Mixing Dynamics at the Large Confluence Between the Yangtze River and Poyang Lake

Xu, Lei; Yuan, Saiyu; Gualtieri, Carlo; Qiu, Jiajian; Xiao, Yang; Whittaker, Colin

doi:10.1029/2022wr032195

Cited by 24 publications

(3 citation statements)

References 88 publications

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“…There are also some more complex methods of handling this, but they all involve describing the dynamic processes at the junction nodes through equations (Kesserwani, Ghostine, Vazquez, Mosé, et al, 2008; Xu, Yuan, et al, 2022).…”

Section: Physical Backgroundmentioning

confidence: 99%

Enhanced physics‐informed neural networks for efficient modelling of hydrodynamics in river networks

Luo,

Yuan,

Tang

et al. 2024

Hydrological Processes

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This article enhances the physics‐informed neural networks (PINNs) method to effectively model the hydrodynamics of real‐world river networks with irregular cross‐sections. First, we pre‐process hydraulic parameters to optimize training speed without compromising accuracy, achieving a 91.67% acceleration compared with traditional methods. To address the vanishing gradient problem, layer normalization is also incorporated into the architecture. We also introduce novel physical constraints—water level range and junction node equations—to ensure effective training convergence and enrich the model with additional physical insights. Two practical case studies using HEC‐RAS benchmarks demonstrate that our improved PINN method can predict river network hydrodynamics with less data and is less sensitive to time step size, allowing for longer computational time steps. Incorporating physical knowledge, our enhanced PINN methodology emerges as an efficient and promising avenue for modelling the complexities of hydrodynamic processes in natural river networks.

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Section: Physical Backgroundmentioning

confidence: 99%

Enhanced physics‐informed neural networks for efficient modelling of hydrodynamics in river networks

Luo,

Yuan,

Tang

et al. 2024

Hydrological Processes

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“…Six distinct regions characterize flow dynamics at channel confluences: flow stagnation, flow deflection, shear layer, flow separation, maximum velocity, and gradual flow recovery (Best, 1987; Figure 1). The separation zone (e.g., Best & Reid, 1984) and the shear layer (e.g., Rhoads & Sukhodolov, 2004, 2008; Xu et al., 2022; Yuan et al., 2018) are of particular research interest. The separation zone develops when the momentum of incoming flow from an angled tributary has sufficient inertia that it detaches from the channel wall.…”

Section: Introductionmentioning

confidence: 99%

Large Wood Transport and Accumulation Near the Separation Zone of a Channel Confluence

Yuan,

Zheng,

Tang

et al. 2024

Water Resources Research

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Fallen trees enter the adjacent stream and are carried away downstream by the current. As the stream joins another one, the complex hydrodynamics near their confluence make the movement of wood hard to predict. These woods may accumulate near the confluence resulting in backwater and subsequent potential flooding. A laboratory study was conducted to investigate the movement and accumulation behavior of individual pieces of wood near the confluence. The characteristics of wood (i.e., the length, diameter, and density) and the hydraulic conditions (i.e., the discharge ratio and the release distance) were varied in this investigation. It was found that the wooden pieces released from the tributary got occasionally trapped in the flow separation zone of the confluence, whereupon they were mainly trapped by a clockwise vortex and continued to stay driven by a reverse cluster of currents within this zone. The accumulation probability of wood was mainly related to its length, the discharge ratio and the release distance. The effect of wood diameter and density within the tested parameters was negligible. The probability increased with an increase in the discharge ratio as well as a decrease in the release distance. The longer pieces had a higher probability of being trapped, whereas for those exceeding some critical value, the probability was nearly the same, or dropped sharply. A generalized model for wood accumulation near the confluence was developed for practical application. These findings carry significant implications for river management, particularly in preventing the risk of flooding caused by wood blockage.

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“…This model mostly emerged from information obtained in field studies (e.g., De Serres et al, 1999;Sukhodolov and Rhoads, 2001;Rhoads and Sukhodolov, 2004;Boyer et al, 2006). Field studies continued until to the present (Sabrina et al, 2021;Xu et al, 2022;Sukhodolov et al, 2023). Also recently, Leite Ribeiro (2011), Guillén-Ludeña et al (2015), Bombar and Cardoso (2020) and Safi et al (2023) resorted again to laboratory studies by performing average-to large-scale experiments on asymmetrical confluences.…”

Section: Introductionmentioning

confidence: 99%

Experimental investigation of the evolution of bed topography in an asymmetrical confluence

Bombar,

Cardoso

2024

Preprint

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This study assessed the time evolution of the bed morphology of six experiments performed by Bombar and Cardoso (2020) on mobile bed confluences. These experiments were characterized by a unique junction angle, a fixed plan layout of the converging channels, constant water discharges and water discharge ratio, and six combinations of the converging sediment transport rates. The end aspect ratio of both channels was sufficiently large to avoid strong wall effects, and the flow was always subcritical and rough turbulent. The bed sediment was a uniform non-ripple forming sand. Detail analysis of the time evolution of the bed morphology showed that balanced fluxes of sediments through the boundaries of the system were a sufficient condition of equilibrium within the range of variables covered by the study. The most important morphological features of the confluence were discussed for several time instants, including those referring to the equilibrium phase. The equilibrium features essentially complied with the morpho-dynamic models reported in the literature for mobile bed confluences, meaning that the time evolution and the end (equilibrium) values of the bed morphologies reported in this paper can be used to validate CFD models thought of as predictive tools of confluence morpho-dynamics. Two experiments are, indeed, ideal for this purpose since they converged to static equilibria, and wrong predictions cannot be attributed to oscillations of the bed, whereas those models may only be able to predict the average, equilibrium values of the bed features of experiments run with sediment supply into the main channel and characterized by the movement of bedforms.

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Mixing Dynamics at the Large Confluence Between the Yangtze River and Poyang Lake

Cited by 24 publications

References 88 publications

Enhanced physics‐informed neural networks for efficient modelling of hydrodynamics in river networks

Enhanced physics‐informed neural networks for efficient modelling of hydrodynamics in river networks

Large Wood Transport and Accumulation Near the Separation Zone of a Channel Confluence

Experimental investigation of the evolution of bed topography in an asymmetrical confluence

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