Research on constructing surrogate model of rocket aerodynamic discipline

Liang Yu Zhao*, Shu Xing Yang, Hao Ping She

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Aiming at long calculation time when computational fluid dynamics method was used for optimization of rocket aerodynamic multidiscipline, a new method for constructing surrogate model for rocket aerodynamic discipline was put forward by means of computational fluid dynamics (CFD), experiment design and radial basis function (RBF) neural network techniques, and its flow chart was analyzed in detail. Through example analysis, the method was proven to be feasible and effective. Under the high-precision precondition, the RBF neural network surrogate modeling method can greatly reduce computational time.

Original languageEnglish
Pages (from-to)1-4+38
JournalGuti Huojian Jishu/Journal of Solid Rocket Technology
Volume30
Issue number1
Publication statusPublished - Feb 2007

Keywords

  • Multidisciplinary design optimization
  • RBF neural network
  • Rocket
  • Surrogate model

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