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At least 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

30 cm Drop Modeling

This report documents modeling and analysis of the 30 cm cask drop scenario for spent nuclear fuel (SNF) casks. The 30 cm drop is an important hypothetical dynamic loading scenario for SNF because 10 CFR 71.71 defines testing requirements for normal conditions of transport (NCT) of SNF packages, and free drop (of 30 cm for the heaviest packages) tests are required. Other NCT conditions specified in 10 CFR 71.71 include vibration normally incident to transport, and DOE’s multimodal transportation test (MMTT) and related analysis concluded that mechanical shock and vibration incident to transportation is negligible for SNF structural integrity because the loads are so small. The SNF cask 30 cm drop has a lot more potential to cause damage to SNF because the kinetic energy and momentum of the system is stopped abruptly during the cask impact event. Test data and model predictions both agree that the SNF assemblies are subjected to significantly higher loads in a cask drop event than normal transportation shock and vibration, but currently there is no reason to expect gross structural damage of the fuel assembly or widespread SNF cladding failure as a result of a 30 cm cask drop. The ultimate goal of this work is to close the knowledge gap of stress profiles on SNF rods and fuel assembly components during NCT, and this report describes the progress toward reaching that goal as well as a plan for finishing this work over the next year or two. Key remaining tasks include refining the fuel assembly model, calculating a selection of specific fuel assembly cases to populate the damage model, and finally running the damage model to predict the full general response range of 30 cm drop loads on SNF.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

DUNE and MINERvA Flux Studies and a Measurement of the Charged-Current Quasielastic Antineutrino Scattering Cross Section with $<E_{\nu}> \sim 6 $ GeV on a CH Target

One of the biggest open questions in physics is the prevalence of matter over anti-matter in the present universe. One of possible answers lies in the violation of charge and parity symmetry in the lepton sector that would favor matter over anti-matter so that the universe becomes dominated by matter over time. Recent results from neutrino experiments like T2K and NOvA have indicated that there is CP violation in the leptonic sector \citep{Abe:2019vii}. However, the precise measurement of CP violation will require an unprecedented level of accuracy in these oscillation experiments. One of the challenges lies with the systematic uncertainties that come with the measurements, which include uncertainties related to the neutrino flux modeling, interaction modeling and detector related systematic uncertainties.\\ The first part of this thesis will go through the challenges related to flux uncertainties. It will discuss the flux uncertainties related to hadron production in the LBN F beamli ne for the DUNE experiment. Hadron production models used in flux simulations vary widely depending on the simulation and choice of physics models used to simulate the hadron production. This thesis will explain the method of using existing hadron production data to constrain these uncertainties, a method which has been used in MINERvA experiment.\\ The second part of the thesis goes through the effect of possible flux mismodeling and MINERvA's approach to address this effect. In doing so, we discovered that the suspected mismodeling could also result from incorrect estimation of the energy scale of the muons. This project not only demonstrated the challenges of flux modeling but also led to a novel approach of using neutrino energy spectra to understand the correlation between detector and neutrino beamline parameters. \\ A high statistics measurement of the anti-neutrino scattering cross section in the charged current quasielastic (CCQE) channel is the final part of the th esis. Th e 2-body interactions in this channel have the advantage of allowing reconstruction of the interaction kinematics from the outgoing muon trajectory which can be measured very well. Double differential cross sections as a function of muon kinematics, one of the deliverables of this analysis, will help future oscillation experiments understand their data. The cross-section as a function of the four-momentum transferred from the leptonic system to the hadronic system ($Q^{2}_{QE}$) can be used to test models used in simulating antineutrino interactions and the nuclear effects that can modify the predicted cross sections. Nuclear effects arise from the complex nuclear environment and both modify the initial scattering and change the fate of the particles produced from in neutrino-nucleus interactions. Study of nuclear effects will help to understand the structure of the atomic nucleus and its impact on neutrino interactions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Machine learning-enabled prediction of chemical durability of A 2 B 2 O 7 pyrochlore and fluorite

