Novel Parameters for the Performance Evaluations of Leading Edge Tubercles on Airfoils
Bibliographic record
Abstract
Researchers have tested tubercles with different amplitude and wavelength combina- tions on a range of low-speed airfoils. However, a systematic approach has never been used for the optimization of tubercles. In this study, tubercles are optimized using an articial neural network known as Self-Organizing Maps (SOM). Data were extracted using reverse engineering from published tubercle research and used for training the SOM. A new vari- able, a Reynolds number based on hydraulic diameter ReDh, is introduced for the tubercle classification directly relating performance. In addition, post-stall operability another new parameter was introduced for tubercle performance assessment. Based on the SOM re- sults, new tubercle geometries were selected for 2 new proof of concept tests to perform further investigation. Stall angle improved due to the reduction of amplitude, wavelength and ReDh, validating the predictions of SOM. However, the one tubercle geometry resulted in lower lift curve slope in the pre-stall region and a reduced CLmax in comparison to the baseline, possibly a result of drastic reduction in tubercle wavelength. In the post-stall regions, the new tubercle geometry showed improvements over the baseline unmodified 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".