基于复模态分析与并行遗传算法的车辆动力参数识别

Vehicle parameter identification based on complex mode analysis and parallel genetic algorithm

关键词:  车桥耦合振动  车辆有限元模型  复模态分析  动力参数识别  多核并行计算  遗传算法

Key words:  vehicle-bridge coupling vibration  vehicle finite element model  complex modal analysis  dynamic parameter identification  multi-core parallel computing  genetic algorithm

摘要:

获取准确的车辆动力参数是车桥耦合振动分析与桥梁健康监测的前提,为此,提出了一种基于复模态分析与多核并行遗传算法的车辆动力参数快速识别方法。首先,改进了复模态理论结合有限元方法获取车辆自振频率、阻尼比、模态振型的算法；然后,提出了车辆动力参数识别的多核并行遗传算法,采用多目标适应度评价,以快速、准确地识别车辆刚度、阻尼、转动惯量；最后,采用车轮跌落振动实验和环境激励峰值法实测车辆的模态,获取用于适应度评价的自振频率、阻尼比和振型。通过对轻型汽车、重型卡车的动力参数进行识别进行验证,结果表明:用识别的车辆动力参数计算得到的车辆振动模态,与实测振动模态吻合,其中前3阶固有频率的最大误差为0.8%、阻尼比最大误差为1.3%,计算与实测振型向量之间的夹角余弦接近1；车辆的分析模型有必要增加车体的扭转阻尼,以准确反映实际车辆的扭转振动特性；多核并行算法大大加速了识别过程。16核心CPU在15核心并行时的加速比达到最大值12.5,在复杂车辆的多目标、多参数识别中,采用多核并行算法是非常有效的。

Abstract:

Accurate vehicle dynamic parameter identification is a prerequisite for vehicle-bridge coupling vibration analysis and bridge health monitoring. This study proposes a rapid identification method for the vehicle dynamic parameters based on complex modal analysis and multi-core parallel genetic algorithm. Firstly, an algorithm combining the complex modal theory with the finite element method is improved to calculate the natural frequencies, damping ratios, and modal shapes of vehicles. Subsequently, a multi-core parallel genetic algorithm for the vehicle dynamic parameter identification is proposed, in which the fitness evaluation of multiple objectives including natural frequencies, damping ratios and mode shapes is adopted, and the dynamic parameters including stiffness coefficient, damping coefficients and moments of inertia of the vehicle model can be rapidly and accurately identified. Finally, the wheel-drop-vibration experiment and ambient-excitation peak method are used in modal analysis of practical vehicle to obtain the measured natural frequencies, damping ratios and vibration modes, which are used in the fitness evaluation. The above methods have been validated by the dynamic parameters identification of the practical light car and heavy truck, and the results show that: vehicle vibration modes calculated with the identified vehicle dynamics parameters are in good agreement with the measured vibration modes, in which the maximum error of the first three natural frequency is 0.8%, the maximum error of the damping ratio is 1.3%, and the cosines of the angle between the calculated and measured vibration mode vectors are close to 1; incorporating body torsional damping is critical to accurately capture the torsional vibration characteristics of real vehicles; the multi-core parallel algorithm greatly accelerates the identification process. The acceleration ratio of 16-core CPU reaches the maximum value of 12.5 when 15 cores are in parallel. Therefore, the multi-core parallel algorithm is very effective in multi-objective and multi-parameter identification of complex vehicles.