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Numerical analysis and optimal design for new automotive door sealing with variable cross-section

机译:新型变截面汽车门密封件的数值分析与优化设计

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Automotive door sealing system isolates passenger compartment from water, dust and wind noise. It has the most direct influences on door-closing performance, which is determined by cross-section design in terms of its appropriate Compression Load Deflection (CLD) property. Traditional sealing structure has uniform geometrical cross-section. It has the shortcomings of bad fitting in corner parts with large curvatures, causing inaccurate door-closing effort design. Regarding the door panel's complex 3D profile, numerical analysis and optimal design for new sealing with variable cross-section are developed in this paper. Firstly, the whole sealing is partitioned into several parts. For four nearly straight segments, conventional 2D numerical analysis can still be used to obtain desired geometrical configuration. For other four curved corner parts with large curvatures, 3D numerical analysis of door closing is applied. Secondly, 2D geometrical cross-section optimization is proposed. Instead of three variables in previous research, five variables are selected for featuring cross-section geometry and used for next CAD reconstruction with more precision. After comparison between Back Propagation (BP) neural network and the Kriging surrogate model, BP neural network which performs better and efficient in this automotive design optimization field is applied for extracting nonlinear mapping between five cross-section parameters and compression load, which were parallely optimized by Genetic Algorithm (GA) and its efficiency and accuracy are compared with another evolutionary algorithm of Particle Swarm Optimization (PSO). Thirdly, 3D numerical modeling of four curved corner parts' closing process is realized, of which twisting and bending effects during seal assembly are taken into account, thus minimizing theoretical error and producing more realistic solution. Consequently, the desired geometrical configurations for both straight parts and corner parts satisfying designated CLD property can be obtained and the whole sealing can be achieved with variable cross-section, resulting in an ideal door closing effort. Finally, a Matlab-based platform has been developed to assist the design and optimization process. Experiment and case study indicates that it provides an effective method for new door sealing design with variable cross-section.
机译:汽车门密封系统可将车厢与水,灰尘和风噪声隔离。它对关门性能具有最直接的影响,这由横截面设计决定,取决于其适当的压缩载荷挠度(CLD)特性。传统的密封结构具有均匀的几何横截面。它具有曲率大的角部零件装配不当的缺点,导致关闭门力度设计不正确。针对门板的复杂3D轮廓,本文针对可变截面的新型密封件进行了数值分析和优化设计。首先,将整个密封分为几个部分。对于四个几乎笔直的线段,仍然可以使用常规的2D数值分析来获得所需的几何形状。对于其他四个曲率较大的弯曲拐角零件,应用了3D数值分析的关门方法。其次,提出了二维几何截面优化方法。代替了先前研究中的三个变量,选择了五个变量来表征横截面几何形状,并以更高的精度用于下一个CAD重建。在将反向传播(BP)神经网络和Kriging替代模型进行比较之后,将在该汽车设计优化领域中表现更好且效率更高的BP神经网络用于提取五个横截面参数和压缩载荷之间的非线性映射,并对其进行了并行优化。将遗传算法(GA)的算法与效率和准确性进行了比较,并与另一种粒子群优化算法(PSO)进行了比较。第三,实现了四个弯角零件闭合过程的3D数值建模,其中考虑了密封件装配过程中的扭曲和弯曲效应,从而使理论误差最小化,并产生了更现实的解决方案。因此,可以获得满足指定的CLD特性的直线部分和拐角部分的期望几何构造,并且可以以可变的横截面实现整个密封,从而实现理想的门关闭效果。最后,开发了一个基于Matlab的平台来辅助设计和优化过程。实验和案例研究表明,它为变截面新门密封设计提供了一种有效的方法。

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