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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Multiphysics Degradation Modeling of Energy Storage Materials via RKPM with a Neural Network-Enhancement

In energy storage materials, strong electrochemical-mechanical coupling and highly anisotropic material properties contribute to the formation and propagation of micro-cracking during charge/discharge cycling, resulting in reduced performance and service life. A coupled electro-chemo-mechanical reproducing kernel particle method (RKPM) formulation is developed, and a patch-test is formulated to certify optimal convergence of the proposed RKPM method for the coupled physics system. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), pixel-based model construction by RKPM is then used to represent the complex material microstructures for modeling the coupled physics of these systems. Further, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1, 2] is introduced to effectively model damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. Reference: [1] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, pp 4422-4454, https://doi.org/10.1002/nme.7040, 2022. [2] Baek, J., Chen, J. S., "A Neural Network-Based Enrichment of Reproducing Kernel Approximation for Modeling Brittle Fracture", Computer Methods in Applied Mechanics and Engineering Vol. 410, 116590, 2024.

electro-chemo-mechanical coupling↗
DOE OSTI2024

Leveraging a Neural Network-Enhanced Reproducing Kernel Particle Method for Multiphysics Degradation Modeling of Energy Storage Materials

Energy storage materials exhibit strong electro-chemo-mechanical coupling and highly anisotropic material properties, contributing to the formation and propagation of micro-cracking during charge/discharge cycling and resulting in reduced performance and service life. A coupled electro-chemo-mechanical reproducing kernel particle method (RKPM) formulation has been developed to analyze this system. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), pixel-based model construction by RKPM is used to represent the complex material microstructures that dictate the coupled physics of these systems. Traditional electro-chemo-mechanical models rely on mesh-based finite element methods, which can lead to difficulties in meshing such complex geometries and capturing crack propagation due to mesh dependency. Here, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1, 2] is introduced to effectively model damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. NN-RKPM is additionally used to inform how crack opening and closure in turn affect the coupled chemical equations and material microstructure. Reference: [1] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, pp 4422-4454, https://doi.org/10.1002/nme.7040, 2022. [2] Baek, J., Chen, J. S., "A Neural Network-Based Enrichment of Reproducing Kernel Approximation for Modeling Brittle Fracture", Computer Methods in Applied Mechanics and Engineering Vol. 410, 116590, 2024.

degradation↗
DOE OSTI2024

Initial Demonstration of New Griffin Technologies for Simulating the Running-In Phase of Pebble Bed Reactors

Griffin is a reactor multiphysics modeling application based on MOOSE (Multiphysics Object-Oriented Simulation Environment) and specifically targeting transient modeling of advanced reactors. Griffin has been used recently to model pebble-bed reactors for the Nuclear Regulatory Commission (NRC) Office of Nuclear Regulatory Research and the Advanced Reactor Technology program. This modeling work has focused thus far on the direct calculation of equilibrium cores. This report documents an initial demonstration of a new running-in simulation capability. The new running-in capability is verified using the existing direct equilibrium core calculation capability. A simplified pebble-bed reactor model is then used to demonstrate the running-in simulation capability. This demonstration shows that Griffin is able to simulate years of operation during the running-in phase efficiently with each depletion step taking only several seconds. Two new technologies are also presented in this report which have been developed in Griffin that will be essential for improved accuracy both of the direct equilibrium core computation and the new running-in simulation capability. The first technology is an online cross section generation capability specifically targeted for pebble-bed reactors. This will improve the accuracy of the depletion calculation as the cross sections are generated at the exact core status. This also avoids the difficult step of pre-generating a separate standalone multigroup cross section set. Secondly, a newly implemented discretization for discontinuous finite element method (DFEM) SN transport in cylindrical (RZ) coordinates, which can be solved efficiently using the existing SN sweep solver, is discussed and some results are shown demonstrating the usefulness of the additional accuracy transport provides over a diffusion approximation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Pronghorn: A Multidimensional Coarse Mesh Application for Advanced Reactor Thermal-Hydraulics

