Tonal Noise Prediction of a Modern Turbofan Engine With Large Upstream and Downstream Distortion
Bibliographic record
Abstract
Ultra-high bypass ratio (UHBR) engines are designed as compact as possible and are characterized by a short asymmetric air inlet and heterogeneous outlet guide vanes (OGVs). The flow close to the fan is therefore circumferentially nonuniform (or distorted) and the resulting noise might be impacted. This is studied here at take-off conditions by means of a simulation of the unsteady Reynolds-averaged Navier–Stokes (URANS) equations of a full-annulus fan stage. The model includes an asymmetric air inlet, a fan, heterogeneous OGVs, and homogeneous inlet guide vanes (IGVs). Direct acoustic predictions are given for both inlet and aft noises. A novel hydrodynamic/acoustic splitting method based on a modal decomposition is developed and is applied for the aft noise analysis. The noise mechanisms that are generally considered (i.e., interaction of fan-blade wakes with OGVs and fan self-noise) are shown to be impacted by the distortion. In addition, new sources caused by the interaction between the stationary distortion and the fan blades appear and contribute to the inlet 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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".