Fluid‐Driven Transport of Round Sediment Particles: From Discrete Simulations to Continuum Modeling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it