Optimum Multidisciplinary and Multi-Objective Wing Design in CFD Using Evolutionary Techniques

Abstract

This paper details some current extensions and applications of hierarchical asynchronous parallel evolutionary algorithms (HAPEA) for multidisciplinary and multi-objective wing design and optimisation problems. In this work the search for the solution takes place in separate hierarchical layers comprising different CFD solvers or resolutions. The performance and advantages of the algorithm are compared to that of a classical EA which would normally use only a single complex model and involve larger computational expense. The formulation and implementation of the algorithm are described and a test case for a multidisciplinary transonic wing design in structures and aerodynamics is presented. The trade-off between the objective functions produced a set of compromise designs represented in an optimal Pareto front. Results indicate that the algorithm is fast and robust for multi-objective and multidisciplinary optimisation problems and as designed produces classical as well as alternative wing configurations.