NUMERICAL AND EXPERIMENTAL ANALYSIS ON HELICOPTER'S MAIN ROTOR TRANSMISSION FOR PREDICTING STRUCTURE-BORNE NOISE
Bibliographic record
Abstract
A large number of a vehicle's mechanical systems are responsible for tonal vibrations, which propagate through the connected structures to radiate structure-borne noise into the cabin.In the literature, transfer path analysis (TPA) methods make it possible to solve vibro-acoustic problems using sub-structuring applications.This paper presents a case study of a heavy-active component connected to a plate backed cavity, using Component-Based transfer path analysis methods.The studied academic system is representative of a helicopter's main transmission.Both numerical and the experimental characterization are used to discuss the effect of several parameters, such as coupling (in-situ) vs decoupling (sub-structuring), completeness of the used transfer function matrix, the accuracy of the inversion method, as well as the rigidity of the test bench used to identify the equivalent forces.It is shown both numerically and experimentally that by using part of the frequency response functions matrix, one can reconstruct the response of both vibration and acoustic target's locations, even by decoupling the system and characterizing the equivalent forces on a test bench.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".