Development and Experimental Validation of a Shipboard Helicopter On-Deck Maneuvering Simulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it