Experimental and numerical estimation of velocity and concentration distributions in partially vegetated open channels.

• PIV and the PCA are used to measure simultaneously the surface velocity field and pollutant concentration. • DBM D2Q16 and LBM D2Q5 are used to simulate the velocity and concentration fields. • Effects of the vegetation's density on water depth, velocity field and pollutant transport are discussed. Transport phenomena are crucial components of the biological and chemical processes that govern fluvial environments. The presence of vegetation along rivers plays a significant role in affecting the flow velocity, which becomes unevenly distributed, and consequently is an important factor that can affect longitudinal and transversal dispersion and mixing of pollutants. Therefore, in this study, to provide a better understanding and further insights of the velocity field and the pollutant dispersion in open channels partially covered by sparse vegetation and dense vegetation, an experimental campaign is presented to compare them with the no-vegetation scenario. Novel datasets are produced by applying the Particle Image Velocity (PIV) and the Planar Concentration Analysis (PCA) technologies, which were used to measure simultaneously the surface velocity field and pollutant concentration map within the open channel tested. Results obtained have been used to calibrate and validate a numerical model designed to simulate velocity fields (via Discrete Boltzmann Model D2Q16) and pollutant concentration maps (via the Lattice Boltzmann model D2Q9). Results have shown that as the density of vegetation increases, the flow velocity in the vegetation zone decreases while the velocity in the no-vegetation zone increases. So, the velocity gradient in the transition zone increases accordingly. Besides, the water surface difference between inlet and outlet is higher as the density increases. Finally, the simulated results have been compared with corresponding experimental data and good agreements have been achieved, which indicates that the model developed can accurately predict features that were observed and measured within the experimental facility. [ABSTRACT FROM AUTHOR]