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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 OSTI2023

Iterative sampling of expensive simulations for faster deep surrogate training

Deep neural network (DNN) surrogates of expensive physics simulations are enabling a rapid change in the way that common experimental design and analysis tasks are approached. Surrogate models allow simulations to be performed in parallel and separately from downstream tasks, thereby enabling analyses that would be impossible with the simulation in-the-loop; surrogates based on DNNs can effectively emulate diverse non-scalar data of the types collected in fusion and laboratory-astrophysics experiments. The challenge is in training the surrogate model, for which large ensembles of physics simulations must be run, preferably without wasting computational effort on uninteresting simulations. Here, in this paper, we present an iterative sampling scheme that can preferentially propose simulations in interesting regions of parameter space without neglecting unexplored regions, allowing high-quality and wide-ranging surrogate models to be trained using 2–3 times fewer simulations compare to space-filling designs. Our approach uses an explicit importance function defined on the simulation output space, balanced against a measure of simulation density which serves as a proxy for surrogate accuracy. It is easy to implement and can be tuned to find interesting simulations early in the study, allowing surrogates to be trained quickly and refined as new simulations become available; this represents an important step towards the routine generation of deep surrogate models quickly enough to be truly relevant to experimental work.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Uncertainty and feasibility of dynamical downscaling for modeling tropical cyclones for storm surge simulation

This paper presents a modeling study conducted to evaluate the uncertainty of a regional model in simulating hurricane wind and pressure fields, and the feasibility of driving coastal storm surge simulation using an ensemble of region model outputs produced by 18 combinations of three convection schemes and six microphysics parameterizations, using Hurricane Katrina as a test case. Simulated wind and pressure fields were compared to observed H*Wind data for Hurricane Katrina and simulated storm surge was compared to observed high-water marks on the northern coast of the Gulf of Mexico. The ensemble modeling analysis demonstrated that the regional model was able to reproduce the characteristics of Hurricane Katrina with reasonable accuracy and can be used to drive the coastal ocean model for simulating coastal storm surge. Results indicated that the regional model is sensitive to both convection and microphysics parameterizations that simulate moist processes closely linked to the tropical cyclone dynamics that influence hurricane development and intensification. The Zhang and McFarlane (ZM) convection scheme and the Lim and Hong (WDM6) microphysics parameterization are the most skillful in simulating Hurricane Katrina maximum wind speed and central pressure, among the three convection and the six microphysics parameterizations. Error statistics of simulated maximum water levels were calculated for a baseline simulation with H*Wind forcing and the 18 ensemble simulations driven by the regional model outputs. The storm surge model produced the overall best results in simulating the maximum water levels using wind and pressure fields generated with the ZM convection scheme and the WDM6 microphysics parameterization.

Yang, Zhaoqing↗
DOE OSTI2012

Evaluation of Regional Climate Simulations over the Great Lakes Region Driven by Three Global Data Sets

The performance of regional climate simulations is evaluated for the Great Lakes region. Three 10-year (1990–1999) current-climate simulations are performed using the MM5 regional climate model (RCM) with 36-km horizontal resolution. The simulations employed identical configuration and physical parameterizations, but different lateral boundary conditions and sea-surface temperatures derived from the NCEP Global Reanalysis and output from the CCSM3 and GISS general circulation models (GCMs). The simulation results are compared to the North American Regional Reanalysis (NARR). The three RCM simulations appeared to be more accurate in winter and least accurate in summer, and more accurate aloft than near the surface. The reanalysis-constrained simulation adequately captured the spatial distribution and seasonal cycle of the observed surface-air temperature and precipitation, but it produced consistently across all seasons a cold bias that is generally larger over the lakes than over land and a wet bias due to an overestimation of nonconvective precipitation. The simulated seasonal cycle of moisture–flux convergence over the region was in very good agreement with NARR. The two GCM-driven runs adequately simulated the spatial and seasonal variation of temperature, but overestimated cold-season precipitation and underestimated summer precipitation, reversing the observed annual precipitation cycle. The GISS-driven run failed to simulate the prevailing low-level flow and moisture convergence patterns. All three RCM simulations successfully captured the impact of the Great Lakes on the region's climate, especially on winter precipitation, a significant improvement over coarse-resolution GCM simulations over the region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Direct comparisons of X-ray scattering and atomistic molecular dynamics simulations for precise acid copolymers and ionomers

