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At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Generalized Bayesian Framework for Evaluation of Integral Benchmark Experiments

A recently published generalized Bayesian optimization framework has provided a way to retract any or all of the three common assumptions underlying the conventional Generalized Linear Least Squares (GLLS) optimization method based on the concepts introduced in Ref. [2]. These assumptions are: 1. Perfection: The model used for data evaluation and the prior probability distribution function (PDF) of generalized data are perfect. 2. Normality: The prior and posterior PDF are normal. 3. Linearity: The model is linear. In this work we outline how the framework in [1] could be directly adopted for improved evaluation of nuclear criticality integral benchmark experiments (IBEs) by: 1. Removing the first assumption alone by utilizing the concept of imperfections introduced in [1] to enable evaluation in the presence of discrepancies between the data and model or of missing covariance information by a GLLS method that will be seen as a generalization of the conventional GLLS method employed by the TSURFER code, and by 2. Removing the remaining two assumptions by implementing a Markov Chain Monte Carlo method for computation of the posterior PDF in the SAMPLER code, where TSURFER and SAMPLER are the uncertainty quantification (UQ) codes for IBEs in the SCALE code system based on the GLLS and the stochastic method, respectively. The graphic in Figure 1 categorizes the methods discussed in terms of the assumptions that they employ to determine posterior PDFs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Generalized Bayesian Framework for Evaluation of Integral Benchmark Experiments

A recently published generalized Bayesian optimization framework has provided a way to retract any or all of the three common assumptions underlying the conventional Generalized Linear Least Squares (GLLS) optimization method based on the concepts introduced in reference two. These assumptions are: 1. Perfection: The model used for data evaluation and the prior probability distribution function (PDF) of generalized* data are perfect; 2. Normality: The prior and posterior PDF are normal; and 3. Linearity: The model is linear. In this work we outline how the framework in 1 could be directly adopted for improved evaluation of nuclear criticality integral benchmark experiments (IBEs) by: 1. Removing the first assumption alone by utilizing the concept of imperfections introduced in 1 to enable evaluation in the presence of discrepancies between the data and model or of missing covariance information by a GLLS method that will be seen as a generalization of the conventional GLLS method employed by the TSURFER code, and by 2. Removing the remaining two assumptions by implement- ing a Markov Chain Monte Carlo method for computation of the posterior PDF in the SAMPLER code, where TSURFER and SAMPLER are the uncertainty quantification (UQ) codes for IBEs in the SCALE code system based on the GLLS, and the stochastic method, respectively.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

WPEC SG44 Subgroup Report: Investigation of Covariance Data in General Purpose Nuclear Data Libraries [Slides]

Presentations were as follows: Progress on the Development of Thermal Scattering Covariance Formats, New Paradigm for Nuclear Data Evaluation, The Role of Fission Yield Correlations to Obtain Realistic Uncertainty Values in the Summation Method, ENDF/B-VIII.0 Augmented Covariance Data: The First Iteration, Status and Requirements of Nuclear Data Variance-Covariance Matrices for the Neutronic Assessment of Fast Reactor Cores, The Identification and Treatment of Unrecognized Uncertainties and the Impact on Evaluated Uncertainties– SG44, Improved Calibration of Nuclear Resonance Parameters, Report on the CSEWG Covariance and Measurement Session Initiative on Creating Templates of Expected Measurement Uncertainties, The Nuclear Data Belief Network, Investigation of Covariance Data in General Purpose Nuclear Data Libraries, and Computational Benchmark Proposal.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Avoidance of disruptions on KSTAR due to vertical displacement events via novel real-time stability assessment

Disruption avoidance via the DECAF approach has been achieved on KSTAR using a novel real-time vertical stability assessment and a multiactuator feedback control strategy. The development of disruption avoidance strategies with reactor-relevant reliability is an urgent activity, enabling future fusion power plants. The stability metric employed is based on a new formulation of a vertical force gradient balance metric evaluated across the poloidal cross section of the plasma, with parameters tuned using historical data. Evaluation of this metric on a validation set of 400 recent KSTAR shots indicates >82% of Vertical displacement events can be avoided via feedback control. Essential to its calculation is the two-dimensional toroidal current density distribution in the plasma. Measurement of this profile faster than fully-converged equilibrium reconstructions can deliver is found to improve forecaster performance and is achieved with a surrogate model that takes as input magnetic diagnostic measurements and outputs the current profile on a basis comprising the top principal components of historical current profiles (from past equilibrium reconstructions). This method solves the non-uniqueness problem typically faced when reconstructing current profiles directly from diagnostics, while improving computational time and accuracy. On average, profiles produced by this model reach coefficients of determination of >0.99 with respect to those from equilibrium reconstructions. The avoidance actuators employed include poloidal field coils and an electron cyclotron current drive system. The multiactuator approach, as shown in this first demonstration, allows disruption avoidance while minimizing impact to operational performance. This ability, along with its flexibility and speed, makes this new approach an attractive option for avoiding these types of disruptions in reactors.

