Numerical Study of Helicopter Rotors in a Ship Airwake
Bibliographic record
Abstract
Operating helicopters in a naval environment is challenging because it imposes a pilot workload significantly higher than that during land-based operations. The aerodynamic interaction between the aircraft and the ship wake is known to play an important role in increasing the pilot workload, hence reducing the aircraft capability as a result of maintaining safety. As a further step toward numerical prediction of ship/helicopter operational limitations, computational-fluid-dynamics simulations are conducted for the Canadian Patrol Frigate. The effect of the rotor is included in the simulation, first using an actuator-disc method together with steady calculations, then using rotor blades and the unsteady Reynolds-averaged Navier–Stokes equations. Results using the actuator-disc method demonstrate the importance of coupling effects on the wake and rotor inflow when the rotor is operating close to the ship and therefore the invalidity of superposition methods. The case of a Sea King helicopter main rotor hovering above the deck just before touchdown is reproduced to overcome the limitations of steady calculations and the actuator-disc method. Predictions of rotor thrust compare well with the experimental data available and give confidence in the results. The findings highlight the differences in rotor loading between forward flight and near-deck operation. The possibility of using such simulations for determining safe flight envelopes is discussed as part of the future work.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".