Physics Informed Neural Network in Turbulent Porous Flow: Pore-scale Flow Reconstruction

Turbulence modelling in porous media presents challenges in Computational Fluid Dynamics (CFD). While various modelling approaches have been employed to analyze turbulent flow properties, achieving both precision and cost-effectiveness remains a significant hurdle. In recent years, Deep Learning (DL), with its capacity for solving nonlinear model, has emerged as a promising solution to address these challenges. The advent of Physics-Informed Neural Networks (PINN) has expanded the scope of Deep learning applications in turbulent flow modelling. However, applying PINN to complex flow physics within porous media remains an underexplored territory. This study employs PINN to solve the Reynolds-Averaged Navier-Stokes (RANS) equations in a composite porous-fluid system, guided by supervised learning and penalized by the RANS equation to ensure fidelity to flow physics. The research aims to enhance flow prediction accuracy and explore the influence of data distribution on PINN performance in complex flow scenarios in composite porous-fluid systems. Results showed that using porous-fluid interface training data provides better accuracy, with improvements of 40% and 2% in second-order statistics. This research contributes to advancing our understanding of turbulent flows in porous media and highlights the potential of PINN as a valuable tool for exploring complex flow physics.