The high quality and the relevance of Romain thesis contributed to get significant results as a part of MDA-MDO project.
About this thesis
In this thesis, we propose two developments: (i) machine learning-based surrogate models capable of approximating aerodynamic experiments and (ii) integrating more classical surrogate models into industrial aerothermal process. The first approach mitigates aerodynamic issues by separating solutions with very different shapes into several subsets using machine learning algorithms. Moreover, a resampling technique takes advantage of the subdomain decomposition by adding extra information in relevant regions. The second development focuses on pylon sizing by building surrogate models substituting aerothermal simulations. The two approaches are applied to aircraft configurations in order to bridge the gap between academic methods and real-world applications. Significant improvements are highlighted in terms of accuracy and cost gains.
|M.V. SALVETTI||Rapporter||Professor / University of Pisa|
|C. CORRE||Rapporter||Professor / Lyon Central School|
|A. IOLLO||Examiner||Professor / University of Bordeaux|
|P. BREITKOPF||Examiner||Engineer / CNRS Search Center, Technological University|
|P. SAGAUT||Thesis Director||Professor / University of Aix, Marseille|
|J.C. JOUHNAUD||Thesis co-Director||Senior researcher / CERFACS|
|A. GAZAIX||Invited / Thesis Advisor||Project Manager / IRT Saint Exupéry|
|C. LOURIOU||Invited||Engineer / Airbus|
- Surrogate modeling of aerodynamic simulations for multiple operating conditions using machine learning – R. Dupuis, J.C. Jouhaud, P. Sagaut – AIAA Journal, vol. 56, pp. 3622-3635 – 2018 – DOI
- Aerodynamic data predictions for transonic flows via a machine-learning-based surrogate model – R. Dupuis, J.C. Jouhaud, P. Sagaut – AIAA – ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, AIAA SciTech Forum – Kissimmee, Florida – 2018 – DOI