Parameter influence law analysis and optimal design of a dual mass flywheel

IF 3.4 Q1 ENGINEERING, MECHANICAL 国际机械系统动力学学报(英文) Pub Date : 2022-07-20 DOI:10.1002/msd2.12046

Guangqiang Wu, Guoqiang Zhao

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引用次数: 8

Abstract

The influence of the dynamic parameters of a dual mass flywheel (DMF) on its vibration reduction performance is analyzed, and several optimization algorithms are used to carry out multiobjective DMF optimization design. First, the vehicle powertrain system is modeled according to the parameter configuration of the test vehicle. The accuracy of the model is verified by comparing the simulation data with the test results. Then, the model is used to analyze the influence of the moment of inertia ratio, torsional stiffness, and damping in reducing DMF vibration. The speed fluctuation amplitude at the transmission input shaft and the natural frequency of the vehicle are taken as the optimization objectives. The passive selection method, multiobjective particle swarm optimization, and the nondominated sorting genetic algorithm based on an elite strategy are used to carry out DMF multiobjective optimization design. The advantages and disadvantages of these algorithms are evaluated, and the best optimization algorithm is selected.

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双质量飞轮参数影响规律分析及优化设计

分析了双质量飞轮(DMF)动力学参数对其减振性能的影响，并采用几种优化算法进行了多目标DMF优化设计。首先，根据试验车辆的参数配置，对整车动力总成系统进行建模。通过仿真数据与试验结果的对比，验证了模型的准确性。然后，利用该模型分析了转动惯量比、扭转刚度和阻尼对DMF减振的影响。以变速器输入轴处的速度波动幅值和车辆固有频率为优化目标。采用被动选择法、多目标粒子群算法和基于精英策略的非支配排序遗传算法进行DMF多目标优化设计。对这些算法的优缺点进行了评价，并选择了最佳的优化算法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

国际机械系统动力学学报(英文)

CiteScore

3.50

自引率

0.00%

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