IDEAS home Printed from https://ideas.repec.org/a/sae/engenv/v30y2019i1p156-175.html
   My bibliography  Save this article

Optimization of the thermal lag Stirling engine performance

Author

Listed:
  • Mojtaba Alborzi
  • Faramarz Sarhaddi
  • Fatemeh Sobhnamayan

Abstract

In this paper, neural network and genetic algorithm is used to obtain the optimal output power of thermal lag Stirling engine. A neural network is trained and developed using the theoretical data of previous literatures in order to predict the performance of Stirling engine. Input parameters to neural network include angular velocity, thermal resistance, stroke length radius, piston diameter, the volume of heat buffer chamber and the volume of gas chamber, and output parameter includes output power. The accuracy of neural network is evaluated by average square error and regression analysis. Also, genetic algorithm is used for the optimization of the output power of the Stirling engine. The results of present study show that the neural network can be used as an appreciate tool to predict the output power of the thermal lag Stirling engine with a high precision and speed. The main deficiency of thermal lag type of Stirling engines is low output power. However, the optimization of design parameters of thermal lag Stirling engine causes an increase of 86.9% in output power.

Suggested Citation

  • Mojtaba Alborzi & Faramarz Sarhaddi & Fatemeh Sobhnamayan, 2019. "Optimization of the thermal lag Stirling engine performance," Energy & Environment, , vol. 30(1), pages 156-175, February.
    • Handle: RePEc:sae:engenv:v:30:y:2019:i:1:p:156-175
    DOI: 10.1177/0958305X18787307
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0958305X18787307
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0958305X18787307?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Chieh-Li Chen & Chia-En Ho & Her-Terng Yau, 2012. "Performance Analysis and Optimization of a Solar Powered Stirling Engine with Heat Transfer Considerations," Energies, MDPI, vol. 5(9), pages 1-13, September.
    2. Kaushik, S.C & Kumar, S, 2000. "Finite time thermodynamic analysis of endoreversible Stirling heat engine with regenerative losses," Energy, Elsevier, vol. 25(10), pages 989-1003.
    3. Cheng, Chin-Hsiang & Yang, Hang-Suin & Jhou, Bing-Yi & Chen, Yi-Cheng & Wang, Yu-Jen, 2013. "Dynamic simulation of thermal-lag Stirling engines," Applied Energy, Elsevier, vol. 108(C), pages 466-476.
    4. Timoumi, Youssef & Tlili, Iskander & Ben Nasrallah, Sassi, 2008. "Design and performance optimization of GPU-3 Stirling engines," Energy, Elsevier, vol. 33(7), pages 1100-1114.
    5. Hooshang, M. & Askari Moghadam, R. & Alizadeh Nia, S. & Masouleh, M. Tale, 2015. "Optimization of Stirling engine design parameters using neural networks," Renewable Energy, Elsevier, vol. 74(C), pages 855-866.
    6. Cheng, Chin-Hsiang & Yang, Hang-Suin, 2013. "Theoretical model for predicting thermodynamic behavior of thermal-lag Stirling engine," Energy, Elsevier, vol. 49(C), pages 218-228.
    7. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    8. Bert, Juliette & Chrenko, Daniela & Sophy, Tonino & Le Moyne, Luis & Sirot, Frédéric, 2014. "Simulation, experimental validation and kinematic optimization of a Stirling engine using air and helium," Energy, Elsevier, vol. 78(C), pages 701-712.
    9. Cheng, Chin-Hsiang & Yu, Ying-Ju, 2010. "Numerical model for predicting thermodynamic cycle and thermal efficiency of a beta-type Stirling engine with rhombic-drive mechanism," Renewable Energy, Elsevier, vol. 35(11), pages 2590-2601.
    10. Timoumi, Youssef & Tlili, Iskander & Ben Nasrallah, Sassi, 2008. "Performance optimization of Stirling engines," Renewable Energy, Elsevier, vol. 33(9), pages 2134-2144.
    11. Tavakolpour-Saleh, A.R. & Jokar, H., 2016. "Neural network-based control of an intelligent solar Stirling pump," Energy, Elsevier, vol. 94(C), pages 508-523.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    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. Marion, Michaël & Louahlia, Hasna & Gualous, Hamid, 2016. "Performances of a CHP Stirling system fuelled with glycerol," Renewable Energy, Elsevier, vol. 86(C), pages 182-191.
    5. Cheng, Chin-Hsiang & Yang, Hang-Suin, 2011. "Analytical model for predicting the effect of operating speed on shaft power output of Stirling engines," Energy, Elsevier, vol. 36(10), pages 5899-5908.
    6. Zare, Sh. & Tavakolpour-Saleh, A.R., 2016. "Frequency-based design of a free piston Stirling engine using genetic algorithm," Energy, Elsevier, vol. 109(C), pages 466-480.
    7. 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.
    8. Schneider, T. & Müller, D. & Karl, J., 2020. "A review of thermochemical biomass conversion combined with Stirling engines for the small-scale cogeneration of heat and power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    9. Bert, Juliette & Chrenko, Daniela & Sophy, Tonino & Le Moyne, Luis & Sirot, Frédéric, 2014. "Simulation, experimental validation and kinematic optimization of a Stirling engine using air and helium," Energy, Elsevier, vol. 78(C), pages 701-712.
    10. Hooshang, M. & Askari Moghadam, R. & Alizadeh Nia, S. & Masouleh, M. Tale, 2015. "Optimization of Stirling engine design parameters using neural networks," Renewable Energy, Elsevier, vol. 74(C), pages 855-866.
    11. 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.
    12. Mabrouk, M.T. & Kheiri, A. & Feidt, M., 2015. "Effect of leakage losses on the performance of a β type Stirling engine," Energy, Elsevier, vol. 88(C), pages 111-117.
    13. Carlos Ulloa & José Luis Míguez & Jacobo Porteiro & Pablo Eguía & Antón Cacabelos, 2013. "Development of a Transient Model of a Stirling-Based CHP System," Energies, MDPI, vol. 6(7), pages 1-19, June.
    14. Cheng, Chin-Hsiang & Yang, Hang-Suin, 2013. "Theoretical model for predicting thermodynamic behavior of thermal-lag Stirling engine," Energy, Elsevier, vol. 49(C), pages 218-228.
    15. Kuban, Lukasz & Stempka, Jakub & Tyliszczak, Artur, 2019. "A 3D-CFD study of a γ-type Stirling engine," Energy, Elsevier, vol. 169(C), pages 142-159.
    16. Cheng, Chin-Hsiang & Yang, Hang-Suin, 2012. "Optimization of geometrical parameters for Stirling engines based on theoretical analysis," Applied Energy, Elsevier, vol. 92(C), pages 395-405.
    17. Hooshang, M. & Askari Moghadam, R. & AlizadehNia, S., 2016. "Dynamic response simulation and experiment for gamma-type Stirling engine," Renewable Energy, Elsevier, vol. 86(C), pages 192-205.
    18. Ferreira, Ana C. & Nunes, Manuel L. & Teixeira, José C.F. & Martins, Luís A.S.B. & Teixeira, Senhorinha F.C.F., 2016. "Thermodynamic and economic optimization of a solar-powered Stirling engine for micro-cogeneration purposes," Energy, Elsevier, vol. 111(C), pages 1-17.
    19. 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.
    20. Carmela Perozziello & Lavinia Grosu & Bianca Maria Vaglieco, 2021. "Free-Piston Stirling Engine Technologies and Models: A Review," Energies, MDPI, vol. 14(21), pages 1-22, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:engenv:v:30:y:2019:i:1:p:156-175. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.