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

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Department of Mechanical Engineering, Pardis Branch, Islamic Azad University, Pardis New City 1658174583, Iran
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Department of Petroleum Engineering, Ahwaz Faculty of Petroleum Engineering, Petroleum University of Technology (PUT), Ahwaz P.O. Box 63431, Iran
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Department of Renewable Energies, Faculty of New Science and Technologies, University of Tehran, Tehran 141764411, Iran
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Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa, ON L1H 7K4, Canada
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Author to whom correspondence should be addressed.
Academic Editor: Francesco Asdrubali
Sustainability 2015, 7(2), 2243-2255; https://doi.org/10.3390/su7022243
Received: 4 December 2014 / Revised: 3 February 2015 / Accepted: 10 February 2015 / Published: 17 February 2015
(This article belongs to the Section Energy Sustainability)
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. View Full-Text
Keywords: GMDH; neural network; Stirling engine; torque; power GMDH; neural network; Stirling engine; torque; power
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Ahmadi, M.H.; Ahmadi, M.-A.; Mehrpooya, M.; Rosen, M.A. Using GMDH Neural Networks to Model the Power and Torque of a Stirling Engine. Sustainability 2015, 7, 2243-2255.

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