Comprehensive emissions models extensively use engine exhaust data from vehicle experiments to represent the relationship between fuel composition and pollutants. However, the predicted emissions from these models often neglect the effects of transients and speed-load history. Fourier transform infrared (FTIR) spectroscopy is a high frequency measurement technique capable of comprehensive speciation. However, due to long residence times of exhaust within a FTIR spectrometer gas cell, FTIR measurements are contaminated by the effects of historical emissions, precluding the attainment of time-resolved engine exhaust data. This work presents a Bayesian estimation model for processing FTIR measurements to obtain accurate estimations of instantaneous engine exhaust composition. The Bayesian model utilizes a simple model of the mixing dynamics of the gas cell and measurement noise statistics to estimate the composition of exhaust entering the FTIR gas cell during a measurement period. To validate the estimation model, synthetic FTIR measurements are generated from simulated engine exhaust data from the Federal Test Procedure driving cycle. These synthetic measurements are processed by the estimation model, which is shown to yield improved estimations of instantaneous composition as compared to the raw FTIR measurements, although the degree of improvement depends on the magnitude of measurement noise and flow rate through the FTIR gas cell. For a measurement noise standard deviation of 0.5% of the maximum measurement, the estimation model improved estimates of instantaneous NO emission by approximately 42.5% on average, while about a 7.5% improvement was achieved for a measurement noise standard deviation of 2% of the maximum measurement for a FTIR flow rate of 10 L/min. For a flow rate of 25 L/min, improvements of approximately 41.5% and 6% were achieved for measurement noise standard deviations of 0.5% and 2% of the maximum measurements, respectively. The application of the model in this work is to generate time-resolved emissions estimates to further elucidate the relationship between fuel composition and engine emissions.

Accurate chemical kinetic models, which predict species evolution and heat release rates in chemically reactive systems, are essential for further advancements in fuel and combustion technology. An experimental facility that is widely used for evaluating the accuracy of kinetic models is a rapid compression machine (RCM), which creates a well-defined reaction environment by compressing a reactive mixture inside a chamber. Generally, RCM experiments are conducted in order to obtain ignition delay data. However, chemical speciation data provides greater insight into reaction pathways, and is therefore a more rigorous benchmark for validating kinetic models.In order for a chemical kinetic model to be evaluated using RCM data, the kinetic model must be coupled with a thermodynamic model that can predict the temporally varying conditions that evolve during an RCM experiment. The most common approach is to utilize a thermally and compositionally homogeneous 0-dimensional reactor model (HRM), which predicts conditions inside the hot core region of the main combustion chamber of an RCM, where a significant portion of the chemical reaction in an RCM takes place. This approach requires an effective volume profile, which is derived from the pressure profile of either a non-reactive experiment with similar transport properties as the condition of interest, or a separate multi-zone model (MZM), via the relationship between pressure and volume for an isentropic process. While HRMs have been shown to yield adequate ignition delay predictions, they cannot be used to predict average speciation data, since the conditions in the core region vary considerably from the average conditions of the total reaction chamber.This work introduces a modified MZM, which simulates chemical reaction throughout the entire temperature-varying main combustion chamber of an RCM, in addition to boundary work, conduction, and crevice flows as the traditional MZM approach. Simulating chemistry in the MZM allows for average speciation predictions, and eliminates the need for an HRM. The new approach is shown to yield similar average speciation data as CFD simulations (within 15% difference) for the combustion of primary reference fuels at various conditions. iii ACKNOWLEDGEMENTS

Fourier transform infrared (FTIR) spectroscopy is a prevalent technique for measuring the comprehensive chemical composition of engine emissions. However, its applicability to transient emissions is limited due to recirculation of exhaust from past engine cycles within a FTIR gas cell and nonstationarity of the infrared beam intensity. An unscented Kalman filter is developed to overcome these limitations and obtain accurate, time-resolved estimations of engine exhaust composition from FTIR measurements. Residence time distribution within the FTIR gas cell is modeled using the well-mixed assumption, while the Fourier transform of an interferogram generated from a linearly evolving, uniformly broadened absorption line is used to deduce transient gas cell composition values from FTIR measurements. The filter utilizes both models, as well as measurement noise statistics, to infer the composition of sample entering the FTIR gas cell during a measurement period. To validate the filter, FTIR measurements of air with transient, trace amounts of acetylene are conducted. A variety of composition profiles are explored with different combinations of composition standard deviation (15 and 45 ppm) and duration between set points (0.4 and 1 s). The results demonstrate that measurement noise becomes less impactful as the magnitudes of composition fluctuations increase, while residence time effects become less significant as the duration of fluctuations increase. Improvements in estimated composition are achieved by the filter in every case, with an average improvement of 32% over unfiltered FTIR measurements. Experiments are conducted using sample flow rates of 12 and 25 standard liters per minute. More accurate measurements and estimations are attained at higher sample flow rates, highlighting the importance of maximizing flow rate to reduce residence time effects for transient measurements.

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Emissions of various fuel components (cyclohexane, ethanol, and pentane) and reaction intermediate species (acetylene, ethylene, formaldehyde, and methane) from a multicylinder, port-fuel-injected, spark-ignited gasoline engine undergoing transient loads are measured using Fourier transform infrared (FTIR) spectroscopy. The load profiles explored herein consist of positive and negative load ramps spanning brake mean effective pressures of 2–7 bar lasting 1, 2.5, and 5 s, as well as periodic load ramps of identical magnitudes and durations. Experiments are performed at two constant speed settings of 1500 and 2000 rpm. Fourier transform infrared spectroscopy measurements are processed with a recently developed unscented Kalman filter [WilsonD. Wilson, D. Energy Fuels2017311115611168; WilsonD. Wilson, D. Energy Fuels2018321189911912], which combats the biasing effects of sample recirculation and signal nonstationarity associated with transient FTIR measurements, to improve emission estimations. Emissions during load transients are compared to quasi-steady model predictions and estimated emission stochasticity. Overall, the data shows that transient effects (i.e., load ramp rate, speed/load history, nonstoichiometric equivalence ratio) substantially influence volatile organic compound (VOC) emissions in a deterministic manner, as quasi-steady prediction errors regularly exceed the combined effects of stochasticity and uncertainty. Negative load ramps (from 7 to 2 bar) result in the greatest quasi-steady prediction errors of all load profiles. For the periodic load ramps, the greatest quasi-steady prediction errors of the intermediate and fuel component emissions occur for 1 and 2.5 s load ramps, respectively. In both cases, these errors surpass the 95% confidence interval of statistical significance for each species except cyclohexane. Benzene and toluene emissions are unreported due to low quantities and excessive measurement noise, whereas 1,3-butadiene emissions show minimal relation to engine speed/load. The results of this work suggest that transient and historical effects must be taken into account when predicting VOC engine emissions and that the quasi-steady approach is insufficient.