OPTIMIZATION STUDY AND PANEL PARAMETER STUDY FOR NOISE RADIATION REDUCTION OF AN AIRCRAFT PANEL EXCITED BY TURBULENT FLOW
Bibliographic record
Abstract
The noise and vibration in an aircraft cabin during cruise conditions is mostly caused by external flow excitations from the turbulent boundary layer (TBL). The TBL causes the fuselage panels on the aircraft to vibrate. These vibrations radiate sound energy in the form of noise. Therefore, it is of great interest to determine which aircraft panel parameter is most sensitive in decreasing the amount of radiated sound power and how to optimize these parameters to reduce the noise into the aircraft cabin. An analytical model was created and validated using Matlab that calculates the acceleration power spectral density, which is proportional to radiated sound power (RSP). A sensitivity study was performed on the panel parameters, to determine the percent change in acceleration power spectral density (PSD), vs. the percent change in seven different panel parameters: panel thickness, material density, panel width and length, Elasticity modulus, Poisson’s ratio, and damping ratio. An analytical method to optimize an aircraft panel is presented, by changing the panel properties, in order to reduce the acceleration PSD of the panel caused by the TBL. It is shown that panel thickness and panel density are the most consistent and sensitive parameters at, reducing the acceleration PSD, at different octave bands in the human hearing range.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".