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The proper formation of fuel-air mixture, which depends to a large extend on the complex in-cylinder air flow, is an important criterion to control the clean and reliable combustion process in spark-ignition direct-injection (SIDI) engines. The in-cylinder flow vorticity field presents highly transient complex characteristics, and the corresponding vorticity field also evolves in the entire engine cycle from intake to exhaust strokes. It is also widely recognized that the vorticity field plays a key role in the in-cylinder turbulent field because it influences the air-fuel mixing and flame development process. In this investigation, the in-cylinder vortex structure and vorticity field characteristics are analyzed using the phase-invariant proper orthogonal decomposition (POD) method.

The preparation of fuel-air mixture and its efficient, clean, and reliable combustion in spark-ignition direct-injection (SIDI) engines depend to a large extend on the complex in-cylinder air flow. It has been widely recognized that the ensemble-averaged flow field provides rather limited understanding of in-cylinder air motion due to the strong cycle-to-cycle variations. In this study, time-resolved particle image velocimetry (PIV) is utilized to measure the in-cylinder air motion in a motored single-cylinder optical engine. Then, the velocity fields from different phases (crank-angle positions during intake and compression strokes) of 200 engine cycles are analyzed using phase-invariant proper orthogonal decomposition (POD) technique. With the phase-invariant POD method, the velocity fields from different phases are decomposed into a single set of POD modes. In this manner, the POD modes can be used to represent any phase of the flow.

Proper Orthogonal Decomposition (POD) is a useful statistical tool for analyzing the cycle-to-cycle variation of internal combustion engine in-cylinder flow field. Given a set of flow fields (also known as snapshots) recorded over multiple engine cycles, the POD analysis optimally decomposes the snapshots into a series of flow patterns (known as POD modes) and corresponding coefficients of successively maximum flow kinetic energy content. These POD results are therefore strongly dependent on the kinetic energy content of the individual snapshots, which may vary over a wide range. However, there is as yet no algorithm in the literature to define, detect, and then remove outlier snapshots from the dataset in a systematic manner to ensure reliable POD results.

The continuous improvement of gasoline direct injection (GDI) engine is largely attributed to the enhanced understanding of air-fuel mixing and combustion processes. This work investigates the transient behavior of the ambient flow fields of hexane spray using the combined diagnostics of fluorescent particle image velocimetry (FPIV) and mie scattering. A hybrid analysis approach is proposed to investigate the residual effect of spray injection on ambient flow fields, including flow similarity measurement, entrainment velocity calculation, and vortex strength detection. The work investigates the residual effect under different injection durations, injection pressure, and flash-boiling extent of the spray, and unveils correlation between vortex strength and the endurance of the residual effect.