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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
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 OSTI2021

The 3D printing and modeling of functionally graded Kelvin foams for controlling crushing performance

Mechanical impact protection is an important consideration in many applications, ranging from product transportation to sports. Cellular materials are typically used due to their desirable energy absorption properties and light weight. However, their large deformation and rate dependent responses (especially of polymer foams) are challenging to consider in design. Additionally, the use of foams with uniform properties, such as uniform density and uniform stiffness, often restricts the designed foams to only be suitable for a narrow range of mechanical impact conditions whereas real applications commonly face unpredictable situations. 3D printing offers fabrication flexibility and thus opens the door to create foams with tailored properties. In this work, we investigate the feasibility of using 3D printing for functionally graded foams (FGFs) that are optimal over a broad range of mechanical environments. The foams are fabricated by the recently developed grayscale digital light processing (g-DLP) method which can print parts with locally designed properties. These foams are tested under drop test conditions and with slower displacement control. We also model the large deformation behavior of FGFs using finite element analysis in which we account for the different viscoelastic behaviors of the distinct grayscale regions. We then use the model to examine the impact mitigation capabilities of FGFs in different loading scenarios. Finally, we show how FGFs can be used to satisfy real-world design goals using the case study of a motorcycle helmet. In contrast to prior work, we investigate continuous, functionally graded foams of a single density that differ in their viscoelastic responses. This work provides further insight into the benefits of viscoelastic properties and modulus graded foams and presents a manufacturing approach that can be used to produce the next generation of flexible lattice foams as mechanical absorbers.

3D printing↗
DOE OSTI2026

Accelerating Traction Motor Optimization Design with AI Surrogate Models

The advancement of artificial intelligence systems enables the use of data-driven physics-based surrogate models to explore design spaces rapidly and deeply for engineering projects. This work presents a surrogate model workflow that accelerates electric traction motor design optimization by replacing finite element analysis (FEA) with an artificial neural network (ANN) and using this model in a genetic algorithm for design optimization. A baseline interior permanent-magnet motor is parameterized and sampled to generate FEA-labeled training data, after which a feed-forward ANN predicts key outputs (e.g., loss components and weight). The validated surrogate enables genetic-algorithm optimization and deep search over the design space without new FEA runs, producing Pareto-optimal trade-offs between weight and losses and set of optimized designs for rapid downselection of manufacturable motor designs.

Ribeiro, Pedro [ORNL] (ORCID:0009000921026641)↗
DOE OSTI2024

Coupled Multiphysics Modeling of Lithium-Ion Batteries for Automotive Crashworthiness Applications

Considerable advances have been made in battery safety models, but achieving predictive accuracy across a wide range of conditions continues to be challenging. Interactions between dynamically evolving mechanical, electrical, and thermal state variables make model prediction difficult during mechanical abuse scenarios. In this study, we develop a physics-based modeling approach that allows for choosing between different mechanical and electrochemical models depending on the required level of analysis. We demonstrate the use of this approach to connect cell-level abuse response to electrode-level and particle-level transport phenomena. A pseudo-two-dimensional model and simplified single-particle models are calibrated to electrical-thermal cycling data and applied to mechanically induced short-circuit scenarios to understand how the choice of electrochemical model affects the model prediction under abuse scenarios. These models are implemented using user-defined subroutines on ls-dyna finite element software and can be coupled with existing automotive crash safety models.

analysis and design of components↗
DOE OSTI2021

Material Models and Credibility for System Level Abnormal Mechanical ModSim Applications

The purpose of this document is to provide evidence for assessing the adequacy of parameterized material models for a collection of materials used in a finite element analyses setting. “Adequacy” is relative to the intended use of the material in particular analyses. The intended application of the material models covered within this document is for system level abnormal mechanical solid mechanics analyses. Generally, material model parameterizations should be valid from temperatures of approximately -50 to 70° C, across a range of strain rates, and (depending on details of the parts involved) large deformations. Each material covered in this document is presented in its own chapter with a common format across materials. Model assumptions, limitations, existing validation results, readiness for use with uncertainty quantification, general usage guidance, and failure considerations are all provided along with specific parameterization inputs suitable for the finite element analysis code Sierra/Solid Mechanics.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Physics-informed Bayesian machine learning case study: Integral blade rotors

