A Decision-Tree-Based Approach to Smoke Spike Detection in a Heavy-Duty Diesel Engine

Smoke spikes occurring during transient engine operation have detrimental health effects and increase fuel consumption by requiring more frequent regeneration of the diesel particulate filter. This paper proposes a decision tree approach to real-time detection of smoke spikes for control and on-board diagnostics purposes. A contemporary, electronically controlled heavy-duty diesel engine was used to investigate the deficiencies of smoke control based on the fuel-to-oxygen-ratio limit. With the aid of transient and steady state data analysis and empirical as well as dimensional modeling, it was shown that the fuel-to-oxygen ratio was not estimated correctly during the turbocharger lag period. This inaccuracy was attributed to the large manifold pressure ratios and low exhaust gas recirculation flows recorded during the turbocharger lag period, which meant that engine control module correlations for the exhaust gas recirculation flow and the volumetric efficiency had to be extrapolated. The engine control module correlations were based on steady state data and it was shown that, unless the turbocharger efficiency is artificially reduced, the large manifold pressure ratios observed during the turbocharger lag period cannot be achieved at steady state. Additionally, the cylinder-to-cylinder variation during this period were shown to be sufficiently significant to make the average fuel-to-oxygen ratio a poor predictor of the transient smoke emissions. The steady state data also showed higher smoke emissions with higher exhaust gas recirculation fractions at constant fuel-to-oxygen-ratio levels. This suggests that, even if the fuel-to-oxygen ratios were to be estimated accurately for each cylinder, they would still be ineffective as smoke limiters. A decision tree trained on snap throttle data and pruned with engineering knowledge was able to use the inaccurate engine control module estimates of the fuel-to-oxygen ratio together with information on the engine control module estimate of the exhaust gas recirculation fraction, the engine speed, and the manifold pressure ratio to predict 94% of all spikes occurring over the Federal Test Procedure cycle. The advantages of this non-parametric approach over other commonly used parametric empirical methods such as regression were described. An application of accurate smoke spike detection in which the injection pressure is increased at points with a high opacity to reduce the cumulative particulate matter emissions substantially with a minimum increase in the cumulative nitrogrn oxide emissions was illustrated with dimensional and empirical modeling.

DESIGN OF A LIQUID FUEL INJECTOR FOR ALTERNATIVE FUEL STUDIES IN AN ATMOSPHERIC MODEL GAS TURBINE COMBUSTOR

A new liquid-fuel injector was designed for use in the atmospheric-pressure, model gas turbine combustor in Bucknell University’s Combustion Research Laboratory during alternative fuel testing. The current liquid-fuel injector requires a higher-than-desired pressure drop and volumetric flow rate to provide proper atomization of liquid fuels. An air-blast atomizer type of fuel injector was chosen and an experiment utilizing water as the working fluid was performed on a variable-geometry prototype. Visualization of the spray pattern was achieved through photography and the pressure drop was measured as a function of the required operating parameters. Experimental correlations were used to estimate droplet sizes over flow conditions similar to that which would be experienced in the actual combustor. The results of this experiment were used to select the desired geometric parameters for the proposed final injector design and a CAD model was generated. Eventually, the new injector will be fabricated and tested to provide final validation of the design prior to use in the combustion test apparatus.

Monitoring Stormwater Redistribution Into Low Resistivity Soils Using Noninvasive Geophysical Techniques

Water held in the unsaturated zone is important for agriculture and construction and is replenished by infiltrating rainwater. Monitoring the soil water content of clay soils using ground-penetrating radar (GPR) has not been researched, as clay soils cause attenuation of GPR signal. In this study, GPR common-midpoint soundings (CMPs) are used in the clayey soils of the Miller Run floodplain to monitor changes in the soil water content (SWC) before and after rainfall events. GPR accomplishes this task because increases in water content will increase the dielectric constant of the subsurface material, and decrease the velocity of the GPR wave. Using an empirical relationship between dielectric constant and SWC, the Topp relation, we are able to calculate a SWC from these velocity measurements. Non-invasive electromagnetics, resistivity, and seismic were performed, and from these surveys, the layering at the field site was delineated. EM characterized the horizontal variation of the soil, allowing us to target the most clay rich area. At the CMP location, resistivity indicates the vertical structure of the subsurface consists of a 40 cm thick layer with a resistivity of 100 ohm*m. Between 40 cm and 1.5 m is a layer with a resistivity of 40 ohm*m. The thickness estimates were confirmed with invasive auger and trenching methods away from the CMP location. GPR CMPs were collected relative to a July 2013 and September 2013 storm. The velocity observations from the CMPs had a precision of +/- 0.001 m/ns as assessed by repeat analysis. In the case of both storms, the GPR data showed the expected relationship between the rainstorms and calculated SWC, with the SWC increasing sharply after the rainstorm and decreasing as time passed. We compared these data to auger core samples collected at the same time as the CMPs were taken, and the volumetric analysis of the cores confirmed the trend seen in the GPR, with SWC values between 3 and 5 percent lower than the GPR estimates. Our data shows that we can, with good precision, monitor changes in the SWC of conductive soils in response to rainfall events, despite the attenuation induced by the clay.

Analysis and Prediction of Transient Opacity Spikes Using Dimensional Modeling

Dimensional modeling, GT-Power in particular, has been used for two related purposes-to quantify and understand the inaccuracies of transient engine flow estimates that cause transient smoke spikes and to improve empirical models of opacity or particulate matter used for engine calibration. It has been proposed by dimensional modeling that exhaust gas recirculation flow rate was significantly underestimated and volumetric efficiency was overestimated by the electronic control module during the turbocharger lag period of an electronically controlled heavy duty diesel engine. Factoring in cylinder-to-cylinder variation, it has been shown that the electronic control module estimated fuel-Oxygen ratio was lower than actual by up to 35% during the turbocharger lag period but within 2% of actual elsewhere, thus hindering fuel-Oxygen ratio limit-based smoke control. The dimensional modeling of transient flow was enabled with a new method of simulating transient data in which the manifold pressures and exhaust gas recirculation system flow resistance, characterized as a function of exhaust gas recirculation valve position at each measured transient data point, were replicated by quasi-static or transient simulation to predict engine flows. Dimensional modeling was also used to transform the engine operating parameter model input space to a more fundamental lower dimensional space so that a nearest neighbor approach could be used to predict smoke emissions. This new approach, intended for engine calibration and control modeling, was termed the "nonparametric reduced dimensionality" approach. It was used to predict federal test procedure cumulative particulate matter within 7% of measured value, based solely on steady-state training data. Very little correlation between the model inputs in the transformed space was observed as compared to the engine operating parameter space. This more uniform, smaller, shrunken model input space might explain how the nonparametric reduced dimensionality approach model could successfully predict federal test procedure emissions when roughly 40% of all transient points were classified as outliers as per the steady-state training data.