Flexible transonic wing design optimization with discipline-oriented decompositions
Bibliographic record
Abstract
This paper discusses the performance of two discipline-oriented decompositions applied to the multidisciplinary design optimization (MDO) problem of a wing in transonic regime. A finite element model (FEM) serves to predict the wing structural deformations under cruise aerodynamic loads obtained from computational fluid dynamics (CFD) analyses. The aim of the design is to obtain a suitable wing structure and external shape that maximizes the cruise range under a lift constraint subjected to structural safety factor constraints for a given upwind gust load case. A methodology to quickly predict the performance of a decomposition method is presented. This methodology is applied to several possible formulations (single and bi-level decomposition) of the optimization problem. Based on the forecasted performance of the different decompositions, a bi-level FIO and a semi-decoupled decompositions are tested on the transonic wing design problem. The optimization results highlight the respective advantages of hierarchical decomposition and decoupling. With the hierarchical decomposition, the line search performed better because the structure is adapted for each external shape configuration. As for the semi-decoupled formulation, it was able to reduce the cost related to the resolution of the MDA. However, the bi-level FIO decomposition obtained a slightly better objective function than the semi-decoupled formulation. Also, the designs obtained by the optimizations are not representative of what is done in the industry because of a weakness in the objective function and because the load case is too conservative.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".