论文标题
多孔微结构的Minkowski张量可以表征其渗透性吗？
Can Minkowski tensors of a porous microstructure characterize its permeability?
论文作者
论文摘要
我们表明，可以从描述孔微结构几何形状的Minkowski Tensor（MTS）可靠地预测多孔介质的渗透性。为此，我们考虑了大量通过包含复杂形状障碍物的周期性单元细胞进行的流动模拟。通过使用MT元素作为属性的仿真数据训练深层神经网络（DNN）来实现该预测。获得的预测允许结论，即孔微结构的MTs包含足够的信息来确定渗透性，尽管MTS与渗透率之间的功能关系可能很复杂，可以确定。
We show that the permeability of porous media can be reliably predicted from the Minkowski tensors (MTs) describing the pore microstructure geometry. To this end, we consider a large number of simulations of flow through periodic unit cells containing complex shaped obstacles. The prediction is achieved by training a deep neural network (DNN) using the simulation data with the MT elements as attributes. The obtained predictions allow for the conclusion that MTs of the pore microstructure contain sufficient information to determine the permeability, although the functional relation between the MTs and the permeability could be complex to determine.
