Aerodynamic Shape Optimization of Wings Using a Parallel Newton-Krylov Approach
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Bibliographic record
Abstract
ANewton–Krylov algorithm for aerodynamic shape optimization in three dimensions is presented for both singlepoint andmultipoint optimization. An inexact Newtonmethod is used to solve the Euler equations, a discrete adjoint method is used to compute the gradient, and an optimizer based on a quasi-Newtonmethod is used tofind the optimal geometry. Theflexible generalizedminimal residualmethod is usedwith approximate Schur preconditioning to solve both the flow equation and the adjoint equation. Thewing geometry is parameterized byB-spline surfaces, and a fast algebraic algorithm is used for grid movement at each iteration. An effective strategy is presented to enable simultaneous optimization of planform variables and section shapes. Optimization results are presented with up to 225 design variables to demonstrate the capabilities and efficiency of the approach.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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