Calibrating hypersonic turbulence flow models with the HIFiRE-1 experiment using data-driven machine-learned models.

Chowdhary, Kenny; Hoang, Chi; Lee, Kookjin; Ray, Jaideep; Weirs, V. Gregory; Carnes, Brian

doi:10.1016/j.cma.2022.115396

Calibrating hypersonic turbulence flow models with the HIFiRE-1 experiment using data-driven machine-learned models.

Journal Article · Fri Sep 16 00:00:00 EDT 2022 · Computer Methods in Applied Mechanics and Engineering

DOI:https://doi.org/10.1016/j.cma.2022.115396· OSTI ID:1888549

^[1]; ^[1]; ^[2]; Ray, Jaideep ^[1]; Weirs, V. Gregory ^[3]; Carnes, Brian ^[3]

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)

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.

View Accepted Manuscript (DOE)

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

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: NA0003525

OSTI ID:: 1888549

Alternate ID(s):: OSTI ID: 1960842

Report Number(s):: SAND2022-10607J; 708881

Journal Information:: Computer Methods in Applied Mechanics and Engineering, Journal Name: Computer Methods in Applied Mechanics and Engineering Vol. 401; ISSN 0045-7825

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (81)

A review of surrogate models and their application to groundwater modeling: SURROGATES OF GROUNDWATER MODELS Asher, M. J.; Croke, B. F. W.; Jakeman, A. J. Water Resources Research, Vol. 51, Issue 8 https://doi.org/10.1002/2015WR016967	journal	August 2015
Model identification of reduced order fluid dynamics systems using deep learning: Model identification in fluid dynamics using deep learning Wang, Z.; Xiao, D.; Fang, F. International Journal for Numerical Methods in Fluids, Vol. 86, Issue 4 https://doi.org/10.1002/fld.4416	journal	August 2017
Reduced-order modeling of parameterized PDEs using time-space-parameter principal component analysis Audouze, C.; De Vuyst, F.; Nair, P. B. International Journal for Numerical Methods in Engineering, Vol. 80, Issue 8 https://doi.org/10.1002/nme.2540	journal	November 2009
Surrogate modeling of multiscale models using kernel methods: KERNEL SURROGATE MULTISCALE MODELS Wirtz, D.; Karajan, N.; Haasdonk, B. International Journal for Numerical Methods in Engineering, Vol. 101, Issue 1 https://doi.org/10.1002/nme.4767	journal	November 2014
Nonintrusive reduced-order modeling of parametrized time-dependent partial differential equations Audouze, Christophe; De Vuyst, Florian; Nair, Prasanth B. Numerical Methods for Partial Differential Equations, Vol. 29, Issue 5 https://doi.org/10.1002/num.21768	journal	February 2013
On design optimization for structural crashworthiness and its state of the art Fang, Jianguang; Sun, Guangyong; Qiu, Na Structural and Multidisciplinary Optimization, Vol. 55, Issue 3 https://doi.org/10.1007/s00158-016-1579-y	journal	September 2016
Managing computational complexity using surrogate models: a critical review Alizadeh, Reza; Allen, Janet K.; Mistree, Farrokh Research in Engineering Design, Vol. 31, Issue 3 https://doi.org/10.1007/s00163-020-00336-7	journal	April 2020
A Bayesian Calibration–Prediction Method for Reducing Model-Form Uncertainties with Application in RANS Simulations Wu, Jin-Long; Wang, Jian-Xun; Xiao, Heng Flow, Turbulence and Combustion, Vol. 97, Issue 3 https://doi.org/10.1007/s10494-016-9725-6	journal	March 2016
