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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Calibration of RAFM Micromechanical Model for Creep Using Bayesian Optimization for Functional Output

A Bayesian optimization procedure is presented for calibrating a multimechanism micromechanical model for creep to experimental data of F82H steel. Reduced activation ferritic martensitic (RAFM) steels based on Fe(8–9)%Cr are the most promising candidates for some fusion reactor structures. Although there are indications that RAFM steel could be viable for fusion applications at temperatures up to 600°C, the maximum operating temperature will be determined by the creep properties of the structural material and the breeder material compatibility with the structural material. Due to the relative paucity of available creep data on F82H steel compared to other alloys such as Grade 91 steel, micromechanical models are sought for simulating creep based on relevant deformation mechanisms. As a point of departure, this work recalibrates a model form that was previously proposed for Grade 91 steel to match creep curves for F82H steel. Due to the large number of parameters (9) and cost of the nonlinear simulations, an automated approach for tuning the parameters is pursued using a recently developed Bayesian optimization for functional output (BOFO) framework (Huang et al., 2021, “Bayesian optimization of functional output in inverse problems,” Optim. Eng., 22, pp. 2553–2574). Incorporating extensions such as batch sequencing and weighted experimental load cases into BOFO, a reasonably small error between experimental and simulated creep curves at two load levels is achieved in a reasonable number of iterations. In conclusion, validation with an additional creep curve provides confidence in the fitted parameters obtained from the automated calibration procedure to describe the creep behavior of F82H steel.

42 ENGINEERING↗
DOE OSTI2020

Interfacial solvation and slow transport of hydrated excess protons in non-ionic reverse micelles

This work employs molecular dynamics simulations to investigate the solvation and transport properties of hydrated excess protons (with a hydronium-like core structure) in non-ionic Igepal CO-520 reverse micelles of various sizes in a non-polar solvent. Multiscale Reactive Molecular Dynamics (MS-RMD) simulations were used to describe vehicular and hopping diffusion during the proton transport process. As detailed herein, an excess proton shows a marked tendency to localize in the interfacial region of micellar water pools. Slow proton transport was observed which becomes faster with increasing micellar size. Further analysis reveals that the slow diffusion of an excess proton is a combined result of slow water diffusion and the low proton hopping rate. This study also confirms that a low proton hopping rate in reverse micelles stems from the interfacial solvation of hydrated excess protons and the immobilization of interfacial water. The low water density in the interfacial region makes it difficult to form a complete hydrogen bond network near the hydrated excess proton, and therefore locks in the orientation of hydrated proton cations. The immobilization of the interfacial water also slows the relaxation of the overall hydrogen bond network.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2009

Hydrogen Cyanide in the Upper Troposphere: GEM-AQ Simulation and Comparison with ACE-FTS Observations

We investigate the spatial and temporal distribution of hydrogen cyanide (HCN) in the upper troposphere through numerical simulations and comparison with observations from a space-based instrument. To perform the simulations, we used the Global Environmental Multiscale Air Quality model (GEM-AQ), which is based on the threedimensional Gobal multiscale model developed by the Meteorological Service of Canada for operational weather forecasting. The model was run for the period 2004-2006 on a 1.5deg x 1.5deg global grid with 28 hybrid vertical levels from the surface up to 10 hPa. Objective analysis data from the Canadian Meteorological Centre were used to update the meteorological fields every 24 h. Fire emission fluxes of gas species were generated by using year-specific inventories of carbon emissions with 8-day temporal resolution from the Global Fire Emission Database (GFED) version 2. The model output is compared with HCN profiles measured by the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) instrument onboard the Canadian SCISAT-1 satellite. High values of up to a few ppbv are observed in the tropics in the Southern Hemisphere; the enhancement in HCN volume mixing ratios in the upper troposphere is most prominent in October. Low upper-tropospheric mixing ratios of less than 100 pptv are mostly recorded at middle and high latitudes in the Southern Hemisphere in May-July. Mixing ratios in Northern Hemisphere peak in the boreal summer. The amplitude of the seasonal variation is less pronounced than in the Southern Hemisphere. The comparison with the satellite data shows that in the upper troposphere GEM-AQ perform7s well globally for all seasons, except at northern hi gh and middle latitudes in surnmer, where the model has a large negative bias, and in the tropics in winter and spring, where it exhibits large positive bias. This may reflect inaccurate emissions or possible inaccuracies in the emission profile. The model is able to explain most of the observed variability in the upper troposphere HCN field, includin g the interannual variations in the observed mixing ratio. A complementary comparison with daily total columns of HCN from two middle latitude ground-based stations in Northern Japan for the same simulation period shows that the model captures the observed seasonal variation and also points to an underestimation of model emissions in the Northern Hemisphere in the summer. The estimated average global emission equals 1.3 Tg N/yr. The average atmospheric burden is 0.53 Tg N, and the corresponding lifetime is 4.9 months.

