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Numerical simulation is a useful and a cost-effective tool for engine cycle prediction. In the present study, a dual Wiebe function is used to approximate the heat release rate in a DI, naturally aspirated diesel engine fuelled with eucalyptus biodiesel/diesel fuel blends and operated at various engine loads. This correlation is fitted to the experimental heat release rate at various operating conditions (fuel nature and engine load) using a least squares regression to find the unknown parameters. The main objective of this study is to propose a model to predict the Wiebe function parameters for more general operating conditions, not only those experimentally tested. For this purpose, an artificial neural network (ANN) is developed on the basis of the experimental data. Engine load and eucalyptus biodiesel/diesel fuel blend are the input layer, while the six parameters of the dual Wiebe function are the output layer.

This study focuses on the development of a simulation software able to predict the car cabin blown air temperature. This software describes the fluid circuits (water, oil and air) and the engine blocks using the nodal method. It aims to enhance the global knowledge of the equipment suppliers in the thermal management between the engine and the rest of the car. A new correlation for the prediction of the engine heat losses is proposed. This correlation predicts the indicated efficiency as a function of engine settings and parameters, obtained from a statistical study. This leads to develop a reduced combustion model, which combined with the simulation software, will offer a real-time running prediction tool.

This paper focuses on the prediction of thermal losses and indicated performance in modern automotive engines. In a previous study, a complete simulation software was developed in order to both predict the car cabin blown air temperature and simulate the fluid circuits temperature. The two-zone, 0-dimensionnal combustion model presented in this paper aims to enhance this software. Theoretical overview reveals that thermal losses can be deduced from a predictive correlation of indicated performance. This correlation is established with a statistical tool and empirical coefficients are proposed. As a result of this study, the simulation software becomes a real-time computing tool that considers variable parameters previously neglected.