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Microstructure reconstruction and compression techniques are designed to identify microstructures with desired properties. While a microstructure reconstruction involves searching for a microstructure with prescribed statistical properties, a microstructure compression focuses on efficient representation of material morphology for the purpose of multiscale modelling. Successful application of these techniques, nevertheless, requires proper understanding of the underlying statistical descriptors quantifying morphology of a material. In this paper, we focus on a lineal path function designed to capture short-range effects and phase connectedness, which can hardly be handled by the commonly used two-point probability function. Usage of the lineal path function is, however, significantly limited because of huge computational requirements. So as to examine the properties of the lineal path function during computationally exhaustive compression and reconstruction processes, we start with an acceleration of the lineal path evaluation, namely by porting part of its code to a graphics processing unit using the CUDA (Compute Unified Device Architecture) programming environment. This allows us to present a unique comparison of the entire lineal path function with the commonly used rough approximation based on the Monte Carlo and/or sampling template. Moreover, this accelerated version of the lineal path function is then compared to the two-point probability function during the compression and reconstruction of two-phase morphologies. Their significant features are discussed and illustrated using a set of artificial periodic as well as real-world random microstructures. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this research is to examine the use of statistical descriptors in the microstructure reconstruction process and the associated effect on microstructure properties. In this work, two-point correlation functions and lineal path functions were used to study the efficiency and accuracy of microstructure reconstruction. A polar plot of the two-point correlation functions is used to quantitatively evaluate the agreement with the target microstructure. In general, as more microstructure descriptors are added for the microstructure reconstruction process, the computational time increases and the reconstructed microstructures quantitatively agree better with the target microstructure. However, in some cases, adding lineal path functions did not always result in a better microstructural representation. Last, finite element analysis of thermal loading in the reconstructed microstructures of loaded nuclear waste glass is used to evaluate the property predictions with those of the target microstructure. Identifying which statistical descriptors are best for different microstructures may provide insight into optimizing the microstructure reconstruction process from limited statistical descriptors. [Copyright &y& Elsevier]

A stochastic reconstruction scheme via the simulated Annealing method (SA) is developed here in order de represent the 3D microstructure of a porous material so, its properties can be estimated. The 2D image, obtained by Scanning Electron Microscope (SEM), is treated and then the different statistical information contained, as the 2-points correlation function and the lineal path function are extracted from this image. [ABSTRACT FROM AUTHOR]