Airfoils Generation Using Neural Networks, CST Curves and Aerodynamic Coefficients
Bibliographic record
Abstract
Fuel consumption has always been a major issue in the aviation industry, as all of its actors try to reduce it, to get the best carbon footprint possible. One of the answers to this issue is the reduction of drag caused by airplanes. The aim of this study was to implement airfoil morphing wing technology using neural networks methods. Specifically, the study was focused on finding an airfoil shape, given a set of aerodynamic coefficients (CL, CD, Cm) as inputs. Networks used lift, drag and pitching moment coefficients in order to generate a parametrized airfoil. Several networks were created using different parameters, and their results were compared, to verify the quality of the results, as well as the importance of the different parameters in the end-outcomes. After, the best network was used to generate airfoils, which aerodynamic properties were verified and compared to their reference aerodynamic performances to validate this method. The best networks reached an important efficiency of almost 70% generating airfoils with errors below 0.005 (Sum squared error). Finally, in order to create a highly effective tool for the objectives of this paper, a complementary study was conducted, in which the angle of attack was included as one of the inputs. This work is useful for determining airfoil shapes based on the knowledge of aerodynamic coefficients.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".