Next Article in Journal
Sustainability Best Paper Awards for 2015
Previous Article in Journal
Soil Quality Impacts of Current South American Agricultural Practices
Article Menu

Export Article

Open AccessArticle
Sustainability 2015, 7(2), 2243-2255;

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

Department of Mechanical Engineering, Pardis Branch, Islamic Azad University, Pardis New City 1658174583, Iran
Department of Petroleum Engineering, Ahwaz Faculty of Petroleum Engineering, Petroleum University of Technology (PUT), Ahwaz P.O. Box 63431, Iran
Department of Renewable Energies, Faculty of New Science and Technologies, University of Tehran, Tehran 141764411, Iran
Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa, ON L1H 7K4, Canada
Author to whom correspondence should be addressed.
Academic Editor: Francesco Asdrubali
Received: 4 December 2014 / Revised: 3 February 2015 / Accepted: 10 February 2015 / Published: 17 February 2015
(This article belongs to the Section Energy Sustainability)
Full-Text   |   PDF [180 KB, uploaded 24 February 2015]   |  


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

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sustainability EISSN 2071-1050 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top