The exact placement of the laminar–turbulent transition has a significant effect on relevant characteristics of the boundary layer and aerodynamics, such as drag, heat transfer and flow separation on e.g. wings and turbine blades. Tripping, which fixes the transition position, has been a valuable aid to wind-tunnel testing during the past 70 years, because it makes the transition independent of the local condition of the free-stream. Tripping helps to obey flow similarity for scaled models and serves as a passive control mechanism. Fundamental fluid-mechanics studies and many engineering developments are based on tripped cases. Therefore, it is essential for computational fluid dynamics (CFD) simulations to replicate the same forced transition, in spite of the advanced improvements in transition modelling. In the last decade, both direct numerical simulation (DNS) and large-eddy simulations (LES) include tripping methods in an effort to avoid the need for modeling the complex mechanisms associated with the natural transition process, which we would like to bring over to Reynolds-averaged Navier–Stokes (RANS) turbulence models. This paper investigates the implementation and performance of such a technique in RANS and specifically in the k- ω SST model. This study assesses RANS tripping with three alternatives: First, a recent approach of turbulence generation, denoted as turbulence-injection method (kI), is evaluated and investigated through different test cases; second, a predefined transition point is used in a traditional transition model (denoted as IM method); and third a novel formulation combining the two previous methods is proposed, denoted γ- kI. The model is compared with DNS, LES and experimental data in a variety of test cases ranging from a turbulent boundary layer on a flat plate to the three-dimensional (3D) flow over a wing section. The desired tripping is achieved at the target location and the simulation results compare very well with the reference results. With the application of the novel model, the challenging transition region can be excluded from a simulation, and consequently more reliable results can be provided.