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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Example on how to (intelligently) augment the nuclear-data pipeline with machine learning [Slides]

The presentation discusses how machine learning has helped the Los Alamos National Laboratory (LANL) nuclear-data pipeline. It also discusses the strengths of machine learning as it finds trends in large amounts of data where human brains are overwhelmed and that this information may be crucial to improve our nuclear data. It does stress, however, that machine learning is no "silver bullet" and that it is critical to feed it expert knowledge and use physics intuition to interpret the results. The presentation discusses the need to develop infrastructure and tools to provide data in an easily readable and unambiguously interpretable format (e.g., EXFOR format), to develop experimental data and theory to solve physics questions, and that statisticians and nuclear-data experts must be brought together to correctly interpret the results. The presentation concludes by stating that machine learning is a great tool and that LANL needs to use the algorithms along with developing physics data, tools and infrastructure.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

HPC for Fission Modeling in Support of Nuclear Data [Slides]

The presentation discusses the background of the High-Performance Computing (HPC) division at the Los Alamos National Laboratory (LANL), with emphasis on its computational needs and computational techniques. It also discusses the highlights of the HPC and the results of calculations, including energy sharing parameterized from DFT calculations, parameterization used in CGMF calculations, angular momentum from microscopic calculations, and correlations between FF angular momenta. The presentation concludes with an outlook including what remains to be done in the HPC division and how it will benefit nuclear data, specifically how it will provide a better understanding of the fission process.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

International Intercomparison for Nuclear Accident Dosimetry Using Godiva-IV

During the week of August 22, 2022, Integral Experiment Request (IER) 538, an international blind intercomparison for nuclear accident dosimetry (NAD) exercise, was completed using the Godiva-IV critical assembly at the National Criticality Experiments Research Center (NCERC) located in the Device Assembly Facility (DAF) at the Nevada National Security Site (NNSS). This exercise builds upon a series of experiments that include the characterization the radiation fields around Godiva (IER-147) and Flattop (IER-252) and follow up intercomparisons of dosimetry around both Godiva IV and Flattop (IER-148 and IER-253, respectively). The participants consisted of seven Department of Energy laboratories and one laboratory each from the US Navy, United Kingdom, and France. The participants of the exercise were Lawrence Livermore National Laboratory (LLNL); Los Alamos National Laboratory (LANL); Sandia National Laboratory (SNL); Savannah River Site (SRS); Hanford Site, Missions Support and Test Services (MSTS); Y-12 National Security Complex (Y-12); Naval Dosimetry Center (NDC); Atomic Weapons Establishment (AWE); and Institut de Radioprotection et de Sûreté Nucléaire (IRSN). MSTS dosimeters were included in the irradiations but not reported for evaluation. This report primarily discusses the performance of the 24 hour results submitted by participants, though available final results are briefly discussed. Information for each irradiation performed is provided for participating laboratories to produce their own final report which will be incorporated into the CED-4a report.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

EDA R-Matrix Evaluations Report on n+ 9 Be, n+ 16 O [Slides]

This presentation discusses new developments in light-element R-matrix evaluation and recent light-element work. The presentation also reviewed R-matrix theory, EDAf90 code implementation, including interface EDAf90 with NJOY, ENDFtk, EXFOR/CSISRS, and a new evaluation for n+ 9 Be. The presentation concludes by stating that further development is needed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Validation of Nuclear Data Sensitivity Calculations by the MCNP PERT Card [Poster]

This work validates a method of estimating sensitivities that utilized the MCNP PERT card and tallies. The agreement of the PERT card and Brute Force results proves the validity of calculating nuclear data sensitivities by use of the MCNP PERT card, especially at higher energies. The overall agreement will be easier to understand once an uncertainty analysis is completed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Thermal Scattering Law Evaluations and Progress at NCSU [Slides]

The presentation discusses the eight new Thermal Scattering Law (TSL) evaluations that have been submitted to the National Nuclear Data Center (NNDC). It explains that evaluations are performed using the NCSU FLASSH code and that NJOY/LEAPR input files are provided to allow for independent testing. The presentation concludes by stating that more evaluations are expected to be submitted before the end of 2020.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Application of Machine Learning Algorithms to Identify Problematic Nuclear Data

