Impacts of Mid-Level Biofuel Content In Gasoline on SIDI Engine-Out and Tailpipe Particulate Matter Emissions

In this work, the influences of ethanol and iso-butanol blended with gasoline on engine-out and post three-way catalyst (TWC) particle size distribution and number concentration were studied using a General Motors (GM) 2.0L turbocharged spark ignition direct injection (SIDI) engine. The engine was operated using the production engine control unit (ECU) with a dynamometer controlling the engine speed and the accelerator pedal position controlling the engine load. A TSI Fast Mobility Particle Sizer (FMPS) spectrometer was used to measure the particle size distribution in the range from 5.6 to 560 nm with a sampling rate of 1 Hz. U.S. federal certification gasoline (E0), two ethanol-blended fuels (E10 and E20), and 11.7% iso-butanol blended fuel (BU12) were tested. Measurements were conducted at 10 selected steady-state engine operation conditions. Bi-modal particle size distributions were observed for all operating conditions with peak values at particle sizes of 10 nm and 70 nm. Idle and low-speed / low-load conditions emitted higher total particle numbers than other operating conditions. At idle, the engine-out particulate matter (PM) emissions were dominated by nucleation mode particles, and the production TWC reduced these nucleation mode particles by more than 50%, while leaving the accumulation mode particle distribution unchanged. At an engine load higher than 6 bar net mean effective pressure (NMEP), accumulation mode particles dominated the engine-out particle emissions, and the TWC had little effect. Compared to the baseline gasoline (E0), E10 does not significantly change PM emissions, while E20 and BU12 both reduce PM emissions under the conditions studied. Iso-butanol was observed to impact PM emissions more than ethanol, with up to 50% reductions at some conditions. In this paper, issues related to PM measurement using the FMPS are also discussed. While some uncertainties are due to engine variation, the FMPS must be carefully maintained in order to achieve repeatable measurement results

Modeling the Fuel Spray and Combustion Process of the Ignition Quality Tester with KIVA-3V Modeling the Fuel Spray and Combustion Process of the Ignition Quality Tester with KIVA-3V

Development of advanced compression ignition and low-temperature combustion engines is increasingly dependent on chemical kinetic ignition models. However, rigorous experimental validation of kinetic models has been limited as a result of several factors. For example, shock tubes and rapid compression machines are often limited to premixed gas-phase studies, precluding the use of more realistic, low-volatility diesel or biodiesel surrogates. The Ignition Quality Tester (IQT) constant-volume spray combustion system measures ignition delay of low-volatility fuels; therefore, the IQT has the potential to validate ignition models experimentally. However, a better understanding of the IQT's fuel spray and combustion processes is necessary to facilitate chemical kinetic studies. KIVA-3V is utilized in developing a three-dimensional computational fluid dynamics (CFD) model that accurately and efficiently reproduces ignition behavior and temporally resolves temperature and equivalence ratio regions inside the IQT. The model's fuel spray characteristics (e.g., velocity, cone-angle, oscillations) are experimentally validated; n-heptane is initially studied because of the simplicity of its chemical kinetics and use as IQT calibration fuel. Reduced/skeletal n-heptane chemical mechanisms (60, 42, and 33 species) and one-step chemistry are employed. The CFD results indicate combustion is governed by autoignition kinetics, and perturbations/oscillations in the fuel spray have significant effects on the combustion process, as verified experimentally. The CFD model provides insight into the complex interaction between the fuel spray and combustion processes, which is vital to expanding the fuel research capabilities of the IQT

Experiments and Computational Fluid Dynamics Modeling Analysis of Large <i>n</i>‑Alkane Ignition Kinetics in the Ignition Quality Tester

This paper presents experimental measurements of ignition delays from low- to high-volatility <i>n</i>-alkanes representative of diesel and jet fuel compounds that are supplemented with a computational fluid dynamics (CFD) analysis. The ignition quality tester (IQT) is shown to be effective for studying ignition of low-volatility fuels, such as <i>n</i>-hexadecane, which are typically difficult to measure. Ignition delays, both experimental and modeled, are presented using an eight-point experimental design matrix (1.5 and 3.0 MPa, 823 and 723 K, and 15 and 21% O₂). A detailed <i>n</i>-alkane mechanism (C₈–C₁₆ with a total of 2115 species) was reduced to a skeletal 237 species <i>n</i>-hexadecane mechanism using a targeted search algorithm. A CFD model of the IQT (developed using KIVA-3V) coupled with skeletal mechanisms predicted ignition delays of <i>n</i>-heptane and <i>n</i>-hexadecane with reasonable accuracy over the eight-point matrix, with the exception of the highest temperature, lowest pressure, and oxygen concentration conditions. Temperature sweeps across a range of pressures (0.1–1.0 MPa) and temperatures (673–973 K) were performed for <i>n</i>-heptane, <i>n</i>-decane, <i>n</i>-dodecane, and <i>n</i>-hexadecane. The negative temperature coefficient (NTC) region was observed experimentally for the first time for <i>n</i>-hexadecane. The NTC region for <i>n</i>-dodecane and <i>n</i>-decane has previously been observed in shock tubes and rapid compression machines and is reported here for the first time in the IQT. The IQT is thus capable of capturing NTC behavior for large alkanes and can serve as an additional experimental validation tool for chemical kinetic mechanisms of low-volatility surrogates for diesel and jet fuels

