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A new thermal model based on polytropic numerical simulation of Stirling engines

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  • Babaelahi, Mojtaba
  • Sayyaadi, Hoseyn

Abstract

The new numerical thermal model based on polytropic expansion/compression processes was developed for predicting thermal performance of Stirling engines. In this regard, differential governing equations of early adiabatic model of Stirling engines were modified to consider polytropic heat transfers of the working fluid to the surrounding through expansion/compression cylinder walls. Therefore, adiabatic expansion/compression processes of the early adiabatic model were substituted with polytropic expansion/compression processes in the new thermal model. In order to increase accuracy of the thermal model, various loss mechanisms including effect of mass leakage from working to buffer spaces and heat leakage from expansion to compression spaces, due to thermal conductivity (shuttle heat transfer effect) of the displacer, were implemented in basic differential equations of polytropic analysis. In addition, in a similar manner to the early simple analysis, effect of non-ideal heat recovery of the regenerator and hydraulic pressure drop were considered in heater, cooler and regenerator. Also, magnitude of the piston back pressure was corrected in terms of piston velocity using the principle of finite speed thermodynamics (FST) and mechanical friction between piston and cylinder was taken into account in the new thermal model. On the other hand, longitudinal heat conduction between the heater and cooler through the regenerator wall was modeled as an additional loss mechanism. Finally, the new model called polytropic analysis of Stirling engine with various losses (PSVL) mechanisms was applied to a prototype Stirling engine, namely GPU-3 Stirling engine, and the obtained results were evaluated through comparison with previous thermal models and experimental results. Based on the new PSVL model, the output power and thermal efficiency of the GPU-3 engine were predicted with +14.34% and +3.14% (as a difference), respectively.

Suggested Citation

  • Babaelahi, Mojtaba & Sayyaadi, Hoseyn, 2015. "A new thermal model based on polytropic numerical simulation of Stirling engines," Applied Energy, Elsevier, vol. 141(C), pages 143-159.
  • Handle: RePEc:eee:appene:v:141:y:2015:i:c:p:143-159
    DOI: 10.1016/j.apenergy.2014.12.033
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    References listed on IDEAS

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    Cited by:

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    9. Patel, Vivek & Savsani, Vimal, 2016. "Multi-objective optimization of a Stirling heat engine using TS-TLBO (tutorial training and self learning inspired teaching-learning based optimization) algorithm," Energy, Elsevier, vol. 95(C), pages 528-541.
    10. Ni, Mingjiang & Shi, Bingwei & Xiao, Gang & Peng, Hao & Sultan, Umair & Wang, Shurong & Luo, Zhongyang & Cen, Kefa, 2016. "Improved Simple Analytical Model and experimental study of a 100W β-type Stirling engine," Applied Energy, Elsevier, vol. 169(C), pages 768-787.
    11. Kanbur, Baris Burak & Xiang, Liming & Dubey, Swapnil & Choo, Fook Hoong & Duan, Fei, 2018. "Finite sum based thermoeconomic and sustainable analyses of the small scale LNG cold utilized power generation systems," Applied Energy, Elsevier, vol. 220(C), pages 944-961.
    12. Luo, Zhongyang & Sultan, Umair & Ni, Mingjiang & Peng, Hao & Shi, Bingwei & Xiao, Gang, 2016. "Multi-objective optimization for GPU3 Stirling engine by combining multi-objective algorithms," Renewable Energy, Elsevier, vol. 94(C), pages 114-125.
    13. Jiang, Han & Xi, Zhongli & A. Rahman, Anas & Zhang, Xiaoqing, 2020. "Prediction of output power with artificial neural network using extended datasets for Stirling engines," Applied Energy, Elsevier, vol. 271(C).
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    15. Ahmed, Fawad & Zhu, Shunmin & Yu, Guoyao & Luo, Ercang, 2022. "A potent numerical model coupled with multi-objective NSGA-II algorithm for the optimal design of Stirling engine," Energy, Elsevier, vol. 247(C).
    16. Wang, Kai & Dubey, Swapnil & Choo, Fook Hoong & Duan, Fei, 2016. "A transient one-dimensional numerical model for kinetic Stirling engine," Applied Energy, Elsevier, vol. 183(C), pages 775-790.
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    18. Carrillo Caballero, Gaylord Enrique & Mendoza, Luis Sebastian & Martinez, Arnaldo Martin & Silva, Electo Eduardo & Melian, Vladimir Rafael & Venturini, Osvaldo José & del Olmo, Oscar Almazán, 2017. "Optimization of a Dish Stirling system working with DIR-type receiver using multi-objective techniques," Applied Energy, Elsevier, vol. 204(C), pages 271-286.
    19. Mohammadi, Mohammad Amin & Jafarian, Ali, 2018. "CFD simulation to investigate hydrodynamics of oscillating flow in a beta-type Stirling engine," Energy, Elsevier, vol. 153(C), pages 287-300.
    20. Li, Ruijie & Grosu, Lavinia & Li, Wei, 2017. "New polytropic model to predict the performance of beta and gamma type Stirling engine," Energy, Elsevier, vol. 128(C), pages 62-76.
    21. Babaelahi, Mojtaba & Sayyaadi, Hoseyn, 2016. "Analytical closed-form model for predicting the power and efficiency of Stirling engines based on a comprehensive numerical model and the genetic programming," Energy, Elsevier, vol. 98(C), pages 324-339.
    22. 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.

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