Unstructured LES of the baseline EXEJET dual-stream jet
Bibliographic record
Abstract
The EXEJET project is a large-scale jet noise collaborative program by Snecma, Onera and Airbus. In this framework, an exhaustive experimental database has been collected on a realistic dual-jet stream mounted around a central plug. The high-bypass ratio jet is operated at approach conditions in the CEPRA19 anechoic wind-tunnel. In the present paper, several Large-Eddy Simulations (LES) have been performed on the EXEJET baseline configuration using the unstructured flow solver AVBP developped at Cerfacs. The nozzle inlet boundary conditions are obtained by performing RANS simulations until the nozzle exit total pressure conditions match the experimental pressure probe survey. Three LES simulations have been performed following the numerical strategy developped by the present authors, the first one with a fully tetrahedral mesh, the second one uses a hybrid mesh including prismatic layers to better resolve the boundary layer development in the nozzles and finally, a finer mesh including an original tripping method near the fan jet exit to trigger the laminar to turbulent transition of the external mixing layer. The boundary layers developping in the nozzles have a major influence on the turbulent jet streams mixing. The mean velocity magnitude and turbulent kinetic energy evolution along the jet from the PIV measurements are well captured with the three simulations but a better agreement is obtained with the finer hybrid tripped simulation. The acoustic predictions obtained using a Ffowcs William and Hawking’s analogy are in good agreement with the experimental measurements especially at 30◦ degree in the downstream direction. The successive meshes tested bring similar acoustic results in the low and middle frequency ranges. The hybrid meshes recover the proper high-frequency decay of the sound levels and the cut-off frequency is increased with the mesh refeinement. Preliminary beamforming results have identified two main noise sources upstream and downstream the core-jet collapse.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".