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DOE OSTI2022

Nuclear Data Adjustment with Whisper for Criticality Safety Applications [Slides]

Nuclear data adjustment is used by Whisper to compute residual nuclear data uncertainties within a full suite of benchmarks; in order to do so, a complete set of nuclear data covariances, benchmarks and k-effective sensitivity profiles (for both benchmarks and applications) is required. For applications that are similar to the full benchmark suite, the residual uncertainties add a small margin to the overall USL calculation. For applications that are dissimilar to the full benchmark suite, the residual uncertainties add a reasonable margin to the overall USL calculation (more conservative). This methodology helps to overcome any issues in the released covariance libraries where nuclear data uncertainties appear to be too large.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Experimental Unevaluated Nuclear Data List (XUNDL)

Contains nuclear structure and decay data from published articles. Data include nuclear level properties, such as energy, half-life, decay modes, spin and parity, as well as energy and intensity for radiation types such as gammas, alphas, betas and neutrons. For articles published in Physical Review C and the European Physical Journal, compilation takes place during the article submission process. Data from approximately 250 articles are added per year. All the compilation effort is supported by the US Nuclear Data Program and coordinated by the National Nuclear Data Center at Brookhaven National Laboratory, which is also responsible for its web dissemination.

Basic Nuclear Research, nuclear structure and deca↗
DOE OSTI2022

United States Nuclear Data Program Annual Report for FY2021

The US Nuclear Data Program (USNDP) Annual Report for Fiscal Year 2021 (FY21) summarizes the work of USNDP for the period of October 1, 2020 through September 30, 2021, with respect to the Work Plan for FY21 that was prepared in 2019. The Work Plan and Final Report for USNDP are prepared for the DOE Office of Science, Office of Nuclear Physics. The support for the nuclear data activity from sources outside the nuclear data program is described in the staffing table and in Appendix A. This leverage amounts to about 3.24 FTE scientific, to be compared with 25.095 FTEs at USNDP units funded by the DOE Office of Science, Office of Nuclear Physics. Since it is often difficult to separate accomplishments funded by various sources, some of the work reported in the present report was accomplished with nuclear data program support leveraged by other funding.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Selection of a Pair of Experiments to Optimally Reduce Uncertainty in Targeted Nuclear Data

We propose a novel process to select a pair of differential and integral experiments that best reduce uncertainties in targeted 239 ⁢Pu nuclear data while compressing the current nuclear data pipeline from 20 to 3 years. 239⁢ Pu nuclear data are poorly understood for neutrons in the intermediate energy range due to sparsity and uncertainty in historical experiments. New experiments targeting this range will enable better understanding of these nuclear data, but choosing the ideal experiments to conduct is challenging. Beginning with a prior distribution represented by samples of nuclear data generated from theory, generalized least squares adjustments are made to incorporate data from historical experiments. To quantify potential uncertainty reduction obtainable from a pair of candidate experiments, we compute the D-optimality criterion of the posterior covariance of intermediate energy range nuclear data compared to the equivalent covariance after additional adjustment to the pair of candidate experiments. Repeating the process for each of many candidate pairs facilitates the final selection. Results support 63⁢ Cu total cross section measurements for differential experiments and alumina and alumina/graphite configurations for integral experiments. This analysis enables choosing differential and integral experiments to be executed concurrently while shortening decision times relative to the current nuclear data pipeline.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Enhancing nuclear data validation analysis by using machine learning

In this work, we demonstrate how machine learning (ML) techniques can be used as an auxiliary tool for nuclear data validation analysis. The output of the ML analysis can inform evaluators and validators on the quality, or lack thereof, of specific nuclear data and benchmark experiments with respect to simulating these experimental benchmark values. To this end, measured and simulated effective neutron multiplication factors, k eff , of 875 selected ICSBEP critical assemblies and the latter's sensitivities with respect to nuclear data as well as benchmarks' features (e.g., material nuclide, core geometry) are used as input for a random forest (RF) regression model. The RF is used to encode the complex inter-dependencies between thousands of nuclear data features (i.e., sensitivity profiles and aspects describing the measurements) and 875 simulated and experimental k eff values in order to understand what nuclear data features are most informative for predicting bias. The complexity of relationships and high-dimensional space is difficult-to-impossible to search fully using simply expert judgment. As a first proof-of-concept—step, it is demonstrated that this technique is able to correctly trace large discrepancies between simulated and experimental k eff back to fabricated shortcomings in nuclear data that were perturbed to simulated k eff values. In a second, real-case scenario, step, the RF algorithm is used to validate the ENDF/B-VIII.0 library in comparison to ENDF/B-VII.1 nuclear data. One case is showcased where the chosen ML algorithms highlighted nuclear data (the 19 F(n,inl) cross section from 0.4–0.9 MeV) that are shown to be problematic by comparing them to associated differential experimental data and nuclear data from other libraries. In addition to that, it is shown that the RF results point towards poor benchmark experiments and associated underestimated uncertainties (e.g., the PU-SOL-THERM-028 series). However, using the RF algorithm for validating nuclear data with respect to k eff is currently limited to pinpointing groups of questionable nuclear data due to the inherent correlations between features introduced by the nuclear data themselves and how k eff is simulated. Due to this, we recommend that the ML methods presented be used to augment—rather than replace—the expert knowledge of evaluators and validators.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

