Integrating CFD and piloted simulation to quantify ship-helicopter operating limits
Bibliographic record
Abstract
Abstract This paper describes a study which has been concerned with numerical predictions of the airwakes resulting from two simplified ship geometries: the internationally agreed Simple Frigate Shape, SFS1, and its successor, SFS2. Extensive steady-state simulations have been carried out for a wide range of wind conditions using Fluent, a commercially available Computational Fluid Dynamics (CFD) code. The CFD predictions have been partially validated against wind tunnel data produced by the National Research Council of Canada (NRC) and have shown good agreement. The resulting airwake velocity components have been exported from Fluent, interpolated onto suitable grids and attached to the FLIGHTLAB flight-simulation environment as look-up tables; piloted flight trials were then carried out using the Liverpool full-motion simulator. The pilot workload and helicopter control margins resulting from a range of wind-over-deck conditions have been used to develop the Ship-Helicopter Operating Limits (SHOL) for a Lynx-like helicopter and the SFS2. The workload was compared to the pilot’s experiences on a similar aircraft and a Type 23 Frigate and the simulated SHOL compared with SHOLs derived from sea trials. The results are very encouraging and open up further the long awaited prospect of such simulations being used in the future to reduce at-sea trials, and to provide a safe environment for pilot training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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".