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.
A computational workflow to study particle transport and filtration in porous media: Coupling CFD and deep learning / Marcato, A.; Boccardo, G.; Marchisio, D.. - In: CHEMICAL ENGINEERING JOURNAL. - ISSN 1385-8947. - ELETTRONICO. - 417(2021), p. 128936.
|Titolo:||A computational workflow to study particle transport and filtration in porous media: Coupling CFD and deep learning|
|Data di pubblicazione:||2021|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1016/j.cej.2021.128936|
|Appare nelle tipologie:||1.1 Articolo in rivista|
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