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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

FY22 Progress on Computational Modeling of the Water-Based NSTF

This report summarizes the system-level modeling effort by Argonne National Laboratory (Argonne) of the Natural convection Shutdown heat removal Test Facility (NSTF) in FY22. As an extension of the effort from FY21, this year’s work focuses primarily on the two-phase modeling of the NSTF using RELAP5-3D, particularly with the inclusion of the cavity model. The results from simulations were used to compare against experimental data for benchmarking purposes of the RELAP5 deck. Additionally, RELAP5 was used as a predictive tool to guide planned test operations and identify expected system behaviors. In the first part of this report, details are provided of the cavity omitted model where heat flux is applied directly as a boundary condition to the risers. The general trend predicted by the RELAP5 model matches that from the experimental data when a single-phase natural circulation flow is first established, followed by an oscillatory two-phase period and finally a stable two-phase flow. However, the onset of oscillations is predicted early by the model due to the smaller thermal mixing region in the tank. However, by expanding the simulated thermal mixing region in the tank, the onset of oscillations predicted by the model is able to match that from the experiment. These oscillations where studied in depth and are deduced to be flashing-induced instability. The model was then modified to simulate an accident scenario case where a representative heat load based on the full-scale Framatome’s 625 MW t SC-HTGR was applied directly to the riser channels. The simulated initial and boundary conditions were identical to those performed experimentally, facilitating direct comparisons between the predicted and experimental results. It was determined that the results showed some discrepancies remain, likely due to the overprediction of vapor generation rate by the computer model. In the second part of this report, the cavity model is re-introduced where it is observed that the RELAP5 prediction is now able to capture the major trends of the observed flow commonly observed during two-phase conditions. However, the onset of oscillations is once again predicted early by the model, possibly caused by the underprediction of heat loss from the heater and cavity. This is likely due to the omission of support structures in the cavity that can act as additional pathways for heat to escape to the environment. To overcome the underprediction of heat loss, part of the insulation surrounding the cavity side panels and the back of the heaters are removed to allow heat to escape directly to the environment, which then improves the RELAP5 prediction. Parametric studies are also performed to investigate the effects of heater power, tank inventory level, and tank gas space pressure on flow behaviors, also in direct comparison to conditions tested experimentally. User option-61 in the RELAP5-3D input deck, which changes the heat transfer coefficient correlations used for calculating the vapor generation, is also investigated where it is found that by enabling the option, the overall duration of oscillations is increased and matches that from the experiment better. The RELAP5 model is further benchmarked with a header inlet- throttling case where it is observed that the prediction from the model fails to capture some major features observed in the experiment. By using a modified loss coefficient curve for the valve, the accuracy of the prediction is improved where most of the major features observed in the experiment are predicted by the model. Lastly, the model is benchmarked with an inventory depletion scenario where it is observed that despite the modeling limitation of RELAP5, the prediction shows good agreement with the experimental data where major trends and features are captured by the model. Future work will see continued development of the current RELAP5-3D input deck of the NSTF to both improve the accuracy of the model’s predictive capability and continuing serving the experimental program. The mutually beneficial relationship between analysis and experimental efforts has become integral to the parent NSTF program, and the greater objective to fully understand and accurately predict the heat removal performance of a full scale RCCS concept.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Accessing the Single-Particle Structure of the Pygmy Dipole Resonance in 208 Pb

New experimental data on the neutron single-particle character of the Pygmy Dipole Resonance (PDR) in Pb 208 are presented. They were obtained from ( d , p ) and resonant proton scattering experiments performed at the Q3D spectrograph of the Maier-Leibnitz Laboratory in Garching, Germany. The new data are compared to the large suite of complementary, experimental data available for Pb 208 and establish ( d , p ) as an additional, valuable, experimental probe to study the PDR and its collectivity. Furthermore, besides the single-particle character of the states, different features of the strength distributions are discussed and compared to large-scale shell model (LSSM) and energy-density functional plus quasiparticle-phonon model theoretical approaches to elucidate the microscopic structure of the PDR in Pb 208 .

