Data Assimilation of Transitional and Separated Turbomachinery Flows With Physics-Informed Neural Networks

Abstract

Despite the demonstrated utility of Reynolds-averaged Navier-Stokes (RANS) calculations for many industrially relevant problems, the method yields unsatisfactory representations of many flows of engineering interest, such as nonequilibrium turbulence, massive flow separation, coherent unsteadiness, and secondary flow features. Due to the Reynolds-averaging process, a turbulence model is required to close the RANS equations, and the simple physical arguments and approximations used in many turbulence models can cause erroneous results when applied to the flows that feature strong pressure gradients, sudden changes in mean-strain-rate, surface curvature, and turbulence anisotropy. Physics-info..