This paper provides a physics-informed Bayesian machine learning (PIBML) description and case study. The PIBML approach applies three physics-based models to establish the initial beliefs before testing to determine the probability of milling stability (or prior). These include: receptance coupling substructure analysis (RCSA) prediction for the tool tip frequency response functions; finite element software prediction of the mechanistic force model coefficients; and a spindle speed-dependent power law model for process damping. Testing was then performed to identify optimal stable machining conditions using an expected improvement in material removal rate criterion. The prior probability of stability was updated using the test results to determine the posterior probability of stability. The test results were compared to the parameter recommendations provided by the endmill manufacturer. A demonstration integral blade rotor was machined at the optimal stable machining conditions for 304 stainless steel and 6061-T6 aluminum. Finally, the disagreement between manufacturer recommendations and milling performance in both materials tested emphasizes the need for broad implementation of PIBML approaches to increase machining productivity and efficiency.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Unified cohesive zone model (UCZM) for fracturing and fragmenting solids

Here, a Unified Cohesive Zone Model (UCZM), which inherits most of the advantages while overcoming the shortcomings of existing Cohesive Zone Models (CZMs), is proposed. Similar to the traditional extrinsic CZM approach, UCZM dynamically inserts the cohesive elements into the system based on local material states (e.g., stress, strain). However, the transition from continua to discontinua is smoothly achieved, thereby eliminating the “time-discontinuous” issue seen in the extrinsic CZM. Moreover, within the novel UCZM framework, the point of transition from continua to discontinua is controllable through the introduction of crack initialization criteria. As a result, the UCZM allows any material models (e.g., elastic, plastic, damage models) for continuum solids and for discrete fracture behavior to work together. In essence, both an enhanced extrinsic cohesive zone model and an intrinsic cohesive zone model can be represented by the proposed unified model. The proposed UCZM has been verified through different numerical examples. The work demonstrates that the UCZM is a highly effective approach for modeling fracture and fragmentation processes in solids.

42 ENGINEERING↗
DOE OSTI2022

Thermal conductivity of crack-containing media: A numerical study

The present study aims to evaluate the effective thermal conduction behavior of crack-containing media through micromechanical modeling. Numerical analyses were performed using the finite element method, first using periodic arrays of elliptical pores and then reducing the elliptical minor axis to reach the crack limit. This parametric approach was seen to be able to capture the effect of discontinuity across the crack face in a straightforward manner. The overall thermal conductivity of the model structures, encompassing a range of crack densities and spatial distributions, was investigated. It was found that, for cracks with mixed orientations, the effective thermal conductivity is insensitive to the actual crack alignment and can be uniquely represented by the crack density. The mixed-orientation periodic crack configurations are also representative of random sizes, orientations, and spatial distributions of cracks, thus exhibiting a high degree of generality. The modeling analyses can also be used to determine simple empirical expressions for two- and three-dimensional media containing random cracks.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A numerical-homogenization based phase-field fracture modeling of linear elastic heterogeneous porous media

Most porous media, such as geomaterials and biomaterials are highly heterogeneous in nature, and they contain large variations of microscopic pore structures, such as pore sizes, pore distribution, and pore shapes. The oscillation of microscopic structures is a substantial challenge in theoretical characterization and is usually ignored in continuous modeling. However, mechanical behavior of porous media such as deformation and failure, are essentially impacted by the microscopic heterogeneity which needs to be considered in modeling a porous media. Here, this research proposes a numerical modeling framework with a capability to investigate the effect of microscopic heterogeneity on the macroscopic fracture behavior in porous media by using a numerical homogenization technique, combined with the phase-field fracture modeling method. This numerical modeling strategy computes a homogenized elasticity tensor based on microscopic heterogeneous pore structures heterogenous porous domain by solving boundary value problems at microscopic domain. The strain energy and subsequent propagation of macroscopic fractures will be updated using homogenized stiffness information. Using this numerical scheme, the microscopic pore structure’s impact on the fracture behavior through the homogenized elastic tensor will be taken into account. This multiscale technique is benchmarked against classical problems. Finally, the results highlight the importance of the underlying pore structure and reveal that both fracture strength and propagation path can be influenced by the microscopic heterogeneity.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Numerical and experimental evaluation of mechanical performance of the multifunctional energy storage composites