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

SG50 Data-format Requirement Document for an Automatically Readable, Comprehensive and Curated Experimental Reaction Database MEDUSA

This report constitutes the requirement document that guides the development of the experimental reaction database, MEDUSAL (Machine-readable Experimental Data User App & Library), created by OECD/NEA/WPEC SubGroup 50. Experimental reaction data are usually stored in the EXFOR library in EXFOR format. With MEDUSAL, the WPEC sub-group 50 wants to go beyond the EXFOR format and database to generate a library that is (a) automatically readable, (b) comprehensive, and (c) curated.

Nuclear Criticality Safety Program (NCSP)↗
DOE OSTI2024

Bridging the gap between experiments and simulations using machine learning

The physics of inertial confinement fusion is rich and complex. Simulation codes that are used to design experiments are computationally expensive and lack the predictive capability required for extensive parameter exploration in search of a high-performing design for laser direct drive. In this work we use deep learning to build a fast emulator of experiments. To facilitate the development of the deep-learning model, an autoencoder is used to reduce the dimensionality of the input space. Two deep learning models are developed. One model is trained on a vast array of simulation data and is subsequently calibrated to expensive and limited experimental data using a technique known as “transfer learning.” The other model is trained on a statistical model and is subsequently calibrated using experimental data. A comparative study of the two predictive models is carried out. The models potentially reproduce key experimental observables with high accuracy and unprecedented inference times relative to those achieved with simulation codes. These models facilitate rapid exploration of a high dimensional input parameter space.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Calibrating the SPECTACULAR constitutive model using legacy Sandia data for two filled epoxy systems: 828/CTBN/DEA/GMB and 828/DEA/GMB

The SPECTACULAR model is a development extension of the Simplified Potential Energy Clock (SPEC) model. Both models are nonlinear viscoelastic constitutive models used to predict a wide range of time-dependent behaviors in epoxies and other glass-forming materials. This report documents the procedures used to generate SPECTACULAR calibrations for two particulate-filled epoxy systems, 828/CTBN/DEA/GMB and 828/DEA/GMB. No previous SPECTACULAR or SPEC calibration exists for 828/CTBN/DEA/GMB, while a legacy SPEC calibration exists for 828/DEA/GMB. To generate the SPECTACULAR calibrations, a step-by-step procedure was executed to determine parameters in groups with minimal coupling between parameter groups. This procedure has often been deployed to calibrate SPEC, therefore the resulting SPECTACULAR calibration is backwards compatible with SPEC (i.e. none of the extensions specific to SPECTACULAR are used). The calibration procedure used legacy Sandia experimental data stored on the Polymer Properties Database website. The experiments used for calibration included shear master curves, isofrequency temperature sweeps under oscillatory shear, the bulk modulus at room temperature, the thermal strain during a temperature sweep, and compression through yield at multiple temperatures below the glass transition temperature. Overall, the calibrated models fit the experimental data remarkably well. However, the glassy shear modulus varies depending on the experiment used to calibrate it. For instance, the shear master curve, isofrequency temperature sweep under oscillatory shear, and the Young's modulus in glassy compression yield values for the glassy shear modulus at the reference temperature that vary by as much as 15 %. Also, for 828/CTBN/DEA/GMB, the temperature dependence of the glassy shear modulus when fit to the Young's modulus at different temperatures is approximately four times larger than when it is determined from the isofrequency temperature sweep under oscillatory shear. For 828/DEA/GMB, the temperature dependence of the shear modulus determined from the isofrequency temperature sweep under oscillatory shear accurately predicts the Young's modulus at different temperatures. When choosing values for the shear modulus, fitting the glassy compression data was prioritized. The new and legacy calibrations for 828/DEA/GMB are similar and appear to have been calibrated from the same data. However, the new calibration improves the fit to the thermal strain data. In addition to the standard calibrations, development calibrations were produced that take advantage of development features of SPECTACULAR , including an updated equilibrium Helmholtz free energy that eliminates undesirable behavior found in previous work. In addition to the previously mentioned experimental data, the development calibrations require data for the heat capacity during a stress-free temperature sweep to calibrate thermal terms.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Critical Review of Heat Pipe Experiments in Nuclear Energy Applications

