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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Neural Network-Enhanced Reproducing Kernel Particle Method for Image-Based Multiphysics Damage Modeling of Energy Storage Materials

Energy storage materials undergo significant stresses during charge/discharge cycling, which makes understanding their reliability and durability fundamental in predicting performance and service life. Strong electrochemical-mechanical coupling and highly anisotropic material properties contribute to the formation and propagation of micro-cracking, largely along material interfaces and grain boundaries. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), image-based modeling techniques are used to represent the complex material microstructures that dictate the coupled physics of these systems. Traditional electrochemical-mechanical models rely on mesh-based finite element methods, which can lead to difficulties in capturing crack propagation due to mesh dependency. Additionally, commonly used damage models, such as the continuous damage model and the cohesive zone model, often have steep tradeoffs between discontinuous field accuracy and computational expense. In this work, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1] is leveraged to accurately capture damage and crack propagation throughout the material by learning the location, orientation, and sharpness of discontinuity while allowing for a coarser nodal distribution than that necessary for capturing sharp solution transitions using traditional mesh-based methods. NN-RKPM is 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.

damage modeling↗
DOE OSTI2023

Neural Network Enhanced RKPM for Electrochemical-Mechanical Coupled Damage Modeling of Energy Storage Materials

Energy storage materials undergo significant charge cycling, which makes understanding their reliability and durability fundamental in predicting performance and service life. Strong electrochemical-mechanical coupling and highly anisotropic material properties contribute to the formation and propagation of micro-cracking, largely along material interfaces and grain boundaries. For Li-ion batteries, for example, lithium moving between electrodes during charging and discharging process causes expansion and contraction of grains, and the strongly anisotropic and nonlinearly [Li]-dependent grain material properties can cause grains to expand into and contract away from each other, leading to chemo-mechanical cracking. In the first part of this work, a RKPM based computational framework for solving the coupled solid-phase lithium conservation with Fickian diffusion and the lithium concentration dependent anisotropic mechanical problem subjected to a highly nonlinear Butler-Volmer boundary condition is introduced. The choice of RKPM completeness conditions for lithium concentration and mechanical deformation fields, and the variational consistency condition for the domain integration of the coupled problem is first determined. In the second part of this work, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1] is leveraged to accurately capture damage and crack propagation throughout the material, by learning the location, orientation, and sharpness of discontinuity while allowing for a coarser nodal distribution than that is necessary for capturing sharp solution transitions using traditional mesh-based methods. NN-RKPM is used to inform how crack opening and closure in turn affect the coupled chemical equations and material microstructure.

damage modeling↗
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 OSTI2026

Kernel Enriched Meshfree 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 ultimately diminishing performance and service life. With microstructural images supplied by the National Laboratory of the Rockies (NLR), pixel-based meshfree model construction by the reproducing kernel particle method (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. The first kernel enrichment discussed will be the interface modified reproducing kernel (IM-RK) [1, 2], constructed by scaling a smooth kernel function with an interface-distance function to achieve strategic discontinuity types (i.e. weak discontinuities for strain discontinuities and strong discontinuities for cracks) and alleviate Gibbs oscillations near these transition zones. The IM-RK is especially useful for areas in which a known discontinuity-type is expected a priori. The second kernel enrichment to be discussed is a neural network-enhanced reproducing kernel (NN-RK) [3, 4], which is introduced to effectively model non-obvious 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-RK is additionally used to inform how crack opening and closure in turn affect the electro-chemo-mechanical responses in the material microstructure. References: [1] Wang, Y., Baek, J., Tang, Y. et al. "Support vector machine guided reproducing kernel particle method for image-based modeling of microstructures," Comput Mech 73, 907-942 (2024). https://doi.org/10.1007/s00466-023-02394-9. [2] Susuki, K., Allen, J. & Chen, J. S.. "Image-based modeling of coupled electro-chemo-mechanical behavior of Li-ion battery cathode using an interface-modified reproducing kernel particle method," Engineering with Computers (2024). https://doi.org/10.1007/s00366-024-02016-9. [3] 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, 4422-4454 (2022). https://doi.org/10.1002/nme.7040.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

