Fluid‐Driven Transport of Round Sediment Particles: From Discrete Simulations to Continuum Modeling
Bibliographic record
Abstract
Abstract Bedload sediment transport is ubiquitous in shaping natural and engineered landscapes, but the variability in the relation between sediment flux and driving factors is not well understood. At a given Shields number, the observed dimensionless transport rate can vary over a range in controlled systems and up to several orders of magnitude in natural streams. Here, we (a) experimentally validate a resolved fluid‐grain numerical scheme (Lattice Boltzmann Method‐Discrete Element Method or DEM‐LBM), and use it to (b) explore the parameter space controlling sediment transport in simple systems. Wide wall‐free simulations show the dimensionless transport rate is not influenced by the slope, fluid depth, mean particle size, particle surface friction, or grain‐grain damping for gentle slopes (0.01–0.03) at a medium to high fixed Shields number. (c) Examination of small‐scale fluid‐grain interactions shows fluid torque is non‐negligible for the entrainment and sediment transport near the threshold. And (d) the simulations guide the formulation of continuum models for the transport process. We present an upscaled two‐phase continuum model for grains in a turbulent fluid and validate it against bedload transport DEM‐LBM simulations. To model the creeping granular flow under the bed surface, we use an extension of the Nonlocal Granular Fluidity model, which was previously shown to account for flow cooperativity from grain‐size‐effects in dry media. The model accurately predicts the exponentially decaying velocity profile deeper into the bed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".