This critical review provides heat pipe (HP) experimental data sets that contain pertinent information regarding nuclear technology that may be beneficial to researchers. Heat pipes have been shown to have a tremendously positive impact on nuclear technologies and will continue to become a more prevalent technology as more nuclear reactor concepts embrace this robust technology. Most previous reviews may focus on only a specific HP design or application, and some are backdated. Herein, this critical review extends previous efforts; integrates and summarizes previously reported HP experimental efforts; and provides updates with recently reported results in the literature for HPs in all nuclear-related applications, including space power (thermal radiators, core cooling, and electricity production), microreactors (emergency core cooling, hybrid control rods, and reactor core cooling), and HP involvement in other nuclear-related technologies (spent fuel pool cooling). The two main objectives of this critical review are (1) to facilitate the development of HP codes by outlining some of the existing experimental data sets to validate their codes and directing developers to these efforts and (2) to provide comprehensive information regarding the vast applicability of HPs used in the nuclear industry, including the theory of operation and limitations to supplement researchers in the development of new ideas for potential applications in nuclear-related technologies. The review clearly shows extensive and diverse experimental data sets for HPs developed under diverse testing conditions depending on the available nuclear application for validation purposes. Thus, this critical review is oriented to providing attention to the existing efforts rather than determining gaps in HP research.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Methodology for physics-informed generation of synthetic neutron time-of-flight measurement data

Accurate neutron cross section data are a vital input to the simulation of nuclear systems for a wide range of applications from energy production to national security. The evaluation of experimental data is a key step in producing accurate cross sections. There is a widely recognized lack of reproducibility in the evaluation process due to its artisanal nature and therefore there is a call for improvement within the nuclear data community. This can be realized by automating/standardizing viable parts of the process, namely, parameter estimation by fitting theoretical models to experimental data. This automation effort could greatly benefit from a synthetic data resource. This work leverages problem-specific physics, Monte Carlo sampling, and a general methodology for data synthesis to generate unlimited, labelled experimental cross-section data that is statistically indistinguishable to the observed data. Heuristic and, where applicable, rigorous statistical comparisons to observed data support this claim. The demonstration is based on/limited to transmission measurements at Rensselaer Polytechnic Institute (RPI) and energy-differential cross sections in the resolved resonance region (RRR). An open-source software is published alongside this article that executes the complete methodology to produce high-utility synthetic datasets. The goal of this work is to provide an approach and corresponding tool that will allow the evaluation community to begin exploring more data-driven, ML-based solutions to long-standing challenges in the field.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Nuclear masses learned from a probabilistic neural network

Machine learning methods and uncertainty quantification have been gaining interest throughout the last several years in low-energy nuclear physics. In particular, Gaussian processes and Bayesian neural networks have increasingly been applied to improve mass model predictions while providing well-quantified uncertainties. In this work, we use the probabilistic Mixture Density Network (MDN) to directly predict the mass excess of the 2016 Atomic Mass Evaluation within the range of measured data, and we extrapolate the inferred models beyond available experimental data. The MDN provides not only mean values but also full posterior distributions both within the training set and extrapolated testing set. We show that the addition of physical information to the feature space increases the accuracy of the match to the training data as well as provides for more physically meaningful extrapolations beyond the the limits of experimental data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Non-Boltzmann Effects in Chain Branching and Pathway Branching for Diethyl Ether Oxidation

