Computational optimization of trailing-edge designs to reduce airfoil self-noise
Bibliographic record
Abstract
This paper presents an investigation and optimization of Trailing Edge (TE) design to reduce airfoil self-noise using Computational Fluid Dynamics (CFD). The case for study is a NACA0012 airfoil with a chord length (C) of 0.2286 m, a varying Angle of Attack (AoA) between 0° and 15°, and free stream velocity between 35 and 70 m/s. The flow domain consists of a c-type domain with a length and height of 18C and 9C, respectively. The parametric mesh maintains a structured mesh on the entire domain for different designs and TE shapes. Simulations employ a hybrid Stress-Blended Embedded Large-Eddy Simulations (SB-ELES) model to calculate the flow properties. Different turbulence models are tested to address their performance in determining pressure fluctuations. A correlation length also accounts for spanwise effects in the Ffowcs-Williams and Hawkings (FW-H) acoustic analogy approach to forecasting the far-field noise. Furthermore, multi-objective optimization is employed to determine the optimum airfoil TE configuration for different flow velocities and AoAs. The optimum designs generate the lowest Sound Pressure Level (SPL) without significantly sacrificing the aerodynamic performance of the airfoil within specified parameters.
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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) | 0.001 | 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".