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 language | English |
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Pages (from-to) | 1-4+38 |
Journal | Guti Huojian Jishu/Journal of Solid Rocket Technology |
Volume | 30 |
Issue number | 1 |
Publication status | Published - Feb 2007 |
Keywords
- Multidisciplinary design optimization
- RBF neural network
- Rocket
- Surrogate model