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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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At least 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

National Renewable Energy Laboratory (NREL) Topic 2 Final Report: End-to-End Communication and Control System to Support Clean Energy Technologies

This document is the final report of a two-year development, test, and demonstration project, 'Cohesive Application of Standards- Based Connected Devices to Enable Clean Energy Technologies.' The project was part of the National Renewable Energy Laboratory's (NREL's) Integrated Network Testbed for Energy Grid Research and Technology (INTEGRATE) initiative hosted at Energy Systems Integration Facility (ESIF). This project demonstrated techniques to control distribution grid events using the coordination of traditional distribution grid devices and high-penetration renewable resources and demand response. Using standard communication protocols and semantic standards, the project examined the use cases of high/low distribution voltage, requests for volt-ampere-reactive (VAR) power support, and transactive energy strategies using Volttron. Open source software, written by EPRI to control distributed energy resources (DER) and demand response (DR), was used by an advanced distribution management system (ADMS) to abstract the resources reporting to a collection of capabilities rather than needing to know specific resource types. This architecture allows for scaling both horizontally and vertically. Several new technologies were developed and tested. Messages from the ADMS based on the common information model (CIM) were developed to control the DER and DR management systems. The OpenADR standard was used to help manage grid events by turning loads off and on. Volttron technology was used to simulate a homeowner choosing the price at which to enter the demand response market. Finally, the ADMS used newly developed algorithms to coordinate these resources with a capacitor bank and voltage regulator to respond to grid events.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Epidural anesthesia needle guidance by forward-view endoscopic optical coherence tomography and deep learning

Epidural anesthesia requires injection of anesthetic into the epidural space in the spine. Accurate placement of the epidural needle is a major challenge. To address this, we developed a forward-view endoscopic optical coherence tomography (OCT) system for real-time imaging of the tissue in front of the needle tip during the puncture. We tested this OCT system in porcine backbones and developed a set of deep learning models to automatically process the imaging data for needle localization. A series of binary classification models were developed to recognize the five layers of the backbone, including fat, interspinous ligament, ligamentum flavum, epidural space, and spinal cord. The classification models provided an average classification accuracy of 96.65%. During puncture, it is important to maintain a safe distance between the needle tip and the dura mater. Regression models were developed to estimate that distance based on the OCT imaging data. Based on the Inception architecture, our models achieved a mean absolute percentage error of 3.05% ± 0.55%. Overall, our results validated the technical feasibility of using this novel imaging strategy to automatically recognize different tissue structures and measure the distances ahead of the needle tip during the epidural needle placement.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Improved Parametric Models for Explosion Pressure Signals Derived From Large Datasets

Accurate recording and characterization of explosion-induced pressure signals are key components of the forensic analysis of explosion events in the atmosphere. Parametric overpressure models based on several key waveform features (peak overpressure, positive pulse duration, and impulse) are widely used to estimate explosion energy in terms of trinitrotoluene equivalent yield. However, those models are often developed by a limited dataset, including only a few events or recordings at relatively short propagation distances. Here, we develop empirical waveform-parameter models based on a regression analysis of a large set of data curated from four chemical explosion experiments including 16 detonations. We measured peak overpressure and impulse for positive and negative phases from 1000 pressure signals recorded at local ranges (<20 km) with scaled distance up to 8000 m/kg 1/3 . Additionally, the measured waveform parameters showed large variation with respect to observing distances indicating the effects of atmospheric propagation. In this study, a second-order polynomial model was used in a least-squares regression to account for those propagation effects and to improve data fitting. In addition to model parameters for waveform features, we also determined range-dependent model uncertainties based on data variance. The model uncertainty represents the prediction error of our models and can be critical to evaluating the uncertainty of yield estimate.

