Mesh generation for periodic 3D microstructure models and computation of effective properties

Understanding and optimizing effective properties of porous functional materials, such as permeability or conductivity, is one of the main goals of materials science research with numerous applications. For this purpose, understanding the underlying 3D microstructure is crucial since it is well known that the materials? morphology has an significant impact on their effective properties. Because tomographic imaging is expensive in time and costs, stochastic microstructure modeling is a valuable tool for virtual materials testing, where a large number of realistic 3D microstructures can be generated and used as geometry input for spatially-resolved numerical simulations. Since the vast majority of numerical simulations is based on solving differential equations, it is essential to have fast and robust methods for generating high-quality volume meshes for the geometrically complex microstructure domains. The present paper introduces a novel method for generating volume-meshes with periodic boundary conditions based on an analytical representation of the 3D microstructure using spherical harmonics. Due to its generality, the present method is applicable to many scientific areas. In particular, we present some numerical examples with applications to battery research by making use of an already existing stochastic 3D microstructure model that has been calibrated to eight differently compacted cathodes

Mesh generation for periodic 3D microstructure models and computation of effective properties

Understanding and optimizing effective properties of porous functional materials, such as permeability or conductivity, is one of the main goals of materials science research with numerous applications. For this purpose, understanding the underlying 3D microstructure is crucial since it is well known that the materials? morphology has an significant impact on their effective properties. Because tomographic imaging is expensive in time and costs, stochastic microstructure modeling is a valuable tool for virtual materials testing, where a large number of realistic 3D microstructures can be generated and used as geometry input for spatially-resolved numerical simulations. Since the vast majority of numerical simulations is based on solving differential equations, it is essential to have fast and robust methods for generating high-quality volume meshes for the geometrically complex microstructure domains. The present paper introduces a novel method for generating volume-meshes with periodic boundary conditions based on an analytical representation of the 3D microstructure using spherical harmonics. Due to its generality, the present method is applicable to many scientific areas. In particular, we present some numerical examples with applications to battery research by making use of an already existing stochastic 3D microstructure model that has been calibrated to eight differently compacted cathodes

Influence of Electrode Structuring Techniques on the Performance of All-Solid-State Batteries

All-solid-state batteries (ASSBs) offer a promising route to safer batteries with superior energy density compared to conventional Li-ion batteries (LIBs). However, the design of the composite cathode and optimization of the underlying microstructure is one of the aspects requiring intensive research. Achieving both high energy and power density remains challenging due to limitations in ionic conductivity and active material loading. Using structure-resolved simulations, we investigate the potential of perforated and layered electrode designs to enhance ASSB performance. Design strategies showing significant performance increase in LIBs are evaluated regarding their application to ASSBs. Composite cathodes with solid electrolyte channels in the structure do not significantly increase cell performance compared to unstructured electrodes. However, the design with a two-layer cathode proves promising. The layered structure effectively balances improved ionic transport due to increased solid electrolyte fraction at the separator side and substantial active material loading through increased active material fraction at the current collector side of the cathode. Our research highlights key challenges in ASSB development and provides a clear direction for future studies in the field.Comment: 46 pages, 15 figure

Large-Scale Statistical Learning for Mass Transport Prediction in Porous Materials Using 90,000 Artificially Generated Microstructures

Effective properties of functional materials crucially depend on their 3D microstructure. In this paper, we investigate quantitative relationships between descriptors of two-phase microstructures, consisting of solid and pores and their mass transport properties. To that end, we generate a vast database comprising 90,000 microstructures drawn from nine different stochastic models, and compute their effective diffusivity and permeability as well as various microstructural descriptors. To the best of our knowledge, this is the largest and most diverse dataset created for studying the influence of 3D microstructure on mass transport. In particular, we establish microstructure-property relationships using analytical prediction formulas, artificial (fully-connected) neural networks, and convolutional neural networks. Again, to the best of our knowledge, this is the first time that these three statistical learning approaches are quantitatively compared on the same dataset. The diversity of the dataset increases the generality of the determined relationships, and its size is vital for robust training of convolutional neural networks. We make the 3D microstructures, their structural descriptors and effective properties, as well as the code used to study the relationships between them available open access

Understanding Electrolyte Filling of Lithium‐Ion Battery Electrodes on the Pore Scale Using the Lattice Boltzmann Method

Electrolyte filling is a time-critical step during battery manufacturing that also affects battery performance. The underlying physical phenomena mainly occur on the pore scale and are hard to study experimentally. Therefore, here, the lattice Boltzmann method is used to study the filling of realistic 3D lithium-ion battery cathodes. Electrolyte flow through the nanoporous binder is modelled adequately. Besides process time, the influences of particle size, binder distribution, volume fraction and wetting behavior of active material and binder are investigated. Optimized filling conditions are discussed by pressure-saturation relationships. It is shown how the influencing factors affect the electrolyte saturation. The amount and distribution of entrapped residual gas are analyzed in detail. Both can adversely affect the battery performance. The results indicate how the filling process, the final electrolyte saturation, and also the battery performance can be optimized by adapting process parameters as well as electrode and electrolyte design

Understanding Electrolyte Filling of Lithium-Ion Battery Electrodes on the Pore Scale Using the Lattice Boltzmann Method

Electrolyte filling is a time-critical step during battery manufacturing that also affects battery performance. The underlying physical phenomena mainly occur on the pore scale and are hard to study experimentally. Therefore, here, the lattice Boltzmann method is used to study the filling of realistic 3D lithium-ion battery cathodes. Electrolyte flow through the nanoporous binder is modelled adequately. Besides process time, the influences of particle size, binder distribution, volume fraction and wetting behavior of active material and binder are investigated. Optimized filling conditions are discussed by pressure-saturation relationships. It is shown how the influencing factors affect the electrolyte saturation. The amount and distribution of entrapped residual gas are analyzed in detail. Both can adversely affect the battery performance. The results indicate how the filling process, the final electrolyte saturation, and also the battery performance can be optimized by adapting process parameters as well as electrode and electrolyte design