Mapping the Operation of the Miniature Combustion Aerosol Standard (Mini-CAST) Soot Generator

The Jing Ltd. miniature combustion aerosol standard (Mini-CAST) soot generator is a portable, commercially available burner that is widely used for laboratory measurements of soot processes. While many studies have used the Mini-CAST to generate soot with known size, concentration, and organic carbon fraction under a single or few conditions, there has been no systematic study of the burner operation over a wide range of operating conditions. Here, we present a comprehensive characterization of the microphysical, chemical, morphological, and hygroscopic properties of Mini-CAST soot over the full range of oxidation air and mixing N-2 flow rates. Very fuel-rich and fuel-lean flame conditions are found to produce organic-dominated soot with mode diameters of 10-60nm, and the highest particle number concentrations are produced under fuel-rich conditions. The lowest organic fraction and largest diameter soot (70-130nm) occur under slightly fuel-lean conditions. Moving from fuel-rich to fuel-lean conditions also increases the O:C ratio of the soot coatings from similar to 0.05 to similar to 0.25, which causes a small fraction of the particles to act as cloud condensation nuclei near the Kelvin limit (kappa similar to 0-10(-3)). Comparison of these property ranges to those reported in the literature for aircraft and diesel engine soots indicates that the Mini-CAST soot is similar to real-world primary soot particles, which lends itself to a variety of process-based soot studies. The trends in soot properties uncovered here will guide selection of burner operating conditions to achieve optimum soot properties that are most relevant to such studies.

Development of a model-based transient calibration process for diesel engine electronic control module tables – Part 1: data requirements, processing, and analysis

This is the first part of a study investigating a model-based transient calibration process for diesel engines. The motivation is to populate hundreds of parameters (which can be calibrated) in a methodical and optimum manner by using model-based optimization in conjunction with the manual process so that, relative to the manual process used by itself, a significant improvement in transient emissions and fuel consumption and a sizable reduction in calibration time and test cell requirements is achieved. Empirical transient modelling and optimization has been addressed in the second part of this work, while the required data for model training and generalization are the focus of the current work. Transient and steady-state data from a turbocharged multicylinder diesel engine have been examined from a model training perspective. A single-cylinder engine with external air-handling has been used to expand the steady-state data to encompass transient parameter space. Based on comparative model performance and differences in the non-parametric space, primarily driven by a high engine difference between exhaust and intake manifold pressures (ΔP) during transients, it has been recommended that transient emission models should be trained with transient training data. It has been shown that electronic control module (ECM) estimates of transient charge flow and the exhaust gas recirculation (EGR) fraction cannot be accurate at the high engine ΔP frequently encountered during transient operation, and that such estimates do not account for cylinder-to-cylinder variation. The effects of high engine ΔP must therefore be incorporated empirically by using transient data generated from a spectrum of transient calibrations. Specific recommendations on how to choose such calibrations, how many data to acquire, and how to specify transient segments for data acquisition have been made. Methods to process transient data to account for transport delays and sensor lags have been developed. The processed data have then been visualized using statistical means to understand transient emission formation. Two modes of transient opacity formation have been observed and described. The first mode is driven by high engine ΔP and low fresh air flowrates, while the second mode is driven by high engine ΔP and high EGR flowrates. The EGR fraction is inaccurately estimated at both modes, while EGR distribution has been shown to be present but unaccounted for by the ECM. The two modes and associated phenomena are essential to understanding why transient emission models are calibration dependent and furthermore how to choose training data that will result in good model generalization.