https://www.mdu.se/

We propose a novel technique that combines physics-informed machine learning (PIML) with the wall-adapting local eddy viscosity model for predicting flow patterns over time. In order to generate loss functions for turbulence flow and calculate dissipation rates within the domain, a new form of machine learning-based WALE-LES model includes updating the boundary matrix and tensor of component velocity and pressure tensors over time. By utilizing boundary conditions, Navier-Stokes-based equations will be discretized via automatic differentiation in PIML. We present a novel point cloud-based method for pore-scale porous media (PSPM) for the first time. This method transforms complex geometry into a matrix of data based on a point cloud. As a result of this method, boundary conditions are included in transient calculations. We modified the standard form of momentum equations to obtain the loss function for flows passing through a PSPM. According to the findings, high-fidelity numerical simulation and PIML prediction results are very similar in pressure and velocity. Additionally, this method can reduce calculation costs by up to 41% compared to the standard method.