In this work we aim to show that Machine learning algorithms are promising tools for the identification of nuclear data that contribute to increased errors in transport simulations. We demonstrate this through an application of a machine learning algorithm (Random Forest) to the Whisper/MCNP6 criticality validation library to identify nuclear data that are associated with an increase of the bias (simulated - experimental $k_{eff}$) in the calculations. Specifically, the $k_{eff}$ sensitivity profiles (w.r.t. nuclear data) of 233 U solution benchmarks are used to predict the bias and Shapley Additive Explanations (SHAP) are used to explain how the sensitivities are related to the predicted bias. The SHAP values can be interpreted as sensitivity coefficients of the machine learning model to the $k_{eff}$ sensitivities which are used to make predictions of bias. Using the SHAP values we can identify specific subsets of nuclear data which have the highest probability of influencing bias. We demonstrate the utility of this method by showing how SHAP values were used to identify an inconsistency in the 19 F inelastic scattering nuclear data. The methodology presented here is not limited to transport problems and can be applied to other simulations if there are experimental measurements to compare against, simulations of those experimental measurements, and the ability to calculate sensitivities of the model output with respect to the data inputs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Flattop Benchmark CED-2 (IER-423)

The final design for the Flattop HEU Core Benchmark is presented in this report. Physical measurements will be performed on Flattop components using modern instruments and techniques to define uncertainties necessary to meet contemporary ICSBEP standards. A preliminary re-evaluation of ICSBEP HMF-0028 (Flattop HEU Core) led to the development of IER-423 CED-1, wherein total relative uncertainties in $k_{eff}$ were provided based on a new detailed model. The estimated uncertainty on the total uncertainty is +100%/-0%, given a lack of documented uncertainties on mass, dimensions, and densities of predominantly the natural uranium (NU) and highly enriched uranium (HEU) components.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Thermal Neutron Scattering Law Benchmark and Validation at NCSU [Slides]

The presentation discusses how the NCSU graphite SDT ORELA experiment is under development as a benchmark. It discusses the testing of various ENDF/B-VII.1 and ENDF/B-VIII.0 graphite libraries that have been performed using the PROTEUS benchmark. The presentation demonstrates that the NSCU Polyethylene ENDF/B-VIII.0 TSL improves agreement with total cross section measurements. The presentation concludes by stating that the NCSU single crystal sapphire TSL library is based on realistic data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Improving the probability tables of the cross section of near closed-shell nuclei [Slides]

The level density distribution of near closed-shell nuclei is much lower than the typical nucleus, therefore, the cross sections show significant fluctuations, and these fluctuations are not predictable. The current methodology used to describe such behavior and construct the probability table of the cross section is based on the extrapolation of the average resonance widths and average resonance spacings from the resonance region and use these parameters to construct the probability table. Although this is a standard and widely used technique, it does not take into account the existing experimental data, such for total and the elastic cross section. Our goal is to extend the current theory and provide a more general approach to compute the probability distribution function of the cross section combining the existing probability tables and the available experimental data. Results will be presented for 90 Zr.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2020

R.Q. Wright’s Hf updates [Slides]

This presentation details R.Q. Wright’s Hf updates. It discusses the stable Hf isotope (174, 176-180), RRR and URR + fast (n, g), and the motivation and deficiencies in the current ENDF/B-VIII.0. It also discusses what new data and/or theory will motivate a new evaluation and/or update and what validation testing has been/will be done.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2020

GNDS Expert Group Report [Slides]

The presentation discusses the GNDS-1.9 specifications that were published during the Summer 2020 and a webinar that occurred on 8 July 2020. It also discusses the goals of the GNDS-2.0, including the intention to satisfy the remaining SG-38 goals, to ensure “forwards compatibility” with ENDF-6, and to respond to user needs. It discusses the 2020 status and a poll that was implemented to find out more about how GNDS is being implemented. The presentation states that some projects are far along in their GNDS support and that most are open source with 20% closed source. It also states that GNDS's purpose is to inspire younger scientists with a histogram displaying what fraction of the developers are student, post-docs, and junior staff. The presentation concludes by discussing what is next including major format changes post-GNDS-2.0 and an ENDF to GNDS transition.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Summary of changes to the ENDF library [Slides]

