Entrained-flow slagging gasifiers are characterized by operating conditions that promote ash migration/ deposition onto the reactor walls, whence ash is drained as a molten phase. Experimental investigation on ashes generated by full-scale plants suggested that both char entrapment inside the melt and carboncoverage of the slag can occur. Because of the wide range of spatial and temporal scales involved in these phenomena, numerical simulation of the fate of the flying fine char particles is a very difficult task. This work illustrates how different numerical modeling approaches can be jointly used to understand segregation patterns of char particles in full-scale entrained-flow coal gasifiers operated in the slagging regime. A multilevel approach has been developed for this purpose. RANS-based simulations of the full-scale geometry with coal particle injection and tracking aimed to obtain the general behavior of the flow field and particle trajectories. Simulations enabled to estimate the effect of swirl and tangential flow on the bulk-to-wall char particle deposition rate. Then, RANS results were adopted in a more detailed numerical model based on the solution of the filtered Navier-Stokes equations. In this last model, a turbulence LES approach for the Eulerian gas phase was applied. The equations of particles motion were solved via a Lagrangian particle tracking algorithm with the TrackToFace method. Simulations were performed involving a level of detail that allowed to obtain a clear picture of the multiphase flow behavior responsible for char deposition phenomena. This multilevel approach enabled the assessment of the char particle deposition rates and the nature of char-slag interaction (segregation/entrapment) that are likely to occur in full-scale slagging gasifiers. Results of numerical simulations have been critically discussed in the light of experimental findings. They represent a useful source of information for the implementation of constitutive equations and parameters in design-oriented reduced compartmental models. © 2013 Elsevier Ltd. All rights reserved.