This paper presents an overview of Pronghorn, a multiscale thermal-hydraulic (T/H) application developed by Idaho National Laboratory and the University of California, Berkeley. Pronghorn, built on the open-source finite element Multiphysics Object-Oriented Simulation Environment (MOOSE), leverages state-of-the-art physical models, numerical methods, and nonlinear solvers to deliver fast-running advanced reactor T/H simulation capabilities within a modern software engineering environment. This work summarizes the physical models, multiphysics and multiscale coupling, and numerical discretization in Pronghorn with emphasis on our initial target application to pebble bed reactors (PBRs). A diverse set of applications are shown to depressurized natural circulation in the SANA experiments, forced convection in the Pebble Bed Modular Reactor, three-dimensional (3-D)/one-dimensional coupling of Pronghorn and RELAP-7 systems T/H for loop analysis in the High Temperature Reactor Power Module, and forced convection in the Mark-1 Pebble Bed Fluoride-Salt-Cooled High-Temperature Reactor. A multiphysics coupling of Pronghorn, RELAP-7, and Griffin deterministic neutronics for a gas-cooled PBR demonstrates the capability of the MOOSE framework for reactor design calculations. These applications highlight the verification and validation underlying Pronghorn’s software development while emphasizing features that improve upon capabilities offered by legacy tools in areas such as 3-D unstructured meshing, physics modeling, and multiphysics coupling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

In-situ magnetic alignment model for additive manufacturing of anisotropic bonded magnets

In this work, we report a mathematical framework which predicts the degree of alignment (DoA) in an in-situ aligned additively manufactured 3D printed bonded magnets. A multiphysics model is developed which couples the harmonious interactions of magnetic particles in a viscous flowing polymer under the presence of an external magnetic field. The hydrodynamic fluid-particle interaction is paired with the magnetophoretic force to predict the particle trajectory and distribution during extrusion through a 3D printer nozzle. Succeeding the force balance, a magnetohydrodynamic torque equilibrium analysis is performed to predict the net-orientation of the magnetic particles as a function of the applied field strength, viscous forces, and particle-to-particle interactions (P2P). Experimental validation of the DoA predictions is performed using 65 vol% Nd-Fe-B+Sm-Fe-N in Nylon12 (DoA exp = 0.620 and DoA theory = 0.686) and 15 vol% Sm-Co in PLA (DoA exp = 0.830 and DoA theory = 0.863). A parametric analysis is performed to analyze the effect of operating and design parameters like alignment field strength, magnetic loading fraction, extrusion load, and particle size. The model predicts a competing behavior between particle-fluid and particle-particle interactions under the presence of an applied field. Lastly, the model provides a framework to efficiently predict the DoA in tandem with a functionalized-magnetic 3D printer and allows the user to adjust the operating parameters according to the desired DoA.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Generalized thermo-mechanical framework for heterogeneous materials through asymptotic homogenization

Abstract A fundamental understanding of the interaction between microstructure and underlying physical mechanisms is essential, especially for developing more accurate multi-physics models for heterogeneous materials. Effects of microstructure on the material response at the macroscale are modeled by using the generalized thermomechanics. In this study, strain gradient theory is employed as a higher-order theory on the macroscale with thermodynamics modeled as a first-order theory on the microscale. Hence, energy depends only on the temperature such that we circumvent an extension of Fourier’s law and analyze the “simplest” thermo-mechanical model in strain gradient elasticity. Developing multiphysics models for heterogeneous materials is indeed a challenge and even this “simplest” model in generalized thermomechanics creates dozens of parameters to be determined. We develop a thermo-mechanical framework, in which microstructure is modeled as a periodic structure and through asymptotic homogenization approach, higher-order parameters at macroscopic scale are calculated. To illustrate the importance of higher-order parameters in overall thermo-mechanical response of a heterogeneous materials, finite element method (FEM) is employed with the aid of open-source codes (FEniCS). Verification example of a bulk system and several case studies of porous structures demonstrate how such numerical framework can be beneficial in the design of materials with tailored microstructures.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

New Developments of MOOSE/FENIX Capabilities for Fusion Neutronics Calculations

This poster summarizes internship work to support new developments of MOOSE/FENIX Capabilities for fusion neutronics calculations. A new feature was added to allow coupling OpenMC models with thermomechanics models and was verified. Two coupled multiphysics models are demonstrated for a tokamak model and monoblock divertor model. Results show heating results, temperature distributions, tritium production and transport.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Differentiable Multiphysics Codes: A Breakthrough Technology for Simulation and Computing

