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.

Publication
Journal of Computational Physics
Date
Citation
R. G. Ghanem, C. Soize, C. Safta, X. Huan, G. Lacaze, J. C. Oefelein, and H. N. Najm. Design optimization of a scramjet under uncertainty using probabilistic learning on manifolds. Journal of Computational Physics, Vol. 399, pp. 108930, 2019. https://dx.doi.org/10.1016/j.jcp.2019.108930

BibTeX

@article{Ghanem2019a,
author = {Ghanem, Roger G. and Soize, Christian and Safta, Cosmin and Huan, Xun and Lacaze, Guilhem and Oefelein, Joseph C. and Najm, Habib N.},
doi = {10.1016/j.jcp.2019.108930},
journal = {Journal of Computational Physics},
number = {},
pages = {108930},
title = {{Design optimization of a scramjet under uncertainty using probabilistic learning on manifolds}},
volume = {399},
year = {2019}
}