Designing acid- and ion-containing polymers for optimal proton, ion, or water transport would benefit profoundly from predictive models or theories that relate polymer structures with ionomer morphologies. Recently, atomistic molecular dynamics (MD) simulations were performed to study the morphologies of precise poly(ethylene-co-acrylic acid) copolymer and ionomer melts. Here, we present the first direct comparisons between scattering profiles, I(q), calculated from these atomistic MD simulations and experimental X-ray data for 11 materials. This set of precise polymers has spacers of exactly 9, 15, or 21 carbons between acid groups and has been partially neutralized with Li, Na, Cs, or Zn. In these polymers, the simulations at 120 °C reveal ionic aggregates with a range of morphologies, from compact, isolated aggregates (type 1) to branched, stringy aggregates (type 2) to branched, stringy aggregates that percolate through the simulation box (type 3). Excellent agreement is found between the simulated and experimental scattering peak positions across all polymer types and aggregate morphologies. The shape of the amorphous halo in the simulated I(q) profile is in excellent agreement with experimental I(q). We found that the modified hard-sphere scattering model fits both the simulation and experimental I(q) data for type 1 aggregate morphologies, and the aggregate sizes and separations are in agreement. Given the stringy structure in types 2 and 3, we develop a scattering model based on cylindrical aggregates. Both the spherical and cylindrical scattering models fit I(q) data from the polymers with type 2 and 3 aggregates equally well, and the extracted aggregate radii and inter- and intra-aggregate spacings are in agreement between simulation and experiment. Furthermore, these dimensions are consistent with real-space analyses of the atomistic MD simulations. By combining simulations and experiments, the ionomer scattering peak can be associated with the average distance between branches of type 2 or 3 aggregates. Furthermore, this direct comparison of X-ray scattering data to the atomistic MD simulations is a substantive step toward providing a comprehensive, predictive model for ionomer morphology, gives substantial support for this atomistic MD model, and provides new credibility to the presence of stringy, branched, and percolated ionic aggregates in precise ionomer melts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Open-source framework for power system transmission and distribution dynamics co-simulation

The promise of the smart grid entails more interactions between the transmission and distribution networks, and there is an immediate need for tools to provide the comprehensive modelling and simulation required to integrate operations at both transmission and distribution levels. Existing electromagnetic transient simulators can perform simulations with integration of transmission and distribution systems, but the computational burden is high for large-scale system analysis. For transient stability analysis, currently there are only separate tools for simulating transient dynamics of the transmission and distribution systems. In this paper, we introduce an open source co-simulation framework “Framework for Network Co-Simulation” (FNCS), together with the decoupled simulation approach that links existing transmission and distribution dynamic simulators through FNCS. FNCS is a middleware interface and framework that manages the interaction and synchronization of the transmission and distribution simulators. Preliminary testing results show the validity and capability of the proposed open-source co-simulation framework and the decoupled co-simulation methodology.

Huang, Renke↗
DOE OSTI2022

Simulation of plasma transport in MPS-LD linear plasma device by using BOUT++

Here a linear plasma device (LPD) module based on the 2D transport module under the BOUT++ framework is developed in this paper to simulate plasma transport in the LPD. The LPD module includes three parts, i.e. magnetic field calculation, simulation mesh generation and plasma transport setup. The magnetic field is calculated with the circular current loop using the location and current information of each coil. The mesh generation code can produce a simulation mesh for LPD by employing the magnetic field. The plasma transport model is based on the reduced Braginskii equations, which consist of the continuity equation, momentum equation, and energy equation. The fluid neutral model is applied for neutral particle simulation. Deuterium (D) and helium (He) ions and atoms can be simulated by the model. The first attempt to simulate plasma transport in the new LPD, called multiple plasma simulation linear plasma device (MPS-LD), is presented using the developed model. The D plasma transport in the MPS-LD is simulated and benchmarked against the two-point model, showing the validation of the BOUT++ simulation. The effects of radial transport on the heat load to the target are studied, which illustrates the significant impact of the radial diffusivities $D_⟂$ and $\mathcal{X}_{⟂i,e}$ on the plasma. Moreover, the He impurity injection process during the discharge is studied with emphasis on plasma–impurity interactions. The simulation results show that He injection can reduce the plasma energy load to the target significantly, and the efficiency depends on the He source density and injection velocity. The present work provides an alternative and flexible simulation tool for plasma transport in LPDs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Accuracy Guarantees and Quantum Advantage in Analog Open Quantum Simulation with and without Noise

