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

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.
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https://hal-uphf.archives-ouvertes.fr/hal-03448100
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Submitted on : Thursday, November 25, 2021 - 8:24:45 AM
Last modification on : Friday, November 26, 2021 - 3:45:39 AM

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Mohammad Hossein Ahmadi, Mohammad Ali Ahmadi, Milad Ashouri, Astarei Razie, Roghayeh Ghasempour, et al.. Prediction of performance of Stirling engine using least squares support machine technique. Mechanics & Industry, EDP Sciences, 2016, 17 (5), pp.506. ⟨10.1051/meca/2015098⟩. ⟨hal-03448100⟩

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