Fast Prediction of Solute Concentration Field in Rotationally Influenced Fluids Using a Parameter-Based Field Reconstruction Convolutional Neural Network
Abstract:Many high-performance fluid dynamic models do not consider fluids in a rotating environment and often require a significant amount of computational time. The current study proposes a novel parameter-based field reconstruction convolutional neural network (PFR-CNN) approach to model the solute concentration field in rotationally influenced fluids. A new three-dimensional (3D) numerical solver, TwoLiquidMixingCoriolisFoam, was implemented within the framework of OpenFOAM to simulate effluents subjected to the in… Show more
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