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.

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.

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.

Investigation of Solid-State Shear Pulverization Processing Parameters for Polyethylene-Clay Nanocomposites

Investigates multiple processing parameters, includingpolymer type, filler type, processing technique, severity of SSSP (Solid-state shear pulverization)processing, and postprocessing, of SSSP. HDPE and LLDPE polymers with pristine clay and organo-clay samples are explored. Effects on crystallization, high-temperature behavior, mechanicalproperties, and gas barrier properties are examined. Thermal, mechanical, and morphological characterization is conducted to determine polymer/filler compatibility and superior processing methods for the polymer-clay nanocomposites.

The Crystallization Kinetics of Polylactic Acid (Pla) Processed Through Solid-State/Melt Extrusion

Polylactic acid (PLA) is a bio-derived, biodegradable polymer with a number of similar mechanical properties to commodity plastics like polyethylene (PE) and polyethylene terephthalate (PETE). There has recently been a great interest in using PLA to replace these typical petroleum-derived polymers because of the developing trend to use more sustainable materials and technologies. However, PLA¿s inherent slow crystallization behavior is not compatible with prototypical polymer processing techniques such as molding and extrusion, and in turn inhibits its widespread use in industrial applications. In order to make PLA into a commercially-viable material, there is a need to process the material in such a way that its tendency to form crystals is enhanced. The industry standard for producing PLA products is via twin screw extrusion (TSE), where polymer pellets are fed into a heated extruder, mixed at a temperature above its melting temperature, and molded into a desired shape. A relatively novel processing technique called solid-state shear pulverization (SSSP) processes the polymer in the solid state so that nucleation sites can develop and fast crystallization can occur. SSSP has also been found to enhance the mechanical properties of a material, but its powder output form is undesirable in industry. A new process called solid-state/melt extrusion (SSME), developed at Bucknell University, combines the TSE and SSSP processes in one instrument. This technique has proven to produce moldable polymer products with increased mechanical strength. This thesis first investigated the effects of the TSE, SSSP, and SSME polymer processing techniques on PLA. The study seeks to determine the process that yields products with the most enhanced thermal and mechanical properties. For characterization, percent crystallinity, crystallization half time, storage modulus, softening temperature, degradation temperature and molecular weight were analyzed for all samples. Through these characterization techniques, it was observed that SSME-processed PLA had enhanced properties relative to TSE- and SSSP-processed PLA. Because of the previous findings, an optimization study for SSME-processed PLA was conducted where throughput and screw design were varied. The optimization study determined PLA processed with a low flow rate and a moderate screw design in an SSME process produced a polymer product with the largest increase in thermal properties and a high retention of polymer structure relative to TSE-, SSSP-, and all other SSME-processed PLA. It was concluded that the SSSP part of processing scissions polymer chains, creating defects within the material, while the TSE part of processing allows these defects to be mixed thoroughly throughout the sample. The study showed that a proper SSME setup allows for both the increase in nucleation sites within the polymer and sufficient mixing, which in turn leads to the development of a large amount of crystals in a short period of time.