Augmented Prediction of Turbulent Flows via Sequential Estimators Meldi, Marcello Flow, Turbulence and Combustion, Vol. 101, Issue 2 https://doi.org/10.1007/s10494-018-9967-6	journal	August 2018
Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression Schmelzer, Martin; Dwight, Richard P.; Cinnella, Paola Flow, Turbulence and Combustion, Vol. 104, Issue 2-3 https://doi.org/10.1007/s10494-019-00089-x	journal	December 2019
Estimation of the Continuous Ranked Probability Score with Limited Information and Applications to Ensemble Weather Forecasts Zamo, Michaël; Naveau, Philippe Mathematical Geosciences, Vol. 50, Issue 2 https://doi.org/10.1007/s11004-017-9709-7	journal	November 2017
DRAM: Efficient adaptive MCMC Haario, Heikki; Laine, Marko; Mira, Antonietta Statistics and Computing, Vol. 16, Issue 4 https://doi.org/10.1007/s11222-006-9438-0	journal	December 2006
An Overview of Gradient-Enhanced Metamodels with Applications Laurent, Luc; Le Riche, Rodolphe; Soulier, Bruno Archives of Computational Methods in Engineering, Vol. 26, Issue 1 https://doi.org/10.1007/s11831-017-9226-3	journal	July 2017
A Survey of Bayesian Calibration and Physics-informed Neural Networks in Scientific Modeling Viana, Felipe A. C.; Subramaniyan, Arun K. Archives of Computational Methods in Engineering, Vol. 28, Issue 5 https://doi.org/10.1007/s11831-021-09539-0	journal	February 2021
Recent progress in augmenting turbulence models with physics-informed machine learning Zhang, Xinlei; Wu, Jinlong; Coutier-Delgosha, Olivier Journal of Hydrodynamics, Vol. 31, Issue 6 https://doi.org/10.1007/s42241-019-0089-y	journal	December 2019
Modeling and control of physical processes using proper orthogonal decomposition Ly, Hung V.; Tran, Hien T. Mathematical and Computer Modelling, Vol. 33, Issue 1-3 https://doi.org/10.1016/S0895-7177(00)00240-5	journal	January 2001
A constrained reduced-order method for fast prediction of steady hypersonic flows Cao, Changqiang; Nie, Chunsheng; Pan, Shucheng Aerospace Science and Technology, Vol. 91 https://doi.org/10.1016/j.ast.2019.07.016	journal	August 2019
Krylov projection framework for Fourier model reduction Gugercin, Serkan; Willcox, Karen Automatica, Vol. 44, Issue 1 https://doi.org/10.1016/j.automatica.2007.05.007	journal	January 2008
A reduced order aerothermodynamic modeling framework for hypersonic vehicles based on surrogate and POD Chen, Xin; Liu, Li; Long, Teng Chinese Journal of Aeronautics, Vol. 28, Issue 5 https://doi.org/10.1016/j.cja.2015.06.024	journal	October 2015
Bayesian calibration of the constants of the k–ε turbulence model for a CFD model of street canyon flow Guillas, Serge; Glover, Nina; Malki-Epshtein, Liora Computer Methods in Applied Mechanics and Engineering, Vol. 279 https://doi.org/10.1016/j.cma.2014.06.008	journal	September 2014
An efficient Bayesian uncertainty quantification approach with application to k - ω - γ transition modeling Zhang, Jincheng; Fu, Song Computers & Fluids, Vol. 161 https://doi.org/10.1016/j.compfluid.2017.11.007	journal	January 2018
Projection-based model reduction: Formulations for physics-based machine learning Swischuk, Renee; Mainini, Laura; Peherstorfer, Benjamin Computers & Fluids, Vol. 179 https://doi.org/10.1016/j.compfluid.2018.07.021	journal	January 2019
An efficient approach for quantifying parameter uncertainty in the SST turbulence model Zhang, Jincheng; Fu, Song Computers & Fluids, Vol. 181 https://doi.org/10.1016/j.compfluid.2019.01.017	journal	March 2019
