Toward Practical Aerodynamic Design through Numerical Optimization
Bibliographic record
Abstract
A Newton-Krylov algorithm for aerodynamic optimization is applied to the multipoint design of an airfoil for eighteen dierent operating conditions. The operating conditions include four cruise conditions and four long-range cruise conditions at maximum and minimum cruise weights and altitudes. In addition, eight operating points are included in order to provide adequate maneuvering capabilities under dive conditions at the same maximum and minimum weights and altitudes with two dierent load factors. Finally, two low-speed operating conditions are included at the maximum and minimum weights. The problem is posed as a multipoint optimization problem with a composite objective function that is formed by a weighted sum of the individual objective functions. The Newton-Krylov algorithm, which employs the discrete-adjoint method, has been extended to include the lift constraint among the governing equations, leading to an improved lift-constrained drag minimization capability. The optimized airfoil performs well throughout the flight envelope. This example demonstrates how numerical optimization can be applied to practical aerodynamic 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".