Field and Laboratory Investigation of Frazil Floc and Surface Ice Properties
Bibliographic record
Abstract
Frazil ice particles and flocs can adhere to underwater structures causing blockage to water intakes and large accumulation of frazil ice in the channel may cause flooding and property damage. After frazil floc rises to the water surface the resulting surface ice profoundly impacts river hydraulics and bank stability. Significant progress has been made in investigating the properties and evolution of frazil ice particles. However, the physical process in which frazil particles flocculate into flocs and rise to the surface forming ice pans remains largely unknown due to limited data available on the properties of frazil floc and surface ice. The motivation of this study was to determine the properties of frazil floc and surface ice as well as their evolution under changing environmental conditions to better model and predict their physical behavior throughout the river freeze-up. The use of oblique images of river surfaces captured at long focus distances for long-term monitoring of surface ice conditions and ice pan properties was explored. Image data from a public camera mounted on a building rooftop captured during five freeze-up seasons was used. A deep learning based hybrid image processing algorithm was developed and evaluated to compute surface ice concentrations as well as ice pan sizes and shapes. The ice pans detected were generally elliptical shaped and their diameters ranged from 0.55 to 15.03 m. A lognormal distribution was a good fit for the ice pan size distributions for all years. Time series analysis showed that the appearance of ice pans coincided with supercooling and daily mean ice pan diameter varied from 1 to 3 m. These results demonstrate the viability of this method, which may open opportunities to identify and use public camera images for surface ice quantification. Properties of frazil flocs were measured for the first time in field by deploying a submersible camera system a total of eleven times during supercooling in the North Saskatchewan, Peace, and Kananaskis Rivers. A lognormal distribution was found to be a good fit for the floc size distribution. The mean floc size ranged from 1.19 to 5.64 mm and decreased linearly as the local Reynolds number increased. The average floc number concentration ranged from 1.80 × 10-4 to 1.15 × 10-1 cm-3. The average floc volumetric concentration ranged from 2.05 × 10-7 to 4.56 × 10-3 and was found to correlate strongly with the fractional height above the bed through a power law relationship. No significant correlations were found between the air-water heat flux and floc properties. Floc number concentration and mean size increased significantly just before peak supercooling and reached a maximum near the end of principal supercooling. To explore how the supercooling curve and frazil ice particle and floc properties vary under different air-water heat flux scenarios, a series of laboratory experiments were conducted in which frazil particles and flocs were generated and imaged when the cold room air temperature was increased or decreased threefold at different times during supercooling events. It was found that increasing the heat flux raised the mean particle number concentration by 25 – 33 % but did not significantly affect the mean floc number concentration. Decreasing the heat flux only produced significant effects when the change occurred before peak supercooling, reducing mean particle and floc number concentration by 10 and 22 %, respectively. Time series analysis showed that varying heat flux during different supercooling phases led to significantly different responses in the supercooling curve and particle and floc evolution. Additional laboratory frazil tank experiments were performed to investigate the correlation between the time series of frazil particle and floc properties under different air temperatures and turbulent dissipation rates. A strong linear relationship between particle and floc number concentrations was found with the floc-to-particle number concentration ratio ranging from 0.29 - 0.35. The ratio was reduced by 12 – 17 % when the turbulent dissipation rate was lower. A moderate to strong nonlinear correlation was found between mean particle and floc sizes described by an exponential relationship when particle mean sizes increased or decreased significantly. When particle mean size reaches an approximate equilibrium, a weak to moderate linear correlation was found between mean particle and floc size and the negative slope suggests they are inversely correlated.
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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 | 0.001 | 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".