Prediction of performance of Stirling engine using least squares support machine technique

¹ Renewable Energies and Environmental Department, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
² Department of Petroleum Engineering, Ahwaz Faculty of Petroleum Engineering, Petroleum University of Technology (PUT), Ahwaz, Iran
³ University of Valenciennes, LAMIH CNRS UMR 8201, Department of Mechanics, Campus Mont Houy, 59313 Valenciennes Cedex 9, France

^a Corresponding author: mohammadhosein.ahmadi@gmail.com

Received: 2 July 2015
Accepted: 22 October 2015

Abstract

Stirling engine is an environmental friendly heat engine which could reduce CO₂ emission through combustion process. Output power, shaft torque and brake specific fuel consumption represent the efficiency and robustness of the Stirling engines. The present research tries to determine the three aforementioned parameters with high accuracy and low uncertainty. In this research a new type of intelligent models named “least square support vector machine (LSSVM) was employed to predict output power, shaft torque and brake specific fuel consumption. Furthermore, high accurate actual values of the required parameters from previous studies were implemented to develop the robust intelligent model. A great advantage of LSSVM model over ANN is that in the present model over fitting does not happen. Expected statistical parameters of the suggested intelligent model have been indicated and validate the high efficiency of the suggested LSSVM model. Good agreement between LSSVM results and actual values was observed. Solutions obtained from the developed support vector machine model could help us in exact designing of Stirling engine with low uncertainty.