Reduction of Transient Particulate Matter Spikes with Decision Tree Based Control

Decision trees have been proposed as a basis for modifying table based injection to reduce transient particulate spikes during the turbocharger lag period. It has been shown that decision trees can detect particulate spikes in real time. In well calibrated electronically controlled diesel engines these spikes are narrow and are encompassed by a wider NOx spike. Decision trees have been shown to pinpoint the exact location of measured opacity spikes in real time thus enabling targeted PM reduction with near zero NOx penalty. A calibrated dimensional model has been used to demonstrate the possible reduction of particulate matter with targeted injection pressure pulses. Post injection strategy optimized for near stoichiometric combustion has been shown to provide additional benefits. Empirical models have been used to calculate emission tradeoffs over the entire FTP cycle. An empirical model based transient calibration has been used to demonstrate that such targeted transient modifiers are more beneficial at lower engine-out NOx levels.

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.

Characterization of Halyomorpha Halys (Brown Marmorated Stink Bug) Biogenic Volatile Organic Compound Emissions and Their Role in Secondary Organic Aerosol Formation

The formation of aerosols is a key component in understanding cloud formation in the context of radiative forcings and global climate modeling. Biogenic volatile organic compounds (BVOCs) are a significant source of aerosols, yet there is still much to be learned about their structures, sources, and interactions. The aims of this project were to identify the BVOCs found in the defense chemicals of the brown marmorated stink bug Halymorpha halys and quantify them using gas chromatography-mass spectrometry (GC/MS) and test whether oxidation of these compounds by ozone-promoted aerosol and cloud seed formation. The bugs were tested under two conditions: agitation by asphyxiation and direct glandular exposure. Tridecane, 2(5H)-furanone 5-ethyl, and (E)-2-decenal were identified as the three most abundant compounds. H. halys were also tested in the agitated condition in a smog chamber. It was found that in the presence of 100-180 ppm ozone, secondary aerosols do form. A scanning mobility particle sizer (SMPS) and a cloud condensation nuclei counter (CCNC) were used to characterize the secondary aerosols that formed. This reaction resulted in 0.23 mu g/bug of particulate mass. It was also found that these secondary organic aerosol particles could act as cloud condensation nuclei. At a supersaturation of 1%, we found a kappa value of 0.09. Once regional populations of these stink bugs stablilize and the populations estimates can be made, the additional impacts of their contribution to regional air quality can be calculated. Implications: Halymorpha halys (brown marmorated stink bugs) are a relatively new invasive species introduced in the United States near Allentown, Pennsylvania. The authors chemically speciated the bugs' defense pheromones and found that tridecane, 5-ethyl-2(5H)-furanone, and (E)-2-decenal dominated their emissions. Their defense emissions were reacted with atmospherically relevant concentrations of ozone and resulted in 0.23 g of particulate matter per emission per bug. Due to the large population of these bugs in some regions, these emissions could contribute appreciably to a region's PM2.5 (particulate matter with an aerodynamic diameter 2.5 m) levels.

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.