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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Reinforcement Learning-Guided Long-Timescale Simulation of Hydrogen Transport in Metals

Diffusion in alloys is an important class of atomic processes. However, atomistic simulations of diffusion in chemically complex solids are confronted with the timescale problem: the accessible simulation time is usually far shorter than that of experimental interest. In this work, long-timescale simulation methods are developed using reinforcement learning (RL) that extends simulation capability to match the duration of experimental interest. Two special limits, RL transition kinetics simulator (TKS) and RL low-energy states sampler (LSS), are implemented and explained in detail, while the meaning of general RL are also discussed. As a testbed, hydrogen diffusivity is computed using RL TKS in pure metals and a medium entropy alloy, CrCoNi, and compared with experiments. The algorithm can produce counter-intuitive hydrogen-vacancy cooperative motion. We also demonstrate that RL LSS can accelerate the sampling of low-energy configurations compared to the Metropolis–Hastings algorithm, using hydrogen migration to copper (111) surface as an example.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Recommended direct simulation Monte Carlo collision model parameters for modeling ionized air transport processes

A systematic approach for calibrating the direct simulation Monte Carlo (DSMC) collision model parameters to achieve consistency in the transport processes is presented. The DSMC collision cross section model parameters are calibrated for high temperature atmospheric conditions by matching the collision integrals from DSMC against ab initio based collision integrals that are currently employed in the Langley Aerothermodynamic Upwind Relaxation Algorithm (LAURA) and Data Parallel Line Relaxation (DPLR) high temperature computational fluid dynamics solvers. The DSMC parameter values are computed for the widely used Variable Hard Sphere (VHS) and the Variable Soft Sphere (VSS) models using the collision-specific pairing approach. The recommended best-fit VHS/VSS parameter values are provided over a temperature range of 1000-20 000 K for a thirteen-species ionized air mixture. Use of the VSS model is necessary to achieve consistency in transport processes of ionized gases. The agreement of the VSS model transport properties with the transport properties as determined by the ab initio collision integral fits was found to be within 6% in the entire temperature range, regardless of the composition of the mixture. The recommended model parameter values can be readily applied to any gas mixture involving binary collisional interactions between the chemical species presented for the specified temperature range.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Kinetic Simulations of Microstructural Evolution (KSOME)

"Kinetic simulations of microstructural evolution" (KSOME) is an object Kinetic Monte Carlo (OMKC) simulation code. It is developed to simulate microstructural evolution in materials during irradiation. KSOME is developed from ground up with priority given to the flexibility, ease of upgradability, and computation efficiency, in that order. With previously developed simulation codes, various processes (more precisely, the diffusion-reaction processes) that can occur in a system were hardwired into the code. Hence, to perform higher fidelity simulations, a significant coding effort was required every time to incorporate either a more accurate description of existing processes or new processes. With KSOME, the necessity for frequent code upgrades whenever there is a need to perform higher fidelity simulations is either eliminated or reduced significantly. In KSOME, the categories of various diffusion-reaction processes and the data-management system are hardwired, while the execution of the diffusion-reaction processes specific to a system is specified via input files. In the present version of KSOME, the analytical expression required to calculate defect interaction radii are inputted via a text file. However, the ability to parse mathematical expressions from a text file can be extended to input the analytical expression to calculate other defect properties; including the expressions to calculate the change in the migration barriers of mobile defects due to long-range interaction with extended defects

Nandipati, Giridhar [Pacific Northwest National La↗
DOE OSTI2017

Implications of z ~ 6 Quasar Proximity Zones for the Epoch of Reionization and Quasar Lifetimes