Lupu, A.↗
DOE OSTI2024

Collaborative Research: Vlasov–Maxwell Simulations to resolve electron heating and dissipation, in quasiperpendicular shocks (Final Technical Report)

The project is to study electron heating at quasi-perpendicular shocks. We have analyzed measurements from the Magnetosphere Multiscale (MMS) spacecraft and particle-in-cell simulations to address the question. One key discovery is that waves and are excited in the shock transition region, which further develop into complicated structures and thin current layers, which contribute to accelerate and heat electrons. We further investigated the excitation and evolution of these waves and structures, and their association with ion and electron dynamics. In addition, the analysis technique of Vlasov equation terms is further applied to study electron dynamics at the dayside magnetopause, to understand the effect of density, velocity and temperature variations on electron distributions and kinetics.

58 GEOSCIENCES↗
NASA NTRS2008

Multiscale Modeling of Damage Processes in Aluminum Alloys: Grain-Scale Mechanisms

This paper has two goals related to the development of a physically-grounded methodology for modeling the initial stages of fatigue crack growth in an aluminum alloy. The aluminum alloy, AA 7075-T651, is susceptible to fatigue cracking that nucleates from cracked second phase iron-bearing particles. Thus, the first goal of the paper is to validate an existing framework for the prediction of the conditions under which the particles crack. The observed statistics of particle cracking (defined as incubation for this alloy) must be accurately predicted to simulate the stochastic nature of microstructurally small fatigue crack (MSFC) formation. Also, only by simulating incubation of damage in a statistically accurate manner can subsequent stages of crack growth be accurately predicted. To maintain fidelity and computational efficiency, a filtering procedure was developed to eliminate particles that were unlikely to crack. The particle filter considers the distributions of particle sizes and shapes, grain texture, and the configuration of the surrounding grains. This filter helps substantially reduce the number of particles that need to be included in the microstructural models and forms the basis of the future work on the subsequent stages of MSFC, crack nucleation and microstructurally small crack propagation. A physics-based approach to simulating fracture should ultimately begin at nanometer length scale, in which atomistic simulation is used to predict the fundamental damage mechanisms of MSFC. These mechanisms include dislocation formation and interaction, interstitial void formation, and atomic diffusion. However, atomistic simulations quickly become computationally intractable as the system size increases, especially when directly linking to the already large microstructural models. Therefore, the second goal of this paper is to propose a method that will incorporate atomistic simulation and small-scale experimental characterization into the existing multiscale framework. At the microscale, the nanoscale mechanics are represented within cohesive zones where appropriate, i.e. where the mechanics observed at the nanoscale can be represented as occurring on a plane such as at grain boundaries or slip planes at a crack front. Important advancements that are yet to be made include: 1. an increased fidelity in cohesive zone modeling; 2. a means to understand how atomistic simulation scales with time; 3. a new experimental methodology for generating empirical models for CZMs and emerging materials; and 4. a validation of simulations of the damage processes at the nano-micro scale. With ever-increasing computer power, the long-term ability to employ atomistic simulation for the prognosis of structural components will not be limited by computation power, but by our lack of knowledge in incorporating atomistic models into simulations of MSFC into a multiscale framework.

Hochhalter, J. D.↗
DOE OSTI2020

Solitary Magnetic Structures at Quasi–Parallel Collisionless Shocks: Formation

Solitary magnetic structures known as SLAMS (short large-amplitude magnetic structures) have been considered as essential elements of collisionless shocks with quasi-parallel geometries. Yet the physics underlying their formation remains an open question. In this paper, we use measurements from the magnetospheric multiscale mission combined with fully kinetic simulations to study the formation of SLAMS. We find that gyro-resonance between solar wind ions and right-hand circularly polarized electromagnetic waves results in magnetic field amplification. Gyro-trapping by the growing magnetic field builds up the plasma density that further enhances the current and field. The solitary nature of SLAMS stems from a beat-like magnetic field envelope where the maximum sets the initial location for nonlinear growth. Our results present a conceptual advance on SLAMS, and may shed new light on the open question of magnetic field amplification at astrophysical shocks.