Many-body open quantum systems, described by Lindbladian master equations, are a rich class of physical models that display complex equilibrium and out-of-equilibrium phenomena which remain to be understood. In this paper, we theoretically analyze noisy analog quantum simulation of geometrically local open quantum systems and provide evidence that this problem both is hard to simulate on classical computers and could be approximately solved on near-term quantum devices. First, given a noiseless quantum simulator, we show that the dynamics of local observables and the fixed-point expectation values of rapidly mixing local observables in geometrically local Lindbladians can be obtained to a precision of ϵ in time that is poly ( ϵ − 1 ) and uniform in system size. Furthermore, we establish that the quantum simulator would provide a superpolynomial advantage, in run-time scaling with respect to the target precision and either the evolution time (when simulating dynamics) or the Lindbladian’s decay rate (when simulating fixed points), over any classical algorithm for these problems, assuming BQP ≠ BPP . We then consider the presence of noise in the quantum simulator in the form of additional geometrically local Lindbladian terms. We show that the simulation tasks considered in this paper are stable to errors; i.e., they can be solved to a noise-limited, but system-size independent, precision. Finally, we establish that, assuming BQP ≠ BPP , there are stable geometrically local Lindbladian simulation problems such that, as the noise rate on the simulator is reduced, classical algorithms must take time superpolynomially longer in the inverse noise rate to attain the same precision as the analog quantum simulator. Published by the American Physical Society 2025

Kashyap, Vikram (ORCID:0000000208195207)↗
DOE OSTI2023

A Deep Learning Pipeline for Optimizing Large-scale Phase Field Simulations

Phase field (PF) simulations are computationally expensive but remain a key analysis tool to understand the complex mechanisms of additive manufacturing (AM) processes. Each PF simulation-aided analysis requires thousands of node hours on leadership-class supercomputers. One of the main goals of these analyses is the study of microstructure evolution during the build process which begins with the onset of nucleation. Nucleation occurs under certain thermomechanical conditions which are not known a priori and many PF simulations are required to identify ranges of input thermo-mechanical parameters that can result in the onset of nucleation. Since many of the simulations do not result in nucleation, an analysis campaign often ends up wasting tremendous amounts of precious computing resources executing nucleation-absent simulations. The goal of this work is to design and train deep learning models to inform a PF simulation about the likelihood of the occurrence of nucleation in a future simulation time-step based on the state summary over a finite number of past time-steps of a running simulation. If the prediction determines that the running simulation is unlikely to reach nucleation in the allotted time, then its execution is stopped immediately ultimately resulting in vast reduction in wasted computations when accrued over all the PF simulations typically performed in a single or multiple analysis campaign(s). The paper presents the performance of a machine learning pipeline that uses a convolutional neural network (CNN) model to learn an embedding which is then used with a self-attention network to build a multi-task deep learning model to predict the likelihood of nucleation. The model also predicts the input parameters used in a simulation. Performance is compared with a baseline pipeline that uses an off-the-shelf LeNet-5 model to learn the initial embedding. Despite their smaller size, performance results indicate significant improvement in accuracy of the proposed models compared to the larger baseline models.

Kannan, Ramakrishnan {ramki}↗
DOE OSTI2024

Automated Calibration of Parallel and Distributed Computing Simulators: A Case Study

Many parallel and distributed computing research results are obtained in simulation, using simulators that mimic real-world executions on some target system. Each such simulator is configured by picking values for parameters that define the behavior of the underlying simulation models it implements. The main concern for a simulator is accuracy: simulated behaviors should be as close as possible to those observed in the real-world target system. This requires that values for each of the simulator's parameters be carefully picked, or “calibrated,” based on ground-truth real-world executions. Examining the current state of the art shows that simulator calibration, at least in the field of parallel and distributed computing, is often undocumented (and thus perhaps often not performed) and, when documented, is described as a labor-intensive, manual process. In this work we evaluate the benefit of automating simulation calibration using simple algorithms. Specifically, we use a real-world case study from the field of High Energy Physics and compare automated calibration to calibration performed by a domain scientist. Our main finding is that automated calibration is on par with or significantly outperforms the calibration performed by the domain scientist. Furthermore, automated calibration makes it straightforward to operate desirable tradeoffs between simulation accuracy and simulation speed.

