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This study examines the scour depth induced by turbulent wall jets and proposes novel mathematical formulations to predict the depth of scouring. Through a comprehensive gamma test, key parameters influencing the scour depth are identified, including the apron length, densimetric Froude number, median sediment size, tailwater level, Reynolds number...
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Citations
... The process of contaminant transport through river confluences is complex and influenced by several hydrodynamic and morphological factors. River confluences can be regarded as nodes in the fluvial network where two or more streams meet, which influence flow dynamics and sediment transport and generally affect contaminant dispersion (Dong et al. 2024;Devi et al. 2024;Sandilya et al. 2024). Such processes are important for efficient water quality management and environmental protection. ...
The transport processes of pollutants in river confluences with coarse-grained beds are the focus of this study, addressed through experimental work and mathematical modeling. The goal of this study is to develop explicit and dimensionless models to investigate pollutant transport in river confluences with gravel-filled upstream branches through experimental and analytical methods. A physical model was built with a Y-junction of open channels, each filled with gravel but with different dimensions. The experiments involved tracer tests on the sodium chloride solution under different flow rates and initial concentrations. The explicit mathematical relationship has been derived for contaminant transport taking into consideration the important parameters such as flow rates, cross-sectional areas, porosities, dispersion coefficients, and branch lengths. The predictivity for peak concentration of the model is good as relative errors are always under 6%, and particularly, the RBL (River Branch Length) model has an inadequacy of the limit in the prediction of peak time and does not perform better for upstream instances, i.e., for far-stream locations. In this way, it was also possible to obtain a dimensionless relationship using dimensional analysis and nonlinear regression, which got an equation with a great value of the coefficient of determination. The sensitivities for both models presented great dependence with the porosity and the interstitial velocity. Better performance of the analytical model was slightly confirmed by other statistical indicators: the model with thickness showed R² at 0.958, and root mean square error at 721.46 mg/L, while the dimensionless model showed 0.937 and 853.79 mg/L. It showed a lower percent bias for the dimensionless model (−2.37% against −4.91%), which means more balanced predictions across the range of observed concentrations. Both models showed very good performance with respect to predicting pollutant concentrations, with Nash–Sutcliffe values > 0.9.