Pyrochlore-structure type and its derivative in a general formula A 2 B 2 O 7 (A = rare earth elements and actinides; B = Ti, Sn, Zr, Hf, Pb, Si, etc.) display excellent structural flexibility and rich crystal chemistry as promising nuclear waste form materials capable of immobilizing actinides and fission products. It is essential to understand these materials’ chemical durability and element release of radionuclides in order to evaluate their performance in near-field environment. However, it is a formidable grand technological challenge to experimentally perform durability testing across hundreds of thousands of possibilities resulting from their extreme compositional complexities due to cation substitutions at both A and B-sites. In this work, we demonstrate a machine learning approach to determine the key materials parameters and structural characteristics governing the leaching behaviors from a small set of selected compositions as model systems, enabling a science-based prediction of their chemical durability that can be extended to a wide range of chemical compositions. The combination of four key structural characteristics and materials parameters, including ionic radius size difference , ionic potential difference , electronegativity difference , and lattice parameter , creates features an optimized prediction of the chemical durability. Two machine learning models, linear regression and Kernel ridge regression models, are trained on the randomly-split training dataset derived from the experimentally-determined elemental release rates, and subsequently tested on the testing dataset. The predicted leaching rates from both machine learning models show an excellent agreement with the experimental data, demonstrating the feasibility of rapidly evaluating the material properties of new compositions. These results highlight the immense potential of synergizing informatics through machine learning-based models and well-controlled experiments of selected model systems to accelerate materials design and discovery with optimized compositions and performance of promising materials for effective nuclear waste management.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Assessment and Improvement of Fission Product Transport Predictions of Particle Fuel in BISON

The U.S. Department of Energy’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aims to develop predictive capabilities by applying computational methods to the analysis and design of advanced reactor and fuel cycle systems. This program has been providing engineering-scale support for the development of BISON, a high-fidelity and high-resolution fuel performance tool. This study was motivated by the need to incorporate more physics-based models in BISON in order to foster tri-structural isotropic (TRISO) applications. This document details the integration of new modeling capabilities in BISON, including (1) development of pyrolytic carbon (PyC) and silicon carbide (SiC) layer anisotropic thermal and mass transport capabilities, (2) verification of the mass diffusion solution in TRISO modeling, (3) calibration of fission product diffusivity using Advanced Gas Reactor (AGR) experiments, (4) improved fission product release modeling by developing compact diffusion modeling capabilities, and (5) documentation of accelerated failure analysis on the BISON website. Improvements made to the diffusion models and parameters were documented and validated against AGR-1 and -2 experiment data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Summary of US DOE R&D Activities on Graphite Oxidation (2006–2021)

The objective of the international collaboration between United States Department of Energy (U.S.-DOE) and Generation IV International Forum (GIF) is the development of the next generation of nuclear energy systems. The current GIF Project Arrangement (PA) on Materials (2018-2022) was revised in 2019 and extended for another 10 years (2020-2030). The Work Package 1 (“Graphite”) of the extended Project Plan (PP) on Materials specifies technical tasks and High Level Deliverables for research and development (R&D) activities related to using graphite in fuel elements, reflectors, and support structures of Very High Temperature Reactors (VHTR). The graphite tasks include specification and acquisition, qualification and development of new grades, characterization of properties, and development of behavior models. Specifically, Task 1.4 (“Graphite Oxidation Behavior”) outlines planned activities related to acute oxidation by air and chronic oxidation by impurities in the helium coolant. A final report on experimental data regarding graphite oxidation behavior is scheduled for 2022 (deliverable 3.1.1.4.a). In preparation of this deliverable, this document summarizes the R&D activities funded by U.S.-DOE from 2006 (the inception of the VHTR system arrangement) through present (2021). This report is being submitted to the GIF Graphite Working Group (GWG) to serve as input for the GWG high-level deliverable to the Project Management Board (PMB) of PA on Materials. Besides U.S.-DOE, other organizations participating to Task 1.4 of the current PA on Materials are: European Commission’s Joint Research Center (JRC), Korea Atomic Energy Research Institute (KAERI), and Japan Atomic Energy Agency (JAEA). U.S.-DOE is the main contributor on graphite oxidation R&D, with 85 % commitment of total funding during 2018-2022.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Machine learning molecular dynamics simulations toward exploration of high-temperature properties of nuclear fuel materials: case study of thorium dioxide

