Aeroacoustic Performances of the ECL5 UHBR Turbofan Model With Serrated OGVs: Design, Predictions and Comparisons With Measurements
Bibliographic record
Abstract
This work is devoted to the design and evaluation of a low-noise OGV aiming to reduce the rotor-stator interaction noise using leading edge serrations and tested on the ECL5 UHBR model in the PHARE-B3 rig at Ecole Centrale Lyon. First, a radially varying 2D design is proposed and evaluated by means of a fast analytical prediction tool and using a strip approach. Then, an iterative process combining 3D RANS calculations with an in-house modeler is carried out to achieve a suitable 3D geometry, while minimizing the aerodynamic penalties. Following this design process, high fidelity simulations based on a lattice-Boltzmann solver are performed to assess the sound power level reduction achieved by the serrated OGV (by comparison to the untreated baseline case primarily tested). Due to the low-compressibility assumptions of the present LBM, simulations are limited to the reachable higher regime (45% of nominal rotational speed). Hence, available analytical and numerical predictions are compared to the experimental data, both in terms of aerodynamic and acoustic performances. A rather good agreement is obtained with sound power level reductions up to 3 dB from intake radiation and 6 dB in the bypass duct on the considered operating points.
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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 | 0.000 | 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 | 0.001 |
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".