This document summarizes the findings of a strategic planning exercise commissioned by the Weapons Simulation and Computing, Computational Physics (WSC/CP) program at the Lawrence Livermore National Laboratory (LLNL) in FY24. During the year, the committee met with multiple stakeholder communities to gather input, opinions, suggestions and concerns which have been incorporated throughout this document. The key findings from this exercise are summarized: • The development of multiphysics modelling and simulation (mod/sim) codes and software technologies, their deployment on exascale compute platforms, and their broad adoption across the NNSA is a major success of the Advanced Simulation and Computing (ASC) program and the Exascale Computing Project (ECP). Sustained investment in these core technologies is essential. • Today’s state of the art involves running ensembles of O(100K) simulations to perform uncertainty quantification (UQ) and design studies using multiple statistical methods such as Bayesian optimization to understand sensitivities of our models and explore parameterized design spaces. Even with exascale computing, we are practically limited to O(10) parameters in these studies since the number of simulations required to sample the space scales exponentially with the number of design parameters. • The data from these simulation ensembles is increasingly being used to train machine learned (ML) surrogates (or reduced order models, ROMs) which can then be used for optimization or real time design exploration. However, the trained surrogates are still limited in the number of parameters they can represent due to the sampling limitations previously noted. • Augmenting our suite of integrated multiphysics simulation codes, both current and emerging, with the ability to compute gradients (solution derivatives) of arbitrary simulation outputs with respect to (some or all) simulation inputs would be a breakthrough technology, opening the door to a new era of efficient and automated inverse design based on verified and validated mod/sim capabilities. • This capability, which we refer to as differentiable multiphysics codes (DMCs), would revolutionize both UQ and optimization studies by breaking the curse of dimensionality that presently limits our “gradient-free” ensemble based computing approach. A similar breakthrough occurred in the AI/ML community once the ability to compute gradients of arbitrary loss functions using back-propagation became commonplace. Gradient information from the multiphysics codes can also be used to dramatically improve the efficiency and scale of training of ML/ROM surrogates for rapid assessments. • Achieving this in our suite of codes will be a grand challenge, similar to the amount of effort that was required to transition from CPU to GPU computing. It will require buy-in from the entire WSC/CP program and beyond, including all integrated codes, physics and engineering models, third-party library dependencies and performance portability abstractions. It will also require investment in research and development of numerical methods for computing adjoints of coupled physics across multiple adaptively refined moving meshes and of stochastic (Monte Carlo) and mesh free (SPH) methods. • New software and numerical techniques, largely pioneered by the AI/ML community, make this feasible. Chief among these is automatic differentiation (AD), the ability to employ AD at point-wise locations in a physics calculation (instead of traditional black-box approaches) and the ability to perform “back-propagation in time” (or reverse mode AD) for non-linear partial differential equations (PDEs). Fundamentally, the conclusion of this strategic planning exercise is that the time is right to undertake a large scale effort in WSC, centered on the existing integrated codes, to continue the natural evolution of mod/sim in the age of AI/ML. Instead of attempting to replace mod/sim with purely data driven AI/ML models, we believe the key to success is to integrate AI/ML by building on top of the decades of hard-won knowledge and the verified/validated multiphysics modelling capability that is the hallmark of the ASC program.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Modeling of a Solid Oxide Fuel Cell as Part of a Predictive Functional Model for Aerospace Fuel-Cell Systems

This paper will discuss initial efforts at modeling a solid oxide fuel cell (SOFC) for aerospace applications. Fuel cells historically have been used in spacecraft from the Gemini to the Shuttle era for providing spacecraft power with the added benefit of producing water for crew use. However, there are many potential applications for fuel cells and electrolyzers in spaceflight, including oxygen generation, in-situ resource utilization (ISRU) and propellant production. This proof-of-concept model has been developed using a commercial multiphysics modeling software package. Two-dimensional and one-dimensional isothermal models were created based on a certain SOFC design and results were compared to test data from the real system. Local Butler-Volmer kinetic relations were adjusted, and an effective porous medium approach was taken in order to capture how the many interconnects between cells in the gas channels affected fluid flow. The model was able to reproduce polarization curves derived from test data within around 0.02 Volts for a given current density. This model could be adapted to model a solid oxide electrolyzer, proton exchange membrane fuel cell, or other type of fuel cell technology in order to understand how these types of technologies could fit into broader spacecraft designs and advance the capabilities of spaceflight systems.

Mary Lou Nadeau↗
NASA NTRSDate not supplied

Modeling of a Solid Oxide Fuel Cell as Part of a Predictive Functional Model for Aerospace Fuel-Cell Systems

This paper will discuss initial efforts at modeling a solid oxide fuel cell (SOFC) for aerospace applications. Fuel cells historically have been used in spacecraft from the Gemini to the Shuttle era for providing spacecraft power with the added benefit of producing water for crew use. However, there are many potential applications for fuel cells and electrolyzers in spaceflight, including oxygen generation, in-situ resource utilization (ISRU) and propellant production. This proof-of-concept model has been developed using a commercial multiphysics modeling software package. Two-dimensional and one-dimensional isothermal models were created based on a certain SOFC design and results were compared to test data from the real system. Local Butler-Volmer kinetic relations were adjusted, and an effective porous medium approach was taken in order to capture how the many interconnects between cells in the gas channels affected fluid flow. The model was able to reproduce polarization curves derived from test data within around 0.02 Volts for a given current density. This model could be adapted to model a solid oxide electrolyzer, proton exchange membrane fuel cell, or other type of fuel cell technology in order to understand how these types of technologies could fit into broader spacecraft designs and advance the capabilities of spaceflight systems.