Predicting materials properties of nuclear fuel compounds is a challenging task in materials science. Their thermodynamical behaviors around and above the operational temperature are essential for the design of nuclear reactors. However, they are not easy to measure, because the target temperature range is too high to perform various standard experiments safely and accurately. Moreover, theoretical methods such as first-principles calculations also suffer from the computational limitations in calculating thermodynamical properties due to their high calculation-costs and complicated electronic structures stemming from f-orbital occupations of valence electrons in actinide elements. Here, we demonstrate, for the first time, machine-learning molecular-dynamics to theoretically explore high-temperature thermodynamical properties of a nuclear fuel material, thorium dioxide. The target compound satisfies first-principles calculation accuracy because f-electron occupation coincidentally diminishes and the scheme meets sampling sufficiency because it works at the computational cost of classical molecular-dynamics levels. We prepare a set of training data using first-principles molecular dynamics with small number of atoms, which cannot directly evaluate thermodynamical properties but captures essential atomistic dynamics at the high temperature range. Then, we construct a machine-learning molecular-dynamics potential and carry out large-scale molecular-dynamics calculations. Consequently, we successfully access two kinds of thermodynamic phase transitions, namely the melting and the anomalous λ transition induced by large diffusions of oxygen atoms. Furthermore, we quantitatively reproduce various experimental data in the best agreement manner by selecting a density functional scheme known as SCAN. Our results suggest that the present scale-up simulation-scheme using machine-learning techniques opens up a new pathway on theoretical studies of not only nuclear fuel compounds, but also a variety of similar materials that contain both heavy and light elements, like thorium dioxide.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Independent normalization for γ -ray strength functions: The shape method

Here, the shape method, a novel approach to obtain the functional form of the γ-ray strength function (γSF), is introduced. In connection with the Oslo method the slope of the nuclear level density (NLD) and γSF can be obtained simultaneously even in the absence of neutron resonance spacing data. The foundation of the shape method lies in the primary γ-ray transitions which preserve information on the functional form of the γSF. The shape method has been applied to 56 Fe, 92 Zr, and 164 Dy, which are representative cases for the variety of situations encountered in typical NLD and γSF studies. The comparisons of results from the shape method to those from the Oslo method demonstrate that the functional form of the γSF is retained regardless of nuclear structure details or J π values of the states fed by the primary transitions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Quantum Monte Carlo Calculations of Magnetic Form Factors in Light Nuclei

Here, we present Quantum Monte Carlo calculations of magnetic form factors in A = 6-10 nuclei, based on Norfolk two- and three-nucleon interactions, and associated one- and two-body electromagnetic currents. Agreement with the available experimental data for 6 Li, 7 Li, 9 Be and 10 B up to values of momentum transfer q ~ 3 fm -1 is achieved when two-nucleon currents are accounted for. We present a set of predictions for the magnetic form factors of 7 Be, 8 Li, 9 Li, and 9 C. In these systems, two body currents account for ~ 40-60% of the total magnetic strength. Measurements in any of these radioactive systems would provide valuable insights on the nuclear magnetic structure emerging from the underlying many-nucleon dynamics. A particularly interesting case is that of 7 Be, as it would enable investigations of the magnetic structure of mirror nuclei

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Hydroxyl radical yields in the heavy ion radiolysis of water

