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Optimization of an auxetic jounce bumper based on Gaussian process metamodel and series hybrid GA-SQP algorithm

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Abstract

Jounce bumpers are indispensable components in vehicle suspension systems. It can attenuate impact energy and improve the ride comfort performances during transient pulse impacts from roads. However, the traditional jounce bumpers cannot achieve ideal mechanical performance and are difficult for parametric optimization due to its various shapes which are hard for parameterization. In this paper, a cylinder auxetic structure was introduced and applied as the suspension jounce bumper. Auxetic jounce bumper can be primarily defined by several variables. With tunable structure parameters, a large region of mechanical performances can be realized. In order to evaluate and optimize the performance of auxetic jounce bumpers, they were assembled into vehicle models to conduct virtual ride comfort tests under bump road. The maximum vertical acceleration of vehicle was defined as the objective function of the optimization problem of auxetic jounce bumper. On the purpose of improving optimization efficiency, Gaussian process regression method was employed to establish the metamodel of auxetic jounce bumper based on the sample set created by orthogonal design of experiments method. A series hybrid GA-SQP (Genetic algorithm-Sequential quadratic programming) algorithm, provided with both global and fast local searching abilities, was used for the optimization of auxetic jounce bumper. For comparison, GA algorithm was also applied for auxetic jounce bumper optimization alone. It is proved that the proposed optimization process is accurate and can improve optimization efficiency. Then the optimized design through hybrid algorithm was compared with original design. The maximum vertical acceleration of vehicle when travelling through bump was reduced with optimized design, and the vehicle ride comfort performance was improved.

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Acknowledgements

This research presented within this article is supported by the Natural Science Foundation of Jiangsu Province (Grant No. BK20170784) and Aeronautical Science Foundation of China (Granted No. 2016ZA52003).

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Authors and Affiliations

  1. College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, People’s Republic of China

    Yuanlong Wang, Wanzhong Zhao, Guan Zhou & Chunyan Wang

  2. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, People’s Republic of China

    Qiang Gao

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  1. Yuanlong Wang
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  2. Wanzhong Zhao
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  3. Guan Zhou
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  4. Qiang Gao
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  5. Chunyan Wang
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Correspondence to Wanzhong Zhao.

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Wang, Y., Zhao, W., Zhou, G. et al. Optimization of an auxetic jounce bumper based on Gaussian process metamodel and series hybrid GA-SQP algorithm. Struct Multidisc Optim 57, 2515–2525 (2018). https://doi.org/10.1007/s00158-017-1869-z

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  • DOI: https://doi.org/10.1007/s00158-017-1869-z

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