论文标题
一种基于机器学习的方法，可生成具有所需孔隙率和渗透性的随机包装的各向同性多孔培养基
A machine learning based method to generate random packed isotropic porous media with desired porosity and permeability
论文作者
论文摘要
多孔材料用于许多领域，包括能源行业，农业，医疗行业等。数字多孔媒体的产生促进了真正的多孔媒体的制造及其物业的分析。过去的随机数字多孔媒体生成方法无法生成具有特定渗透性的多孔介质。在本研究中提出了一种新方法，该方法可以生成具有特定孔隙率和渗透性的随机堆积的各向同性多孔培养基。首先，详细介绍了生成随机包装的各向同性多孔介质的过程。其次，生成的多孔介质的渗透性是通过多释放时间（MRT）晶格玻尔兹曼方法（LBM）计算的，该方法是为卷积神经网络（CNN）培训准备的。第三，关于多孔介质微观结构的3000个样品及其渗透率用于训练CNN模型。训练有素的模型非常有效地预测多孔介质的渗透性。最后，我们的方法是详细说明的，并讨论了该方法中目标渗透性的选择。在强大的计算机的支持下，可以在短时间内生成满足孔隙率和渗透率的误差条件的多孔介质。
Porous materials are used in many fields, including energy industry, agriculture, medical industry, etc. The generation of digital porous media facilitates the fabrication of real porous media and the analysis of their properties. The past random digital porous media generation methods are unable to generate a porous medium with a specific permeability. A new method is proposed in the present study, which can generate the random packed isotropic porous media with specific porosity and permeability. Firstly, the process of generating the random packed isotropic porous media is detailed. Secondly, the permeability of the generated porous media is calculated with the multi-relaxation time (MRT) lattice Boltzmann method (LBM), which is prepared for the training of convolutional neural network (CNN). Thirdly, 3000 samples on the microstructure of porous media and their permeabilities are used to train the CNN model. The trained model is very effective in predicting the permeability of a porous medium. Finally, our method is elaborated and the choice of target permeability in this method is discussed. With the support of a powerful computer, a porous medium that satisfies the error condition of porosity and permeability can be generated in a short time.
