Stirling engine is an Environmental friendly heat engine which can reduce CO2 emission through combustion process. Various criteria should be considered for designing and optimizing stirling heat engines such as power, torque, and pressure loss in heat exchangers of stirling engine, efficiency and so forth. In the aforementioned criteria, output power and shaft torque are the most important criteria which represent the performance and efficiency of the stirling engines. So, determination of output power and shaft torque with low uncertainty and high precision are required. In this paper, a new generation of intelligent models named “least square support vector machine (LSSVM)” is employed to predict output power and shaft torque of stirling engines. To build, train and test the LSSVM model, various accurate experimental data from open literature are employed. The outputs of the LSSVM model are compared to experimental ones and statistical parameters of the LSSVM model including correlation coefficient, average absolute relative deviation (AARD) and root mean square error (RMSE) are calculated. According to the results obtained via LSSVM model, the LSSVM model can predict output power and shaft torque of Stirling heat engine with reasonable and acceptable accuracy. Finally, the LSSVM model can help us in designing of Stirling engine with low degree of uncertainty and high precision.
کلید واژگان :LSSVM; RSME
ارزش ریالی : 600000 ریال
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