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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A High-Fidelity Modeling Tool to Support the Design of Oxy-Combustors for Direct-Fired Supercritical CO2 Cycles

The challenge in the design of oxy-combustors for direct-fired supercritical CO 2 (sCO 2 ) cycles is in addressing disparate performance metrics and objectives. Key design parameters to consider include, among others, injector design for mixing and flame stability, split of recycled CO 2 diluent between injectors and cooling films, target flame temperatures to control noncondensable products, and strategies to inject the diluent CO 2 for film cooling and thermal control. In order to support novel oxy-combustor designs, a high-fidelity yet numerically efficient modeling framework based on the CRUNCH CFD® flow solver is presented, featuring key physics-based submodels relevant in this regime. For computational efficiency in modeling large kinetic sets, a flamelet/progress variable (FPV) based tabulated-chemistry approach is utilized featuring a three-stream extension to allow for the simulation of the CO 2 film cooling stream in addition to the fuel and oxidizer streams. Finite rate chemistry effects are modeled in terms of multiple progress variables for the primary flame as well as for slower-evolving chemical species such as NO x and SO x contaminants. Real fluid effects are modeled using advanced equations of states. The predictive capabilities of this computationally tractable design support tool are demonstrated on a conceptual injector design for an oxy-combustor operating near 30 MPa. Simulations results provide quantitative feedback on the effectiveness of the film cooling as well as the level of contaminants (CO, NO, and N) in the exhaust due to impurities entering from the injectors. These results indicate that this framework would be a useful tool for refining and optimizing the oxy-combustor designs as well as risk mitigation analyses.

Engineering↗
DOE OSTI2024

High-Temperature Gas-Cooled Pebble-Bed Reactors Running In And Transient Modeling Capabilities Demonstration

This study presents a comprehensive benchmarking and verification effort of several thermal-hydraulic and multiphysics capabilities for high-temperature gas-cooled reactor (HTGR) applications. The first part of this effort focuses on the running-in verification of Griffin's multiphysics capabilities, specifically for simulating the evolution of Pebble Bed reactor cores from startup to equilibrium. In the absence of validation data, code-to-code comparisons are conducted with Kugelpy, showing good agreement for key quantities like maximum power density and fresh core k-eigenvalue predictions. However, discrepancies in equilibrium core predictions suggest potential issues with cross sections, underscoring the need for further refinement and evaluation. The HTTF system analysis code benchmark involves RELAP5-3D, SAM, and GAMMA+ to assess their predictive capabilities for HTTF behavior under both normal operation and pressurized conduction cooldown (PCC) transient conditions. While there is good agreement in predicting major parameters such as coolant temperature, solid temperature, and flow distribution, discrepancies in transient behavior highlight differences in modeling approaches, nodalizations, and heat transfer models. The HTTF lower plenum CFD benchmark employs nekRS to simulate flow mixing phenomena, successfully capturing relevant flow physics and demonstrating mesh independence in complex geometries. Preliminary results suggest a relatively uniform temperature field but significant unsteadiness in the flow, requiring time-averaging analyses. The GPBR200 system analysis code benchmark uses SAM's core channel and porous media models, incorporating an RCCS loop for decay heat removal. During steady-state and transient conditions, including protected de-pressurized and pressurized loss of forced cooling (DLOFC and PLOFC), both models show good agreement in predicting temperature profiles and key parameters. Notably, while the core channel model underpredicts convective heat transfer effects, both models maintain temperatures well below the TRISO fuel safety limit. These benchmarking efforts collectively enhance the predictive capabilities of the tools used in HTGR design and safety analysis, guiding developments to improve their accuracy and applicability.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Hybrid learning techniques for scientific data reduction with performance guarantees

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Final report- UFL - RAPIDS2: A SciDAC Institute for Computer Science, Data, and Artificial Intelligence

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Studies of Alternative Ventilation Configurations to Mitigate Airborne Exposure Risks in Office Spaces

