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Optimal design for torsional vibration suppression of non-smooth NES

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Abstract

Nonlinear energy sinks (NESs) have received increasing attention for their ability to passively inhibit a large amount of vibration energy over a wide range of frequencies. However, although many studies have focused on dynamics of NES, few have addressed the optimal design method of NES, especially for the non-smooth NES (NSNES). Therefore, the parameter optimization method for an NSNES which can be applied to supress torsional vibration of rotor system was developed. First, the design variables were reduced by using piece-wise linear torsional stiffness to equivalently fit cubic torsional stiffness; then, taking the torsional vibration of single-disk rotor system as an example, the genetic algorithm (GA) was used to solve the optimization problem of the NSNES in torsional vibration suppression of rotor system. Finally, the optimized NSNES was verified by numerical simulation and experiment to suppress the torsional vibration of a rotor system. The results show that the optimally designed NSNES can effectively suppress torsional vibration of a rotor system. In transient vibration suppression, the optimal percentage of accumulated energy dissipation of NSNES can reach 95.3 %. For steady-state vibration suppression, the peak vibration suppression of NSNES can reach 82.2 % in the simulation and 81.9 % in the test.

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Acknowledgments

The authors gratefully acknowledge the National Natural Science Foundation of China (Grant No. 52075084) and the Equipment Pre-Research Foundation of China (Grant No. 50910050302) for the financial support for this study.

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Correspondence to Hongliang Yao.

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Yanbo Cao received the Master in Mechanical Engineering from Northeastern University, Shenyang, China, in 2019. He is currently pursuing the Ph.D. in Mechanical Design and Theory at Northeastern University. His research interest is mechanical dynamics.

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Cao, Y., Yao, H., Dou, J. et al. Optimal design for torsional vibration suppression of non-smooth NES. J Mech Sci Technol 36, 5399–5412 (2022). https://doi.org/10.1007/s12206-022-1006-9

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