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Purpose <p>Nonlinear Energy Sink (NES) is a vibration control device characterized by structural simplicity, strong robustness, and broadband vibration reduction capabilities. However, its state evolution under random excitation environments is challenging to effectively capture using traditional analytical methods. Currently, researchers tend to use traditional methods to explore the vibration reduction performance of NES, and there are relatively few studies that apply digital twin (DT) to the response analysis of nonlinear dynamics under random excitation.</p> Method <p>To address this, we apply a DT to the analysis of the NES system. The virtual mapping platform built with DT technology can dynamically model the behavior of physical entities, enabling real-time monitoring and predictive analysis of the main structure and NES. The constructed DT comprises three main components: (a) a data collection module; (b)a state update module that is based on the Unscented Kalman Filter (UKF), which achieves multi-time-scale state tracking; and (c) a response prediction module that is based on the Gaussian Process Regression (GPR), which learns the temporal evolution patterns of parameters. Through two case studies, the responses of a system coupled to two different types of NES under harmonic and Gaussian white noise excitation are investigated.</p> Results <p>The DT accurately tracks system states with parameter estimation accuracy exceeding 98% and effectively predicts future parameter trends, confirming its applicability and effectiveness in NES-coupled systems.</p> Conclusion <p>The proposed DT framework provides a new method for dynamic analysis of NES systems under random excitation, supporting real-time monitoring and predictive analysis of NES-coupled structures.</p>

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Digital Twin for Stochastic Systems Coupled with a Nonlinear Energy Sink Under Harmonic and Gaussian White Noise Excitation

  • 期刊论文

  • 发表日期：

    2026年2月5日

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  • Yong-Ge Yang,
  • Guan-Fang Li

摘要

Purpose

Nonlinear Energy Sink (NES) is a vibration control device characterized by structural simplicity, strong robustness, and broadband vibration reduction capabilities. However, its state evolution under random excitation environments is challenging to effectively capture using traditional analytical methods. Currently, researchers tend to use traditional methods to explore the vibration reduction performance of NES, and there are relatively few studies that apply digital twin (DT) to the response analysis of nonlinear dynamics under random excitation.

Method

To address this, we apply a DT to the analysis of the NES system. The virtual mapping platform built with DT technology can dynamically model the behavior of physical entities, enabling real-time monitoring and predictive analysis of the main structure and NES. The constructed DT comprises three main components: (a) a data collection module; (b)a state update module that is based on the Unscented Kalman Filter (UKF), which achieves multi-time-scale state tracking; and (c) a response prediction module that is based on the Gaussian Process Regression (GPR), which learns the temporal evolution patterns of parameters. Through two case studies, the responses of a system coupled to two different types of NES under harmonic and Gaussian white noise excitation are investigated.

Results

The DT accurately tracks system states with parameter estimation accuracy exceeding 98% and effectively predicts future parameter trends, confirming its applicability and effectiveness in NES-coupled systems.

Conclusion

The proposed DT framework provides a new method for dynamic analysis of NES systems under random excitation, supporting real-time monitoring and predictive analysis of NES-coupled structures.