The objective of this study was to evaluate the impact of alternative ventilation configurations on airflow patterns and potential exposure risks in office spaces. Two existing conference rooms at Sandia NM were modeled using Computational Fluid Dynamics (CFD) simulations to characterize airflow patterns and potential airborne exposure risks in well-mixed and once-through (through-flow) ventilation conditions. Multiple scenarios were studied to evaluate the impact of occupancy, Plexiglass barriers, and a modified-return airflow configuration. Experimental and visualization tests were also conducted to validate the well-mixed and through-flow models and findings. The simulations demonstrated that the modified-return airflow configuration that promoted through-flow conditions reduced pathogen concentrations within the space compared to the well-mixed airflow configuration; occupancy reduction only reduced the number of exposed individuals, and Plexiglass barriers had almost no effect. The experimentally measured air speeds at nine anemometer locations generally matched the simulated airflow velocities, and a fog-purge visualization test was also consistent with simulated results of plume movement and dissipation. The visualization tests demonstrated improvements in air change rate with the modified return, which promoted through-flow conditions, versus the original well-mixed ventilation configuration. The results of this study demonstrate that minor modifications to a space that promote through-flow conditions can improve air quality and reduce pathogen concentrations. Additional airflow modeling and testing of alternative occupied space configurations are recommended to further inform room designs that mitigate airborne exposure risks for occupants.

42 ENGINEERING↗
DOE OSTI2021

INTEGRATE - Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements

The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. This AI-based design technology can capture complex non-linear aerodynamic effects while being 100 times faster than design approaches based on computational fluid dynamics. This project enables innovation in wind turbine design by accelerating time to market through higher-accuracy early design iterations to reduce the levelized cost of energy. INVERTIBLE NEURAL NETWORKS Researchers are leveraging a specialized invertible neural network (INN) architecture along with the novel dimension-reduction methods and airfoil/blade shape representations developed by collaborators at the National Institute of Standards and Technology (NIST) learns complex relationships between airfoil or blade shapes and their associated aerodynamic and structural properties. This INN architecture will accelerate designs by providing a cost-effective alternative to current industrial aerodynamic design processes, including: - Blade element momentum (BEM) theory models: limited effectiveness for design of offshore rotors with large, flexible blades where nonlinear aerodynamic effects dominate - Direct design using computational fluid dynamics (CFD): cost-prohibitive - Inverse-design models based on deep neural networks (DNNs): attractive alternative to CFD for 2D design problems, but quickly overwhelmed by the increased number of design variables in 3D problems AUTOMATED COMPUTATIONAL FLUID DYNAMICS FOR TRAINING DATA GENERATION - MERCURY FRAMEWORK The INN is trained on data obtained using the University of Marylands (UMD) Mercury Framework, which has with robust automated mesh generation capabilities and advanced turbulence and transition models validated for wind energy applications. Mercury is a multi-mesh paradigm, heterogeneous CPU-GPU framework. The framework incorporates three flow solvers at UMD, 1) OverTURNS, a structured solver on CPUs, 2) HAMSTR, a line based unstructured solver on CPUs, and 3) GARFIELD, a structured solver on GPUs. The framework is based on Python, that is often used to wrap C or Fortran codes for interoperability with other solvers. Communication between multiple solvers is accomplished with a Topology Independent Overset Grid Assembler (TIOGA). NOVEL AIRFOIL SHAPE REPRESENTATIONS USING GRASSMAN SPACES We developed a novel representation of shapes which decouples affine-style deformations from a rich set of data-driven deformations over a submanifold of the Grassmannian. The Grassmannian representation as an analytic generative model, informed by a database of physically relevant airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously captured in the data , (ii) improved low-dimensional parameter domain for inferential statistics informing design/manufacturing, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal shapes. TECHNOLOGY TRANSFER DEMONSTRATION - COUPLING WITH NREL WISDEM Researchers have integrated the inverse-design tool for 2D airfoils (INN-Airfoil) into WISDEM (Wind Plant Integrated Systems Design and Engineering Model), a multidisciplinary design and optimization framework for assessing the cost of energy, as part of tech-transfer demonstration. The integration of INN-Airfoil into WISDEM allows for the design of airfoils along with the blades that meet the dynamic design constraints on cost of energy, annual energy production, and the capital costs. Through preliminary studies, researchers have shown that the coupled INN-Airfoil + WISDEM approach reduces the cost of energy by around 1% compared to the conventional design approach. This page will serve as a place to easily access all the publications from this work and the repositories for the software developed and released through this pr...

