A computational workflow to study particle transport and filtration in porous media: Coupling CFD and deep learning

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.

Automated summary

This work developed an open-source workflow for constructing data-driven models from a large campaign of CFD simulations, focusing on predicting the permeability and filtration effectiveness of porous media models. Through extensive simulations and neural network training, the predictive performance of the model was evaluated.