58 GEOSCIENCES↗
DOE OSTI2021

Enhanced Planar Antenna Efficiency Through Magnetic Thin-Films

This work proposes to use magnetic material as the substrate of planar antennas to overcome the platform effect caused by the conducting ground plane. The upper bound of the radiation efficiency of an electric-current-driven low-profile antenna is theoretically derived, which is inversely proportional to the Gilbert damping factor of the magnetic material. Meanwhile, the improvement of radiation due to the use of magnetic material is demonstrated by a three-dimensional (3D) multiphysics and multiscale time-domain model. The simulation results match the theoretical derivation, showing 25% radiation efficiency from a planar antenna backed by a FeGaB thin film with 2.56 m thickness. Furthermore, for conductive ferromagnetic materials, it is shown that the eddy current loss can be well suppressed by laminating the thin film into multiple layers. Here, the radiation efficiency of the modeled antenna with a conductive ferromagnetic substrate is improved from 2.2% to 11.8% by dividing the substrate into 10 layers, with a ferromagnetic material fill factor of 93%.

42 ENGINEERING↗
DOE OSTI2025

Beyond Magic Barrels: Digital manufacturing for crystallization, process development and optimization of explosive materials: Part II Resveratrol Exemplar

This SAND report summarizes work supported by an Engineering Sciences Research Foundation (ESRF) Lab Directed Research and Development (LDRD) project entitled “Beyond Magic Barrels: Digital manufacturing for crystallization, process development and optimization of explosive materials.” This SAND report is written in two parts with Part 1 discusses recrystallization of our explosive exemplar and Part 2 summarizing our work with recrystallization of resveratrol. We have studied resveratrol recrystallization with a multiscale approach combining experiments, modeling and simulation. At the single crystal scale, microscopy experiments illuminate crystal time-dependent growth rates using advanced image analysis. Bench scale experiments were carried out to look at growth of multiple particles in a small reactor creating thousands of particles and analyzing the results with microscopy and μCT. For the modeling we combine kinetic Monte Carlo (kMC) models with subscale information from density functional theory (DFT) or molecular dynamics. This work is discussed in Part 1 and can also be found in a paper from the project discussing a coarse-grained kMC model specifically developed for resveratrol. For well-mixed systems, we have population balance equations (PBE) linked with species mass conservation forming a set of ordinary differential equations that can be solved quickly. For more complicated geometries, such as the vat crystallization used throughout the complex, a coupled computational fluid dynamic (CFD)/PBE method was developed to account for gradients in temperature and concentration and differences in crystallization rates throughout the domain. These simulations are more complex and require high performance computing. We present results for two cases: 5% seed fast cool with parameters fit to the well-mixed case and 5% seed slow cool using the same parameters. We show reasonable agreement with experiments though are particles are significantly larger than the experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Micropolar deep material network

This study extends the Deep Material Network (DMN), a physics-informed machine learning framework, to predict the homogenized mechanical response of composite materials with micropolar (Cosserat-type) constitutive behavior. This extension incorporates microstructure-dependent size effects, enabling accurate, efficient, and size-aware predictions for composites with complex internal architectures. While traditional, direct numerical simulation micropolar models effectively capture size effects by introducing extra local degrees of freedom, they bring significant computational challenges, particularly for multiscale analyses relevant to engineering applications. The micropolar DMN developed in this paper achieves high accuracy while significantly reducing computation time compared to micropolar direct numerical simulations. This advancement enables multiscale analyses and parameter studies that were previously impractical, such as high-cycle fatigue simulations and comprehensive investigations of internal length scale effects notably in size-dependent plastic response and the optimization of lattice structures. By uniting microstructure-sensitive modeling, physics-driven learning, and scalable surrogate modeling, the micropolar DMN paves the way for accelerated material design, large-scale parametric studies, and the reliable incorporation of size-dependent effects across a wide range of engineering applications, including optimization and next-generation composite design.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Develop a new integrated macro→micro←nano (MMN) multiscale modeling framework to optimize high strength aluminum alloys and processes for vehicle light-weighting​

