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In the Big Data era, the capability in statistical and probabilistic data characterization, data pattern identification, data modeling and analysis is critical to understand the data, to find the trends in the data, and to make better use of the data. In this paper the fundamental probability concepts and several commonly used probabilistic distribution functions, such as the Weibull for spectrum events and the Pareto for extreme/rare events, are described first. An event quadrant is subsequently established based on the commonality/rarity and impact/effect of the probabilistic events. Level of measurement, which is the key for quantitative measurement of the data, is also discussed based on the framework of probability. The damage density function, which is a measure of the relative damage contribution of each constituent is proposed. The new measure demonstrates its capability in distinguishing between the extreme/rare events and the spectrum events.

Spray characteristics of injectors depend on, among other factors, not only the level of turbulence upstream of the nozzle plate, but also on whether cavitation arises. "Bulk" cavitation, by which we mean cavitation which arises far from walls and thus far from streamline curvature associated with salient points on a wall, has not been investigated thoroughly experimentally and moreover it is quite challenging to predict by means of computational fluid dynamics. Information about the effect of the injector geometry on the formation of bulk cavitation and quantitative measurements of the flow field that promotes this phenomenon in gasoline injectors does not exist and this forms the background to this work. Evolution of bulk cavitation was visualized in two gasoline multi-hole injectors by means of a fast camera.