Effective diffusivity is one of the basic transport coefficients used to describe the mass transport capability of a porous medium.In this study,a deep learning method based on a convolutional neural network(CNN)with ...Effective diffusivity is one of the basic transport coefficients used to describe the mass transport capability of a porous medium.In this study,a deep learning method based on a convolutional neural network(CNN)with sam-ple structure information self-amplification is proposed to predict the effective diffusivity of a porous medium,which is considerably influenced by the morphological and topological parameters of the porous medium.In this method,the geometric structures of three-dimensional(3D)porous media are reproduced via a stochastic reconstruction method.Datasets of the effective diffusivities of the reconstructed porous media were first estab-lished by the pore-scale lattice Boltzmann method(LBM)simulation.A large number of geometric structures of 3D porous media are obtained using the proposed sample structure information self-amplification approach.The 3D geometric structure information and corresponding effective diffusivities are directionally applied to a CNN for training and prediction.The effective diffusivities of media with porosities ranging from 0.48 to 0.58 are employed as training datasets,and the effective diffusivities of media with a broader porosity range of 0.39 to 0.79 are predicted by CNN.The CNN model can achieve a fast and accurate prediction of the effective diffusivity.The relative error between the CNN and LBM is 0.026%–8.95%with porosities ranging from 0.39 to 0.79.For a typical case with a porosity of 0.5,the computation time required by the CNN model is only 3×10^(−4) h,while the computation time for the same case is 16.96 h using the LBM.These findings indicate that the proposed deep learning method has a powerful learning ability;it is time-saving,provides accurate predic-tions,and can serve as a promising and powerful tool to predict the transport coefficients of complex porous media.展开更多
基金This work was supported by the Foundation for Innovative Re-search Groups of the National Natural Science Foundation of China(No.51721004)the Fundamental Research Funds for the Central Universi-ties(No.G2018KY0303)the 111 Project(B16038).
文摘Effective diffusivity is one of the basic transport coefficients used to describe the mass transport capability of a porous medium.In this study,a deep learning method based on a convolutional neural network(CNN)with sam-ple structure information self-amplification is proposed to predict the effective diffusivity of a porous medium,which is considerably influenced by the morphological and topological parameters of the porous medium.In this method,the geometric structures of three-dimensional(3D)porous media are reproduced via a stochastic reconstruction method.Datasets of the effective diffusivities of the reconstructed porous media were first estab-lished by the pore-scale lattice Boltzmann method(LBM)simulation.A large number of geometric structures of 3D porous media are obtained using the proposed sample structure information self-amplification approach.The 3D geometric structure information and corresponding effective diffusivities are directionally applied to a CNN for training and prediction.The effective diffusivities of media with porosities ranging from 0.48 to 0.58 are employed as training datasets,and the effective diffusivities of media with a broader porosity range of 0.39 to 0.79 are predicted by CNN.The CNN model can achieve a fast and accurate prediction of the effective diffusivity.The relative error between the CNN and LBM is 0.026%–8.95%with porosities ranging from 0.39 to 0.79.For a typical case with a porosity of 0.5,the computation time required by the CNN model is only 3×10^(−4) h,while the computation time for the same case is 16.96 h using the LBM.These findings indicate that the proposed deep learning method has a powerful learning ability;it is time-saving,provides accurate predic-tions,and can serve as a promising and powerful tool to predict the transport coefficients of complex porous media.
