On the use of particle image velocimetry (PIV) data for the validation of Reynolds averaged Navier-Stokes (RANS) simulations during the intake process of a spark ignition direct injection (SIDI) engine

Computational fluid dynamics (CFD) simulations of the in-cylinder flow field are widely used in the design of internal combustion engines (ICEs) and must be validated against experimental measurements to enable a robust predictive capability. Such validation is complicated by the presence of both large-scale cycle-to-cycle variations and small-scale turbulent fluctuations in experimental measurements of in-cylinder flow fields. Reynolds averaged Navier-Stokes (RANS) simulations provide overall flow structures with acceptable accuracy and affordable computational cost for widespread industrial applications. Due to the nature of averaging physical parameters in RANS, its validation against experimental results obtained by particle image velocimetry (PIV) requires consideration of how best to average or filter the measured turbulent flows. In this paper, PIV measurements on the cross-tumble plane were recorded every five crank angle degrees for 300 cycles during the intake process of a motored, optically accessible spark ignition direct injection (SIDI) engine. Several methods including ensemble averaging, speed-based averaging and low-order proper orthogonal decomposition (POD) reconstruction were applied to remove the fluctuations from experimental PIV vector fields and thus enable comparison to RANS simulations. Quantitative comparison metrics were used to evaluate the performances of each method in representing the intake jet. Recommendations are made on how to provide a fair validation between measured data and simulation results in highly fluctuating flow fields such as the engine intake jet