An MBSE Certification-Driven Design of a UAV MALE Configuration in the AGILE 4.0 Design Environment
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2021-3080.vid This paper presents a certification-driven design process for an Unmanned Medium-Altitude-Long-Endurance (UAV MALE) air vehicle, including on-board system design and placements, electro-magnetic compatibility analysis, and thermal risk assessments. In literature, the preliminary aircraft design phase is mainly driven by mission performances and structural integrity aspects. However, the inclusion of other disciplines, like on-board system design or electro-magnetic compatibility, or thermal analysis, can lead to more efficient and cost-effective solutions and becomes paramount for non-conventional configurations like unmanned vehicles or highly electrified platforms. In the EC-funded AGILE 4.0 project (2019-2022), the traditional scope of the preliminary aircraft design is extended by including domains that are usually considered only in later design phases, such as certification, production and maintenance. In this paper, the AGILE 4.0 design environment supports the definition and execution of a certification-driven design process of a UAV MALE configuration, using a Model-Based Systems Engineering (MBSE) approach.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".