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Optimization and Engineering

, Volume 2, Issue 4, pp 431–452 | Cite as

Polynomial Response Surface Approximations for the Multidisciplinary Design Optimization of a High Speed Civil Transport

  • Serhat Hosder
  • Layne T. Watson
  • Bernard Grossman
  • William H. Mason
  • Hongman Kim
  • Raphael T. Haftka
  • Steven E. Cox
Article

Abstract

Surrogate functions have become an important tool in multidisciplinary design optimization to deal with noisy functions, high computational cost, and the practical difficulty of integrating legacy disciplinary computer codes. A combination of mathematical, statistical, and engineering techniques, well known in other contexts, have made polynomial surrogate functions viable for MDO. Despite the obvious limitations imposed by sparse high fidelity data in high dimensions and the locality of low order polynomial approximations, the success of the panoply of techniques based on polynomial response surface approximations for MDO shows that the implementation details are more important than the underlying approximation method (polynomial, spline, DACE, kernel regression, etc.). This paper selectively surveys some of the ancillary techniques—statistics, global search, parallel computing, variable complexity modeling—that augment the construction and use of polynomial surrogates.

global optimization multidisciplinary design parallel computing response surface 

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Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • Serhat Hosder
    • 1
  • Layne T. Watson
    • 1
  • Bernard Grossman
    • 1
  • William H. Mason
    • 1
  • Hongman Kim
    • 1
  • Raphael T. Haftka
    • 2
  • Steven E. Cox
    • 2
  1. 1.Multidisciplinary Analysis and Design Center for Advanced VehiclesVirginia Polytechnic Institute and State UniversityBlacksburgUSA
  2. 2.Department of Aerospace Engineering, Mechanics & Engineering ScienceUniversity of FloridaGainesvilleUSA

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