Plunging Flow and Coherent Turbulent Structures in a Straight Pool‐Riffle
Bibliographic record
Abstract
Abstract Pool‐riffle units are naturally occurring topographical elements of rivers. Various hypotheses have been proposed to describe how the hydrodynamics in pool‐riffle units may lead to scour and sediment transport. However, most hypotheses have focused on time averaged properties, and a physical mechanism for observations of high near‐bed velocities has not been proposed. In this study, we apply Large Eddy Simulations to examine the dynamics of flow over straight riffles. We show that a high velocity core of fluid can alternately skim or plunge toward the bed as a function of the Froude number (Fr) at the crest of the riffle, even within a subcritical flow regime. Coherent turbulent structures are generated in the pool and induce high magnitude fluctuations of shear stress and bed pressure. Four different turbulent structures are described and the concept of vortex resistance is used to discuss how turbulence can induce the observed bifurcation in pool hydrodynamics. Despite the simple geometry, the hydraulic scaling means that the results provide a novel physical explanation of near‐bed high velocity flow observations in natural pool‐riffles. The results are also significant because turbulent shear stresses in the head of a pool exceed what occurs elsewhere in the channel, including the riffles. Further research should examine the conditions under which plunging flow may occur and turbulence may lead to sediment entrainment in other riffle pool geometries.
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.
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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".