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

Marcato, Agnese ; Boccardo, Gianluca ; Marchisio, Daniele

Chemical engineering journal (Lausanne, Switzerland : 1996), 2021-08, Vol.417, p.128936, Article 128936 [Periódico revisado por pares]

Elsevier B.V

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  • Título:
    A computational workflow to study particle transport and filtration in porous media: Coupling CFD and deep learning
  • Autor: Marcato, Agnese ; Boccardo, Gianluca ; Marchisio, Daniele
  • Assuntos: CFD ; Machine learning ; Neural networks ; OpenFOAM ; Porous media ; Tensorflow
  • É parte de: Chemical engineering journal (Lausanne, Switzerland : 1996), 2021-08, Vol.417, p.128936, Article 128936
  • Descrição: [Display omitted] •We employed neural networks for the prediction of 2D porous media properties.•We setup a workflow for the creation of a dataset of CFD simulations on OpenFOAM.•The results of CFD simulations were the training data for the neural networks.•The permeability and the filtration rate were efficiently predicted by the networks.•The neural networks predicted better the results compared to analytical correlations. 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.
  • Editor: Elsevier B.V
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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