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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Low-temperature failure mechanism of [001] niobium micropillars under uniaxial tension

The plasticity of body-centered cubic (bcc) metals is dependent of temperature as well as sample dimension at the micrometer scale, but the effects of cryogenic temperature on the plasticity and the related failure process in micron-sized bcc metals have not been studied under uniaxial tension. In this work, we utilized in situ cryogenic micro-tensile tests, transmission electron microscopy, and dislocation dynamic simulations to examine the plasticity and failure processes of [001]-oriented bcc niobium micropillars. Our study reveals that a strong suppression of cross-slip at low temperatures prevents dislocation multiplication and leads to a dislocation starvation state, at which no mobile dislocation exists due to the rapid annihilation of dislocations at free surfaces. New dislocations are then nucleated until stress concentration at a slip step creates a micro-crack, the propagation of which leads to catastrophic failure. As a result, this unique failure process results from the combined effects of sample dimension and temperature.

36 MATERIALS SCIENCE↗
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 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 OSTI2026

Cluster Dynamics Simulations of Intra-Granular Fission Gas Bubble Size and Pressure Evolution in UO 2

Fission gases such as xenon (Xe) play a critical role in determining the behavior and response of nuclear fuel. Given that Xe has little solubility in UO 2 , it accumulates and forms bubbles, which significantly impact fuel performance. Intra- and inter-granular bubble nucleation and growth can lead to fuel swelling, and once bubbles interconnect at grain boundaries, fission gas can be released into the plenum. At low temperatures, limited uranium vacancy mobility can restrict swelling, therefore causing the bubbles to become highly pressurized. Consequently, this can induce micro-cracking, promote fission gas release (increasing the likelihood of cladding failure), and even lead to fuel pulverization under accident conditions such as a loss of coolant accident. As bubble evolution is strongly influenced by local temperature and fission rate, markedly different behavior occurs across the radial profile of the fuel pellet. Capturing the mechanisms that underpin bubble evolution is therefore important to predict these behaviors in the fuel. Previous models describing important mechanisms informed by lower length scale simulations have been developed under the NEAMS program. These can describe the evolution of a single bubble type (i.e., single value for radius and pressure) at each position in the pellet, for instance using the Centipede cluster dynamic code. However, in reality, a full distribution in bubble sizes and pressures exists within the microstructure at a given position in the pellet. To address this the cluster dynamics code Xolotl, which can predict Xe and vacancy phase space (i.e., bubble distributions) for intra-granular bubbles, has been used before. Prior work benchmarked the Xolotl code against the Centipede cluster dynamics code to ensure compatibility and to verify that mobile defect properties are adequately transferred between the two codes, along with some physics improvements. In this work, we go further by introducing a physics-based set of improvements that will allow us to accurately predict bubble size distributions and internal bubble pressures under representative UO 2 irradiation conditions. The improvements include (i) coupling bubble-defect reaction energies to a virial equation of state (EOS), (ii) including a bubble surface tension contribution, (iii) incorporating radiation-induced re-solution of Xe and vacancies, (iv) enabling pressure-driven dislocation loop punching through an effective emission of interstitial clusters informed by interstitial loop energetics, (v) accounting for radiation induced athermal diffusion of Xe, and (vi) implementing a Booth-type grain boundary sink representation for all mobile defects and defect clusters. After these modifications, we observe good agreement of Xolotl fission gas bubble size and concentration predictions with legacy experimental measurements. Additionally, it allows the distribution of Xe bubble pressures and radius to also be predicted and compared to data produced through the Advanced Fuels Campaign (AFC) program. Here, we have done this by running simulations under conditions similar to the AFC post-irradiation examination (PIE) samples irradiated at North Anna 2 light water reactor (LWR). Our results shows excellent agreement with these experimental measurements.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1993

The effects of long-duration space exposure on the mechanical properties of some carbon-reinforced resin matrix composites

