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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.

As emission restrictions become more stringent and conventional fuel supplies become more limited, dual-fuel engines are emerging as a promising solution that offers both environmental and economic benefits. However, the performance of these engines is often hampered by the issue of knocking, which can negatively impact their overall operation, and also by the increase in NOx emissions at high load. This work investigates the use of pilot injection properties by combining the use of emulsified diesel of different water percentages with injection timing to reduce both knock intensity and NOx emission rate. Specifically, a dual fuel operation case at full load with high enrichment of the primary fuel (natural gas) with hydrogen is considered in order to create conditions for high knocking and high NOx emission rates.