Modal analysis of the laminar boundary layer instability and tonal noise of an airfoil at Reynolds number 150,000
Bibliographic record
Abstract
A direct numerical simulation of the flow field around a controlled-diffusion airfoil within an anechoic wind-tunnel at [Formula: see text] incidence and a high Reynolds number of [Formula: see text] is performed for the first time using a lattice Boltzmann method. The simulation compares favorably with experimental measurements of wall-pressure, wake statistics, and far-field sound. The simulation noticeably captures experimentally observed high-amplitude acoustic tones that rise above a broadband hump. Both noise components are related to a breathing of a recirculation bubble formed around 65–70% of the chord, and to Kelvin-Helmholtz instabilities in the separated shear layer that yield rollers that break down into turbulent vortices whose diffraction at the trailing edge produces a strong dipole acoustic field. A wavelet analysis of the wall-pressure signals combined with some flow visualization has shown that the flow statistics are dominated by intense events caused by intermittent, large and intense bursting rollers. Several modal analyses of these events are performed on both the wall-pressure fluctuations and the span averaged flow field in order to analyse the boundary layer instability which triggers the typical sharp tones over a broadband hump in airfoil noise. A suction side Kelvin-Helmholtz instability is observed to be coupled with a pressure side vortex shedding induced by the sudden transition to turbulence and the blunt trailing edge.
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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) | 0.000 | 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) | 0.001 | 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".