Mc donald, Jesse↗
DOE OSTI2020

Progress on Understanding Rayleigh–Taylor Flow and Mixing Using Synergy Between Simulation, Modeling, and Experiment

Simultaneous advances in numerical methods and computing, theoretical techniques, and experimental diagnostics have all led independently to better understanding of Rayleigh–Taylor (RT) instability, turbulence, and mixing. In particular, experiments have provided significant motivation for many simulation and modeling studies, as well as validation data. Numerical simulations have also provided data that is not currently measurable or very difficult to measure accurately in RT unstable flows. Thus, simulations have also motivated new measurements in this class of buoyancy-driven flows. This overview discusses simulation and modeling studies synergistic with experiments and examples of how experiments have motivated simulations and models of RT instability, flow, and mixing. First, a brief summary of measured experimental and calculated simulation quantities, of experimental approaches, and of issues and challenges in the simulation and modeling of RT experiments is presented. Implicit large-eddy, direct numerical, and large-eddy simulations validated using RT experimental data are then discussed. This is followed by a discussion of modeling using analytical, modal, buoyancy–drag, and turbulent transport models of RT mixing experiments. The discussion will focus on three-dimensional RT mixing arising from multimode perturbations. Finally, this focused review concludes with a perspective on future simulation, modeling, and experimental directions for further research. Research in simulation and modeling of RT unstable flows, coupled with experiments, has made significant progress over the past several decades. This overview serves as an opportunity to both discuss progress and to stimulate future research on simulation and modeling of this unique class of hydrodynamically unstable turbulent flows.

42 ENGINEERING↗
DOE OSTI2014

TH-A-BRF-11: Image Intensity Non-Uniformities Between MRI Simulation and Diagnostic MRI

Purpose: MRI simulation for MRI-based radiotherapy demands that patients be setup in treatment position, which frequently involves use of alternative radiofrequency (RF) coil configurations to accommodate immobilized patients. However, alternative RF coil geometries may exacerbate image intensity non-uniformities (IINU) beyond those observed in diagnostic MRI, which may challenge image segmentation and registration accuracy as well as confound studies assessing radiotherapy response when MR simulation images are used as baselines for evaluation. The goal of this work was to determine whether differences in IINU exist between MR simulation and diagnostic MR images. Methods: ACR-MRI phantom images were acquired at 3T using a spin-echo sequence (TE/TR:20/500ms, rBW:62.5kHz, TH/skip:5/5mm). MR simulation images were obtained by wrapping two flexible phased-array RF coils around the phantom. Diagnostic MR images were obtained by placing the phantom into a commercial phased-array head coil. Pre-scan normalization was enabled in both cases. Images were transferred offline and corrected for IINU using the MNI N3 algorithm. Coefficients of variation (CV=σ/μ) were calculated for each slice. Wilcoxon matched-pairs and Mann-Whitney tests compared CV values between original and N3 images and between MR simulation and diagnostic MR images. Results: Significant differences in CV were detected between original and N3 images in both MRI simulation and diagnostic MRI groups (p=0.010, p=0.010). In addition, significant differences in CV were detected between original MR simulation and original and N3 diagnostic MR images (p=0.0256, p=0.0016). However, no significant differences in CV were detected between N3 MR simulation images and original or N3 diagnostic MR images, demonstrating the importance of correcting MR simulation images beyond pre-scan normalization prior to use in radiotherapy. Conclusions: Alternative RF coil configurations used in MRI simulation can Result in significant IINU differences compared to diagnostic MR images. The MNI N3 algorithm reduced MR simulation IINU to levels observed in diagnostic MR images. Funding provided by Advancing a Healthier Wisconsin.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Estimating building simulation parameters via Bayesian structure learning

