Vortex-Resistance Hypothesis: Large Eddy Simulation of Turbulent Flow in Isolated Pool- Riffle Units
Bibliographic record
Abstract
By numerical simulations of turbulent flow in isolated poolriffle units with various riffle heights, four different types of vortices were found and named as follows: surface rollers (SR), corner rollers (CR), ramp rollers (CR), and axial tails (AT). Surface rollers are shaped on the flow surface due to submerged hydraulic jump or any obstacles in the forced poolriffle units. Corner rollers are shaped close to the corners near the walls at the pool head. Ramp rollers are formed at the bed of the channel on the ramp into the head of the pool. All kinds of vortices stretch in the streamwise direction as they travel to the downstream, which they are called axial tails. The simulations showed that all four types of vortices interact with each other, combine, amplify or cancel out each other as they travel downstream. The strength of vortices and how they interact result into different types of flow patterns. The surface rollers combine with corner rollers to make a jet like plunging flow near the pool bed. In other cases with lower riffle heights, ramp rollers tend to push the flow up, which in turn leads to higher turbulence near the bed and higher velocity near the flow surface (skimming flow). Moreover, if both surface rollers and ramp rollers have the similar strength (e.g., vorticity) and scale, the streamwise velocity profile has a peak around the middle of the flow, and minimum velocities near the bed and free surface. This flow pattern was named as “rifting flow.” Based on these findings, a new hypothesis is proposed called ‘vortex-resistance,’ which states that the turbulent structures, by increasing the eddy viscosity and changing the pressure domain, act as an obstacle that steers the flow. Plunging and skimming flow can thus be understood as the products of different types of turbulent structures. These findings provide new clarifications to long-standing questions related to the hydraulics of pools and riffles.
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 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 | 0.000 | 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) | 0.004 | 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".