Large‐Scale Particle Image Velocimetry Reveals Pulsing of Incoming Flow at a Stream Confluence
Bibliographic record
Abstract
Abstract Despite widespread recognition that confluences are characterized by complex hydrodynamic conditions, few studies have mapped in detail spatial patterns of flow at confluences and variation in these patterns over time. Recent developments in large‐scale particle image velocimetry (LSPIV) have created novel opportunities to explore the spatial and temporal dynamics of flow patterns at confluences. This study uses LSPIV to map two‐dimensional flow structure at the water surface at a confluence and to examine variation in this structure over time. Results show that flow within the confluence is characterized by a large region of flow stagnation at the junction apex, a region of low velocities at the downstream junction corner, and a region of merging of the two flows along a mixing interface within the center of the confluence. Interaction between the incoming flows varies over time in the form of episodic pulsing in which one of the two tributary flows first decelerates and then subsequently accelerates into the confluence. The cause of this pulsing remains uncertain, but it may reflect unsteadiness in the water‐surface pressure‐gradient field as the two flows compete for space within the confluence. No large‐scale vortices are evident within the mixing interface for the particular flow conditions documented in this study, but such vortices do occur along the margins of the stagnation zone where shearing action between fast‐moving and slow‐moving fluid is strong. The results of the study provide insight into the time‐dependent dynamics of the spatial structure of flow at stream confluences.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".