Evolutionary Computational Methods for Complex Design in Aerodynamics

This paper describes how new evolutionary tools such as Genetic Algorithms (GAs) can solve complex optimization problems in aerospace industry. With the increasing demand for high innovation rates for new and improved products there is a constant pressure to reduce the costs and time for marketing. Concurrent engineering and multidisciplinary design optimization on distributed parallel computers are emergent tools for the reduction of time cycle design which can drastically contribute to those industrial targets. Robustness of search algorithms is moving on top priority to extract globa) solution from rugged multi-dimensional landscapes. Based on Darwin's natural selection principle and digital DNA representation, the GAs approach is a new sharing Information Technology. The blending of stochastic properties of genetic operators with a deterministic fitness classification of individuals guarantees diversity for GAs and makes them able to escape local minima where traditional methods fail. Therefore, they produce a much larger optimization effort. Numerical results are presented for the global solution of complex optimization or control problems in the following areas: 1) CFD : localisation and shape design for high-lift maximization of complex multi body; 2) GEM: multi-objective optimization using Pareto or Nash game strategies for the RCS minimisation of scattered field by active aerodynamic reflectors ; 3) Parallel Computing : parallelized GAS applied to shape optimum design for the reduction of drag. Copyright © 1998 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. The above results illustrate the robustness and promising highly parallel properties of GAs for future concurrent aerospace technologies.