aerodynamics↗
DOE OSTI2024

Development of an Improved RELAP5-3D Model for the High Temperature Test Facility

High-temperature gas-cooled reactors (HTGRs) are rapidly approaching deployment. Confidence in transient analysis of these systems for design, optimization, and licensing calculations requires modeling and simulation tools that have been validated against data relevant to HTGR conditions. The High Temperature Test Facility (HTTF) is an integral effects thermal hydraulics test facility for prismatic HTGRs. In spring and summer of 2019, HTTF was used for a series of experiments that now serve as the basis for the OECD/NEA Thermal Hydraulic Code Validation Benchmark for High Temperature Gas-Cooled Reactors using HTTF Data (HTGR T/H Benchmark). This benchmark contains problems for systems code, computational fluid dynamics (CFD), and coupled systems code/CFD modeling representing lower plenum mixing and both the depressurized and pressurized conduction cooldown (DCC and PCC respectively) transients. Benchmark problems include exercises for code-to-code and code-to-data comparisons as well as an exercise for error scaling between HTTF and the Modular High Temperature Gas-Cooled Reactor, which serves as the basis for the HTTF design. Previous analysis as part of the HTGR T/H benchmark used a RELAP5-3D model developed at Idaho National Laboratory (INL) and demonstrated an ability to reproduce trends in the measured data but difficulties reproducing experimental values within their uncertainty. These difficulties were largely attributed to assumptions made during the development of the initial RELAP5-3D model, which predated the HTTF experiments. A significant cause of difficulty reproducing the measured temperatures may be the radial nodalization of the previous RELAP5-3D model. The new model provides a finer nodalization to assess the impact of radial nodalization and allows for asymmetric heating within the core, which was a feature of multiple HTTF experiments. In this paper, we present the new RELAP5-3D model of HTTF. In addition to describing the new model, this paper compares the new and old models and provides results for a full-power steady state, a DCC, and a PCC in HTTF. These analyses are based on the code-to-code comparison exercises for the DCC and PCC problems of the HTGR T/H benchmark. We present the results of these exercises from the new model and compare them to the results of the old model.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

High-Speed and High-Quality Field Welding Repair Based on Advanced Non-Destructive Evaluation and Numerical Modeling

