5743468
doi
10.5281/zenodo.5743468
oai:zenodo.org:5743468
user-agile4
user-eu
Bartoli, N.
ONERA
Diouane, Y.
ISAE-SUPAERO
Lefebvre, T.
ONERA
Morlier, J.
ISAE-SUPAERO
David, C.
ONERA
Nguyen Van, E.
ISAE-SUPAERO
Defoort, S.
ONERA
CONSTRAINED BAYESIAN OPTIMIZATION OVER MIXED CATEGORICAL VARIABLES, WITH APPLICATION TO AIRCRAFT DESIGN
Saves, P.
ONERA/ISAE-SUPAERO
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Multidisciplinary Design Optimization
Bayesian Optimization
Surrogate-based Optimization
<p>Multidisciplinary Design Optimization (MDO) methods aim at adapting nu- merical optimization techniques to the design of engineering systems involving multiple disciplines or components. Among MDO architectures, various ones are considering the resolution of the Multidisciplinary Design Analysis (MDA). In our study, the system of interest being an aircraft, the resolution of the MDA will be provided by the Future Air- craft Sizing Tool with Overall Aircraft Design (FAST-OAD), a point mass approach that estimates the required fuel and energy consumption for a given set of top-level aircraft requirements. In this context, a large number of mixed continuous, integer and categorical variables that arise from aircraft design has to be tackled by the optimization process.</p>
<p>Recently, there has been a growing interest in mixed variables constrained Bayesian optimization based on Gaussian process surrogate models. In this setting, most existing approaches severely increase the dimension of the covariance matrix related to the sur- rogate. In fact, the construction of the Gaussian process model may not be scalable to practical applications involving a large number of mixed variables.</p>
<p>In this paper, we address this issue by constructing a covariance kernel for the surrogate model that depends on only a few hyperparameters. The new kernel is constructed based on the information obtained from the partial least squares method. The obtained numerical results lead to interesting results for the optimization of a baseline aircraft and to reduce the fuel consumption of “DRAGON”, a new hybrid electric propulsion aircraft, with a high number of mixed variables and for a small budget of time-consuming evaluations.</p>
Zenodo
2021-07-23
info:eu-repo/semantics/conferencePaper
5743467
user-agile4
user-eu
award_title=AGILE 4.0: Towards cyber-physical collaborative aircraft development; award_number=815122; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/815122; funder_id=00k4n6c32; funder_name=European Commission;
1638323335.539268
3281571
md5:0ef98448644f21f78b8ea6466db58d89
https://zenodo.org/records/5743468/files/AeroBest_2021_PhDPaul.pdf
public
10.5281/zenodo.5743467
isVersionOf
doi