This work presents numerical simulation methods to model the mechanical behavior of the multifunctional energy storage composites (MESCs), which consist of a stack of multiple thin battery layers reinforced with through-the-hole polymer rivets and embedded inside carbon fiber composite laminates. MESC has been demonstrated through earlier experiments on its exceptional behavior as a structural element as well as a battery. However, the inherent complex infrastructure of the MESC design has created significant challenges in simulation and modeling. A novel homogenization technique was adopted to characterize the multi-layer properties of battery material using physics-based constitutive equations combined with nonlinear deformation theories to handle the interface between the battery layers. Second, mechanical damage and failure modes among battery materials, polymer reinforcements, and carbon fiber-polymer interfaces were characterized through appropriate models and experiments. The model of MESCs has been implemented in a commercial finite element code in ABAQUS. A comparison of structural response and failure modes from numerical simulations and experimental tests are presented. The results of the study showed that the predictions of elastic and damage responses of MESCs at various loading conditions agreed well with the experimental data. © 2021

Materials Science↗
DOE OSTI2024

Simplified inelastic constitutive models for ASME Section III, Division 5 design by inelastic analysis

This report describes the development of simplified, universal constitutive model that captures the high temperature monotonic and cyclic behavior of a range of commonly-used high temperature materials. The goal of the work is to provide a simple, universal constitutive model to replace the current bespoke models for Grade 91, 316H, and Alloy 617 included in Nonmandatory Appendix HBB-Z of the ASME Boiler & Pressure Vessel Code, and to extend this model to cover Alloy 800H. We initiated this work in response to feedback from reactor vendors and other Code users requesting simplified models, compared to the current models, that are easier to implement and use in commercial finite element analysis software. This report describes the completion of this effort by developing a model to correct the defects in standard model forms presently used for high temperature material modeling, described in past work, developing and implementing new numerical methods to train this model against test data, and then actually training the model for the four materials. The report provides a complete mathematical description of the model along with the tabulated material coefficients for the four materials. The final step will be to formulate an ASME Code change to introduce the new models into the Code.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A general framework for substructuring-based domain decomposition methods for models having nonlocal interactions [including apppendix B]

A rigorous mathematical framework is provided for a substructuring-based domain-decomposition approach for nonlocal problems that feature interactions between points separated by a finite distance. Here, by substructuring it is meant that a traditional geometric configuration for local partial differential equation problems is used in which a computational domain is subdivided into non-overlapping subdomains. In the nonlocal setting, this approach is substructuring-based in the sense that those subdomains interact with neighboring domains over interface regions having finite volume, in contrast to the local PDE setting in which interfaces are lower dimensional manifolds separating abutting subdomains Key results include the equivalence between the global, single-domain nonlocal problem and its multi-domain reformulation, both at the continuous and discrete levels. These results provide the rigorous foundation necessary for the development of efficient solution strategies for nonlocal domain-decomposition methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

