Predicting Effective Diffusivity of Porous Media from Images by Deep Learning
Predicting Effective Diffusivity of Porous Media from Images by Deep Learning
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DOI：
10.1038/s41598-019-56309-x
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发表时间：
2019-12-31
影响因子：
4.6
通讯作者：
Qiao, Rui
中科院分区：
文献类型：
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作者：
Wu, Haiyi;Fang, Wen-Zhen;Qiao, Rui
我们报告了机器学习方法的应用，用于根据二维多孔介质的结构图像预测其有效扩散率（De）。孔隙结构使用重建方法构建并表示为图像，其有效扩散率通过格子玻尔兹曼（LBM）模拟计算。由此生成的数据集用于训练卷积神经网络（CNN）模型并评估其性能。经过训练的模型可以预测多孔结构的有效扩散率，计算成本比 LBM 模拟低几个数量级。优化模型在具有真实拓扑、孔隙率变化较大 (0.28-0.98) 和有效扩散率跨越一个数量级以上（0.1 小于或类似于 D-e < 1）的多孔介质上表现良好，例如，>95% 的预测 D-e 已截断相对误差
We report the application of machine learning methods for predicting the effective diffusivity (De) of two-dimensional porous media from images of their structures. Pore structures are built using reconstruction methods and represented as images, and their effective diffusivity is computed by lattice Boltzmann (LBM) simulations. The datasets thus generated are used to train convolutional neural network (CNN) models and evaluate their performance. The trained model predicts the effective diffusivity of porous structures with computational cost orders of magnitude lower than LBM simulations. The optimized model performs well on porous media with realistic topology, large variation of porosity (0.28-0.98), and effective diffusivity spanning more than one order of magnitude (0.1 less than or similar to D-e < 1), e.g., >95% of predicted D-e have truncated relative error of