Aerodynamic shape optimization of a supersonic transport including a subsonic static margin constraint
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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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".