A Moment-Based Depth-Averaged K-ε Model for Predicting the True Turbulence Intensity over Bedforms

Turbulence models are critical for depth-averaged flow in at least two ways: (i) as closures momentum equations and (ii) indicators of the spatial variability turbulence intensity field, which is crucial sediment transport bedform evolutions. This paper introduces a novel moment-based k-ε (MDAKE) model that could be considered revised version standard Rastogi–Rodi (SDAKE) can used to estimate true values kinetic energy more complex varied conditions with accelerating–decelerating fields. The study hand shows SDAKE tends overestimate turbulent (k¯u) by 50 130% benchmark case uniform over flatbed. Further, assumes bed shear velocity an appropriate scale generation terms both dissipation. When topographic features vary, zone formed assumption invalid. Since most generated zones away from bed, model’s estimates field out phase measurements train bedforms. Therefore, newly developed KE based on moment concept presented here. replaces integral (u1). calibrated MDAKE predict results generally reasonable agreement measurements.