摘要：Accurate prediction of nitrogen oxide, soot, carbon monoxide and unburned hydrocarbon emissions from diesel engines plays a crucial role during the design and development phases of vehicle powertrain systems due to increasingly more strict emission legislation. Undoubtedly, generating accurate and robust emission prediction methods will serve to global optimization of engine components at very early stages of engine development. Engine component selection, accurate specific fuel consumption prediction and defining the correct exhaust gas recirculation strategy (low and mid-high) can only be performed via accurate and fast emission prediction. There are many possible ways of emission prediction in the literature, such as three-dimensional computational fluid dynamics, stochastic reactor, semi-empirical, phenomenological models and neural networks. However, these prediction methods either need excessive test data or simulation duration. However, using one-dimensional simulation tools is a faster way of emission prediction, but has low accuracy. In this study, it is aimed to develop a fast NOx emission prediction methodology by utilizing one-dimensional models generated in GT-Suite software. Two different heavy-duty engines are modelled, and the models are correlated with test data. An NOx emission prediction methodology is developed with the 9-L heavy-duty diesel engine model. Extended Zeldovich mechanism included in the software is tuned via embedding different calibration multiplier maps. Comparison of simulation results with test data shows that turbine inlet temperature, in-cylinder maximum temperature, maximum pressure, load, CA50, exhaust gas recirculation rate and fuel–air ratio are the most critical map parameters for enhanced emission prediction capability. The methodology developed is applied to the 12.7-L engine with these selected map parameters. The results show that the methodology can be used to predict NOx values with high speed and sufficient accuracy for different heavy-duty diesel engine variants.
