Bibliographic record
Abstract
Noise generated by aircraft is a common complain nowadays, specially for communities near airports. The reduction of noise from aerodynamic origin is one of today major industrial challenge, specially in the field of aviation, even more considering the gradually more stringent noise standard from ICAO (International Civil Aviation Organization). It is therefore desirable to accurately predict the noise generated by turbulent flows, so the noise reduction can effectively be addressed. Aeroacoustics is the study of noise generated by turbulent fluid motion, and by aerodynamic forces interacting with solid surfaces. Aeroacoustic involves coupled aerodynamic and acoustics phenomena. Because of the multi-disciplinary nature of Aeroacoustics and the diversity of the potential applications, Aeroacoustics courses are typically complex and taught at the graduate level and/or at the last year of graduation, requiring the student to be familiar with mathematics, physics and computational techniques. This paper will outline a number of important topics to consider when studying aeroacoustics and aircraft noise, from fundamental concepts to the more advanced and complex topics. Applications to engineering and industry problems are also presented. Both subsonic and supersonic flows are discussed, with emphasis given to turbulent flow induced noise in aircraft.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".