Discrete Simulations of Fluid‐Driven Transport of Naturally Shaped Sediment Particles
Bibliographic record
Abstract
Abstract The particles in natural bedload transport processes are usually aspherical and span a range of shapes and sizes, which is challenging to be represented in numerical simulations. We assemble existing numerical methods to simulate the transport of natural gravel (NG). Starting with computerized tomographic scans of natural grains, our method approximates the shapes of these grains by “gluing” spheres (SP) of different sizes together with overlaps. The conglomerated SP move using a Discrete Element Method which is coupled with a Lattice Boltzmann Method 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. Beyond the flume, in a more generalized wide wall‐free geometry, the numerical tool is used to further test 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 NG independently of the material friction. The results support the modified sediment transport relation. The simulations also provide insights into particle‐level kinematics, such as particle orientations. Though particles below the bed surface prefer to orient with their shortest axes perpendicular to the bed surface, with a decaying tendency with an increasing height above the bed surface, the orientational preferences in transport processes are much weaker than those in settling processes. NG rotates relatively freely during bedload transport.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".