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

Title: 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 »« less

Authors:

^[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)

Publication Date:: Fri Sep 16 00:00:00 EDT 2022

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.

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                    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

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                    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.

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@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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Flow, Turbulence and Combustion, Vol. 104, Issue 2-3
DOI: 10.1007/s10494-019-00089-x

Deep Convolutional Encoder‐Decoder Networks for Uncertainty Quantification of Dynamic Multiphase Flow in Heterogeneous Media
journal, January 2019

Mo, Shaoxing; Zhu, Yinhao; Zabaras, Nicholas
Water Resources Research, Vol. 55, Issue 1
DOI: 10.1029/2018WR023528

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

Abstract

Citation Formats

Modeling and control of physical processes using proper orthogonal decomposition journal, January 2001

A data assimilation methodology for reconstructing turbulent flows around aircraft journal, February 2015

Nonintrusive reduced order modeling framework for quasigeostrophic turbulence journal, November 2019

Ground Test Studies of the HIFiRE-1 Transition Experiment Part 2: Computational Analysis journal, November 2008

A Bayesian Calibration–Prediction Method for Reducing Model-Form Uncertainties with Application in RANS Simulations journal, March 2016

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Computer Model Calibration Using High-Dimensional Output journal, June 2008

Model reduction of aerothermodynamic for hypersonic aerothermoelasticity based on POD and Chebyshev method journal, November 2018

Aerothermoelastic Analysis of a Hypersonic Vehicle Based on Thermal Modal Reconstruction journal, March 2019

Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier–Stokes simulations: A data-driven, physics-informed Bayesian approach journal, November 2016

Turbulence Modeling in the Age of Data journal, January 2019

Computation of turbulent axisymmetric and nonaxisymmetric jet flows using the K-epsilon model journal, February 1996

Krylov projection framework for Fourier model reduction journal, January 2008

Subgrid-scale scalar flux modelling based on optimal estimation theory and machine-learning procedures journal, June 2017

Application of the Arnoldi Algorithm to the solution of the advection-dispersion equation journal, October 1990

Robust Bayesian Calibration of a k−ε Model for Compressible Jet-in-Crossflow Simulations journal, December 2018

RANS turbulence model development using CFD-driven machine learning journal, June 2020

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A reduced order aerothermodynamic modeling framework for hypersonic vehicles based on surrogate and POD journal, October 2015

Rapid Steady-State Hypersonic Aerothermodynamic Loads Prediction Using Reduced Fidelity Models journal, May 2021

Strictly Proper Scoring Rules, Prediction, and Estimation journal, March 2007

Optimization of Parameter Values in the Turbulence Model Aided by Data Assimilation journal, May 2016

High-Order Collocation Methods for Differential Equations with Random Inputs journal, January 2005

Large-eddy simulation of turbulent flow over a parametric set of bumps journal, March 2019

Leveraging Bayesian analysis to improve accuracy of approximate models journal, October 2019

Parameterized neural ordinary differential equations: applications to computational physics problems journal, September 2021

Surrogate modeling of multiscale models using kernel methods: KERNEL SURROGATE MULTISCALE MODELS journal, November 2014

Quantification of model uncertainty in RANS simulations: A review journal, July 2019

Parameter Estimation for Reynolds-Averaged Navier–Stokes Models Using Approximate Bayesian Computation journal, November 2021

An Overview of Gradient-Enhanced Metamodels with Applications journal, July 2017

An efficient approach for quantifying parameter uncertainty in the SST turbulence model journal, March 2019

Reduced-order modeling of parameterized PDEs using time-space-parameter principal component analysis journal, November 2009

Managing computational complexity using surrogate models: a critical review journal, April 2020

Greedy Nonintrusive Reduced Order Model for Fluid Dynamics journal, December 2018

A paradigm for data-driven predictive modeling using field inversion and machine learning journal, January 2016

Machine-Learning-Augmented Predictive Modeling of Turbulent Separated Flows over Airfoils journal, July 2017

Towards a general data-driven explicit algebraic Reynolds stress prediction framework journal, October 2019

Estimation of Inflow Uncertainties in Laminar Hypersonic Double-Cone Experiments journal, October 2020

Bayesian estimation of Karhunen–Loève expansions; A random subspace approach journal, August 2016

The development of algebraic stress models using a novel evolutionary algorithm journal, December 2017

A Survey of Bayesian Calibration and Physics-informed Neural Networks in Scientific Modeling journal, February 2021

Metamodel based high-fidelity stochastic analysis of composite laminates: A concise review with critical comparative assessment journal, July 2017

Bayesian Parameter Estimation of a k-ε Model for Accurate Jet-in-Crossflow Simulations journal, August 2016

Using field inversion to quantify functional errors in turbulence closures journal, April 2016

Time-series learning of latent-space dynamics for reduced-order model closure journal, April 2020

Data-driven modeling for unsteady aerodynamics and aeroelasticity journal, August 2021

