GPU Solutions to Multi-scale Problems in Science and Engineering
GPU Solutions to Multi-scale Problems in Science and Engineering
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DOI：
10.1007/978-3-642-16405-7
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发表时间：
2011-07
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影响因子：
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通讯作者：
D. Yuen;Jianfeng Wang;L. Johnsson;Chi-Hung Chi-Chi-Hung-Chi-36452710;Yaolin Shi
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作者：
D. Yuen;Jianfeng Wang;L. Johnsson;Chi-Hung Chi-Chi-Hung-Chi-36452710;Yaolin Shi
我们利用结合了显微断层摄影术和计算分析的工作流程，对不同样本的多个空间尺度上的微观结构进行了表征。高分辨率显微层析成像数据由台式和同步辐射X射线层析成像获得。在最近的一些四维实验中，随着时间的推移，微结构被产生并在情景中被记录下来。我们的材料中的微结构是用基于渗流理论的数值例程来表征的。在预处理步骤中，从断层扫描数据中分割感兴趣的材料。该分析方法可以应用于任何可分割的特征。我们通过微结构的体积分数、比表面积、连通性(渗流)和各向异性来表征微结构。此外，还可以计算渗透率和弹性参数等属性。通过移动窗口方法，得到了尺度相关的性质，并确定了代表性体积单元(RVE)的尺寸。通过将特定特征的数量与其归一化大小相关联来估计特定微结构构型的分维。关联长度的临界指数可以由微结构的渗流概率得到。有了这两个独立的参数，就可以确定所有其他的临界指数，从而得出特定微观结构的标度定律。这些被用来提升微结构模型和属性。形象化是进行表征时必不可少的工具之一。这些特性背后的高性能计算包括：(1)用于标记大数据集中材料的Hoshen-Kolpeman算法；(2)移动窗口方法的OpenMP并行化和随机分析(高达体素)的性能；(3)移动窗口方法的MPI并行化和随机分析的性能，这使得计算能够在分布式存储机器上运行并采用大规模并行；(4)并行MPI版本的Hoshen-Kolpeman算法和移动窗口方法，它允许分析理论上无限大的数据集。
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 tovoxels); (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.