In situ and operando force-based atomic force microscopy for probing local functionality in energy storage materials

Electrochemical energy storage is the key enabling component of electric vehicles and solar-/wind-based energy technologies. The enhancement of energy stored requires the detailed understanding of charge storage mechanisms and local electrochemical and electromechanical phenomena over a variety of length scales from atoms to full cells. Classical electrochemical techniques, such as voltammetry, represent the macroscopic electrochemical properties, and consequently do not allow to extract important information about local electrochemical reactions, ions adsorption, intercalations, and transport. Understanding, controlling, and tuning the local electrochemical functionalities in functional energy materials require in situ/operando techniques which limit the use of structural and functional characterization techniques that provide local information. Here, force-based atomic force microscopies (AFMs) have provided novel insights into locally probing electrochemical mechanisms on tens of nanometer and even molecular length scales and provide a viable pathway to probe electrochemical processes in situ/operando. In this review, we highlight the contributions in the development and application of force-based AFM methods to elucidate the local charge storage mechanism in a variety of energy-related materials. We will focus in particular on methods or AFM modalities in a liquid electrolyte environment.

25 ENERGY STORAGE↗
DOE OSTI2025

Multiphysics Meshfree Degradation Modeling of Energy Storage Materials with Kernel Enrichment

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 ultimately diminishing performance and service life. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), pixel-based meshfree model construction by the reproducing kernel particle method (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. The first kernel enrichment discussed will be the interface modified reproducing kernel (IM-RK) [1, 2], constructed by scaling a smooth kernel function with an interface-distance function to achieve strategic discontinuity types (i.e. weak discontinuities for strain discontinuities and strong discontinuities for cracks) and alleviate Gibbs oscillations near these transition zones. The IM-RK is especially useful for areas in which a known discontinuity-type is expected a priori. The second kernel enrichment to be discussed is a neural network-enhanced reproducing kernel (NN-RK) [3, 4], which is introduced to effectively model non-obvious 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-RK is additionally used to inform how crack opening and closure in turn affect the electro-chemo-mechanical responses in the material microstructure. Reference: [1] Wang, Y., Baek, J., Tang, Y. et al. "Support vector machine guided reproducing kernel particle method for image-based modeling of microstructures," Comput Mech 73, 907-942 (2024). https://doi.org/10.1007/s00466-023-02394-9. [2] Susuki, K., Allen, J. & Chen, J. S.. "Image-based modeling of coupled electro-chemo-mechanical behavior of Li-ion battery cathode using an interface-modified reproducing kernel particle method," Engineering with Computers (2024). https://doi.org/10.1007/s00366-024-02016-9. [3] 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, 4422-4454 (2022). https://doi.org/10.1002/nme.7040.

25 ENERGY STORAGE↗
DOE OSTI2024

High temperature thermochemical energy storage materials

Disclosed are high enthalpy thermochemical energy storage materials that exhibit high thermal conductivity and stability at high temperature reaction conditions. Disclosed materials include hydride-based alloys that can undergo high temperature reversible hydrogenation/dehydrogenation reactions without phase change of any metal or metalloid components of the alloy. The materials undergo a reversible exothermic hydrogenation reaction to form a metal hydride and a ternary alloy that includes a high thermal conductivity metal that, in its pure state, would exhibit a phase change at the hydrogenation reaction conditions.

Ward, Patrick A.↗
DOE OSTI2023

High temperature thermochemical energy storage materials

Disclosed are thermal energy storage systems and methods that utilize metal carbonate eutectics that can undergo high temperature reversible reactions to form mixtures of metal oxides. The metal oxides undergo an exothermic reaction with carbon dioxide to form the molten metal carbonate eutectics, and the molten metal carbonate eutectics undergo an endothermic decarbonization reaction to form the metal oxides and carbon dioxide. By carrying out the reversible reactions at a temperature above the melting point of the carbonate eutectic, the systems provide high thermal conductivity and reversible stability for thermal energy storage.

