Solving reliability-based design optimization (RBDO) by combining surrogate models is a powerful tool to deal with the output variation induced by uncertainties during actual engineering design. This paper aims to develop a strategy for solving RBDO problems by support vector regression (SVR) under the Bayesian inference, referred to as Bayesian SVR (BSVR). The BSVR model possesses the features of original SVR as well as providing the prediction variance to direct the sequential sampling process for probabilistic analysis in RBDO. In the meanwhile, a learning function combined with the sample pool truncation strategy is proposed to select the new training samples to adaptively update the BSVR model, which can accurately and efficiently approximate the important probabilistic constraint boundaries in the desired regions, and the newly selected training samples tend to be far away from the existing training points under the current design to avoid clustering. The computational capability and the good engineering applicability of the proposed method are verified by three numerical examples and two engineering applications about a stiffened rib of the wing edge and a latch-lock mechanism of the cabin hatch.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Bayesian Support Vector Regression for Reliability-Based Design Optimization


    Beteiligte:
    Ling, Chunyan (Autor:in) / Lu, Zhenzhou (Autor:in) / Zhang, Wenxin (Autor:in)

    Erschienen in:

    AIAA Journal ; 59 , 12 ; 5141-5157


    Erscheinungsdatum :

    01.12.2021




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Support Vector Regression-based Multidisciplinary Design Optimization in Aircraft Conceptual Design

    Zhang, K. / Han, Z. / American Institute of Aeronautics and Astronautics | British Library Conference Proceedings | 2013



    A Genetic Optimization Design Methodology Based on Support Vector Regression

    Xiang, G. / Huang, D. | British Library Conference Proceedings | 2008


    Design Space Decomposition Using Support Vector Machines for Reliability-Based Design Optimization

    Sanchez, Antonio / Missoum, Samy / Martinez Teuscher, Juan | AIAA | 2006


    Design Space Decomposition Using Support Vector Machines for Reliability-Based Design Optimization

    Sanchez, A. / Missoum, S. / Teuscher, J. M. et al. | British Library Conference Proceedings | 2006