The ability to predict cyclic variations is certainly useful in studying engine operating regimes, especially under unstable operating conditions where one single cycle may differ from another substantially and a single simulation may give rather misleading results. PDF based models such as Stochastic Reactor Models (SRM) are able to model cyclic variations, but these may be overpredicted if discretization is too coarse.
The range of cyclic variations and the dependence of the ability to correctly assess their mean values on the number of cycles simulated were investigated. In most cases, the average values were assessed correctly on the basis of as few as 10 cycles, but assessing the complete range of cyclic variations could require a greater number of cycles. In studying average values, variations due too coarse discretization being employed are smaller than variations originating from changes in physical parameters, such as heat transfer and mixing parameters. Thus it feels safe to conclude that, even with such coarse discretization and fast execution, the findings obtained with use of the SRM are fundamentally correct. For studies of cyclic variations in engines, discretization needs to have a higher level of resolution to provide trustworthy results.
In the case of high levels of turbulence and evenly distributed heat transfer, the in-cylinder conditions become homogeneous more quickly. The results indicate that in HCCI engines inhomogeneties tend to promote earlier ignition and more stable operating conditions as well as lesser cyclic variations. The pressure rate was shown to generally increase under homogeneous conditions, which could lead to unwanted noise and even to engine damage. According to calculations for HCCI engines, the level of turbulence and the heat transfer distribution had little impact on the duration of combustion or on the amount of HC and NO at EVO, except for HC which rocketed in the odd misfiring cycles.