Optimization and design of an aircraft's morphing wing-tip demonstrator for drag reduction at low speeds, Part II - Experimental validation using Infra-Red transition measurement from Wind Tunnel tests
Bibliographic record
Abstract
In the present paper, an ‘in-house’ genetic algorithm was numerically and experimentally validated. The genetic algorithm was applied to an optimization problem for improving the aerodynamic performances of an aircraft wing tip through upper surface morphing. The optimization was performed for 16 flight cases expressed in terms of various combinations of speeds, angles of attack and aileron deflections. The displacements resulted from the optimization were used during the wind tunnel tests of the wing tip demonstrator for the actuators control to change the upper surface shape of the wing. The results of the optimization of the flow behavior for the airfoil morphing upper-surface problem were validated with wind tunnel experimental transition results obtained with infra-red Thermography on the wing-tip demonstrator. The validation proved that the 2D numerical optimization using the ‘in-house’ genetic algorithm was an appropriate tool in improving various aspects of a wing’s aerodynamic performances.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".