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Although the near-wall turbulence is not fully developed in the engine combustion chamber, wall heat transfer models based on flow characteristics in fully developed near-wall turbulence are typically employed in engine simulations to predict heat transfer. Only few studies reported the wall heat transfer mechanism in near-wall flow where the near-wall turbulence was not fully developed as expected in the engine combustion chamber. In this study, the velocity distribution and wall heat flux in such a near-wall flow were evaluated using a rapid compression and expansion machine. In addition to the experimental approach, a numerical simulation with highly resolved calculation mesh was applied in various flow fields expected in the engine combustion chamber. As a result, the turbulent Reynolds number that represents the relationship between turbulent production and dissipation varied in the wall boundary layer according to the near-wall flow condition. This behavior affects the wall heat transfer. Considering this finding, a new model was formulated by introducing a ratio of turbulent Reynolds number in an intended near-wall flow to that in fully developed near-wall turbulence. It was confirmed that the proposed model could improve the prediction accuracy of wall heat flux even in near-wall flow where the near-wall turbulence was not fully developed. By applying the proposed model in engine computational fluid dynamics, it was found that the proposed model could predict the wall heat flux in a homogeneous charge compression ignition gasoline engine with acceptable accuracy.

This study simulates soot formation processes in diesel combustion using a large eddy simulation (LES) model, based on a one-equation subgrid turbulent kinetic energy model. This approach was implemented in the KIVA4 code, and used to model diesel spray combustion within a constant volume chamber. The combustion model uses a direct integration approach with a fast explicit ordinary differential equation (ODE) solver, and is additionally parallelized using OpenMP. The soot mass production within each computation cell was determined using a phenomenological soot formation model developed by Waseda University. This model was combined with the LES code mentioned above, and included the following important steps: particle inception during which acenaphthylene (A 2 R 5 ) grows irreversibly to form soot; surface growth with driven by reactions with C 2 H 2 ; surface oxidation by OH radical and O 2 attack; and particle coagulation.The results obtained using our new model are compared to those generated using a RANS (RNG k-epsilon) model, and also to experimental data from the engine combustion network (ECN) of Sandia National Laboratories. The sensitivity of the LES results to mesh resolution is also discussed. The results show that both RANS and LES simulations predict the dispersion and vapor penetration of the injected fuel fairly well. LES generally provides flow and spray characteristics in better agreement with experimental data than RANS. It is also shown that the phenomenological soot model is useful for investigating soot particle production and distribution. The LES model was better than the RANS model at describing instantaneous soot concentration contour.

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