粒子(生态学)
多相流
颗粒沉积
材料科学
CFD-DEM公司
流量(数学)
网格
过滤(数学)
作者
Agnese Marcato,Gianluca Boccardo,Daniele Marchisio
标识
DOI:10.1016/j.cej.2021.128936
摘要
Abstract In this work we developed an open-source work-flow for the construction of data-driven models from a wide Computational Fluid Dynamics (CFD) simulations campaign. We focused on the prediction of the permeability of bidimensional porous media models, and their effectiveness in filtration of a transported colloidal species. CFD simulations are performed with OpenFOAM , where the colloid transport is solved by the advection–diffusion equation. A campaign of two thousands simulations was performed on a HPC cluster, the permeability is calculated from the simulations with Darcy’s law and the filtration (i.e. deposition) rate is evaluated by an appropriate upscaled parameter. Finally a dataset connecting the input features of the simulations with their results is constructed for the training of neural networks, executed on the open-source machine learning platform Tensorflow (integrated with Python library Keras ). The predictive performance of the data-driven model is then compared with the CFD simulations results and with traditional analytical correlations.
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