Search Results

In 1999, the first generation NOx sensor from NGK Spark Plug, Co., Ltd. was commercialized for use in gasoline LNT NOx after-treatment systems [ 1 ]. Since then, as emissions regulations and OBD requirements have become more stringent, the demand for a high-accuracy NOx sensor with fast light-off has increased, particularly for diesel after-treatment systems. To meet such market demands, NGK Spark Plug, Co., Ltd. has developed, in collaboration with Ford Motor Company, a second generation NOx sensor.

A diesel fuel and air diffusion flame burner system has been designed for laboratory simulation of diesel exhaust gas. The system consists of mass flow controllers and a fuel pump, and employs several unique design and construction features. It produces particulate emissions with size, number distribution, and morphology similar to diesel exhaust. At the same time, it generates NOx emissions and HC similar to diesel. The system has been applied to test plasma discharges. Different design discharge devices have been tested, with results indicating the importance of testing devices with soot and moisture. Both packed bed reactor and flat plate dielectric barrier discharge systems remove some soot from the gas, but the designs tested are susceptible to soot fouling and related electrical failures. The burner is simple and stable, and is suitable for development and aging of plasma and catalysts systems in the laboratory environment.

Environmental concerns have prompted increasingly stringent government legislation regulating automotive fuel economy and emissions. Recent rules not only mandate lower total emissions, but also require on-board diagnostics which monitor the vehicle exhaust systems. In order to satisfy these requirements, new and improved exhaust gas sensors are continually being developed to serve as part of the engine feedback control and emissions monitoring systems. Before we can properly design these new sensors, we must attempt to better understand the harsh environment in which they will operate. In this paper, we examine the high frequency nature of pressure fluctuations found in the exhaust system for both steady state and transient engine operating conditions. We also investigate temperature fluctuations, but restrict these measurements to the sampling environment found in the packaging of a Ford Si-based microcalorimeter.

The present research is concerned with the quality of the shearing process, which is an integral part of automotive vehicle body stamping. Experiments were done to analyse the influence of the gap between the cutting edges and the geometry of the cutting blades, on the quality of the sheared surface. Experimental results are presented as microstructures of polished samples. To predict the quality of sheared surfaces, a numerical code based on solid mechanics equations, elastoplastic flow theory and cummulative theory of damages was created.

In-cylinder pressure traces vary significantly from cycle-to-cycle in spark-ignition (SI) engines. The variations, substantially present even when engine is stable, are magnified under certain engine operating conditions. As a result, engine torque output oscillates and engine operation becomes unstable. EGR tolerance, lean burn limit and spark retard capabilities at CSSRE (Cold Start Spark Retard and Enleanment) are mostly determined by the levels of cycle-to-cycle variations. None of the engine computer models, however, have included cyclic variations for routine industrial applications. As the application domain of engine simulation models expands into unstable engine operating conditions, the modeling of cyclic variations becomes increasingly important. In this research, reviews were conducted regarding different approaches for the simulation of cyclic variation.

Seated vertical vibration thresholds were tested using an adaptive Levitt algorithm. All such testing raises issues concerning potential shifting of thresholds during testing as subjects improve at the task. Additional testing was done at 4 and 16 Hz to quantify the adequacy of training within the algorithm. A 3-down 1-up algorithm starting at 8 mG descended in 3 dB steps until the first error, then switched to 1 dB steps and continued for 9 more reversals, with the last 6 averaged for threshold. Stimuli were paired with intervals containing no vibration in random order. Subjects closed their eyes and were presented with sounds in earphones to indicate the stimulus intervals, and chose the interval they thought contained the stimulus. A combination of eyes closed for concentration, gradual approach to the threshold, 4 reversals before data was used, and feedback on each trial provided built-in training to avoid threshold shift.