Metamodel based high-fidelity stochastic analysis of composite laminates: A concise review with critical comparative assessment Dey, S.; Mukhopadhyay, T.; Adhikari, S. Composite Structures, Vol. 171 https://doi.org/10.1016/j.compstruct.2017.01.061	journal	July 2017
The development of algebraic stress models using a novel evolutionary algorithm Weatheritt, J.; Sandberg, R. D. International Journal of Heat and Fluid Flow, Vol. 68 https://doi.org/10.1016/j.ijheatfluidflow.2017.09.017	journal	December 2017
Towards a general data-driven explicit algebraic Reynolds stress prediction framework Sotgiu, Corrado; Weigand, Bernhard; Semmler, Klaus International Journal of Heat and Fluid Flow, Vol. 79 https://doi.org/10.1016/j.ijheatfluidflow.2019.108454	journal	October 2019
Uncertainty and sensitivity analysis of SST turbulence model on hypersonic flow heat transfer Zhao, Yatian; Yan, Chao; Wang, Xiaoyong International Journal of Heat and Mass Transfer, Vol. 136 https://doi.org/10.1016/j.ijheatmasstransfer.2019.03.012	journal	June 2019
Bayesian estimates of parameter variability in the k–ε turbulence model Edeling, W. N.; Cinnella, P.; Dwight, R. P. Journal of Computational Physics, Vol. 258 https://doi.org/10.1016/j.jcp.2013.10.027	journal	February 2014
A data assimilation methodology for reconstructing turbulent flows around aircraft Kato, Hiroshi; Yoshizawa, Akira; Ueno, Genta Journal of Computational Physics, Vol. 283 https://doi.org/10.1016/j.jcp.2014.12.013	journal	February 2015
A paradigm for data-driven predictive modeling using field inversion and machine learning Parish, Eric J.; Duraisamy, Karthik Journal of Computational Physics, Vol. 305 https://doi.org/10.1016/j.jcp.2015.11.012	journal	January 2016
Bayesian estimation of Karhunen–Loève expansions; A random subspace approach Chowdhary, Kenny; Najm, Habib N. Journal of Computational Physics, Vol. 319 https://doi.org/10.1016/j.jcp.2016.02.056	journal	August 2016
Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier–Stokes simulations: A data-driven, physics-informed Bayesian approach Xiao, H.; Wu, J. -L.; Wang, J. -X. Journal of Computational Physics, Vol. 324 https://doi.org/10.1016/j.jcp.2016.07.038	journal	November 2016
A novel evolutionary algorithm applied to algebraic modifications of the RANS stress–strain relationship Weatheritt, Jack; Sandberg, Richard Journal of Computational Physics, Vol. 325 https://doi.org/10.1016/j.jcp.2016.08.015	journal	November 2016
A reduced order model based on Kalman filtering for sequential data assimilation of turbulent flows Meldi, M.; Poux, A. Journal of Computational Physics, Vol. 347 https://doi.org/10.1016/j.jcp.2017.06.042	journal	October 2017
Non-intrusive reduced order modeling of nonlinear problems using neural networks Hesthaven, J. S.; Ubbiali, S. Journal of Computational Physics, Vol. 363 https://doi.org/10.1016/j.jcp.2018.02.037	journal	June 2018
Leveraging Bayesian analysis to improve accuracy of approximate models Nadiga, Balasubramanya; Jiang, Chiyu; Livescu, Daniel Journal of Computational Physics, Vol. 394 https://doi.org/10.1016/j.jcp.2019.05.015	journal	October 2019
RANS turbulence model development using CFD-driven machine learning Zhao, Yaomin; Akolekar, Harshal D.; Weatheritt, Jack Journal of Computational Physics, Vol. 411 https://doi.org/10.1016/j.jcp.2020.109413	journal	June 2020
