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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

NJOY21: A Successor to the NJOY Nuclear Data Processing System

NJOY21 is an initiative at Los Alamos National Laboratory (LANL) to develop software to succeed the NJOY nuclear data processing system. Specifically, the aim of the project is to provide: - a stand-alone executable for nuclear data processing in the vein of the original NJOY and, - a hierarchy of reusable loosely-coupled, object-oriented component libraries. The name NJOY21 was chosen to denote a commitment to provide software suitable for the 21. Century; integrating modern development practices and tools from the inception of the project. This summary will expand upon the current goals and design of the project. Before going onto discuss NJOY21, it is worthwhile to briefly establish the historical context and motivating factors. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2014

The Activities of the European Consortium on Nuclear Data Development and Analysis for Fusion

This paper presents an overview of the activities of the European Consortium on Nuclear Data Development and Analysis for Fusion. The Consortium combines available European expertise to provide services for the generation, maintenance, and validation of nuclear data evaluations and data files relevant for ITER, IFMIF and DEMO, as well as codes and software tools required for related nuclear calculations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

The Role of Nuclear Data Sensitivities in Prompt α-Eigenvalue Predictions of Delayed Critical Benchmarks

Alpha (α) eigenvalues, which describe the logarithmic time derivative of the neutron population in a multiplying system, are integral to time-dependent behavior and diagnostic applications. However, uncertainties in the evaluated nuclear data can significantly impact the accuracy of transport simulations for such quantities. This work explores the use of machine learning models to predict two key outputs, α-eigenvalues and keff bias, using input features derived from α-eigenvalue sensitivities to nuclear data. The criticality safety benchmark models used in this study come from the International Handbook of Evaluated Criticality Safety Benchmark Experiments. Three models, random forest, XGBoost, and NGBoost, are trained on both energy-resolved and energy-summed α sensitivities. For the α-eigenvalue bias prediction, NGBoost achieved the highest R 2 (0.9476) using energy-resolved features, while XGBoost performed best using summed sensitivities. In contrast, when predicting the keff bias, all the models showed moderate predictive capability (best R 2 ≈ 0.72), as the mapping from the static α-sensitivities to the static keff bias was less direct. SHAP (SHapley Additive exPlanations) analysis was used to interpret the model predictions. Across both prediction tasks, the features associated with neutron capture [H-1 (n, γ)], uranium scattering reactions (such as 235 U elastic/inelastic), and actinide capture/fission reactions (such as 239 Pu and 234 U) were consistently identified as the most impactful. This highlights the key role of specific nuclear reactions and energy ranges in shaping both time-dependent and steady-state criticality behavior. These results demonstrated that α-sensitivities, despite being computed for time-dependent metrics, can provide valuable insights for predicting both α-eigenvalues and the keff bias. Moreover, machine learning models offer a promising pathway for uncovering important nuclear data dependencies and guiding future data evaluation efforts.

Nuclear data↗
DOE OSTI2023

Nuclear Data for Spallation Neutron Radioisotope Production (Final Technical Report)

Over 50 million nuclear medicine procedures are performed annually, leading to a multi-billion dollar market for radioisotope production. The demand for new medical and research isotopes is growing, and radioisotope supply is insufficient. Most radioisotope production today utilizes charged particle or low-energy neutron irradiation of a target. Isotope production with tens to hundred MeV incident energies is a relatively unexplored option. There is a tremendous opportunity associated with a growing number of suitable domestic and international facilities buttressed by hundred million dollar global investments (e.g., the Los Alamos and Brookhaven Isotope Production Facilities, the European Spallation Source in Lund, and the Korean Multi-purpose Accelerator Complex in Gyeongbuk). In part due to a lack of supporting nuclear data that would make modeling radioisotope yields and purities possible, these facilities do not utilize their high-energy neutron fluxes for isotope production. This project attempted to measure neutron reaction excitation functions relevant to the large-scale production of critical radioisotopes, enabling development of cost-efficient isotope production methods, contributing to the improvement of theoretical models, and enhancing the value of national isotope production facilities. Reactions which form 67 Cu, 32 Si, and alpha-emitting isotopes like 225 Ac were chosen for their consistent prioritization by expert panels, representation of diverse reaction mechanisms, and relative lack of supporting nuclear data. Accurate measurement of these data is presently made only using quasi-monoenergetic neutron beams, which are produced by bombarding thin lithium targets with protons at only a few laboratories in the world. Ultimately, the projects efforts were incompletely accomplished due to a combination of multi-year technical issues at the only laboratory in the world which can make the proposed measurements, iThemba Labs in South Africa, and the onset of a global pandemic, which precluded the significant effort and time required to develop an independent measurement capability in the United States.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

