Multipoint aerodynamic design optimization involves no more than tens of flight conditions, which cannot thoroughly represent the actual demand. A comprehensive evaluation of the performance may consider hundreds or even thousands of flight conditions, and this leads to a massively multipoint optimization problem. Existing optimization methods are inefficient in such cases. This paper presents a surrogate-assisted gradient-based optimization architecture that efficiently solves massively multipoint design problems. To avoid the curse of dimensionality, surrogate models are constructed only in the low-dimensional space spanned by flow condition variables. With the aerodynamic functions and gradients computed by surrogate models, efficient gradient-based optimization is performed to find the optimal design. To ensure convergence, an adaptive sampling criterion is proposed to refine the surrogate models. In a transonic aircraft wing design case, the results show that the optimal design found by the proposed method with 342 missions yields a fuel burn reduction by a factor of two as compared to a regular multipoint optimal design. This work highlights the demand and provides an efficient way to conduct massively multipoint optimization in aircraft design.


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    Titel :

    Massively Multipoint Aerodynamic Shape Design via Surrogate-Assisted Gradient-Based Optimization


    Beteiligte:
    Li, Jichao (Autor:in) / Cai, Jinsheng (Autor:in)

    Erschienen in:

    AIAA Journal ; 58 , 5 ; 1949-1963


    Erscheinungsdatum :

    01.05.2020




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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