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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.
Cited literature [34 references]
https://hal-upec-upem.archives-ouvertes.fr/hal-02341912
Contributor : Christian Soize <>
Submitted on : Sunday, November 17, 2019 - 9:57:08 AM
Last modification on : Wednesday, February 17, 2021 - 10:42:26 AM
Long-term archiving on: : Tuesday, February 18, 2020 - 2:10:37 PM
Contributor : Christian Soize <>
Submitted on : Sunday, November 17, 2019 - 9:57:08 AM
Last modification on : Wednesday, February 17, 2021 - 10:42:26 AM
Long-term archiving on: : Tuesday, February 18, 2020 - 2:10:37 PM
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