Data analysis for a model podded propulsor in ice (pusher mode)
Bibliographic record
Abstract
Non-hospitable areas are frequently being explored for new energy resources and most of these places are ice covered. Understanding the interactions between a ship’s propeller and sea ice is fundamental in the production and manufacturing of podded propulsors. The test facilities here at IOT were used to conduct experiments whereby a model-podded propeller was used in the ice tank and propeller-ice interaction parameters were measured and recorded by the use of dynamometers. Tests were conducted using the two operating conditions, (tractor and pusher), different depths of cut, varying azimuth angles, and a range of propeller rotational speeds and carriage velocities. The analysis of ice loads in pusher mode is different to that of tractor mode because of the un-uniform ice conditions experienced during pusher mode. The acquired results must be represented in either individual blade angular positions or individual revolutions. This can be easily done with computer programs such as Sweet. Parameters, such as advance coefficient, thrust coefficient, and torque coefficient, can be calculated from the newly represented data. Plotting these parameters against an exceedance probability can easily show that as the depth of cut, azimuth angles and advance coefficients increase, the maximum torque values also increase. After setting an appropriate return period of the probability, deterministic values of ice loads, (here we used shaft torque), can be provided depending on the design criteria (such as 100-year load).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".