Effect of Turbulence Intensity on Frazil Flocculation
Bibliographic record
Abstract
The processes of secondary nucleation and flocculation of frazil ice are relatively poorly understood. In order to better understand the effect that turbulence intensity has on these processes, a series of experiments were undertaken at the Hydraulics Research & Testing Facility in the University of Manitoba using a counter-rotating flume. Five sets of bed plates ranging in roughness from smooth PVC to 20 mm gravel were used to generate the turbulence in the flume. Velocity measurements in open water were made using a constant temperature anemometer with a conical hot-film probe. The ability to rotate the flume walls at any given rate enabled the researchers to perform experiments where the average velocity was kept constant, while the turbulence intensity increased with increasing bed roughness. Measurements of water temperature, air temperature, and digital images taken during ice formation were analyzed. It was found that the rate of secondary nucleation increased with increasing turbulence intensity, however, the trend was not well defined. A multiple linear regression model using turbulence intensity in addition to the maximum degree of supercooling as the two independent variables was found to reasonably model the rate of secondary nucleation. Initial results demonstrate that turbulence intensity increases the uniformity of the vertical distribution of frazil particles and tends to inhibit frazil flocculation. 1.
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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 | 0.001 | 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 | 0.000 | 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".