Nuclear data uncertainty propagation applied to the versatile test reactor conceptual design

We report the Versatile Test Reactor (VTR) currently under development is a 300 MWth sodium-cooled fast reactor (SFR) fueled with ternary metal alloy fuel, which aims to accelerate the testing of advanced nuclear fuels, materials, instrumentation, and sensors in high flux environments that are necessary to license the next generation of advanced reactor concepts. To support the VTR design process, uncertainties associated with the nuclear data has been propagated through the reactor core neutronics calculation to global parameters of interest, such as the core multiplication factor, kinetic parameters, and various reactivity feedback coefficients, following the sensitivity based uncertainty propagation approach. By folding the sensitivity coefficients, separately computed by the generalized perturbation theory code PERSENT and Monte Carlo code Serpent 2, with the variance-covariance matrices from COMMARA-2.0, we obtain the reaction-wise, isotope-wise, and overall uncertainties for each response of interest due to nuclear data uncertainty. With Serpent 2, the statistical error of the uncertainty is obtained by propagating the statistical error of the sensitivity coefficients through the same process using a newly developed uncertainty propagation method. From both codes, the overall top uncertainty contributors are found to be the cross section of Fe-56 elastic scattering, Na-23 elastic scattering, and U 238 inelastic scattering. The large contributions of the Fe-56 elastic scattering cross sections to global parameters are due to its relatively large relative uncertainty of 5–10% in nuclear data and the large volume of Fe-containing reflector assemblies in the fairly compact VTR core design. Both codes agreed well for the overall uncertainty estimates of all responses of interest, except the delayed neutron fraction, prompt neutron generation time, and the coolant density feedback coefficient, where Serpent 2 yielded a much larger value than PERSENT due to the large statistical error of sensitivity coefficients. The calculated uncertainties are also compared to those associated with other SFR cores. Another outcome of this study is a variance-covariance matrix of reactivity coefficients, which can be used in the subsequent uncertainty propagation to the system level to investigate the impact of identified uncertainties on system responses in the safety analysis.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Nuclear Data Impact on Key Metrics for a Representative Molten Chloride Fast Reactor Model

Nuclear data are an essential component of the foundation on which all modeling and simulation methods and tools are relying upon, from the front end to the back end of the nuclear fuel cycle. In this study, the impact of uncertainties in nuclear data is investigated for a representative molten chloride fast reactor, for several important metrics, including eigenvalue, reactivity differences, and nuclide inventories in fuel at 5-yr irradiation. Uncertainty of keff for a full core model was found to be similar between the fresh fuel and the irradiated fuel states (1.7-1.8%), with its primary driver being the uncertainty in the 235U (n,γ) cross section. The results obtained for the reactivity differences show large uncertainties, of over 100%, in elastic scattering sensitivities of several nuclides, which led to large uncertainties of temperature reactivity differences for cladding and reflector. These results provide evidence that the currently applied methods may not be sufficiently adequate for ensuring the reliable determination of such metrics.

Procop, Germina [ORNL] (ORCID:0000000342226393)↗
DOE OSTI2024

AIACHNE's contribution for Nuclear Energy Agency Working Party on International Nuclear Data Evaluation Co-operation Subgroup 50

