Study of fluid–thermal–structural interaction in high-temperature high-speed flow using multi-fidelity multi-variate surrogates. Huang, D., Sadagopan, A., Düzel, Ü., & Hanquist, K. M. Journal of Fluids and Structures, 2022. doi abstract bibtex This study investigates the impact of the high-temperature effect, especially the real gas effect and chemical reactions, on hypersonic aerothermodynamic solutions of double cone and double wedge configurations, as well as the fluid–thermal–structural interaction of a double wedge configuration in hypersonic flow. First, a high-temperature computational fluid dynamics (CFD) code was benchmarked and correlated with experimental results, emphasizing the impact of high-temperature effects as well as turbulence modeling on heat flux prediction. Subsequently, the multi-fidelity multi-variate Gaussian process regression (M2GPR ) method for problems with high-dimensional outputs was developed to create an aerothermal surrogate model. The model achieves a balance between model accuracy and computational cost of sample generation, using the combination of a few high-fidelity samples and many low-fidelity samples. The numerical examples show that, using the M2GPR formulation, the required number of high-fidelity samples may be reduced by over 80% while maintaining an accuracy comparable to the high-fidelity CFD solvers. In addition, a geodesic-distance-based metric is developed to inform the choice of high-dimensional datasets of different fidelities for the M2GPR surrogate with improved accuracy. Finally, the aerothermal surrogate was applied to study the impact of the high-temperature effect on the aerothermoelastic response of a hypersonic skin panel, emphasizing the necessity of the accurate characterization of the localized heat flux for reasonable assessment of the response of a compliant structure in high-speed high-temperature flowfield.
@article{huang2022,
title = {Study of fluid–thermal–structural interaction in high-temperature high-speed flow using multi-fidelity multi-variate surrogates},
volume = {113},
doi = {10.1016/j.jfluidstructs.2022.103682},
abstract = {This study investigates the impact of the high-temperature effect, especially the real gas effect and chemical reactions, on hypersonic aerothermodynamic solutions of double cone and double wedge configurations, as well as the fluid–thermal–structural interaction of a double wedge configuration in hypersonic flow. First, a high-temperature computational fluid dynamics (CFD) code was benchmarked and correlated with experimental results, emphasizing the impact of high-temperature effects as well as turbulence modeling on heat flux prediction. Subsequently, the multi-fidelity multi-variate Gaussian process regression (M2GPR ) method for problems with high-dimensional outputs was developed to create an aerothermal surrogate model. The model achieves a balance between model accuracy and computational cost of sample generation, using the combination of a few high-fidelity samples and many low-fidelity samples. The numerical examples show that, using the M2GPR formulation, the required number of high-fidelity samples may be reduced by over 80\% while maintaining an accuracy comparable to the high-fidelity CFD solvers. In addition, a geodesic-distance-based metric is developed to inform the choice of high-dimensional datasets of different fidelities for the M2GPR surrogate with improved accuracy. Finally, the aerothermal surrogate was applied to study the impact of the high-temperature effect on the aerothermoelastic response of a hypersonic skin panel, emphasizing the necessity of the accurate characterization of the localized heat flux for reasonable assessment of the response of a compliant structure in high-speed high-temperature flowfield.},
journal = {Journal of Fluids and Structures},
author = {Huang, Daning and Sadagopan, Aravinth and Düzel, Ümran and Hanquist, Kyle M.},
year = {2022},
keywords = {Grassmannian geodesic distance, High-speed fluid–thermal–structural interaction, High-temperature effects, Hypersonic aerothermodynamics, Multi-fidelity surrogate modeling, Multivariate Gaussian process regression, graduate\_student, own},
}
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First, a high-temperature computational fluid dynamics (CFD) code was benchmarked and correlated with experimental results, emphasizing the impact of high-temperature effects as well as turbulence modeling on heat flux prediction. Subsequently, the multi-fidelity multi-variate Gaussian process regression (M2GPR ) method for problems with high-dimensional outputs was developed to create an aerothermal surrogate model. The model achieves a balance between model accuracy and computational cost of sample generation, using the combination of a few high-fidelity samples and many low-fidelity samples. The numerical examples show that, using the M2GPR formulation, the required number of high-fidelity samples may be reduced by over 80% while maintaining an accuracy comparable to the high-fidelity CFD solvers. In addition, a geodesic-distance-based metric is developed to inform the choice of high-dimensional datasets of different fidelities for the M2GPR surrogate with improved accuracy. Finally, the aerothermal surrogate was applied to study the impact of the high-temperature effect on the aerothermoelastic response of a hypersonic skin panel, emphasizing the necessity of the accurate characterization of the localized heat flux for reasonable assessment of the response of a compliant structure in high-speed high-temperature flowfield.","journal":"Journal of Fluids and Structures","author":[{"propositions":[],"lastnames":["Huang"],"firstnames":["Daning"],"suffixes":[]},{"propositions":[],"lastnames":["Sadagopan"],"firstnames":["Aravinth"],"suffixes":[]},{"propositions":[],"lastnames":["Düzel"],"firstnames":["Ümran"],"suffixes":[]},{"propositions":[],"lastnames":["Hanquist"],"firstnames":["Kyle","M."],"suffixes":[]}],"year":"2022","keywords":"Grassmannian geodesic distance, High-speed fluid–thermal–structural interaction, High-temperature effects, Hypersonic aerothermodynamics, Multi-fidelity surrogate modeling, Multivariate Gaussian process regression, graduate_student, own","bibtex":"@article{huang2022,\n\ttitle = {Study of fluid–thermal–structural interaction in high-temperature high-speed flow using multi-fidelity multi-variate surrogates},\n\tvolume = {113},\n\tdoi = {10.1016/j.jfluidstructs.2022.103682},\n\tabstract = {This study investigates the impact of the high-temperature effect, especially the real gas effect and chemical reactions, on hypersonic aerothermodynamic solutions of double cone and double wedge configurations, as well as the fluid–thermal–structural interaction of a double wedge configuration in hypersonic flow. First, a high-temperature computational fluid dynamics (CFD) code was benchmarked and correlated with experimental results, emphasizing the impact of high-temperature effects as well as turbulence modeling on heat flux prediction. Subsequently, the multi-fidelity multi-variate Gaussian process regression (M2GPR ) method for problems with high-dimensional outputs was developed to create an aerothermal surrogate model. The model achieves a balance between model accuracy and computational cost of sample generation, using the combination of a few high-fidelity samples and many low-fidelity samples. The numerical examples show that, using the M2GPR formulation, the required number of high-fidelity samples may be reduced by over 80\\% while maintaining an accuracy comparable to the high-fidelity CFD solvers. In addition, a geodesic-distance-based metric is developed to inform the choice of high-dimensional datasets of different fidelities for the M2GPR surrogate with improved accuracy. 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