58 GEOSCIENCES↗
DOE OSTI2013

Neutron Diffusion Model for Prompt Burst Simulation in Fissile Solutions

A simulation model, based on neutron diffusion, has been developed to predict prompt burst excursions in fissile solutions. This model combines neutron diffusion with simple expressions for radiolytic gas generation and solution temperature change through a neutron cross-section model that accounts for reactivity changes. The simulation model tracks neutron flux as a function of time and position during transients. The model is intended to serve as a general survey tool for the behavior of unreflected aqueous fissile solutions commonly encountered at a variety of nuclear materials processing facilities. The model has been tested against experimental data from the KEWB and Silene research reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Feasibilities of a Coal-Biomass to Liquids Plant in Southern West Virginia

This project has generated comprehensive and realistic results of feasibilities for a coal-biomass to liquids (CBTL) plant in southern West Virginia; and evaluated the sensitivity of the analyses to various anticipated scenarios and parametric uncertainties. Specifically the project has addressed economic feasibility, technical feasibility, market feasibility, and financial feasibility. In the economic feasibility study, a multi-objective siting model was developed and was then used to identify and rank the suitable facility sites. Spatial models were also developed to assess the biomass and coal feedstock availabilities and economics. Environmental impact analysis was conducted mainly to assess life cycle analysis and greenhouse gas emission. Uncertainty and sensitivity analysis were also investigated in this study. Sensitivity analyses on required selling price (RSP) and greenhouse gas (GHG) emissions of CBTL fuels were conducted according to feedstock availability and price, biomass to coal mix ratio, conversion rate, internal rate of return (IRR), capital cost, operational and maintenance cost. The study of siting and capacity showed that feedstock mixed ratio limited the CBTL production. The price of coal had a more dominant effect on RSP than that of biomass. Different mix ratios in the feedstock and conversion rates led to RSP ranging from $104.3 - $157.9/bbl. LCA results indicated that GHG emissions ranged from 80.62 kg CO 2 eq to 101.46 kg CO2 eq/1,000 MJ of liquid fuel at various biomass to coal mix ratios and conversion rates if carbon capture and storage (CCS) was applied. Most of water and fossil energy were consumed in conversion process. Compared to petroleum-derived-liquid fuels, the reduction in GHG emissions could be between -2.7% and 16.2% with CBTL substitution. As for the technical study, three approaches of coal and biomass to liquids, direct, indirect and hybrid, were considered in the analysis. The process models including conceptual design, process modeling and process validation were developed and validated for different cases. Equipment design and capital costs were investigated on capital coast estimation and economical model validation. Material and energy balances and techno-economic analysis on base case were conducted for evaluation of projects. Also, sensitives studies of direct and indirect approaches were both used to evaluate the CBTL plant economic performance. In this study, techno-economic analysis were conducted in Aspen Process Economic Analyzer (APEA) environment for indirect, direct, and hybrid CBTL plants with CCS based on high fidelity process models developed in Aspen Plus and Excel. The process thermal efficiency ranges from 45% to 67%. The break-even oil price ranges from $86.1 to $100.6 per barrel for small scale (10000 bbl/day) CBTL plants and from $65.3 to $80.5 per barrel for large scale (50000 bbl/day) CBTL plants. Increasing biomass/coal ratio from 8/92 to 20/80 would increase the break-even oil price of indirect CBTL plant by $3/bbl and decrease the break-even oil price of direct CBTL plant by about $1/bbl. The order of carbon capture penalty is direct > indirect > hybrid. The order of capital investment is hybrid (with or without shale gas utilization) > direct (without shale gas utilization) > indirect > direct (with shale gas utilization). The order of thermal efficiency is direct > hybrid > indirect. The order of break-even oil price is hybrid (without shale gas utilization) > direct (without shale gas utilization) > hybrid (with shale gas utilization) > indirect > direct (with shale gas utilization).

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Studies of Reactive Amorphous Compounds and Surfaces: Their Pathways to Crystallinity and Surface Functionality

This project was originally headed by Dr. Abraham Clearfield (Texas A&M University, ret.) and focused on developing structural models of zirconium phosphate (ZrP) based materials at different stages of crystalline order using PDF techniques developed by co-PI Dr. Simon Billinge (Columbia University). When Dr. Clearfield retired, the project was taken over by Dr. Hong-Cai Zhou (Texas A&M University). The project proposed looking at defective metal-organic frameworks (MOFs) supported on the surface of the ZrP materials. During the project, the personnel conducted studies of the dispersion of different phosphate ligands in mixed organic phosphonates, and how they could be utilized to incorporate different metals within the ZrP interlayer spacing.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Adaptive Data-Driven Model Predictive Control for Heat Pipe Microreactors