In this paper, we study quasar proximity zones in the redshift range $5.77\leqslant z\leqslant 6.54$ by homogeneously analyzing 34 medium-resolution spectra, encompassing both archival and newly obtained data, and exploiting recently updated systemic redshift and magnitude measurements. Whereas previous studies found strong evolution of proximity zone sizes with redshift and argued that this provides evidence for a rapidly evolving intergalactic medium (IGM) neutral fraction during reionization, we measure a much shallower trend $\propto {(1+z)}^{-1.44}$. We compare our measured proximity zone sizes to predictions from hydrodynamical simulations post-processed with one-dimensional radiative transfer and find good agreement between observations and theory, irrespective of the ionization state of the ambient IGM. This insensitivity to IGM ionization state has been previously noted, and results from the fact that the definition of proximity zone size as the first drop of the smoothed quasar spectrum below the 10% flux transmission level probes locations where the ionizing radiation from the quasar is an order of magnitude larger than the expected ultraviolet ionizing background that sets the neutral fraction of the IGM. Our analysis also uncovered three objects with exceptionally small proximity zones (two have ${R}_{p}\lt 1$ proper Mpc), which constitute outliers from the observed distribution and are challenging to explain with our radiative transfer simulations. Finally, we consider various explanations for their origin, such as strong absorption line systems associated with the quasar or patchy reionization, but find that the most compelling scenario is that these quasars have been shining for ≲10 5 years.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Behavior of contaminants and simulant fission products in the advanced chlorination process

The University of Tennessee, Knoxville and Oak Ridge National Laboratory have completed proof-of principle testing on an advanced sulfur-based zirconium and aluminum chlorination process for the recovery of Zr or Al from spent fuel claddings and purification of the products. This process could be used to recover hafnium-free nuclear-grade zirconium for reuse or disposal as low-level waste. Testing showed that even non-optimized reaction protocols could produce zirconium product streams from simulated irradiated fuel with purities >90%. The chlorination process was tested on representative fission product species that would be found in Advanced Test Reactor fuel (ATR). First, fission product generation was determined through modeling and simulation of the ATR. The generation of fission products was used to selected contaminants and potentially volatile fission product simulants for laboratory testing. The goal is to identify which species may react with the sulfur-chlorine reagents and be transported into the advanced chlorination off-gas.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

The EGS Collab Project – Stimulations at Two Depths

The EGS Collab project, supported by the US Department of Energy, is performing intensively monitored rock stimulation and flow tests at the 10-m scale in an underground research laboratory to address challenges in implementing enhanced geothermal systems (EGS). Data and observations from the field tests are compared to simulations to understand processes and build confidence in numerical modeling of the processes. We have completed Experiment 1 (of 3), which examined hydraulic fracturing in a well-characterized underground fractured phyllite test bed at a depth of approximately 1.5 km at the Sanford Underground Research Facility (SURF) in Lead, South Dakota. Testbed characterization included fracture mapping, borehole acoustic and optical televiewers, full waveform sonic, conductivity, resistivity, temperature, campaign p- and s-wave investigations and electrical resistance tomography. Borehole geophysical techniques including passive seismic, continuous active source seismic monitoring, electrical resistance tomography, fiber-based distributed strain, distributed temperature, and distributed acoustic monitoring, were used to carefully monitor stimulation events and flow tests. More than a dozen stimulations and nearly one year of flow tests were performed. Quality data and detailed observations were collected and analyzed during stimulation and water flow tests using ambient temperature and chilled water. We achieved adaptive control of the tests using real-time monitoring and rapid dissemination of data and near-real-time simulation. More detailed numerical simulation was performed to answer key experimental design questions, forecast fracture propagation trajectories and extents, and analyze and evaluate results. Data are freely available from the Geothermal Data Repository. Experiment 2 examines the potential for hydraulic shearing in amphibolite at a depth of about 1.25 km at SURF. This site has a different set of stress and fracture conditions than Experiment 1. The Experiment 2 testbed consists of nine subhorizontal boreholes configured in two fans of two boreholes which surround the testbed and contain grouted-in electrical resistance tomography, seismic sensors, active seismic sources and distributed fiber sensors. A “five-spot” set of test wells that extends from a custom mined alcove includes an injection well and four production/monitoring wells. The testbed was characterized geophysically and hydrologically, and three stimulations have been performed using the Step-Rate Injection Method for Fracture In-Situ Properties (SIMFIP) tool to measure strains, and a new strain quantifying tool (downhole robotic strain analysis tool -DORSA) was deployed in a monitoring hole during stimulation. Real-time data were broadcast during stimulations to allow real-time response to arising issues.

