Resistance and propulsion of CCGS Terry Fox in ice from model tests to full scale correlation
Bibliographic record
Abstract
The first resistance and propulsion tests of a Terry Fox model were carried out in 1988 at the Institute for Ocean Technology, IOT (formerly Institute of Marine Dynamics, IMD). More recent resistance and propulsion model tests were again conducted in 2007. This paper describes not only the correlation with full scale but also the reproducibility and quality of the test data and test method over 20 years. The model has been tested with three different hull-ice friction coefficients, 0.11, 0.045, and 0.005 and several different ice conditions. The empirical formula to predict the full-scale resistance is given based on the IOT's standard analysis method. Towed propulsion tests were carried out in ice and in open water using an overload method. During the full-scale tests conducted in 1990 by Fleet Technology Limited, the flexural strength of the ice was 150 kPa and the thickness was 1.55 m. It was quite soft but thick ice. Due to the thick ice, significant propeller ice interaction was reported, but unfortunately model tests were not done with corresponding full-scale ice thickness. Some of the other full-scale measurements (in 1986 by Arctec Canada Limited) had a snow cover, hummocks, and melt-pools which could affect the resistance value. These effects were not taken into account in the model tests. The present paper shows the usefulness of a non-dimensional method to predict resistance with four components (breaking term, clearing term, buoyancy term and open water term). Overall resistance prediction is good, but the power prediction shows some discrepancies possibly due to propeller ice interaction. The model results from the 1988 to 2007 tests were consistent over the twenty years between the tests, and the prediction method for full-scale power is appropriate with a friction coefficient of about 0.05, as has been found before at IOT.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".