HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Journal articles

Design optimization of a scramjet under uncertainty using probabilistic learning on manifolds

Abstract : We demonstrate, on a scramjet combustion problem, a constrained probabilistic learning approach that augments physics-based datasets with realizations that adhere to underlying constraints and scatter. The constraints are captured and delineated through diffusion maps, while the scatter is captured and sampled through a projected stochastic differential equation. The objective function and constraints of the optimization problem are then efficiently framed as non-parametric conditional expectations. Different spatial resolutions of a large-eddy simulation filter are used to explore the robustness of the model to the training dataset and to gain insight into the significance of spatial resolution on optimal design.
Complete list of metadata

Cited literature [34 references]  Display  Hide  Download

Contributor : Christian Soize Connect in order to contact the contributor
Submitted on : Sunday, November 17, 2019 - 9:57:08 AM
Last modification on : Saturday, January 15, 2022 - 4:13:28 AM
Long-term archiving on: : Tuesday, February 18, 2020 - 2:10:37 PM


Files produced by the author(s)




Roger Ghanem, Christian Soize, C. Safta, X. Huan, G. Lacaze, et al.. Design optimization of a scramjet under uncertainty using probabilistic learning on manifolds. Journal of Computational Physics, Elsevier, 2019, 399, pp.108930. ⟨10.1016/j.jcp.2019.108930⟩. ⟨hal-02341912⟩



Record views


Files downloads