Journal of Zhejiang University SCIENCE A 2017 Vol.18 No.5 P.393-412

http://doi.org/10.1631/jzus.A1600124


Numerical model and multi-objective optimization analysis of vehicle vibration


Author(s):  Peng Guo, Jun-hong Zhang

Affiliation(s):  1. State Key Laboratory of Engine, Tianjin University, Tianjin 300072, China more

Corresponding email(s):   pengguo@tju.edu.cn, zhangjh@tju.edu.cn

Key Words:  Vehicle model, Hamming method, Runge-Kutta method, Design of experiment, Multi-objective optimization


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Peng Guo, Jun-hong Zhang. Numerical model and multi-objective optimization analysis of vehicle vibration[J]. Journal of Zhejiang University Science A, 2017, 18(5): 393-412.

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author="Peng Guo, Jun-hong Zhang",
journal="Journal of Zhejiang University Science A",
volume="18",
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pages="393-412",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1600124"
}

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T1 - Numerical model and multi-objective optimization analysis of vehicle vibration
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J0 - Journal of Zhejiang University Science A
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DOI - 10.1631/jzus.A1600124


Abstract: 
It is crucial to conduct a study of vehicle ride comfort using a suitable physical model, and a precise and effective problem-solving method is necessary to describe possible engineering problems to obtain the best analysis of vehicle vibration based on the numerical model. This study establishes different types of vehicle models with different degrees of freedom (DOFs) that use different types of numerical methods. It is shown that results calculated using the Hamming and runge-Kutta methods are nearly the same when the system has a small number of DOFs. However, when the number is larger, the hamming method is more stable than other methods. The hamming method is multi-step, with four orders of precision. The research results show that this method can solve the vehicle vibration problem. Orthogonal experiments and multi-objective optimization are introduced to analyze and optimize the vibration of the vehicle, and the effects of the parameters on the dynamic characteristics are investigated. The solution F1 (vertical acceleration root mean square of the vehicle) reduces by 0.0352 m/s2, which is an improvement of 7.22%, and the solution F2 (dynamic load coefficient of the tire) reduces by 0.0225, which is an improvement of 6.82% after optimization. The study provides guidance for the analysis of vehicle ride comfort.

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Full Text:   <5322>

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CLC number: TH133.31

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2017-04-11

Cited: 1

Clicked: 6716

Citations:  Bibtex RefMan EndNote GB/T7714

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