Bending tests provide a means to study plane strain fracture performance of 6000 series aluminum alloys. Metrics from bending tests have been correlated with self-pierce riveting (SPR) performance of a high strength AA6111 automotive aluminum alloy in previous works. Using the ORNL HPC resources, this project developed an innovative macro→micro←nano (MMN) multiscale microstructure-based finite element (FE) code to further understanding of the relationship between microstructure and fracture properties of high-strength 6000-series alloys. This work started with microstructural characterization in both mesoscale and nanoscale and bending performance characterization of AA6111 HS2-T6 alloy at Ford, the MMN framework was applied to this alloy to simulate 3-point VDA bending. From the results of the macro-modeling of 3-point VDA bending, the critical region of fracture was identified, and the region geometry was used to construct the micro-model. The fracture criterion of micron-scale precipitates and aluminum matrix which contains submicron and nano particles (AL-SMP-NP) in the micro-model was calibrated and validated by comparing simulated and measured bending results. With the AL-SMP-NP fracture strain obtained, the fracture strain of Al-matrix containing nano particles (ALNP) will similarly be determined by a submicron scale-model using an edge-constrained FE modeling approach developed by Hu et al. With the ALNP fracture strain obtained, the fracture strain of Al-matrix containing no particles will similarly be determined by a nano scale-model using an edge-constrained FE modeling approach. After the MMN framework is built and fracture criterion calibrated, nano-model FE simulations with virtual microstructures was performed to obtain a reduced order model (ROM) of the fracture criterion of the ALNP as a function of volume fraction, size, and distribution of the nanoparticle. This nano→submicron→micro modeling part allows exploration of the influence of different material nanostructures from different process conditions on the bending properties within the multiscale bending simulation framework and the ROM of material bendability as a function of nanoparticle size and shape was established. This obtained reduced order model (ROM) could help guide the design and selection of materials to improve existing SPR process models that could replace trial-and-error rivet/die selection and help to design new rivet/die combinations capable of robustly joining new higher strength 6000 5 series alloys in automotive body structures. This would enable lightweighting of Ford vehicles leading to greater fuel efficiency and reduce manufacturing time and energy.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Multiscale electro-chemo-mechanical model of high-capacity amorphous silicon anode material in sodium-ion batteries

The growing interest in sodium-ion batteries (SIBs) is fueled by their abundant resources and environmentally friendly nature, with amorphous silicon (a-Si) emerging as a promising anode material for enhancing capacity. However, the key challenge lies in sustaining reversible capacity during cycling. Here, in this work, we developed a multiscale electrochemical model incorporating an a-Si anode to elucidate the performance parameters of SIBs. Additionally, we integrated an electro-chemo-mechanical model at the particle level to capture stress generation, an essential factor in the degradation of high-capacity electrodes. Unlike existing models, our approach accounts for large-deformation chemo-mechanics at the particle scale and includes simulations under varying charge rates to explore multiscale behavior. The results reveal that coupled sodiation significantly prolongs complete cycling times and influences discharge dynamics, indicating that neglecting this coupling leads to an underestimation of actual capacity. Furthermore, we observed pronounced polarization effects at higher charge rates, resulting in heterogeneous stress distributions across the anode. With the identification of critical failure parameters for both active particles and binder materials, offering novel insights for mitigating degradation in high-capacity electrode systems.

Amorphous silicon anode↗
DOE OSTI2025

Integrating Ultra-Coarse-Grained Protein Models into Accessible Workflows for Multiscale Molecular Dynamics

