Modelling and simulation of skid-equipped shipboard rotorcraft
Bibliographic record
Abstract
Mathematical modelling and computer simulation has proven to be the tool of choice for supporting the development and operation of shipboard aircraft securing and handling equipment. While other alternatives have been used for specific phases and aspects of operation, the variety of nonlinear effects present and range of analysis types that are required strongly support transient time-domain simulation as the most versatile option. The DYNAFACE® simulation program has been developed over the past fifteen years and is widely used for this purpose, particularly for the analysis of conventional shipboard aircraft designed with wheeled landing gears. Increasingly, a requirement has emerged for the ability to model shipboard aircraft having skid landing gear -- both due to occasional use of land-based aircraft aboard ships and for supporting the design and operation of ship-based UAVs that are often fitted with skid landing gear. This paper describes in detail for the first time recent mathematical modelling resulting in the extension of DYNAFACE® capabilities to include the modelling of skid-equipped rotorcraft both using a fast linear stiffness modelling approach for the gear and using a more-general nonlinear finite element structural modelling approach. In both cases, an efficient skid/deck interface model is used. The two modelling options provide versatility in the type of analysis that can be performed. Sample results from a typical analysis are also presented and discussed.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".