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Quantitative planar laser-induced fluorescence (PLIF) of gaseous acetone as a fuel-tracer has been used in an optically accessible engine, fueled by direct hydrogen injection. The purpose of this article is to assess the accuracy and precision of the measurement and the associated data reduction procedures. A detailed description of the acetone seeding system is given as well. The key features of the experiment are a high-pressure bubbler saturating the hydrogen fuel with acetone vapor, direct injection into an optical engine, excitation of acetone fluorescence with an Nd:YAG laser at 266 nm, and detection of the resulting fluorescence by an unintensified camera. Key steps in the quantification of the single-shot imaging data are an in-situ calibration and a correction for the effect of local temperature on the fluorescence measurement.

Spark plugs instrumented with a ring of optical fibers in the threaded-body region have seen considerable use in the past ten years, and it is expected that their application to unmodified production engines will increase in the years to come. Interpretation of the optical signals obtained with the probe is often difficult, particularly under lean operating conditions where the low luminosity of the flame leads to imprecise flame arrival detection. A systematic look at the optical signals, along with direct imaging of the flame, has been undertaken to calibrate and optimize the determination of flame arrival times. In addition, an evaluation of the different models available for the analysis of the flame arrival data is made. Data fits are compared with real flame images, to determine which model best estimates the convective velocity of the flow and the expansion speed of the flame kernel.

RNG k-ε closure turbulence dissipation equations are evaluated employing the CFD code KIVA-3V Release 2. The numerical evaluations start by considering simple jet flows, including incompressible air jets and compressible helium jets. The results show that the RNG closure turbulence model predicts lower jet tip penetration than the "standard" k-ε model, as well as being lower than experimental data. The reason is found to be that the turbulence kinetic energy is dissipated too slowly in the downstream region near the jet nozzle exit. In this case, the over-predicted R term in RNG model becomes a sink of dissipation in the ε-equation. As a second step, the RNG turbulence closure dissipation models are further tested in complex engine flows to compare against the measured evolution of turbulence kinetic energy, and an estimate of its dissipation rate, during both the compression and expansion processes.

Transients in the rate of injection (ROI) with respect to time are ever-present in direct-injection engines, even with common-rail fueling. The shape of the injection ramp-up and ramp-down affects spray penetration and mixing, particularly with multiple-injection schedules currently in practice. Ultimately, the accuracy of CFD model predictions used to optimize the combustion process depends upon the accuracy of the ROI utilized as fuel input boundary conditions. But experimental difficulties in the measurement of ROI, as well as real-world affects that change the ROI from the bench to the engine, add uncertainty that may be mistaken for weaknesses in spray modeling instead of errors in boundary conditions. In this work we use detailed, time-resolved measurements of penetration at the Spray A conditions of the Engine Combustion Network to rigorously guide the necessary ROI shape required to match penetration in jet models that allow variable rate of injection.

For 2D surface temperature monitoring applications, a variant of Electrical Impedance Tomography (EIT) was evaluated computationally in this study. Literature examples of poor sensor performance in the center of the 2D domains away from the side electrodes motivated these efforts which seek to overcome some of the previously noted shortcomings. In particular, the use of ‘sensing skins’ with novel tailored baseline conductivities was examined using the EIDORS package for EIT. It was found that the best approach for detecting a temperature hot spot depends on several factors such as the current injection (stimulation) patterns, the measurement patterns, and the reconstruction algorithms. For well-performing combinations of these factors, customized baseline conductivities were assessed and compared to the baseline uniform conductivity.