Customized data-driven RANS closures for bi-fidelity LES–RANS optimization Zhang, Yu; Dwight, Richard P.; Schmelzer, Martin Journal of Computational Physics, Vol. 432 https://doi.org/10.1016/j.jcp.2021.110153	journal	May 2021
Improvement of k-epsilon turbulence model for CFD simulation of atmospheric boundary layer around a high-rise building using stochastic optimization and Monte Carlo Sampling technique Shirzadi, Mohammadreza; Mirzaei, Parham A.; Naghashzadegan, Mohammad Journal of Wind Engineering and Industrial Aerodynamics, Vol. 171 https://doi.org/10.1016/j.jweia.2017.10.005	journal	December 2017
A review on design of experiments and surrogate models in aircraft real-time and many-query aerodynamic analyses Yondo, Raul; Andrés, Esther; Valero, Eusebio Progress in Aerospace Sciences, Vol. 96 https://doi.org/10.1016/j.paerosci.2017.11.003	journal	January 2018
Quantification of model uncertainty in RANS simulations: A review Xiao, Heng; Cinnella, Paola Progress in Aerospace Sciences, Vol. 108 https://doi.org/10.1016/j.paerosci.2018.10.001	journal	July 2019
Data-driven modeling for unsteady aerodynamics and aeroelasticity Kou, Jiaqing; Zhang, Weiwei Progress in Aerospace Sciences, Vol. 125 https://doi.org/10.1016/j.paerosci.2021.100725	journal	August 2021
Time-series learning of latent-space dynamics for reduced-order model closure Maulik, Romit; Mohan, Arvind; Lusch, Bethany Physica D: Nonlinear Phenomena, Vol. 405 https://doi.org/10.1016/j.physd.2020.132368	journal	April 2020
Bayesian uncertainty analysis with applications to turbulence modeling Cheung, Sai Hung; Oliver, Todd A.; Prudencio, Ernesto E. Reliability Engineering & System Safety, Vol. 96, Issue 9 https://doi.org/10.1016/j.ress.2010.09.013	journal	September 2011
Reynolds averaged turbulence modelling using deep neural networks with embedded invariance Ling, Julia; Kurzawski, Andrew; Templeton, Jeremy Journal of Fluid Mechanics, Vol. 807 https://doi.org/10.1017/jfm.2016.615	journal	October 2016
Large-eddy simulation of turbulent flow over a parametric set of bumps Matai, Racheet; Durbin, Paul Journal of Fluid Mechanics, Vol. 866 https://doi.org/10.1017/jfm.2019.80	journal	March 2019
Deep Convolutional Encoder‐Decoder Networks for Uncertainty Quantification of Dynamic Multiphase Flow in Heterogeneous Media Mo, Shaoxing; Zhu, Yinhao; Zabaras, Nicholas Water Resources Research, Vol. 55, Issue 1 https://doi.org/10.1029/2018WR023528	journal	January 2019
Application of the Lanczos Algorithm to the solution of the groundwater flow equation Dunbar, W. Scott; Woodbury, Allan D. Water Resources Research, Vol. 25, Issue 3 https://doi.org/10.1029/WR025i003p00551	journal	March 1989
Application of the Arnoldi Algorithm to the solution of the advection-dispersion equation Woodbury, Allan D.; Dunbar, W. Scott; Nour-Omid, Bahram Water Resources Research, Vol. 26, Issue 10 https://doi.org/10.1029/WR026i010p02579	journal	October 1990
Groundwater Management Using Model Reduction via Empirical Orthogonal Functions McPhee, James; Yeh, William W. -G. Journal of Water Resources Planning and Management, Vol. 134, Issue 2 https://doi.org/10.1061/(ASCE)0733-9496(2008)134:2(161)	journal	March 2008
Using field inversion to quantify functional errors in turbulence closures Singh, Anand Pratap; Duraisamy, Karthik Physics of Fluids, Vol. 28, Issue 4 https://doi.org/10.1063/1.4947045	journal	April 2016
