The efficient global optimization approach was often used to reduce the computational cost in the optimization of complex engineering systems. This algorithm can, however, remain expensive for large-scale problems because each simulation uses the full numerical model. A novel optimization approach for such problems is proposed in this paper, in which the numerical model solves partial differential equations involving the resolution of a large system of equations, such as by finite element. This method is based on the combination of the efficient global optimization approach and reduced-basis modeling. The novel idea is to use inexpensive, sufficiently accurate reduced-basis solutions to significantly reduce the number of full system resolutions. Two applications of the proposed surrogate-based optimization approach are presented: an application to the problem of stiffness maximization of laminated plates and an application to the problem of identification of orthotropic elastic constants from full-field displacement measurements based on a tensile test on a plate with a hole. Compared with the crude efficient global optimization algorithm, a significant reduction in computational cost was achieved using the proposed efficient reduced-basis global optimization.


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    Titel :

    Accelerated Adaptive Surrogate-Based Optimization Through Reduced-Order Modeling


    Beteiligte:

    Erschienen in:

    AIAA Journal ; 55 , 5 ; 1681-1694


    Erscheinungsdatum :

    2017-02-09


    Format / Umfang :

    14 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch






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