The yields of hydroxyl radicals in the radiolysis of water with protons and carbon ions have been examined using experimental scavenging techniques coupled with Monte Carlo track simulations. Combined with previous results using helium ions, this set of data gives valuable information on the potential for radiation induced damage to biological systems for light ions with very different track structures. Carbon dioxide production from aerated formic acid solutions in concentrations ranging from 10 -3 to 1 M was used as a probe of hydroxyl radical yields from about 8 ns to 8 µs. Numerical interpolation of the results at slightly different ion energies in combination with data from gamma radiolysis allows for a systematic analysis of both track average and track segment yields. As expected, considerable track chemistry is found to occur on the nanosecond to microsecond time scales. Monte Carlo track simulations employing stochastic diffusion-kinetic calculations of product yields are found to reproduce experimental observations satisfactorily. The track simulations are used to extract hydroxyl radical kinetics in pure water at neutral conditions.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2020

Dynamically Reconfigurable Data Readout of Pixel Detectors for Automatic Synchronization with Data Acquisition Systems

Reconfigurable detectors with dynamically selectable sensing and readout modes are highly desirable for implementing edge computing as well as enabling advanced imaging techniques such as foveation. The concept of a camera system capable of simultaneous passive imaging and dynamic ranging in different regions of the detector is presented. Such an adaptive-autonomous detector with both spatial and temporal control requires programmable window of exposure (time frames), ability to switch between readout modes such as full-frame imaging and zero-suppressed data, modification of the number of pixel data bits and independent programmability for distinct detector regions. In this work, a method is presented for seamlessly changing time frames and readout modes without data corruption while still ensuring that the data acquisition system (DAQ) does not need to stop and resynchronize at each change of setting, thus avoiding significant dead time. Data throughput is maximized by using a minimum unique data format, rather than lengthy frame headers, to differentiate between consecutive frames. A data control and transmitter (DCT) synchronizes data transfer from the pixel to the periphery, reconfigures the data to transmit it serially off-chip, while providing optimized decision support based on a DAQ definable mode. Measurements on a test structure demonstrate that the DCT can operate at 1 GHz in a 65 nm LP CMOS process.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Non-destructive structural characterization of graphite components using mechanical resonance and deep learning

As compared to conventional nuclear reactors, microreactors have the potential to significantly reduce construction timelines and capital costs, decreasing the barriers for advanced nuclear reactor technologies. However, the lower power output of these microreactors (typically < 20 MWe) creates challenging economics if operation and maintenance costs cannot be sufficiently reduced. The compact size of these designs presents an opportunity for comprehensive in-situ structural health monitoring to provide real-time feedback in order to reduce operational costs associated with maintenance and downtime. Many microreactor concepts use graphite for both in-core neutron moderation and as a structural material, which has typically required some form of periodic and laborious inspection. This report provides a description and assessment of recent work with graphite to couple acoustic-based experimental measurements and characterization with machine learning models to mature structural health monitoring capabilities and generate benefits for the nuclear microreactor industry. With resilient embedded sensors in development in other programs funded by the US Department of Energy’s Office of Nuclear Energy and elsewhere, the work described herein builds upon previously funded efforts to mature non-destructive testing technology that relates measured vibrational signatures to structural changes, using a combination of new experimental measurements and machine learning processing. Building on past successful demonstrations of predictive workflows to identify structural changes in a hexagonal stainless steel test article with excellent acoustic propagation, we first performed baseline characterization on graphite samples with canonical geometries to ensure compatibility and confidence in the applied techniques for a material with distinctly different mechanical properties. In contrast to efforts in previous years, we worked exclusively with unidirectional vibration data that is more comparable to those expected from the existing embedded sensor technologies which are suitable for deployment in a reactor setting. Established acoustic and modern machine-learning-based characterization approaches were applied to the resulting datasets from these simple geometries. Both approaches were found to be highly capable of detecting even small geometric irregularities amongst nominally identical samples. As such, we then moved to testing these approaches for detection of artificial local stress perturbations introduced into a more complex geometry: a hexagonal block with drilled holes. A main outcome of this work is that a generalizable ML workflow can be used to detect and predict the characteristics of small artificial anomalies in a graphite component with a relevant geometry. While this work was performed using surficial vibration data, we expect the approach to be flexible and viable for other monitoring scenarios, such as those with different arrangements or types of sensor arrays. As compared to previously funded efforts, an existing ML workflow based on neural networks was enhanced through the addition of recently developed Fourier neural operators. As applied to previously collected and new vibration datasets, prediction accuracies of anomaly characterizations were greatly improved with minimal added computational cost. As trained on small durations of vibration data (tens of seconds) collected over a realistic number of locations, the model was able to reliably determine the presence of a subtle stress anomaly and begin to provide location estimates. Such an approach is likely to be viable for more relevant reactor damage scenarios for graphite components, such as progressive crack growth or creep.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Comparison Study of Upper Subcritical Limits Derived Using Sensitivity/Uncertainty Tools Case Studies of Benchmarks and Applications

