Evaluation of a dynamic positioning thruster wake using stereoscopic particle image velocimetry
Bibliographic record
Abstract
Thrusters working at low advance coefficients are employed in a wide range of offshore and marine applications on Floating, Production, Storage and Offloading (FPSO) systems; shuttle tankers; tug boats; and mobile offshore units. Therefore, an understanding of the flow around the thrusters is of great practical interest. Despite this interest, there is lack of knowledge in the description of the hydrodynamic characteristics of thrusters wake at bollard pull and low advance coefficient values. This work was aimed at providing detailed data about the hydrodynamic characteristics of a Dynamic Positioning (DP) thruster wake flow at different low advance coefficient values. Wake measurements were made during cavitation tunnel tests carried out on a ducted propeller model at the Italian Ship Model Basin (INSEAN), Rome, Italy. Through these experiments, the DP thruster far wake velocity components at different downstream axial planes, up to 15 diameters downstream, were obtained using a Stereoscopic Particle Image Velocimetry (SPIV) system in the INSEAN cavitation tunnel. These experiments were carried out at two different advance coefficient (J) values [bollard pull (J = 0), and J = 0.4].
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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 | 0.000 |
| 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) | 0.000 | 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".