Modeling Heterogeneous Materials via Two-Point Correlation Functions: II. Algorithmic Details and Applications
arXiv:0801.1314 · doi:10.1103/PhysRevE.77.031135
Abstract
In the first part of this series of two papers, we proposed a theoretical formalism that enables one to model and categorize heterogeneous materials (media) via two-point correlation functions S2 and introduced an efficient heterogeneous-medium (re)construction algorithm called the "lattice-point" algorithm. Here we discuss the algorithmic details of the lattice-point procedure and an algorithm modification using surface optimization to further speed up the (re)construction process. The importance of the error tolerance, which indicates to what accuracy the media are (re)constructed, is also emphasized and discussed. We apply the algorithm to generate three-dimensional digitized realizations of a Fontainebleau sandstone and a boron carbide/aluminum composite from the two- dimensional tomographic images of their slices through the materials. To ascertain whether the information contained in S2 is sufficient to capture the salient structural features, we compute the two-point cluster functions of the media, which are superior signatures of the micro-structure because they incorporate the connectedness information. We also study the reconstruction of a binary laser-speckle pattern in two dimensions, in which the algorithm fails to reproduce the pattern accurately. We conclude that in general reconstructions using S2 only work well for heterogeneous materials with single-scale structures. However, two-point information via S2 is not sufficient to accurately model multi-scale media. Moreover, we construct realizations of hypothetical materials with desired structural characteristics obtained by manipulating their two-point correlation functions.
35 pages, 19 figures
References in corpus (2)
Cited by in corpus (30)
- A Superior Descriptor of Random Textures and Its Predictive Capacity
- Hyperuniformity in point patterns and two-phase random heterogeneous media
- Classical Disordered Ground States: Super-Ideal Gases, and Stealth and Equi-Luminous Materials
- Accurate and Fast reconstruction of Porous Media from Extremely Limited Information Using Conditional Generative Adversarial Network
- Designing Disordered Hyperuniform Two-Phase Materials with Novel Physical Properties
- Microstructure synthesis using style-based generative adversarial network
- Density of States for a Specified Correlation Function and the Energy Landscape
- Improved reconstructions of random media using dilation and erosion processes
- Heterogeneous Force Chains in Cellularized Biopolymer Network
- Geometrical Ambiguity of Pair Statistics. I. Point Configurations
- Geometrical Ambiguity of Pair Statistics. II. Heterogeneous Media
- Precise algorithms to compute surface correlation functions of two-phase heterogeneous media and their applications
- Fractal Heterogeneous Media
- Entropic descriptor of a complex behaviour
- Microstructure reconstruction using entropic descriptors
- Generation and Structural Characterization of Debye Random Media
- Quantifying Microstructural Evolution via Time-Dependent Reduced-Dimension Metrics Based on Hierarchical -Point Polytope Functions
- Neural Cellular Automata for Solidification Microstructure Modelling
- Information Content of Hierarchical n-Point Polytope Functions for Quantifying and Reconstructing Disordered Systems
- Multiscale reconstruction of porous media based on multiple dictionaries learning
- Compression failure of porous ceramics: A computational study about the effect of volume fraction on damage evolution and failure
- A Two-Stage Reconstruction of Microstructures with Arbitrarily Shaped Inclusions
- Statistical mechanics characterization of spatio-compositional inhomogeneity
- Statistical Reconstruction of Microstructures Using Entropic Descriptors
- 3D Multiphase Heterogeneous Microstructure Generation Using Conditional Latent Diffusion Models
- An efficient optimization based microstructure reconstruction approach with multiple loss functions
- AI-driven Bayesian inference of statistical microstructure descriptors from finite-frequency waves
- Novel Hierarchical Correlation Functions for Quantitative Representation of Complex Heterogeneous Materials and Microstructural Evolution
- Understanding Degeneracy of Two-Point Correlation Functions via Debye Random Media
- Microstructure Design of Low-Melting-Point Alloy (LMPA)/ Polymer Composites for Dynamic Dry Adhesion Tuning in Soft Gripping