Large-eddy simulation of the flow over a realistic riverine geometry
Bibliographic record
Abstract
In this paper, we discuss the transport of sediment and the formation of bedforms in turbulent river flows, under flow conditions typical of flooding events. Through the implementation of an immersed boundary method, a wall model and a morphological model, we were able to simulate complex and mobile geometries under high Reynolds numbers at an affordable computational cost. In particular, we examined the evolution of bedforms on a loose sediment bed under turbulent flow conditions, using input parameters obtained from laboratory measurements. Over time, the bedforms become more three-dimensional and irregular in shape, leading to changes in the shear layer, crest angle and separation patterns. The bedforms continue to evolve until a quasi-steady equilibrium is reached. Our simulations highlight the crucial role played by the small-scale bedforms, which significantly affect the flow dynamics: an increase in the total drag is observed, related to the form drag generated by the local recirculation and the increased size of the large-scale recirculation bubble. Furthermore, a stronger turbulent activity ensues from the shear layers forming on the crests of the small-scale bedforms. Finally, a wider shedding angle of the shear layer is caused by the irregular crest line.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".