A constrained reduced-order method for fast prediction of steady hypersonic flows journal, August 2019

Recent progress in augmenting turbulence models with physics-informed machine learning journal, December 2019

Customized data-driven RANS closures for bi-fidelity LES–RANS optimization journal, May 2021

Application of the Lanczos Algorithm to the solution of the groundwater flow equation journal, March 1989

Nonintrusive reduced-order modeling of parametrized time-dependent partial differential equations journal, February 2013

Surrogate Modeling Approach to Support Real-Time Structural Assessment and Decision Making journal, June 2015

Reynolds averaged turbulence modelling using deep neural networks with embedded invariance journal, October 2016

Bayesian uncertainty analysis with applications to turbulence modeling journal, September 2011

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 journal, December 2017

A novel evolutionary algorithm applied to algebraic modifications of the RANS stress–strain relationship journal, November 2016

Estimation of the Continuous Ranked Probability Score with Limited Information and Applications to Ensemble Weather Forecasts journal, November 2017

A review of surrogate models and their application to groundwater modeling: SURROGATES OF GROUNDWATER MODELS journal, August 2015

A reduced order model based on Kalman filtering for sequential data assimilation of turbulent flows journal, October 2017

Bayesian estimates of parameter variability in the k–ε turbulence model journal, February 2014

Uncertainty and sensitivity analysis of SST turbulence model on hypersonic flow heat transfer journal, June 2019

Ground Test Studies of the HIFiRE-1 Transition Experiment Part 1: Experimental Results journal, November 2008

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A review on design of experiments and surrogate models in aircraft real-time and many-query aerodynamic analyses journal, January 2018

An efficient Bayesian uncertainty quantification approach with application to k - ω - γ transition modeling journal, January 2018

Projection-based model reduction: Formulations for physics-based machine learning journal, January 2019

Augmented Prediction of Turbulent Flows via Sequential Estimators journal, August 2018

DRAM: Efficient adaptive MCMC journal, December 2006

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Two-equation eddy-viscosity turbulence models for engineering applications journal, August 1994

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Groundwater Management Using Model Reduction via Empirical Orthogonal Functions journal, March 2008

Non-intrusive reduced order modeling of nonlinear problems using neural networks journal, June 2018

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On design optimization for structural crashworthiness and its state of the art journal, September 2016

Model Reduction of Computational Aerothermodynamics for Hypersonic Aerothermoelasticity journal, January 2012

An explanation of the turbulent round-jet/plane-jet anomaly journal, March 1978

Modeling and control of physical processes using proper orthogonal decomposition
journal, January 2001

A data assimilation methodology for reconstructing turbulent flows around aircraft
journal, February 2015

Nonintrusive reduced order modeling framework for quasigeostrophic turbulence
journal, November 2019

Ground Test Studies of the HIFiRE-1 Transition Experiment Part 2: Computational Analysis
journal, November 2008

A Bayesian Calibration–Prediction Method for Reducing Model-Form Uncertainties with Application in RANS Simulations
journal, March 2016

Probabilistic forecasts, calibration and sharpness
journal, April 2007

Computer Model Calibration Using High-Dimensional Output
journal, June 2008

Model reduction of aerothermodynamic for hypersonic aerothermoelasticity based on POD and Chebyshev method
journal, November 2018

Aerothermoelastic Analysis of a Hypersonic Vehicle Based on Thermal Modal Reconstruction
journal, March 2019

Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier–Stokes simulations: A data-driven, physics-informed Bayesian approach
journal, November 2016

Turbulence Modeling in the Age of Data
journal, January 2019

Computation of turbulent axisymmetric and nonaxisymmetric jet flows using the K-epsilon model
journal, February 1996

Krylov projection framework for Fourier model reduction
journal, January 2008

Subgrid-scale scalar flux modelling based on optimal estimation theory and machine-learning procedures
journal, June 2017

Application of the Arnoldi Algorithm to the solution of the advection-dispersion equation
journal, October 1990

Robust Bayesian Calibration of a k−ε Model for Compressible Jet-in-Crossflow Simulations
journal, December 2018

RANS turbulence model development using CFD-driven machine learning
journal, June 2020

Uncertainty Quantification of Turbulence Model Closure Coefficients for Transonic Wall-Bounded Flows
journal, January 2017

A reduced order aerothermodynamic modeling framework for hypersonic vehicles based on surrogate and POD
journal, October 2015

Rapid Steady-State Hypersonic Aerothermodynamic Loads Prediction Using Reduced Fidelity Models
journal, May 2021

Strictly Proper Scoring Rules, Prediction, and Estimation
journal, March 2007

Optimization of Parameter Values in the Turbulence Model Aided by Data Assimilation
journal, May 2016

High-Order Collocation Methods for Differential Equations with Random Inputs
journal, January 2005

Large-eddy simulation of turbulent flow over a parametric set of bumps
journal, March 2019

