A state-of-the-art review on the dynamic design of nonlinear energy sinks

Nonlinear energy sink (NES) is a type of vibration absorbers that does not have linear stiffness. Through establishing strongly nonlinear coupling between a primary system and a NES, the targeted energy transfer can be achieved from the primary system to the NES, thereby realizing vibration absorption in a wide frequency range. A significant amount of research work has been conducted on developing the NES for unidirectional vibration energy transfer over the last decade. More research is expected to develop further NESs to address various engineering vibration problems. Meanwhile, the question of whether NES can be practically applied to engineering is always being asked. The main objective of this paper is to review the research progress on dynamic design of NESs to promote the application of NESs to reduce engineering structure vibration. To do so, this paper first summarizes the characteristics of NESs, including vibration absorption mechanism, the threshold of targeted energy transfer, and strong nonlinearity characteristics. Then, dynamic designs of the NESs proposed in the literation are reviewed in terms of nonlinear stiffness design, mass design and damping design. Special attention is placed on the nonlinear stiffness design for NESs, including design principle, multi-stability design, track design, and magnetic design. The gaps between these design approaches and applications are explained. NES cells and their distributed vibration control strategy are also introduced. The research progress on the NES optimization design is also briefly discussed. Following the extensive review on NES dynamic design research, future promising research topics are recommended with an attempt to advancing the engineering application of NES. It is expected that this paper would help readers to understand the progress of NES dynamic design research and the future NES development for more practical applications. © 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.