The AIACHNE (AI/ML Informed cAlifornium CHi Nuclear data Experiment) project aims at designing an experiment for the 252 Cf Prompt Fission Neutron Spectrum (PFNS) that explores systematic biases in an experimental database retrieved from the EXFOR databases. To that end, machine learning (ML) methods were applied to pint-point measurement features likely related to bia. From that information, we selected a feature that should be explored by the AIACHNE experiment. Measurement features are metadata encapsulating all pertinent information about the physical measurement and analysis techniques. Examples are, for instance, what neutron and fission detectors were used for the physical metadata, and what background reduction techniques were employed for analysis techniques. Such metadata were retrieved both from EXFOR entries as well as the literature of data sets described in detail in Ref. [2]. The prerequisite for applying machine learning techniques is casting the metadata into a format that can be parsed by the algorithm. This step might seem trivial but requires to find a unique language where metadata that carry the same physics meaning across several experiments must have the same identifier. One example is, for instance, the neutron detector. As seen in Figure 1, the machine learning code identified the use of 6 Li detectors as being related to bias in some datasets of the AIACHNE 252 Cf PFNS experimental database. In fact, here are several experiments that used neutron detectors containing 6Li in the database, for instance for the example below. EXFOR format has a unique keywords describing detectors such as “SCIN” or “GLASD”. One may think that these keywords are already sufficient descriptors for ML to uniquely find an issue. However, “SCIN” (used for [3, 4]) and “GLASD” (used for [5]) fail to inform the algorithm what is the active material in the detector. And, the key common issue leading to bias in 252 Cf related to neutron detectors is not whether it is a glass detector or a scintillator. No, the issue is that 6 Li was within both detector types and that even small mistakes in the detector response functions around approximately 200 keV are amplified by the 6 Li(n,α) resonance there leading to bias in data as highlighted in Fig. 1 and Ref. [1]. Hence, the features describing the neutron detector must call out the active material in the detector, rather than the existing EXFOR detector keyword, that the ML algorithm can find physically meaningful features related to bias. The AIACHNE team used a precursor of the WPEC (Working Party on International Nuclear Data Evaluation Co-operation) SG(Subgroup)-50 format to store the metadata for the ML analysis.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Nuclear Data Sheets for A = 146

Evaluated nuclear structure and decay data for all nuclei within the A = 146 mass chain are presented. The experimental data are evaluated and best values for level and gamma-ray energies, quantum numbers, lifetimes, gamma-ray intensities, and other nuclear properties are recommended. Inconsistencies and discrepancies that exist in the literature are noted. This work supersedes the earlier evaluation by L.K. Peker, J.K. Tuli (1997Pe22), published in Nuclear Data Sheets82, 187 (1997).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Bayesian Monte Carlo Evaluation Framework for Cross Sections Nuclear Data and Integral Benchmark Experiments

The new Bayesian Monte Carlo (MC) evaluation framework described in this abstract has been conceived as an attempt to improve nuclear data evaluations of differential crosssection data by removing the following two approximations conventionally employed for nuclear data evaluations: all probability density functions (PDFs) of all data and model parameters, both prior and posterior, are assumed to be normal (i.e., Gaussian) PDFs, and all uncertainties and covariances are propagated using a linear approximation. With these approximations removed, the Bayesian MC (BMC) framework could be used to account for nonlinear effects and would enable improved evaluations of differential cross sections and IBE data that are presently performed based on the assumptions itemized above. The BMC would also improve upon the uniform sampling of IBE parameters from within ranges defined by their evaluated uncertainties.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Improvements of Nuclear Data Evaluations for Lead Isotopes in Support of Next Generation Lead-Cooled Fast Systems

The neutron evaluation of the isotopes that comprise natural lead were undertaken as a part of DOE-NEUP Project #19-16739. The goal of the project was to update the neutron cross sections to account for new differential measurements and incorporate the most up-to-date physics. Shortcomings in the lead cross sections was made known by several independent reports. The work performed here repeated the simulation of all the “benchmark” validation systems and concluded that the major issue is the scattering cross sections in the major lead isotopes above 100 keV. Re-evaluation of 206,207,208 Pb included both the resolved resonance region and fast region evaluations of the cross sections. Combined these regions cover energies from thermal to 20 MeV. The most drastic improvement is the resolved resonance region evaluation of 208 Pb which is now extended from 1.0 to 1.5 MeV. Parameterization of these resonances in the R-matrix provides a superior reconstruction of not only the experimental cross section but also the scattering distributions via the Blatt-Biedenharn formalism. Fast region evaluations of the three major isotopes were done to include new inelastic experimental data from the neutron Time-of-Flight facility at CERN. The culmination of all the changes to the cross section is a drastic improvement in the scattering kernel as shown in Rensselaer Polytechnic Institute (RPI) Quasi-Differential scattering measurements and improved prediction of keff for fast integral experiments. Alongside the new cross sections, new nuclear data covariance (uncertainties) have been computed and are included in the evaluation. Little is changed in the magnitude of the uncertainties but the correlations within the covariance display non-trivial changes. The new evaluations of 206,207,208 Pb have been submitted to the National Nuclear Data Center to be included in the ENDF/B-VIII.1 library. While the cross sections have been updated extensively, knowledge and modeling of the cross sections between 1.0 and 3.0 MeV for the isotopes remain a challenge. Most notably is the double differential elastic cross section for 208 Pb and 206 Pb. New quasi-differential measurements for pure samples of these two nuclei would go a long way in reducing compensating errors in the evaluations. Qualitatively, the project has produced a prosperous collaboration between the nuclear data group at RPI with staff scientists at Brookhaven National Laboratory, Naval Nuclear Laboratory, Oak Ridge National Laboratory, Los Alamos National Laboratory, Lawrence Livermore National Laboratory, and Sandia National Laboratories. Quantitatively this is reflected in seven conference presentations, at least one journal submission, and one doctoral thesis.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