To capture protein conformational transitions using molecular dynamics (MD), several simulation resolutions covering different spatial and temporal scales are typically needed. All-atom (AA) simulations provide fine resolution, but are computationally infeasible for large systems over longer durations. Coarse-grained (CG) and ultra-coarse-grained (UCG) models have a lower resolution and computational cost while still being able to conserve essential protein features. Prior work on a Multiscale Machinelearned Modeling Infrastructure (MuMMI) combined both AA and CG simulations to study RAS-RAF protein interactions, leveraging CG models for longer time scales and using AA to investigate unusual conformations in greater detail. However, MuMMI is still resource-intensive, and this study aims to maximize exploration of the protein conformational space while reducing computational cost. In this paper, we build on prior work that integrates UCG models based on heterogeneous elastic network modeling (hENM) into the MuMMI workflow. We demonstrate that UCG models enable accurate sampling of protein conformations, focusing on simulating RAS-RAF protein interactions. Using higher-resolution CG Martini simulation data, we can automatically refine intramolecular interactions in UCG models. We present a scalable Python package that uses fluctuations observed in higher-resolution CG Martini simulations to estimate bond coefficients of the UCG model. We built novel machine learning-based backmapping methods to recover more detailed CG Martini structures from UCG structures, using diffusion models to learn the mapping between scales. Finally, we present UCG-mini-MuMMI, an accessible and less compute-intensive version of MuMMI as a resource for the scientific community. Incorporating UCG models into MD studies is applicable to a broad range of systems and proteins, and our study offers insights into the advantages and limitations of these methods.

Chemical structure↗
DOE OSTI2023

Multiscale Reactive Model for 1,3,5-Triamino-2,4,6-trinitrobenzene Inferred by Reactive MD Simulations and Unsupervised Learning

When high-energy-density materials are subjected to thermal or mechanical insults at extreme conditions (shock loading), a coupled response between the thermo-mechanical and chemical behaviors is systematically induced. Herein we develop a reaction model for the fast chemistry of 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) at the mesoscopic scale, where the chemical behavior is determined by underlying microscopic reactive simulations. The slow carbon cluster formation is not discussed in the present work. All-atom reactive molecular dynamics (MD) simulations are performed with the ReaxFF potential, and a reduced-order chemical kinetics model for TATB is fitted to isothermal and adiabatic simulations of single crystal chemical decomposition. Unsupervised machine learning techniques based on non-negative matrix factorization are applied to MD trajectories to model the decomposition kinetics of TATB in terms of a four-component model. The associated heats of reaction are fit to the temperature evolution from adiabatic decomposition trajectories. Using a chemical species analysis, we show that non-negative matrix factorization captures the main chemical decomposition steps of TATB and provides an accurate estimation of their evolution with temperature. The final analytical formulation, coupled to a diffusion term, is incorporated into a continuum formalism, and simulation results are compared one-to-one against MD simulations of 1D reaction propagation along different crystallographic directions and with different initial temperatures. A good agreement is found for both the temporal and spatial evolution of the temperature field.

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

Constraint energy minimizing generalized multiscale finite element method for multi-continuum Richards equations

In fluid flow simulation, the multi-continuum model is a useful strategy. When the heterogeneity and contrast of coefficients are high, the system becomes multiscale, and some kinds of reduced order methods are demanded. Combining these techniques with nonlinearity, we will consider in this paper a dual-continuum model which is generalized as a multi-continuum model for a coupled system of nonlinear Richards equations as unsaturated flows, in complex heterogeneous fractured porous media; and we will solve it by a novel multiscale approach utilizing the constraint energy minimizing generalized multiscale finite element method (CEM-GMsFEM). In particular, such a nonlinear system will be discretized in time and then linearized by Picard iteration (whose global convergence is proved theoretically). Subsequently, we tackle the resulting linearized equations by the CEM-GMsFEM and obtain proper offline multiscale basis functions to span the multiscale space (which contains the pressure solution). More specifically, we first introduce two new sources of samples, and the GMsFEM is used over each coarse block to build local auxiliary multiscale basis functions via solving local spectral problems, that are crucial for detecting high-contrast channels. Second, per oversampled coarse region, local multiscale basis functions are created through the CEM as constrainedly minimizing an energy functional. Various numerical tests for our approach reveal that the error converges with the coarse-grid size and that only few oversampling layers as well as basis functions are needed.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2006

PAB3D: Its History in the Use of Turbulence Models in the Simulation of Jet and Nozzle Flows

This is a review paper for PAB3D s history in the implementation of turbulence models for simulating jet and nozzle flows. We describe different turbulence models used in the simulation of subsonic and supersonic jet and nozzle flows. The time-averaged simulations use modified linear or nonlinear two-equation models to account for supersonic flow as well as high temperature mixing. Two multiscale-type turbulence models are used for unsteady flow simulations. These models require modifications to the Reynolds Averaged Navier-Stokes (RANS) equations. The first scheme is a hybrid RANS/LES model utilizing the two-equation (k-epsilon) model with a RANS/LES transition function, dependent on grid spacing and the computed turbulence length scale. The second scheme is a modified version of the partially averaged Navier-Stokes (PANS) formulation. All of these models are implemented in the three-dimensional Navier-Stokes code PAB3D. This paper discusses computational methods, code implementation, computed results for a wide range of nozzle configurations at various operating conditions, and comparisons with available experimental data. Very good agreement is shown between the numerical solutions and available experimental data over a wide range of operating conditions.