25 ENERGY STORAGE↗
DOE OSTI2021

Development of High Capacity Energy Storage Materials

Hydrogen fuel cells have the potential to offer energy and power density advantages over lithium ion batteries in automotive and portable power applications when paired with an appropriate hydrogen storage system. Development of the ideal hydrogen storage material has been immensely sought after but plagued by limitations present in each type of material. These limitations typically include nonidealistic operational temperatures, low capacities, excessive costs, lack of reversibility, or evolution of impurities which irreversibly damage fuel cell performance. Many complex metal hydrides possess suitable hydrogen capacities but unfortunately suffer from impurity release. In order to address this problem, our team has explored in-situ catalyst doping of alkali metal amides to demonstrate methodologies to reduce ammonia release. The incorporation of iridium metal in lithium amide significantly reduces the ammonia release and decomposes the ammonia to provide additional hydrogen. Furthermore, we demonstrate, for the first time, high resistance of low temperature fuel cells to ammonia in comparison with typical proton exchange membrane fuel cells. The utilization of these two approaches in tandem provides a novel pathway for the development and implementation of high capacity energy storage materials for fuel cell applications.

25 ENERGY STORAGE↗
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

High-temperature thermochemical energy storage materials using doped magnesium-transition metal spinel oxides

High-temperature thermochemical energy storage materials using doped magnesium-transition metal spinel oxides are provided. —transition metal spinel oxides, such as magnesium manganese oxide (MgMn) 3 O 4 , are promising candidates for high-temperature thermochemical energy storage applications. However, the use of these materials has been constrained by the limited extent of their endothermic reaction. Embodiments described herein provide for doping magnesium-transition metal spinel oxides to produce a material of low material costs and with high energy densities, creating an avenue for plausibly sized modules with high energy storing capacities.

Muhich, Christopher↗
DOE OSTI2020

Pseudocapacitance: From Fundamental Understanding to High Power Energy Storage Materials

There is an urgent global need for electrochemical energy storage that includes materials that can provide simultaneous high power and high energy density. One strategy to achieve this goal is with pseudocapacitive materials that take advantage of reversible surface or near-surface Faradaic reactions to store charge, surpassing the capacity limitations of electrical double-layer capacitors and mass transfer limitations of batteries. The past decade has seen tremendous growth in the understanding of pseudocapacitance as well as materials that exhibit this phenomenon. The purpose of this study is to examine the fundamental development of the concept of pseudocapacitance and how it came to prominence in electrochemical energy storage as well as to describe new classes of materials whose electrochemical energy storage behavior can be described as pseudocapacitive.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1996

Sol-gel Technology and Advanced Electrochemical Energy Storage Materials

Advanced materials play an important role in the development of electrochemical energy devices such as batteries, fuel cells, and electrochemical capacitors. The sol-gel process is a versatile solution for use in the fabrication of ceramic materials with tailored stoichiometry, microstructure, and properties. This processing technique is particularly useful in producing porous materials with high surface area and low density, two of the most desirable characteristics for electrode materials. In addition,the porous surface of gels can be modified chemically to create tailored surface properties, and inorganic/organic micro-composites can be prepared for improved material performance device fabrication. Applications of several sol-gel derived electrode materials in different energy storage devices are illustrated in this paper. V2O5 gels are shown to be a promising cathode material for solid state lithium batteries. Carbon aerogels, amorphous RuO2 gels and sol-gel derived hafnium compounds have been studied as electrode materials for high energy density and high power density electrochemical capacitors.

Chung-tse Chu↗
NASA NTRS1977

Cryogenic temperature control by means of energy storage materials

An investigation was conducted to study the concept of thermal control by means of physical or chemical reaction heats for applications involving the storage of cryogens during long-term space voyages. The investigation included some preliminary experimental tests of energy storage material (ESM) effectiveness. The materials considered can store and liberate large amounts of thermal energy by means of mechanisms such as sensible heat, heat of fusion, and physical or chemical reaction heat. A differential thermal analysis was utilized in the laboratory tests. Attention is given to the evaluation of cryogenic ESM thermal control concepts, the experimental determination of phase change materials characteristics, and adsorption ESMs. It is found that an ESM shield surrounded by multiple layer insulation provides the best protection for a cryogen store.

Grodzka, P. G.↗