基于改进遗传算法的车身板件厚度优化

Vehicle body panel thickness optimization by a genetic algorithm

为了在车身设计阶段降低车内噪声,以HyperMesh软件建立的车身声固耦合模型为研究对象,提出一种改进的遗传算法优化车身板件厚度。采用Hammersley实验设计方法,建立白车身一阶整体模态、车身质量、车内目标点最大声压级响应面。以目标点最大声压级为性能指标,改进的遗传算法用于车身板件厚度优化。目标点声压级最大值降低4.0 dB,相对遗传算法、全局响应面法、自适应响应面法和可行方向法优化车内噪声的结果,改进的遗传算法分别提高了2.2%、2.2%、2.3%和2.5%。结果表明:对遗传算法的改进,提升了遗传算法的稳定性和寻优能力,改进的遗传算法可用于设计阶段的车身板件厚度优化。

The interior noise in a vehicle can be reduced by optimizing the body panel thickness. A genetic algorithm was used to optimize the vehicle body panel thickness based on a coupled acoustic structure model of the vehicle body in HyperMesh. The Hammersley experimental design method was used to determine the response surfaces of the first-order global modal of the body-in-white, the body mass and the maximum sound pressure of a target point inside the vehicle. The maximum sound pressure at the target point was then used as the performance index in a genetic algorithm to optimize the vehicle body panel thickness. The optimum thickness reduced the maximum peak sound pressure at the target point by 4.0 dB. This algorithm gave 2.2% lower sound levels than a standard genetic algorithm, 2.2% lower than the global response search method, 2.3% lower than the adaptive response surface method and 2.5% lower than the feasible direction method, respectively. The results show that this genetic algorithm improves the stability and optimization ability of the genetic algorithm and this algorithm can efficiently optimize the vehicle body panel thickness.