Toward the prediction of fan noise: From low-speed to high-speed turbomachineries.
Bibliographic record
Abstract
Environmental concern and comfort trigger more and more acoustic specifications and regulations on aircraft impact on airports and on ventilation systems in buildings or transportation systems (cars, trains, or airplanes), which in turn impose lower and lower maximum noise levels to such systems. For instance, turbofan engines have increased their bypass ratio in order to improve the aircraft performance while diminishing the nominal speed of rotation. The jet noise is then reduced, and the fan noise becomes a dominant source of noise, especially at approach. A quick calculation of the overall noise generated by a given fan geometry, either low speed or high speed, would be a valuable asset to any fan design designer and manufacturer prior to any installation in a building or an airplane, for instance. However, an accurate prediction of the sound by any full turbomachinery still remains a challenging goal and a daunting task to be achieved by a direct computation. In the present study, the noise predictions will then rely on a strip theory combined with an acoustic analogy based on the wall pressure fluctuations. For the low-speed fans, the model is an extension of the development by Schlinker & Amiet for helicopters. Turbofan engines induce two additional difficulties to noise modeling: the cascade effects and the duct configuration which are presently modeled.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".