EGS Collab, Enhanced Geothermal Systems, EGS, fiel↗
DOE OSTI2021

Modelling a compressible packed bed flow-through washing and deacetylation reactor for corn stover pretreatment

The deacetylation process is a key step in the lignocellulosic biomass pretreatment process that currently is done in batch reactors. A flow-through diffuser type reactor is a promising approach to make the process more efficient and economically favorable. A washing step before deacetylation can further reduce chemical usage and increase the process efficiency. However, the highly compacted biomass particles can cause significant pressure-drop for fluid flow through the bed, which in the worst case can cause self-enhancing pressure-drop leading to reactor blocking. Detailed understanding of the fundamentals of flow through a compressible packed bed is necessary to scale up the washing and deacetylation process. This work closely examines the classical Kozeny Carman equation and proposes modifications to accurately predict the pressure-drop at different compaction states. Here, a compressible packed bed model is further developed based on the interaction between solid mechanics and fluid dynamics. Experimental measurements of water flow through a packed bed of corn stover show good agreement with the developed model. Corn stover washing process scale up simulations show that smaller reactor diameter, higher friction lining material, lower process throughput, and higher process temperature is favorable in stabilizing the bed from extensive compression when the bed height is fixed.

09 BIOMASS FUELS↗
DOE OSTI2024

Spatiotemporal dynamics of fast electron heating in solid-density matter via XFEL

Abstract High-intensity, short-pulse lasers are crucial for generating energetic electrons that produce high-energy-density (HED) states in matter, offering potential applications in igniting dense fusion fuels for fast ignition laser fusion. High-density targets heated by these electrons exhibit spatially non-uniform and highly transient conditions, which have been challenging to characterize due to limitations in diagnostics that provide simultaneous high spatial and temporal resolution. Here, we employ an X-ray Free Electron Laser (XFEL) to achieve spatiotemporally resolved measurements at sub-micron and femtosecond scales on a solid-density copper foil heated by laser-driven fast electrons. Our X-ray transmission imaging reveals the formation of a solid-density hot plasma localized to the laser spot size, surrounded by Fermi degenerate, warm dense matter within a picosecond, and the energy relaxation occurring within the hot plasma over tens of picoseconds. These results validate 2D particle-in-cell simulations incorporating atomic processes and provide insights into the energy transfer mechanisms beyond current simulation capabilities. This work significantly advances our understanding of rapid fast electron heating and energy relaxation in solid-density matter, serving as a key stepping stone towards efficient high-density plasma heating and furthering the fields of HED science and inertial fusion energy research using intense, short-pulse lasers.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Development of High-Performance Cast Crankshafts. Final Technical Report

