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Using GMDH Neural Networks to Model the Power and Torque of a Stirling Engine

Author

Listed:
  • Mohammad Hossein Ahmadi

    (Department of Mechanical Engineering, Pardis Branch, Islamic Azad University, Pardis New City 1658174583, Iran)

  • Mohammad-Ali Ahmadi

    (Department of Petroleum Engineering, Ahwaz Faculty of Petroleum Engineering, Petroleum University of Technology (PUT), Ahwaz P.O. Box 63431, Iran)

  • Mehdi Mehrpooya

    (Department of Renewable Energies, Faculty of New Science and Technologies, University of Tehran, Tehran 141764411, Iran)

  • Marc A. Rosen

    (Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa, ON L1H 7K4, Canada)

Abstract

Different variables affect the performance of the Stirling engine and are considered in optimization and designing activities. Among these factors, torque and power have the greatest effect on the robustness of the Stirling engine, so they need to be determined with low uncertainty and high precision. In this article, the distribution of torque and power are determined using experimental data. Specifically, a novel polynomial approach is proposed to specify torque and power, on the basis of previous experimental work. This research addresses the question of whether GMDH (group method of data handling)-type neural networks can be utilized to predict the torque and power based on determined parameters.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:7:y:2015:i:2:p:2243-2255:d:46058
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    References listed on IDEAS

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    3. 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.
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    Cited by:

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    2. Zare, Shahryar & Tavakolpour-saleh, A.R. & Aghahosseini, A. & Sangdani, M.H. & Mirshekari, Reza, 2021. "Design and optimization of Stirling engines using soft computing methods: A review," Applied Energy, Elsevier, vol. 283(C).
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    5. Khosravi, A. & Machado, L. & Nunes, R.O., 2018. "Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil," Applied Energy, Elsevier, vol. 224(C), pages 550-566.
    6. 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.

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