Development and Experimental Validation of a Shipboard Helicopter On-Deck Maneuvering Simulation
Bibliographic record
Abstract
Routine shipboard helicopter operation on many classes of ships requires that helicopters be maneuvered and traversed along the ship deck using installed helicopter securing and handling equipment. This paper describes the derivation, implementation, and validation of a four-degrees-of-freedom mathematical model for predicting and analyzing the behavior of shipboard aircraft under the influence of external aircraft handling forces. The resulting model is suitable both for engineering analysis and training applications. The helicopter model includes the coupled dynamics of the aircraft, landing gear, and optionally steerable or castorable auxiliary wheel assembly. Detailed tire modeling addresses the transient development and release of tire deflection and cornering forces related to yawed and unyawed relaxation lengths as well as direct application of forces by the handling system. Verification and both qualitative and full-scale experimental validation of the model, performed using a number of simple maneuvers to validate specific aspects of the simulation, are discussed. It is shown that the resulting HeliMan simulation captures the underlying dynamics of the shipboard helicopter maneuvering process. Full-scale validation data show that in many cases the simulation is able to reproduce closely the measured data. The effect of tire side loading on longitudinal rolling resistance has been identified as a shortcoming of existing rolling tire models and the most probable cause for differences that exist between simulated and measured results in some validation cases. Nomenclature A = vector representing the accelerations for the two bodies
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".