Disciplinary proper orthogonal decomposition and interpolation for the resolution of parameterized multidisciplinary analysis - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Article Dans Une Revue International Journal for Numerical Methods in Engineering Année : 2022

Disciplinary proper orthogonal decomposition and interpolation for the resolution of parameterized multidisciplinary analysis

Résumé

This article proposes a new method for the resolution of parameterized coupled systems of equations, such as typical in multidisciplinary analysis (MDA). It is based on the use of disciplinary surrogate models within a multi-query context. The main idea is to replace the costly disciplinary solvers of the MDA by Proper Orthogonal Decomposition and Interpolation models. The main challenge we address is the high-dimensional coupling variables whose ranges are unknown. To overcome this issue, a training strategy is developed by uncoupling the disciplinary solvers from the MDA context. This new surrogate MDA called Disciplinary Proper Orthogonal Decomposition and Interpolation is iteratively enriched to solve the analysis with an estimation of the error made by the surrogate models. The disciplinary surrogate model which has the most influence on this error is determined by a sensitivity analysis and thus enriched. This approach allows to uncouple the disciplinary solvers during the training and enrichment phases. The approach is applied to various aeroelastic problems of an aircraft wing and allows to reduce by a factor of 5 the mean number of disciplinary solver calls needed for the resolution of the MDA.
Fichier non déposé

Dates et versions

hal-03878308 , version 1 (29-11-2022)

Identifiants

Citer

Gaspard Berthelin, Sylvain Dubreuil, Michel Salaün, Nathalie Bartoli, Christian Gogu. Disciplinary proper orthogonal decomposition and interpolation for the resolution of parameterized multidisciplinary analysis. International Journal for Numerical Methods in Engineering, 2022, 123 (15), pp.3594-3626. ⟨10.1002/nme.6981⟩. ⟨hal-03878308⟩
18 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More