Can Thickness Froude Number be an Influencing Parameter of Ice-Induced Pressure on Vertical Structure?
Bibliographic record
Abstract
Aspects of expressing ice-induced pressure on vertical structures in dimensionless form as a function of dimensionless influencing parameters are discussed. Based on these, a few previously published experimental data of other researchers on sheet-ice interaction with vertical structures are analyzed, with particular emphasis on the influence of thickness Froude number on dimensionless ice-induced pressure. The data analyzed in this paper, were obtained using fresh-water ice, saline ice, from field and laboratory tests, wherein the ice-thickness-based strain-rate (u/h) varied between 1.0×10-4 sec-1 and 9.0×100 sec-1 while the structure-widthbased strain-rate (u/B) varied from 3.0×10-4 sec-1 to 8.0×100 sec-1. Thus, a wide range of strain rates, from ductile to brittle deformation of ice, has been considered in the analysis. The aspect ratio (B/h) varied from 0.20 to 45 while the contact area varied between 5.0×10-5 m2 to 2.0 m2. The analyses showed that dimensionless ice-induced pressure varies as a decreasing function of increased thickness Froude number, when all other dimensionless independent parameters are held constant. The shape of the indentor was found to have no influence on the dimensionless ice-induced pressure expressed as a function of u/√(gh). The ice-induced pressure data used in this analysis are also presented in dimensional form as a function of pressure-area and as a function of aspect ratio to show the usefulness of expressing parameters in dimensionless form to understand the physics of complex ice-structure interaction problem.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".