Benchmark Reynolds-averaged Navier-Stokes study of a generic marine rudder’s static stall characteristics
Bibliographic record
Abstract
When assessing manoeuvring performance it is common to perform steady Reynolds-Averaged Navier-Stokes computations for a range of turning conditions. It is then critical to reliably and accurately predict rudder forces. A recent collaborative benchmark study showed inconsistent capturing of stall effects on aft control surfaces for a generic submarine. To investigate this issue, we present a new generic marine rudder and the influence of the numerical setup on the prediction of its static stall characteristics. We demonstrate the existence of a static hysteresis loop, which we compute with Menter’s SST turbulence model at a full-scale Reynolds number of . One branch shows a light trailing-edge stall with a high lift coefficient, and the other a deep stall with a low lift coefficient. We show how the initial conditions and solution methodology affect which solution is achieved. Notably, even below the low limit in the hysteresis loop, an incorrect deep-stall result can be obtained when using a free-stream initialization, due to poor iterative convergence. In contrast, potential-flow initialization and unsteady RANS methods effectively produce the correct high-lift solution below the hysteresis loop. A discretization analysis at shows that six flow solvers produce consistent results to within a small discretization uncertainty.
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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".