A Novel Design of Landing Gear Oleo Strut Damper Using MR Fluid for Aircraft and UAV’s
Bibliographic record
Abstract
Most landing gears used in aircraft employ very efficient oleo-pneumatic dampers to absorb and dissipate the impact kinetic energy of the aircraft body frame. A single-acting shock absorber is most commonly used in the oleo strut that has a metering pin extending through the orifice, which can vary the orifice area upon compression and extension of the strut. This variation is adjusted by shaping the metering pin so that the strut load is fairly constant under dynamic loading. In this paper, it is proposed to further change the damping coefficient as a function of time in order to achieve a semi-active control of the aircraft vibrations during landing by using Magnetorheological (MR) fluid in the Oleo. With the metering pin designed for a nominal flight condition, further variation in the fluid viscosity would help achieve the optimal performance in off-nominal flight conditions. A simulation approach is employed to show the effect of different profiles for viscosity variation in the MR fluid. The utility of such a damper can be very well exploited to include different criteria such as the landing distance after touchdown. This type of system can be used also in Unmanned Aerial Vehicle (UAV) application where the focus of design may be to accomplish the task without the consideration of passenger comfort.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".