基于遗传算法的带内流低阻车身气动优化

通过建立18个参数的参数化模型,并开发了基于遗传算法的全局优化方法,展开带内流的车身气动优化,获得了气动阻力系数为0.261的低阻优化外形.比较最优车身的仿真和试验结果发现,气动阻力系数仅相差4%,表面压力系数和不同截面速度分布趋势相同、量值相差较小,表明所采用数值仿真方法是正确、可行的.利用本征正交分解对车身尾部截面流场进行能量分解发现,前9阶模态占总能量的54.5%;能量占比最高的1阶模态呈现出尾部拖曳涡的形态,并且拖曳涡的涡核位置不随时间变化而变化.建立了带内流的全局优化方法,获得了经试验验证的带内流低阻车身,为相关产品开发提供借鉴方法和外形参考.

Aerodynamic Optimization of Low drag Vehicle with Internal Flow Based on Genetic Algorithm

A parametric model of 18 parameters was established and a global optimization method based on genetic algorithm was developed. The aerodynamic optimization of the vehicle body with internal flow was carried out and a low-drag optimization shape with aerodynamic drag coefficient of 0.261 was obtained. Comparing the results of numerical simulation and test, it is found that the difference in aerodynamic drag coefficient is only 4%. The surface pressure coefficient and different cross-section velocity contours have the same distribution and small difference of magnitude, which indicates that the numerical simulation method is correct and feasible. Through the energy decomposition of the flow field in the tail section using the Proper Orthogonal Decomposition, it can be observed that the first nine modes occupy 54.5% of the total energy. Among them, the first-order mode with the highest energy shows the shape of the tail drag vortex and its vortex position does not change with time. In this paper, the global optimization method of the vehicle with internal flow was established, and the low-drag model with internal flow was validated by test, which can provide a method and reference for the development of related products.