Neutron transport methods used to establish subcriticality require validation by comparison to critical experiments considered to be benchmarks. Whisper is a sensitivity/uncertainty analysis tool developed to assist with the task of validation in nuclear criticality safety. Details on the Whisper methodology can be found in References 1-3 on the MCNP ® reference collection website at https://mcnp.lanl.gov. Whisper-1.0 was originally developed in 2014 and used to assist with nuclear criticality safety validation at Los Alamos National Laboratory. Whisper was upgraded in 2016 to Whisper-1.1 and prepared for release with MCNP6.2. Whisper contains a library of over 1100 critical experiment benchmarks and quantifies neutronic similarity of an application to benchmarks in the library. Using highest similarity benchmarks, Whisper computes a calculational margin (CM) encompassing of the worst-case bias and bias uncertainty at a 99% confidence level for each application. In addition, portions of the margin of subcriticality (MOS) for nuclear data uncertainty and potential code errors are computed. The baseline upper subcritical limit (USL) computed by Whisper is comprised of the CM, MOS nuclear data , and MOS code errors . The Whisper baseline USL is absent a portion of the MOS due to the area of application, which is applied based upon judgment by the criticality safety analyst. An objective of this paper is to present the baseline USL, CM and portions of the MOS as computed by Whisper for comparison with similar sensitivity/uncertainty tools, such as those used by IRSN and ORNL. The initial comparison involves four critical experiment benchmarks: HEU-MET-FAST-013-001, HEU-SOLTHERM-001-008, PU-MET-FAST-022-001, AND PU-SOL-THERM-001-001, which have been: 1. modeled independently by LANL, IRSN, and ORNL based upon information provided in the ICSBEP Handbook, 2. are common in S/U libraries for LANL, IRSN, and ORNL, 3. span a range of energy spectrum and fissile material, and 4. taken as applications for the purposes of this study and therefore excluded from use as a benchmark for calculating the upper subcritical limit. Results presented in this paper have been computed using covariance data for all isotopes in ENDF/BVII.0 using a 44-group energy structure. Benchmarks in the Whisper library were run in MCNP6.2 using 100,000 neutrons per cycle, skipping 100 cycles for 500 active cycles. Reference 10 also compares the results for baseline USL with an order of magnitude greater neutrons, using the same total number of cycles with 1,000,000 neutrons per cycle. Subsequent to the results presented in Reference 10 changes were made to the benchmark library, as discussed in Reference 11. Newer results using the revised benchmark library are presented herein.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Spectroscopic characterization of neptunium(VI), plutonium(VI), americium(VI) and neptunium(V) encapsulated in uranyl nitrate hexahydrate

The coordination of crystalline products resulting from the co-crystallization of Np(VI), Pu(VI), Am(VI), and Np(V) with uranyl nitrate hexahydrate (UNH) has been revealed through solid-state spectroscopic characterization via diffuse reflectance UV-Vis-NIR spectroscopy, SEM-EDS, and extended X-ray absorption fine structure (EXAFS) spectroscopy. Density functional and multireference wavefunction calculations were performed to analyze the An(VI/V)O 2 (NO 3 ) 2 ·2H 2 O electronic structures and to help assign the observed transitions in the absorption spectra. EXAFS show a similar coordination between the U(VI) in UNH and Np(VI) and Pu(VI); while Am resulted in a similar coordination to Am(III), as reduction of Am(VI) occurred prior to EXAFS data being obtained. Finally, the co-crystallization of the oxidized transuranic species—penta- and hexavalent—with UNH, represents a significant advance from not only a practical standpoint in providing an elegant solution for used nuclear fuel recycle, but also as an avenue to expand the fundamental understanding of the 5f electronic behavior in the solid-state.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Spectroscopic characterization of neptunium(VI), plutonium(VI), americium(VI) and neptunium(V) encapsulated in uranyl nitrate hexahydrate

