New methodology combining neural network and extended great deluge algorithms for the ATR-42 wing aerodynamics analysis
Bibliographic record
Abstract
ABSTRACT The fast determination of aerodynamic parameters such as pressure distributions, lift, drag and moment coefficients from the known airflow conditions (angles of attack, Mach and Reynolds numbers) in real time is still not easily achievable by numerical analysis methods in aerodynamics and aeroelasticity. A flight parameters control system is proposed to solve this problem. This control system is based on new optimisation methodologies using Neural Networks (NNs) and Extended Great Deluge (EGD) algorithms. Validation of these new methodologies is realised by experimental tests using a wing model installed in a wind tunnel and three different transducer systems (a FlowKinetics transducer, an AEROLAB PTA transducer and multitube manometer tubes) to determine the pressure distribution. For lift, drag and moment coefficients, the results of our approach are compared to the XFoil aerodynamics software and the experimental results for different angles of attack and Mach numbers. The main purpose of this new proposed control system is to improve, in this paper, wing aerodynamic performance, and in future to apply it to improve aircraft aerodynamic performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".