DNN surrogates for turbulence closure in CFD-based shape optimization

Abstract

A DNN-based surrogate for turbulence (and transition) closure of the Reynolds-Averaged Navier–Stokes (RANS) equations is presented. The DNN configuration, namely the hyperparameters (number of layers, number of neurons, type of activation functions) and the DNN input data result from a Metamodel-Assisted Evolutionary Algorithm (MAEA)-based optimization. The trained DNN replaces the numerical solution of the turbulence and/or transition model PDEs during the solution of the RANS equations, by estimating the necessary turbulent viscosity field in each pseudo-time step iteration. The gain from using such a computational tool becomes pronounced in cases in which many calls to the CFD tools are necessary; a typical example is CFD-based (shape) optimization. Thus, the new model (abbreviated to RANS-DNN) is used as the evaluation software in MAEA-based shape optimization problems. Three aerodynamic cases covering a wide gamut of applications from 2D to 3D, internal to external and compressible to incompressible flows are selected to demonstrate the capabilities of the RANS-DNN model. Using the RANS-DNN instead of the standard RANS solver a significant reduction in the optimization turnaround time is achieved in cases dealing with an isolated airfoil, a turbomachinery cascade and a car.