Creep strength-enhanced ferritic (CSEF) steels such as Grade 91 (9Cr-1Mo-V) and Grade 92 (Fe-9Cr-2W-0.5Mo) steels are widely used in the fossil-fuel-fired and nuclear power plants. The weld integrity of these steels is crucial for power plants' safe and reliable operations. Due to harsh service conditions, the steel weld can become susceptible to environmental degradation. Field welding repair is used to restore the degraded weld’s performance where a controlled temper-bead welding technique is commonly used to temper the freshly formed martensite during welding. However, knowledge of weld repairability is limited and experimental trial and error optimization to achieve desired microstructure and joint properties is expensive and time-consuming. Many existing computational models, e.g., finite element models, are limited to solving heat conduction equation and ignoring convective heat transfer due to molten metal flow. These models can result in over-prediction of peak temperatures of weld pool and heat-affected zone (HAZ), which in turn can affect the accuracy of tempering prediction. Moreover, these finite element models require an input of the deposit profiles in advance and thus limits the usability of these models. Here, a molten pool-based, multi-pass multi-layer model has been developed based on computational fluid dynamics (CFD) approach with the Volume of Fluid (VOF) method. The model calculates the bead formation, thereby eliminating the need for pre-determined bead profiles required by finite element models. For computational efficiency, a coordinate system attached to the moving heat source is utilized. A subroutine is developed to convert the temperature profiles in the reference frame stationary to the heat source to that stationary to the workpiece. The converted thermal cycles are then imported into a microstructure model to compute the tempering kinetics and resultant hardness using a Johnson-Mehl-Avrami-Kolmogorov (JMAK), and modified Grange-Baughman parameter. The modeling approach is first developed and validated on single- and multi-pass deposition of stainless steel filler metal onto a SA-533 high strength steel substrate. The models are then applied to a multi-pass V-groove repair weld of Grade 91 steel plate as well as directed energy deposition of Grade 92 steel. Non-destructive characterization of microstructures was performed on Grade 91 and 92 steel welds. Two welding processes, cold metal transfer (CMT) and flux-cored arc welding (FCAW), were investigated for the Grade 91 steel weld samples. For the Grade 92 weld samples, three different heat inputs (low, medium, and high) of gas tungsten arc welding (GTAW) were utilized to replicate traditional field welding processes. The non-destructive evaluation (NDE) method used for this research was immersion ultrasonic testing (UT) using a micro-resolution ultrasonic imaging methodology specifically designed to operate in the through-transmission configuration operating at 20 MHz of frequency. The system used a focused ultrasonic beam spot size diameter between 250-300 μm, and a 6 μm laser vibrometer spot size for detection, to produce highly defined images with longitudinal and mode-converted shear waves. From the micro-resolution ultrasonic C-scan images, three microstructural regions, i.e., weld metal (WM), HAZ, and base metal (BM), were clearly identifiable. Various levels of ultrasonic amplitudes distributed over the three regions were correlated with electron beam backscattered diffraction (EBSD) images using grain size, grain boundaries, and dislocation densities. The results showed that areas with relatively higher ultrasonic amplitude levels were associated with smaller grains and higher dislocation densities, while areas with lower amplitude levels were associated with larger grains and lower dislocation densities. In addition, ultrasonic velocity data obtained across the three different weld microstructural regions of Grade 91 test samples were correlated with optical metallographic images and hardness measurements. The results showed distinctive decreases in ultrasonic velocity and hardness over the HAZ region, where weld failures often occur during service.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Sulfur TES ML [SWR-23-02]

Sulfur TES ML contains code originally created to develop surrogate ML models to mimic the utility of CFD simulations of Sulfur TES systems. It has been adapted to provide a comprehensive package for building, training, validating, testing, and optimizing regression models with customizable features and targets.

Menear, Kevin↗
DOE OSTI2022

Preliminary Design of Reactor Cavity Cooling System for a Horizontal Compact HTGR