ANDES (ARGONNE NUCLEAR DATA EXPLORATION SOFTWARE) - Physics Tools

A collection of physics tools used by the ANDES. The Argonne Nuclear Data Exploration Software (ANDES) is a collection of open-source C++ codes and ROOT scripts that enable the visualization of patterns emerging from nuclear data. From this global view, well-known phenomena such as major nuclear shell closures and pairing effects near N=Z nuclei, as well as more subtle features, such as quantum phase transition around the Zr isotopes or the charge radius staggering in the Hg isotopes, can be observed. This visualization tool apart from having pedagogic application can serve as a tool for discovering previously unknown phenomena in the nuclear chart.

Santiago-Gonzalez, Daniel↗
DOE OSTI2015

A nuclear data approach for the Hubble constant measurements

An extraordinary number of Hubble constant measurements challenges physicists with selection of the best numerical value. The standard U.S. Nuclear Data Program (USNDP) codes and procedures have been applied to resolve this issue. The nuclear data approach has produced the most probable or recommended Hubble constant value of 67.00(770) (km/sec)/Mpc. This recommended value is based on the last 25 years of experimental research and includes contributions from different types of measurements. The present result implies (14.6±1.7) x 10 9 years as a rough estimate for the age of the Universe. The complete list of recommended results is given and possible implications are discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

“Full Model” Nuclear Data and Covariance Evaluation Process Using TALYS, Total Monte Carlo and Backward-forward Monte Carlo

The “Full model” evaluation process, that is used in CEA DAM DIF to evaluate nuclear data in the continuum region, makes extended use of nuclear models implemented in the TALYS code to account for experimental data (both differential and integral) by varying the parameters of these models until a satisfactory description of these experimental data is reached. For the evaluation of the covariance data associated with this evaluated data, the Backward-forward Monte Carlo (BFMC) method was devised in such a way that it mirrors the process of the “Full model” evaluation method. When coupled with the Total Monte Carlo method via the T6 system developed by NRG Petten, the BFMC method allows to make use of integral experiments to constrain the distribution of model parameters, and hence the distribution of derived observables and their covariance matrix. Together, TALYS, TMC, BFMC, and T6, constitute a powerful integrated tool for nuclear data evaluation, that allows for evaluation of nuclear data and the associated covariance matrix, all at once, making good use of all the available experimental information to drive the distribution of the model parameters and the derived observables.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

The ENDF/B Nuclear Data Library and Its Impact on Reactor Simulations

The ENDF/B library, which is developed, maintained, and distributed by the Cross Section Evaluation Working Group, is the main source of nuclear data for analyses and computational simulations in nuclear applications, like different nuclear reactors concepts, radiation shielding, medical applications, astrophysics, etc. The library is constantly being improved and updated, with each release bringing an optimal representation of nuclear interactions as they are understood in their time. The most recent release, ENDF/B-VIII.1, represents a significant improvement in terms of the performance and consistency of the measured differential data relative to previous versions, as it combines the most recent experimental differential data and advanced theoretical nuclear models. As one of the many highlights, ENDF/B-VIII.1 restores a high-burnup depletion performance, comparable to ENDF/B-VII.1, that had been degraded in ENDF/B-VIII.0, while further improving the performance in criticality benchmarks, such as those in the Mosteller’s suite. Additionally noteworthy is the improved performance of ENDF/B-VIII.1 in radiation shielding and thick-target leakage spectrum integral experiments, which are also important for fusion and reactor applications. In this work we present in a very summarized way the main updates implemented in the ENDF/B-VIII.1 release and its main impacts, and also begin to delineate the path forward as to what to expect in the future for the next ENDF/B release, which, based on the timeline of the past few releases, is estimated to happen around 5 years from now. We emphasize that the ENDF/B-VIII.1 release was the product of an enormous collaborative effort among many authors and that for a complete detailed picture, the reader is strongly encouraged to refer to the article accompanying the release, which is currently in the publication process, but available as preprint [G. P. A. Nobre et al. arXiv Preprint arXiv: 2511.03564 (2025)].

