Publications Details

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

Safta, Cosmin S.; Ghanem, R.G.; Huan, X.; Lacaze, G.; Oefelein, J.C.; Najm, H.N.

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.