Aerodynamic optimization of a biplane configuration using differential evolution
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents our work on designing a biplane configuration that has a minimum drag to lift ratio. This problem is a mixed optimization problem in that both discrete and continuous variables are used. Fourteen parameters were used to fully describe the biplane configuration and calculate performance. Performance calculations were based on Munk's general biplane theory. Each wing required six parameters; airfoil profile type, span, tip and root chord lengths, angle of attack, and sweep angle. Two parameters were used to define the horizontal stagger and vertical gap between the two planes. The airfoil profile types were stored in an indexed database which allowed us to obtain the section's aerodynamic characteristics. Our analysis showed that differential evolution found the optimum solution quickly. The characteristics of the resultant optimum solution will be discussed in detail, along with our observations of how the process needs to be adjusted for optimum performance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it