PIV type 2 Grade 2

Large-eddy simulation has been increasingly applied to internal combustion engine flows because of their improved potential to capture the spatial and temporal evolution of turbulent flow structures compared with Reynolds-averaged Navier Stokes simulation. Furthermore, large-eddy simulation is universally recognized as capable of simulating highly unsteady and random phenomena, which drive cycle-to-cycle variability and cycle-resolved events such as knocks and misfires. To identify large-scale structure fluctuations, many methods have been proposed in the literature. This article describes the application of several analysis methods for the comparison between different datasets (experimental or numerical) and the identification of large-structure fluctuations. The reference engine is the well-known TCC-III single-cylinder optical unit from the University of Michigan and GM Global R&D center; the analyses were carried out under motored engine conditions. A deep analysis of in-cylinder gas dynamics and flow structure evolution was performed by comparing the experimental results (particle image velocimetry of the velocity fields) with a dataset of consecutive large-eddy simulation cycles on four different cutting planes at engine-relevant crank angle positions. Phase-dependent proper orthogonal decomposition was used to obtain further conclusions regarding the accuracy of the simulation results and to apply conditional averaging methods. A two-point correlation and an analysis of the tumble center are proposed. Finally, conclusions are drawn to be used as guidelines in future large-eddy simulation analyses of internal combustion engines.

A non-linear eddy-viscosity transition model is presented, tuned by a large experimental data set describing transitional boundary layers. Data have been acquired by TR-PIV on a flat plate placed in a 2D converging-diverging channel with variable opening angle, allowing variation of the adverse pressure gradient, the free-stream turbulence intensity and the flow Reynolds number. Overall, 48 different combinations of these flow parameters encompass different modes of transition from bypass to separated-flow mechanisms, thus allowing fine tuning of the model, spanning significantly different conditions. The model is tuned locally as a function of the turbulent kinetic energy, a Reynolds number based on the wall distance and the l(2)-norm of the shear rate tensor. A first correlation determines the rotation for alignment of the principal axes of the shear and stress tensors. By a second correlation, the eigenvalues of the stress tensor are obtained. The non-linear eddy-viscosity relation reproduces the anisotropy of the turbulence field observed for both bypass and separated-flow transitional cases. The relation has been applied to another experimental data set that did not participate to the fitting of the model and that is characterized by a different range of Reynolds number and turbulence intensity and a significantly stronger adverse pressure gradient with respect to the tuning dataset. Such application further strengthens the capability of the proposed correlations, that can easily be implemented in existing CFD solvers.