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基于代理模型的二元收扩喷管流道型面优化设计
引用本文:施小娟,吉洪湖.基于代理模型的二元收扩喷管流道型面优化设计[J].航空动力学报,2016,31(9):2124-2131.
作者姓名:施小娟  吉洪湖
作者单位:南京航空航天大学 能源与动力学院 江苏省航空动力系统重点实验室, 南京 210016
摘    要:在二元收扩(2D-CD)喷管设计参数对喷管气动性能影响研究的基础上,以获得尽可能高的喷管推力系数为目标,以喉道宽高比、喉道型面半径比、收敛半角和扩张半角为设计变量,对二元收扩喷管的流道型面进行了优化设计。设计过程中,利用正交试验设计方法确定初始样本点,建立喷管推力系数与设计参数间的Kriging代理模型,采用自适应模拟退火算法(ASA)对代理模型进行分析求解。结果表明,二元收扩喷管的优化型面参数为:喉道宽高比为6,喉道型面半径比为0.3,收敛半角为15°,扩张半角为5.64°,此时最大推力系数为0.97847,流量系数为0.98778. 

关 键 词:二元收扩喷管    优化设计    代理模型    自适应模拟退火算法    设计参数
收稿时间:2014/12/2 0:00:00

Optimization design of two dimensional convergent and divergent nozzles' profile based on surrogate models
SHI Xiao-juan and JI Hong-hu.Optimization design of two dimensional convergent and divergent nozzles'' profile based on surrogate models[J].Journal of Aerospace Power,2016,31(9):2124-2131.
Authors:SHI Xiao-juan and JI Hong-hu
Institution:Jiangsu Province Key Laboratory of Aerospace Power System, College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Abstract:Based on the influence research of design parameters on the aerodynamic performance of two dimensional convergent and divergent (2D-CD) nozzle, the optimization design of 2D-CD nozzles'' profile was conducted. The goal of optimization design was to get thrust coefficient as high as possible. The design variables of optimization design were throat aspect ratio, throat radius ratio, convergent half angle, divergent half angle. In the design process, the initial sample points were selected using the orthogonal experiment design method, then the Kriging surrogate model of thrust coefficient for design parameters was established. The analysis and optimization was conducted with the Adaptive Simulated Annealing Algorithm (ASA). The results show that when the throat aspect ratio is 6, throat radius ratio is 0.3, convergent half angle is 15°, divergent half angle is 5.64°, the maximum thrust coefficient of the 2D-CD nozzle is 0.97847, and the flow coefficient is 0.98778.
Keywords:two dimensional convergent and divergent nozzle  optimization design  surrogate model  adaptive simulated annealing algorithm  design parameters
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