Airfoil Generation and Optimization using Multiresolution B-Spline Control with Geometrical Constraints
Bibliographic record
Abstract
This paper presents a scheme that combines multiresolution curve editing with linear constraints: thickness and camber. This framework allows to perform multiresolution manipulation of piecewise polynomial B-spline curves, while specifying and satisfying various constraints on the curves. An easy computable multiresolution curve editing technique is investigated for the purpose of airfoil design as a novel tool for the generation and optimization of 2D wing profile. Thickness and camber constraints imposed at several sections of the profile are incorporated into this freeform curve generating environment, as it will be shown. The aimed objective was the improvement of an aerodynamic module included into a complete wing generator designed for multidisciplinary design optimization (MDO) purposes. The behavior of the profile generator under multiresolution decomposition while applying thickness and camber constraints will be presented, as well as some geometric and aerodynamic optimization test cases involving large changes in shape. A noteworthy aspect of this technique is the possibility of using a variable number of parameters in the optimization process from low to high, and the smoothness of the profile shape obtained.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".