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Time-resolved particle image velocimetry and Mie-scattering of fuel droplets at 16 kHz were used to capture simultaneously the temporal evolution of the in-cylinder flow field and spray formation within a direct-injection spark-ignition engine. The engine was operated in stratified combustion mode, with stratified mixtures created by a triple injection late in the compression stroke. The impact of geometric variation of the intake port on in-cylinder flow and flow-spray interactions was investigated, focusing on the second injection, since it provides ignitable mixtures at the time of ignition and is subject to strong fluctuations, rather than the first injection, which is very reproducible. Flow field statistics conditioned on the spray shape of the second injection revealed regions with macroscopic cycle-to-cycle flow variations, which correlated with the spray for all recorded cycles. The flow-spray interaction was traced back to before the first injection using correlation analysis, which revealed that cycle-to-cycle fluctuations of the large-scale tumble vortex had a big impact on the spray shape of the second injection, while the first injection was unaffected. This indicates that the origin of the spray fluctuations may be during intake. Despite significant flow modifications due to the intake port geometry variation, fluctuation levels of the second injection were the same for both geometries, that is, spray fluctuations were not sensitive to the geometric change.

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Cycle-to-cycle variations in an optically accessible four-stroke direct injection spark-ignition gasoline engine are investigated using high-speed scanning particle image velocimetry and in-cylinder pressure measurements. Particle image velocimetry allows to measure in-cylinder flow fields at high spatial and temporal resolution. Binary classifiers are used to predict combustion cycles of high indicated mean effective pressure based on in-cylinder flow features and engineered tumble features obtained during the intake and the compression stroke. Basic in-cylinder flow features of the mid-cylinder plane are sufficient to predict combustion cycles of high indicated mean effective pressure as early as 180 degree crank angle before the top dead center at 0 degree crank angle. Engineered characteristic tumble features derived from the flow field are not superior to the basic flow features. The results are independent of the tested machine learning method (multilayer perceptron and boosted decision trees) and robust to hyper-parameter selection.