Efficient parametric study of a stochastic airfoil system based on hybrid surrogate modeling with advanced automatic kriging construction

Flutter is one of the most important aeroelastic instability phenomena that arises from the interaction between the structural dynamics of the mechanical airfoil system and the surrounding airflow. This instability phenomenon can lead not only to a reduction in aircraft performance but also to catas...

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Published inEuropean journal of mechanics, A, Solids Vol. 99; p. 104926
Main Authors Denimal, E., Sinou, J.-J.
Format Journal Article
LanguageEnglish
Published Elsevier Masson SAS 01.05.2023
Elsevier
Subjects
Airfoil flutter
Engineering Sciences
Hybrid uncertainties
Kriging
Mechanics
Polynomial chaos expansion
Sensitivity analysis
Stability analysis
Vibrations
Airfoil flutter
Stability analysis
Sensitivity analysis
Polynomial chaos expansion
Hybrid uncertainties
Kriging
hybrid uncertainties
polynomial chaos expansion
airfoil flutter
sensitivity analysis
stability analysis
kriging
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Abstract Flutter is one of the most important aeroelastic instability phenomena that arises from the interaction between the structural dynamics of the mechanical airfoil system and the surrounding airflow. This instability phenomenon can lead not only to a reduction in aircraft performance but also to catastrophic structural failure. Therefore, one of the major challenges is to perform parametric and sensitivity studies on the stability behavior of a wing system subject to many random uncertainties in order to achieve a thorough understanding and reliable estimation of the role played by each parameter in the flutter phenomenon. To carry out such a study, an advanced surrogate modeling technique based on kriging and polynomial chaos expansion (PCE) is proposed for the prediction of flutter instability. In addition, a methodology based on hybrid surrogate modeling with advanced automatic kriging construction is discussed to promote an efficient parametric study of the airfoil system with uncertainties subjected to flutter. The Sobol indices highlight that the role played by each random parameter depends strongly on the flow speed and airfoil geometry with complex behaviors, giving valuable insights into the physics and the complexity of flutter. •Extensive parametric and sensitivity analysis of a stochastic airfoil system.•Efficient surrogate modeling techniques associating kriging and polynomial chaos.•Comparison of automatic criterion of kriging construction in the hybrid surrogate model.
AbstractList Flutter is one of the most important aeroelastic instability phenomena that arises from the interaction between the structural dynamics of the mechanical airfoil system and the surrounding airflow. This instability phenomenon can lead not only to a reduction in aircraft performance but also to catastrophic structural failure. Therefore, one of the major challenges is to perform parametric and sensitivity studies on the stability behavior of a wing system subject to many random uncertainties in order to achieve a thorough understanding and reliable estimation of the role played by each parameter in the flutter phenomenon. To carry out such a study, an advanced surrogate modeling technique based on kriging and polynomial chaos expansion (PCE) is proposed for the prediction of flutter instability. In addition, a methodology based on hybrid surrogate modeling with advanced automatic kriging construction is discussed to promote an efficient parametric study of the airfoil system with uncertainties subjected to flutter. The Sobol indices highlight that the role played by each random parameter depends strongly on the flow speed and airfoil geometry with complex behaviors, giving valuable insights into the physics and the complexity of flutter. •Extensive parametric and sensitivity analysis of a stochastic airfoil system.•Efficient surrogate modeling techniques associating kriging and polynomial chaos.•Comparison of automatic criterion of kriging construction in the hybrid surrogate model.
Flutter is one of the most important aeroelastic instability phenomena that arises from the interaction between the structural dynamics of the mechanical airfoil system and the surrounding airflow. This instability phenomenon can lead not only to a reduction in aircraft performance but also to catastrophic structural failure.Therefore, one of the major challenges is to perform parametric and sensitivity studies on the stability behavior of a wing system subject to many random uncertainties in order to achieve a thorough understanding and reliable estimation of the role played by each parameter in the flutter phenomenon. To carry out such a study, an advanced surrogate modeling technique based on kriging and polynomial chaos expansion (PCE) is proposed for the prediction of flutter instability. In addition, a methodology based on hybrid surrogate modeling with advanced automatic kriging construction is discussed to promote an efficient parametric study of the airfoil system with uncertainties subjected to flutter. The Sobol indices highlight that the role played by each random parameter depends strongly on the flow speed and airfoil geometry with complex behaviors, giving valuable insights into the physics and the complexity of flutter.
ArticleNumber 104926
Author Sinou, J.-J.
Denimal, E.
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Keywords Airfoil flutter
Stability analysis
Sensitivity analysis
Polynomial chaos expansion
Hybrid uncertainties
Kriging
hybrid uncertainties
polynomial chaos expansion
airfoil flutter
sensitivity analysis
stability analysis
kriging
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Snippet Flutter is one of the most important aeroelastic instability phenomena that arises from the interaction between the structural dynamics of the mechanical...
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StartPage 104926
SubjectTerms Airfoil flutter
Engineering Sciences
Hybrid uncertainties
Kriging
Mechanics
Polynomial chaos expansion
Sensitivity analysis
Stability analysis
Vibrations
Title Efficient parametric study of a stochastic airfoil system based on hybrid surrogate modeling with advanced automatic kriging construction
URI https://dx.doi.org/10.1016/j.euromechsol.2023.104926
https://inria.hal.science/hal-04093089
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