The study of morphing aircraft has become a hot topic. Investigating aerodynamic features under various operating conditions and full configurations is essential. This paper proposes a multi-fidelity surrogate model method based on HDMR-Kriging (MFS-HK) to predict aerodynamic characteristics of morphing craft quickly and accurately. First, two computational models of aerodynamic characteristics for morphing aircraft are obtained based on the parametric geometric model of morphing aircraft: one with high-fidelity and the other with low fidelity. Second, the low-fidelity aerodynamic characteristics surrogate model based on the HDMR-Kriging method is established. Furthermore, the multi-fidelity surrogate model is constructed based on the hybrid scaling function. MFS-HK can achieve fast and accurate global prediction of morphing aircraft’s aerodynamic characteristics. The accuracy and efficiency of the method are verified by analyzing the computational model of morphing aircraft aerodynamic characteristics.


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    Titel :

    A Multi-fidelity Surrogate Model Method Based on HDMR-Kriging for Aerodynamic Characteristics Prediction of Morphing Aircraft


    Weitere Titelangaben:

    Learning and Analytics in Intelligent Systems


    Beteiligte:
    Tsihrintzis, George A. (Herausgeber:in) / Favorskaya, Margarita N. (Herausgeber:in) / Kountcheva, Roumiana (Herausgeber:in) / Patnaik, Srikanta (Herausgeber:in) / Ma, Shuaichao (Autor:in) / Gao, Jingwei (Autor:in) / Li, Jiaxin (Autor:in) / Xu, Xinyi (Autor:in) / Xie, Zeyang (Autor:in) / Wu, Zeping (Autor:in)

    Kongress:

    International Conference on Computational Vision and Robotics ; 2024 ; Qingdao, China August 24, 2024 - August 25, 2024



    Erscheinungsdatum :

    01.04.2025


    Format / Umfang :

    15 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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