The objective of this project was to develop technologies that would enable the production of cast crankshafts that can replace high performance forged steel crankshafts. To achieve this, the Ultimate Tensile Strength (UTS) of the new material needs to be 850 MPa with a desired minimum Yield Strength (YS; 0.2% offset) of 615 MPa and at least 10% elongation. Perhaps more challenging, the cast material needs to be able to achieve sufficient local fatigue properties to satisfy the durability requirements in today’s high performance gasoline and diesel engine applications. The project team focused on the development of cast steel alloys for application in crankshafts to take advantage of the higher stiffness over other potential material choices. The material and process developed should be able to produce high-performance crankshafts at no more than 110% of the cost of current production cast units, perhaps the most difficult objective to achieve. To minimize costs, the primary alloy design strategy was to design compositions that can achieve the required properties with minimal alloying and post-casting heat treatments. An Integrated Computational Materials Engineering (ICME) based approach was utilized, rather than relying only on traditional trial-and-error methods, which has been proven to accelerate alloy development time. Prototype melt chemistries designed using ICME were cast as test specimens and characterized iteratively to develop an alloy design within a stage-gate process. Standard characterization and material testing was done to validate the alloy performance against design targets and provide feedback to material design and manufacturing process models. Finally, the project called for Caterpillar and General Motors (GM) to develop optimized crankshaft designs using the final material and manufacturing processing path developed. A multi-disciplinary effort was to integrate finite element analyses by engine designers and geometry-specific casting simulations with existing materials models to optimize crankshaft cost and performance. Prototype crankshafts of the final design were to be produced and validated using laboratory bench testing and on-engine durability testing. ICME process simulation tools were used to investigate a broad range of processing concepts. These concepts included casting orientation, various mold and core materials, and various filling and feeding strategies. Each crankshaft was first simulated without gating and risers, which is termed natural solidification. The natural solidification results were used as a baseline for strategy development of each concept. Casting process simulations and ICME tools were proven to be reasonable predictors of real world results. Potential alloys were developed that could meet the project material property goals with appropriate normalization and temper treatments. For the alloys considered, post-normalization temper treatments proved to be necessary to achieve the desired yield strengths and elongations and appropriate heat treatments were designed using ICME tools. The experimental data of all the alloys were analyzed in combination with ICME tools to establish chemistry-process-structure relations. Several GM small gas engine (SGE) crankshafts were successfully cast in sand molds using two different sprue, runner, gate, riser, chill designs. These crankshafts were cast in two different steel alloys developed during the project, but casting finishing (e.g. riser removal) remains a cost challenge. A long list of future work was left unfinished when this project was unexpectedly terminated.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Seasonal responses of terrestrial ecosystem water‐use efficiency to climate change

Abstract Ecosystem water‐use efficiency ( EWUE ) is an indicator of carbon–water interactions and is defined as the ratio of carbon assimilation ( GPP ) to evapotranspiration ( ET ). Previous research suggests an increasing long‐term trend in annual EWUE over many regions and is largely attributed to the physiological effects of rising CO 2 . The seasonal trends in EWUE , however, have not yet been analyzed. In this study, we investigate seasonal EWUE trends and responses to various drivers during 1982–2008. The seasonal cycle for two variants of EWUE , water‐use efficiency ( WUE , GPP / ET ), and transpiration‐based WUE ( WUE t , the ratio of GPP and transpiration), is analyzed from 0.5° gridded fields from four process‐based models and satellite‐based products, as well as a network of 63 local flux tower observations. WUE derived from flux tower observations shows moderate seasonal variation for most latitude bands, which is in agreement with satellite‐based products. In contrast, the seasonal EWUE trends are not well captured by the same satellite‐based products. Trend analysis, based on process‐model factorial simulations separating effects of climate, CO 2 , and nitrogen deposition ( NDEP ), further suggests that the seasonal EWUE trends are mainly associated with seasonal trends of climate, whereas CO 2 and NDEP do not show obvious seasonal difference in EWUE trends. About 66% grid cells show positive annual WUE trends, mainly over mid‐ and high northern latitudes. In these regions, spring climate change has amplified the effect of CO 2 in increasing WUE by more than 0.005 gC m −2 mm −1 yr −1 for 41% pixels. Multiple regression analysis further shows that the increase in springtime WUE in the northern hemisphere is the result of GPP increasing faster than ET because of the higher temperature sensitivity of GPP relative to ET . The partitioning of annual EWUE to seasonal components provides new insight into the relative sensitivities of GPP and ET to climate, CO 2, and NDEP .

