Numerical investigation of continuous, high density turbidity currents response, in the variation of fundamental flow controlling parameters

During floods, the density of river water usually increases due to the increase in the concentration of the suspended sediment that the river carries, causing the river to plunge underneath the free surface of a receiving water basin and form a turbidity current that continues to flow along the bottom. The study and understanding of such complex and rare phenomena is of great importance, as they constitute one of the major mechanisms for suspended sediment transport from rivers into the ocean, lakes or reservoirs. In the present paper a previously tested and verified numerical model[1]is applied in laboratory scale numerical experiments of continuous, high density turbidity currents. The turbidity currents are produced by the steady discharge of fresh water – suspended sediment mixtures, into an inclined channel which is connected at its downstream end to a wide horizontal tank. Both, channel and tank are initially filled with fresh water. This configuration serves as a simplified experimental analog of natural, hyperpycnal turbidity currents that are formed at river outflows in the sea, lakes or reservoirs and usually travel within subaqueous canyon-fan complexes. The main aim is to investigate the exact qualitative and quantitative effect of fundamental, flow controlling parameters in the hydrodynamic and depositional characteristics of continuous, high density turbidity currents. According to the authors’ best knowledge, the present paper constitutes the first attempt in the literature, where the isolated effects of each individual controlling parameter as well as their relative importance on the hydrodynamic characteristics of continuous, high-density turbidity currents are quantitatively evaluated in detail. The numerical model used, is based on a multiphase modification of the Reynolds Averaged Navier–Stokes equations (RANS). For turbulence closure the Renormalization-group (RNG) k–ε model is applied, which is an enhanced version of the widely used standard k–ε model