Lagrangian Tracking of Specified Flow Parcels in an Open Channel Embayment Using Phosphorescent Particles
Bibliographic record
Abstract
Better understanding of Lagrangian flow patterns will improve predictions of the movement of pollutants and allow for verification of numerical models. It is difficult to efficiently, and non-intrusively, obtain Lagrangian data on a specified region of flow, thus very few methods exist for their effective determination. In this work, nearly neutrally buoyant phosphorescent tracer particles have been developed for use in a new laboratory technique, which will allow for easy visualization of the Lagrangian path of specified flow parcels. After seeding an entire flow with uncharged phosphorescent particles, it is possible to excite one chosen particle (or a chosen region) using a focused source of UVA light, which includes the optimal excitation wavelengths of the phosphorescent pigments. Once excited, the particles continue to glow and their Lagrangian path can be photographically recorded for analysis (for up to 30 seconds). To demonstrate this technique a qualitative analysis was performed on the entrapment process of pollutants from an open channel flow into an embayment. Previous work has shown the existence of two distinct flow regions within an embayment: a core region, which exhibits relatively non-turbulent flow, and an outer region, which is governed by the circulation of eddies. The retention time scale in the inner core region was found to be significantly greater than in the outer region. In the current study, the Lagrangian flow paths of particles in the region of entrapment, and in the center of the recirculating region are observed in order to gain a better understanding of the entrapment process and the exchange processes between regions. The results indicate the presence of a secondary vortex structure that transports settling particles to the center of the core region, where they then accumulate.
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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 | 0.000 | 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) | 0.000 | 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".