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Study on a Pi-type mean flow acoustic engine capable of wind energy harvesting using a CFD model

Author

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  • Yu, Yan S.W.
  • Sun, Daming
  • Zhang, Jie
  • Xu, Ya
  • Qi, Yun

Abstract

A mean flow with remarkable kinetic energy passing deep cavities excites stable acoustic oscillations at certain mean flow velocity ranges. Based on this aerodynamic effect, a mean flow acoustic engine (MFAE) can be built to convert wind energy or other fluid energy into acoustic energy. The MFAE without mechanical moving parts has potential applications in driving thermoacoustic refrigerators and transducers to provide cooling power and electrical power, respectively. With two resonators spaced on one side of the driver, a Pi-type MFAE can be built. A computational fluid dynamics model with large-eddy simulation of turbulence was used to simulate the operation performance of a Pi-type MFAE. With mean flow velocity below 62.22m/s, five acoustic modes with different pressure wave frequencies were observed in the Pi-type MFAE. Pressure amplitudes in the resonators, phase lags between two resonators, hydrodynamic vortices shedding and non-dimensional Strouhal numbers were presented. We found that the maximum pressure amplitude happens at the third acoustic mode with mean flow velocity 49.98m/s. The maximum non-dimensional pressure amplitudes at four acoustic modes were found at Strouhal number close to 0.4 indicating the first hydrodynamic mode. The Strouhal number suggests an optimal working condition to harness wind energy. Furthermore, it is found that the phase difference between the pressure waves at the front resonator and at the rear resonator of the Pi-type MFAE differs from the cross-junction MFAE. Appropriately utilizing the phase difference between two resonators could enhance the energy exploitation.

Suggested Citation

  • Yu, Yan S.W. & Sun, Daming & Zhang, Jie & Xu, Ya & Qi, Yun, 2017. "Study on a Pi-type mean flow acoustic engine capable of wind energy harvesting using a CFD model," Applied Energy, Elsevier, vol. 189(C), pages 602-612.
    • Handle: RePEc:eee:appene:v:189:y:2017:i:c:p:602-612
    DOI: 10.1016/j.apenergy.2016.12.022
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    Cited by:

    1. Liu, Tongxiang & Zhao, Qiujun & Wang, Jianzhou & Gao, Yuyang, 2021. "A novel interval forecasting system for uncertainty modeling based on multi-input multi-output theory: A case study on modern wind stations," Renewable Energy, Elsevier, vol. 163(C), pages 88-104.
    2. Ya Xu & Jiangqi Yuan & Daming Sun & Dailiang Xie, 2022. "Piezoelectric Harvesting of Fluid Kinetic Energy Based on Flow-Induced Oscillation," Energies, MDPI, vol. 15(23), pages 1-11, December.
    3. Liuyi Jiang & Hong Zhang & Qingquan Duan & Xiaoben Liu, 2021. "Numerical Simulation of Acoustic Resonance Enhancement for Mean Flow Wind Energy Harvester as Well as Suppression for Pipeline," Energies, MDPI, vol. 14(6), pages 1-17, March.
    4. Wang, Jianzhou & Niu, Tong & Lu, Haiyan & Guo, Zhenhai & Yang, Wendong & Du, Pei, 2018. "An analysis-forecast system for uncertainty modeling of wind speed: A case study of large-scale wind farms," Applied Energy, Elsevier, vol. 211(C), pages 492-512.
    5. Silva-Leon, Jorge & Cioncolini, Andrea & Nabawy, Mostafa R.A. & Revell, Alistair & Kennaugh, Andrew, 2019. "Simultaneous wind and solar energy harvesting with inverted flags," Applied Energy, Elsevier, vol. 239(C), pages 846-858.
    6. Zhou, Zhiyong & Qin, Weiyang & Zhu, Pei & Shang, Shijie, 2018. "Scavenging wind energy by a Y-shaped bi-stable energy harvester with curved wings," Energy, Elsevier, vol. 153(C), pages 400-412.

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