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Combustion efficiency of internal combustion engine is closely influenced by the air flow pattern in the engine cylinder. Some researchers use high-speed particle image velocimetry to visualize and measure the temporally and spatially resolved in-cylinder velocity flow fields in the optically assessable engine. However, the transparent cylindrical liner makes it difficult to accurately determine the particle displacements inside the cylinder due to the optically distorted path of scattering light from seeding particles through the curved liner. To correct for the distortion-induced error in the seeding particle positions through the optical liner, the distortion mapping function is modeled using the Gaussian optics theory. Two artificial flow patterns with 5 by 5 vectors were made to illustrate the mapping correction. Distortion-induced error of velocity vectors was precisely mapped in six different planes inside the cylinder.

Rotating flow inside an internal combustion engine cylinder is deliberately engineered for improved fuel-air mixing and combustion. The details of the rotating flow structure vary temporally over an engine cycle as well as cyclically at the same engine phase. Algorithms in the literature to identify these structural details of the rotating flow invariably focus on locating its center and, on occasion, measuring its rotational strength and spatial extent. In this paper, these flow structure parameters are evaluated by means of complex moments, which have been adapted from image (scalar field) recognition applications to two-dimensional flow pattern (vector field) analysis. Several additional detailed characteristics of the rotating flow pattern - the type and extent of its deviation from the ideal circular pattern, its rotational and reflectional symmetry (if exists), and thus its orientation - are also shown to be related to the first few low-order complex moments of the flow pattern.

The continuous improvement of spark-ignition direct-injection (SIDI) engines is largely attributed to the enhanced understanding of air-fuel mixing and combustion processes. The intricate interaction between transient spray behavior and the ambient flow field is important to unveil the airflow dynamics during the spray injection process. This study investigates the fuel-spray boundary interactions under different superheated conditions by analyzing the ambient flow field pattern with constraint-based modeling (CBM). In the experimental setup, superheated conditions are facilitated by adjusting different fuel temperatures and ambient pressures. By adding the tracer particles containing Rhodamine 6G to the ambient air, the combined diagnostic of fluorescent particle image velocimetry (FPIV) and Mie-scattering is implemented to measure the velocity distribution and flow trajectory of the air surrounding the spray formation and propagation.