Huang, Mengtian↗
DOE OSTI2017

Application of the new IAPWS Guideline on the fast and accurate calculation of steam and water properties with the Spline-Based Table Look-Up Method (SBTL) in RELAP-7

The numerical simulation of thermalhydraulic processes in nuclear power plants requires very accurate and extremely fast algorithms for calculating the thermophysical properties of water and steam. In order to provide such algorithms, the International Association for the Properties of Water and Steam (IAPWS) has adopted the new “IAPWS Guideline on the Fast Calculation of Steam and Water Properties with the Spline-Based Table Look-Up Method (SBTL)”. In this article, the SBTL method is applied to property functions of specific volume and specific internal energy (v,e) based on the scientific formulation IAPWS-95 and the latest IAPWS formulations for transport properties. From the newly generated SBTL functions, thermodynamic and transport properties as well as their derivatives and inverse functions are calculable in the fluid range of state for pressures up to 100 MPa and for temperatures up to 1273 K, including the metastable liquid and the metastable vapor regions. The SBTL functions reproduce the underlying formulations with an accuracy of 10–100 ppm and significantly reduced computing times. The SBTL method has been implemented into the nuclear reactor system safety analysis code RELAP-7 [2] to consider the real fluid behavior of water and steam in a novel 7-equation two-phase flow model.

42 ENGINEERING↗
DOE OSTI2025

Establishing a process-structure-property-performance framework for SLS additive manufacturing through integrated multiscale modeling

This study presents a comprehensive suite of high-fidelity computational models that integrate multiscale and multiphysics simulations to capture the full Selective Laser Sintering (SLS) additive manufacturing process—from initial melting and solidification to mechanical response under external loads. Process simulations are linked with mechanical analysis through Representative Volume Elements (RVEs), establishing a process-structure–property-performance framework. The interaction between laser light and polyamide 12 (PA12) powder is modeled, accounting for laser characteristics and the optical, thermal, and geometrical properties of the powder. The heat source is incorporated into a heat transfer model, coupled with crystallization kinetics and densification models to predict material density and crystallinity. The porosity distribution from the densification model and crystallinity interpolated from experimental data are used to construct the RVEs. A multi-mechanism constitutive model is then calibrated using mechanical tests to predict the stress–strain response. Simulation results show good agreement with experimental data in terms of porosity, crystallinity, and mechanical performance when sufficient laser power (62 W or higher) is used. This research supports the inverse design of 3D-printed structures by introducing a high-fidelity framework that combines multiscale and multiphysics modeling with experimental calibration for predictive and performance-driven additive manufacturing.

SLS↗
DOE OSTI2019

Kinetic Monte Carlo Simulations of Chemical Reactions on Surfaces

Lattice based kinetic Monte Carlo (kMC) simulations are a powerful tool for micro-kinetic modeling. The kinetic Monte Carlo technique stochastically simulates chemical reaction processes (e.g. adsorption, desorption, diffusion), where the probability of a given reaction process being selected is weighted by its rate constant. The open source software package, KMOS1, allows fast construction of kMC simulations by automatically implementing a computationally efficient kMC framework, thus allowing the researcher to focus on accurate parameterization and formulation of the kinetic model. From this, the spatiotemporal evolution (e.g. concentration, surface configuration, turnover frequencies) of crystal surfaces or bulk crystalline structures is obtained. KMOS uses the variable step-size method implementation of kMC. At each step, the VSSM updates available events, calculates the corresponding transition rates and simulation time, and selects one of the available events. The lattice based kMC technique has many potential applications within SRNL where chemical kinetics are of interest. By transitioning through time along a trajectory of configuration space, fundamental insights to a system's dominant reactions and parameters can be gained, leading to improved materials and process design. In this work, we have established capabilities for fast implementation of kMC simulations through the installation and testing of the KMOS software on SRNL high performance computing resources. The parameterization of kMC simulations is often performed from first-principles energetic calculations. For the purposes of this work, the test cases use arbitrary species and thus, have arbitrary parameterization. In order to demonstrate the use of KMOS, two primary test cases are shown. Case 1 involves simple adsorption and desorption. Case 2 involves a slightly more complex mechanism. The evolution of the surface concentration and the final surface configuration for simulation Case 2 is shown. Rate-limiting processes and rate-limiting configurations are not always known or understood, but kMC provides information pertaining to both. The capability to implement efficient micro-kinetic models using the kinetic Monte Carlo technique has been established through the testing and installation of KMOS on SRNL HPC resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Shortening the Design and Certification Cycle for Additively Manufactured Materials by Improved Mesoscale Simulations and Validation Experiments: Fiscal Year 2019 Status Report.