Many key building design policies are made using sophisticated computer simulations such as EnergyPlus (E+), the DOE flagship whole-building energy simulation engine. E+ and other sophisticated computer simulations have several major problems. The two main issues are 1) gaps between the simulation model and the actual structure, and 2) limitations of the modeling engine's capabilities. Currently, these problems are addressed by having an engineer manually calibrate simulation parameters to real world data or using algorithmic optimization methods to adjust the building parameters. However, some simulations engines, like E+, are computationally expensive, which makes repeatedly evaluating the simulation engine costly. This work explores addressing this issue by automatically discovering the simulation's internal input and output dependencies from ~20 Gigabytes of E+ simulation data, future extensions will use ~200 Terabytes of E+ simulation data. The model is validated by inferring building parameters for E+ simulations with ground truth building parameters. Our results indicate that the model accurately represents parameter means with some deviation from the means, but does not support inferring parameter values that exist on the distribution's tail.

Edwards, Richard E.↗
DOE OSTI2023

Model simulations of Plum Island Ecosystems LTER low marsh site using ELM-PFLOTRAN

Model simulations using the E3SM Land Model (ELM) coupled to the PFLOTRAN reactive transport model via the Alquimia interface. The simulations were conducted for a tidal salt marsh at the Plum Island Ecosystems LTER near Rowley, Massachusetts, USA. Model simulations were forced using site-specific tidal cycles and salinity, and the simulations used a biogeochemical reaction network including aerobic decomposition, sulfate reduction, iron reduction, and methanogenesis. Model outputs include simulated carbon stocks, carbon dioxide and methane fluxes, and porewater concentrations of key solutes related to sulfur, iron, and carbon cycling. The model simulations included a saline simulation (with tidal sulfate inputs), a fresh simulation (with low salinity and low sulfate inputs), and a saline simulation with lower vegetation productivity to represent the effect of salinity on vegetation. These simulations were conducted to demonstrate that a new model framework incorporating subsurface redox and biogeochemical interactions into a land surface model could reproduce measured surface greenhouse gas fluxes and biogeochemical dynamics in tidal marsh ecosystems, and to test whether including redox interactions in a land surface model would allow the model to resolve contrasts in biogeochemical cycling and greenhouse gas production between saline and freshwater wetlands.The data package includes gzipped tar archives (which can be expanded using standard tar and gzip utilities) of model outputs from three model configurations: saline subsurface and reduced vegetation productivity related to salinity; saline subsurface with vegetation productivity not reduced; and freshwater. Also included are code for the modified E3SM model, Alquimia interface, and PFLOTRAN reactive transport simulator in gzipped tar format; plain text parameter and configuration files; python code files for visualizing model output and defining model configurations; and model output, tide and salinity forcing, and configuration files in netCDF format. See the README.md file in the data package for a detailed description of all files contained in the package. All files are in netCDF (.nc), gzipped tar archive (.tar.gz or .tgz), or text (all other files).Updated: May 13, 2024. Model output, E3SM code, PFLOTRAN input files, and python codes for visualizing results were updated to reflect changes made for the manuscript revision. The updated archive reflects the code and model output from the final accepted manuscript. Changes included updated reaction parameters reflecting improved parameterization and additional comparisons with field measurements. E3SM code changes included better support for multiple grid cells and improved flow and transport parameterization.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Monthly averages of ED2 model simulations initialized with airborne lidar structure, Jan 1981-Dec 2018, Brazilian Amazon

