Discrete simulations of fluid-driven transport of naturally shaped sediment particles
Bibliographic record
Abstract
{A numerical scheme is developed to simulate the transport of natural gravel. Starting with computerized tomographic (CT) scans of natural grains, our method approximates the shapes of these grains by “gluing” spheres of different sizes together with overlaps. The conglomerated spheres move using a Discrete Element Method (DEM) which is coupled with a Lattice Boltzmann Method (LBM) fluid solver, forming the first complete workflow from particle shape measurement to high resolution simulations with hundreds of distinct shapes. The simulations are quantitatively benchmarked by flume experiments. The numerical tool is used to further validate a recently proposed modified sediment transport relation, which takes particle shape effects into account, including the competition between hydrodynamic drag and material friction. Unlike a physical experiment, our simulations allow us to vary the hydrodynamic drag coefficient of the natural gravel independently of the material friction. Our studies support the modified sediment transport relation. The simulations also provide insights on the particle-level kinematics, such as particle orientations, in the bedload transport process. Particles below the bed surface prefer to orient with their shortest axes perpendicular to the bed surface, but the tendency goes down as the packing fraction decreases far from the bed surface. The particles rotate freely in the dilute particle flow regime. }
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".