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

Mohammad Hossein Ahmadi, Mohammad-Ali Ahmadi, Mehdi Mehrpooya and Marc A. Rosen
Additional contact information
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

Sustainability, 2015, vol. 7, issue 2, 1-13

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.

Keywords: GMDH; neural network; Stirling engine; torque; power (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://www.mdpi.com/2071-1050/7/2/2243/pdf (application/pdf)
https://www.mdpi.com/2071-1050/7/2/2243/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:7:y:2015:i:2:p:2243-2255:d:46058

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().