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

Abstract

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 are taken from a research which is performed on a single-rotor, four stroke and port fuel injection 13B Wankel engine. The number of data which are taken from experimental results are 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 is 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.

Artificial Intelligence in Diesel Engines

“…Among these extended subjects, applying AI to internal combustion engines is a new approach on which researchers are working [15,16]. Although the most of efforts have been devoted to the control approaches [17,18], the range of employing AI in engine performance development is still wide from the optimized redesign [19,20] to fuel/charge mixture [21,22] and combustion characteristics [23,24]. Furthermore, due to the strong research background in this field, it can be asserted that AI technology is simultaneously applied to all known combustion modes, from spark ignition engines [25,26] to diesel [27,28] and low-temperature combustion ones [29,30].…”