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Aircraft design optimization

Author

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  • Alonso, J.J.
  • LeGresley, P.
  • Pereyra, V.

Abstract

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.

Suggested Citation

  • Alonso, J.J. & LeGresley, P. & Pereyra, V., 2009. "Aircraft design optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(6), pages 1948-1958.
    • Handle: RePEc:eee:matcom:v:79:y:2009:i:6:p:1948-1958
    DOI: 10.1016/j.matcom.2007.07.001
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    References listed on IDEAS

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    1. Pereyra, V. & Scherer, G. & Wong, F., 2006. "Variable projections neural network training," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 73(1), pages 231-243.
    2. Pereyra, Victor, 2009. "Fast computation of equispaced Pareto manifolds and Pareto fronts for multiobjective optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(6), pages 1935-1947.
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    Cited by:

    1. Abbes, Dhaker & Martinez, André & Champenois, Gérard, 2014. "Life cycle cost, embodied energy and loss of power supply probability for the optimal design of hybrid power systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 98(C), pages 46-62.

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