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Nuclear Data Libraries Sensitivity Studies for ORSA Using SCALE

Subcritical assemblies offer valuable training capabilities in nuclear criticality safety (NCS) for individuals handling fissile material. At Oak Ridge National Laboratory(ORNL), the Oak Ridge Subcritical Assembly (ORSA), a new experimental facility, is being established to provide hands-on training for the Nuclear Criticality Safety Program (NCSP).It is essential to accurately determine the neutron multiplication factor (keff) to ensure that ORSA remains subcritical and safe during operations. This study investigated the sensitivity of keff to variations across Evaluated Nuclear Data File (ENDF/B) libraries, consisting of ENDF/B-VII.1, ENDF/B-VIII.0, and ENDF/B-VIII.1. The analysis was conducted using the CSAS6 sequence in the SCALE-6.3 code system. Individual isotopes in the ORSA model were replaced one at a time with ENDF/B-VII.1 as the base library and changing to ENDF/B-VIII.0or ENDF/B-VIII.1. The results demonstrated that the changes in keffof the nuclides associated with the ORSA model were mostly within the uncertainty of the base model (~24 pcm), except for primary nuclides like Uranium-235and H-poly with few other nuclides. The relative delta keff values of Uranium-235 and H-poly, expressed in pcm, were +280 and -212 in ENDF/B-VIII.0 and +332 and -275 in ENDF/B-VIII.1, respectively, which were notable changes in reactivity. These results demonstrated that ORSA was largely insensitive to variations across these nuclear data libraries.

Hong, Evan [North Carolina State University]↗
DOE OSTI2022

Horizontal Split Table Conceptual Design for Validation of Nuclear Data used in Advanced Reactors [Slides]

This presentation discusses a methodology that was developed to create conceptual designs of benchmark critical experiments for advanced reactors and nuclear data testing. A first concept that was explored was a pebble-bed high-temperature gas cooled reactor, based on the HTR-10 reactor. The very high correlation is a proof of concept that the design is similar to the application, and performing such critical experiments would help nuclear data testing and validation. Other concepts could be explored if needed, such as a molten-salt reactor, a sodium-cooled fast reactor, or heat pipe reactors/microreactor.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Identifying Nuclear Data Correlated Through Predicting Bias in Integral Experiments via Applying Principal Component Analysis to Random Forest

ABSTRACT Nuclear data (ND) are the input data for neutron‐transport simulations to answer questions related to nuclear technologies. Subsets of ND, here > 20,000 data points, are validated with respect to thousands of criticality experiments that represent various applications on a small scale. The aim of validation with these experiments is to find errors in ND or methods. The key challenge here is that several hundreds of ND are used to simulate one integral value. Hence, one cannot clearly identify what ND are leading to bias in criticality measurements. In fact, a mistake in one nuclear‐data observable can be compensated with an error in another, and the predicted criticality value would still be predicted in agreement with experimental data. Random forest (RF) was previously employed to predict bias in criticality measurements using sensitivities of simulated criticality experiments to ND. The SHapley Additive exPlanations (SHAP) metric was then applied to attribute the importance of each ND experiment and observable to bias prediction. This, however, did not highlight what ND were jointly related to predicting bias. This is important as it could inform us about where compensating errors in ND could hide. We tackle this shortcoming here by first decomposing the ND sensitivities to integral‐experiment simulations into principal components. Then we use principal component projections to predict bias via the RF and SHAP. The SHAP values and principal components are employed to reconstruct detailed SHAP values for each ND observable. We demonstrate that these extended SHAP bias predictions are more robust, less noisy, and more efficient. In addition, we show that this approach accounts for covariance in ND sensitivities and automates the identification of where compensating errors could hide in ND.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