PREDICTING TRANSITIONAL AND TURBULENT FLOW AROUND A TURBINE BLADE WITH A PHYSICS-INFORMED NEURAL NETWORK

Abstract

Despite the demonstrated usefulness of RANS for many industrially-relevant problems, it can be challenging to accurately simulate certain flow features with the method. Due to the Reynolds-averaging process, the Reynolds-averaged Navier-Stokes equations require a turbulence model to close the equations, and the simple physical arguments and approximations used in many turbulence models can cause erroneous results when applied to flows featuring separation or strong pressure gradients. Physics-informed neural networks (PINNs) offer a way to model aerodynamic problems without explicitly requiring a closure. The network can use sparse training data and the unclosed RANS equations to reconstruct..