Multi-objective optimal design of magnetorheological engine mount based on an improved non-dominated sorting genetic algorithm

Ling Zheng; Xuwei Duan; Zhaoxue Deng; Yinong Li

doi:10.1117/12.2044974

10 March 2014 Multi-objective optimal design of magnetorheological engine mount based on an improved non-dominated sorting genetic algorithm

Ling Zheng, Xuwei Duan, Zhaoxue Deng, Yinong Li

Author Affiliations +

Proceedings Volume 9057, Active and Passive Smart Structures and Integrated Systems 2014; 90572N (2014) https://doi.org/10.1117/12.2044974
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2014, San Diego, California, United States

Abstract

A novel flow-mode magneto-rheological (MR) engine mount integrated a diaphragm de-coupler and the spoiler plate is designed and developed to isolate engine and the transmission from the chassis in a wide frequency range and overcome the stiffness in high frequency. A lumped parameter model of the MR engine mount in single degree of freedom system is further developed based on bond graph method to predict the performance of the MR engine mount accurately. The optimization mathematical model is established to minimize the total of force transmissibility over several frequency ranges addressed. In this mathematical model, the lumped parameters are considered as design variables. The maximum of force transmissibility and the corresponding frequency in low frequency range as well as individual lumped parameter are limited as constraints. The multiple interval sensitivity analysis method is developed to select the optimized variables and improve the efficiency of optimization process. An improved non-dominated sorting genetic algorithm (NSGA-II) is used to solve the multi-objective optimization problem. The synthesized distance between the individual in Pareto set and the individual in possible set in engineering is defined and calculated. A set of real design parameters is thus obtained by the internal relationship between the optimal lumped parameters and practical design parameters for the MR engine mount. The program flowchart for the improved non-dominated sorting genetic algorithm (NSGA-II) is given. The obtained results demonstrate the effectiveness of the proposed optimization approach in minimizing the total of force transmissibility over several frequency ranges addressed.

Citation Download Citation

Ling Zheng, Xuwei Duan, Zhaoxue Deng, and Yinong Li "Multi-objective optimal design of magnetorheological engine mount based on an improved non-dominated sorting genetic algorithm", Proc. SPIE 9057, Active and Passive Smart Structures and Integrated Systems 2014, 90572N (10 March 2014); https://doi.org/10.1117/12.2044974

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