Aerodynamic Optimization Design of Compressor Cascade Based on Parallel Multi-Objective Genetic Algorithm and Artificial Neural Network

被引：1
|
作者：
Chen Li-hai [1 ]
Yang Qing-zhen [1 ]
Cui Jin-hui [1 ]
机构：
[1] NW Polytech Univ, Sch Power & Energy, Xian 710072, Peoples R China
来源：
APPLIED MECHANICS AND MECHANICAL ENGINEERING II, PTS 1 AND 2 | 2012年 / 138-139卷
关键词：
Compressor; cascade; multi-objective genetic algorithm(MGA); artificial neural network(ANN); aerodynamic optimization design;
D O I：
10.4028/www.scientific.net/AMM.138-139.534
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
Genetic algorithm(GA) is improved with fast non-dominated sort approach and crowded comparison operator. A new algorithm called parallel multi-objective genetic algorithm(PMGA) is developed with the support of Massage Passing Interface (MPI). Then, PMGA is combined with Artificial Neural Network (ANN) to improve the optimization efficiency. Training samples of the ANN are evaluated based on the two-dimensional Navier-Stokes equation solver of cascade. To demonstrate the feasibility of the hybrid algorithm, an optimization of a controllable diffusion cascade is performed. The optimization results show that the present method is efficient and trustiness.
引用
收藏
页码：534 / 539
页数：6
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