Numerical study of optimized airfoil trailing-edge serrations for broadband noise reduction
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 0.000 |
Machine scores (provisional)
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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 it