Numerical study of optimized airfoil trailing-edge serrations for broadband noise reduction
Bibliographic record
Abstract
A surrogate-based global optimization study is performed to predict the optimum airfoil trailing-edge serration shape for the broadband noise reduction. The Controlled Diffusion airfoil is used. The optimization employs Ayton’s analytical model for the broadband noise prediction and Reynolds-Averaged Navier-Stokes (RANS) computations for the aerodynamic performance prediction. A parametric 3D geometrical and numerical model is constructed for the RANS computations. A design of experiments is carried out for the aerodynamic performance to construct the surrogate models based on Gaussian Process technique. The resulting response surfaces show that the lift-to-drag ratio and the pitching moment change non-linearly with the change in the serrations size. The optimization is performed for the maximization of noise reduction constrained by the lift-to-drag ratio and by the moment. The optimized shape shows the overall noise reduction of 15% compared to the reference airfoil. The maximum noise reduction appears after Stc = 26.3. The solution shows that the constraint on the moment is much more important than that of the lift-to-drag ratio. The aerodynamic constraints affect both the size and the shape of the serrations. The resulting noise reduction is lowered compared to previously computed unconstrained optimization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".