This presentation discusses the major revisions that the TSL sublibrary is undergoing. It also discusses mass(ive) sublibrary changes, charged particle evaluations, INDEN evaluations, and the non-INDEN neutron sublibrary. The presentation concludes by stating that there are conflicts that need to be resolved and that they have enough contributions that the executive committee feels that an ENDF/B-VIII.1 release is warranted by February 2023.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Covariance Testing at BNL [Slides]

This presentation discusses testing implemented in FUDGE & ADVANCE in regard to proper ENDF formatting: STAN, STANEF, CHECKR, FUDGE, NJOY, convert to BOXR format, plotting, endfcovreport.py which is an overview of ENDF library’s covariance contents, and covariance Matrix class implements that are the core of all GNDS covariance. Several checks are performed, including symmetric, real, positive definite, condition # of Eigenvalues, and too big uncertainties. The presentation states that it can fix too big and/or small uncertainties and that it can plot matrices. It states that further testing is recommended by CSEWG but has not yet been implemented and suggests methods such as documentation which needs a human to execute, completeness which is partially complete, and reasonableness which has not been started. The presentation concludes by elaborating on reasonableness and states that only cross section is documented, that similar limits on CP reactions cross sections are needed, that maximum limits are needed too which is tricky since ENDF assumes Normal PDFs, and that there may be other observables.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Status of the Atlas of Neutron Resonances [Slides]

The Atlas of Neutron Resonances is the most comprehensive compilation of neutron resonances, thermal cross sections, resonance integrals and Maxwellian averaged cross sections generally available. For decades, the Atlas was carefully curated and maintained by Dr. Said Mughabghab who sadly passed on during the summer of 2018 after publishing the 2018 edition of the Atlas . We are continuing the development of this important compendium. To a large extent, the Atlas book is generated from a series of text files given in a single purpose domain-specific format. Therefore, we developed a software API and began the systematic assessment of the Atlas files. With this work past, we are now focusing on new efforts to expand the quality and scope of the Atlas . Current and recently completed projects include a cross comparison of the Atlas bibliography with Nuclear Science References and the EXFOR data library, a better determination of average resonance parameters, and using machine learning to assess the correctness of the spin group assignments of resonances tabulated in the Atlas .

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Machine Learning for Neutron Resonance Evaluations [Slides]

The performance of nuclear reactors and other nuclear systems depends on a precise understanding of the neutron interaction cross sections for materials used in these systems. These cross sections exhibit a resonance structure whose shape is determined in part by the angular momentum quantum numbers of the resonances. The correct assignment of the quantum numbers of neutron resonances is therefore of paramount importance. In this presentation, we describe the application of machine learning to automate the quantum number assignments. Scikit-learn classifiers were trained on simulated resonance data whose statistical properties were chosen to mimic real data. We explored the use of several physics (and random matrix theory)-motivated features for training the classifiers, including the nearest neighbor spacing distribution, cumulative level distribution, and channel width distributions. Initial results demonstrated that we can determine resonance spin groups somewhat reliably. We are now investigating the application of our approach to 52 Cr resonance data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Updated ENDF-6 Format Approval Process [Slides]

The presentation begins by discussing the original ENDF/B-VIII.0 format proposal lifecycle and states that the GNDS Expert Group has developed a gitlab-friendly review process that addresses concerns raised by the format proposal lifecycle. As a result, a revised ENDF/B-VIII.0 format proposal lifecycle has been created. The presentation then discusses the necessary steps in the lifecycle that are as follows: create an issue in the issue tracker, make your changes directly to the manual, submit it for review and final approval. The presentation concludes by discussing two other things, including the transition CI/CD from buildbot system to built-in gitlab CI/CD and that the revised format review process document will follow, pending approval.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