JWSS - Journal of Water and Soil Science

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In this paper, velocity profiles were analyzed under different conditions such as bed slope, discharge and concentration of density current, and water entrainment. Experiments were carried out in a tilting flume with the density currents being provided using salt and water solution. Results showed that the above mentioned factors have significant effects on the velocity profile characteristics. Dimensionless velocity profiles were also provided and compared for sub-critical, critical and supercritical flow conditions and the results showed that for supper critical conditions the velocity profiles are generally thicker due to the more ambient water entrainment. The coefficients of velocity profile equations were also derived for the jet and wall zones, which showed good agreements with the experimental measurements. Relative values of the velocity profile characteristics were also calculated in order to have a better understanding about the velocity profile structure.

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While the mass density current penetrates the stagnant fluid, a plunge point occurs. In this regard, the boundary of the dense fluid with ambient fluid is determined at the plunge point height. In this research, the hydraulic parameters of the dense flow and the bed slope of the stagnant fluid which have a significant effect on the plunge point have been investigated under the two turbulence models: the k- and the RNG at the Flow-3D model. To achieve the purpose of this research, a physical model was set up at the hydraulics laboratory of Shahid Chamran University (SCU), Ahwaz, Iran. Then, using the Flow-3D model with both the k- and the RNG turbulence model, the height of the plunge point was simulated according to the same experimental condition. Findings showed that the predicted depth under the RNG model is closer to the results of the physical model. For example, the k- and RNG model for the 12% slope can estimate the plunge point depth by 30% and 12.28% respectively more than the experimental data. However, for all the slopes, the k-e model can on average overestimate by 27% and RNG model 10.5% more than the results of experimental data. The statistical analysis showed that the RNG model predicts the plunge point depths with a satisfactory precision.