Predicting permeability via statistical learning on higher-order microstructural information
Predicting permeability via statistical learning on higher-order microstructural information
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DOI：
10.1038/s41598-020-72085-5
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发表时间：
2020-09-17
影响因子：
4.6
通讯作者：
Torquato, Salvatore
中科院分区：
文献类型：
--
作者：
Roeding, Magnus;Ma, Zheng;Torquato, Salvatore
定量结构-性质关系对于理解和预测复杂材料的物理性质至关重要。对于多孔材料中的流体流动，表征孔隙微结构的几何形状有助于预测渗透性，渗透性是材料科学、物理学和化学工程中广泛研究的关键性质。在这项工作中，我们通过线性回归和神经网络研究了不同结构描述符的可预测性。为此，创建了30，000个不同类型的虚拟多孔微结构的大型数据集，包括颗粒和连续固相。我们计算这些结构的渗透率，使用晶格玻尔兹曼方法，并使用一点相关函数（孔隙度，比表面），两点表面，表面-空隙，和空隙-空隙相关函数，以及作为一个隐式描述符的测地线弯曲度的孔隙空间几何特征。然后，我们研究了使用这些描述符的不同组合的渗透率的预测。我们获得显着的性能改善相比，只有最低阶描述符（孔隙率和比表面）的Kozeny-Carman回归。我们发现，结合所有三个两点相关函数和曲折提供了最好的预测渗透率，与空隙-空隙相关函数是最翔实的个人描述符。此外，孔隙度、比表面和测地线弯曲度的组合提供了非常好的预测性能。这表明高阶相关函数对于形成预测复杂材料物理性质的通用模型是非常有用的。此外，我们的研究结果表明，人工神经网络是上级更传统的回归方法建立定量结构-性质的关系。我们公开使用的数据和代码，以促进渗透率预测方法的进一步发展。
Quantitative structure-property relationships are crucial for the understanding and prediction of the physical properties of complex materials. For fluid flow in porous materials, characterizing the geometry of the pore microstructure facilitates prediction of permeability, a key property that has been extensively studied in material science, geophysics and chemical engineering. In this work, we study the predictability of different structural descriptors via both linear regressions and neural networks. A large data set of 30,000 virtual, porous microstructures of different types, including both granular and continuous solid phases, is created for this end. We compute permeabilities of these structures using the lattice Boltzmann method, and characterize the pore space geometry using one-point correlation functions (porosity, specific surface), two-point surface-surface, surface-void, and void-void correlation functions, as well as the geodesic tortuosity as an implicit descriptor. Then, we study the prediction of the permeability using different combinations of these descriptors. We obtain significant improvements of performance when compared to a Kozeny-Carman regression with only lowest-order descriptors (porosity and specific surface). We find that combining all three two-point correlation functions and tortuosity provides the best prediction of permeability, with the void-void correlation function being the most informative individual descriptor. Moreover, the combination of porosity, specific surface, and geodesic tortuosity provides very good predictive performance. This shows that higher-order correlation functions are extremely useful for forming a general model for predicting physical properties of complex materials. Additionally, our results suggest that artificial neural networks are superior to the more conventional regression methods for establishing quantitative structure-property relationships. We make the data and code used publicly available to facilitate further development of permeability prediction methods.