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

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

doi:10.1016/j.jcp.2019.108930

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

Journal Article · Mon Sep 09 00:00:00 EDT 2019 · Journal of Computational Physics

DOI:https://doi.org/10.1016/j.jcp.2019.108930· OSTI ID:1769912

^[1]; ^[2]; Safta, C. ^[3]; ^[4]; Lacaze, G. ^[5]; Oefelein, J. C. ^[6]; Najm, H. N. ^[3]

Univ. of Southern California, Los Angeles, CA (United States)
Univ. Paris-Est Marne-la-Vallée, Marne-La-Vallée (France)
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Univ. of Michigan, Ann Arbor, MI (United States)
Space Exploration Technologies Corporation, Hawthorne, CA (United States)
Georgia Institute of Technology, Atlanta, GA (United States)

Here, 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.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-CA), Livermore, CA (United States)

Sponsoring Organization:: Defense Advanced Research Projects Agency (DARPA); USDOE Office of Science (SC); USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC04-94AL85000; AC02-05CH11231; NA0003525

OSTI ID:: 1769912

Alternate ID(s):: OSTI ID: 1775900

Report Number(s):: SAND--2021-2057J; 693983

Journal Information:: Journal of Computational Physics, Journal Name: Journal of Computational Physics Vol. 399; ISSN 0021-9991

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited By (2)

Computation of Sobol Indices in Global Sensitivity Analysis from Small data sets by Probabilistic Learning on Manifolds Arnst, Maarten; Soize, Christian; Bulthuis, Kevin International Journal for Uncertainty Quantification, Vol. 11, Issue 2 https://doi.org/10.1615/int.j.uncertaintyquantification.2020032674	journal	January 2021
Physics‐constrained non‐Gaussian probabilistic learning on manifolds Soize, Christian; Ghanem, Roger International Journal for Numerical Methods in Engineering, Vol. 121, Issue 1 https://doi.org/10.1002/nme.6202	journal	September 2019

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Diffusion maps
Machine learning
Optimization under uncertainty
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Design optimization of a scramjet under uncertainty using probabilistic learning on manifolds

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