Experimental Investigation of the Pressure Distribution beneath a Floating Ice Block
Bibliographic record
Abstract
Discrete ice floes approaching an ice cover from upstream will contribute to the lengthening of the cover or will become entrained in the flow. Currently, ice process models rely on empirical relationships to predict the behavior of ice floes at the leading edge of an intact ice cover. Knowledge of the hydrodynamic forces acting on individual ice floes is an important component of any model attempting to predict ice-cover progression. Experimental studies were conducted in a recirculating flume in the T. Blench Hydraulics Laboratory at the University of Alberta to increase the knowledge of the physical behavior of ice floes in water and the hydrodynamic forces that act on them. A hollow Plexiglas acrylic ice block outfitted with pressure taps was constructed to facilitate measurements of the pressure distribution beneath an ice block. The dynamic pressure was measured under the block for various block thickness-to-depth ratios and flow velocities. The dynamic pressure was found to decrease for increasing block thickness-to-approach flow depth ratios and increasing flow rates. A block with a rounded leading edge was also tested, and it showed a significantly reduced leading-edge pressure effect. The rectangular ice block results were categorized into separate effects: a pressure reduction attributable to Venturi effects and a pressure reduction attributable to leading-edge effects. A predictive relationship was developed for the pressure distribution beneath a floating ice block and the subsequent submerging force and underturning moment.
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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 | 0.001 |
| 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) | 0.002 | 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".