An Aeroacoustic Study of the Nose Landing Gear with Emphasis on Steering Actuators, Torque Link and Tow Hook
Bibliographic record
Abstract
Aeroacoustic experiments are performed at the UTIAS anechoic wind tunnel on a 30%-scale, simplified model of a generic nose landing gear. The far-field noise contributions resulting from the individual additions and/or variations of the landing gear components are evaluated at a Mach number of M = 0.17 through linear- and phased-array microphone measurements. Specific focus is placed on the torque link, tow hook and steering system components of the gear. Tow hook is found to suppress the low-frequency overhead noise originating from around the axle-strut connection region. Torque links mounted upstream of the strut induce prominent low-frequency noise in the sideline direction. Also, torque links mounted upstream of the strut generate higher broadband noise both in the overhead and sideline directions at low separation distances of the torque link arms compared to their downstream counterparts. Increasing the separation between fixed-length torque link arms decreases the broadband noise contribution of the upstream-mounted torque links to levels achieved with a torque link mounted downstream of the strut. The angle between steering actuators and the freestream flow is found to be crucial for the overall acoustic response of the nose landing gear. Steering actuators placed nearly perpendicular to the freestream flow were found to generate the quietest response.
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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 | 0.001 |
| 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 | 0.001 |
| 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".