Velocity field and transverse dispersion in vegetated flows

Abstract

In recent years aquatic vegetation has become more accepted and important in the river restoration schemes and preserving river ecology. The purpose of this thesis is to investigate the influence of emergent vegetation on velocity and turbulence fields in order to have a better understanding of the effect of vegetation on the transverse mixing processes. To achieve this objective, a series of experiments was conducted in an open channel flume with emergent rigid rods in both staggered and aligned arrangements. Detailed velocity, turbulence and dye tracer measurement were carried out for six vegetation densities relating to solid volume fractions (SVF) in the range 0.51 % to 7.79 %. In sparse vegetation (SVF < 10 %) as expected the normalised spatially-averaged longitudinal velocity reduces as the vegetation density increases with approximately 30 % to 50 % reduction when the solid volume fraction is doubled. Results indicated that in sparse vegetation, the normalised turbulence intensities increased with increasing solid volume fraction. The bulk drag coefficient increased with increasing vegetation density whilst decreased with increasing stem Reynolds number. The transverse mixing coefficient increases with both increasing vegetation density and stem Reynolds number. The current study showed that for sparse vegetation (SVF < 10%), the transverse mixing coefficient has a stronger correlation with turbulence intensity compared to transverse shear. Therefore indicating that within sparse vegetated flows, turbulence dominates over transverse shear in transverse mixing. In addition to that, transverse mixing also correlate better with double-averaged turbulence intensity compared to turbulent kinetic energy. This reflects that the turbulence in the longitudinal direction plays a greater contribution to the overall transverse dispersion than the contribution of the total turbulence in all three directions. Finally two vegetation transverse dispersions models proposed by other researcher for randomly distributed vegetation were tested against data from the current study. Both models were found to predict reasonably well