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This study examines the feasibility of broadening the fuel capabilities of a direct-injected two-stroke engine with stratified combustion. A three cylinder, direct-injected two-stroke engine was modified to operate on JP-5, a kerosene-based jet fuel that is heavier, more viscous, and less volatile than gasoline. Demonstration of engine operation with such a fuel after appropriate design modifications would significantly enhance the utilization of this engine in a variety of applications. Results have indicated that the performance characteristics of this engine with jet fuel are similar to that of gasoline with respect to torque and power output at low speeds and loads, but the engine's performance is hampered at the higher speeds and loads by the occurrence of knock.

A process for creating a Customer Correlated, Accelerated, Life Test is presented. This process, which results in a model for predicting reliability, has been applied to a cold weather piston scuff problem. In this paper, the authors will discuss development of frequency distributions for customer environmental and operational use, establishment of customer based failure criteria, development of an accelerated test based on degradation, selection of testing strategies, data analyses, and measurement techniques.

An accurate air flow rate model is critical for high-quality air-fuel ratio control in Spark-Ignition engines using a Three-Way-Catalyst. Emerging Variable Valve Timing technology complicates cylinder air charge estimation by increasing the number of independent variables. In our previous study (SAE 2004-01-3054), an Artificial Neural Network (ANN) has been used successfully to represent the air flow rate as a function of four independent variables: intake camshaft position, exhaust camshaft position, engine speed and intake manifold pressure. However, in more general terms the air flow rate also depends on ambient temperature and pressure, the latter being largely a function of altitude. With arbitrary cam phasing combinations, the ambient pressure effects in particular can be very complex. In this study, we propose using a separate neural network to compensate the effects of altitude on the air flow rate.