CFD design in aeronautics using a robust multilevel parallel evolutionary optimiser

This chapter explores the potential merit of an innovative parallel evolutionary algorithm (EA) coupled with current computational fluid dynamics (CFD) solvers. The chapter outlines the hierarchical asynchronous parallel evolution algorithm (HAPEA), and its important differences to a more conventional evolutionary method. A multi-objective test case involving the reconstruction of a set of two dimensional aerofoil geometries from scratch that are found by considering multiple prescribed pressure distributions at two different flow states is described. The chapter presents conclusions on the increased speed and robustness of the HAPEA algorithm operating in various parallel states, as compared to a more traditional method. Numerical experiments presented in this chapter provide the designer a gateway for practical applicability of parallel EAs in 3D industrial environments and MDO approaches using Navier-Stokes flow analysis solvers coupled with complex turbulence models. The increased speed and robustness obtained from the results underscore that the proper application of sound engineering judgment in conjunction with evolutionary techniques and parallel computing architectures can lead to optimal design solutions and significant computational savings when applied to real world problems.