[1]方正,王大镇.高速列车单节车体空气动力特性优化[J].集美大学学报(自然版),2018,23(6):461-466.
 FANG Zheng,WANG Dazhen.Optimization of Aerodynamic Characteristicson the Unit Body of High-speed Train Based on GRNN Model and GA Algorithm[J].Journal of Jimei University,2018,23(6):461-466.
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高速列车单节车体空气动力特性优化()
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《集美大学学报(自然版)》[ISSN:1007-7405/CN:35-1186/N]

卷:
第23卷
期数:
2018年第6期
页码:
461-466
栏目:
船舶与机械工程
出版日期:
2018-11-28

文章信息/Info

Title:
Optimization of Aerodynamic Characteristicson the Unit Body of High-speed Train Based on GRNN Model and GA Algorithm
作者:
方正王大镇
(集美大学机械与能源工程学院,福建 厦门 361021)
Author(s):
FANG ZhengWANG Dazhen
(School of Mechanical and Energy Engineering,Jimei University,Xiamen 361021,China)
关键词:
高速列车单节车体空气动力特性GRNN模型GA算法
Keywords:
high speed trainunit body of trainaerodynamic characteristicsGRNN modelGA algorithm
文献标志码:
A
摘要:
针对传统高速列车单节车体空气动力特性优化方法的不足,设计了一种基于广义回归神经网络和遗传算法的单节车体空气动力特性优化方法,该方法首先利用流体动力学软件获得单节车体的实验数据,然后用广义回归神经网络对实验数据进行训练,建立优化模型,并采用遗传算法对该模型进行优化。结果表明,优化后的单节车体的结构参数能够改善列车的空气动力特性,优化后的升力、侧向力和倾覆力矩系数分别降低了11.5%、8.05%和17.5%,并且优化后的单节车体压力系数与原有单节车体相比得到了改善。
Abstract:
The paper mainly focuses on the optimization of deficiencies in the features of the traditional high-speed train,which lacks the aerodynamic characteristics for the unit body of train.Based on the generalized regression neural network and the genetic algorithm,a new approach to obtaining the experimental data of the unit body of train through fluid dynamics software was proposed,from which simultaneously establishes and trains the optimized model.By generalized regression neural network,and then,employing the particle swarm algorithm optimizes the model.The results show that the parameters determined by train structural optimization can improve the aerodynamic characteristics of the train,while the coefficients of the lift,lateral force and overturning moment after perfection have been decreased by 11.5%,8.05% and 17.5%,respectively.Compared to the original parameters of the unit body of train,the pressure coefficient was improved after optimization.
更新日期/Last Update: 2019-01-18