Nuclear Data: From Measurement, Evaluation, Processing to Practical Applications [Slides]

Any analysis, design of a nuclear system will be as good as the knowledge of the nuclear data used whether differential and integral. ORNL is engaged in generating high level nuclear data for reactor, shielding, and safety applications. Nuclear data remains a major source of data uncertainties. Uncertainties on nuclear data rely upon measurements and evaluations. Tight connections with experimental facilities around the world have helped ORNL to address issues on ND and uncertainty. It is extremely important for ORNL to retain expertise on nuclear data in support of the research and expertise activities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Nuclear Data Uncertainty Quantification for Adjoint-Weighted Quantities via XGPT in Monte Carlo Codes

The Total Monte Carlo method (TMC) is an innovative approach to nuclear data uncertainty propagation. In TMC, a large number of independent ENDF files are randomly generated starting from nuclear models parameters and their uncertainties. These files are then processed with NJOY or similar codes to produce a set of formatted continuous-energy cross sections (i.e., ACE files), and independent Monte Carlo neutron transport simulations are run with these different ACE files as input. Finally the distributions of the considered response functions are obtained directly from the results of the Monte Carlo runs. Despite several improvements of the TMC method, this approach is often considered an inefficient way of propagating nuclear data uncertainties, as it requires several computationally expensive Monte Carlo runs. Covariance-based methods present important advantages for several applications, especially when different sources of uncertainties are to be investigated. For this reason, sensitivity analysis and uncertainty quantification capabilities are being implemented in several Monte Carlo neutron transport codes. On the other hand, the fact that no artificial multi-group covariance matrices are required and that higher moments of the responses distributions can be easily obtained, are definite advantages of the TMC method. In an effort to overcome the limitations of both approaches, a new method, called eXtended Generalized Perturbation Theory (XGPT) is under development. When performing nuclear data uncertainty propagation, such XGPT approach does not require generating discretized (i.e., multigroup) covariance matrices, and allows to estimate higher moments of the response probability distributions, via linear perturbation theory. One of the promising applications of the XGPT method involves its adoption in conjunction with the Proper Orthogonal Decomposition (POD) of nuclear data, to build a Reduced Order Model (ROM) for the approximation of thousands of independent TMC results in a single criticality source simulation. In the present work, this approach is employed for the first time to evaluate the impact of uncertainty in nuclear data on adjoint-weighted quantities. The method has been implemented in an extended version of the Serpent-2.1.24 Monte Carlo code and is demonstrated on two fast spectrum benchmarks, focusing on {sup 208}Pb nuclear data. A new method for propagating nuclear data uncertainties in criticality source Monte Carlo simulations has been developed. This eXtended Generalized Perturbation Theory (XGPT) approach allows to accurately reproduce reference Total Monte Carlo (TMC) uncertainty estimates in a single criticality source simulation. The present work aims to demonstrate that the proposed method can quantify nuclear data uncertainty based on adjoint-weighted quantities. The XGPT approach was applied to evaluate the impact of {sup 208}Pb nuclear data uncertainties on the effective prompt neutron lifetime (l{sub eff}) in two fast spectrum systems. The obtained XGPT results show a good agreement with the reference TMC results, obtained from independent Monte Carlo runs with {sup 208}Pb nuclear data from random TENDL-2013 evaluations. The XGPT method, implemented in a extended version of the Serpent Monte Carlo code and adopted along with the Proper Orthogonal Decomposition (POD) of the nuclear data, is able to provide an accurate reduced order model to approximate thousands TMC results within a single run, ensuring a large saving in computational requirements. The effect of {sup 208}Pb nuclear data uncertainties appears to be rather large. This is consistent with previous studies that adopted the same input random evaluations data from TENDL. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