Tobin, Matthew [Columbia Univ., New York, NY (Unit↗
DOE OSTI2012

Reactive Transport Modeling

This special section in the Vadose Zone Journal focusing on reactive transport modeling was developed from a special symposium jointly sponsored by the Soil Physics and Soil Chemistry Divisions of the Soil Science Society of America at the 2010 annual meetings held in Long Beach, CA. It contains eight contributions reflecting a diverse range of reactive transport modeling applications that address the fate and transport of both reactive solutes and colloidal material (i.e., viruses). Further, the eight studies range in scale from laboratory batch and column experiments to the regional scale. Three of the submissions reflect efforts to model experimental data that were already reported in the literature, providing additional insight concerning underlying physical and chemical processes. In addition to the obvious predictive applications, the eight contributions to this special section illustrate the additional advantages to including reactive transport modeling as an active component within a diverse research agenda. In addition to scenario testing, reactive transport modeling provides a framework for synthesizing diverse experimental data, evaluating the relative importance of various competing and interrelated biogeochemical processes, and identifying important data gaps and research priorities under more natural conditions at multiple temporal and spatial scales.

Seaman, J. C.↗
NASA NTRS1981

Wheat cultivation: Identifying and estimating area by means of LANDSAT data

Automatic classification of LANDSAT data supported by aerial photography for identification and estimation of wheat growing areas was evaluated. Data covering three regions in the State of Rio Grande do Sul, Brazil were analyzed. The average correct classification of IMAGE-100 data was 51.02% and 63.30%, respectively, for the periods of July and of September/October, 1979.

Dejesusparada, N.↗
DOE OSTI2014

AVTA Federal Fleet PEV Readiness Data Logging and Characterization Study for NASA Glenn Research Center

The Advanced Vehicle Testing Activity’s study seeks to collect and evaluate data to validate the utilization of advanced plug-in electric vehicle (PEV) transportation. This report focuses on the NASA Glenn Research Center (GRC) fleet to identify daily operational characteristics of select vehicles and report findings on vehicle and mission characterizations to support the successful introduction of PEVs into the agencies’ fleets. Individual observations of these selected vehicles provide the basis for recommendations related to electric vehicle adoption and whether a battery electric vehicle or plug-in hybrid electric vehicle (collectively referred to as PEVs) can fulfill the mission requirements.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1979

A program and data base for evaluating SMMR algorithms

A program (PARAM) is described which enables a user to compare the values of meteorological parameters derived from data obtained by the scanning multichannel microwave radiometer (SMMR) instrument on NIMBUS 7 with surface observations made over the ocean. The input to this program is a data base, also described, which contains the surface observations and coincident SMMR data. The evaluation of meteorological parameters using SMMR data is done by a user supplied subroutine. Instruments are given for executing the program and writing the subroutine.

Source record↗
DOE OSTI2023

Outcomes of WPEC SG47 on "Use of Shielding Integral Benchmark Archive and Database for Nuclear Data Validation"

The Working Party on International Nuclear Data Evaluation Co-operation Subgroup 47 (WPECSG47) entitled "Use of Shielding Integral Benchmark Archive and Database for Nuclear Data Validation" was organised from 2019 and 2022 with the objectives to promote more systematic and wider use of shielding benchmark experiments in nuclear data (ND) and transport code validation and development, to provide feedback on the Shielding Integral Benchmark Archive and Database (SINBAD), and to promote its further development in coordination with the Expert Group on Physics of Reactor Systems (EGPRS). Altogether 9 meetings, the large majority (8) held remotely, were organised during the past 3 years to discuss the experience on the use of SINBAD, evaluation of new benchmarks and improvements to be contributed to the database which was severely neglected and lacking maintenance over the past ← 10+ years. Several proposals for new or updated benchmark evaluation were presented and discussed, such as FNG copper, LLNL pulsed spheres, CIAE iron sphere, KFK 1977 gamma measurements, Rez Fe sphere, ASPIS, ORNL Oxygen broomstick, TIARA and others. Complementing the database with new features was also discussed, for example providing the nuclear data sensitivity profiles more systematically would facilitate and better guide the use of data. Information on the geometry, (radiation source) and materials available in CAD format is expected to allow an easier and less error prone reference for computational model preparation and a potential input to CAD based workflows. Inputs for various transport codes and other benchmark data from participants have been shared via the NEA GitLab which could hopefully in the future evolve and form a bases for critically checked and validated benchmark data. Future development of SINBAD will be monitored by EGPRS and the newly created SINBAD Task Force.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

