

    E-Print Network [OSTI]

    Shyamasundar, R.K.

    Shock wave propagation in composites and active Vinamra Agrawal California Institute of Technology travel through a material. These waves are characterized as a discontinuity propagating through shock waves propagate in heterogeneous materials. Shock waves are also being used to o pulsed currents

  5. Matching of asymptotic expansions for the wave propagation in media

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    Matching of asymptotic expansions for the wave propagation in media with thin slot S-SAM Matching of asymptotic expansions for the wave propagation in media with thin slot ­ p.1/29 inria-00528072 The wavelength The width of the slot ¡ Matching of asymptotic expansions for the wave propagation in media

  6. Love wave propagation in layered magneto-electro-elastic structures

    E-Print Network [OSTI]

    Wang, Ji

    Love wave propagation in layered magneto-electro-elastic structures with initial stress J. Du, X that the initial stress has an important effect on the Love wave propagation in layered piezomagnetic at their interface. He concluded that shear surface waves propagate in the layer and attenuate along the thickness

  7. Singular value decomposition methods for wave propagation analysis

    E-Print Network [OSTI]

    Santolik, Ondrej

    Singular value decomposition methods for wave propagation analysis O. Santoli´k,1 M. Parrot, and F planarity. Simulations of Z-mode waves, which simultaneously propagate with different wave vectors, indicate the waves simultaneously propagate with wave vectors in two opposite hemispheres. Finally, we show

  8. Propagating waves mediate information transfer in the motor cortex

    E-Print Network [OSTI]

    Hatsopoulos, Nicholas

    Propagating waves mediate information transfer in the motor cortex Doug Rubino1, Kay A Robbins2-delay reaching task, we found that these oscillations propagated as waves across the surface of the motor cortex oscillations propagated as waves across the primary motor (MI) and premotor (PMd) cortices as monkeys planned

  9. Feedback stabilization of unstable propagating waves Eugene Mihaliuk,1

    E-Print Network [OSTI]

    Showalter, Kenneth

    Feedback stabilization of unstable propagating waves Eugene Mihaliuk,1 Tatsunari Sakurai,1 Florin Received 29 July 2001; revised manuscript received 10 March 2002; published 26 June 2002 Propagating wave s : 82.40.Ck, 47.54. r Propagating waves in active media arise from the cou- pling of a positive feedback

  10. TIME-PERIODIC SOUND WAVE PROPAGATION COMPRESSIBLE EULER EQUATIONS

    E-Print Network [OSTI]

    A PARADIGM FOR TIME-PERIODIC SOUND WAVE PROPAGATION IN THE COMPRESSIBLE EULER EQUATIONS BLAKE consistent with time-periodic sound wave propagation in the 3 Ã? 3 nonlinear compressible Euler equations description of shock-free waves that propagate through an oscillating entropy field without breaking or dis

  11. Efficient Numerical Simulation for Long Range Wave Propagation 1

    E-Print Network [OSTI]

    Solna, Knut

    Efficient Numerical Simulation for Long Range Wave Propagation 1 Kai Huang 2 George Papanicolaou 3 for simulating wave propagation over long dis- tances with both weak and strong scatterers. In domains with weak heterogeneities the wave field is decomposed into forward propagating and back scattered modes using two coupled

  12. Electromagnetic Waves Propagation in 3D Plasma Configurations

    E-Print Network [OSTI]

    Electromagnetic Waves Propagation in 3D Plasma Configurations Pavel Popovich, W. Anthony Cooper in a plasma strongly depends on the frequency, therefore the tools used for wave propagation studies are very that will allow for the calculation of the fields and energy deposition of a low-frequency wave propagating

  13. Detection of Cardiac Occlusions Using Viscoelastic Wave Propagation

    E-Print Network [OSTI]

    Detection of Cardiac Occlusions Using Viscoelastic Wave Propagation H.T. Banks and J. R. Samuels driven viscoelastic (VE) waves propagated through biotissue to body surface sensors. We in- vestigate: Inverse problems, viscoelastic models, wave propagation in biotissue, statistical models. AMS Subject

  14. Handwritten Digit Recognition with a Back-Propagation Network

    E-Print Network [OSTI]

    Parker, Gary B.

