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<p>The galloping energy harvesting technique has been widely employed to capture fluid energy by exploiting the aeroelastic instability of mechanical structures in flow fields, providing power for low-energy electronic devices.&#xa0;However, the dynamics of such systems under realistic stochastic wind fluctuations remain a critical challenge, especially when mechanical impacts are involved. To address this, this paper investigates the stochastic response and reliability of a galloping energy harvester system with mechanical impacts under Gaussian colored noise excitation. Based on the energy envelope stochastic averaging, the governing equation of the probability density function and the backward equation of the conditional reliability function are derived. The effectiveness of the proposed approach is verified through Monte Carlo numerical simulations. This study analyzes the effects of system parameters on the probability density function, mean square voltage, conditional reliability function, and mean first passage time. The results indicate that the adjustment of parameters can not only enhance the energy harvesting efficiency of the system but also improve its reliability.</p>

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Stochastic dynamics of galloping energy harvesting with mechanical impact under colored noises

  • 期刊论文

  • 发表日期：

    2026年4月9日

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  • Li Liu,
  • Huiling ma,
  • Meng Su,
  • Wei Xu,
  • Wei Wang

摘要

The galloping energy harvesting technique has been widely employed to capture fluid energy by exploiting the aeroelastic instability of mechanical structures in flow fields, providing power for low-energy electronic devices. However, the dynamics of such systems under realistic stochastic wind fluctuations remain a critical challenge, especially when mechanical impacts are involved. To address this, this paper investigates the stochastic response and reliability of a galloping energy harvester system with mechanical impacts under Gaussian colored noise excitation. Based on the energy envelope stochastic averaging, the governing equation of the probability density function and the backward equation of the conditional reliability function are derived. The effectiveness of the proposed approach is verified through Monte Carlo numerical simulations. This study analyzes the effects of system parameters on the probability density function, mean square voltage, conditional reliability function, and mean first passage time. The results indicate that the adjustment of parameters can not only enhance the energy harvesting efficiency of the system but also improve its reliability.