Application of Deep Neural Network Technology for Multi-scale CFD Modeling in Porous Media

System-scale computational fluid dynamics (CFD) simulations in chemical and process engineering remain limited owing to the complexity of integrating the results obtained at different scales. The present study addresses this issue by correlating the flow behaviors calculated by CFD in porous media at the micro-scale and the macro-scale using deep neural network (DNN) technology. The DNN model is trained using a dataset constructed from the results obtained for a large number of particle-scale CFD simulations that are coupled to macroscopic governing equations. Comparisons with experimental results obtained with a packed bed show that the proposed CFD-DNN method provides predictions of pressure drop with an accuracy that is 28% greater than that of a method based on the Ergun equation. The proposed multi-scale modeling method integrates a DNN trained using simulation data obtained at the particle-scale with CFD calculations for conducting the system-scale modeling of material transport in porous media subject to the local flow properties arising from the microstructural characteristics of the system. The prediction performance of the integrated DNN-CFD method is also demonstrated. image