The Horizontal Compact High Temperature Gas Reactor (HC-HTGR) is being designed by a multi-disciplinary team of nuclear, mechanical, and structural engineers under the support of a DOE-NE Advanced Reactor Demonstration Program’s Advanced Reactor Concepts-20 (ARC-20) award. The objective of this ARC-20 project is to deliver a conceptual design for the proposed MIGHTR in 3 years and support its commercialization as a safe and low-cost HTGR. Argonne National Laboratory (Argonne) is responsible for the design and analysis of the reactor cavity cooling system (RCCS) as a safety system for passive decay heat removal of the reactor concept. This report documents the preliminary design study of the RCCS for the HC-HTGR. It includes the establishment of the design requirements, a high-level design study by initial scoping calculations, and preliminary performance calculations of the HC-HTGR RCCS design. Design requirements for the HC-HTGR RCCS have been established to guide preliminary design activities and scoping performance calculations. Initial scoping calculations including estimation of the water inventory, estimation of HVAC thermal capability, and a parametric study on loop dimensions by standalone RCCS analysis. Based on scoping calculation results, a set of baseline dimensions of the HC-HTGR RCCS was derived. A water panel modeling approach was investigated to explore various potential design options for the water panel under consideration for the HC-HTGR RCCS using RELAP5-3D. A test case study was performed to assess the prediction capability of two modeling approaches. The results were compared with CFD simulations conducted in constant RPV temperature and heat flux boundary conditions. It confirms the capability of the RELAP5-3D modeling approach to include all important heat transfer mechanisms expected in the HC-HTGR RCCS operation conditions. Then, a reference RELAP5-3D model for the 1/8 th of a compartment of the preliminary design of the HC-HTGR RCCS was developed. A preliminary performance analysis was conducted to evaluate single-phase natural circulation performance with different top tank temperature values and panel conduction performance in various operation conditions. From single-phase natural circulation performance analysis, the system operation mode was investigated in normal operating and limiting design conditions. It showed operation mode in a subcooled state with a proper top tank water cooling system. Parasitic heat loss by both internal air flow and RCCS was estimated, showing it satisfies maintaining below target maximum heat loss of the HC-HTGR RCCS. From the panel conduction performance analysis, two candidate materials for the riser tube such as carbon steel and stainless steel were compared in the thermal performance of HC-HTGR RCCS. From a single water panel test compared with CFD simulation results, it was confirmed that the current capability of the RELAP5-3D modeling approach for the water panel predicts the thermal conduction of two different materials of the water panel. Then, system-level thermal performance analysis was performed for 1/8 th of the compartment of the preliminary HC-HTGR RCCS design. It was first observed that the current preliminary HC-HTGR RCCS design had minimal impact on the overall thermal performance of the water panel by changing pipe material from carbon steel to stainless steel. From Argonne’s effort on the ongoing water-based NSTF testing program, several considerations other than the thermal performance point of view were addressed to be considered in selecting pipe materials.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Feasibility of full-core pin resolved CFD simulations of small modular reactor with momentum sources

Complex flow structure interactions and heat transfer processes take place in nuclear reactor cores. Given the extreme pressure/temperature and radioactive conditions inside the core, numerical simulations offer an attractive and sometimes more feasible approach to study the related flow and heat transfer phenomena in addition to the experiments. Under the Exascale Computing Project, the full-core simulation of a small modular reactor (SMR) has been pursued coupling Computational Fluid Dynamics (CFD) and neutronics. A key aspect of the modeling of SMR fuel assemblies is the presence of spacer grids and the mixing promoted by mixing vanes or the equivalent. A reduced order methodology is adopted based on momentum sources to mimic the mixing of the vanes. The momentum sources have been carefully calibrated with detailed Large Eddy Simulations (LES) of spacer grids performed with Nek5000. Modeling the spacer grid and mixing vanes (SGMV) effect without body-fitted computational grid avoids the excessive costs in resolving the local geometric details, and thus supports the simulation to be scaled up to the full core. Besides the progress on momentum source modeling, this paper also features the first full-core pin resolved CFD simulation ever performed to the authors' knowledge. This represents a significant advancement in capability for the CFD of nuclear reactors, which will hopefully serve as an inspiration for further integrating high-fidelity numerical simulations in actual engineering designs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Improving NO x Entitlement with Axial Staging

Manufacturers are constantly under pressure to increase the efficiency of air breathing gas turbines for the next generation of ground based combined cycle power plants without a significant increase in NO x emissions. Axially staging some of the fuel and air is one method manufacturers are exploring to increase the firing temperature with a minimal NO x increase. This project used a high-pressure combustion facility to systematically explore the axial stage design space at conditions relevant to engine manufacturers. Chemiluminescence and PIV images of the reacting jets were obtained as well as exit emissions. This data was used to create correlations for reacting jets-in-crossflow, analogous to what currently exists for non-reacting jets-in-crossflow. Two CFD programs were used to model these results to quantifying the accuracy of the codes and help elucidate the underlying physics that governs the high-pressure reacting jet in crossflow. This report indicates a change in flame/flow-field behavior, and emissions characteristics with increasing pressure from 1-5.5 atm. Additionally, the effects of preheating and premixing the axial jet are explored at various conditions and show increased jet liftoff leads to a decrease in NO x emissions.

