Development of a Shipboard Skid-equipped Rotary-wing Aircraft Manoeuvering and Securing Simulation Package
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-0341.vid Helicopters have been leveraged to expand operational capabilities in many maritime applications such as naval intelligence, defence, and search and rescue. The proliferation of vertical-take-off unmanned aircraft systems (VTUAS) is further reshaping and broadening these maritime applications. Existing simulation capabilities for studying the dynamic interactions at the interface between aircraft and ship are not well suited to the operational constraints which VTUAS currently present. Their high thrust-to-weight ratio and typical stiff ‘skid-type’ landing gear constrain mechanical securing and traversing options. They also present more complex ship-helicopter interface behaviour compared to the conventional wheeled-landing gear helicopters widely deployed in maritime operations. The Skid-equipped Rotary-wing Aircraft Manoeuvring and Securing Simulation (SRAMSS) dynamic interface analysis package is tailored to skid-equipped, maritime-capable, rotary-wing aircraft. The package is capable of simulating the aircraft’s response to ship-deck interactions under varying deck motion, aerodynamic, and mechanical conditions. With the implemented models, SRAMSS distinguishes itself through its capability of simulating landing, securing, manoeuvring, touchdown, and take-off phases of flight.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".