Comparison of different noise sources for the simulative cabin noise assessment of an electrically propelled regional aircraft
Bibliographic record
Abstract
The rising number of passengers transported by aircraft leads to more flight traffic, further increasing the environmental impact of the aviation sector. In order to combat the growing environmental impact, the Cluster of Excellence Sustainable and Energy Efficient Aviation of TU Braunschweig aims to advance research towards a climate neutral aviation industry, especially with the design of an electrically propelled short-range regional aircraft, among others. In the conscience of passengers, the focus is also shifted towards a healthy and comfortable travel experience. One of the main factors influencing these aspects is noise inside the aircraft cabin. A lower noise impact can help increase the technology acceptance and further push towards more sustainable airborne transport solutions. This contribution aims to simulatively assess and compare the sound pressure levels inside the passenger cabin of an electric propeller aircraft. The focus is laid on two of the most important noise sources: the tonal propeller excitation as well as the sound field beneath the turbulent boundary layer. The paper presents a wave-resolving FE model considering both sources and shows, which sound pressure levels can be expected, while also comparing the frequency spectra separately, therefore enabling early design changes to help reduce the cabin noise.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".