Aircraft design optimization
In this paper we describe briefly a set of procedures for the optimal design of full mission aerospace systems. This involves multi-physics simulations at various fidelity levels, Surrogates, distributed computing and multi-objective optimization. Low-fidelity analysis is used to populate a database of inputs and outputs of the system simulation and Neural Networks are then designed to generate inexpensive surrogates. Higher fidelity is used only where is warranted and also to do a local exploration after global optimization techniques have been used on the surrogates in order to provide plausible initial values. The ideas are exemplified on a generic supersonic aircraft configuration, where one of the main goals is to reduce the ground sonic boom. (C) 2007 IMACS. Published by Elsevier B.V. All rights reserved.
optimal design aerospace systems surrogates multi-objective optimization neural networks