Modelling A Single-Rotor Wankel Engine Performance With Artificial Neural Network At Middle Speed Range

• Main Authors: Cemal Baykara, Osman Azmi Özsoysal, Akın Kutlar, Ömer Ci̇han, Mehmet İ̇lter Özmen
• Format: Article
• Language: English
• Published: Turkish Society of Automotive Engineers 2020-09-01
• Series: International Journal of Automotive Science and Technology
• Subjects: artificial neural network; engine performance; exhaust emissions; scarce data; wankel engine
• Online Access: https://dergipark.org.tr/en/pub/ijastech/issue/55251/771165

The researches on Wankel engines are very rare and considered new in modelling and prediction. Therefore this study deals with the artificial neural network (ANN) modelling of a Wankel engine to predict the power, volumetric efficiency and emissions, including nitrogen oxide, carbon dioxide, carbon monoxide and oxygen by using the change of mean effective pressure, intake manifold pressure, start of ignition angle and injection duration as inputs. The experiment results were taken from a research which is performed on a single-rotor, four stroke and port fuel injection 13B Wankel engine. The number of datas which are taken from experimental results were scarce and varied in six different data set (for example; mean effective pressure, from 1 to 6 bar) at 3000 rpm engine speed. The standard back-propagation (BPNN) Levenberg-Marquardt neural network algorithm is applied to evaluate the performance of middle speed range Wankel engine. The model performance was validated by comparing the prediction data sets with the measured experimental data. Results approved that the artificial neural network (ANN) model provided good agreement with the experimental data with good accuracy while the correlation coefficient R varies between 0.79 and 0.97.