The water contamination on the side windows of moving vehicles is a crucial issue in improving the driving safety and the comfort. In this paper, an effective optimization method is proposed to reduce the water contamination on the side windows of automobiles. The accuracy and the efficiency of the numerical simulation are improved by using the lattice Boltzmann method, and the Lagrangian particle tracking method. Optimized parameters are constructed on the basis of the occurrence of the water deposition on a vehicle’s side window. The water contamination area of the side window and the aerodynamic drag are considered simultaneously in the design process; these two factors are used to form the multi- objective optimization function in the genetic algorithm (GA) method. The approximate model, the boundary-seeded domain method, and the GA method are combined in this study to enhance the optimization efficiency. After optimization, the optimal parameters for the A-pillar section are determined by setting the boundary to an area of W = 7.77 mm, L = 1.27 mm and H =11.22 mm. The side window’s soiling area in the optimized model is reduced by 66.93%, and the aerodynamic drag is increased by 0.41% only, as compared with the original model. It is shown that the optimization method can effectively solve the water contamination problem of side windows.

在提高行驶安全性和舒适性方面，行驶中的车辆的侧窗玻璃上的水污染是至关重要的问题。本文提出了一种有效的优化方法，以减少汽车侧窗上的水污染。通过使用格子Boltzmann方法和拉格朗日粒子跟踪方法，可以提高数值模拟的准确性和效率。根据车辆侧窗上积水的情况构造优化参数。在设计过程中同时考虑了侧窗的水污染区域和空气阻力。这两个因素在遗传算法（GA）方法中用于形成多目标优化函数。近似模型，边界种子域方法，结合遗传算法和遗传算法提高了优化效率。优化后，通过将边界设置为以下区域来确定A柱截面的最佳参数：W = 7.77毫米，L = 1.27毫米，H = 11.22毫米。与原始模型相比，优化模型中的侧窗污染面积减少了66.93％，空气阻力仅增加了0.41％。结果表明，该优化方法可以有效解决侧窗水污染问题。