Bibliographic record
Abstract
An evaluation of using Bezier Curves to create leading edge geometries for high speed vehicles, such as for hypersonic waveriders, has been performed. In such applications, Bezier Curves offer advantages over previously employed leading edge geometry approaches such as hemi-cylindrical or power-law curve based designs. Third-sixth order Bezier Curve Leading Edges have been generated and their performance quantified using a number of criteria including; pressure drag, surface pressure gradient, stagnation point heating, and laminar and turbulent acreage heating. Genetic Algorithms have been used to perform the optimization and have generated geometries for a number of cost function/constraint combinations. From this analysis it has been found that fourth order Bezier Curves offer the best combination of geometric flexibility and optimization performance for use in defining leading edges for high speed vehicles. Additionally, Pareto fronts of leading edge geometries have been created for combinations of influences such as stagnation point heating and pressure drag, and for either laminar or turbulent acreage heating.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".