Full-Scale/Model-Scale Comparison of Podded Icebreaker’s Performance in Ice with Flexural Strength Measurement Study
Bibliographic record
Abstract
_ This paper provides the results of model tests in ice to evaluate the performance of the USCG Mackinaw Icebreaker that was equipped with two podded propulsors and compares with the data obtained from the full-scale ice trials. The objective of this collaborative model test program between the NRC and USCG was to understand the capability and limitation of the model tests with podded vessels in ice. As a result, the model tests showed a good agreement with attainable speeds at selected power levels but an overestimation of the ice resistance by an average of 7% (from 10% to 25%). Further discussion of podded icebreaker performance including turning circle tests in ice is provided and future work is proposed. This paper also provides a discussion of two different flexural strength test methods, which are simple beam and cantilever beam tests. Introduction The number of icebreakers with podded propulsors has been increasing in recent years and many new icebreakers are planning to use the pods because of high maneuverability and additional benefits such as low noise and vibration, and various usages of the propeller wake. The first pod unit (1.3 MW) was installed in a utility vessel Seili in 1990. Since then, several ice-going tankers/ icebreakers have used single or multiple pod units, which had up to 16 MW power (Wilkman et al. 2018).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".