Reynolds Averaged Navier-Stokes turbulence models have been widely used in many industrial applications because of their lower computational cost compared to other simulation approaches such as DNS and LES. This is a consequence of considering mean-ow quantities achieved by the t
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Reynolds Averaged Navier-Stokes turbulence models have been widely used in many industrial applications because of their lower computational cost compared to other simulation approaches such as DNS and LES. This is a consequence of considering mean-ow quantities achieved by the time-averaging process. However, the time-averaging introduces the Reynolds stresses, which make the system of the RANS equations underdetermined, thus requiring modelling approaches. As a consequence of modelling the Reynolds stresses based on the Boussinesq hypothesis, RANS simulations contain model errors. To improve the RANS predictive capability, this thesis aims to identify momentum forcing f that is required to reduce base-model errors with respect to any given data, and determine its characteristics.
