In spark ignition (SI) engines, high efficiencies are typically obtained near limits of stable operation which may result in high cycle-to-cycle variations (CCV). Traditional computational fluid dynamics (CFD) tools like Reynolds-averaged Navier-Stokes simulations (RANS) may not predict the CCV in engines. Higher fidelity CFD tools like large-eddy simulations (LES) have been shown to capture these CCV. In this paper, LES of a motored transparent combustion chamber (TCC) engine is performed to simulate the CCV introduced during the gas exchange process. A grid convergence study is performed, and it is shown that using a 1 mm in-cylinder grid size leads to similar flowfield statistics as compared to using a 0.5 mm in-cylinder grid size. The phase-averaged mean and root mean square (RMS) flowfields predicted by LES are validated comprehensively using particle image velocimetry (PIV) measurements. The validation is performed for 4 different crank angles, corresponding to the intake, compression, expansion and exhaust strokes, and for three different measurement planes. It is shown that LES is able to accurately predict the mean velocities, whereas the RMS velocity magnitudes are under-predicted. The inaccuracy in the RMS velocities are largest during the intake stroke, whereas good agreement with the measurements is observed during the expansion and exhaust strokes. A similarity index analysis provides a quantitative measure of the number of cycles that are required to be simulated to capture the flowfield statistics. This analysis is applied to both the PIV dataset and CFD dataset. It is shown that approximately 20 cycles are sufficient to obtain converged mean and RMS flowfields from the simulations, whereas the PIV measurements require approximately 40 cycles. Faster convergence for the LES results is because the simulations do not take into account additional uncertainties in the rpm, plenum pressures, boundary temperatures and so on, which are present in the experiments.

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