Aerodynamic wing shape optimization based on the computational design framework CEASIOM
Bibliographic record
Abstract
Purpose A collaborative design environment is needed for multidisciplinary design optimization (MDO) process, based on all the modules those for different design/analysis disciplines, and a systematic coupling should be made to carry out aerodynamic shape optimization (ASO), which is an important part of MDO. Design/methodology/approach Computerized environment for aircraft synthesis and integrated optimization methods (CEASIOM)-ASO is developed based on loosely coupling all the existing modules of CEASIOM by MATLAB scripts. The optimization problem is broken down into small sub-problems, which is called “sequential design approach”, allowing the engineer in the loop. Findings CEASIOM-ASO shows excellent design abilities on the test case of designing a blended wing body flying in transonic speed, with around 45 per cent drag reduction and all the constraints fulfilled. Practical implications Authors built a complete and systematic technique for aerodynamic wing shape optimization based on the existing computational design framework CEASIOM, from geometry parametrization, meshing to optimization. Originality/value CEASIOM-ASO provides an optimization technique with loosely coupled modules in CEASIOM design framework, allowing engineer in the loop to follow the “sequential approach” of the design, which is less “myopic” than sticking to gradient-based optimization for the whole process. Meanwhile, it is easily to be parallelized.
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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 | 0.000 |
| 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) | 0.006 | 0.002 |
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".