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The overall goal of this project is to design and develop an engine monitoring and control system for spark ignition engines that will help to reduce emissions and increase efficiency. Certain engine parameters are already measured by existing measurement sensors. Other parameters necessary or desirable for intelligent engine monitoring or control are not currently measured, either because those measurements would be too costly or too slow to be of use in real time. The approach is to use the suite of available sensor measurements along with neural networks with online learning capabilities to develop "virtual sensors" for the parameters that are needed but cannot be easily or rapidly measured. The data from these virtual sensors can then be used for performance monitoring and to make intelligent engine control decisions. A general aviation (GA) aircraft engine was used for data collection for this phase of the project.Three virtual sensors were developed in this project. These virtual sensors estimate parameters for pilot aid, diagnostics, and emission monitoring. High quality outputs were obtained for all parameters for normal operating conditions. The estimation errors ranged from +3% to f6%. This level of accuracy demonstrates feasibility of the virtual sensor concept for this application.

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Internal combustion engines are being required to comply with increasingly stringent government exhaust emissions regulations. Compression ignition (CI) piston engines will continue to be used in cost-sensitive fuel applications such as in heavy-duty buses and trucks, power generation, locomotives and off-highway applications, and will find application in hybrid electric vehicles. Close control of combustion in these engines will be essential to achieve ever-increasing efficiency improvements while meeting increasingly stringent emissions standards. The engines of the future will require significantly more complex control than existing map-based control strategies, having many more degrees of freedom than those of today. Neural network (NN)-based engine modelling offers the potential for a multidimensional, adaptive, learning control system that does not require knowledge of the governing equations for engine performance or the combustion kinetics of emissions formation that a conventional map-based engine model requires. The application of a neural network to model the output torque and exhaust emissions from a modern heavy-duty diesel engine (Navistar T444E) is shown to be able to predict the continuous torque and exhaust emissions from a heavy-duty diesel engine for the Federal heavy-duty engine transient test procedure (FTP) cycle and two random cycles to within 5 per cent of their measured values after only 100 min of transient dynamometer training. Applications of such a neural net model include emissions virtual sensing, on-board diagnostics (OBD) and engine control strategy optimization.

A new, unobtrusive method for liquid-liquid two-phase flow data collection was proved to be reliable in this research. Drop size distributions and concentration profiles were determined for a dilute water-inkerosene system under horizontal straight pipe flow using this technique, and the Segev model for predicting concentration profiles was tested with the data collected here.The drop size distributions were found to follow a Rosin-Rammler function for a limited droplet diameter range, and the average value of the exponent in the Rosin-Rammler equation was determined to be 2.0. The velocity where the flow regime makes a transition from stratified to adequately dispersed flow was found to be between 2.0 and 2.2 m/s. Concentration profiles predicted by the Segev model were in general agreement with the profiles determined in this work, and using the proper choice of model parameters noticeably improves the model predictions.

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