tdavydze/MachineLearning-CFD-DEM
Runtime ANN drag prediction for CFDEM/OpenFOAM—trained on fine‑mesh data using fluid/particle velocities and void fraction—boosts coarse‑mesh CFD‑DEM fidelity
This project helps chemical engineers and process designers improve the accuracy of their coarse-mesh CFD-DEM simulations. It takes high-resolution simulation data and uses it to train an Artificial Neural Network, which then predicts drag forces more accurately during subsequent coarse-mesh simulations. The end-user is a CFD-DEM practitioner who wants to better model fluid-particle interactions in systems like fluidized beds or pneumatic transport.
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Use this if you need to perform high-fidelity simulations of fluid-particle interactions using coarse meshes and want to improve the accuracy of drag force predictions beyond traditional empirical models.
Not ideal if you are working with very simple fluid-particle systems where standard empirical drag models like Di Felice or Koch-Hill are already sufficient.
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Jul 28, 2025
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