Service interruption on Monday 11 July from 12:30 to 13:00: all the sites of the CCSD (HAL, EpiSciences, SciencesConf, AureHAL) will be inaccessible (network hardware connection).
Skip to Main content Skip to Navigation
Journal articles

Surrogate Modeling of Aerodynamic Simulations for Multiple Operating Conditions Using Machine Learning

Abstract : This paper describes a methodology, called local decomposition method, which aims at building a surrogate model based on steady turbulent aerodynamic fields at multiple operating conditions. The various shapes taken by the aerodynamic fields due to the multiple operation conditions pose real challenges as well as the computational cost of the high-fidelity simulations. The developed strategy mitigates these issues by combining traditional surrogate models and machine learning. The central idea is to separate the solutions with a subsonic behavior from the transonic and high-gradient solutions. First, a shock sensor extracts a feature corresponding to the presence of discontinuities, easing the clustering of the simulations by an unsupervised learning algorithm. Second, a supervised learning algorithm divides the parameter space into subdomains, associated to different flow regimes. Local reduced-order models are built on each subdomain using proper orthogonal decomposition coupled with a multivariate interpolation tool. Finally, an improved resampling technique taking advantage of the subdomain decomposition minimizes the redundancy of sampling. The methodology is assessed on the turbulent two-dimensional flow around the RAE2822 transonic airfoil. It exhibits a significant improvement in terms of prediction accuracy for the developed strategy compared with the classical method of surrogate modeling.
Document type :
Journal articles
Complete list of metadata

Cited literature [61 references]  Display  Hide  Download
Contributor : Olivier Boutin Connect in order to contact the contributor
Submitted on : Monday, April 29, 2019 - 12:29:24 PM
Last modification on : Monday, May 16, 2022 - 8:20:27 AM


Files produced by the author(s)




Romain Dupuis, Jean-Christophe Jouhaud, Pierre Sagaut. Surrogate Modeling of Aerodynamic Simulations for Multiple Operating Conditions Using Machine Learning. AIAA Journal, American Institute of Aeronautics and Astronautics, 2018, 56 (9), pp.3622-3635. ⟨10.2514/1.J056405⟩. ⟨hal-02113987⟩



Record views


Files downloads