Investigation on population-based optimization of complex flows

Optimization of complex high-speed flows involving multi-component and multiphase flow phenomena is often challenging due to their unsteady and multi-scale nature. In this study, we present a computational strategy for model-free, population-based optimization of such flows. In the method, to optimize a set of flow parameters for targeted objectives, groups of simulation samples are distributed and iteratively search the parameter space until convergence. We evaluate the efficiency and accuracy of this method using canonical model problems. The method is implemented in an in-house compressible flow code to demonstrate optimization of three-dimensional high-speed reacting flows on multiple GPUs in a scalable fashion.