To establish a technical basis for self-regulating microreactors, a model predictive control (MPC) system is investigated to proactively respond to anomalies and disturbances in anticipation of potential deviations from operating setpoints. Due to the difficulty of developing a physics-based surrogate model that can accurately match plant data in various operating conditions, machine learning algorithms are used in MPC, which allow for learning from both simulation and operation data, thus efficiently describing the targeted transient with arbitrary accuracy. However, one of the biggest concerns in applying ML algorithms like artificial neural networks (ANNs) is that the predictive capabilities of ANN are limited by training data. If there are gaps between the training and target domain, the accuracy of an ANN can degrade significantly when it is used to predict unseen data. To improve the predictive capability of ANN and enable a confident use of data-driven MPCs outside the training data, this study proposes an adaptive data-driven MPC framework. The system will monitor the discrepancy between plant responses and surrogate predictions, fine-tune the ANN-based surrogate when a large discrepancy is detected, and continue MPC operation with updated surrogates. The framework is demonstrated on a point kinetic model for microreactors. The hyperparameters of the update strategy, including layers to update, error thresholds, learning rate discount, and number of data points used for fine-tuning, are optimized so the simulated microreactor is able to follow changes in setpoint with the smallest of deviations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Adaptable Data Driven Model Predictive Control for Heat Pipe Microreactors

To establish a technical basis for self-regulating microreactors, a model predictive control (MPC) system is investigated to proactively respond to anomalies and disturbances in anticipation of potential deviations from operating setpoints. Due to the difficulty of developing a physics-based surrogate model that can accurately match plant data in various operating conditions, machine learning algorithms are used in MPC, which allow for learning from both simulation and operation data, thus efficiently describing the targeted transient with arbitrary accuracy. However, one of the biggest concerns in applying ML algorithms like artificial neural networks (ANNs) is that the predictive capabilities of ANN are limited by training data. If there are gaps between the training and target domain, the accuracy of an ANN can degrade significantly when it is used to predict unseen data. To improve the predictive capability of ANN and enable a confident use of data-driven MPCs outside the training data, this study proposes an adaptive data-driven MPC framework. The system will monitor the discrepancy between plant responses and surrogate predictions, fine-tune the ANN-based surrogate when a large discrepancy is detected, and continue MPC operation with updated surrogates. The framework is demonstrated on a point kinetic model for microreactors. The hyperparameters of the update strategy, including layers to update, error thresholds, learning rate discount, and number of data points used for fine-tuning, are optimized so the simulated microreactor is able to follow changes in setpoint with the smallest of deviations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Computational model for predicting the performance of fluid-powered longiudinal piston modules and motors.

A Matlab-based computational model line code has been developed for predicting the performance of fluid-powered longitudinal piston modules and motors. A system of equations has been developed based upon applying the conservation of mass to a control volume defined by a reciprocating piston. The method results in a differential equation that predicts the pressure gradient in the control volume as a function of rotor rotation angle. The model uses an integration scheme to predict the pressure state in the control volume. The orifice equation is used to predict the flowrate in and out of the control volume. From the resulting solution the torque and speed characteristics of the motor configuration can be predicted, optimized, or tailored for a specific requirement.SAND2019-13983 M 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.

Taylor, Michael↗
DOE OSTI2022

Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanics

Conventional machine-learning (ML) models in computational chemistry learn to directly predict molecular properties using quantum chemistry only for reference data. While these heuristic ML methods show quantum-level accuracy with speeds several orders of magnitude faster than traditional quantum chemistry methods, they suffer from poor extensibility and transferability; i.e., their accuracy degrades on large or new chemical systems. Incorporating quantum chemistry frameworks into the ML models directly solves this problem. Here we take the structure of semiempirical quantum mechanics (SEQM) methods to construct dynamically responsive Hamiltonians. SEQM methods use empirical parameters fitted to experimental properties to construct reduced-order Hamiltonians, facilitating much faster calculations than ab initio methods but with compromised accuracy. By replacing these static parameters with machine-learned dynamic values inferred from the local environment, we greatly improve the accuracy of the SEQM methods. Trained on molecular energies and atomic forces, these dynamically generated Hamiltonian parameters show a strong correlation with atomic hybridization and bonding. Trained with only about 60,000 small organic molecular conformers, the resulting model retains interpretability, extensibility, and transferability when testing on much larger chemical systems and predicting various molecular properties. Overall, this work demonstrates the virtues of incorporating physics-based descriptions with ML to develop models that are simultaneously accurate, transferable, and interpretable.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