This report outlines the fiscal year (FY) 2019 status of an ongoing multi-year effort to develop a general, microstructurally-aware, continuum-level model for representing the dynamic response of material with complex microstructures. This work has focused on accurately representing the response of both conventionally wrought processed and additively manufactured (AM) 304L stainless steel (SS) as a test case. Additive manufacturing, or 3D printing, is an emerging technology capable of enabling shortened design and certification cycles for stockpile components through rapid prototyping. However, there is not an understanding of how the complex and unique microstructures of AM materials affect their mechanical response at high strain rates. To achieve our project goal, an upscaling technique was developed to bridge the gap between the microstructural and continuum scales to represent AM microstructures on a Finite Element (FE) mesh. This process involves the simulations of the additive process using the Sandia developed kinetic Monte Carlo (KMC) code SPPARKS. These SPPARKS microstructures are characterized using clustering algorithms from machine learning and used to populate the quadrature points of a FE mesh. Additionally, a spall kinetic model (SKM) was developed to more accurately represent the dynamic failure of AM materials. Validation experiments were performed using both pulsed power machines and projectile launchers. These experiments have provided equation of state (EOS) and flow strength measurements of both wrought and AM 304L SS to above Mbar pressures. In some experiments, multi-point interferometry was used to quantify the variation is observed material response of the AM 304L SS. Analysis of these experiments is ongoing, but preliminary comparisons of our upscaling technique and SKM to experimental data were performed as a validation exercise. Moving forward, this project will advance and further validate our computational framework, using advanced theory and additional high-fidelity experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2017

The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6

Our primary objective of CFMIP is to inform future assessments of cloud feedbacks through improved understanding of cloud–climate feedback mechanisms and better evaluation of cloud processes and cloud feedbacks in climate models. But, the CFMIP approach is also increasingly being used to understand other aspects of climate change, and so a second objective has now been introduced, to improve understanding of circulation, regional-scale precipitation, and non-linear changes. CFMIP is supporting ongoing model inter-comparison activities by coordinating a hierarchy of targeted experiments for CMIP6, along with a set of cloud-related output diagnostics. CFMIP contributes primarily to addressing the CMIP6 questions How does the Earth system respond to forcing? and What are the origins and consequences of systematic model biases? and supports the activities of the WCRP Grand Challenge on Clouds, Circulation and Climate Sensitivity. A compact set of Tier 1 experiments is proposed for CMIP6 to address this question: (1) what are the physical mechanisms underlying the range of cloud feedbacks and cloud adjustments predicted by climate models, and which models have the most credible cloud feedbacks? Additional Tier 2 experiments are proposed to address the following questions. (2) Are cloud feedbacks consistent for climate cooling and warming, and if not, why? (3) How do cloud-radiative effects impact the structure, the strength and the variability of the general atmospheric circulation in present and future climates? (4) How do responses in the climate system due to changes in solar forcing differ from changes due to CO 2 , and is the response sensitive to the sign of the forcing? (5) To what extent is regional climate change per CO 2 doubling state-dependent (non-linear), and why? (6) Are climate feedbacks during the 20th century different to those acting on long-term climate change and climate sensitivity? (7) How do regional climate responses (e.g. in precipitation) and their uncertainties in coupled models arise from the combination of different aspects of CO 2 forcing and sea surface warming? CFMIP also proposes a number of additional model outputs in the CMIP DECK, CMIP6 Historical and CMIP6 CFMIP experiments, including COSP simulator outputs and process diagnostics to address the following questions. How well do clouds and other relevant variables simulated by models agree with observations? What physical processes and mechanisms are important for a credible simulation of clouds, cloud feedbacks and cloud adjustments in climate models? Which models have the most credible representations of processes relevant to the simulation of clouds? How do clouds and their changes interact with other elements of the climate system?

