Knock prediction for dual fuel engines by using a simplified combustion model

The present work used a methane-air mixture chemical kinetics scheme consisting of 119 elementary reaction steps and 41 chemical species to develop a simplified combustion model for prediction of the knock in dual fuel engines. Calculated values by the model for natural gas operation showed good agreement with corresponding experimental values over a broad range of operating conditions.

Effect of exhaust gas recirculation and intake pre-heating on performance and emission characteristics of dual fuel engines at part loads

Achieving simultaneous reduction of NOx,CO and unburned hydrocarbon(UHC) emissions without compromising engine performance at part loads is the current focus of dual fuel engine research.The present work focuses on an experimental investigation conducted on a dual fuel(diesel-natural gas) engine to examine the simultaneous effect of inlet air pre-heating and exhaust gas recirculation(EGR) ratio on performance and emission characteristics at part loads.The use of EGR at high levels seems to be unable to improve the engine performance at part loads.However,it is shown that EGR combined with pre-heating of inlet air can slightly increase thermal efficiency,resulting in reduced levels of both unburned hydrocarbon and NOx emissions.CO and UHC emissions are reduced by 24% and 31%,respectively,The NOx emissions decrease by 21% because of the lower combustion temperature due to the much inert gas brought by EGR and decreased oxygen concentration in the cylinder.

Study of CNG／diesel dual fuel engine＇s emissions by means of RBF neural network

Great efforts have been made to resolve the serious environmental pollution and inevitable declining of energy resources. A review of Chinese fuel reserves and engine technology showed that compressed natural gas (CNG)/diesel dual fuel engine (DFE) was one of the best solutions for the above problems at present. In order to study and improve the emission performance of CNG/diesel DFE, an emission model for DFE based on radial basis function (RBF) neural network was developed which was a black-box input-output training data model not require priori knowledge. The RBF centers and the connected weights could be selected automatically according to the distribution of the training data in input-output space and the given approximating error. Studies showed that the predicted results accorded well with the experimental data over a large range of operating conditions from low load to high load. The developed emissions model based on the RBF neural network could be used to successfully predict and optimize the emissions performance of DFE. And the effect of the DFE main performance parameters, such as rotation speed, load, pilot quantity and injection timing, were also predicted by means of this model. In resumé, an emission prediction model for CNG/diesel DFE based on RBF neural network was built for analyzing the effect of the main performance parameters on the CO, NOx emissions of DFE. The predicted results agreed quite well with the traditional emissions model, which indicated that the model had certain application value, although it still has some limitations, because of its high dependence on the quantity of the experimental sample data.

Application of neural network in the study of combustion rate of natural gas／diesel dual fuel engine

In order to predict and improve the performance of natural gas/diesel dual fuel engine (DFE), a combustion rate model based on forward neural network was built to study the combustion process of the DFE. The effect of the operatin g parameters on combustion rate was also studied by means of this model. The stu dy showed that the predicted results were good agreement with the experimental d a ta. It was proved that the developed combustion rate model could be used to succ essfully predict and optimize the combustion process of dual fuel engine.