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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 (ws) in turbulence and its still water counterpart (wso). 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 equatewstowsoresult 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 whyws/wsois 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.
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Machine‐Assisted Physical Closure for Coarse Suspended Sediments in Vegetated Turbulent Channel Flows
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 ofavH ∈ [0.3, 1.0] by examining turbulent dispersive flux. While conventional studies disregard dispersive momentum flux foravH> 0.1, our findings reveal significant dispersive sediment flux for large particles with a diameter‐to‐Kolmogorov length ratio whendp/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.
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- Award ID(s):
- 2019625
- PAR ID:
- 10555393
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 51
- Issue:
- 20
- ISSN:
- 0094-8276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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