Prediction of Turbulent Flow-Induced Noise in Aircraft Cabins
Bibliographic record
Abstract
Flow-induced noise in aircraft cabins can be predicted through analytical models or numerical methods. However, the analytical methods existent nowadays were obtained for simple structures and cabins, in which, usually, a single panel is excited by the turbulent flow, and coupled with an acoustic enclosure. This paper discusses the development of analytical models for the prediction of aircraft cabin noise induced by the external turbulent boundary layer (TBL). The coupled structural-acoustic analytical model is developed using the contribution of both structural and acoustic natural modes. While, in previous works, only the contribution of an individual panel to the cabin interior noise was considered, here, the simultaneous contribution of multiple flow-excited panels is also analyzed. The analytical models were developed for rectangular and cylindrical cabins. The mathematical models were successfully validated through the good agreement with several independent experimental studies. Analytical predictions are presented for the interior sound pressure level (SPL) at different locations inside the cabins. It is shown that identical panels located at different positions have dissimilar contributions to the cabin interior noise, showing that the position of the vibrating panel is an important variable for the accurate prediction of cabin interior noise. Additionally, the results show that the number of vibrating panels significantly affects the interior noise levels. It is shown that the average SPL, over the cabin volume, increases with the number of vibrating panels. The space-averaged SPL is usually accepted to provide the necessary information for the noise prediction. However, in some real life applications, the local sound pressure may be desirable. To overcome this point, the model is also able to predict local SPL values, at specific locations in the cabin, which are also affected by number of vibrating panels, and often differ from the average SPL values. The developed analytical model can be used to study a wide range of different systems involving a cabin coupled with vibrating panels, excited by the TBL. The properties of the external flow, acoustic cabin, and panels, as well as the number of vibrating panels, can be easily changed to represent different systems. These abilities of the model make it a solid basis for future investigations involving the implementation of noise reduction techniques and multidisciplinary design optimization analyzes.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".