Global Sensitivity Analysis and Estimation of Model Error, Toward Uncertainty Quantification in Scramjet Computations

Huan, Xun; Safta, Cosmin; Sargsyan, Khachik; Geraci, Gianluca; Eldred, Michael S.; Vane, Zachary P.; Lacaze, Guilhem; Oefelein, Joseph C.; Najm, Habib N.

doi:10.2514/1.J056278

Title: Global Sensitivity Analysis and Estimation of Model Error, Toward Uncertainty Quantification in Scramjet Computations

Journal Article · 09 February 2018 · AIAA Journal

DOI:https://doi.org/10.2514/1.J056278· OSTI ID:1429819

Huan, Xun ^[1]; Safta, Cosmin ^[1]; Sargsyan, Khachik ^[1]; Geraci, Gianluca ^[2]; Eldred, Michael S. ^[2]; Vane, Zachary P. ^[1]; Lacaze, Guilhem ^[1]; Oefelein, Joseph C. ^[1]; Najm, Habib N. ^[1]

Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

The development of scramjet engines is an important research area for advancing hypersonic and orbital flights. Progress toward optimal engine designs requires accurate flow simulations together with uncertainty quantification. However, performing uncertainty quantification for scramjet simulations is challenging due to the large number of uncertain parameters involved and the high computational cost of flow simulations. These difficulties are addressed in this paper by developing practical uncertainty quantification algorithms and computational methods, and deploying them in the current study to large-eddy simulations of a jet in crossflow inside a simplified HIFiRE Direct Connect Rig scramjet combustor. First, global sensitivity analysis is conducted to identify influential uncertain input parameters, which can help reduce the system’s stochastic dimension. Second, because models of different fidelity are used in the overall uncertainty quantification assessment, a framework for quantifying and propagating the uncertainty due to model error is presented. Finally, these methods are demonstrated on a nonreacting jet-in-crossflow test problem in a simplified scramjet geometry, with parameter space up to 24 dimensions, using static and dynamic treatments of the turbulence subgrid model, and with two-dimensional and three-dimensional geometries.

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

Sponsoring Organization:: USDOE; Defense Advanced Research Projects Agency (DARPA)

DOE Contract Number:: AC04-94AL85000

OSTI ID:: 1429819

Report Number(s):: SAND-2017-8103J; 655797

Journal Information:: AIAA Journal, Vol. 56, Issue 3; ISSN 0001-1452

Publisher:: AIAA

Country of Publication:: United States

Language:: English

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