Two-Level Free-Form and Axial Deformation for Exploratory Aerodynamic Shape Optimization
Bibliographic record
Abstract
An intuitive shape parameterization and control technique suitable for high-fidelity aerodynamic shape optimization is presented. It relies on the principles of free-form and axial deformation, enabling thorough exploration of the design space while keeping the number of design variables manageable. Surface sensitivities to the design variables are readily available; their inclusion in a highly efficient and robust adjoint-based optimization methodology involving linearly elastic volume mesh deformation and a Newton–Krylov solver for the Euler equations is described. The flexibility of the proposed approach is demonstrated through the exploratory shape optimization of a three-pronged feathered winglet, leading to a span efficiency of 1.19 under a height-to-span ratio constraint of 0.1, and an optimization of a regional jet wing at transonic speed where a winglet is allowed to develop starting from a planar wingtip extension, leading to an 18.8% reduction in drag.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".