Bayesian multi-objective optimization with constraints: Application to the design of a commercial aircraft environment control system

Abstract : We present the BMOO algorithm for multi-objective optimization in the presence of non-linear and expensive-to-evaluate constraints and an application to the design of a commercial aircraft environment control system (ECS). The BMOO algorithm implements a Bayesian approach to this optimization problem. The emphasis is on conducting the optimization using a limited number of system simulations and, as a particularity, the algorithm is run on a non-hypercubic design domain and implements hidden constraints handling capabilities. The ECS is composed of two cross-flow heat exchangers, a centrifugal compressor and a radial turbine, the geometries of which are simultaneously optimized to achieve minimal weight and entropy generation of the system as a whole, while respecting strict specifications. While both objectives impact the overall performance of the aircraft, they are shown to be antagonistic and a set of trade-off design solutions is identified.
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Autre publication
GdR MASCOT-NUM working meeting "Dealing with stochastics in optimization problems", May 26, 2016,.. 2016
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https://hal-centralesupelec.archives-ouvertes.fr/hal-01323028
Contributeur : Julien Bect <>
Soumis le : lundi 30 mai 2016 - 08:09:30
Dernière modification le : vendredi 17 février 2017 - 16:10:41

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  • HAL Id : hal-01323028, version 1

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Paul Feliot, Julien Bect, Emmanuel Vazquez. Bayesian multi-objective optimization with constraints: Application to the design of a commercial aircraft environment control system. GdR MASCOT-NUM working meeting "Dealing with stochastics in optimization problems", May 26, 2016,.. 2016. 〈hal-01323028〉

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