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Abstract The behavior of suspended particles in turbulent flows is a recalcitrant problem spanning wide‐ranging fields including geomorphology, hydrology, and dispersion of particulate matter in the atmosphere. One key mechanism underlying particle suspension is the difference between particle settling velocity (w_s) in turbulence and its still water counterpart (w_so). This difference is explored here for a range of particle‐to‐fluid densities (1–10) and particle diameter to Kolmogorov micro‐eddy sizes (0.1–10). Conventional models of particle fluxes that equatew_stow_soresult in eddy diffusivities and turbulent Schmidt numbers contradictory to laboratory experiments. Incorporating virtual mass and Basset history forces resolves these inconsistencies, providing clarity as to whyw_s/w_sois sub‐unity for the aforementioned conditions. The proposed formulation can be imminently used to model particle settling in turbulence, especially when sediment distribution outcomes over extended time scales far surpassing turbulence time scales are sought. 

Abstract The parameterization of suspended sediments in vegetated flows presents a significant challenge, yet it is crucial across various environmental and geophysical disciplines. This study focuses on modeling suspended sediment concentrations (SSC) in vegetated flows with a canopy density ofa_vH ∈ [0.3, 1.0] by examining turbulent dispersive flux. While conventional studies disregard dispersive momentum flux fora_vH> 0.1, our findings reveal significant dispersive sediment flux for large particles with a diameter‐to‐Kolmogorov length ratio whend_p/η > 0.1. Traditional Rouse alike approaches therefore must be revised to account for this effect. We introduce a hybrid methodology that combines physical modeling with machine learning to parameterize dispersive flux, guided by constraints from diffusive and settling fluxes, characterized using recent covariance and turbulent settling methods, respectively. The model predictions align well with reported SSC data, demonstrating the versatility of the model in parameterizing sediment‐vegetation interactions in turbulent flows. 

Abstract Floating treatment wetlands are new ecological infrastructures for stormwater treatment. Despite a recent proliferation in their usage, their contaminant removal efficiencyecontinues to draw research attention. Here, theefrom idealized FTWs is numerically computed across a wide range of flow and geometric conditions while accommodating joint contributions of advection, turbulent dispersion, and vegetation removal. The emerging mathematical structure describingebears resemblance to a simplified plug flow model and supports an empirical shallow-basin model from long-term field measurements. The present model indicates thateremains significantly influenced by a Dämkohler number that quantifies the effects of both vegetation and flow properties. The impacts oneof the underflow region and contaminant blockage on the removal mechanisms are also investigated. 

Abstract Floating treatment wetlands (FTWs) are efficient at wastewater treatment; however, data and physical models describing water flow through them remain limited. A two‐domain model is proposed dividing the flow region into an upper part characterizing the flow through suspended vegetation and an inner part describing the vegetation‐free zone. The suspended vegetation domain is represented as a porous medium characterized by constant permeability thereby allowing Biot's Law to be used to describe the mean velocity and stress profiles. The flow in the inner part is bounded by asymmetric stresses arising from interactions with the suspended vegetated (porous) base and solid channel bed. An asymmetric eddy viscosity model is employed to derive an integral expression for the shear stress and the mean velocity profiles in this inner layer. The solution features an asymmetric shear stress index that reflects two different roughness conditions over the vegetation‐induced auxiliary bed and the physical channel bed. A phenomenological model is then presented to explain this index. An expression for the penetration depth into the porous medium defined by 10% of the maximum shear stress is also derived. The predicted shear stress profile, local mean velocity profile, and bulk velocity agree with the limited experiments published in the literature.