Crashworthiness Study of Helicopter Skid Landing Gear System Equipped With a Magnetorheological Energy Absorber
Bibliographic record
Abstract
The present study concerns with the performance of a skid landing gear (SLG) system of a rotorcraft impacting the ground at a vertical sink rate of 5.0 m/s. The impact attitude is per chapter 527 of the Airworthiness Manual (AWM) of Transport Canada Civil Aviation and FAR Part 27 of the U.S. Federal Aviation Regulation. A single degree of freedom helicopter model is investigated under two rotor lift factors 0.67 and 1.0. Three Configurations are evaluated: a) A conventional SLG; b) SLG equipped with a passive viscous damper and c) SLG incorporated with a magnetorheological energy absorber. The non-dimensional solutions of the helicopter model show that the passive damper system could reduce the maximum acceleration experienced by the helicopter occupants by 21% and 19.8% in comparison to the undamped system for the above rotor lift factors, respectively. However, the passive damper fails to constrain the non-dimensional energy absorption stroke of the damper within the given 18 cm maximum stroke and a bottoming out of the damper piston was noticed. Therefore, the alternative and successful choice was to employ a magnetorheological energy absorber (MREA). To improve the MREA controllability and to resettle the payload with no oscillations, i.e. in one cycle, two different Bingham numbers for compression stroke and rebound stroke were defined in the non-dimensional solution. Several simulations were conducted for different values of Bingham numbers. Among these numerical simulation results, the solution that implemented the optimum Bingham numbers was found to be the only one feasible solution. In this case the MREA with optimum Bingham number for compression could utilize the full energy absorption stroke to attain soft landing. In the rebound stroke, the generated optimal on-state damping force successfully controls the bounce of the payload until the payload settles down to its original equilibrium position with no oscillations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".