Enhancing Model Predictability for a Scramjet Using Probabilistic Learning on Manifolds

Soize, Christian; Ghanem, Roger; Safta, Cosmin; Huan, Xun; Vane, Zachary P.; Oefelein, Joseph C.; Lacaze, Guilhem; Najm, Habib N.

doi:10.2514/1.j057069

Title: Enhancing Model Predictability for a Scramjet Using Probabilistic Learning on Manifolds

Journal Article · Thu Dec 06 00:00:00 EST 2018 · AIAA Journal

DOI:https://doi.org/10.2514/1.j057069· OSTI ID:1501635

Soize, Christian ^[1]; Ghanem, Roger ^[2]; Safta, Cosmin ^[3]; Huan, Xun ^[3]; Vane, Zachary P. ^[3]; Oefelein, Joseph C. ^[3]; Lacaze, Guilhem ^[3]; Najm, Habib N. ^[3]

University of Paris-Est Marne-la-Vallée (France)
Univ. of California, Los Angeles, CA (United States)
Sandia National Lab. (SNL-CA), Livermore, CA (United States)

In this study, the computational burden of a large-eddy simulation for reactive flows is exacerbated in the presence of uncertainty in flow conditions or kinetic variables. A comprehensive statistical analysis, with a sufficiently large number of samples, remains elusive. Statistical learning is an approach that allows for extracting more information using fewer samples. Such procedures, if successful, will greatly enhance the predictability of models in the sense of improving exploration and characterization of uncertainty due to model error and input dependencies, all while being constrained by the size of the associated statistical samples. In this paper, it is shown how a recently developed procedure for probabilistic learning on manifolds can serve to improve the predictability in a probabilistic framework of a scramjet simulation. The estimates of the probability density functions of the quantities of interest are improved together with estimates of the statistics of their maxima. Lastly, it is also demonstrated how the improved statistical model adds critical insight to the performance of the model.

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Research Organization:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

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

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

OSTI ID:: 1501635

Report Number(s):: SAND-2018-0463J; 659989

Journal Information:: AIAA Journal, Vol. 57, Issue 1; ISSN 0001-1452

Publisher:: AIAA

Country of Publication:: United States

Language:: English

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