Design optimization of a scramjet under uncertainty using probabilistic learning on manifolds
Résumé
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.
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publi-2019-JCP-399()1-14-ghanem-soize-sfta-et-al-preprint.pdf (1.48 Mo)
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