03 NATURAL GAS↗
DOE OSTI2021

Investigation of Nonlinear Difference-Frequency Wave Excitation on a Semisubmersible Offshore-Wind Platform with Bichromatic-Wave CFD Simulations: Preprint

The natural surge and pitch frequencies of semisubmersible offshore wind platforms are typically designed to be below the wave frequencies to avoid direct excitation. However, surge or pitch resonance can be excited by the nonlinear low-frequency loads generated by irregular incident waves. Second-order potential-flow models with added Morison drag have been found to underpredict this low-frequency excitation and response. As part of the OC6 project, Computational Fluid Dynamics (CFD) simulations were performed to enable a better understanding of the low-frequency loads and the limitations of lower-fidelity models. The focus of the current paper is to set up a computationally cost-effective CFD simulation of a fixed semisubmersible platform to investigate nonlinear difference-frequency loads and establish the corresponding uncertainty in the results. Due to the high computing cost, CFD simulations of irregular waves can be challenging. Instead, simulations were performed with bichromatic waves having a shorter repeat period. A preliminary comparison with Quadratic Transfer Functions from second-order potential-flow theory shows that CFD models consistently predict higher nonlinear wave loads at the difference frequency, likely due to flow separation and viscous drag not accounted for in potential-flow theory.

2nd order↗
DOE OSTI2021

Preparing an Incompressible-Flow Fluid Dynamics Code for Exascale-Class Wind Energy Simulations

The U.S. Department of Energy has identified exascale-class wind farm simulation as critical to wind energy scientific discovery. A primary objective of the ExaWind project is to build high-performance, predictive computational fluid dynamics (CFD) tools that satisfy these modeling needs. GPU accelerators will serve as the computational thoroughbreds of next-generation, exascale-class supercomputers. Here, we report on our efforts in preparing the ExaWind unstructured mesh solver, Nalu-Wind, for exascale-class machines. For computing at this scale, a simple port of the incompressible-flow algorithms to GPUs is insufficient. To achieve high performance, one needs novel algorithms that are application aware, memory efficient, and optimized for the latest-generation GPU devices. The result of our efforts are unstructured-mesh simulations of wind turbines that can effectively leverage thousands of GPUs. In particular, we demonstrate a first-of-its-kind, incompressible-flow simulation using Algebraic Multigrid solvers that strong scales to more than 4000 GPUs on the Summit supercomputer.

algebraic multigrid↗
DOE OSTI2024

Enhanced Models for the Simulation of Two-Phase Boiling Flow and Critical Heat Flux with the STAR-CCM+ and NEK-2P CFD Codes: Executive Summary (Final Report)

The goal of the project was to develop, verify and validate an advanced two-phase boiling flow modeling capability that enhances the existing two-phase flow modeling of the STAR-CCM+ Computational Fluid Dynamics (CFD) code. The project focused on the simulation of two phase flow and heat transfer phenomena that occur in Boiling Water Reactor (BWR) and Pressurized Water Reactor (PWR) fuel bundles under various operating conditions. The extended STAR-CCM+ two-phase flow models implemented during the project follow the approach similar to the Extended Boiling Framework [1-6] previously developed at Argonne but include more fundamental physical models of boiling phenomena. The development and validation of the extended two-phase models is supported by the U.S. DOE GAIN program and performed in collaboration with Framatome U.S. Inc.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