Investigation of Iso-octane Ignition and Validation of a Multizone Modeling Method in an Ignition Quality Tester

An ignition quality tester was used to characterize the autoignition delay times of iso-octane. The experimental data were characterized between temperatures of 653 and 996 K, pressures of 1.0 and 1.5 MPa, and global equivalence ratios of 0.7 and 1.05. A clear negative temperature coefficient behavior was seen at both pressures in the experimental data. These data were used to characterize the effectiveness of three modeling methods: a single-zone homogeneous batch reactor, a multizone engine model, and a three-dimensional computational fluid dynamics (CFD) model. A detailed 874 species iso-octane ignition mechanism (Mehl, M.; Curran, H. J.; Pitz, W. J.; Westbrook, C. K. Chemical kinetic modeling of component mixtures relevant to gasoline. Proceedings of the European Combustion Meeting; Vienna, Austria, April 14–17, 2009) was reduced to 89 species for use in these models, and the predictions of the reduced mechanism were consistent with ignition delay times predicted by the detailed chemical mechanism across a broad range of temperatures, pressures, and equivalence ratios. The CFD model was also run without chemistry to characterize the extent of mixing of fuel and air in the chamber. The calculations predicted that the main part of the combustion chamber was fairly well-mixed at longer times (> ∼30 ms), suggesting that the simpler models might be applicable in this quasi-homogeneous region. The multizone predictions, where the combustion chamber was divided into 20 zones of temperature and equivalence ratio, were quite close to the coupled CFD–kinetics results, but the calculation time was ∼11 times faster than the coupled CFD–kinetics model. Although the coupled CFD–kinetics model captured the observed negative temperature coefficient behavior and pressure dependence, discrepancies remain between the predictions and the observed ignition time delays, suggesting improvements are still needed in the kinetic mechanism and/or the CFD model. This approach suggests a combined modeling approach, wherein the CFD calculations (without chemistry) can be used to examine the sensitivity of various model inputs to in-cylinder temperature and equivalence ratios. These values can be used as inputs to the multizone model to examine the impact on ignition delay. The speed of the multizone model also makes it feasible to quickly test more detailed kinetic mechanisms for comparison to experimental data and sensitivity analysis

A Quantitative Model for the Prediction of Sooting Tendency from Molecular Structure

Particulate matter emissions negatively affect public health and global climate, yet newer fuel-efficient gasoline direct injection engines tend to produce more soot than their port-fuel injection counterparts. Fortunately, the search for sustainable biomass-based fuel blendstocks provides an opportunity to develop fuels that suppress soot formation in more efficient engine designs. However, as emissions tests are experimentally cumbersome and the search space for potential bioblendstocks is vast, new techniques are needed to estimate the sooting tendency of a diverse range of compounds. In this study, we develop a quantitative structure–activity relationship (QSAR) model of sooting tendency based on the experimental yield sooting index (YSI), which ranks molecules on a scale from <i>n</i>-hexane, 0, to benzene, 100. The model includes a rigorously defined applicability domain, and the predictive performance is checked using both internal and external validation. Model predictions for compounds in the external test set had a median absolute error of ∼3 YSI units. An investigation of compounds that are poorly predicted by the model lends new insight into the complex mechanisms governing soot formation. Predictive models of soot formation can therefore be expected to play an increasingly important role in the screening and development of next-generation biofuels

Modeling multiple risks during infancy to predict quality of the caregiving environment: Contributions of a person-centered approach

► Nine risk factors for poor quality caregiving environment explored using four methods. ► Compared bivariate approach, regression, cumulative risk, latent class analysis. ► Five risk classes identified, from married low-risk to single low-income/education. ► Latent class analysis provided more intuitive and useful summary of multiple risks. The primary goal of this study was to compare several variable-centered and person-centered methods for modeling multiple risk factors during infancy to predict the quality of caregiving environments at six months of age. Nine risk factors related to family demographics and maternal psychosocial risk, assessed when children were two months old, were explored in the understudied population of children born in low-income, non-urban communities in Pennsylvania and North Carolina ( N = 1047). These risk factors were (1) single (unpartnered) parent status, (2) marital status, (3) mother's age at first child birth, (4) maternal education, (5) maternal reading ability, (6) poverty status, (7) residential crowding, (8) prenatal smoking exposure, and (9) maternal depression. We compared conclusions drawn using a bivariate approach, multiple regression analysis, the cumulative risk index, and latent class analysis (LCA). The risk classes derived using LCA provided a more intuitive summary of how multiple risks were organized within individuals as compared to the other methods. The five risk classes were: married low-risk; married low-income; cohabiting multiproblem; single low-income; and single low-income/education. The LCA findings illustrated how the association between particular family configurations and the infants’ caregiving environment quality varied across race and site. Discussion focuses on the value of person-centered models of analysis to understand complexities of prediction of multiple risk factors