The coordination of crystalline products resulting from the co-crystallization of Np(VI), Pu(VI), Am(VI), and Np(V) with uranyl nitrate hexahydrate (UNH) has been revealed through solid-state spectroscopic characterization via diffuse reflectance UV-Vis-NIR, SEM-EDS, and extended X-ray absorption fine structure (EXAFS). Density functional and multireference wavefunction calculations were performed to analyze the An(VI/V)O 2 (NO 3 ) 2 ·2H 2 O electronic structures and to help assign the observed transitions in the absorption spectra. EXAFS show a similar coordination between the U(VI) in UNH and Np(VI) and Pu(VI); while Am resulted in a similar coordination to Am(III), as reduction of Am(VI) occurred prior to EXAFS data being obtained. Here, the co-crystallization of the oxidized transuranic species—penta- and hexavalent—with UNH, represents a significant advance from not only a practical standpoint in providing an elegant solution for used nuclear fuel recycle, but also as an avenue to expand the fundamental understanding of the 5f electronic behavior in the solid-state.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Impact of T - and ρ -dependent decay rates and new (n, γ ) cross-sections on the s process in low-mass asymptotic giant branch stars

Aims. We study the impact of nuclear input related to weak-decay rates and neutron-capture reactions on predictions for the slow neutron-capture process (s process) in asymptotic giant branch (AGB) stars. We provide the first database of surface abundances and stellar yields of the isotopes heavier than iron from the Monash models. Methods. We ran nucleosynthesis calculations with the Monash post-processing code for seven stellar structure evolution models of low-mass AGB stars with three different sets of nuclear inputs. The reference set has constant decay rates and represents the set used in the previous Monash publications. The second set contains the temperature and density dependence of β decays and electron captures based on the default rates of nuclear NETwork GENerator (NETGEN). In the third set, we further update 92 neutron-capture rates based on re-evaluated experimental cross sections from the ASTrophysical Rate and rAw data Library. We compare and discuss the predictions of the sets relative to each other in terms of isotopic surface abundances and total stellar yields. We also compare the results to isotopic ratios measured in presolar stardust silicon carbide (SiC) grains from AGB stars. Results. The new sets of models result in a ∼66% solar s-process contribution to the p-nucleus 152 Gd, confirming that this isotope is predominantly made by the s process. The nuclear input updates result in predictions for the 80 Kr/ 82 Kr ratio in the He intershell and surface 64 Ni/ 58 Ni, 94 Mo/ 96 Mo, and 137 Ba/ 136 Ba ratios that are more consistent with the corresponding ratios measured in stardust; however, the new predicted 138 Ba/ 136 Ba ratios are higher than the typical values of the SiC grains. The W isotopic anomalies are in agreement with data from the analyses of other meteoritic inclusions. We confirm that the production of 176 Lu and 205 Pb is affected by too large uncertainties in their decay rates from NETGEN.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Availability of Critical Benchmark Experiments for the Pebble Tanker Transportation Model for Nuclear Criticality Safety Validation of TRISO Pebbles

