Installation effects on airfoil self-noise estimated by direct numerical simulations
Bibliographic record
Abstract
Several Direct Numerical Simulations (DNS) have been achieved using the Lattice-Boltzmann Method (LBM) on a Controlled-Diffusion (CD) airfoil at R e c = 1 . 5 × 1 0 5 and α = 8 ° to evaluate installation effects on airfoil self-noise. 2D DNS provides unrealistic airfoil noise sources and a significant overprediction of the far-field noise with possibly a change in the ranking of the noise sources. Conversely, all 3D simulations of the airfoil immersed in the wind tunnel jet show excellent agreement with the flow around the airfoil and the far-field noise. Two new 3D DNS with some shear-layer refinement and possibly an additional zigzag trip to trigger turbulence have better resolved the jet development. Even though all 3D DNS identify two main noise sources, the laminar separation bubble (LSB) at the leading edge and the turbulent eddies at the trailing edge , their intensity varies because of a strong coupling between the jet and the airfoil that yields different LSB sizes, increased inlet turbulent intensity, oscillations of the angle-of-attack, and variations in the noise sources. The state of the jet shear layer at the nozzle exit of a given open-jet anechoic wind tunnel is seen to influence the flow field around the airfoil and its noise radiation, with potentially a larger jet contribution.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".