United States Nuclear Data Program Annual Report for FY 23

The US Nuclear Data Program (USNDP) Annual Report for Fiscal Year 2023 (FY23) summarizes the work of USNDP for the period of October 1, 2022 through September 30, 2023, with respect to the Work Plan for FY23 that was prepared in 2022. The Work Plan and Final Report for USNDP are prepared for the DOE Office of Science, Office of Nuclear Physics. The support for the nuclear data activity from sources outside the nuclear data program is described in the staffing table and in Appendix A. This leverage amounts to about 17.4 FTE scientific, to be compared with 23.8 FTEs at USNDP units funded by the DOE Office of Science, Office of Nuclear Physics. Since it is often difficult to separate accomplishments funded by various sources, some of the work reported in the present report was accomplished with nuclear data program support leveraged by other funding.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

Nuclear Data Needs and Capabilities for Applications

In July 2014, DOE NP carried out a review of the US Nuclear Data Program. This led to several recommendations, including that the USNDP should “devise effective and transparent mechanisms to solicit input and feedback from all stakeholders on nuclear data needs and priorities.” The review also recommended that USNDP pursue experimental activities of relevance to nuclear data; the revised 2014 Mission Statement accordingly states that the USNDP uses “targeted experimental studies” to address gaps in nuclear data. In support of these recommendations, DOE NP requested that USNDP personnel organize a Workshop on Nuclear Data Needs and Capabilities for Applications (NDNCA). This Workshop was held at Lawrence Berkeley National Laboratory (LBNL) on 27-29 May 2015. The goal of the NDNCA Workshop was to compile nuclear data needs across a wide spectrum of applied nuclear science, and to provide a summary of associated capabilities (accelerators, reactors, spectrometers, etc.) available for the required measurements. The first two days of the workshop consisted of 25 plenary talks by speakers from 16 different institutions, on nuclear energy (NE), national security (NS), isotope production (IP), and industrial applications (IA). There were also shorter “capabilities” talks that described the experimental facilities and instrumentation available for the measurement of nuclear data. This was followed by a third day of topic-specific “breakout” sessions and a final closeout session. The agenda and copies of these talks are available online at http://bang.berkeley.edu/events/NDNCA/agenda. The importance of nuclear data to both basic and applied nuclear science was reflected in the fact that while the impetus for the workshop arose from the 2014 USNDP review, joint sponsorship for the workshop was provided by the Nuclear Science and Security Consortium, a UC-Berkeley based organization funded by the National Nuclear Security Administration (NNSA).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

ENDF-6 Formats Manual: Data Formats and Procedures for the Evaluated Nuclear Data Files

In December 2006 and again in December 2011, the Cross Section EvaluationWorking Group (CSEWG) of the United States released new versions of the ENDF/B-VII library. Each major release with minor revision number “0” coincides with the release of a new Neutron Standards Library. In December 2017, CSEWG released a new major version of ENDF/B, ENDF/B-VIII.0, again coinciding with the release of the Neutron Stanards Library. This ENDF release also contains the results of the Collaborative International Evaluation Library Organization (CIELO) Pilot Project, namely standards level full evaluations of 1 H, 16 O, 56 Fe, 235 U, 238 U and 239 Pu. It is our expectation that this major revision will be embraced by the data evaluators as well as members of numerous nuclear data user communities worldwide.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

2011.5 Revision of the Evaluated Nuclear Data Library: ENDL2011.5 (legacy and GNDS), and ENDL2011.5-direct

LLNL’s Nuclear Data and Theory Group has created the 2011.5 revised release of the Evaluated Nuclear Data Library (ENDL2011.5). ENDL2011.5 is designed to support LLNL’s current and future nuclear data needs and will be employed in nuclear reactor, nuclear security and stockpile stewardship simulations with ASC codes. This database is currently the most complete nuclear database for Monte Carlo and deterministic transport of neutrons and charged particles. This library was assembled with strong support from the ASC PEM and Attribution programs, leveraged with support from Campaign 4 and the DOE/Office of Science’s US Nuclear Data Program. This document lists the revisions made in ENDL2011.5 compared with the data existing in the original ENDL2011.2 and ENDL2011.3 releases. These changes are made in parallel with some similar revisions for ENDL2009.5. We also describe ENDL2011.5-direct that directly uses ENDF sources wherever possible.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Nuclear Data to Reduce Uncertainties in Reactor Antineutrino Measurements