This study addresses the need for comprehensive investigations into TRi-structural ISOtropic (TRISO) fuel pebble transportation validation. In this work, an exploratory model, the pebble tanker(PT), was developed with the aim of facilitating the validation of nuclear criticality safety calculations in the context of industrial-scale transportation of TRISO fuel. The PT model was designed to investigate the availability and applicability of critical benchmark experiments crucial for assessing the transportation of these pebbles. This work incorporated sensitivity/uncertainty (S/U) similarity studies to quantify the applicability of critical benchmark experiments and to address nuclear data uncertainties in the context of TRISO transportation. Two container models were investigated: one for the Hermes-type pebble and one for the Pebble Bed Modular Reactor (PBMR)–type pebble. The models were simplified, considering fuel, containment, and either water or air, to enable a focus on the underlying physics of applications involving TRISO fuel pebbles using the PT model. A crucial aspect under consideration was the capacity of the transport package to hold pebbles while ensuring subcriticality in the flooded state. An approach in the criticality validation process involves assessing the similarity between systems through an integral index parameter evaluation. This involves calculating a correlation coefficient (referred to as c k ) based on shared nuclear data–induced uncertainty between a benchmark experiment and the application of the PT model. To facilitate this analysis, the SCALE tools, particularly the CSAS6-Shift, TSUNAMI-3D-Shift, and TSUNAMI-IP sequences, were employed for comprehensive studies in neutronics and S/U analysis. Our findings showed that there are sufficient critical experimental benchmarks to perform this validation of the PT model in the most reactive state, i.e. when the tanker is flooded. This paper provides valuable insights into validating a transport package for Generation IV TRISO fuel pebbles.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

MrHyDE v.1.0

SAND2024-01324O MrHyDE, which stands for Multi-resolution Hybridized Differential Equations, is a general-purpose C++ package for the solution of coupled multiphysics and multiscale systems on massively parallel computing systems. MrHyDE is designed to enable moving beyond forward simulation for multiscale applications which includes optimization, control, uncertainty quantification, and stochastic inversion. The framework provides interfaces to several packages within the Trilinos framework and leverages automatic differentiation to enable adjoint capabilities for large-scale, gradient-based optimization. MrHyDE provides automated multiscale capabilities through a subgrid model interface and multiscale Dirichlet-to-Neumann maps. For extreme-scale applications, MrHyDE provides in situ data-compression algorithms to reduce memory requirements while maintaining performance. MrHyDE is a general-purpose, computational framework for the solution of multiscale and multiphysics applications. It uses a combination of structure-preserving, physics-compatible discretizations, fully implicit methods, multi-resolution schemes, or fully explicit methods. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗
DOE OSTI2022

Distances to Prompt Effects for a Nuclear Device

In 2010, nuclear weapon effects experts at Sandia National Laboratories (SNL) were asked to provide a quick reference document containing estimated prompt nuclear effects. This report is an update to the 2010 document that includes updated model assumptions. This report addresses only the prompt effects associated with a nuclear detonation (e.g., blast, thermal fluence, and prompt ionizing radiation). The potential medium- and longer-term health effects associated with nuclear fallout are not considered in this report because, in part, of the impracticality of making generic estimates given the high dependency of fallout predictions on the local meteorological conditions at the time of the event. The results included in this report also do not consider the urban environment (e.g., shielding by or collapse of structures) which may affect the extent of prompt effects. It is important to note that any operational recommendations made using the estimates in this report are limited by the generic assumptions considered in the analysis and should not replace analyses made for a specific scenario/device. Furthermore, nuclear effects experts (John Hogan, SNL, and Byron Ristvet, Defense Threat Reduction Agency (DTRA)) have indicated that the accuracy of effects predictions below 0.5 kilotons (kT) or 500 tons nuclear yield have greater uncertainty because of the limited data available for the prompt effects in this regime. The Specialized Hazard Assessment Response Capability (SHARC) effects prediction tool was used for these analyses. Specifically, the NUKE model within SHARC 2021 Version 10.2 was used. NUKE models only the prompt effects following a nuclear detonation. The algorithms for predicting range-to-output data contained within the NUKE model are primarily based on nuclear test effects data. Probits have been derived from nuclear test data and the U.S. Environmental Protection Agency (EPA) protective action guides. Probits relate the probability of a hazard (e.g., fatality or injury) caused by a given insult (e.g., overpressure, thermal fluence, dose level). Several probits have been built into SHARC to determine the fatality and injury associated with a given level of insult. Some of these probits differ with varying yield. Such probits were used to develop the tables and plots in this report.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