Abdol-Hamid, Khaled S.↗
DOE OSTI2024

Topological Data Analysis for Particulate Gels

Soft gels, formed via the self-assembly of particulate materials, exhibit intricate multiscale structures that provide them with flexibility and resilience when subjected to external stresses. Here, this work combines particle simulations and topological data analysis (TDA) to characterize the complex multiscale structure of soft gels. Our TDA analysis focuses on the use of the Euler characteristic, which is an interpretable and computationally scalable topological descriptor that is combined with filtration operations to obtain information on the geometric (local) and topological (global) structure of soft gels. We reduce the topological information obtained with TDA using principal component analysis (PCA) and show that this provides an informative low-dimensional representation of the gel structure. We use the proposed computational framework to investigate the influence of gel preparation (e.g., quench rate, volume fraction) on soft gel structure and to explore dynamic deformations that emerge under oscillatory shear in various response regimes (linear, nonlinear, and flow). Our analysis provides evidence of the existence of hierarchical structures in soft gels, which are not easily identifiable otherwise. Moreover, our analysis reveals direct correlations between topological changes of the gel structure under deformation and mechanical phenomena distinctive of gel materials, such as stiffening and yielding. In summary, we show that TDA facilitates the mathematical representation, quantification, and analysis of soft gel structures, extending traditional network analysis methods to capture both local and global organization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2023

Multiscale Hybrid Modeling of the Impact Response of the Earth's Magnetotail to Ionospheric O+ Outflow

Ionospheric outflow plays an important role in coupling the ionosphere with the solar wind-magnetosphere system. Previous multi-fluid MHD studies explored the global influence of oxygen ions of ionospheric origin (O+) on magnetospheric dynamics. A detailed exploration of the interaction of ionospheric ions with the magnetotail requires kinetic treatment for ions. We perform a self-consistent investigation of these processes with a three-dimensional space-time adaptive hybrid code, HYPERS, powered by an intelligent Event-driven Multi-Agent Planning System (EMAPS). By comparing simulations with and without outflow we conclude that oxygen ions, flowing from the ionosphere through the lobes into the tail or directly entering the inner magnetosphere, are able to significantly modify the magnetotail configuration and induce X-points and current sheet structures at locations where magnetic reconnection does not occur in a simulation without outflow, potentially very close to the Earth. This finding may have implications for interpreting substorms and magnetotail reconnection events observed for southward magnetic field simultaneously with significant contents of oxygen ions of ionospheric origin.

multiscale↗
DOE OSTI2023

Machine learning from RANS and LES to inform coarse grid simulations

Nuclear system thermal hydraulic analysis has historically relied on computationally inexpensive 1D codes. However, such tools are unable to capture multiscale multidimensional effects in large nuclear reactor enclosures. On the other hand, simulations with higher fidelity can be too expensive for such purposes. One of the ways to reduce computational cost is to perform simulations on a coarse grid, which, unfortunately, introduces large discretization errors. In this paper, two high-to-low data-driven approaches are investigated: (1) a coarse grid turbulence model to predict eddy viscosity and (2) correction of errors in coarse grid velocity fields. The approaches aim to reduce grid- and turbulence model-induced errors in coarse grid Reynolds-averaged Navier–Stokes (RANS) simulations. Two sources of high-fidelity data, RANS and large eddy simulations (LES), are explored. To extract the eddy viscosity from the LES data, an inverse optimization problem is solved. However, the LES eddy viscosity is shown to be comparable to the RANS eddy viscosity in terms of error reduction. Therefore, the directly available RANS eddy viscosity was used to develop a coarse grid data-driven turbulence model. Additionally, error correction in velocity is used to reduce the remaining uncertainties and bring the results closer to reality. In conclusion, the performance of the frameworks is demonstrated for a scaled upper plenum of a gas-cooled reactor facility.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