Calibrating hypersonic turbulence flow models with the HIFiRE-1 experiment using data-driven machine-learned models.
Abstract
In this paper we study the efficacy of combining machine-learning methods with projection-based model reduction techniques for creating data-driven surrogate models of computationally expensive, high-fidelity physics models. Such surrogate models are essential for many-query applications e.g., engineering design optimization and parameter estimation, where it is necessary to invoke the high-fidelity model sequentially, many times. Surrogate models are usually constructed for individual scalar quantities. However there are scenarios where a spatially varying field needs to be modeled as a function of the model’s input parameters. Here we develop a method to do so, using projections to represent spatial variability while a machine-learned model captures the dependence of the model’s response on the inputs. The method is demonstrated on modeling the heat flux and pressure on the surface of the HIFiRE-1 geometry in a Mach 7.16 turbulent flow. The surrogate model is then used to perform Bayesian estimation of freestream conditions and parameters of the SST (Shear Stress Transport) turbulence model embedded in the high-fidelity (Reynolds-Averaged Navier–Stokes) flow simulator, using shock-tunnel data. The paper provides the first-ever Bayesian calibration of a turbulence model for complex hypersonic turbulent flows. We find that the primary issues in estimating the SST model parameters are themore »
- Authors:
-
- Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Arizona State Univ., Tempe, AZ (United States)
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Publication Date:
- Research Org.:
- Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1888549
- Alternate Identifier(s):
- OSTI ID: 1960842
- Report Number(s):
- SAND2022-10607J
Journal ID: ISSN 0045-7825; 708881
- Grant/Contract Number:
- NA0003525
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Computer Methods in Applied Mechanics and Engineering
- Additional Journal Information:
- Journal Volume: 401; Journal ID: ISSN 0045-7825
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 42 ENGINEERING; reduced order modeling; machine learning; Bayesian; MCMC; neural networks; uncertainty quantification
Citation Formats
Chowdhary, Kenny, Hoang, Chi, Lee, Kookjin, Ray, Jaideep, Weirs, V. Gregory, and Carnes, Brian. Calibrating hypersonic turbulence flow models with the HIFiRE-1 experiment using data-driven machine-learned models.. United States: N. p., 2022.
Web. doi:10.1016/j.cma.2022.115396.
Chowdhary, Kenny, Hoang, Chi, Lee, Kookjin, Ray, Jaideep, Weirs, V. Gregory, & Carnes, Brian. Calibrating hypersonic turbulence flow models with the HIFiRE-1 experiment using data-driven machine-learned models.. United States. https://doi.org/10.1016/j.cma.2022.115396
Chowdhary, Kenny, Hoang, Chi, Lee, Kookjin, Ray, Jaideep, Weirs, V. Gregory, and Carnes, Brian. Fri .
"Calibrating hypersonic turbulence flow models with the HIFiRE-1 experiment using data-driven machine-learned models.". United States. https://doi.org/10.1016/j.cma.2022.115396. https://www.osti.gov/servlets/purl/1888549.
@article{osti_1888549,
title = {Calibrating hypersonic turbulence flow models with the HIFiRE-1 experiment using data-driven machine-learned models.},
author = {Chowdhary, Kenny and Hoang, Chi and Lee, Kookjin and Ray, Jaideep and Weirs, V. Gregory and Carnes, Brian},
abstractNote = {In this paper we study the efficacy of combining machine-learning methods with projection-based model reduction techniques for creating data-driven surrogate models of computationally expensive, high-fidelity physics models. Such surrogate models are essential for many-query applications e.g., engineering design optimization and parameter estimation, where it is necessary to invoke the high-fidelity model sequentially, many times. Surrogate models are usually constructed for individual scalar quantities. However there are scenarios where a spatially varying field needs to be modeled as a function of the model’s input parameters. Here we develop a method to do so, using projections to represent spatial variability while a machine-learned model captures the dependence of the model’s response on the inputs. The method is demonstrated on modeling the heat flux and pressure on the surface of the HIFiRE-1 geometry in a Mach 7.16 turbulent flow. The surrogate model is then used to perform Bayesian estimation of freestream conditions and parameters of the SST (Shear Stress Transport) turbulence model embedded in the high-fidelity (Reynolds-Averaged Navier–Stokes) flow simulator, using shock-tunnel data. The paper provides the first-ever Bayesian calibration of a turbulence model for complex hypersonic turbulent flows. We find that the primary issues in estimating the SST model parameters are the limited information content of the heat flux and pressure measurements and the large model-form error encountered in a certain part of the flow.},
doi = {10.1016/j.cma.2022.115396},
journal = {Computer Methods in Applied Mechanics and Engineering},
number = ,
volume = 401,
place = {United States},
year = {Fri Sep 16 00:00:00 EDT 2022},
month = {Fri Sep 16 00:00:00 EDT 2022}
}
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