AN AUTOMATED MACHINE LEARNING-GENETIC ALGORITHM FRAMEWORK WITH ACTIVE LEARNING FOR DESIGN OPTIMIZATION

The use of machine learning (ML)-based surrogate models is a promising technique to significantly accelerate simulation-driven design optimization of internal combustion (IC) engines, due to the high computational cost of running computational fluid dynamics (CFD) simulations. However, training the ML models requires hyperparameter selection, which is often done using trial-and-error and domain expertise. Another challenge is that the data required to train these models are often unknown a priori. In this work, we present an automated hyperparameter selection technique coupled with an active learning approach to address these challenges. The technique presented in this study involves the use of a Bayesian approach to optimize the hyperparameters of the base learners that make up a super learner model. In addition to performing hyperparameter optimization (HPO), an active learning approach is employed, where the process of data generation using simulations, ML training, and surrogate optimization is performed repeatedly to refine the solution in the vicinity of the predicted optimum. The proposed approach is applied to the optimization of a compression ignition engine with control parameters relating to fuel injection, in-cylinder flow, and thermodynamic conditions. It is demonstrated that by automatically selecting the best values of the hyperparameters, a 1.6% improvement in merit value is obtained, compared to an improvement of 1.0% with default hyperparameters. Overall, the framework introduced in this study reduces the need for technical expertise in training ML models for optimization while also reducing the number of simulations needed for performing surrogate-based design optimization.

Owoyele, Opeoluwa↗
DOE OSTI2021

Application of an automated machine learning-genetic algorithm (AutoML-GA) coupled with computational fluid dynamics simulations for rapid engine design optimization

In recent years, the use of machine learning-based surrogate models for computational fluid dynamics (CFD) simulations has emerged as a promising technique for reducing the computational cost associated with engine design optimization. However, such methods still suffer from drawbacks. One main disadvantage is that the default machine learning (ML) hyperparameters are often severely suboptimal for a given problem. This has often been addressed by manually trying out different hyperparameter settings, but this solution is ineffective in case of a high-dimensional hyperparameter space. Besides this problem, the amount of data needed for training is also not known a priori. In response to these issues that need to be addressed, the present work describes and validates an automated active learning approach, AutoML-GA, for surrogate-based optimization of internal combustion engines. In this approach, a Bayesian optimization technique is used to find the best machine learning hyperparameters based on an initial dataset obtained from a small number of CFD simulations. Subsequently, a genetic algorithm is employed to locate the design optimum on the ML surrogate surface. In the vicinity of the design optimum, the solution is refined by repeatedly running CFD simulations at the projected optima and adding the newly obtained data to the training dataset. It is demonstrated that AutoML-GA leads to a better optimum with a lower number of CFD simulations, compared to the use of default hyperparameters. The proposed framework offers the advantage of being a more hands-off approach that can be readily utilized by researchers and engineers in industry who do not have extensive machine learning expertise.

Owoyele, Opeoluwa↗
DOE OSTI2023

Aerosol Particle Deposition on a Spent Nuclear Fuel Assembly Spacer Grid

The flow and particle deposition patterns on surfaces in an idealized spacer grid for a 17x17 pressurized water reactor (PWR) assembly in a spent fuel canister are modeled using computational fluid dynamics (CFD) with laminar flow. The effects of gravitational settling, non-Stokesian flow, and particle slip are first rigorously analyzed. From the analysis, non-Stokesian effects and slip may be neglected for the particle sizes and conditions expected in a canister. For particles that do not settle out, a swirling flow pattern at the corners of a spacer grid channel directs particles to the leeward side of the flow vanes where much of the deposition occurs. Particle deposition increases with increasing particle diameter. Deposition also increases with decreasing flow velocity as this provides more time for particles to settle and deposit on the leeward side of the flow vanes. The fraction of particles that are transmitted through a spacer grid is determined as a function of inlet gas velocity and particle diameter by running the CFD calculation for each set of conditions and for each particle diameter. Curve fits of the transmission curve as a function of particle diameter for a specified spacer grid and flow velocity are applied to a lognormal particle mass density function for the inlet particles. The resulting mass density function and aerosol mass fraction that passes through the spacer grid can be determined analytically without resorting to numerical iteration. A sample calculation of the analytical solution is demonstrated for a lognormal particle mass density function.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