Velocity distribution characteristics for rigid vegetation model with spherical canopy: An analytical study adopting multiple mathematical methods

Wang, Weijie; Zhao, Fang; Mavrommatis, Aristotelis; Christodoulou, George C.; Stamou, Anastasios I.; Jia, Feng-Cong

doi:10.1016/j.jhydrol.2023.129376

Cited by 1 publication

(1 citation statement)

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“…There are several analytical methods and numerical modeling methods for solving the velocity distribution in vegetated rivers. The analytical solution mainly solves the N-S equation with a vegetation drag force term through determined river boundary conditions [13]. Zhang et al [14] defined two depth-averaged velocities for vegetation areas (including the depth-averaged velocity and vegetation-height-averaged velocity) and obtained analytical solutions for predicting the lateral distribution of the water depth averaged flow velocity.…”

Section: Introductionmentioning

confidence: 99%

A Numerical Model of the Pollutant Transport in Rivers with Multi-Layer Rigid Vegetation

Xuan,

Yang,

et al. 2024

Water

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River water pollution is a key environmental issue to human society. How to effectively simulate the flow velocity and pollution transport in a vegetated river can provide a theoretical basis for solving such problems. Based on previous experimental data, this article uses the lattice Boltzmann method and random displacement method to simulate the velocity distribution and nutrient transport in multi-layer rigid vegetated rivers. The simulation results indicate that incorporating the drag force of the vegetation into the model according to different vegetation layers can effectively simulate the velocity in a multi-layer vegetated river. Incorporating the turbulent diffusion and mechanical diffusion effects of nutrients caused by vegetation into the model can effectively simulate the effects of multi-layer vegetation on nutrient transport. This model can provide effective predictions of the flow velocity and pollution transport in multi-layer vegetation.

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Section: Introductionmentioning

confidence: 99%