On the Effects of Trailing Edge Bluntness Size Over Airflow-Induced Noise for a Naca0012 Airfoil
Bibliographic record
Abstract
This project investigates the effects of Trailing Edge (TE) bluntness size on airfoil self-noise using Computational Fluid Dynamics (CFD). The study case is a NACA0012 airfoil with the original chord length (C) of 0.2286m; the airfoil has a varying TE blunted between 0 and 10mm, lowering the effective chord length. The airflow has an Angle of Attack (AoA) of 4° and a free stream velocity of 40m/s. To conduct the CFD simulations, the flow domain consisted of a C-type domain with a length and height of 40C and 10C, respectively. Simulations employ a hybrid Embedded Large-Eddy Simulation (ELES) technique to calculate the flow properties. A correlation length of 0.5C also accounts for spanwise effects in the Ffowcs-Williams and Hawkings (FW-H) acoustic analogy approach to predict far-field noise. Mesh convergence and domain size stability have been checked after verifying the simulation method against available literature. The current study focuses on the relationship between TE bluntness size and Sound Pressure Level (SPL), peak frequencies, and aerodynamic performance of the airfoil.
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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 | 0.002 |
| 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 | 0.000 |
| 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".