The large quantities of antineutrinos produced through the decay of fission fragments in nuclear reactors provide an opportunity to study the properties of these particles and investigate their use in reactor monitoring. The reactor antineutrino spectra are measured using specialized, large area detectors that detect antineutrinos through inverse beta decay, electron elastic scattering, or coherent elastic neutrino nucleus scattering; although, inverse beta decay is the only demonstrated method so far. Reactor monitoring takes advantage of the differences in the antineutrino yield and spectra resulting from uranium and plutonium fission providing an opportunity to estimate the fissile material composition in the reactor. Recent experiments reveal a deviation between the measured and calculated antineutrino flux and spectra (the reactor anomaly) indicating either the existence of yet undiscovered neutrino physics, uncertainties in the reactor source term calculation, incorrect nuclear data, or a combination of all three. To address the nuclear data that impact the antineutrino spectrum calculations and measurements, an international group of over 180 experts in antineutrino physics, reactor analysis, detector development, and nuclear data came together during the Workshop on Nuclear Data for Reactor Antineutrino Measurements (WoNDRAM) to discuss nuclear data needs and achieve concordance on a set of recommended priorities for nuclear data improvements. Three topical sessions focused on the reactor source term, the antineutrino spectrum, and the detector response, provided a forum to gain consensus amongst the participants on the most important data improvements to address two goals: 1) understand the reactor anomaly and 2) improve the ability to monitor reactors using antineutrinos. This report summarizes the outcomes of the workshop discussions and the recommendations for nuclear data efforts that reduce reactor antineutrino measurement uncertainties.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Using Whisper-1.1 to Guide Improvements to Nuclear Data Evaluations

Along with the latest release of the general purpose Monte Carlo radiation transport code, MCNP6.2, came the first officially released version of Whisper, version 1.1, a statistical analysis code used for nuclear criticality safety (NCS) validation. The Whisper software package is primarily used to calculate the upper subcritical limit (USL) of a particular application case along with components of the margin of subcriticality (MOS). In the calculation of the MOS due to nuclear data uncertainties, Whisper uses the generalized linear-least-squares (GLLS) approach to obtain adjusted nuclear data uncertainties which are then propagated to estimate the nuclear data uncertainties in the k eff of the application. Due to the fact that Whisper contains the simulated and measured k eff values for over 1100 benchmarks, nuclear data sensitivity profiles for each benchmark along with the nuclear data covariance matrices, all of which are used in the GLLS method, it is possible to easily utilize these data available to help guide improvements to nuclear data evaluations. In the present work, the Whisper data and the GLLS method are used together to suggest an adjusted nuclear data cross section evaluation specifically for 233 U such that the agreement between the calculated and experimental k eff measurements of the relevant benchmarks are improved. With this adjusted nuclear data available, feedback to the theoretical and experimental nuclear data community is provided.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Using Whisper-1.1 to guide improvements to nuclear data evaluations

Along with the latest release of the general purpose Monte Carlo radiation transport code, MCNP6.2, came the first officially released version of Whisper, version 1.1, a statistical analysis code used for nuclear criticality safety (NCS) validation. The Whisper software package is primarily used to calculate the upper subcritical limit (USL) of a particular application case along with components of the margin of subcriticality (MOS). In the calculation of the MOS due to nuclear data uncertainties, Whisper uses the generalized linear-least-squares (GLLS) approach to obtain adjusted nuclear data uncertainties which are then propagated to estimate the nuclear data uncertainties in the k{sub eff} of the application. Due to the fact that Whisper contains the simulated and measured k{sub eff} values for over 1100 benchmarks, nuclear data sensitivity profiles for each benchmark along with the nuclear data covariance matrices, all of which are used in the GLLS method, it is possible to easily utilize these data available to help guide improvements to nuclear data evaluations. In the present work, the Whisper data and the GLLS method are used together to suggest an adjusted nuclear data cross section evaluation specifically for {sup 233}U such that the agreement between the calculated and experimental k{sub eff} measurements of the relevant benchmarks are improved. With this adjusted nuclear data available, feedback to the theoretical and experimental nuclear data community is provided. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

United States Nuclear Data Program Annual Report for Fiscal Year 2024

The US Nuclear Data Program (USNDP) Annual Report for Fiscal Year 2024 (FY24) summarizes the work of USNDP for the period of October 1, 2023 through September 30, 2024, with respect to the Work Plan for FY24 that was prepared in 2022. The Work Plan and Final Report for USNDP are prepared for the DOE Office of Science, Office of Nuclear Physics. The support for the nuclear data activity from sources outside the nuclear data program is described in the staffing table and in Appendix A. This leverage amounts to about 14.8 FTE scientific, to be compared with 13.9 FTEs at USNDP units funded by the DOE Office of Science, Office of Nuclear Physics. Since it is often difficult to separate accomplishments funded by various sources, some of the work reported in the present report was accomplished with nuclear data program support leveraged by other funding.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