Machine learning methods for turbulence modeling in subsonic flows around airfoils Zhu, Linyang; Zhang, Weiwei; Kou, Jiaqing Physics of Fluids, Vol. 31, Issue 1 https://doi.org/10.1063/1.5061693	journal	January 2019
Subgrid-scale scalar flux modelling based on optimal estimation theory and machine-learning procedures Vollant, A.; Balarac, G.; Corre, C. Journal of Turbulence, Vol. 18, Issue 9 https://doi.org/10.1080/14685248.2017.1334907	journal	June 2017
Parameterized neural ordinary differential equations: applications to computational physics problems Lee, Kookjin; Parish, Eric J. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 477, Issue 2253 https://doi.org/10.1098/rspa.2021.0162	journal	September 2021
Nonintrusive reduced order modeling framework for quasigeostrophic turbulence Rahman, Sk. M.; Pawar, S.; San, O. Physical Review E, Vol. 100, Issue 5 https://doi.org/10.1103/PhysRevE.100.053306	journal	November 2019
Probabilistic forecasts, calibration and sharpness Gneiting, Tilmann; Balabdaoui, Fadoua; Raftery, Adrian E. Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 69, Issue 2 https://doi.org/10.1111/j.1467-9868.2007.00587.x	journal	April 2007
High-Order Collocation Methods for Differential Equations with Random Inputs Xiu, Dongbin; Hesthaven, Jan S. SIAM Journal on Scientific Computing, Vol. 27, Issue 3 https://doi.org/10.1137/040615201	journal	January 2005
Fourier Series for Accurate, Stable, Reduced-Order Models in Large-Scale Linear Applications Willcox, K.; Megretski, A. SIAM Journal on Scientific Computing, Vol. 26, Issue 3 https://doi.org/10.1137/S1064827502418768	journal	January 2005
Turbulence Modeling in the Age of Data Duraisamy, Karthik; Iaccarino, Gianluca; Xiao, Heng Annual Review of Fluid Mechanics, Vol. 51, Issue 1 https://doi.org/10.1146/annurev-fluid-010518-040547	journal	January 2019
Aerothermoelastic Analysis of a Hypersonic Vehicle Based on Thermal Modal Reconstruction Chen, Zhiqiang; Zhao, Yonghui International Journal of Aerospace Engineering, Vol. 2019 https://doi.org/10.1155/2019/8384639	journal	March 2019
Model reduction of aerothermodynamic for hypersonic aerothermoelasticity based on POD and Chebyshev method Xiaoxuan, Yan; Jinglong, Han; Bing, Zhang Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, Vol. 233, Issue 10 https://doi.org/10.1177/0954410018808634	journal	November 2018
Strictly Proper Scoring Rules, Prediction, and Estimation Gneiting, Tilmann; Raftery, Adrian E. Journal of the American Statistical Association, Vol. 102, Issue 477 https://doi.org/10.1198/016214506000001437	journal	March 2007
Computer Model Calibration Using High-Dimensional Output Higdon, Dave; Gattiker, James; Williams, Brian Journal of the American Statistical Association, Vol. 103, Issue 482 https://doi.org/10.1198/016214507000000888	journal	June 2008
Ground Test Studies of the HIFiRE-1 Transition Experiment Part 2: Computational Analysis MacLean, Matthew; Wadhams, Timothy; Holden, Michael Journal of Spacecraft and Rockets, Vol. 45, Issue 6 https://doi.org/10.2514/1.37693	journal	November 2008
Ground Test Studies of the HIFiRE-1 Transition Experiment Part 1: Experimental Results Wadhams, T. P.; Mundy, E.; MacLean, M. G. Journal of Spacecraft and Rockets, Vol. 45, Issue 6 https://doi.org/10.2514/1.38338	journal	November 2008
