基于RBF神经网络代理模型的装载机空调送风参数优化  

Optimization of air supply parameters of loader air conditioner based on RBF neural network agent model

作　　者：张喜清[1] 孙世成 王亚龙 ZHANG Xiqing;SUN Shicheng;WANG Yalong(School of Mechanical Engineering,Taiyuan University of Science and Technology,Taiyuan 030024,Shanxi,China)

机构地区：[1]太原科技大学机械工程学院,山西太原030024

出　　处：《中国工程机械学报》2023年第1期16-21,共6页Chinese Journal of Construction Machinery

基　　金：山西省科技平台项目(201805D121006);山西省研究生教育改革研究课题(2019JG169)。

摘　　要：装载机在夏季高温环境中作业时,为提高其空调降温效果,故以驾驶室内座椅区域的热流量和平衡温度为优化目标,对送风速度、温度、角度3个空调送风参数进行优化研究。首先,对装载机驾驶室内部流场进行分析,利用Isight优化设计平台集成Fluent;其次,选用最优拉丁超立方设计获取样本点,采用径向基函数(RBF)神经网络代理模型;最后,结合遗传算法对装载机空调送风参数进行多目标优化,通过实验对优化结果进行验证。结果表明:优化后装载机驾驶室内座椅区域的热流量增加了55.03 W,平衡温度略有降低,驾驶室内获得较好的气流组织,散热效果明显改善。When the loader operates in the summer high temperature environment,in order to improve the cooling effect of its air conditioning,the heat flow and balance temperature of the cab seat area are optimized and studied,and the three air supply parameters of air supply speed,temperature and angle are optimized and studied. In this paper,the internal flow field of the loader cab is analyzed,the Isight optimization design platform is used to integrate Fluent,the optimal Latin hypercube design is selected to obtain sample points,the radial basis function(RBF) neural network agent model is used,and the multi-objective optimization of the air supply parameters of the loader air conditioner is combined with the genetic algorithm,and the optimization results are verified by experiments. The optimization results show that the heat flow in the seat area of the loader cab after optimization is increased by 55. 03 W,the balance temperature is slightly reduced,the airflow organization in the cab is better,and the heat dissipation effect is significantly improved.

关 键 词：装载机驾驶室 送风参数 多目标优化 代理模型 遗传算法 

分 类 号：TH138[机械工程—机械制造及自动化] U469.5[机械工程—车辆工程]