Dual-stream jet noise simulations with realistic nozzle geometries using a fully unstructured LES solver
Bibliographic record
Abstract
Different jet simulations with realistic nozzle geometries for dual-stream jets are performed using the fully unstructured Large Eddy Simulation solver AVBP developed at Cerfacs. First, an isothermal low-Mach coaxial jet which has been experimentally investigated at the P Institute of Poitiers is simulated. The second investigated configuration is a tenth reduction of a generic model of a high-bypass ratio engine operated at take-off conditions experimentally investigated in the EXEJET project framework at the CEPRA19 Onera anechoic wind-tunnel. The third one is a 12-lobes internal mixer investigated at NASA Glenn Research Center. A similar meshing and numerical strategy has been followed for the three simulations in order to validate the methodology on different challenging jet noise configurations of increasing complexity. The aerodynamic results are in good agreement with the experimental measurements available for the three configurations. The sensitivity to the nozzle exit conditions and especially to the boundary layers resolution at the exit of the nozzle are demonstrated by the different meshes used for each configuration. These results validate the present numerical approach to capture the proper jet developments in all three configurations. For the three configurations, the acoustic results computed using the Ffowcs Williams and Hawking’s analogy are also in good agreement with the experimental measurements, particularly at 30 degrees in the downstream direction. The low Mach number of the coaxial jet configuration makes it more challenging as the noise level decreases. For all the simulations, proper control of the laminar to turbulent transition, to avoid large vortex pairing in the external mixing layers, seems to be the key for accurate acoustic predictions.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".