United States Nuclear Data Program Annual Report for Fiscal Year 2025

The US Nuclear Data Program (USNDP) Annual Report for Fiscal Year 2025 summarizes the work of USNDP for the period of October 1, 2024 through September 30, 2025, with respect to the Work Plan for FY 2025 that was prepared in 2024. The Work Plan and Final Report for USNDP are prepared for the DOE Office of Science, Office of Nuclear Physics. The support for the nuclear data activity from sources outside the US Nuclear Data Program is summarized in the staffing table and Appendix A. This leverage amounts to about 22.8 FTE scientific, to be compared with 21.9 FTEs at USNDP units funded by the DOE Office of Science, Office of Nuclear Physics. Since it is often difficult to separate accomplishments funded by various sources, some of the work reported in the present report was accomplished with nuclear data program support leveraged by other funding. FY 2025 was the 25th year in which the USNDP has operated under a Work Plan developed by the program participants. The program continued to carry out important work in support of the DOE mission. The work balances the ongoing collecting, analyzing, and archiving of nuclear physics information critical to basic nuclear research and to the development and improvement of nuclear technologies with the electronic distribution of this information to users in a timely and easily accessible manner. The present section of the report consists of activity summaries for the major components of the USNDP. This is followed by an updated staff level assignment table that reflects the final distribution of effort among the tasks carried out during FY 2025. Then, we continue with the detailed status of work performed during FY 2025.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Nuclear Data Adjustment for Nonlinear Applications in the OECD/NEA WPNCS SG14 Benchmark—A Bayesian Inverse UQ-Based Approach for Data Assimilation

The Organisation for Economic Co-operation and Development Working Party on Nuclear Criticality Safety has proposed a benchmark exercise to assess the performance of current nuclear data adjustment techniques applied to nonlinear applications and experiments with low correlation to applications. This work introduces Bayesian inverse uncertainty quantification (IUQ) employing scientific machine learning surrogate models as a method for nuclear data adjustments in this benchmark, and compares IUQ to the more traditional methods of generalized linear least squares (GLLS) and Monte Carlo Bayes (MOCABA). Posterior predictions from IUQ showed agreement with GLLS and MOCABA for linear applications. Here, when comparing GLLS, MOCABA, and IUQ posterior predictions to computed model responses using adjusted parameters, we observe that the GLLS predictions failed to replicate the computed response distributions for nonlinear applications, while MOCABA showed near agreement, and IUQ used the computed model responses directly. We also discuss observations on why experiments with low correlation to applications can be informative to nuclear data adjustments and identify some properties useful in selecting experiments for inclusion in nuclear data adjustment. Performance in this benchmark indicates potential for Bayesian IUQ in nuclear data adjustments.

Bayesian calibration↗
DOE OSTI2024

The MUSIC Critical Benchmark and Nuclear Data

The Measurement of Uranium Subcritical and Critical (MUSIC) experiment was a series of measurements of critical and subcritical configurations of bare highly enriched uranium. The goal was to compare measurement methods, analysis techniques, and simulation methods across regimes of criticality and to provide high-quality validation of 235 U nuclear data. A benchmark evaluation of the two critical configurations of the MUSIC experiments will soon be published in the release of the International Criticality Safety Benchmark Evaluation Project Handbook. The recent execution of the experiment aids in proper quantification of model simplifications and all uncertainties associated with the experiment. Historical benchmark evaluations are heavily relied on for uranium nuclear data validation despite the fact that the same level of documentation and comparable uncertainty analysis may not be present. The MUSIC evaluation is less likely to include “unknown unknowns” that could impede accurately modeling the system. Presented are both highly detailed and very simplified models, which represent the experimental configurations accurately, aiding the users of the benchmark for nuclear data or transport code validation. The sensitivities of k eff to nuclear data and nuclear data–related uncertainties are very similar between this experiment and previous bare uranium sphere experiments. In addition, the nuclear data uncertainties to any nuclides other than 235 U are small. For all these reasons, the recently evaluated MUSIC benchmark critical configurations could prove very useful for 235 U nuclear data validation. Currently, major libraries have good agreement with the experimental results, within 200 pcm for all nuclear data libraries, and within one standard deviation of the experimental result for most. Suggested nuclear data adjustments based on MUSIC and Lady Godiva are also presented, with posterior improvements to both the agreement in k eff and the uncertainty associated with the nuclear data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