An application of tomographic PIV to investigate the spray-induced turbulence in a direct-injection engine

Fuel sprays produce high-velocity, jet-like flows that impart turbulence onto the ambient flow field. This spray-induced turbulence augments rapid fuel-air mixing, which has a primary role in controlling pollutant formation and cyclic variability in direct-injection engines. This paper presents tomographic particle image velocimetry (TPIV) measurements to analyze the 3D spray-induced turbulence during the intake stroke of a direct-injection spark-ignition (DISI) engine. The spray produces a strong spray-induced jet (SIJ) in the far field, which travels through the cylinder and imparts turbulence onto the surrounding flow. Planar high-speed PIV measurements at 4.8 kHz are combined with TPIV at 3.3 Hz to evaluate spray particle distributions and validate TPIV measurements in the particle laden flow. A comprehensive uncertainty analysis is performed to assess the uncertainty associated with individual vorticity and strain rate components. TPIV analyses quantify the spatial domain of the turbulence in relation to the SIJ and describe how turbulent flow features such as turbulent kinetic energy (TKE), strain rate (S) and vorticity (Omega) evolve into the surrounding flow field. Access to the full S and Omega tensors facilitate the evaluation of turbulence for individual spray events. TPIV images reveal the presence of strong shear layers (visualized by high S magnitudes) and pockets of elevated vorticity along the immediate boundary of the SIJ. S and Omega values are extracted from spatial domains extending in 1 mm increments from the SIJ. Turbulence levels are greatest within the 0-1 mm region from the SIJ boarder and dissipate with radial distance. Individual strain rate and vorticity components are analyzed in detail to describe the relationship between local strain rates and 3D vortical structures produced within strong shear layers of the SIJ. Analyses are intended to understand the flow features responsible for rapid fuel-air mixing and provide valuable data for the development of numerical models. (C) 2019 Elsevier Ltd. All rights reserved.