基于神经网络的汽车空调送风格栅角度优化与乘员舱降温性能改进  

Neural Network-Based Optimization of Air Conditioning Supply Grille and Cabin Cooling Performance in Vehicles

作　　者：冯英 祁祺 李小华[1] 黄晖[1] 余显忠[1] FENG Ying;QI Qi;LI XiaoHua;HUANG Hui;YU Xianzhong(R&D Center,Jiangling Motors Co.,Ltd.,Nanchang 330200,China)

机构地区：[1]江铃汽车股份有限公司产品研发总院,南昌330200

出　　处：《汽车工程学报》2025年第2期177-186,共10页Chinese Journal of Automotive Engineering

基　　金：国家重点研发计划项目(2022YFB2503505):电动载货车多材料底盘结构轻量化关键技术开发。

摘　　要：围绕汽车乘员舱降温性能下的吹脸速度指标,针对格栅风口叶片位置参数,搭建了同时考虑主驾驶侧吹脸流速性能和副驾驶侧吹脸流速性能的多目标优化自动设计流程。基于CFD仿真和多学科优化设计平台,采用拉丁超立方采样方法设计样本点,获取DOE矩阵,利用神经网络建立代理模型,进行吹脸流速性能参数预测,使用第三代非支配性排序遗传算法(NSGA-Ⅲ)获得多目标优化问题的帕累托前沿图。优化后的格栅叶片位置最高能使主驾驶侧呼吸面速度提升109.1%,副驾驶侧呼吸面速度提升137.5%。对优化前后的方案进行温度场瞬态仿真和乘员降温性能试验,验证了其优化结果的可靠性。A multi-objective optimized automatic design process is developed based on the airflow velocity required for cooling performance in the passenger cabin.This process considers the airflow performance on the driver's side and the passenger's side,focusing on the positioning of the grille vent blades.Based on CFD simulations and a multi-disciplinary optimization design platform,the Latin hypercube sampling method is used to generate sample points and construct the DOE matrix.A neural network-based proxy model is then built to predict the blow-face airflow velocity performance parameters.The NSGA-Ⅲalgorithm is used to obtain the Pareto frontier diagram for the multi-objective optimization problem.The optimized grille blade position increases the airflow speed by 109.1%on the driver's side and by 137.5%on the front passenger's side.The reliability of the optimization results is verified through unsteady CFD simulations and cooling performance tests before and after the optimization.

关 键 词：汽车空调 降温性能 多目标优化 神经网络代理模型 

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]