Low-temperature (LT) engine applications have several potential benefits, including reduced emissions and increased efficiency. Attaining these benefits requires accurate kinetic modeling of LT chain branching, which depends heavily on ketohydroperoxide (KHP) decomposition. For diethyl ether (DEE), a promising biofuel, current estimates of the KHP decomposition rate constant are largely based on empirical fits to data. In this study, we investigate the most important KHP isomer in DEE LT oxidation by applying variable reaction coordinate transition state theory to the main pathway for KHP decomposition: OO bond fission to produce •OH and a keto-alkoxy radical, •OQ'O. We also use ab initio kinetics methods to investigate the decomposition of •OQ'O, where we find dominant branching to acetic acid, with the remaining flux going to CH 3 C(O)OCHO. Additionally, new time-resolved measurements of DEE and acetic acid concentrations during LT (450–600 K) DEE oxidation are obtained in a laser photolysis flow reactor coupled with multiplexed photoionization mass spectrometry. These new experimental data, along with jet-stirred reactor data in the literature, are compared with the predictions of a recent DEE mechanism (Tran et al. Proc. Comb. Inst. 2019, 37, 511-519) that was modified with the newly calculated ab initio rate constants for KHP and •OQ'O decomposition. The predictions of the modified mechanism are quite poor when compared to the experimental data; this is primarily due to the new KHP ⇌ •OQ'O + •OH rate constant, which is 1–2 orders of magnitude slower than empirical values employed in recent mechanisms. To reconcile the new KHP rate constant and the experimental data, we explore and quantify the possible role of non-Boltzmann (nB) reaction sequences. The nB reactions have a substantial effect on both the overall mechanism reactivity and the •OQ'O branching to acetic acid. We also provide guidance on the proper implementation of nB reactions in kinetic mechanisms.

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

Understanding Model Inadequacy in TRISO Nuclear Fuel Fission Products Release Models: Empirical and Mechanistic Approaches

The increasing use of tristructural isotropic (TRISO) particle fuel in both advanced and existing reactors necessitates a thorough evaluation of uncertainties and shortcomings in TRISO fission product release models. These inadequacies arise from the simplifications made in computational models compared to experimental data. Utilizing the BISON fuel performance code and experimental data from the Advanced Gas Reactor (AGR) program provides a unique chance to rigorously assess these inadequacies within a Bayesian uncertainty quantification (UQ) framework. This study contrasts the standard Bayesian framework with the Kennedy-O'Hagan (KOH) framework, which explicitly accounts for modeling inadequacies, in the context of UQ for TRISO silver release models. It examines both the traditional Arrhenius equation and a more advanced lower-length-scale (LLS)-informed model that incorporates microstructure information. The inverse UQ process applied to AGR-2 and AGR-3/4 datasets identified modeling inadequacy as the primary source of uncertainty, with experimental noise also being significant, while model parameter uncertainty was minimal. Both the Arrhenius and LLS-informed models showed similar levels of modeling inadequacy. For forward predictive UQ using the AGR-1 dataset, the KOH framework enhanced the accuracy and quality of quantified uncertainties by approximately 30% and 40%, respectively, compared to the standard Bayesian framework. This improvement was observed for both the Arrhenius and LLS-informed models. At the engineering scale, both models performed similarly, but the LLS-informed model outperformed the Arrhenius equation at the mesoscale. These findings underscore the importance of explicitly considering modeling inadequacy in the UQ process and highlight the need for ongoing refinement of physics-based models to address these shortcomings.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

YAHFC: A Code Framework to Model Nuclear Reactions and Estimate Correlated Uncertainties

Reaction modeling is a key ingredient in designing experiments and interpreting their results, and is an essential component in the process of evaluating nuclear data and assembling nuclear data libraries used in nuclear technology applications. Typically, experimental data are available only for a handful of reaction channels and theory models are used to fill in the gaps. In addition, theory is often called upon as the arbitrator between discrepant data. Most importantly, theory and modeling are required for an accurate determination of uncertainties in the evaluated data and the correlations between the multiple channels. A fast, accurate, and flexible modeling capability has been developed at LLNL with the code system YAHFC (Yet Another Hauser-Feshbach Code). YAHFC is a Monte Carlo, Hauser-Feshbach code framework, making full use of dynamic memory allocation, derived types, and parallel computing. YAHFC can generate events to simulate experiments and is guiding experiments designed to measure inelastic neutron scattering from actinide targets. YAHFC is also being used to analyze decays from surrogate experiments, thereby enabling the inference of reaction cross sections inaccessible by direct measurement. Finally, by modeling nuclear reactions with constraints from experimental data, YAHFC can deliver complete nuclear data libraries, with evaluated uncertainties, using the modernized Generalized Nuclear Data Structure (GNDS).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Prediction of Redox Potentials for Ac, Th, and Pa in Aqueous Solution

