Particle settling in turbidity currents: inertia-independent biased sampling

[2606.08241] Particle settling in turbidity currents: concentration-gradient-driven biased sampling Skip to main content Search Submit Donate Log in Physics > Fluid Dynamics arXiv:2606.08241 (physics) [Submitted on 6 Jun 2026 ( v1 ), last revised 18 Oct 2026 (this version, v3)] Title: Particle settling in turbidity currents: concentration-gradient-driven biased sampling Authors: Lianzheng Cui , Eric Climent , Graham O. Hughes , Maarten van Reeuwijk View PDF HTML (experimental) Abstract: We investigate the mechanisms governing particle settling in turbidity currents using Eulerian$\unicode{x2013}$Lagrangian direct numerical simulations. The Eulerian carrier flow is driven either by solutal buoyancy or particle feedback, with the Lagrangian phase comprising passive tracers or inertial particles, respectively. The effective particle settling velocity is decomposed into a fluid velocity sampled at particle positions and a particle-fluid slip velocity. The Eulerian mean profiles of these velocities are obtained using a concentration-weighted average of the coarse-grained fields. The mean sampled fluid velocity is shown to be approximately equal to the ratio of the vertical turbulent flux of particles to their mean concentration and reflects biased sampling of upward turbulent fluctuations at particle positions, despite the zero Eulerian mean vertical fluid velocity. The passive-tracer cases show that the upward bias does not require particle inertia and arises from turbulent transport acting on concentration gradients, as it persists for inhomogeneous tracer seeding but disappears under uniform seeding. For the weakly inertial regime considered here, the upward bias dominates downward-directed biases associated with particle inertia. The mean slip velocity is well approximated by the terminal settling velocity predicted for a quiescent fluid. This is consistent with a leading-order balance between buoyancy and drag in the slope-normal direction. Modelling the sampled fluid velocity from the turbulent flux and using the slip-velocity approximation yield an Eulerian prediction for the settling velocity, in good agreement with the simulation data for the dilute, weakly inertial particles considered. Subjects: Fluid Dynamics (physics.flu-dyn) Cite as: arXiv:2606.08241 [physics.flu-dyn] (or arXiv:2606.08241v3 [physics.flu-dyn] for this version) https://doi.org/10.48550/arXiv.2606.08241 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Lianzheng Cui [ view email ] [v1] Sat, 6 Jun 2026 15:54:43 UTC (3,532 KB) [v2] Fri, 21 Aug 2026 21:31:38 UTC (3,898 KB) [v3] Sun, 18 Oct 2026 13:55:04 UTC (6,126 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: physics.flu-dyn < prev | next > new | recent | 2026-06 Change to browse by: physics References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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