基于Kriging和NSGA-Ⅲ算法的汽车交流发电机温度场预测

Prediction of temperature field for automotive generator based on Kriging and NSGA-Ⅲ algorithms

基于克里金(Kriging)代理模型和非支配解排序遗传算法Ⅲ型(NSGA-Ⅲ),对汽车交流发电机在不同工况下各部件的温度场分布情况进行预测。首先采用流固耦合有限元计算软件,对发电机全局温度场分布进行了仿真分析,结果表明在10 000 r/min下稳定运行时,整机最高温度位于三相定子绕组上及最低温区域出现在冷却扇叶,并进行了试验校验,同时选择了合适的样本测量位置及运行工况。然后基于Pareto多目标优化理论,构建温度场预测的Kriging代理模型,并通过NSGA-Ⅲ优化算法逐代寻优,选取Mobw为子代选择评价指标,最后利用MAPE进行预测误差分析。研究表明,采用该方法预测温度场分布的最大误差为2.78%,高速、长时间运行工况的预测更加精确,误差为0.53%,所述预测算法具有较高的有效性、准确性和便捷性。

Based on the Kriging proxy model and non-dominated sorting genetic algorithm Ⅲ(NSGA-Ⅲ), the temperature field distribution of each component of the automotive alternator is predicted under different operating conditions. The simulation analysis of the global temperature field distribution of the generator was carried out by the fluid-solid coupling calculation software of finite element modeling. The results show that when running at 10 000 r/min, the maximum temperature of the machine is located on the three-phase stator winding and the lowest temperature area appears in the cooling fan blades. The experimental measurements were established and validated against the analysis with simulations and then the suitable sampling locations and operating conditions were selected for experimental measurement. With the Pareto multi-objective optimization theory, the Kriging proxy model for temperature field prediction was constructed, and the optimization algorithm of NSGA-Ⅲ was implemented for optimizing generation by generation. Mobw was referred as the evaluation index for the offspring selection and MAPE was adopted for the predicting error analysis. The results demonstrate that the maximum error of temperature field distribution by prediction with this method is 2.78%, and for the high-speed and long-running conditions, the prediction results are more accurate with the error of 0.53%. The validity, accuracy and convenience are detected in the prediction algorithm.