Robust design of supercritical wing aerodynamic optimization considering fuselage interfering

Huang Jiangtao, Gao Zhenghong, Zhao Ke, Bai Junqiang

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

Abstract Robust optimization approach for aerodynamic design has been developed and applied to supercritical wing aerodynamic design. The aerodynamic robust optimization design system consists of genetic optimization algorithm, improved back propagation (BP) neural network and deformation grid technology. In this article, the BP neural network has been improved in two major aspects to enhance the training speed and precision. Uniformity sampling is adopted to generate samples which will be used to establish surrogate model. The testing results show that the prediction precision of the improved BP neural network is reliable. On the assumption that the law of Mach number obeys normal distribution, supercritical wing configuration considering fuselage interfering of a certain aerobus has been taken as a typical example, and five design sections and twist angles have been optimized. The results show that the optimized wing, which considers robust design, has better aerodynamic characteristics. What's more, the intensity of shock wave has been reduced.

Original languageEnglish
Pages (from-to)523-528
Number of pages6
JournalChinese Journal of Aeronautics
Volume23
Issue number5
DOIs
StatePublished - Oct 2010

Keywords

  • aircraft design
  • BP neural network
  • genetic algorithm
  • grid deformation
  • normal distribution
  • robust design

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