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Title: Model reduction for hypersonic aerodynamics via conservative LSPG projection and hyper-reduction

Abstract

High-speed aerospace engineering applications rely heavily on computational fluid dynamics (CFD) models for design and analysis due to the expense and difficulty of flight tests and experiments. This reliance on CFD models necessitates performing accurate and reliable uncertainty quantification (UQ) of the CFD models. However, it is very computationally expensive to run CFD for hypersonic flows due to the fine grid resolution required to capture the strong shocks and large gradients that are typically present. Furthermore, UQ approaches are “many-query” problems requiring many runs with a wide range of input parameters. One way to enable computationally expensive models to be used in such many-query problems is to employ projection-based reduced-order models (ROMs) in lieu of the (high-fidelity) full-order model. In particular, the least-squares Petrov–Galerkin (LSPG) ROM (equipped with hyper-reduction) has demonstrated the ability to significantly reduce simulation costs while retaining high levels of accuracy on a range of problems including subsonic CFD applications. This allows computationally inexpensive LSPG ROM simulations to replace the full-order model simulations in UQ studies, which makes this many-query task tractable, even for large-scale CFD models. This work presents the first application of LSPG to a hypersonic CFD application. In particular, we present results for LSPGmore » ROMs of the HIFiRE-1 in a three-dimensional, turbulent Mach 7.1 flow, showcasing the ability of the ROM to significantly reduce computational costs while maintaining high levels of accuracy in computed quantities of interest.« less

Authors:
 [1];  [2];  [3];  [4];  [4]
  1. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
  2. Univ. of Washington, Seattle, WA (United States)
  3. NexGen Analytics, Sheridan, WY (United States)
  4. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-CA), Livermore, CA (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1619242
Report Number(s):
SAND-2020-4925J
Journal ID: ISBN 978-1-62410-595-1; 685991
DOE Contract Number:  
AC04-94AL85000; NA0003525
Resource Type:
Conference
Journal Name:
AIAA Scitech Forum
Additional Journal Information:
Journal Volume: 2020; Conference: AIAA Scitech 2020 Forum, Orlando, FL (United States), 6-10 Jan 2020; Related Information: AIAA 2020-0104; Session: Hypersonic and Non-Equilibrium Flows I
Publisher:
American Institute of Aeronautics and Astronautics
Country of Publication:
United States
Language:
English
Subject:
Fluid Dynamics

Citation Formats

Blonigan, Patrick Joseph, Carlberg, Kevin T., Rizzi, Francesco, Howard, Micah, and Fike, Jeffrey A.. Model reduction for hypersonic aerodynamics via conservative LSPG projection and hyper-reduction. United States: N. p., 2020. Web. doi:10.2514/6.2020-0104.
Blonigan, Patrick Joseph, Carlberg, Kevin T., Rizzi, Francesco, Howard, Micah, & Fike, Jeffrey A.. Model reduction for hypersonic aerodynamics via conservative LSPG projection and hyper-reduction. United States. https://doi.org/10.2514/6.2020-0104
Blonigan, Patrick Joseph, Carlberg, Kevin T., Rizzi, Francesco, Howard, Micah, and Fike, Jeffrey A.. 2020. "Model reduction for hypersonic aerodynamics via conservative LSPG projection and hyper-reduction". United States. https://doi.org/10.2514/6.2020-0104. https://www.osti.gov/servlets/purl/1619242.
@article{osti_1619242,
title = {Model reduction for hypersonic aerodynamics via conservative LSPG projection and hyper-reduction},
author = {Blonigan, Patrick Joseph and Carlberg, Kevin T. and Rizzi, Francesco and Howard, Micah and Fike, Jeffrey A.},
abstractNote = {High-speed aerospace engineering applications rely heavily on computational fluid dynamics (CFD) models for design and analysis due to the expense and difficulty of flight tests and experiments. This reliance on CFD models necessitates performing accurate and reliable uncertainty quantification (UQ) of the CFD models. However, it is very computationally expensive to run CFD for hypersonic flows due to the fine grid resolution required to capture the strong shocks and large gradients that are typically present. Furthermore, UQ approaches are “many-query” problems requiring many runs with a wide range of input parameters. One way to enable computationally expensive models to be used in such many-query problems is to employ projection-based reduced-order models (ROMs) in lieu of the (high-fidelity) full-order model. In particular, the least-squares Petrov–Galerkin (LSPG) ROM (equipped with hyper-reduction) has demonstrated the ability to significantly reduce simulation costs while retaining high levels of accuracy on a range of problems including subsonic CFD applications. This allows computationally inexpensive LSPG ROM simulations to replace the full-order model simulations in UQ studies, which makes this many-query task tractable, even for large-scale CFD models. This work presents the first application of LSPG to a hypersonic CFD application. In particular, we present results for LSPG ROMs of the HIFiRE-1 in a three-dimensional, turbulent Mach 7.1 flow, showcasing the ability of the ROM to significantly reduce computational costs while maintaining high levels of accuracy in computed quantities of interest.},
doi = {10.2514/6.2020-0104},
url = {https://www.osti.gov/biblio/1619242}, journal = {AIAA Scitech Forum},
number = ,
volume = 2020,
place = {United States},
year = {2020},
month = {1}
}

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Works referenced in this record:

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journal, November 2008


Proper Orthogonal Decomposition for Reduced-Order Thermal Solution in Hypersonic Aerothermoelastic Simulations
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Efficient non-linear model reduction via a least-squares Petrov-Galerkin projection and compressive tensor approximations
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  • Carlberg, Kevin; Bou-Mosleh, Charbel; Farhat, Charbel
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  • https://doi.org/10.1002/nme.3050

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