Mary Lou Nadeau↗
NASA NTRSDate not supplied

Modeling of a Solid Oxide Fuel Cell as Part of a Predictive Functional Model for Aerospace Fuel-Cell Systems

This paper will discuss initial efforts at modeling a solid oxide fuel cell (SOFC) for aerospace applications. Fuel cells historically have been used in spacecraft from the Gemini to the Shuttle era for providing spacecraft power with the added benefit of producing water for crew use. However, there are many potential applications for fuel cells and electrolyzers in spaceflight, including oxygen generation, in-situ resource utilization (ISRU) and propellant production. This proof-of-concept model has been developed using a commercial multiphysics modeling software package. Two-dimensional and one-dimensional isothermal models were created based on a certain SOFC design and results were compared to test data from the real system. Local Butler-Volmer kinetic relations were adjusted, and an effective porous medium approach was taken in order to capture how the many interconnects between cells in the gas channels affected fluid flow. The model was able to reproduce polarization curves derived from test data within around 0.02 Volts for a given current density. This model could be adapted to model a solid oxide electrolyzer, proton exchange membrane fuel cell, or other type of fuel cell technology in order to understand how these types of technologies could fit into broader spacecraft designs and advance the capabilities of spaceflight systems.

Mary Lou Nadeau↗
NASA NTRSDate not supplied

Modeling of a Solid Oxide Fuel Cell as Part of a Predictive Functional Model for Aerospace Fuel-Cell Systems

This paper will discuss initial efforts at modeling a solid oxide fuel cell (SOFC) for aerospace applications. Fuel cells historically have been used in spacecraft from the Gemini to the Shuttle era for providing spacecraft power with the added benefit of producing water for crew use. However, there are many potential applications for fuel cells and electrolyzers in spaceflight, including oxygen generation, in-situ resource utilization (ISRU) and propellant production. This proof-of-concept model has been developed using a commercial multiphysics modeling software package. Two-dimensional and one-dimensional isothermal models were created based on a certain SOFC design and results were compared to test data from the real system. Local Butler-Volmer kinetic relations were adjusted, and an effective porous medium approach was taken in order to capture how the many interconnects between cells in the gas channels affected fluid flow. The model was able to reproduce polarization curves derived from test data within around 0.02 Volts for a given current density. This model could be adapted to model a solid oxide electrolyzer, proton exchange membrane fuel cell, or other type of fuel cell technology in order to understand how these types of technologies could fit into broader spacecraft designs and advance the capabilities of spaceflight systems.

Mary Lou Nadeau↗
DOE OSTI2022

Quantification of electrochemical-mechanical coupling in lithium-ion batteries

Lithium-ion battery safety and durability by nature are dependent on electrochemical and mechanical coupling. Interdisciplinary efforts are required to understand and quantify coupling behaviors. Here we design and conduct mechanically constrained charge and discharge characterizations with efforts supported by multiphysics modeling to unravel the coupling mechanisms of solid-liquid electrode-electrolyte and solid-solid active materials in lithium-ion batteries. We demonstrate that a lithium-ion battery cell under mechanical constraint exhibits a higher voltage during charging and a shorter charging time because of increased electrolyte resistance and decreased diffusivity caused by decreased electrode porosity. The reaction force response of the cell is a combined result of the cell structural response mechanically and lithium-ion intercalation/de-intercalation-induced volume variation electrochemically. Under mechanical constraint, cell capacity is significantly reduced in fast-charge scenarios; however, it can be recovered by a constant-voltage charge protocol. The results highlight the promise of multiphysics approaches to unravel the electrochemical-mechanical coupling mechanisms to direct battery system design and management.

