Stochastic modelling of 3D fiber structures imaged with X-ray microtomography

Townsend, Philip; Larsson, Emanuel; Karlson, Tomas; Hall, Stephen A.; Lundman, Malin; Bergström, Per; Hanson, Charlotta; Lorén, Niklas; Gebäck, Tobias; Särkkä, Aila; Röding, Magnus

doi:10.1016/j.commatsci.2021.110433

Cited by 10 publications

(6 citation statements)

References 20 publications

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“…The fiber systems considered in this paper are generated using essentially the method described in Townsend et al (2021), with modifications to allow for periodic and isotropic structures. Individual fibers are first represented as a set of nodes generated by a random walk.…”

Section: Fiber Systemsmentioning

confidence: 99%

“…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).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Prifling¹,

Röding

Townsend

et al. 2021

Front. Mater.

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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.

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Section: Fiber Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Prifling¹,

Röding

Townsend

et al. 2021

Front. Mater.

Self Cite

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“…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.…”

Section: Introductionmentioning

confidence: 99%

Inverse design of anisotropic spinodoid materials with prescribed diffusivity

Röding

Skärström

Lorén

2022

Sci Rep

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The three-dimensional microstructure of functional materials determines its effective properties, like the mass transport properties of a porous material. Hence, it is desirable to be able to tune the properties by tuning the microstructure accordingly. In this work, we study a class of spinodoid i.e. spinodal decomposition-like structures with tunable anisotropy, based on Gaussian random fields. These are realistic yet computationally efficient models for bicontinuous porous materials. We use a convolutional neural network for predicting effective diffusivity in all three directions. We demonstrate that by incorporating the predictions of the neural network in an approximate Bayesian computation framework for inverse problems, we can in a computationally efficient manner design microstructures with prescribed diffusivity in all three directions.

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“…The heat transfer performance through fibrous porous media can be investigated numerically using 3D virtual samples (i.e., computer models), which are either 3D real fibrous structures reconstructed from a sequence of high resolution 2D X-ray images (i.e., tomograms) acquired with an X-ray micro-computed tomography (lCT) [27][28][29][30][31][32][33][34] (known as image-reconstruction method), or 3D artificial fibrous structures constructed based on algorithms for generating random fibrous networks. The real virtual fibrous samples can be reconstructed by using a skeleton-based fiber tracing algorithm (see Ref.…”

mentioning

confidence: 99%

“…[35] and references therein). The artificial virtual samples can be fitted to the microstructure of real fibrous structures using the geometric characteristics extracted from lCT images (fiber volume fraction, fiber orientation distribution, etc) as inputs [36][37][38]3,4,19,32,33].…”

mentioning

confidence: 99%