The LLNL nuclear data infrastructure for the GNDS data format

The next generation of nuclear data infrastructure tools at the Livermore National Laboratory (LLNL) consists of pipeline of codes that read and process nuclear data from evaluated files saved in the new GNDS (Generalised Nuclear Data Structure) nuclear data format. The processing code FUDGE (For Updating Data and Generating Evaluations) is at the front-end of this pipeline as it reads and process the evaluated data for use in downstream transport codes. FUDGE is Python based with C and C++ extensions for computationally intensive tasks. As is the case for the evaluated data, the processed output is also saved in the GNDS format and the GIDI+ API is provided as the interface between the processed data and the transport codes. GIDI+ is a C++ based suite of codes and it includes GIDI (General Interaction Data Interface), a library for reading and writing GNDS data, and MCGIDI which is the cross section lookup, and reaction and product distribution sampling interface between Monte Carlo transport codes and the GNDS data. GIDI provides methods for easy access to the multi-group processed GNDS data and this is demonstrated through its implementation in ARDRA, the LLNL deterministic transport code. The evaluation and sampling methods in MCGIDI are available as both CPU and GPU methods which facilitates the use of MCGIDI in both traditional CPU-based as well as the next generation mixed model computational architectures. This is demonstrated through the GIDI+ implementation in MERCURY, the LLNL Monte Carlo transport code. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

A New Module for Large Scale Bayesian Evaluation in the Fast Neutron Energy Region

We present an improved update scheme for the Linearized Bayesian Update procedure (LBUP). The revised procedure extends the application of the LBUP to a large number of observables. The consistent update of dozens of millions of observables becomes manageable by circumventing the costly calculation of the prior covariance matrix. Nuclear data evaluations based on the revised scheme may exhaustively enclose all differential and angle-integrated channels, treating all correlations between them exactly in the update procedure.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1984

Shuttle payload dynamic environments - Update

This paper represents a brief summary of a report titled, 'Shuttle Payload Bay Dynamic Environments Summary and Conclusion Report' prepared by the Jet Propulsion Laboratory for the NASA Office of Aeronautics and Space Technology (OAST). The report provides a summary of the dynamic environmental data taken during the first five Space Shuttle flights. The present paper is concerned with a brief synopsis of the report's acoustic and high frequency data evaluation, taking into account also an example acoustic prediction method. Attention is given to an acoustic data summary, a vibration data summary, environmental uncertainties, data reduction errors, spatial bias errors, payload effects, spatial variation, flight to flight variation, payload prediction, and an example prediction.

Oconnell, M.↗
NASA NTRS1997

Tests of a Semi-Analytical Case 1 and Gelbstoff Case 2 SeaWiFS Algorithm with a Global Data Set

A semi-analytical algorithm was tested with a total of 733 points of either unpackaged or packaged-pigment data, with corresponding algorithm parameters for each data type. The 'unpackaged' type consisted of data sets that were generally consistent with the Case 1 CZCS algorithm and other well calibrated data sets. The 'packaged' type consisted of data sets apparently containing somewhat more packaged pigments, requiring modification of the absorption parameters of the model consistent with the CalCOFI study area. This resulted in two equally divided data sets. A more thorough scrutiny of these and other data sets using a semianalytical model requires improved knowledge of the phytoplankton and gelbstoff of the specific environment studied. Since the semi-analytical algorithm is dependent upon 4 spectral channels including the 412 nm channel, while most other algorithms are not, a means of testing data sets for consistency was sought. A numerical filter was developed to classify data sets into the above classes. The filter uses reflectance ratios, which can be determined from space. The sensitivity of such numerical filters to measurement resulting from atmospheric correction and sensor noise errors requires further study. The semi-analytical algorithm performed superbly on each of the data sets after classification, resulting in RMS1 errors of 0.107 and 0.121, respectively, for the unpackaged and packaged data-set classes, with little bias and slopes near 1.0. In combination, the RMS1 performance was 0.114. While these numbers appear rather sterling, one must bear in mind what mis-classification does to the results. Using an average or compromise parameterization on the modified global data set yielded an RMS1 error of 0.171, while using the unpackaged parameterization on the global evaluation data set yielded an RMS1 error of 0.284. So, without classification, the algorithm performs better globally using the average parameters than it does using the unpackaged parameters. Finally, the effects of even more extreme pigment packaging must be examined in order to improve algorithm performance at high latitudes. Note, however, that the North Sea and Mississippi River plume studies contributed data to the packaged and unpackaged classess, respectively, with little effect on algorithm performance. This suggests that gelbstoff-rich Case 2 waters do not seriously degrade performance of the semi-analytical algorithm.