Deforestation and forest degradation (selective logging, fires, fragmentation) have impacted nearly 40% of the original extent of the Brazilian Amazon, and have markedly impacted forest structure across the region. To date, few studies analysed how shifts in forest structure from degradation influence the forest sensitivity to climate extremes, because of the complex interactions between forest structure and micro-environmental conditions. To address this knowledge gap, we carried out a series of simulations across the Brazilian Amazon using the Ecosystem Demography Model (ED2), using observed forest structure derived from 541 airborne lidar transects (375 ha each) and two scenarios representing forest recovery and expansion of degradation to investigate how shifts in forest structure impact ecosystem function under near-average and extreme climate conditions, as part of the manuscript Longo et al 2025 "Degradation and Deforestation Increase the Sensitivity of the Amazon Forest to Climate Extremes". This dataset provides the output results from the ED2 model simulations for the three simulations at monthly time scales, in NetCDF format. For all simulations, we used bias-corrected hourly reanalyses (WFDE5) for most meteorological drivers, except for precipitation, which was obtained from CHIRPS. The meteorological drivers used in the study span 38 years (Jan 1981–Dec 2018). The output results correspond to the last 38 years of simulation (one full cycle of meteorological drivers), in which ED2 simulations used static stand structure (i.e., the forest structure was held constant). The following files are provided:ED2_emean_Global_R004_BrAmaz_s1c0t0l0f0.nc. This corresponds to the Control simulation. The forest structure was obtained from the airborne lidar.ED2_emean_Global_R005_BrAmaz_s1c0t1l1f0.nc. This corresponds to the Degraded simulation. The forest structure was obtained from a spin-up simulation initialized with airborne lidar and a scenario that expanded deforestation and selective logging across the Amazon.ED2_emean_Global_R006_BrAmaz_s1c0t1l0f0.nc. This corresponds to the Recovery simulation. The forest structure was obtained from a spin-up simulation initialized with airborne lidar and a scenario that completely halted deforestation and degradation, allowing degraded forests to recover for 38 years.We also provide file ED2_zones_R004_BrAmaz_s1c0t0l0f0.nc, which classifies each grid cell into zones used in the reference manuscript: 1: Southeast. 2: South. 3: West. 4: Central. 5: Northeast. 6: North. 7: Northwest". Index 0 corresponds to grid cells excluded from sub-region analyses because they were dominated by flooded forests, deforestation, and naturally non-forest vegetation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Quantitative Prediction of Uranium Speciation and Amidoxime Binding in Seawater from Advanced Simulation Techniques

The goal of this project was to quantitatively predict stability of uranium species and thermochemistry of uranium binding with amidoxime ligands in realistic seawater conditions. The specific aims of the project were: (i) determine the stable species of uranium and its solvation structure in realistic seawater conditions; also speciation and solvation of uranium's major competing species in seawater, vanadium; (ii) accurately predict the free energies (and thereby stability constants) of binding between uranium and amidoxime; (iii) accurately predict the reaction enthalpies and entropies of binding between uranium and amidoxime as well as between vanadium and amidoxime. Main results and conclusions are as follows. First-principles molecular dynamic simulations found that the structure of the Ca 2 UO 2 (CO 3 ) 3 complex is very stable and that one Ca ion binds to the center UO 2 (CO 3 ) 3 4- anion stronger than the other Ca ion. This finding suggests that using time-resolved EXAFS spectra may confirm the asymmetry in binding of the two Ca ions in the aqueous Ca 2 UO 2 (CO 3 ) 3 complex. To consider the common ions, solvation of the Ca 2 UO 2 (CO 3 ) 3 complex in seawater was simulated by classical molecular dynamics simulation. It was found that the structure of the Ca 2 UO 2 (CO 3 ) 3 complex is very stable in the model seawater. A Na+ ion was found to be closely associated with the Ca 2 UO 2 (CO 3 ) 3 complex by indirectly interacting with one axial oxygen atom of the UO 2 group bridged by a water molecule. In addition, the Na + ion interacts closely with one Ca 2+ ion than the other. The present simulations revealed the key role of common ions such as Na + in impacting the solvation, structure, and apparent charge of the Ca 2 UO 2 (CO 3 ) 3 complex in seawater. The sequential processes of UO 2 2+ binding with the three CO 3 2- groups and the two Ca 2+ ions in pure water and in 0.1 M NaCl were simulated by classical molecular dynamics with both the non-constrained brute-force approach and umbrella sampling. The simulated free energies show excellent agreement with the experiment. The coordination structures of UO 2 (CO 3 ) 3 4- and CaUO 2 (CO 3 ) 3 2- were found to be significantly affected by the presence of Na + ions, leading to a monodentate binding of a carbonate group to U and a water molecule entering the first coordination shell of U. To shed light on the binding between uranyl and glutardiamidoxime (H 2 B), an important model and state-of-the-art ligand for seawater uranium extraction, a suite of computational methods was employed. From molecular dynamics (MD) simulations, it was found that the binding configurations of B 2- with uranyl favor the twofold distorted η 2 binding between the oximate ends (C=N-O-) and U, while HB - prefers the chelating mode for the oximate end with the neutral end being solvated by water. The free energies of sequential ligand binding to form UO 2 B, [UO 2 B 2 ] 2- , and [UO 2 (HB)B] - were simulated with umbrella sampling and very good agreement with the experimental values was achieved, which corroborates the structural insights into the binding mode. State-of-the-art polymeric sorbents employ both amidoximate and carboxylate groups on the side chains to achieve optimal U uptake and selectivity, so we simulated the binding of a model amidoximate–carboxylate bifunctional ligand with uranyl. Classical MD and free-energy simulations in 0.5 M NaCl showed that the carboxylate group binds first to uranyl, leading to a loose intermediate state, and then, the amidoximate group binds, resulting in a more stable and tight chelate state. Binding of the second bifunctional ligand follows a similar process, and the two ligands prefer a trans configuration around the uranyl group. The simulated free energies indicate that the two bifunctional ligands bind with uranyl 14 kcal/mol stronger than the two ligands with only amidoximate groups, confirming an important synergy between amidoximate and carboxylate groups in binding uranyl.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Investigation of thermolytic hydrogen generation rate of tank farm simulated and actual waste

