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A kinetic carbon monoxide (CO) emission model is developed to simulate engine out CO emissions for conventional diesel combustion. The model also incorporates physics governing CO emissions for low temperature combustion (LTC). The emission model will be used in an integrated system level model to simulate the operation and interaction of conventional and low temperature diesel combustion with aftertreatment devices. The Integrated System Model consists of component models for the diesel engine, engine-out emissions (such as NOx and Particulate Matter), and aftertreatment devices (such as DOC and DPF). The addition of CO emissions model will enhance the capability of the Integrated System Model to predict major emission species, especially for low temperature combustion. In this work a CO emission model is developed based on a two-step global kinetic mechanism [8].

A procedure has been developed to build system level predictive models that incorporate physical laws as well as information derived from experimental data. In particular a soot model was developed, trained and tested using experimental data. It was seen that the model could fit available experimental data given sufficient training time. Future accuracy on data points not encountered during training was estimated and seen to be good. The approach relies on the physical phenomena predicted by an existing system level phenomenological soot model coupled with ‘weights’ which use experimental data to adjust the predicted physical sub-model parameters to fit the data. This approach has developed from attempts at incorporating physical phenomena into neural networks for predicting emissions. Model training uses neural network training concepts.

Neural networks have been used for engine computations in the recent past. One reason for using neural networks is to capture the accuracy of multi-dimensional CFD calculations or experimental data while saving computational time, so that system simulations can be performed within a reasonable time frame. This paper describes three methods to improve upon neural network predictions. Improvement is demonstrated for in-cylinder pressure predictions in particular. The first method incorporates a physical combustion model within the transfer function of the neural network, so that the network predictions incorporate physical relationships as well as mathematical models to fit the data. The second method shows how partitioning the data into different regimes based on different physical processes, and training different networks for different regimes, improves the accuracy of predictions.

Diesel exhaust particulate trap system is one of the most effective means to control diesel particulate emissions from diesel vehicles. In this paper, a recently developed diesel exhaust particulate trap system was described and experimentally studied. This system employed a wall-flow ceramic foam filter, which was made of silicon carbide or chromium oxide. And this system was equipped with a microwave heater for the purpose of filter regeneration. Engine dynamometer testing, vehicle bench testing and on-road evaluation of this system were conducted. The experiments studied on the filtration efficiency of this system, the effectiveness of filter regeneration, the power penalty of the vehicle, the ability of noise suppression of this system, and the durability of this particulate trap system. The experimental results showed that this diesel particulate trap system was effective, reliable, and durable.

A new diesel exhaust particulate trap system was developed to control diesel particulate emissions from buses in large cities in China. This system was equipped with a microwave heater for the purpose of filter regeneration. To achieve effective and efficient filter regeneration, a radio-frequency (RF) technology was employed. The RF technology measured the amount of particulate trapped in filter, and it controlled filter regeneration using microwave signal. In this paper, the on-line diesel particulate measurement system was described, and experimental study of this measurement system was reported. The experimental results proved the effectiveness of the RF technology in the application of this diesel particulate trap system.