Long Duration Exposure Facility (LDEF) Experiment A0175 involved the non-instrumented exposure of seven carbon-fiber reinforced resin-matrix advanced composite panels contained in two trays - A7 and A1. These two trays were located, respectively, on the leading and trailing faces of LDEF, obliquely oriented to the RAM (Row 9) and WAKE (Row 3) directions. The identity and location of the seven panels, which consisted of six flat laminates of the following material systems are shown: carbon/epoxy (T300/934), carbon/bismaleimide (T300/F178), and carbon/polyimide (C6000/LARC-160 and C6000/PMR-15), plus one bonded honeycomb sandwich panel (T300/934 face sheets and Nomex core) patterned after the Space Shuttle payload bay door construction. These material systems were selected to represent a range of then-available matrix resins which, by virtue of their differing polymer chemistry, could conceivably exhibit differing susceptibility to the low-earth orbit (LEO) environment. The principal exposure conditions of the LDEF environment at these tray locations are shown. Noteworthy to some of the observations discussed is the four-orders-of magnitude difference in the atomic oxygen (AO) fluence, which made a shallow incidence angle (approximately 22 deg) to Tray A7, while Tray A1 on the trailing face was essentially shielded from AO exposure. This evaluation focused on determining the individual and relative suitability of a variety of resin-matrix composite systems for long-term space structural applications. This was accomplished primarily by measuring and comparing a range of engineering mechanical properties on over 300 test coupons sectioned from the flight panels and from identical control panels, and tested at ambient and elevated temperatures. This testing was supported by limited physical characterization, involving visual examination of flight panel surface features, measurements of weight loss and warpage, and examination for changes in internal integrity (micro cracking, delamination) by ultrasonic c-scan and polished cross-sections.

Vyhnal, Richard F.↗
NASA NTRS1991

Space Shuttle Orbiter carbon-carbon oxidation performance

Reusable, oxidation protected reinforced carbon carbon (RCC) has been successfully flown on forty Shuttle Orbiter flights. Thermal testing of the silicon carbide coated, reinforced carbon-carbon to determine its oxidation characteristics has been performed in both radiant and convective (plasma arc jet) heating test facilities. Subsurface oxidation of the RCC substrate as a result of oxygen penetrating micro cracks (fizzures) in the coating was characterized as a function of temperature and pressure for both convective and radiant environments. High temperature testing was performed to establish coating recession for over-temperature flight conditions experienced on abort trajectories. Suggested methods for using these test data to establish multi-mission reuse (i.e., mission life) and single mission limits are presented.

Curry, D. M.↗
NASA NTRS2004

Habitat Inspection Scanner, Bio-Structure Scanner, and In Situ Sub-Surface Composition Sensor

The extension of dielectric and inductive spectroscopy into in situ observations represents a significant exploration-enabling tool. This technology can be widely applied from microscopic to macroscopic. Dielectrometry and inductometry can measure sub-surface composition and its distribution. The primary environment that we cannot easily explored is the sub-surface of solid bodies. Weather as part of our equipment that we bring with us, or the locations we are exploring. These fundamental questions lie at the core of the exploration Initiative. To answer them we must use a whole host of complimentary tools including those that allow us to practically examine the sub-surface environment. A nondestructive approach offers significant advantages for both the initial identification of likely samples but also the monitoring of ecosystems and crew health. These include materials characterization, nondestructive inspection, and process quality control, damage monitoring, and hidden object detection and identification. The identification of natural resources such as water on the Moon or Mars is of great importance to the utilization of local resource in the support of human exploration crews. On the macroscopic scale, the understanding of what resources are available and how they are distributed is of primary importance to their productive utilization. Even if initial explorations do not require the use of local resources to succeed, eventual settlement and commercial development will. The routine examination of the structural integrity (micro cracks, leaks) of hi.inafi habitats in harsh envkmments ww!d also be enabled.

VanSteenberg, Michael↗
NASA NTRS2008

Mixed Mode Fracture of Plasma Sprayed Thermal Barrier Coatings: Effects of Anisotropy and Heterogeneity

The combined mode I-mode II fracture behavior of anisotropic ZrO2-8wt%Y2O3 thermal barrier coatings was determined in asymmetric flexure loading at both ambient and elevated temperatures. A fracture envelope of KI versus KII was determined for the coating material at ambient and elevated temperatures. Propagation angles of fracture as a function of KI/KII were also determined. The mixed-mode fracture behavior of the microsplat coating material was modeled using Finite Element approach to account for anisotropy and micro cracked structures, and predicted in terms of fracture envelope and propagation angle using mixed-mode fracture theories.