BISON TRISO Modeling Advancements and Validation to AGR-1 Data

BISON is a finite element-based nuclear fuel performance code. Among its unique characteristics are its ability to model 1D, 2D, and 3D geometries and its applicability to a wide variety of nuclear fuels. For the last eight years, BISON has included a beginning capability to model tri-structural isotropic (TRISO) fuel. Recently, interest in TRISO fuel has grown, and a significant effort has been made to improve BISON’s capabilities in this area. Capability development has occurred for each material present in TRISO fuel particles: the buffer, inner pyrolytic carbon, silicon carbide, and outer pyrolytic carbon layers, as well as the fuel kernel. New elastic, creep, swelling, thermal expansion, thermal conductivity, and fission gas release (FGR) models are available. New models for the graphite matrix are also now available. Another important addition is the ability to perform statistical failure analysis of large samples of fuel particles. This new capability, which continues to grow, enables evaluation of failure due to pressure or crack formation by analyzing many thousands of particles. This enables realistic calculations of fission product release from the many particles in a TRISO-fueled reactor. These capabilities were checked via regression and verification tests. A large number of code benchmarking problems were also run, showing that BISON’s results closely match those of other software tools. Finally, a significant validation effort was completed in which fission product release, measured as part of the AGR-1 capsule experiments, was compared to BISON outputs. BISON outputs compared very well to the experimental data and to PARFUME results. Interest in BISON’s TRISO capabilities is growing, with the U.S. Nuclear Regulatory Commission (NRC) and Westinghouse Electric Company receiving training during the past year. Multiple other entities have expressed interest in or are actively using BISON. Kairos Power, LLC, has a strong partnership with Idaho National Laboratory (INL) regarding the use of BISON for TRISO analysis. While its capabilities still continue to grow, BISON has already become a powerful tool for TRISO analysis.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Shear Band Formation in Thin-Film Multilayer Columns Under Compressive Loading: A Mechanistic Study

Micro-pillar compression is a popular experimental technique used for characterizing the mechanical behavior of nano- and micro-laminates. The compressive stress–strain response of the column-shaped thin-film composite can be measured, and the deformation and damage features can be revealed by post-test cross-section microscopy. The development of plastic instability in the form of localized strain concentration (shear bands), leading to eventual failure, is frequently observed. In the present study, a computational approach is used to illustrate the commonality of shear band formation from a continuum standpoint. Systematic finite element analyses are conducted, showing that the strain field tends to become localized once plastic yielding commences. Distinct shear offsets of the layered structure can be revealed from the numerical model, which is similar to those observed in experiments. The actual appearance of shear bands depends on the materials’ constitutive behavior and precise geometries. Post-yield strain hardening reduces the propensity of shear band formation, while strain softening enhances it. Imperfections such as the undulated layer geometry, as well as the frictional characteristics between the specimen and test apparatus, can also influence the shear band morphology and overall stress–strain response.

finite element modeling↗
DOE OSTI2022

Deep Learning-Assisted Real-Time Forward Modeling of Electromagnetic Logging in Complex Formations

Higher dimensional (i.e., 2-D and 3-D) modeling is indispensable to correctly evaluate the responses of electromagnetic logging tools in complex formation environments. However, limited by the high computational cost of rigorous modeling such as finite difference method and the finite element method, the real-time applications in the well logging industry primarily rely on the 1-D forward solver, which would result in erroneous formation evaluation for complex scenarios. As a result, aiming at realizing fast modeling for electromagnetic logging tools in complex formations, this paper proposes a general framework assisted by deep neural networks (DNNs). The framework consists of three modules: earth model classification, parameter extraction, and surrogate construction. Separate DNNs are trained and tested for different modules. The accuracy and efficiency of the DNN assisted fast modeling are validated by several experiments. Here, this study finds that the fast modeling assisted by DNNs is able to calculate the tool responses and reconstruct the subsurface formations in real-time.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Linear viscoelastic characterization of electrically conductive adhesives used as interconnect in photovoltaic modules

Electrically conductive adhesives (ECAs) are incorporated into recent designs of photovoltaic (PV) modules and replace the traditional metallic solders as interconnects. This transition depicts a significant material change, and a proper understanding of the interconnects' mechanical response has not yet been established. However, such an understanding is necessary to (a) identify the driving forces for module degradation and failure, (b) allow for module design optimization, and (c) enable accurate lifetime predictions. This study summarizes the framework for the mechanical materials characterization and modeling of ECAs for PV applications. Only high-fidelity material models are able to capture the rate and temperature dependency of the ECA interconnect and allow for accurate modeling of the materials response. Furthermore, a linear viscoelastic representation is found to describe the mechanical response of the ECAs sufficiently well. The effects of curing conditions and environmental exposure are investigated, and material models for a variety of ECAs are reported and prepared for the use in numerical simulations. Overall, a finite element simulation of a generic submodel of a shingled cell module is used to highlight the need for high-fidelity material models and demonstrates the error made in the predicted stress states by using less sophisticated models.

14 SOLAR ENERGY↗