To support resolution of Potential Inadequacies in the Safety Analysis for the Savannah River Site (SRS) Tank Farm, Savannah River National Laboratory conducted research to determine the thermolytic hydrogen generation rate (HGR) with simulated and actual waste. Gas chromatography methods were developed and used with air-purged flow systems to quantify hydrogen generation from heated simulated and actual waste at rates applicable to the Tank Farm Documented Safety Analysis (DSA). Initial simulant tests with a simple salt solution plus sodium glycolate demonstrated the behavior of the test apparatus by replicating known HGR kinetics. Additional simulant tests with the simple salt solution excluding organics apart from contaminants provided measurement of the detection and quantification limits for the apparatus with respect to hydrogen generation. Testing included a measurement of HGR on actual SRS tank waste from Tank 38. A final series of measurements examined HGR for a simulant with the most common SRS Tank Farm organics at temperatures up to 140 °C. The following conclusions result from this testing. HGR was measured for an actual-waste Tank 38 sample. The HGR measurements at 80, 90, 100, and 110 °C were 1.7×10 -7 , 4.7×10 -7 , 5.9×10 -7 , and 2.6×10 -6 ft 3 hr -1 gal -1 , respectively. The Limit of Detection and Limit of Quantification for the HGR measurements were 2.6×10-8 ft3 hr-1 gal-1 and 5.1×10 -8 ft 3 hr -1 gal -1 , respectively. HGR was measured in a high boiling point (HBP) simulant with major known SRS Tank Farm organic compounds. Testing used fresh organics and did not simulate the long-term exposure of these organics already experienced in the waste. Hence, testing is expected to provide high bias to the hydrogen generation rates. The final HGR measurements at 75, 100, and 140 °C were 6.2×10 -7 , 3.0×10 -6 , and 8.1×10 -5 ft 3 hr -1 gal -1 , respectively. Decreases in the HGR were noted at isothermal conditions with steady gas tracer concentration, which clouds development of a temperature dependent correlation for generation rate. While the simulant contains high concentrations of major SRS organics tested at high temperatures found in the 3H Evaporator, this limited data set may not fully bound values under all possible operating conditions. The HGR measurement apparatus was successfully employed to replicate hydrogen production rate experiments. Simulant without deliberately added organic carbon was found to contain approximately 200 mg/L of organic carbon from tramp organics in the reagents used to prepare the solution. The tests using such simulant generated hydrogen at rates consistent with, but somewhat lower than, those predicted at equivalent carbon and aluminum loadings by methods used at the Hanford Site. Prior to identifying the organic contaminants, potential sources of hydrogen in simulants without deliberately added organic carbon were examined. Testing determined that this hydrogen is not due to caustic reaction of organic materials of construction within the reaction vessel, electrolysis due to current leak, anode/cathode reactions, corrosion, or the method of heating employed in the experiment. During the HBP and Tank 38 testing, a peak was identified at a retention time greater than the retention time of the inert tracer of krypton. This peak has been identified as methane, and the data have been reexamined to obtain methane concentrations. For the HBP testing, methane concentration was constant at the highest temperature studied (140 °C) and higher than hydrogen. At the lower temperatures (100 and 75 °C), methane and hydrogen tracked closely, with methane concentration generally lower than hydrogen. For Tank 38, methane concentration was approximately 1/3 of the hydrogen concentration during testing (100 and 110 °C). At lower temperatures for Tank 38 testing (90, 80, and 27 °C), methane fell below the quantification range.

