Abstract
River meanders form complex 3D flow patterns, including secondary flows and flow separation. In particular, the flow separation traps solutes and delays their transport via storage effects associated with recirculating flows. The simulation of the separated flows highly relies in the performance of turbulence models. Thus, these closure schemes can control dispersion behaviors simulated in rivers. This study performs 3D simulations to quantify the impact of the turbulence models on solute transport simulations in channels under different sinuosity conditions. The 3D Reynolds-averaged Navier-Stokes equations coupled with the k − ε , k − ω and SST k − ω models are adopted for flow simulations. The 3D Lagrangian particle-tracking model simulates solute transport. An increase in sinuosity causes strong transverse gradients of mean velocity, thereby driving the onset of the separated flow recirculation along the outer bank. Here, the onset and extent of the flow separation are strongly influenced by the turbulence models. The k − ε model fails to reproduce the flow separation or underestimates its size. As a result, the k − ε model yields residence times shorter than those of other models. In contrast, the SST k − ω model exhibits a strong tailing of breakthrough curves by generating more pronounced flow separation.
Highlights
River topography exerts a first-order control on flow and solute transport mechanisms
This study quantified the influence of turbulence closure models in simulating curvature-driven flow separation and resulting non-Fickian mixing behaviors in meandering open channels across a
This study quantified the influence of turbulence closure models in simulating curvature-driven flow separation and resulting non-Fickian mixing behaviors in meandering open channels across a wide range of sinuosity
Summary
River topography exerts a first-order control on flow and solute transport mechanisms. In river bends, complex three-dimensional (3D) flow structures such as secondary flows and recirculating flows develop due to the local imbalance between the centrifugal force and the pressure force [1,2]. The velocity gradient driven by the secondary flow increases the longitudinal dispersion via shear flow effects [3]. The helical motion of the secondary flow impacts the primary flow and stimulates the transverse mixing [4,5]. The dead zones with vigorous recirculating flows, developed by the channel irregularity, trap a solute cloud and induce the non-Fickian dispersion characterized as an elevated concentration of tracers downstream at late times [6,7]. The aforementioned flow features are required to be described adequately with numerical models for accurate prediction of solute dispersion behaviors in surface water systems
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