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Modeling and Simulation of the Off-gas in an Electric Arc Furnace

The following paper describes an approach to process modeling and simulation of the gas phase in an electric arc furnace (EAF). The work presented represents the continuation of research by Logar, Dovžan, and Škrjanc on modeling the heat and mass transfer and the thermochemistry in an EAF. Due to the lack of off-gas measurements, Logar et al. modeled a simplified gas phase under consideration of five gas components and simplified chemical reactions. The off-gas is one of the main continuously measurable EAF process values and the off-gas flow represents a heat loss up to 30 pct of the entire EAF energy input. Therefore, gas phase modeling offers further development opportunities for future EAF optimization. This paper presents the enhancement of the previous EAF gas phase modeling by the consideration of additional gas components and a more detailed heat and mass transfer modeling. In order to avoid the increase of simulation time due to more complex modeling, the EAF model has been newly implemented to use an efficient numerical solver for ordinary differential equations. Compared to the original model, the chemical components H{sub 2}, H{sub 2}O, and CH{sub 4} are included in the gas phase and equilibrium reactions are implemented. The results show high levels of similarity between the measured operational data from an industrial scale EAF and the theoretical data from the simulation within a reasonable simulation time. In the future, the dynamic EAF model will be applicable for on- and offline optimizations, e.g., to analyze alternative input materials and mode of operations.

42 ENGINEERING↗
DOE OSTI2023

Physics informed deep neural network embedded in a chemical transport model for the Amazon rainforest

Secondary organic aerosols (SOA) are fine particles in the atmosphere, which interact with clouds, radiation and affect the Earth’s energy budget. SOA formation involves chemistry in gas phase, aqueous aerosols, and clouds. Simulating these chemical processes involve solving a stiff set of differential equations, which are computationally expensive steps for three-dimensional chemical transport models. Deep neural networks (DNNs) are universal function approximators that could be used to represent the complex nonlinear changes in aerosol physical and chemical processes; however, key challenges such as generalizability to extended time periods, preservation of mass balance, simulating sparse model outputs, and maintaining physical constraints have limited their use in atmospheric chemistry. Here, we develop an approach of using a physics-informed DNN that overcomes previous such challenges and demonstrates its applicability for the chemical formation processes of isoprene epoxydiol SOA (IEPOX-SOA) over the Amazon rainforest. The DNN is trained with data generated by simulating IEPOX-SOA over the entire atmospheric column, using the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem). The trained DNN is then embedded within WRF-Chem to replace the computationally expensive default solver of IEPOX-SOA formation. The trained DNN predictions generalizes well with the default model simulation of the IEPOX-SOA mass concentrations and its size distribution (20 size bins) over several days of simulations in both dry and wet seasons. The embedded DNN reduces the computational expense of WRF-Chem by a factor of 2. Our approach shows promise in terms of application to other computationally expensive chemistry solvers in climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Atomic masses with machine learning for the astrophysical r process

The astrophysical r process plays a vital role in the production of heavy elements. Modeling of the r process is sensitive to masses and further requires knowledge of masses beyond current experimental reach. Therefore, simulations of the r process offer a unique test bed for predicting mass extrapolations. We take a Machine-Learning (ML) approach to model the masses across the entire chart of nuclides. For the first time, we simulate r-process nucleosynthesis with a physics-based ML mass model. We compare simulated abundances to solar data in order to evaluate the model's performance far from stability. The resulting r-process abundances up to thorium and uranium qualitatively match those of the observed solar system abundance pattern, with the characteristic peaks well positioned. We propagate the mass uncertainties obtained from the ML model to r-process abundance yields to estimate an uncertainty band associated with our approach. The size of the uncertainty band is approximately one order of magnitude which aligns with the uncertainty reported using alternative techniques.

79 ASTRONOMY AND ASTROPHYSICS↗