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Influence of Discretization Schemes and LES Subgrid Models on Flow Field Predictions for a Motored Optical Engine
ISSN: 1946-3936, e-ISSN: 1946-3944
Published April 03, 2018 by SAE International in United States
Citation: Nichani, V., Jaime, R., Singh, S., Yang, X. et al., "Influence of Discretization Schemes and LES Subgrid Models on Flow Field Predictions for a Motored Optical Engine," SAE Int. J. Engines 11(6):1505-1529, 2018, https://doi.org/10.4271/2018-01-0185.
Large-eddy simulations (LES) of a motoring single-cylinder engine with transparent combustion chamber (TCC-II) are carried out using a commercially available computer code, CONVERGE. Numerical predictions are compared with high-speed particle image velocimetry (PIV) measurements. Predictions of two spatial discretization schemes, namely, numerically stabilized central difference scheme (CDS) and fully upwind scheme are compared. Four different subgrid scale (SGS) models; a non-eddy viscosity dynamic structure turbulence (DST) model of Pomraning and Rutland, one-equation eddy-viscosity (1-Eqn) model of Menon et al., a zeroequation eddy-viscosity model of Vreman, and the zeroequation standard Smagorinsky model are employed on two different grid configurations. Additionally, simulations are also performed by deactivating the LES SGS models. It is found that the predictions when using the numerically stabilized CDS are significantly better than using the fully upwind scheme. The LES SGS models clearly make an impact on predicted flow field although the impact is not always positive. Overall, the eddy-viscosity model by Vreman provided the best predictions of flow statistics compared to the other LES models used in this work. A parameter called Convergence Index (CI), which is an indication of the magnitude of similarity or dissimilarity between two velocity fields, is introduced to assess the number of cycles required to calculate flow statistics. Interestingly, it is found that for the present study only 10 cycles are sufficient to obtain statistically convergent mean and root mean squared (RMS) values of velocity fluctuations.