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Coupled Aerostructural Design Optimization Using the Kriging Model and Integrated Multiobjective Optimization Algorithm

Author

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  • X. B. Lam

    (Gyeongsang National University)

  • Y. S. Kim

    (Gyeongsang National University)

  • A. D. Hoang

    (Gyeongsang National University)

  • C. W. Park

    (Gyeongsang National University)

Abstract

The paper develops and implements a highly applicable framework for the computation of coupled aerostructural design optimization. The multidisciplinary aerostructural design optimization is carried out and validated for a tested wing and can be easily extended to complex and practical design problems. To make the framework practical, the study utilizes a high-fidelity fluid/structure interface and robust optimization algorithms for an accurate determination of the design with the best performance. The aerodynamic and structural performance measures, including the lift coefficient, the drag coefficient, Von-Mises stress and the weight of wing, are precisely computed through the static aeroelastic analyses of various candidate wings. Based on these calculated performance, the design system can be approximated by using a Kriging interpolative model. To improve the design evenly for aerodynamic and structure performance, an automatic design method that determines appropriate weighting factors is developed. Multidisciplinary aerostructural design is, therefore, desirable and practical.

Suggested Citation

  • X. B. Lam & Y. S. Kim & A. D. Hoang & C. W. Park, 2009. "Coupled Aerostructural Design Optimization Using the Kriging Model and Integrated Multiobjective Optimization Algorithm," Journal of Optimization Theory and Applications, Springer, vol. 142(3), pages 533-556, September.
    • Handle: RePEc:spr:joptap:v:142:y:2009:i:3:d:10.1007_s10957-009-9520-9
    DOI: 10.1007/s10957-009-9520-9
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    References listed on IDEAS

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    1. Atin Basuchoudhary & James T. Bang & Tinni Sen, 2017. "Methodology," SpringerBriefs in Economics, in: Machine-learning Techniques in Economics, chapter 0, pages 19-28, Springer.
    2. Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 65-99.
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    Cited by:

    1. Juliane Müller & Robert Piché, 2011. "Mixture surrogate models based on Dempster-Shafer theory for global optimization problems," Journal of Global Optimization, Springer, vol. 51(1), pages 79-104, September.
    2. Juliane Müller & Christine Shoemaker & Robert Piché, 2014. "SO-I: a surrogate model algorithm for expensive nonlinear integer programming problems including global optimization applications," Journal of Global Optimization, Springer, vol. 59(4), pages 865-889, August.
    3. Juliane Müller & Christine Shoemaker, 2014. "Influence of ensemble surrogate models and sampling strategy on the solution quality of algorithms for computationally expensive black-box global optimization problems," Journal of Global Optimization, Springer, vol. 60(2), pages 123-144, October.

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