Minimum Trim Drag for a Three-Surface Supersonic Transport Aircraft
Bibliographic record
Abstract
Three-surface configurations offer theoretical drag benefits over two-surface configurations, but the literature is inconclusive on what is the best configuration for a supersonic aircraft. This work uses trim-constrained drag minimization to investigate the impact of different trim surface configurations on supersonic transport design. We first use Reynolds-averaged Navier–Stokes (RANS)-based optimization to compare the trim drag at a supersonic cruise condition for three-surface, canard, and conventional variants of a supersonic transport aircraft. The three-surface configuration has the lowest trim drag at the supersonic condition. We then construct a supersonic buildup model to study the effects of variable trim surface sizing. When the trim surface spans are included as design variables, the design for minimum supersonic drag depends on the desired static margin. Canard configurations are optimal from 0% to 5% static margin, whereas three-surface configurations are optimal from 10% to 25% static margin. We also show that the canard configuration with 5% supersonic static margin is unstable at subsonic conditions. This emphasizes the need to consider subsonic stability and supersonic performance simultaneously for supersonic transport design.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".