Modeling Helicopter Blade Sailing: Dynamic Formulation in the Planar Case
Bibliographic record
Abstract
As part of a research project aimed at simulating rotor dynamic response during shipboard rotor startup and shutdown operations, a dynamic model of the ship–helicopter–rotor system that is appropriate for use in predicting rotor elastic response was developed. This planar model consists of a series of rigid bodies connected by rotational stiffness and damping elements that allow motion in the flapwise direction. The rotors were partitioned into an arbitrary number of rigid beam segments having the inertial and geometrical properties of a typical rotor. Helicopter suspension flexibility and damping were also modeled, although the helicopter was otherwise considered as a rigid body. Lagrange’s equation was used to derive the governing dynamic equations for the helicopter–rotor model. The effect of ship motion on blade deflection was also considered. The ship motion supplied as input to the model included representative frigate flight deck motion in three dimensions corresponding to an actual sea spectrum, ship particulars and ship operating conditions. This paper is intended to detail the dynamic approach adopted for this blade sailing study, and its conceptual validation in the planar case. The methodologies that have been developed lend themselves to easy expansion into three dimensions, and into torsion and lead/lag modeling. The amount of blade motion induced by ship motion on nonrotating helicopter blades is included. Although aerodynamic loads are a major contributor to blade sailing, this paper focuses on the dynamics aspect of the problem, and thus does not include aerodynamic effects.
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 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 | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".