The Potential of Machine Learning Methods for Separated Turbulent Flow Simulations: Classical Versus Dynamic Methods

Feasible and reliable predictions of separated turbulent flows are a requirement to successfully address the majority of aerospace and wind energy problems. Existing computational approaches such as large eddy simulation (LES) or Reynolds-averaged Navier–Stokes (RANS) methods have suffered for decades from well-known computational cost and reliability issues in this regard. One very popular approach to dealing with these questions is the use of machine learning (ML) methods to enable improved RANS predictions. An alternative is the use of minimal error simulation methods (continuous eddy simulation (CES), which may be seen as a dynamic ML method) in the framework of partially or fully resolving simulation methods. Characteristic features of the two approaches are presented here by considering a variety of complex separated flow simulations. The conclusion is that minimal error CES methods perform clearly better than ML-RANS methods. Most importantly and in contrast to ML-RANS methods, CES is demonstrated to be well applicable to cases not involved in the model development. The reason for such superior CES performance is identified here: it is the ability of CES to properly account for causal relationships induced by the structure of separated turbulent flows.