Numerical Aerodynamic Optimization Incorporating Laminar-Turbulent Transition Prediction
Bibliographic record
Abstract
A two-dimensional Newton–Krylov aerodynamic shape optimization algorithm is applied to several optimization problems in which the location of laminar-turbulent transition is free. The coupled Euler and boundary-layer solver MSES is used to obtain transition locations through the eN method, which are then used in Optima2D, a Newton–Krylov discrete-adjoint optimization algorithm based on the compressible Reynolds-averaged Navier– Stokes equations. The algorithm is applied to the design of airfoils with maximum lift-to-drag ratio, endurance factor, and lift coefficient. The design examples demonstrate that the optimizer is able to control the transitionpoint locations to provide optimum performance, often producing pressure distributions with laminar rooftops followed by concave pressure recovery. In particular, the optimization algorithm is able to design an airfoil that is very similar, in terms of both shape and performance, to one of the high-lift airfoils designed by Liebeck (Liebeck, R. H., “A Class of Airfoils Designed for High Lift in Incompressible Flow, ” Journal of Aircraft, Vol. 10, No. 10, 1973, pp. 610–617) in the 1970s. The results provide a striking demonstration of the capability of the Newton– Krylov aerodynamic optimization algorithm to design airfoils with characteristics that previously required a great deal of expertise to achieve. I.
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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 | 0.000 | 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".