Reconstruction of multiphase microstructure based on statistical descriptors

Chen, Dongdong; He, Xiaohai; Teng, Qizhi; Xu, Zhi; Li, Zhengji

doi:10.1016/j.physa.2014.07.066

Cited by 25 publications

(3 citation statements)

References 41 publications

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“…The autocorrelation function (ACF) was used by the simulated annealing algorithm because it indicates the statistical characteristics of spatial correlation of different phases [31][32][33][34]; hence, it is also used in this paper as the constraint conditions for selecting the edge sampling points. The definition of ACF will be provided in Sec.…”

Section: The Edge Sampling Proceduresmentioning

confidence: 99%

Reconstruction of three-dimensional porous media from a single two-dimensional image using three-step sampling

Gao

Teng

et al. 2015

Phys. Rev. E

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A random three-dimensional (3D) porous medium can be reconstructed from a two-dimensional (2D) image by reconstructing an image from the original 2D image, and then repeatedly using the result to reconstruct the next 2D image. The reconstructed images are then stacked together to generate the entire reconstructed 3D porous medium. To perform this successfully, a very important issue must be addressed, i.e., controlling the continuity and variability among adjacent layers. Continuity and variability, which are consistent with the statistics characteristic of the training image (TI), ensure that the reconstructed result matches the TI. By selecting the number and location of the sampling points in the sampling process, the continuity and variability can be controlled directly, and thus the characteristics of the reconstructed image can be controlled indirectly. In this paper, we propose and develop an original sampling method called three-step sampling. In our sampling method, sampling points are extracted successively from the center of 5×5 and 3×3 sampling templates and the edge area based on a two-point correlation function. The continuity and variability of adjacent layers were considered during the three steps of the sampling process. Our method was tested on a Berea sandstone sample, and the reconstructed result was compared with the original sample, using tests involving porosity distribution, the lineal path function, the autocorrelation function, the pore and throat size distributions, and two-phase flow relative permeabilities. The comparison indicates that many statistical characteristics of the reconstructed result match with the TI and the reference 3D medium perfectly.

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Section: The Edge Sampling Proceduresmentioning

confidence: 99%

Reconstruction of three-dimensional porous media from a single two-dimensional image using three-step sampling

Gao

Teng

et al. 2015

Phys. Rev. E

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“…To extract this kind of structural information, different types of descriptors can be used. Some statistical descriptors naturally define the "energy" cost function for the SA method preferred here [44][45][46]. For structurally complex random composites, a hybrid combination of two-point correlation function and cluster function [47] is particularly useful.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Reconstruction of Microstructures with Arbitrarily Shaped Inclusions

et al. 2020

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The main goal of our research is to develop an effective method with a wide range of applications for the statistical reconstruction of heterogeneous microstructures with compact inclusions of any shape, such as highly irregular grains. The devised approach uses multi-scale extended entropic descriptors (ED) that quantify the degree of spatial non-uniformity of configurations of finite-sized objects. This technique is an innovative development of previously elaborated entropy methods for statistical reconstruction. Here, we discuss the two-dimensional case, but this method can be generalized into three dimensions. At the first stage, the developed procedure creates a set of black synthetic clusters that serve as surrogate inclusions. The clusters have the same individual areas and interfaces as their target counterparts, but random shapes. Then, from a given number of easy-to-generate synthetic cluster configurations, we choose the one with the lowest value of the cost function defined by us using extended ED. At the second stage, we make a significant change in the standard technique of simulated annealing (SA). Instead of swapping pixels of different phases, we randomly move each of the selected synthetic clusters. To demonstrate the accuracy of the method, we reconstruct and analyze two-phase microstructures with irregular inclusions of silica in rubber matrix as well as stones in cement paste. The results show that the two-stage reconstruction (TSR) method provides convincing realizations for these complex microstructures. The advantages of TSR include the ease of obtaining synthetic microstructures, very low computational costs, and satisfactory mapping in the statistical context of inclusion shapes. Finally, its simplicity should greatly facilitate independent applications.

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“…CF-based reconstruction often involves optimization procedures to minimize the error between the actual CF and the target ones. This approach has been extended for reconstructing multiphase microstructures, for which each phase has its own CF [13,14]. Although CF-based approaches are flexible and can be adapted for different microstructures, it is computationally expensive and prohibitive for use as a part of the iterative material design procedure.…”

mentioning

confidence: 99%

Microstructure reconstruction and structural equation modeling for computational design of nanodielectrics

Zhang

Zhao

Hassinger

et al. 2015

Integr Mater Manuf Innov

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Nanodielectric materials, consisting of nanoparticle-filled polymers, have the potential to become the dielectrics of the future. Although computational design approaches have been proposed for optimizing microstructure, they need to be tailored to suit the special features of nanodielectrics such as low volume fraction, local aggregation, and irregularly shaped large clusters. Furthermore, key independent structural features need to be identified as design variables. To represent the microstructure in a physically meaningful way, we implement a descriptor-based characterization and reconstruction algorithm and propose a new decomposition and reassembly strategy to improve the reconstruction accuracy for microstructures with low volume fraction and uneven distribution of aggregates. In addition, a touching cell splitting algorithm is employed to handle irregularly shaped clusters. To identify key nanodielectric material design variables, we propose a Structural Equation Modeling approach to identify significant microstructure descriptors with the least dependency. The method addresses descriptor redundancy in the existing approach and provides insight into the underlying latent factors for categorizing microstructure. Four descriptors, i.e., volume fraction, cluster size, nearest neighbor distance, and cluster roundness, are identified as important based on the microstructure correlation functions (CF) derived from images. The sufficiency of these four key descriptors is validated through confirmation of the reconstructed images and simulated material properties of the epoxy-nanosilica system. Among the four key descriptors, volume fraction and cluster size are dominant in determining the dielectric constant and dielectric loss.

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Reconstruction of multiphase microstructure based on statistical descriptors

Cited by 25 publications

References 41 publications

Reconstruction of three-dimensional porous media from a single two-dimensional image using three-step sampling

Reconstruction of three-dimensional porous media from a single two-dimensional image using three-step sampling

A Two-Stage Reconstruction of Microstructures with Arbitrarily Shaped Inclusions

Microstructure reconstruction and structural equation modeling for computational design of nanodielectrics

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