Reconstruction of nonstationary disordered materials and media: Watershed transform and cross-correlation function

Tahmasebi, Pejman; Sahimi, Muhammad

doi:10.1103/physreve.91.032401

Cited by 53 publications

(22 citation statements)

References 48 publications

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“…Our model was found to fit the type of porous polymer film we study well . A larger range of pore geometries, for example, for particle based systems, can be obtained using simulated annealing based reconstructions and pattern‐based simulation methods . Examples of other types of stochastic models that have been used to connect microstructure to transport properties can be found in Blunt et al, Kim and Pitsch, and Stenzel et al…”

Section: Introductionmentioning

confidence: 57%

Prediction of diffusive transport through polymer films from characteristics of the pore geometry

2018

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Diffusive transport through porous materials is to a large extent determined by the microstructure of the material. To design materials with controlled transport properties, it is hence important to connect properties of the pore geometry to diffusive transport rates. Different kinds of microstructures from a stochastic model are generated and multiplicative regression is used to find relationships between geometric measures of the microstructures and numerically simulated diffusive transport. The main results are that the geodesic tortuosity explains a large part of the transport variation, and that the standard deviations we introduce further improves prediction. It was found that it is best to compute the tortuosity using the whole pore space, instead of using only the inlet, as is commonly done. The effects of calculating the measures using small samples of the pore structure were investigated, and a method for minimizing errors resulting from boundary effects was proposed. © 2018 American Institute of Chemical Engineers AIChE J, 65: 446–457, 2019

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Section: Introductionmentioning

confidence: 57%

Prediction of diffusive transport through polymer films from characteristics of the pore geometry

2018

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“…Accordingly, a variety of material reconstruction schemes have been devised for different microstructure representations. Examples include the Gaussian random field method [35], stochastic optimization method [36,37,38,39], gradient-based method [40], phase-retrieval method [41], iterative methods based on multi-point statistics [42,43], Bayesian Network Method [44] and cross-correlation functions [45,46], and image synthesis method [26,47]. Recently, a new reconstruction method is proved to be efficient at synthesizing Markovian microstructures [48] where the probability distribution of the material composition at each pixel (or voxel) is determined by its local surroundings and the conditional probability model can be applied homogeneously across a microstructure sample, see Fig.…”

Section: Microstructure Parametrization and Reconstructionmentioning

confidence: 99%

Microstructure Representation and Reconstruction of Heterogeneous Materials Via Deep Belief Network for Computational Material Design

Cang

Chen

et al. 2017

Journal of Mechanical Design

158

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Integrated Computational Materials Engineering (ICME) aims to accelerate optimal design of complex material systems by integrating material science and design automation. For tractable ICME, it is required that (1) a structural feature space be identified to allow reconstruction of new designs, and (2) the reconstruction process be property-preserving. The majority of existing structural presentation schemes rely on the designer's understanding of specific material systems to identify geometric and statistical features, which could be biased and insufficient for reconstructing physically meaningful microstructures of complex material systems. In this paper, we develop a feature learning mechanism based on convolutional deep belief network to automate a two-way conversion between microstructures and their lower-dimensional feature representations,and to achieve a 1000-fold dimension reduction from the microstructure space. The proposed model is applied to a wide spectrum of heterogeneous material systems with distinct microstructural features including Ti-6Al-4V alloy, Pb63-Sn37 alloy, Fontainebleau sandstone, and Spherical colloids, to produce material reconstructions that are close to the original samples with respect to 2-point correlation functions and mean critical fracture strength. This capability is not achieved by existing synthesis methods that rely on the Markovian assumption of material microstructures. * The paper is accepted by JMD 1 arXiv:1612.07401v3 [cond-mat.mtrl-sci]

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“…In this paper, an alternative concept of direct use of the available images of complex materials is proposed, in which not only are none of the current statistical descriptors are needed, but also different phases are used directly [37,39]. Thus, the first step in this algorithm is obtaining 2D or 3D images of the disordered material (i.e., I).…”

Section: Methodsmentioning

confidence: 99%

Accurate modeling and evaluation of microstructures in complex materials

Tahmasebi

2018

Phys. Rev. E

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Accurate characterization of heterogeneous materials is of great importance for different fields of science and engineering. Such a goal can be achieved through imaging. Acquiring three- or two-dimensional images under different conditions is not, however, always plausible. On the other hand, accurate characterization of complex and multiphase materials requires various digital images (I) under different conditions. An ensemble method is presented that can take one single (or a set of) I(s) and stochastically produce several similar models of the given disordered material. The method is based on a successive calculating of a conditional probability by which the initial stochastic models are produced. Then, a graph formulation is utilized for removing unrealistic structures. A distance transform function for the Is with highly connected microstructure and long-range features is considered which results in a new I that is more informative. Reproduction of the I is also considered through a histogram matching approach in an iterative framework. Such an iterative algorithm avoids reproduction of unrealistic structures. Furthermore, a multiscale approach, based on pyramid representation of the large Is, is presented that can produce materials with millions of pixels in a matter of seconds. Finally, the nonstationary systems-those for which the distribution of data varies spatially-are studied using two different methods. The method is tested on several complex and large examples of microstructures. The produced results are all in excellent agreement with the utilized Is and the similarities are quantified using various correlation functions.

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Reconstruction of nonstationary disordered materials and media: Watershed transform and cross-correlation function

Cited by 53 publications

References 48 publications

Prediction of diffusive transport through polymer films from characteristics of the pore geometry

Prediction of diffusive transport through polymer films from characteristics of the pore geometry

Microstructure Representation and Reconstruction of Heterogeneous Materials Via Deep Belief Network for Computational Material Design

Accurate modeling and evaluation of microstructures in complex materials

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