A Deterministic and Probabilistic Framework for Forecasting of Time-Series Damage States and Associated End-of-lIfe of a Pressurizer Water Reactor Surge Line under Design-Basis and Grid-Load-Following Loading Conditions

Argonne National Laboratory in support of the Department of Energy’s Light Water Reactor Sustainability (LWRS) program. The present semi-annual report covers the period between May 2018 and September 2018. In this report, we present the work performed to further improve the capability for structural integrity prediction. Part of this work focused on validating a computational fluid dynamics model for modeling thermal stratification. We also present finite element (FE) model based thermal-mechanical stress analysis of a pressurized water reactor (PWR) surge line under grid-load-following loading condition. In addition, we present work related to end-of-life prediction of a PWR surge line based on ASME code and NUREG-6909 deterministic approaches under design-basis and grid-load-following conditions. Our research in this report period also majorly involved with the development of a strategy for probabilistic fatigue life estimation. First, we applied a Weibull probabilistic modeling approach based on end-of-life fatigue data (traditional S~N data under in-air and PWR water conditions) to estimate the probabilistic life for a given strain amplitude. Second, we developed a data analytics based Markov-Chain-Monte-Carlo (MCMC) model for probabilistic modeling of time-series fatigue damage states and the associated probabilistic end-of-life of a component. Unlike the Weibull type approach, the MCMC type may not require a large number of fatigue tests for estimating the end-of-life for given loading and environmental conditions. We demonstrated the use of this framework through symmetric fatigue tests (stress- or strain-ontrolled tests either under in-air or PWR-water conditions) obtained from our earlier experiments. The MCMC model can use base time-series data either obtained from a model or fatigue experiment. Since experimental data are typically more accurate than model-calculated data, we used experiment-based time-series data to demonstrate the MCMC framework. However, when performing a complex experiment is not possible (due to time and costs involved), a model-based approach can be used to generate the time-series data. Similar model-based data are presented in our earlier report (e.g. for cyclic hardening damage states of a PWR surge line under constant amplitude and design-basis loading [1]). However, for the MCMC work in this report, we used experiment-based time-series damage states obtained from fatigue experiments under design-basis and grid-load-following loading conditions. The experimental loading inputs were based on thermal-mechanical FE model results of a typical PWR surge line. Then, we used the experimental time-series damage state data for forecasting the probabilistic time-series damage states and end-of-life of the PWR surge line. Based on this MCMC modeling strategy, and with the assumed loading, environment and thermal-mechanical boundary conditions, we have estimated that a 316SS-pure base metal PWR surge line would survive at least 159 years with zero failure probability.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Application of a Physiologically Based Pharmacokinetic Model to Study Theophylline Metabolism and Its Interactions With Ciprofloxacin and Caffeine: Application of a Physiologically Based Pharmacokinetic

Theophylline is a commonly used bronchodilator. However, due to its narrow therapeutic range, moderate elevation of serum concentration can result in adverse drug reactions (ADRs). ADRs occur because of interhuman pharmacokinetic variability and interactions with coprescribed medicines. We developed a physiologically based pharmacokinetic (PBPK) model of theophylline, caffeine, and ciprofloxacin metabolisms to: examine theophylline pharmacokinetic variability, and predict population-level outcomes of drug–drug interactions (DDIs). A simulation-based equation for personalized dosing of theophylline was derived. Simulations of DDI show that calculated personalized doses are safe even after cotreatment with large doses of strong inhibitors. Simulations of adult populations indicate that the elderly are most susceptible to ADRs stemming from theophylline–ciprofloxacin and theophylline–caffeine interactions. Females, especially Asians, due to their smaller average size, are more susceptible to DDI-induced ADRs following typical dosing practices. Our simulations also show that the higher adipose and lower muscle fractions in females significantly alter the pharmacokinetics of theophylline or ciprofloxacin.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Characterization of volcanic tuff pores pre- and post-underground nuclear detonation using ultra-small and small angle neutron scattering

