Testing propulsion systems for performance in ice
Bibliographic record
Abstract
With an increasing interest in shipping in ice-covered waters, propulsion systems on these vessels are exposed to ice loads, which can be significantly larger than the open water loads. Severe damages can occur if the propulsion system is under-designed. At present rule formulae based on ice torque, which is related to the ice class of the vessel, is used in the design of these systems. The fact that failures continue to happen shows the need to study ice loads on these systems. Hence, at National Research council (NRC) - Institute for Marine Dynamics (IMD) several types of propulsion systems have been tested in ice conditions over the years. Among them are highly skewed propellers and azimuthing podded propellers. This paper describes the capabilities developed at NRC-IMD to test these two different propulsion systems. For the tests for the performance of highly skewed propellers a dynamometer to measure ice loads encountered by an individual blade was designed and built. It is mounted inside the hub and the blade attached to it. It can measure six component loads encountered by the blade. Some sample test results are given in the paper. In a different study, azimuthing podded propellers are being modelled. the experimental model is designed so that ice loads on different locations on the podded system can be measured: blade loads, shaft loads, shaft bearing loads and the global loads on the whole system. a brief description of the system is presented in the paper.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".