Gap effect on performance of podded propulsors in straight and static azimuthing conditions
Bibliographic record
Abstract
The paper presents preliminary results of an experimental study on the effect of gap distance on propulsive characteristics of puller podded propulsors in straight course and static azimuthing open water conditions. The gap distance is the axial distance between the rotating (propeller) and stationary (pod) part of a podded propulsor. The propeller thrust and torque, unit forces and moments in the threecoordinate directions of a podded unit were measured using a custom designed pod dynamometer in puller configurations with varied operating conditions. The model propulsor was tested at the gap distances of 0.3%, 1% and 2% of propeller diameter for a range of advance coefficients combined with the range of static azimuthing angles from +20° to -20° in a 10° increment. The results show that the gap distance does not have significant effect on propeller torque in straight course conditions, but has effects in azimuthing conditions. The propeller thrust and efficiency were influenced by the change of gap distance and the effects were more obvious at high azimuthing angles and high advance coefficient values. The unit thrust and efficiency, transverse and vertical forces, as well as moments in three coordinate directions were not influenced by the gap distance, taking into account the uncertainty in the measurements, both in straight course and azimuthing conditions.
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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 | 0.000 | 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".