Density functional theory in conjunction with small core pseudopotentials and the associated basis sets was used to calculate potentials for multiple redox couples, covering a range of oxidation states for Ac (0 to III), Th (0 to IV), and Pa (0 to V) in aqueous solution. Solvation effects were incorporated using a supermolecule-continuum approach, with 30 water molecules representing two solvation shells, and the COSMO and SMD implicit solvation models. The calculated geometries for Ac(III), Th(IV), and Pa(V) were in reasonable agreement with the available experimental data. Using the COSMO model with the B3LYP functional, the calculated redox potentials were within ± 0.2 V from experiment for most redox couples. Several pathways were explored for the Pa(V/IV) redox couple for different forms of Pa(V) and Pa(IV). Most Pa(V/IV) redox couples have very similar potentials, ranging from 0 to -0.4 V up to a pH of 1.4. At pH = 1.4, the potentials shift to values that are more negative than -0.7 V, reflecting the growing unfavorable nature of the redox process at higher pH levels. The calculated values for An(III/II) potentials were consistent with prior estimates and the available experimental data. The predicted redox potentials for An(II/I) were highly negative, as expected. For An(I/0) potentials, Th and Pa exhibited positive values, contrasting with the negative values calculated for Ac. Furthermore, the An +m /An(0) potentials agreed better with the experimental data when using the COSMO solvation model as compared to the SMD model.

Chemical calculations↗
DOE OSTI2024

Identifying Bayesian optimal experiments for uncertain biochemical pathway models

Abstract Pharmacodynamic (PD) models are mathematical models of cellular reaction networks that include drug mechanisms of action. These models are useful for studying predictive therapeutic outcomes of novel drug therapies in silico. However, PD models are known to possess significant uncertainty with respect to constituent parameter data, leading to uncertainty in the model predictions. Furthermore, experimental data to calibrate these models is often limited or unavailable for novel pathways. In this study, we present a Bayesian optimal experimental design approach for improving PD model prediction accuracy. We then apply our method using simulated experimental data to account for uncertainty in hypothetical laboratory measurements. This leads to a probabilistic prediction of drug performance and a quantitative measure of which prospective laboratory experiment will optimally reduce prediction uncertainty in the PD model. The methods proposed here provide a way forward for uncertainty quantification and guided experimental design for models of novel biological pathways.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Modified Winterbottom Construction Including Boundaries

There has been extensive work on the equilibrium shape of isolated nanoparticles with internal boundaries, and also single crystals on substrates. Surprisingly, almost shockingly, there has been very little work on the equilibrium shape of particles with internal boundaries on substrates. Here in this paper, the general solution is given for the configuration of particles which contain twin and other grain boundaries on a flat substrate, which can be applied to any polycrystalline or multiphase nanoparticle configuration. The solution is based upon combining the established modified-Wulff construction that has been extensively validated for twinned particles with the Winterbottom construction for single particles on a substrate. The solution is illustrated for the specific case of five-fold multiply twinned particles. Good agreement is observed between both existing experimental data in the literature as well as some experimental data included within this work.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Addendum to ICSBEP Logbooks 12r, 13r, 14r and 15r for Polyethylene and Graphite Reflected HEU Metal Critical Experiments

This report documents the experimenters’ data sheets that were recently (2023) discovered for critical experiments with highly enriched uranium metal with polyethylene and graphite reflectors. These data sheets were produced at the time of the measurements, when the dimensional inspection reports for the polyethylene and graphite were available. However, at this writing, those inspection reports are not available but may be in unmarked storage for the Y-12 National Security Complex. However, the data sheets presented herein contain sketches of the experimental configurations and other additional information that is not in the logbooks, such as dimensions and masses of the graphite measurements. This report reproduces the experimental data sheets that complement the logbooks. Some of the data on these sheets and the logbooks can be used to infer the dimensions and masses that are not documented. Some obvious mistakes in the logbook and data sheets have been corrected. In the reproduced data sheets, some of the information is not clearly visible: some information, such as average values, can be obtained from other data in the sheets or logbook. This report should be sent to the International Criticality Safety Benchmark Program at Idaho National Laboratory to complement the existing Oak Ridge Critical Facility logbooks there.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Thermodynamic Consistent Neural Networks for Learning Material Interfacial Mechanics