    Handwritten Digit Recognition with a Back-Propagation Network Y. Le Cun, B. Boser, J. S. Denker, D We present an application of back-propagation networks to hand- written digit recognition. Minimal. 1 INTRODUCTION The main point of this paper is to show that large back-propagation (BP) net- works

  15. Handwritten Digit Recognition with a BackPropagation Network

    E-Print Network [OSTI]

    LeCun, Yann

    Handwritten Digit Recognition with a Back­Propagation Network Y. Le Cun, B. Boser, J. S. Denker, D We present an application of back­propagation networks to hand­ written digit recognition. Minimal. 1 INTRODUCTION The main point of this paper is to show that large back­propagation (BP) net­ works

  16. A Kinematic Model of Wave Propagation John W. Cain1

    E-Print Network [OSTI]

    Cain, John Wesley

    A Kinematic Model of Wave Propagation John W. Cain1 1 Dept. of Mathematics, Virginia Commonwealth Abstract We present a purely kinematic model of wave propagation in an ex- citable medium, namely cardiac- putationally efficient kinematic model [7] of wave propagation, starting from a standard reaction

  17. Electromagnetically Induced Guiding of Counter-Propagating Lasers in Plasmas

    E-Print Network [OSTI]

    - propagating laser pulses and (ii) guiding of an ultra-short tightly focused laser pulse by a counterElectromagnetically Induced Guiding of Counter-Propagating Lasers in Plasmas G. Shvets Princeton for Quantenoptik, D-85748 Garching, Germany Abstract The interaction of counter-propagating laser pulses

  18. Wave Propagation in Jointed Geologic Media

    SciTech Connect (OSTI)

    Antoun, T

    2009-12-17T23:59:59.000Z

    Predictive modeling capabilities for wave propagation in a jointed geologic media remain a modern day scientific frontier. In part this is due to a lack of comprehensive understanding of the complex physical processes associated with the transient response of geologic material, and in part it is due to numerical challenges that prohibit accurate representation of the heterogeneities that influence the material response. Constitutive models whose properties are determined from laboratory experiments on intact samples have been shown to over-predict the free field environment in large scale field experiments. Current methodologies for deriving in situ properties from laboratory measured properties are based on empirical equations derived for static geomechanical applications involving loads of lower intensity and much longer durations than those encountered in applications of interest involving wave propagation. These methodologies are not validated for dynamic applications, and they do not account for anisotropic behavior stemming from direcitonal effects associated with the orientation of joint sets in realistic geologies. Recent advances in modeling capabilities coupled with modern high performance computing platforms enable physics-based simulations of jointed geologic media with unprecedented details, offering a prospect for significant advances in the state of the art. This report provides a brief overview of these modern computational approaches, discusses their advantages and limitations, and attempts to formulate an integrated framework leading to the development of predictive modeling capabilities for wave propagation in jointed and fractured geologic materials.

  19. Nonlinear propagation of light in Dirac matter

    SciTech Connect (OSTI)

    Eliasson, Bengt [Institut fuer Theoretische Physik, Fakultaet fuer Physik und Astronomie, Ruhr-Universitaet Bochum, D-44780 Bochum (Germany); Shukla, P. K. [RUB International Chair, International Centre for Advanced Studies in Physical Sciences, Fakultaet fuer Physik und Astronomie, Ruhr-Universitaet Bochum, D-44780 Bochum (Germany)

    2011-03-15T23:59:59.000Z

    The nonlinear interaction between intense laser light and a quantum plasma is modeled by a collective Dirac equation coupled with the Maxwell equations. The model is used to study the nonlinear propagation of relativistically intense laser light in a quantum plasma including the electron spin-1/2 effect. The relativistic effects due to the high-intensity laser light lead, in general, to a downshift of the laser frequency, similar to a classical plasma where the relativistic mass increase leads to self-induced transparency of laser light and other associated effects. The electron spin-1/2 effects lead to a frequency upshift or downshift of the electromagnetic (EM) wave, depending on the spin state of the plasma and the polarization of the EM wave. For laboratory solid density plasmas, the spin-1/2 effects on the propagation of light are small, but they may be significant in superdense plasma in the core of white dwarf stars. We also discuss extensions of the model to include kinetic effects of a distribution of the electrons on the nonlinear propagation of EM waves in a quantum plasma.

  20. Seismic Wave Propagation in Alluvial Basins and Influence of Site-City Interaction Seismic Wave Propagation in Alluvial Basins

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    Seismic Wave Propagation in Alluvial Basins and Influence of Site-City Interaction 1 Seismic Wave of alluvial deposits have a major influence on seismic wave propagation and amplification. However influence seismic wave propagation near the free surface. In this paper, the influence of surface structures