Applications of microtomography to multiscale system dynamics: Visualisation, characterisation and high performance computation

Abstract

We characterise microstructure over multiple spatial scales for different samples utilising a workflow that combines microtomography with computational analysis. High-resolution microtomographic data are acquired by desktop and synchrotron X-ray tomography. In some recent 4-dimensional experiments, microstructures that are evolving with time are produced and documented in situ.The microstructures in our materials are characterised by a numerical routine based on percolation theory. In a pre-processing step, the material of interest is segmented from the tomographic data. The analytical approach can be applied to any feature that can be segmented. We characterise a microstructure by its volume fraction, the specific surface area, the connectivity (percolation) and the anisotropy of the microstructure. Furthermore, properties such as permeability and elastic parameters can be calculated. By using the moving window method, scale-dependent properties are obtained and the size of representative volume element (RVE) is determined. The fractal dimension of particular microstructural configurations is estimated by relating the number of particular features to their normalized size. The critical exponent of correlation length can be derived from the probability of percolation of the microstructure. With these two independent parameters, all other critical exponents are determined leading to scaling laws for the specific microstructure. These are used to upscale the microstructural model and properties. Visualisation is one of the essential tools when performing characterisation. The high performance computations behind these characterisations include: (1) the Hoshen-Kolpeman algorithm for labelling materials in large datasets; (2) the OpenMP parallelisation of the moving window method and the performance of stochastic analysis (up to 6403 voxels); (3) the MPI parallelisation of the moving window method and the performance of stochastic analysis, which enables the computation to be run on distributed memory machines and employ massive parallelism; (4) the parallelised MPI version of the Hoshen-Kolpeman algorithm and the moving window method, which allows datasets of theoretically unlimited size to be analysed.