Application of dimensional analysis to estimation of ice-induced pressures on rigid vertical structures
Bibliographic record
Abstract
Dimensional analysis requirements for estimating ice-induced pressures during ice-structure interaction are identified, established, and discussed. Analysis of representative experimental results, based on these requirements, showed that for structures with constant surface slope, dimensionless ice-induced pressure (pe/ρiu2) is a function of four independent dimensionless parameters — aspect ratio (B/h), square of thickness Froude number (STFN = u2/gh), Cauchy number (CN = E/ρiu2), and a constitutive similarity condition (E/σ). It was found that (1) when STFN, CN, and E/σ were held constant, ice-induced pressure could be uniquely determined as a decreasing function of increasing B/h; (2) at any constant B/h, dimensionless ice-induced pressure decreased with increasing STFN, if CN was held constant; (3) when more than one independent dimensionless variables were simultaneously varied, ability to determine ice-induced pressure became increasingly difficult; and (4) for ice-structure interaction problems, when one independent dimensional variable was changed, it became difficult to keep some other independent dimensionless variables as constants because of interdependent nature of dimensionless variables defining the problem. This analysis is more useful in forming conclusions and insights on the influence of parameters defining ice-induced pressure and could be extended to develop predictive empirical equation for ice-induced pressures and deformation processes in ice-structure interaction problem.Key words: aspect ratio, dimensional analysis, failure modes, ice-structure interaction pressures, similarity theory.
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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 | 0.001 | 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".