Multi-Fidelity Multidisciplinary Design Optimization of Metallic and Composite Regional and Business Jets
Bibliographic record
Abstract
This paper presents an aircraft manufacturer’s methodology for the high-delity multidisciplinary design optimization of regional and business jets. The formulation of the multiobjective function and the hybrid multi-level optimization architecture are highlighted. The high-speed aerodynamics sub-space is analyzed with a Transonic Small Disturbance code whereas the low-speed sub-space is analyzed using a three-dimensional panel code and the Valarezo criteria. In addition, the multiple design load cases including manoeuvre and landing are presented along with the uid to structure load transfer scheme. Particular emphasis is also placed on the development, the industrial sizing and the structural suboptimization of a high-delity 3D FEM for composite and metallic wing structures. The validation of the structural sizing methodology is highlighted through examples and by comparison with typical aircraft wing structures. The inuence of low-speed aerodynamics on the nal design is emphasized and a comparative study between the multidisciplinary optimization of composite and metallic wings is presented. The methodology is applied to the optimization of a large business jet comprising winglets, rear-mounted engines and a T-tail conguration. The aircraft-level design optimization goal in this instance is to minimize a cost function for a xed range mission assuming a constant Maximum Take-O Weight.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".