论文标题
从其几何状态确定的多孔培养基中不混溶的流体的有效渗透性
Effective permeability of an immiscible fluid in porous media determined from its geometric state
论文作者
论文摘要
基于达西定律的现象学扩展，两流体流程取决于饱和的相对渗透性函数，仅是过程/路径取决于对孔结构的基本依赖性。对于应用程序，燃料电池是地下$ CO_2 $存储的，必须确定传统方法基于时间耗时实验的反向建模的有效相渗透关系。根本的原因是，从毛孔到达西量表的基本升级步骤，该步骤将多孔介质的孔结构与连续液压电导率联系起来。在此，我们开发了一个人工神经网络（ANN），该神经网络依靠基本的几何关系来确定在不混溶的两流体流动过程中的机械能量耗散。开发的ANN基于一组规定的状态变量，基于物理见解，该洞察力预测了4,500个看不见的孔尺度几何状态的有效渗透性，$ r^2 = 0.98 $。
Based on the phenomenological extension of Darcy's law, two-fluid flow is dependent on a relative permeability function of saturation only that is process/path dependent with an underlying dependency on pore structure. For applications, fuel cells to underground $CO_2$ storage, it is imperative to determine the effective phase permeability relationships where the traditional approach is based on the inverse modelling of time-consuming experiments. The underlying reason is that the fundamental upscaling step from pore to Darcy scale, which links the pore structure of the porous medium to the continuum hydraulic conductivities, is not solved. Herein, we develop an Artificial Neural Network (ANN) that relies on fundamental geometrical relationships to determine the mechanical energy dissipation during creeping immiscible two-fluid flow. The developed ANN is based on a prescribed set of state variables based on physical insights that predicts the effective permeability of 4,500 unseen pore-scale geometrical states with $R^2 = 0.98$.