Zhu, Dongming↗
NASA NTRS2016

Design and Growth of Novel Compounds for Radiation Sensors: Multinary Chalcogenides

Increasing threats of radiological weapons have revitalized the researches for low cost large volume -ray and neutron ray sensors In the past few years we have designed and grown ternary and quaternary lead and thallium chalcogenides and lead selenoiodides for detectors to meet these challenges. These materials are congruent, can be tailored to enhance the parameters required for radiation sensors. In addition, this class of compounds can be grown by Bridgman method which promises for large volume productions. We have single crystals of several compounds from the melt including Tl3AsSe3, Tl3AsSe3-xSx, TlGaSe2, AgGaGe3Se8, AgxLi1-xAgGaGe3Se8 and PbTlI5-x Sex compounds. Experimental studies indicate that these have very low absorption coefficient, low defect density and can be fabricated in any shape and sizes. These crystals do not require post growth annealing and do not show any second phase precipitates when processed for electrode bonding and other fabrication steps. In this paper we report purification, growth and fabrication of large Tl3AsSe3 (TAS) crystals. We observed that TAS crystals grown by using further purification of as supplied high purity source materials followed by directionally solidified charge showed higher resistivity than previously reported values. TAS also showed constant value as the function of voltage. A low thermal gradient and high purity source material were used to reduce thermal stresses in large crystals. By improving the purification of the as supplied source materials very high quality thallium, selenium and arsenic was achieved for preparing stoichiometric Tl3AsSe3 compound. Low gradient (<20K/cm) and slow growth rate (1-2 cm/day) produced crystals with reduced stress. Crystals did not show any micro cracking during fabrication of crystals grown with high purity and at low thermal gradient. Since thallium is a major component and very sensitive to surface oxidation, removal of surface and bulk oxides is very important. Intentional increase in the growth rate from 1cm/day to higher speed (>5cm/day) showed very different morphologies on the surface of the crystals. Electrical resistivity was one order of magnitude higher than previously reported value and it was observed to be constant as the function of frequency.

Singh, N. B.↗
NASA NTRS2017

Design, Manufacture and Test of Cryotank Components

On the composite cryotank technology development (CCTD) project, the Boeing Company built two cryotanks as a means of advancing technology and manufacturing readiness levels (TRL and MRL) and lowering the risk of fabricating full-scale fuel containment vessels.1 CCTD focused on upper stage extended duration applications where long term storage of propellants is required. The project involved the design, analysis, fabrication, and test of manufacturing demonstration units (MDU), a 2.4 m (precursor) and a 5.5 m composite cryotank. Key design features included one-piece wall construction to minimize overall weight (eliminating the need for a bellyband joint), 3-dimensionally (3D) reinforced y-joint material to alleviate stress concentrations at the tank to skirt interface and a purge-able fluted core skirt to carry high axial launch loads. The tanks were made with OoA curing pre-impregnated (prepreg) carbon/epoxy (C/E) slit-tape tow (STT) that contained thin micro-crack resistant plies in the tank wall center to impede permeation. The tanks were fabricated at Boeing's Seattle-based Advanced Development Center (ADC) using RAFP and multipiece break-down tooling. The tooling was designed and built by Janicki Industries (JI) at Sedro Woolley, Washington. Tank assemblage consisted of co-bonded dome covers, one-piece fluted core skirts and mechanical fastened cover/sump. Ultrasonic inspection was performed after every cure or bond and a structural health monitoring system (SHMS) was installed to identify potential impact damage events (in-process and/or during transportation). The tanks were low temperature tested at NASA's George C. Marshall Space Flight Center (MSFC) in Huntsville, Alabama. The testing, which consisted of a sequence of fill/drain pressure and thermal cycles using LH2, was successfully concluded in 2012 on the 2.4 m tank and in 2014 on the 5.5 m tank. Structural, thermal, and permeation performance data was obtained. 2 Critical design features and manufacturing advancements, which helped to validate 25% weight and 30% cost reduction projections, were matured. These advancements will help to guide future composite tank integration activities on next generation long duration aircraft and space launch vehicles. Because CCTD addressed innovative design features, heavy lift size scale-up, multipiece captured tooling, new generation automated material placement (AMP) equipment and OoA materials, this chapter should be of interest to educators, students and manufacturers of composite hardware and flight vehicles.

McCarville, Douglas A.↗
NASA NTRS2019

Uniaxial Tensile Properties of AS4 3D Woven Composites with Four Different Resin Systems: Experimental Results and Analysis: Property Computations

As a part of the NASA Composite Technology for Exploration project, eight different AS4 3D orthogonal woven composite panels were manufactured and were subjected to mechanical testing including uniaxial tension along the weaves' warp direction. Each set, with four different resin systems (KCR-IR6070, EP2400, RTM6, and RS-50), included weave architectures designed using 12K and 6K AS4 carbon fiber yarns. For the tension testing conducted at Room Temperature Ambient (RTA) conditions, the elastic modulus and strength of these eight panels (as-processed and thermally-cycled) were measured and compared while the potential evolution of micro-cracking before and after thermal cycling were monitored via optical microscopy and X-Ray Computed Tomography. The data set also included test results of the as-processed materials at Elevated Temperature Wet (ETW) conditions. In the second part of this study, efforts were made to compute elastic constants for AS4 6K/RTM6 and AS4 12K/RTM6 materials by implementing a finite element approach and the Multiscale Generalized Method of Cells (MSGMC) technique developed at NASA Glenn Research Center. Digimat-FE was used to model the weave architectures, assign properties, calculate yarn properties, create the finite element mesh, and compute the elastic properties by applying periodic boundary conditions to finite element models of each repeating unit cell. The required input data for MSGMC was generated using Matlab® from Digimat exported weave information. Experimental and computational results were compared, and the differences and limitations in correlating to the test data were briefly discussed.

