Subsonic Jet Noise Simulations Using Both Structured and Unstructured Grids
Bibliographic record
Abstract
For the last 10 years, large-eddy simulations have become a major tool for investigating jet noise sources because of their intrinsic ability to capture broadband turbulent features. However, many challenges still arise when dealing with complex geometries in terms of method accuracy and computational costs. Two different approaches to compute jet noise in an industrial context are here validated and compared. Both approaches are based on a hybrid methodology combining large-eddy simulation of jet flows for sources computations and Ffowcs Williams and Hawkings’s analogy for far-field noise prediction, but they differ on their grid topologies. The first approach uses classical block structured grids. The numerical scheme is a low-dispersive, low-dissipative finite-volume compact scheme. The second approach uses fully unstructured tetrahedral grids with a low-dispersive, low-dissipative Taylor–Galerkin finite-element scheme. Both approaches are used to compute a Mach 0.9 cold jet at the moderate Reynolds number without accounting for the nozzle geometry. Comparisons between simulations and experimental measurements highlight the need to correctly capture the initial turbulent development of the mixing layer at the nozzle exit. In the present simulations, because the nozzle geometry is not discretized, the turbulent transition is done by injecting perturbations as vortex-ring modes. Results obtained on this benchmark test case demonstrate the capability of both methods to correctly simulate and predict jet noise. The validation of the approach using fully tetrahedral grids provides a promising way to account for complex noise-reduction devices such as chevrons, realistic dual-stream nozzles, or lobed mixers.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".