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An analysis of beta type Stirling engine with rhombic drive mechanism

Author

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  • Shendage, D.J.
  • Kedare, S.B.
  • Bapat, S.L.

Abstract

Stirling engine system is one of the options for electrifying a remote community not serviceable by the grid, which can operate on energy input in the form of heat. Major hurdle for the wide-spread usage of rhombic drive beta type Stirling engine is complexity of the drive and requirement of tight tolerances for its proper functioning. However, if the operating and geometrical constraints of the system are accounted for, different feasible design options can be identified. In the present work, various aspects that need to be considered at different decision making stages of the design and development of a Stirling engine are addressed. The proposed design methodology can generate and evaluate a range of possible design alternatives which can speed up the decision making process and also provide a clear understanding of the system design considerations. The present work is mainly about the design methodology for beta type Stirling engine and the optimization of phase angle, considering the effect of overlapping volume between compression and expansion spaces. It is also noticed that variation of compression space volume with phase angle remains sinusoidal for any phase difference. The aim of the present work is to find a feasible solution which should lead to a design of a single cylinder, beta type Stirling engine of 1.5kWe capacity for rural electrification.

Suggested Citation

  • Shendage, D.J. & Kedare, S.B. & Bapat, S.L., 2011. "An analysis of beta type Stirling engine with rhombic drive mechanism," Renewable Energy, Elsevier, vol. 36(1), pages 289-297.
    • Handle: RePEc:eee:renene:v:36:y:2011:i:1:p:289-297
    DOI: 10.1016/j.renene.2010.06.041
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    References listed on IDEAS

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    1. Karabulut, H. & Çınar, C. & Oztürk, E. & Yücesu, H.S., 2010. "Torque and power characteristics of a helium charged Stirling engine with a lever controlled displacer driving mechanism," Renewable Energy, Elsevier, vol. 35(1), pages 138-143.
    2. Thirugnanasambandam, Mirunalini & Iniyan, S. & Goic, Ranko, 2010. "A review of solar thermal technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(1), pages 312-322, January.
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    Cited by:

    1. Marcin Wołowicz & Piotr Kolasiński & Krzysztof Badyda, 2021. "Modern Small and Microcogeneration Systems—A Review," Energies, MDPI, vol. 14(3), pages 1-47, February.
    2. Ahmadi, Mohammad H. & Ahmadi, Mohammad Ali & Sadatsakkak, Seyed Abbas & Feidt, Michel, 2015. "Connectionist intelligent model estimates output power and torque of stirling engine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 871-883.
    3. 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.
    4. Solmaz, Hamit & Safieddin Ardebili, Seyed Mohammad & Aksoy, Fatih & Calam, Alper & Yılmaz, Emre & Arslan, Muhammed, 2020. "Optimization of the operating conditions of a beta-type rhombic drive stirling engine by using response surface method," Energy, Elsevier, vol. 198(C).
    5. Erol, Derviş & Yaman, Hayri & Doğan, Battal, 2017. "A review development of rhombic drive mechanism used in the Stirling engines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 1044-1067.
    6. Bidart, Christian & Fröhling, Magnus & Schultmann, Frank, 2014. "Livestock manure and crop residue for energy generation: Macro-assessment at a national scale," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 537-550.
    7. 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.
    8. Mohammad Hossein Ahmadi & Mohammad-Ali Ahmadi & Mehdi Mehrpooya & Marc A. Rosen, 2015. "Using GMDH Neural Networks to Model the Power and Torque of a Stirling Engine," Sustainability, MDPI, vol. 7(2), pages 1-13, February.

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