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

All-Atom Simulation of 3D Hot Spot Formation in Shocked TATB Explosive

TATB is an insensitive high explosive (IHE) critical to the stockpile that is challenging to model at the continuum scale. Advanced detonation models in the Cheetah high explosive chemistry code require validation though subscale simulations. High explosive initiation is determined by micron-scale physics of hot spots formed a shock-collapsed pores. Pore sizes between 100 nm and 1 μm are believed to be the most important for determining the shock sensitivity of TATB. This range of pore sizes is difficult to access at the atomic scale through allatom molecular dynamics (MD) simulations, even with Sierra-class computers. Quasi-2D simulations are widely used and allow much larger pore sizes (up to 400 nm) to be studied, but the applicability of 2D simulations to the actual 3D pore response is not understood. Resolving these uncertainties through “full physics” MD modeling is key for generalizing, parameterizing, and validating the kinds of continuum models used to inform design, safety, and performance. This work was a continuation of FY20 efforts pushing simulations to full 3D with the largest-ever all-atom simulations of an explosive. These were the first all-atom full-3D simulations of large hot spots thought to govern explosive detonation and required over a billion atoms. Simulations were performed using LAMMPS, an open SNL science code. MD explosive models present unique challenges, even for established codes such as LAMMPS. Their model forms are more complex than typical models for metals, while simulating high temperature-pressure conditions is demanding and increases computational cost. Scaling problems in GPU-enabled MD algorithms initially limited simulations to <100 million atoms but were resolved through collaboration with SNL. An overall 24x speedup was obtained relative to CPU machines. Specialized analysis of these simulations required a bottom-up refactoring and algorithm parallelization of in-house codes and application of computer vision algorithms to extract meaningful information.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Frequency Domain Reflectometry (FDR) Simulation Techniques for Digital Twin Representation of an Electrical Cable

Simulation of cable system response to frequency domain reflectometry (FDR) tests can be instrumental to understanding these tests and the nature and influence of various cable anomalies on test signatures. Reflectometry simulations are based upon a finite element representation of cable conductors and insulation to produce an S-parameter at each evaluated frequency. The aggregate collection of cable model S-parameters can simulate the influence on a test signal injected into a physical cable. Such an approach was undertaken in this work to produce a digital twin simulation of a low-voltage electrical cable. The electrical cable digital twin examined the influence of test simulation parameters and the relative influence of cable anomalies, including thermal aging, water or moisture exposure, water or moisture ingress, and other anomalies. The digital twin in this work included modeling of the conductors, insulation, jacket, and surrounding environment (air, water, etc.). The digital twin could be expanded to include cable bends, junctions and splices, branch or T systems, and termination impedances of motors or instruments. Observations and conclusions of this work include: 1. Fully 3D digital twin simulation of an electrical cable using an FDR approach is possible. However, there are tradeoffs between simulation fidelity and solution time, which must be balanced to ensure the simulation solves in an adequate amount of time (e.g., less than 20 minutes). Simulation parameters to balance include frequency bandwidth, number of frequencies, mesh density, connection impedance, and permittivity tolerance. 2. The digital twin simulation can explain FDR sensitivity to various cable anomalies, including entry and exit from an oven or water bath. 3. The digital twin simulation FDR response attenuates with distance along the cable and is further affected by the frequency bandwidth, which is similar to that observed with physical measurements. 4. The resolution of the digital twin FDR peaks increased with increasing bandwidth and with increasing number of frequencies, again similar to physical measurements. 5. The presence of multiple anomalies in the digital twin does not substantially attenuate the FDR response to anomalies located beyond the first encountered anomaly and impedance mismatch. 6. Spectral variation of the permittivity did not have a significant effect on the FDR response compared to a fixed nominal value. 7. Extension of the digital twin to 1000 ft still allowed for detection of distal anomalies near the far end of the electrical cable from the instrument connection point. 8. The ARENA test bed facilitates efficient NDE evaluations of well understood cable anomalies with various NDE methods without risking actual plant damage.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