Author(s): Sha Lou; Ming Chen; Shuguang Liu; Guihui Zhong

Linked Author(s): Sha LOU, ming chen

Keywords: Vertically varying vegetation density; Combined wave–current; Stem-scale turbulence prediction; Sediment suspension

Abstract: Aquatic vegetation plays an important role in the natural ecosystem. It can change flow structure and turbulence characteristics, as well as sediment suspension and transport, which have great impacts on the ecosystem and morphology. However, due to the complicated process and multivariate factors, the mechanism of the interaction between vegetation-hydrodynamics-sediment is still not clearly understood. In this paper, a flume study was carried out to investigate the effects of vegetation on flow structure and sediment suspension. All the experiments were conducted in a wave flume constructed in the Hydraulic Laboratory at Tongji University, China. The wave flume is 50 m long, 0.8 m wide and 1.2 m high. Wood cylinders were used to simulate rigid vegetation. The diameter of cylinders was 8 mm. Three vegetation configurations (sparse, dense and vertically varying density) and different hydrodynamic conditions (waves, currents and combined wave–current flows) were tested. Plenty model sand with uniform diameter of 0.16 ± 0.02 mm and density of 1.45 g/cm3 was set at the bottom of canopy zone. The Acoustic Doppler Velocimetry (ADV) was used to measure three dimensional velocities. Turbidity was directly measured by the Optical Backscatter Sensor (OBS). Vegetation had a significant blocking effect on the flow and reduced the velocity. While the presence of vegetation enhanced the turbulent diffusion in water body and promoted sediment initiation and suspension. An improved formula considering vegetated-induced drag coefficient and solid volume fraction was proposed to predict stem-scale turbulence using characteristic velocity. In vegetated flow, neither the bed shear stress, nor the vegetated-induced turbulence, were solely responsible for the sediment suspension. Therefore, we attempted to predict near-bed sediment concentration using the combination of bed shear stress and turbulent kinetic energy.

DOI: https://doi.org/10.3850/IAHR-39WC252171192022706

Year: 2022