Carder, Kendall L.↗
DOE OSTI2012

Nuclear data uncertainty analysis for the generation IV gas-cooled fast reactor

For the European 2400 MW Gas-cooled Fast Reactor (GoFastR), this paper summarizes a priori uncertainties, i.e. without any integral experiment assessment, of the main neutronic parameters which were obtained on the basis of the deterministic code system ERANOS (Edition 2.2-N). JEFF-3.1 cross-sections were used in conjunction with the newest ENDF/B-VII.0 based covariance library (COMMARA-2.0) resulting from a recent cooperation of the Brookhaven and Los Alamos National Laboratories within the Advanced Fuel Cycle Initiative. The basis for the analysis is the original GoFastR concept with carbide fuel pins and silicon-carbide ceramic cladding, which was developed and proposed in the first quarter of 2009 by the 'French alternative energies and Atomic Energy Commission', CEA. The main conclusions from the current study are that nuclear data uncertainties of neutronic parameters may still be too large for this Generation IV reactor, especially concerning the multiplication factor, despite the fact that the new covariance library is quite complete; These uncertainties, in relative terms, do not show the a priori expected increase with bum-up as a result of the minor actinide and fission product build-up. Indeed, they are found almost independent of the fuel depletion, since the uncertainty associated with {sup 238}U inelastic scattering results largely dominating. This finding clearly supports the activities of Subgroup 33 of the Working Party on International Nuclear Data Evaluation Cooperation (WPEC), i.e. Methods and issues for the combined use of integral experiments and covariance data, attempting to reduce the present unbiased uncertainties on nuclear data through adjustments based on available experimental data. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2010

Nuclear Forensics: A Holistic Overview

Discussions of nuclear forensics are often restricted to work performed by radio-chemists measuring nuclear material attributes in the laboratory. However, this represents only one portion of the work required to answer critical questions. Laboratory analysis results in measurements that need to be evaluated. The results of those evaluations must be put into their proper context in order for them to be useful to others and often require merging those results with additional information. This may contribute to attribution, by virtue of inclusion or exclusion. Finally, the end product must be presented such that appropriate actions can be taken. This could include prosecution by law enforcement, policy initiatives on the part of legislative bodies, or military action in the case of nuclear attack (whether that attack is preempted or not). Using the discovery of a sample of plutonium during cleanup activities at Hanford in 2004, we will step through the process of discovery (representing an interdiction), initial field analysis, laboratory analysis, data evaluation and merging with additional data (similar to law enforcement and/or all source), thereby providing an example of an integrated approach.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2012

Re-estimation of Nuclear Data and JEFF 3.1.1 Uncertainty Calculations

This paper describes the method to define relevant targeted integral measurements that allow the improvement of nuclear data evaluations and the determination of corresponding reliable covariances. {sup 235}U and {sup 56}Fe examples are pointed out for the improvement of JEFF3 data. Utilizations of these covariances are shown for Sensitivity and Representativity studies, Uncertainty calculations, and Transposition of experimental results to industrial applications. S/U studies are more and more used in Reactor Physics and Safety-Criticality. However, the reliability of study results relies strongly on the ND covariance relevancy. Our method derives the real uncertainty associated with each evaluation from calibration on targeted integral measurements. These realistic covariance matrices allow reliable JEFF3.1.1 calculation of prior uncertainty due to nuclear data, as well as uncertainty reduction based on representative integral experiments, in challenging design calculations such as GEN3 and RJH reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

Calculating uncertainty on K-effective with MONK10 - 14583

Criticality safety assessments require a demonstration that a particular configuration of fissile material has an adequate sub-critical margin (k-effective sufficiently below unity) to ensure that the risk of criticality under normal operation and accident conditions is acceptable. The required sub-critical margin depends upon the uncertainty in the estimated value of k-effective. The uncertainty in the calculated value of k-effective arises from a number of sources, including: manufacturing tolerances on input data to the code (affecting geometry, compositions and densities), uncertainty in the nuclear data used by the code, stochastic uncertainty resulting from Monte Carlo simulation and modelling approximations/errors, including the inevitable bugs in the software. The ANSWERS Software Service, in collaboration with industrial partners, is developing a number of techniques to better understand and quantify uncertainty on predicted values of k-effective, using MONK. The SPRUCE utility code has been developed to allow uncertainty to be estimated using sampling methods. This can include the sampling of input parameters (including dimensions, compositions and densities) from statistical distributions. It can also include sampling different nuclear data libraries. A set of nuclear data libraries has been generated for this purpose by sampling from statistical distributions that represent the uncertainties in the published nuclear data evaluated files; a set of libraries has been produced for Latin Hypercube Sampling. By varying the input data and nuclear data, separate and combined uncertainties due to manufacturing tolerances and nuclear data can be derived. By performing least squares fitting on the results it is also possible to estimate the contribution of each of the uncertain inputs and a sensitivity method in MONK can break down the nuclear data uncertainty. (authors)

42 ENGINEERING↗