Property Computations↗
NASA NTRS2019

Uniaxial Tensile Properties of AS4 3D Woven Composites with Four Different Resin Systems: Experimental Results & Analysis - Property Computations

As a part of the NASA Composite Technology for Exploration project, eight different AS4 3D orthogonal woven composite panels were manufactured and were subjected to mechanical testing including uniaxial tension along the weaves' warp direction. Each set, with four different resin systems (KCR-IR6070, EP2400, RTM6, and RS-50), included weave architectures designed using 12K and 6K AS4 carbon fiber yarns. For the tension testing conducted at Room Temperature Ambient (RTA) conditions, the elastic modulus and strength of these eight panels (as-processed and thermally cycled) were measured and compared while the potential evolution of micro-cracking before and after thermal cycling were monitored via optical microscopy and X-Ray Computed Tomography. The data set also included test results of the as-processed materials at Elevated Temperature Wet (ETW) conditions. In the second part of this study, efforts were made to compute elastic constants for AS4 6K/RTM6 and AS4 12K/RTM6 materials by implementing a finite element approach and the Multiscale Generalized Method of Cells (MSGMC) technique developed at NASA Glenn Research Center. Digimat-FE was used to model the weave architectures, assign properties, calculate yarn properties, create the finite element mesh, and compute the elastic properties by applying periodic boundary conditions to finite element models of each repeating unit cell. The required input data for MSGMC was generated using Matlab® from Digimat exported weave information. Experimental and computational results were compared, and the differences and limitations in correlating to the test data were briefly discussed.

Property Computations↗
NASA NTRS2026

Biobased Polybenzoxazine Derived from Furfurylamine and Piceol with Low Cure Temperature and Advanced Properties for Composite Matrices

A novel benzoxazine made from furfurylamine, paraformaldehyde, and piceol, Bz-FA-HA, is assessed for applications in fiber reinforced (FR) composites. Piceol is a biobased phenolic compound derived from the roots of Norwegian spruce trees and contains a methyl ketone group at the para position. Bz-FA-HA is a liquid at room temperature, has a viscosity of < 1 Pa.s at temperatures above 90 °C, and a Tonset of cure at 132 °C. The carbonyl is found to react with the furan ring, yielding a crosslinking reaction, when cured above 180 °C as indicated by differential scanning calorimetry and thermogravimetric analysis coupled with Fourier transform infrared spectroscopy. Poly(Bz-FA-HA) has a Tg > 350 °C, attributed to the crosslinking reaction. Furthermore, the storage modulus is > 3 GPa, regardless of cure temperature. Poly(Bz-FA-HA) has a char yield at 800 °C of 65.0 % (62.3 % at 1000 °C), and a Tonset of decomposition of 357 °C in nitrogen. The resulting carbon formed during pyrolysis shrinks during the carbonization reaction and scanning electron microscopy imaging shows a cross-section with micro cracks. The high processability, advanced mechanical properties, and exceptional char yield make it a promising candidate as the matrix for FR composites.

Benzoxazine↗
DOE OSTI2021

Reference electrode for electrochemical measurements at high temperatures

A reference electrode which is stable over a wide range of temperatures, pressures and chemical conditions is provided. The subject reference electrode according to the present invention comprises a tubular enclosure composed of quartz having a distal, closed end and a proximal, open end. An insulating ceramic rod is seemingly connected to the opening in the closed distal end of the enclosure to form micro-cracks between the ceramic rod and the quartz enclosure (called a cracked junction, CJ). The CJ gives a very tortuous path for ion conduction from inside the reference electrode (RE) to a working electrode (WE). Inside the tubular enclosure is an electrical lead (e.g., a silver wire) disposed in an electrolyte comprising a mixture of alkaline metal salts (e.g., AgCl and KCl), extending from the electrolyte upward through a sealing means at the proximal end of the quartz enclosure.

Gervasio, Dominic Francis↗
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 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 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↗