For multilayer materials in thin substrate systems, interfacial failure is one of the most challenges. The traction-separation relations (TSR) quantitatively describe the mechanical behavior of a material interface undergoing openings, which is critical to understand and predict interfacial failures under complex loadings. However, existing theoretical models have limitations on enough complexity and flexibility to well learn the real-world TSR from experimental observations. A neural network can fit well along with the loading paths but often fails to obey the laws of physics, due to a lack of experimental data and understanding of the hidden physical mechanism. In this paper, we propose a thermodynamic consistent neural network (TCNN) approach to build a data-driven model of the TSR with sparse experimental data. The TCNN leverages recent advances in physics-informed neural networks (PINN) that encode prior physical information into the loss function and efficiently train the neural networks using automatic differentiation. We investigate three thermodynamic consistent principles, i.e., positive energy dissipation, steepest energy dissipation gradient, and energy conservative loading path. All of them are mathematically formulated and embedded into a neural network model with a novel defined loss function. A real-world experiment demonstrates the superior performance of TCNN, and we find that TCNN provides an accurate prediction of the whole TSR surface and significantly reduces the violated prediction against the laws of physics.

Zhang, Jiaxin↗
DOE OSTI2025

Predicting the evolution of biomass bulk density through feedstock preprocessing: Discrete element modeling, regression analysis, and pilot-scale validation

Bulk density is an important material property of biomass feedstocks, influencing handling, storage, transport costs, and conversion efficiency. In this study, predictive regression models for loose and tapped bulk densities of Alamo and Cave-in-Rock switchgrass are developed using a comprehensive dataset generated via calibrated bonded-sphere discrete element method (DEM) simulations. Here, a key contribution of this study is the use of a DEM-based approach, which correlates density with moisture content and particle size distribution parameters and enables analysis across a continuous particle size range, overcoming limitations of purely experimental data. For comparison, regression models are also developed using only experimental data from pilot-scale runs at the Biomass Feedstock National User Facility at Idaho National Laboratory. Validation against pilot-scale data showed reasonable prediction accuracy for both model types, particularly for smaller particle sizes (post-secondary grinding). While the experimental model showed slightly better performance matching the validation data in some cases, the DEM-based model benefits from a much larger dataset, reduced predictor multicollinearity, and continuous parameter coverage, highlighting the utility of validated simulation models for developing robust predictive tools for biomass preprocessing applications.

09 - BIOMASS FUELS↗
DOE OSTI2025

Elastic Bayesian Model Calibration

Functional data are ubiquitous in scientific modeling. For instance, quantities of interest are modeled as functions of time, space, energy, density, etc. Uncertainty quantification methods for computer models with functional response have resulted in tools for emulation, sensitivity analysis, and calibration that are widely used. However, many of these tools do not perform well when the computer model’s parameters control both the amplitude variation of the functional output and its alignment (or phase variation). This paper introduces a framework for Bayesian model calibration when the model responses are misaligned functional data. The approach generates two types of data out of the misaligned functional responses: (1) aligned functions so that the amplitude variation is isolated and (2) warping functions that isolate the phase variation. These two types of data are created for the computer simulation data (both of which may be emulated) and the experimental data. The calibration approach uses both types so that it seeks to match both the amplitude and phase of the experimental data. The framework is careful to respect constraints that arise, especially when modeling phase variation, and is framed in a way that it can be done with readily available calibration software. In conclusion, we demonstrate the techniques on two simulated data examples and on two dynamic material science problems: a strength model calibration using flyer plate experiments and an equation of state model calibration using experiments performed on the Sandia National Laboratories’ Z-machine.

97 MATHEMATICS AND COMPUTING↗