The ability to accurately model the subsurface transport of radionuclides is fundamental to the remote detection and characterization of underground nuclear explosion (UNE) events. Developing more sophisticated transport models presents a significant opportunity to enhance monitoring capabilities, particularly in the reliable prediction of signature migration. Experimentally determined characterization of geologic materials associated with transport properties is the pertinent base information for such robust model development and calibration. Here, we report results from an unprecedented study demonstrating changes to the pore and fracture network structures in geological materials in response to UNEs over nanometer to micrometer length scales. Volcanic tuffs of five different lithological formations from pre- and post-UNE environments were collected from the Nevada National Security Site. Combined ultra-small and small-angle neutron scattering techniques were used to characterize the tuff pore structure. The results demonstrate measurable differences in the specific surface area and porosity of samples pre- and post-shot from texturally similar lithological formations, indicating that pore properties can serve as a direct physical signature of a UNE. The results also provide experimentally determined transport parameters in support of advanced model development through the integration of gas migration, hydrodynamic simulations, and geologic framework models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Electrical flicker-noise generated by filling and emptying of impurity states in injectors of quantum-cascade lasers

Free running line-widths (>100 kHz), much broader than intrinsic line-widths ∼100 Hz, of existing quantum-cascade lasers are governed by strong flicker frequency-noise originating from electrical flicker noise. Understanding of microscopic origins of the electrical flicker noises in quantum-cascade lasers is crucially important for the reduction of strength of flicker frequency-noise without assistances of any type of feedback schemes. In this article, an ad hoc model that is based on fluctuating charge-dipoles induced by electron trappings and de-trappings at indispensable impurity states in injector super-lattices of a quantum-cascade laser is proposed, developing theoretical framework based on the model. The validity of the present model is evaluated by comparing theoretical voltage-noise power spectral densities based on the model with experimental ones obtained by using mid-infrared quantum-cascade lasers with designed impurity-positioning. The obtained experimental results on flicker noises, in comparison with the theoretical ones, shed light on physical mechanisms, such as the inherent one due to impurity states in their injectors and extrinsic ones due to surface states on the ridge-walls and due to residual deep traps, for electrical flicker-noise generation in existing mid-infrared quantum-cascade lasers. It is shown theoretically that quasi-delta doping of impurities in their injectors leads to strong suppression of electrical flicker noise by minimization of the dipole length at a certain temperature, for instance ∼300 K and, in turn, is expected to result in substantial narrowing of the free running line-width down below 10 kHz.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Modeling and Simulation Tool to Improve Urban Search Planning (NA22 Final Report)

The venture project Tool to Improve Urban Search Planning (LL14-V-Urban Search Planning ToolPD3RS) supported two major types of effort: 1) development of a software prototype search planning tool, with the goal of transitioning the tool to operational users for evaluation and possible adoption; 2) enabling research to better understand the radiation characteristics of an urban environment, with implications for deployment of radiation detection systems in that environment. The first effort has resulted in prototype software, Optimization Planning Tool for Urban Search (OPTUS), that has been distributed to a limited set of potential users, including users within the NNSA emergency response community. The second effort supported various research topics including high fidelity radiation transport modeling in urban environments, measurement campaigns for model validation, development of vehicle-based high purity germanium (HPGe) detector systems and supporting data analysis, and computational capabilities for archiving and managing large data sets. While some of these related efforts have informed the models and software design of the OPTUS tool, in large part they pursued more basic research, some of which has evolved into separate DNN R&D projects. Project performers included Oak Ridge National Laboratory (ORNL), Lawrence Berkeley National Laboratory (LBNL) and the Remote Sensing Laboratory (RSL), in addition to Lawrence Livermore Laboratory (LLNL), the project lead. The project also supported university research, including several internships at the laboratories.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Performance Analysis of Transposition Models Simulating Solar Radiation on Inclined Surfaces

Transposition models have been widely used in the solar energy industry to simulate solar radiation on inclined photovoltaic panels. Following numerous studies comparing the performance of transposition models, this work aims to understand the quantitative uncertainty in state-of-the-art transposition models and the sources leading to the uncertainty. Our results show significant differences between two highly used isotropic transposition models, with one substantially underestimating the diffuse plane-of-array irradiances when diffuse radiation is perfectly isotropic. In the empirical transposition models, the selection of the empirical coefficients and land surface albedo can both result in uncertainty in the output. This study can be used as a guide for the future development of physics-based transposition models and evaluations of system performance.

