Dynamic analysis of an SDOF helicopter model featuring skid landing gear and an MR damper by considering the rotor lift factor and a Bingham number
Bibliographic record
Abstract
Abstract The present study addresses the performance of a skid landing gear (SLG) system of a rotorcraft impacting the ground at a vertical sink rate of up to 4.5 ms −1 . The impact attitude is assumed to be level as per chapter 527 of the Airworthiness Manual of Transport Canada Civil Aviation and part 27 of the Federal Aviation Regulations of the US Federal Aviation Administration. A single degree of freedom helicopter model is investigated under different values of rotor lift factor, L . In this study, three SLG versions are evaluated: (a) standalone conventional SLG; (b) SLG equipped with a passive viscous damper; and (c) SLG incorporated a magnetorheological energy absorber (MREA). The non-dimensional solutions of the helicopter models show that the two former SLG systems suffer adaptability issues with variations in the impact velocity and the rotor lift factor. Therefore, the alternative successful choice is to employ the MREA. Two different optimum Bingham numbers for compression and rebound strokes are defined. A new chart, called the optimum Bingham number versus rotor lift factor ‘ B i o − L ’, is introduced in this study to correlate the optimum Bingham numbers to the variation in the rotor lift factor and to provide more accessibility from the perspective of control design. The chart shows that the optimum Bingham number for the compression stroke is inversely linearly proportional to the increase in the rotor lift factor. This alleviates the impact force on the system and reduces the amount of magnetorheological yield force that would be generated. On the contrary, the optimum Bingham number for the rebound stroke is found to be directly linearly proportional to the rotor lift factor. This ensures controllable attenuation of the restoring force of the linear spring element. This idea can be exploited to generate charts for different landing attitudes and sink rates. In this article, the response of the helicopter equipped with the conventional undamped, damped, and MREA based SLG are numerically simulated using three sets of Bingham numbers. Namely, an underestimated, optimum, and overestimated Bingham number for every stroke. The simulation results depict that the only feasible solution is when the MREA generates the optimum damping force corresponding to the optimum Bingham numbers. Under this circumstance, the MREA utilizes the available energy absorption stroke to attain a soft landing. Furthermore, in the rebound stroke, the optimum damping force resettles the helicopter to its equilibrium position and prevents oscillations after the end of the rebound stroke.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".