Analysis of Experimental Data and Quantifying Influence of Dimensionless Material Properties, Velocity and Aspect Ratio on Ice-induced Forces on Vertical Structures
Bibliographic record
Abstract
In this paper, the quantitative influence of aspect ratio (B/h), and dimensionless velocity or thickness Froude number [TFN = u/√(gh)] on dimensionless ice-induced pressures (pe/ρiu2) is briefly reviewed and discussed. Since material properties of ice (E, σf, K1c) have not been reported for many data-sets, a strategy for generating appropriate material properties for ice is proposed. Two dimensionless terms for material properties of ice, {(E/σf)×[K1c/(σf√h)]} and {[K1c/(σfu)]×√[E/(ρih)]} were identified and their influence on pe/ρiu2 is discussed. It was found that (1) pe/ρiu2 on rigid vertical structures decreases with (a) increasing B/h at a rate of about 0.42, when u/√(gh) and {[K1c/(σfu)]×√[E/(ρih)]} remain constant; (b) pe/ρiu2 decreases with increasing u/√(gh) at a rate of about 1.80 when u/√(gh) is < about 6.0×10-3and at a rate of about 1.93 when u/√(gh) is > about 6.0×10-3 when B/h and {[K1c/(σfu)]×√[E/(ρih)]} remain constant. (2) Preliminary analyses of the datasets shows that pe/ρiu2 decreases with increasing {(E/σf)× [K1c/(σf√h)]} at a rate of 0.335 and 0.469 and that pe/ρiu2 decreases with increasing {[K1c/(σfu)]×√[E/(ρih)]} at a rate of 0.729 and 0.808. (3) It was also found that shapes of structures do not influence dimensionless ice-induced pressures on structures.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".