25 ENERGY STORAGE↗
NASA NTRSDate not supplied

Coupled Reactor and Engine Nuclear Thermal Propulsion Modeling Methodology

The design and development process of a Nuclear Thermal Propulsion (NTP) system requires extensive multiphysics modeling to couple the neutron physics and thermal feedback effects to determine the reactor’s power shape. Propulsion system performance codes utilize this power shape to determine NTP key performance parameters. While the power shape is heavily dependent on the temperature profile and geometry of the reactor, many analyses either assume a constant power shape, or use neutronics analysis to determine a power shape for a specific reactor configuration. The development of a coupling interface for a propulsion system performance code and a Monte Carlo neutron transport code (OpenMC) allows for the reactor power shape to be calculated in an iteration loop. The interface utilizes a file share system to transfer geometry dimensions, temperatures, and material identifiers to OpenMC, which is used to perform a neutron transport simulation of a design like the government Testing Reference Design reactor. The interface is then able to post-process the results from OpenMC and use the same file share system to share a power shape and other important neutron transport parameters to the system performance code. Initial results show that neglecting the changes to power shape when comparing reactor configurations can yield inaccurate results. Furthermore, utilizing propellants other than hydrogen gas can cause significant changes to the power shape, and thus, the thermal performance of a specific reactor design. This methodology is being expanded to allow for multiple families of NTP reactors to be analyzed, including block moderator, particle bed, and NERVA-derived reactors.

multiphysics coupling↗
NASA NTRSDate not supplied

Coupled Reactor and Engine Nuclear Thermal Propulsion Modeling Methodology

The design and development process of a Nuclear Thermal Propulsion (NTP) system requires extensive multiphysics modeling to couple the neutron physics and thermal feedback effects to determine the reactor’s power shape. Propulsion system performance codes utilize this power shape to determine NTP key performance parameters. While the power shape is heavily dependent on the temperature profile and geometry of the reactor, many analyses either assume a constant power shape, or use neutronics analysis to determine a power shape for a specific reactor configuration. The development of a coupling interface for a propulsion system performance code and a Monte Carlo neutron transport code (OpenMC) allows for the reactor power shape to be calculated in an iteration loop. The interface utilizes a file share system to transfer geometry dimensions, temperatures, and material identifiers to OpenMC, which is used to perform a neutron transport simulation of a design like the government Testing Reference Design reactor. The interface is then able to post-process the results from OpenMC and use the same file share system to share a power shape and other important neutron transport parameters to the system performance code. Initial results show that neglecting the changes to power shape when comparing reactor configurations can yield inaccurate results. Furthermore, utilizing propellants other than hydrogen gas can cause significant changes to the power shape, and thus, the thermal performance of a specific reactor design. This methodology is being expanded to allow for multiple families of NTP reactors to be analyzed, including block moderator, particle bed, and NERVA-derived reactors.

multiphysics coupling↗
DOE OSTI2023

Pressurized Water Reactor Control Rod Ejection Analysis Using PARCS, RELAP5-3D, and BISON for High Burnup Fuel

This paper describes safety assessment of high burnup fuel (HBF) during loss of coolant accidents (LOCA), reactivity-initiated accidents (RIA), and anticipated operational occurrences (AOOs) for Southern Nuclear’s Vogtle Electric Generating plant (VEGP). A goal of this project is to create a multiphysics modeling approach in aspects of thermal hydraulics, neutronics, and fuel performance, as well as to use it in combination with experimental studies (Figure 1). In support of the project, this paper seeks to address fuel performance of a HBF pin during steady state conditions and during a control rod ejection (CRE) scenario using a loose coupling of PARCS, RELAP5-3D, and BISON.. The primary goal of this work was to analyze high burnup fuel behavior during a CRE scenario. The results indicate fuel failure is not expected to occur under the conditions presented.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

TRITIUM TRANSPORT MODELING AT THE PORE SCALE IN CERAMIC BEEDER MATERIALS USING TMAP8

Fusion reactors depend on the blanket material to breed and release tritium at the same rate or faster than it is consumed by the fusion reaction. Cellular ceramic breeders are dense materials that can maintain a high tritium breeding ratio while promoting tritium release thanks to highly connected pores. Assessing the tritium breeding capabilities of these materials requires a combination of extensive experimental and modeling efforts. In this work, we develop and calibrate a multiphysics model of tritium transport. This novel model accounts for ceramic and pore diffusion, trapping and detrapping, and several surface reactions at the pore surface. We perform a sensitivity analysis and calibrate the model by comparing its predictions against experimental measurements of deuterium absorption. The calibrated model is then used to model tritium absorption in samples with different pore microstructures to investigate the effect of pore interconnectivity on tritium absorption. The model is part of the development of the multiscale, multiphysics framework for tritium transport (i.e., the Tritium Migration Analysis Program [TMAP8]), which is itself built on top of the finite element multiphysics framework MOOSE. This study demonstrates some of TMAP8’s capabilities, and is the first step toward assessing the tritium breeding capabilities of ceramic breeder material designs.

08 HYDROGEN↗