Dynamic Geometry Control for Robust Aerodynamic Shape Optimization
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2021-3031.vid This work presents novel progressive and adaptive dynamic geometry control algorithms which seek to improve convergence and reduce user workload by partially automating the design of effective geometry control systems for aerodynamic shape optimization. These algorithms function by beginning in a coarse design space and periodically refining the geometry control with additional design variables when objective improvement becomes asymptotic. When refinement is initiated, progressive geometry control moves through a pre-defined sequence of increasingly fine geometry control schemes, while the adaptive algorithm instead dynamically generates a refined search space. This is accomplished by generating a list of candidate refinements and ranking them based on the minimum of a constrained quadratic suboptimization problem which constitutes an estimate of the maximum objective reduction possible in each candidate search space. The accuracy of this method is first validated on two inviscid problems, after which the progressive and adaptive algorithms are applied to two common aerodynamic shape optimization problems based on the Reynolds-Averaged Navier-Stokes equations, the twist and section optimization of the common research model wing-only geometry, and the planform optimization of a hybrid wing-body aircraft. In both cases, the dynamic geometry control schemes are able to converge to lower drag, often with fewer optimization iterations, compared to the tested static schemes.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.016 | 0.004 |
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".