Computational Aeroacoustic Prediction of Tonal Noise for Low Reynolds Number Airfoils
Bibliographic record
Abstract
Airfoils operating in low Reynolds number, Re, conditions can generate tonal aeroacoustic noise due to the laminar or transitional boundary layer (BL) at the airfoil trailing edge (TE). At modest Re of less than 1×10⁵, an elongated laminar separation bubble (LSB) can occur near the TE which adds complexity to the BL transition and also generates tonal noise. Computational fluid dynamic and computational aeroacoustic simulations of the SD 7037 airfoil at Re=4.1×10⁴ are completed to study this phenomenon. The numerical methods used are incompressible wall-resolved large eddy simulation (LES) and the Ffowcs-Williams and Hawkings acoustic analogy, with the results of both methods validated against experimental data. The LES simulation of the airfoil BL development is critical to the tonal noise prediction and the accuracy of the predicted tones were assessed for a series of mesh refinements in the near-wall and separated BL regions. The mesh refinements in the regions of BL separation resulted in the correct simulation of the LSB and the associated aeroacoustic tonal noise for 1° angle of attack (AOA). Simulations at higher AOAs showed the sensitivity of the transient BL behaviour to the mesh refinements in the BL, while the time-averaged BL behaviour remained stable. Spectral analysis of the velocity in the BL determined that the source of the tone originates from the Tollmien-Schlichting wave frequency in the attached laminar BL, which is then amplified by the Kelvin-Helmholtz instability that forms in the LSB. The accurate tonal noise prediction occurred in the absence of the acoustic feedback mechanism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".