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Parametric study of gamma-type free piston stirling engine using nonlinear thermodynamic-dynamic coupled model

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  • Ye, Wenlian
  • Zhang, Ting
  • Wang, Xiaojun
  • Liu, Yingwen
  • Chen, Pengfan

Abstract

A reliable thermodynamic-dynamic coupled model for a gamma-type FPSE possessing nonlinear load damping coefficient has been proposed in this study. Simultaneously, the experimental results are compared with the model and a satisfactory agreement is obtained. The output power is predicted with less than 5% error under different hot-end temperatures. Then the amplitude of displacer and piston with time at different pistons’ positions are obtained, and the effects of load damping and nonlinear coefficients on the amplitude values of two pistons and output power are presented. Finally, a simulation study is carried out to investigate the sensitivity of thermodynamic-dynamic parameters to the amplitude of two pistons, operating frequency and output power, and the fitting formulas for evaluating the performance of FPSE are presented. It is found that the initial positions of two pistons have little effect on pistons’ amplitude in the FPSE nonlinear system. High charge pressure, hot-end temperature, and spring stiffness of the displacer can increase the output power and amplitude of the pistons while other parameters have a negative effect on them. The fitting equations have high accuracy. This work provides a reliable model and theoretical guidance for improving the FPSE’s performance.

Suggested Citation

  • Ye, Wenlian & Zhang, Ting & Wang, Xiaojun & Liu, Yingwen & Chen, Pengfan, 2020. "Parametric study of gamma-type free piston stirling engine using nonlinear thermodynamic-dynamic coupled model," Energy, Elsevier, vol. 211(C).
    • Handle: RePEc:eee:energy:v:211:y:2020:i:c:s0360544220315668
    DOI: 10.1016/j.energy.2020.118458
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    References listed on IDEAS

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    1. Ahmadi, Mohammad H. & Ahmadi, Mohammad-Ali & Pourfayaz, Fathollah, 2017. "Thermal models for analysis of performance of Stirling engine: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 168-184.
    2. Ye, Wenlian & Wang, Xiaojun & Liu, Yingwen, 2020. "Application of artificial neural network for predicting the dynamic performance of a free piston Stirling engine," Energy, Elsevier, vol. 194(C).
    3. Mou, Jian & Hong, Guotong, 2017. "Startup mechanism and power distribution of free piston Stirling engine," Energy, Elsevier, vol. 123(C), pages 655-663.
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