CFD predictions of drag force for a Slocum ocean glider
Bibliographic record
Abstract
Preceding the installation of a hydro-acoustic monitoring system on an ocean glider, CFD (Computational Fluid Dynamics) tools can be used to assess the impact on the hydrodynamic properties of the glider when the hull is modified because of adding new sensors. In this study, two simple questions are examined. The first one is which CFD code can predict more accurately the drag force of the ocean glider. The second is which turbulence model is appropriate for estimating the drag force at the glide speed of the ocean glider. The CFD results are compared with the results from the drag measurements and self-propulsion experiments which were conducted at the Marine Institute's Flume Tank with a Slocum glider in October and November 2009. Starting from the comparison with the experiments, the comparative study is performed with the help of graphical results such as velocity profiles, pressure contours and wall shear stress distributions. Finally, an analysis is presented for why there are differences between the experimental and predicted values of the drag force according to the two CFD codes and turbulence models which were used.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".