Computational high-throughput screening of fluid permeability in heterogeneous fiber materials

Abstract

We explore computational high-throughput screening as a design strategy for heterogeneous, isotropic fiber materials. Fluid permeability, a key property in the design of soft porous materials, is systematically studied using a multi-scale lattice Boltzmann framework. After characterizing microscopic permeability as a function of solid volume fraction in the microstructure, we perform high-throughput computational screening of in excess of 35 000 macrostructures consisting of a continuous bulk interrupted by spherical/elliptical domains with either lower or higher microscopic permeability (hence with two distinct microscopic solid volume fractions and therefore two distinct microscopic permeabilities) to assess which parameters determine macroscopic permeability for a fixed average solid volume fraction. We conclude that the fractions of bulk and domains and the distribution of solid volume fraction between them are the primary determinants of macroscopic permeability, and that a substantial increase in permeability compared to the corresponding homogenous material is attainable.

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

“…These microstructure models can be calibrated with experimental data gained, e.g., by tomographic imaging or simply be inspired by experimentally observed structures. There are numerous examples for artificial generation and virtual testing of functional materials, including applications for lithium ion batteries (Feinauer et al, 2015;Hein et al, 2016;Westhoff et al, 2018a;Prifling et al, 2019;Hein et al, 2020;Allen et al, 2021;Prifling et al, 2021a;Birkholz et al, 2021;Furat et al, 2021), solid oxide fuel cells (Abdallah et al, 2016;Neumann et al, 2016;Moussaoui et al, 2018), amorphous silica (Prifling et al, 2021b), gas diffusion electrodes (Neumann et al, 2019a), open-cell foams (Westhoff et al, 2018b), organic semiconductors (Westhoff et al, 2015), mesoporous alumina (Wang et al, 2015), solar cells (Stenzel et al, 2011), electric double-layer capacitors (Prill et al, 2017), platelet-filled composites (Röding et al, 2018), fiber-based materials (Röding et al, 2016;Townsend et al, 2021), and pharmaceutical coatings for controlled drug release (Barman et al, 2019).…”

Advances in data‐assisted high‐throughput computations for material design

“…Due to the porous network structure of fiber materials, they possess excellent absorption efficiency. Roding et al [294] . employed an HTC approach based on a multiscale lattice Boltzmann framework to screen over 35,000 heterogeneous and isotropic fiber materials for high fluid permeability.…”

Inverse design of anisotropic spinodoid materials with prescribed diffusivity

“…Numerous stochastic models of realistic porous microstructures found in e.g. solar cells 4 , organic semiconductors 5 , carbon electrodes 6 , platelet-filled composites 7 , lithium ion batteries 8 , mesoporous silica 9 , fiber materials 10 , 11 , and pharmaceutical coatings for controlled release 12 have been developed. By computing mass transport properties like effective diffusivity and/or fluid permeability together with microstructural (geometric) descriptors, microstructure-property relationships have been established using analytical models or machine learning-based regression.…”