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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".