Impact of Grid Density on the LES Analysis of Flow CCV: Application to the TCC-III Engine under Motored Conditions 2018-01-0203

Large-eddy simulation (LES) applications for internal combustion engine (ICE) flows are constantly growing due to the increase of computing resources and the availability of suitable CFD codes, methods and practices. The LES superior capability for modeling spatial and temporal evolution of turbulent flow structures with reference to RANS makes it a promising tool for describing, and possibly motivating, ICE cycle-to-cycle variability (CCV) and cycle-resolved events such as knock and misfire.

Despite the growing interest towards LES in the academic community, applications to ICE flows are still limited. One of the reasons for such discrepancy is the uncertainty in the estimation of the LES computational cost. This in turn is mainly dependent on grid density, the CFD domain extent, the time step size and the overall number of cycles to be run. Grid density is directly linked to the possibility of reducing modeling assumptions for sub-grid scales. The extent of the computational domain influences the impact of the boundary conditions on the CFD results. The time-step size needs to be set according to the size of the resolved turbulent eddies. It is therefore closely tied to local grid size with the constraint that the CFL number should be lower than unity everywhere in the domain for the highest accuracy. The overall number of simulated cycles influences the soundness of the statistical analysis of LES outcomes.

This paper focuses on the impact of grid density on the LES description of the TCC-III single-cylinder optical engine flow under motored conditions. In particular, attention is focused on the intake stroke of the engine cycle, which governs the induced flow motion. LES results are first evaluated by means of well-established quality indices to find the insufficient grid resolution region to be refined. Second, comparisons with available PIV measurements are carried out. Finally, COV and proper orthogonal decomposition analyses are adopted to further assess the impact of grid density on CCV.