14 SOLAR ENERGY↗
DOE OSTI2010

A global 3D P-Velocity model of the Earth%3CU%2B2019%3Es crust and mantle for improved event location.

To test the hypothesis that high quality 3D Earth models will produce seismic event locations which are more accurate and more precise, we are developing a global 3D P wave velocity model of the Earth's crust and mantle using seismic tomography. In this paper, we present the most recent version of our model, SALSA3D (SAndia LoS Alamos) version 1.4, and demonstrate its ability to reduce mislocations for a large set of realizations derived from a carefully chosen set of globally-distributed ground truth events. Our model is derived from the latest version of the Ground Truth (GT) catalog of P and Pn travel time picks assembled by Los Alamos National Laboratory. To prevent over-weighting due to ray path redundancy and to reduce the computational burden, we cluster rays to produce representative rays. Reduction in the total number of ray paths is > 55%. The model is represented using the triangular tessellation system described by Ballard et al. (2009), which incorporates variable resolution in both the geographic and radial dimensions. For our starting model, we use a simplified two layer crustal model derived from the Crust 2.0 model over a uniform AK135 mantle. Sufficient damping is used to reduce velocity adjustments so that ray path changes between iterations are small. We obtain proper model smoothness by using progressive grid refinement, refining the grid only around areas with significant velocity changes from the starting model. At each grid refinement level except the last one we limit the number of iterations to prevent convergence thereby preserving aspects of broad features resolved at coarser resolutions. Our approach produces a smooth, multi-resolution model with node density appropriate to both ray coverage and the velocity gradients required by the data. This scheme is computationally expensive, so we use a distributed computing framework based on the Java Parallel Processing Framework, providing us with {approx}400 processors. Resolution of our model is assessed using a variation of the standard checkerboard method, as well as by directly estimating the diagonal of the model resolution matrix based on the technique developed by Bekas, et al. We compare the travel-time prediction and location capabilities of this model over standard 1D models. We perform location tests on a global, geographically-distributed event set with ground truth levels of 5 km or better. These events generally possess hundreds of Pn and P phases from which we can generate different realizations of station distributions, yielding a range of azimuthal coverage and proportions of teleseismic to regional arrivals, with which we test the robustness and quality of relocation. The SALSA3D model reduces mislocation over standard 1D ak135, especially with increasing azimuthal gap. The 3D model appears to perform better for locations based solely or dominantly on regional arrivals, which is not unexpected given that ak135 represents a global average and cannot therefore capture local and regional variations.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Regional-scale fault-to-structure earthquake simulations with the EQSIM framework: Workflow maturation and computational performance on GPU-accelerated exascale platforms

Continuous advancements in scientific and engineering understanding of earthquake phenomena, combined with the associated development of representative physics-based models, is providing a foundation for high-performance, fault-to-structure earthquake simulations. However, regional-scale applications of high-performance models have been challenged by the computational requirements at the resolutions required for engineering risk assessments. The EarthQuake SIMulation (EQSIM) framework, a software application development under the US Department of Energy (DOE) Exascale Computing Project, is focused on overcoming the existing computational barriers and enabling routine regional-scale simulations at resolutions relevant to a breadth of engineered systems. This multidisciplinary software development—drawing upon expertise in geophysics, engineering, applied math and computer science—is preparing the advanced computational workflow necessary to fully exploit the DOE’s exaflop computer platforms coming online in the 2023 to 2024 timeframe. Achievement of the computational performance required for high-resolution regional models containing upward of hundreds of billions to trillions of model grid points requires numerical efficiency in every phase of a regional simulation. This includes run time start-up and regional model generation, effective distribution of the computational workload across thousands of computer nodes, efficient coupling of regional geophysics and local engineering models, and application-tailored highly efficient transfer, storage, and interrogation of very large volumes of simulation data. This article summarizes the most recent advancements and refinements incorporated in the workflow design for the EQSIM integrated fault-to-structure framework, which are based on extensive numerical testing across multiple graphics processing unit (GPU)-accelerated platforms, and demonstrates the computational performance achieved on the world’s first exaflop computer platform through representative regional-scale earthquake simulations for the San Francisco Bay Area in California, USA.

58 GEOSCIENCES↗