Modeling Unsteady Flows for Aeroelasticity Through Machine Learning and Data-Driven Methods (2024)

Kou, J., Meinke, M., Schröder, W. (2024). Modeling Unsteady Flows for Aeroelasticity Through Machine Learning and Data-Driven Methods. In AIAA SciTech Forum and Exposition, 2024 (AIAA SciTech Forum and Exposition, 2024). American Institute of Aeronautics and Astronautics Inc, AIAA. https://doi.org/10.2514/6.2024-2262

Kou, Jiaqing ; Meinke, Matthias ; Schröder, Wolfgang et al. / Modeling Unsteady Flows for Aeroelasticity Through Machine Learning and Data-Driven Methods. AIAA SciTech Forum and Exposition, 2024. American Institute of Aeronautics and Astronautics Inc, AIAA, 2024. (AIAA SciTech Forum and Exposition, 2024).

@inproceedings{9a06399ad92b4937a781ac7bcc01104b,

title = "Modeling Unsteady Flows for Aeroelasticity Through Machine Learning and Data-Driven Methods",

abstract = "Aeroelasticity studies the interaction of a flexible structure immersed in unsteady fluid flows. The aeroelastic analysis involves coupling the structure model with the aerodynamic model to evaluate the instability and response of the coupled dynamic system. However, simulating unsteady flows for aeroelasticity is computationally expensive and limits the engineering application of modern aeroelastic analysis tools. The emergence of data-driven methods has given rise to a new research paradigm in fluid mechanics, particularly in the modeling of unsteady flows. Through developing data-driven methods from different sources of data, data-driven aerodynamic models maintain reasonable accuracy while largely increasing the efficiency of aeroelastic analysis. This short review summarizes some of the techniques to model unsteady aerodynamics for the analysis and prediction of various aeroelastic problems, mainly based on the recent effort from the authors. These techniques include system identification for integrated aerodynamic loads, feature extraction for field and distributed variables, and data fusion, i.e., multifidelity modeling and data assimilation, for flow data from multiple sources.",

author = "Jiaqing Kou and Matthias Meinke and Wolfgang Schr{\"o}der and Daning Huang",

note = "Publisher Copyright: {\textcopyright} 2024 by Jiaqing Kou, Matthias Meinke, Wolfgang Schr{\"o}der, Daning Huang.; AIAA SciTech Forum and Exposition, 2024 ; Conference date: 08-01-2024 Through 12-01-2024",

year = "2024",

doi = "10.2514/6.2024-2262",

language = "English (US)",

isbn = "9781624107115",

series = "AIAA SciTech Forum and Exposition, 2024",

publisher = "American Institute of Aeronautics and Astronautics Inc, AIAA",

booktitle = "AIAA SciTech Forum and Exposition, 2024",

}

Kou, J, Meinke, M, Schröder, W 2024, Modeling Unsteady Flows for Aeroelasticity Through Machine Learning and Data-Driven Methods. in AIAA SciTech Forum and Exposition, 2024. AIAA SciTech Forum and Exposition, 2024, American Institute of Aeronautics and Astronautics Inc, AIAA, AIAA SciTech Forum and Exposition, 2024, Orlando, United States, 1/8/24. https://doi.org/10.2514/6.2024-2262

Modeling Unsteady Flows for Aeroelasticity Through Machine Learning and Data-Driven Methods. / Kou, Jiaqing; Meinke, Matthias; Schröder, Wolfgang et al.
AIAA SciTech Forum and Exposition, 2024. American Institute of Aeronautics and Astronautics Inc, AIAA, 2024. (AIAA SciTech Forum and Exposition, 2024).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Modeling Unsteady Flows for Aeroelasticity Through Machine Learning and Data-Driven Methods

AU - Kou, Jiaqing

AU - Meinke, Matthias

AU - Schröder, Wolfgang

AU - Huang, Daning

N1 - Publisher Copyright:© 2024 by Jiaqing Kou, Matthias Meinke, Wolfgang Schröder, Daning Huang.

PY - 2024

Y1 - 2024

N2 - Aeroelasticity studies the interaction of a flexible structure immersed in unsteady fluid flows. The aeroelastic analysis involves coupling the structure model with the aerodynamic model to evaluate the instability and response of the coupled dynamic system. However, simulating unsteady flows for aeroelasticity is computationally expensive and limits the engineering application of modern aeroelastic analysis tools. The emergence of data-driven methods has given rise to a new research paradigm in fluid mechanics, particularly in the modeling of unsteady flows. Through developing data-driven methods from different sources of data, data-driven aerodynamic models maintain reasonable accuracy while largely increasing the efficiency of aeroelastic analysis. This short review summarizes some of the techniques to model unsteady aerodynamics for the analysis and prediction of various aeroelastic problems, mainly based on the recent effort from the authors. These techniques include system identification for integrated aerodynamic loads, feature extraction for field and distributed variables, and data fusion, i.e., multifidelity modeling and data assimilation, for flow data from multiple sources.

AB - Aeroelasticity studies the interaction of a flexible structure immersed in unsteady fluid flows. The aeroelastic analysis involves coupling the structure model with the aerodynamic model to evaluate the instability and response of the coupled dynamic system. However, simulating unsteady flows for aeroelasticity is computationally expensive and limits the engineering application of modern aeroelastic analysis tools. The emergence of data-driven methods has given rise to a new research paradigm in fluid mechanics, particularly in the modeling of unsteady flows. Through developing data-driven methods from different sources of data, data-driven aerodynamic models maintain reasonable accuracy while largely increasing the efficiency of aeroelastic analysis. This short review summarizes some of the techniques to model unsteady aerodynamics for the analysis and prediction of various aeroelastic problems, mainly based on the recent effort from the authors. These techniques include system identification for integrated aerodynamic loads, feature extraction for field and distributed variables, and data fusion, i.e., multifidelity modeling and data assimilation, for flow data from multiple sources.

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U2 - 10.2514/6.2024-2262

DO - 10.2514/6.2024-2262

M3 - Conference contribution

AN - SCOPUS:85196809410

SN - 9781624107115

T3 - AIAA SciTech Forum and Exposition, 2024

BT - AIAA SciTech Forum and Exposition, 2024

PB - American Institute of Aeronautics and Astronautics Inc, AIAA

T2 - AIAA SciTech Forum and Exposition, 2024

Y2 - 8 January 2024 through 12 January 2024

ER -

Kou J, Meinke M, Schröder W, Huang D. Modeling Unsteady Flows for Aeroelasticity Through Machine Learning and Data-Driven Methods. In AIAA SciTech Forum and Exposition, 2024. American Institute of Aeronautics and Astronautics Inc, AIAA. 2024. (AIAA SciTech Forum and Exposition, 2024). doi: 10.2514/6.2024-2262