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An Optimized Entropy-Based Model for Estimating Velocity Distributions at Confluences

Author(s): Hao Lin; Saiyu Yuan; Hongwu Tang; Kun Li; Xiao Luo; An Wan

Linked Author(s): Saiyu Yuan, Hongwu Tang

Keywords: Yangtze river; Flow monitoring; River confluence; Entropy theory; Velocity dip

Abstract: The entropy theory method has been successfully utilized for river monitoring, starting from only near-surface flow velocity measurements. The hydrodynamics of natural rivers could be measured with non-contact methodology using this entropy-based method. The river confluences are characterized by complex flow patterns, including secondary currents and vortices, which pose challenges for the flow estimation. The application of the method in the confluence area is still lacking. In this study, the application of the entropybased approach in a large-scale confluence with complex flow structures is examined and an optimized method is proposed to improve its accuracy. The work was undertaken with field flow data under various flow conditions at the large confluence between the Yangtze River and the Poyang Lake. The entropic function Φ (M) shows the inverse trend compared with the secondary flow velocity. The velocity dip phenomenon is the location of maximum flow velocity, Umax, which may occur below the water surface. It has been usually accurately that the velocity dip is inapparent in large rivers, while the velocity dip was found to be significant despite the large aspect ratio here. An optimized method was proposed to determine the velocity dip based on the available data, and estimate the velocity and discharge. The results show that the proposed approach can provide satisfactory estimation of the hydrodynamics at a confluence. Furthermore, the study provided valuable insights into the entropic parameter and velocity dip under various discharge ratios.


Year: 2023

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