Numerical and Experimental Research on a Podded Propulsor
Bibliographic record
Abstract
This paper presents methodologies and some results of a numerical and experimental program to evaluate the effects of static azimuthing conditions on the propulsive characteristics of a puller podded propulsor in open water. In the experimental effort, the model propulsor was instrumented to measure thrust, torque and rotational speed of the propeller, and three orthogonal forces and moments, and azimuthing angle of the pod. The experimental results included the bare propeller (ahead only) and the combined propeller and pod over a range of advance coefficients at various static azimuthing angles in the range of −180° to 180°. A complementary numerical study is being carried out to predict the hydrodynamic forces of podded propulsor in static azimuthing conditions. A Reynolds-Averaged Navier Stokes solver is used to predict the propulsive performance of the bare propeller as well as the podded propulsor system. The thrust and torque for the bare propeller were compared to the corresponding measurements. The propeller thrust and torque as well as the loads on the pod in straight-ahead condition and at static azimuthing angles were then compared with the measurements. Preliminary analysis demonstrates that the RANS solver could predict the performance coefficients of the bare propeller as well as the podded propulsor in straight-ahead and static azimuthing angles in puller configurations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".