Leveraging Bayesian analysis to improve accuracy of approximate models
journal, October 2019

Parameterized neural ordinary differential equations: applications to computational physics problems
journal, September 2021

Surrogate modeling of multiscale models using kernel methods: KERNEL SURROGATE MULTISCALE MODELS
journal, November 2014

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journal, July 2019

Parameter Estimation for Reynolds-Averaged Navier–Stokes Models Using Approximate Bayesian Computation
journal, November 2021

An Overview of Gradient-Enhanced Metamodels with Applications
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An efficient approach for quantifying parameter uncertainty in the SST turbulence model
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journal, December 2018

A paradigm for data-driven predictive modeling using field inversion and machine learning
journal, January 2016

Machine-Learning-Augmented Predictive Modeling of Turbulent Separated Flows over Airfoils
journal, July 2017

Towards a general data-driven explicit algebraic Reynolds stress prediction framework
journal, October 2019

Estimation of Inflow Uncertainties in Laminar Hypersonic Double-Cone Experiments
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Bayesian estimation of Karhunen–Loève expansions; A random subspace approach
journal, August 2016

The development of algebraic stress models using a novel evolutionary algorithm
journal, December 2017

A Survey of Bayesian Calibration and Physics-informed Neural Networks in Scientific Modeling
journal, February 2021

Metamodel based high-fidelity stochastic analysis of composite laminates: A concise review with critical comparative assessment
journal, July 2017

Bayesian Parameter Estimation of a k-ε Model for Accurate Jet-in-Crossflow Simulations
journal, August 2016

Using field inversion to quantify functional errors in turbulence closures
journal, April 2016

Time-series learning of latent-space dynamics for reduced-order model closure
journal, April 2020

Data-driven modeling for unsteady aerodynamics and aeroelasticity
journal, August 2021

A constrained reduced-order method for fast prediction of steady hypersonic flows
journal, August 2019

Recent progress in augmenting turbulence models with physics-informed machine learning
journal, December 2019

Customized data-driven RANS closures for bi-fidelity LES–RANS optimization
journal, May 2021

Application of the Lanczos Algorithm to the solution of the groundwater flow equation
journal, March 1989

Nonintrusive reduced-order modeling of parametrized time-dependent partial differential equations
journal, February 2013

Surrogate Modeling Approach to Support Real-Time Structural Assessment and Decision Making
journal, June 2015

Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
journal, October 2016

Bayesian uncertainty analysis with applications to turbulence modeling
journal, September 2011

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
journal, December 2017

A novel evolutionary algorithm applied to algebraic modifications of the RANS stress–strain relationship
journal, November 2016

Estimation of the Continuous Ranked Probability Score with Limited Information and Applications to Ensemble Weather Forecasts
journal, November 2017

A review of surrogate models and their application to groundwater modeling: SURROGATES OF GROUNDWATER MODELS
journal, August 2015

A reduced order model based on Kalman filtering for sequential data assimilation of turbulent flows
journal, October 2017

Bayesian estimates of parameter variability in the k–ε turbulence model
journal, February 2014

Uncertainty and sensitivity analysis of SST turbulence model on hypersonic flow heat transfer
journal, June 2019

Ground Test Studies of the HIFiRE-1 Transition Experiment Part 1: Experimental Results
journal, November 2008

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journal, May 1991

A review on design of experiments and surrogate models in aircraft real-time and many-query aerodynamic analyses
journal, January 2018

An efficient Bayesian uncertainty quantification approach with application to k - ω - γ transition modeling
journal, January 2018

Projection-based model reduction: Formulations for physics-based machine learning
journal, January 2019

Augmented Prediction of Turbulent Flows via Sequential Estimators
journal, August 2018

DRAM: Efficient adaptive MCMC
journal, December 2006

Probabilistic Transient Heat Conduction Analysis Considering Uncertainties in Thermal Loads Using Surrogate Model
journal, July 2021

Two-equation eddy-viscosity turbulence models for engineering applications
journal, August 1994

Machine learning methods for turbulence modeling in subsonic flows around airfoils
journal, January 2019

Groundwater Management Using Model Reduction via Empirical Orthogonal Functions
journal, March 2008

Non-intrusive reduced order modeling of nonlinear problems using neural networks
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Fourier Series for Accurate, Stable, Reduced-Order Models in Large-Scale Linear Applications
journal, January 2005

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journal, September 2016

Model Reduction of Computational Aerothermodynamics for Hypersonic Aerothermoelasticity
journal, January 2012

An explanation of the turbulent round-jet/plane-jet anomaly
journal, March 1978

Model identification of reduced order fluid dynamics systems using deep learning: Model identification in fluid dynamics using deep learning
journal, August 2017

Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression
journal, December 2019

Deep Convolutional Encoder‐Decoder Networks for Uncertainty Quantification of Dynamic Multiphase Flow in Heterogeneous Media
journal, January 2019