Jet noise simulation with realistic nozzle geometries using fully unstructured LES solver
Bibliographic record
Abstract
Different jet simulations with realistic nozzle geometries for single and dual jets are performed using the fully unstructured Large Eddy Simulation solver AVBP. Two single jet nozzle geometries from NASA are considered: the Single Metal Chevron 000 which is the base line of the series and the Acoustic Reference Nozzle 2 (ARN2). Both are convergent nozzle but with different section variations. Isothermal Mach 0.9 jets at moderate Reynolds number are simulated using these nozzles. The operating condition corresponds to the set point 7 of Tanna matrix. Grids with different resolutions are used and perturbations are injected in boundary layers to get a fully turbulent mixing layer at the nozzle exit. Apart from a laminar to turbulent transition at the nozzle exit, results show a good agreement with experimental data in terms of aerodynamics and acoustics. A co-axial jet nozzle geometry experimentally studied at the P’ Institute of Poitiers is also simulated. It is a isothermal configuration with a primary jet Mach number of 0.5 and a secondary jet Mach number of 0.35. This low Mach configuration makes it more challenging as the noise level reduces and the acoustic propagation is slower. For the mean flow quantities, an overall good agreement with experimental measurements is obtained. However the flow dynamics is dominated by a strong vortex pairing phenomenon due to the late laminar to turbulent transition of the mixing layers and the discretization of their interactions on the present grid might be unsufficient.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".