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A Review of Intelligent Airfoil Aerodynamic Optimization Methods Based on Data-Driven Advanced Models

Author

Listed:
  • Liyue Wang

    (Department of Aeronautics & Astronautics, Fudan University, Shanghai 200433, China)

  • Haochen Zhang

    (Department of Aeronautics & Astronautics, Fudan University, Shanghai 200433, China)

  • Cong Wang

    (Department of Aeronautics & Astronautics, Fudan University, Shanghai 200433, China)

  • Jun Tao

    (Department of Aeronautics & Astronautics, Fudan University, Shanghai 200433, China)

  • Xinyue Lan

    (Department of Aeronautics & Astronautics, Fudan University, Shanghai 200433, China)

  • Gang Sun

    (Department of Aeronautics & Astronautics, Fudan University, Shanghai 200433, China)

  • Jinzhang Feng

    (Department of Aeronautics & Astronautics, Fudan University, Shanghai 200433, China)

Abstract

With the rapid development of artificial intelligence technology, data-driven advanced models have provided new ideas and means for airfoil aerodynamic optimization. As the advanced models update and iterate, many useful explorations and attempts have been made by researchers on the integrated application of artificial intelligence and airfoil aerodynamic optimization. In this paper, many critical aerodynamic optimization steps where data-driven advanced models are employed are reviewed. These steps include geometric parameterization, aerodynamic solving and performance evaluation, and model optimization. In this way, the improvements in the airfoil aerodynamic optimization area led by data-driven advanced models are introduced. These improvements involve more accurate global description of airfoil, faster prediction of aerodynamic performance, and more intelligent optimization modeling. Finally, the challenges and prospect of applying data-driven advanced models to aerodynamic optimization are discussed.

Suggested Citation

  • Liyue Wang & Haochen Zhang & Cong Wang & Jun Tao & Xinyue Lan & Gang Sun & Jinzhang Feng, 2024. "A Review of Intelligent Airfoil Aerodynamic Optimization Methods Based on Data-Driven Advanced Models," Mathematics, MDPI, vol. 12(10), pages 1-21, May.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:10:p:1417-:d:1389512
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    References listed on IDEAS

    as
    1. Yufei Zhang & Chongyang Yan & Haixin Chen, 2020. "An Inverse Design Method for Airfoils Based on Pressure Gradient Distribution," Energies, MDPI, vol. 13(13), pages 1-18, July.
    2. Martin J. Osborne & Ariel Rubinstein, 1994. "A Course in Game Theory," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262650401, April.
    3. Justin Sirignano & Konstantinos Spiliopoulos, 2017. "DGM: A deep learning algorithm for solving partial differential equations," Papers 1708.07469, arXiv.org, revised Sep 2018.
    4. Owoyele, Opeoluwa & Pal, Pinaki, 2021. "A novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design," Applied Energy, Elsevier, vol. 285(C).
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