PROPELLA: A NUMERICAL TOOL TO STUDY VARIOUS ASPECTS OF PODDED PROPULSORS
Bibliographic record
Abstract
This paper describes the numerical aspects of a research program on podded propulsors, which is being undertaken jointly by the Ocean Engineering Research Centre at Memorial University of Newfoundland, the National Research Council's Institute for Ocean Technology, Oceanic Consulting Corporation, and Thordon Bearings Ltd. The numerical tool is an in-house panel method code, PROPELLA. The code is a low order source-doublet, steady/unsteady time domain panel method code having capabilities to predict hydrodynamic performance of screw propellers with various configurations. Under the research program, the code was extended and used to model the propellers, pod-strut combinations and strut-wake impingement model. Amongst the hydrodynamic issues that have been addressed through numerical predictions were questions regarding the effects of hub taper angle (propeller only case and pod-strut-propeller case), pod-strut configuration (push and pull), geometric variations, azimuthing conditions and pod-strut interactions (wake impingement effect) on podded propeller performance. Predictions were made both in pusher and puller configurations for the pods and reasonable agreement was achieved between the predictions and measurements. The code is being modified to study the podded propulsors' performance at static and dynamic azimuthing conditions. The code is also capable of performing simulations with propellers and bodies like ship hull, underwater vehicles with fins.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".