Probabilistic Transient Heat Conduction Analysis Considering Uncertainties in Thermal Loads Using Surrogate Model Zhang, Kun; Yao, Jianyao; He, Zhongjun Journal of Spacecraft and Rockets, Vol. 58, Issue 4 https://doi.org/10.2514/1.A34832	journal	July 2021
Rapid Steady-State Hypersonic Aerothermodynamic Loads Prediction Using Reduced Fidelity Models Dreyer, Emily R.; Grier, Benjamin J.; McNamara, Jack J. Journal of Aircraft, Vol. 58, Issue 3 https://doi.org/10.2514/1.C035969	journal	May 2021
Model Reduction of Computational Aerothermodynamics for Hypersonic Aerothermoelasticity Crowell, Andrew R.; McNamara, Jack J. AIAA Journal, Vol. 50, Issue 1 https://doi.org/10.2514/1.J051094	journal	January 2012
Surrogate Modeling Approach to Support Real-Time Structural Assessment and Decision Making Mainini, L.; Willcox, K. AIAA Journal, Vol. 53, Issue 6 https://doi.org/10.2514/1.J053464	journal	June 2015
Optimization of Parameter Values in the Turbulence Model Aided by Data Assimilation Kato, Hiroshi; Ishiko, Keiichi; Yoshizawa, Akira AIAA Journal, Vol. 54, Issue 5 https://doi.org/10.2514/1.J054109	journal	May 2016
Bayesian Parameter Estimation of a k-ε Model for Accurate Jet-in-Crossflow Simulations Ray, Jaideep; Lefantzi, Sophia; Arunajatesan, Srinivasan AIAA Journal, Vol. 54, Issue 8 https://doi.org/10.2514/1.J054758	journal	August 2016
Uncertainty Quantification of Turbulence Model Closure Coefficients for Transonic Wall-Bounded Flows Schaefer, John; Hosder, Serhat; West, Thomas AIAA Journal, Vol. 55, Issue 1 https://doi.org/10.2514/1.J054902	journal	January 2017
Machine-Learning-Augmented Predictive Modeling of Turbulent Separated Flows over Airfoils Singh, Anand Pratap; Medida, Shivaji; Duraisamy, Karthik AIAA Journal, Vol. 55, Issue 7 https://doi.org/10.2514/1.J055595	journal	July 2017
Greedy Nonintrusive Reduced Order Model for Fluid Dynamics Chen, Wang; Hesthaven, Jan S.; Junqiang, Bai AIAA Journal, Vol. 56, Issue 12 https://doi.org/10.2514/1.J056161	journal	December 2018
Robust Bayesian Calibration of a k−ε Model for Compressible Jet-in-Crossflow Simulations Ray, Jaideep; Dechant, Lawrence; Lefantzi, Sophia AIAA Journal, Vol. 56, Issue 12 https://doi.org/10.2514/1.J057204	journal	December 2018
Estimation of Inflow Uncertainties in Laminar Hypersonic Double-Cone Experiments Ray, J.; Kieweg, S.; Dinzl, D. AIAA Journal, Vol. 58, Issue 10 https://doi.org/10.2514/1.J059033	journal	October 2020
Parameter Estimation for Reynolds-Averaged Navier–Stokes Models Using Approximate Bayesian Computation Doronina, Olga A.; Murman, Scott M.; Hamlington, Peter E. AIAA Journal, Vol. 59, Issue 11 https://doi.org/10.2514/1.J060308	journal	November 2021
Application of a Reynolds stress turbulence model to the compressible shear layer Sarkar, S.; Lakshmanan, B. AIAA Journal, Vol. 29, Issue 5 https://doi.org/10.2514/3.10649	journal	May 1991
Two-equation eddy-viscosity turbulence models for engineering applications Menter, F. R. AIAA Journal, Vol. 32, Issue 8 https://doi.org/10.2514/3.12149	journal	August 1994
Computation of turbulent axisymmetric and nonaxisymmetric jet flows using the K-epsilon model Thies, Andrew T.; Tam, Christopher K. W. AIAA Journal, Vol. 34, Issue 2 https://doi.org/10.2514/3.13065	journal	February 1996
An explanation of the turbulent round-jet/plane-jet anomaly Pope, S. B. AIAA Journal, Vol. 16, Issue 3 https://doi.org/10.2514/3.7521	journal	March 1978

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