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Planar laser-induced fluorescence (PLIF) has become a useful diagnostic for the quantification of in-cylinder flowfield conditions, and in many applications determining the homogeneity of the in-cylinder flowfield is of primary importance. In some cases, noise associated with this imaging technique (i.e., camera noise, shot-to-shot laser energy variation, and laser sheet profile variations) can dominate the flowfield inhomogeneities, leading to biased mixture stratification statistics. Presented herein are three data normalization schemes (global-, image-, and ray-mean) that can be used to correct for these noise sources when assessing mixture stratification from PLIF data. The normalization schemes are applied to in-cylinder PLIF data obtained over a wide range of inhomogeneity levels, and the conditions over which the use of each normalization scheme is appropriate are discussed.

The KIVA 3V code has been applied to predict combustion chamber heat flux in an air-cooled utility engine. The KIVA heat flux predictions were compared with experimentally measured data in the same engine over a wide range of operating conditions. The measured data were found to be approximately two times larger than the predicted results, which is attributed to the omission of chemical heat release in the near-wall region for the heat transfer model applied. Modifying the model with a simple scaling factor provided a good comparison with the measured data for the full range of engine load, heat flux sensor location, air-fuel ratio and spark timings tested. The detailed spatially resolved results of the KIVA predictions were then used to develop a simplified model of the combustion chamber temporally integrated heat flux using an artificial neural network (ANN).

Cycle-to-cycle variation in combustion phasing and combustion rate cause knock to occur differently in every cycle. This is found to be true even if the end gas thermo-chemical time history is the same. Three cycles are shown that have matched combustion phasing, combustion rate, and time of knock onset, but have knock intensity that differs by a factor of six. Thus, the prediction of knock intensity must include a stochastic component. It is shown that there is a relationship between the maximum possible knock intensity and the unburned fuel energy at the time of knock onset. Further, for a small window of unburned energy at knock onset, the probability density function of knock intensity is self similar when scaled by the 95th percentile of the cumulative distribution, and log-normal in shape.

Thermal swing coatings that have low volumetric heat capacity and low thermal conductivity are attractive because they have the potential to significantly reduce heat transfer to the combustion chamber walls. This paper presents an analytical method for determining the exact solution of the time-resolved wall temperature during the engine cycle for any number of coating layers and properties using the Laplace transformed heat diffusion equation. The method relies only on material properties and the past heat flux history, and represents the exact solution of the heat diffusion equation. The analytical nature of the solution enables fast computation and, therefore, application to system-level optimization calculations. The model relies on an assumption of one-dimensional heat flow, and constant material properties.