Aerodynamic shape optimization of a supersonic transport including a subsonic static margin constraint
Why this work is in the frame
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Bibliographic record
Abstract
Designing supersonic transport aircraft requires accounting for performance and stability at high-speed and low-speed conditions. Previous work demonstrated that there is a trade-off between high-speed performance and low-speed stability. Numerical optimization presents the opportunity to obtain the best high-speed performance while requiring stability at low speeds. We perform RANS-based aerodynamic shape optimization with a component-based geometry parameterization approach that enables the optimization of a three-surface supersonic transport configuration. We minimize drag at a supersonic cruise condition with and without a constraint on subsonic pitch stability. The stability constraint enforces a target static margin at a subsonic takeoff condition. The stable optimized designs use larger leading-edge flap deflections at the subsonic condition and have thicker wings. The thicker wings increase the supersonic drag by 0.5% for neutral stability and 0.85% for a 10% static margin. These results demonstrate that aerodynamic shape optimization is a valuable tool for designing supersonic transport aircraft accounting for supersonic performance and subsonic stability.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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