Identification of Noise Sources in a Realistic Turbofan Rotor Using Large Eddy Simulation
Bibliographic record
Abstract
Large Eddy Simulation is performed using the NASA Source Diagnostic Test turbofan at approach conditions (62% of the design speed). The simulation is performed in a periodic domain containing one fan blade (rotor-alone configuration). The aerodynamic and acoustic results are compared with experimental data. The dilatation field and the dynamic mode decomposition (DMD) are employed to reveal the noise sources around the rotor. The trailing-edge radiation is effective starting from 50% of span. The strongest DMD modes come from the tip region. Two major noise contributors are shown, the first being the tip noise and the second being the trailing-edge noise. The Ffowcs Williams and Hawkings’ (FWH) analogy is used to compute the far-field noise from the solid surface of the blade. The analogy is computed for the full blade, for its tip region (outer 20% of span) and for lower 80% of span to see the contribution of the latter. The acoustics spectrum below 6 kHz is dominated by the tip part (tip noise), whereas the rest of the blade (trailing-edge noise) contributes more beyond that frequency.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".