Collaborative Design of a Business Jet Family Using the AGILE 4.0 MBSE Environment
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2022-3934.vid This paper presents the collaborative model-based design of a business jet family. In family design, a trade-off is made between aircraft performance, reducing fuel burn, and commonality, reducing manufacturing costs. The family is designed using Model-Based Systems Engineering (MBSE) methods developed in the AGILE 4.0 project. The EC-funded AGILE 4.0 project extends the scope of the preliminary aircraft design process to also include systems engineering phases and new design domains like manufacturing, maintenance, and certification. Stakeholders, needs, requirements, and architecture models of the business jet family are presented. Then, the collaborative Multidisciplinary Design Analysis and Optimization (MDAO) capabilities are used to integrate various aircraft design disciplines, including overall aircraft design, onboard systems design, wing structural sizing, tailplane sizing, mission analysis, and cost estimation. Decisions regarding the degree of commonality are implemented by optionally fixing the design of a shared component when sizing an aircraft.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".