A multidisciplinary design optimization approach to preliminary wing design using multifidelity analysis
Bibliographic record
Abstract
A multidisciplinary design optimization (MDO) strategy for the preliminary design of a small utility class aircraft wing has been developed. The proposed approach applies MDO techniques and multifidelity analysis methods, which have seen successful use in many aerospace design applications. A genetic algorithm was adopted to control the optimization. Multifidelity analysis methods were employed including a wing box aeroelastic analysis procedure using a finite element method structural solver in combination with a nonplanar vortex lattice aerodynamic solver. An adaptive meshing routine was developed to allow for more accurate pressure load mapping onto the geometric dependent structures mesh. Design parameters of an existing wing were provided from industry and used as a baseline design to compare with the optimized results. The proposed application of MDO and multifidelity analysis yielded optimum designs that compared well with the baseline design and showed improvements based on the desired objectives. The results of this paper demonstrate the benefits of modern optimization techniques in preliminary wing design.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".