Connecting Model-based Systems Engineering and Multidisciplinary Design Analysis and Optimization for Aircraft Systems Architecting
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2021-3077.vid The aerospace industry has set ambitious targets to meet environmental goals while under pressure to develop novel and optimized aircraft configurations effectively. Multidisciplinary Design Analysis and Optimization (MDAO) are increasingly used to optimize aircraft and their systems. Model-Based System Engineering (MBSE) methods show the potential to make the design process more effective, integrate new disciplines, and capture complex certification constraints. Today, MBSE and MDAO are not connected; different methods and tools are used, not harvesting the full potential of both approaches. This paper discusses the need for improved system architecting in the aircraft conceptual design process and introduces a framework to use MBSE in connection to MDAO. In this framework, the MBSE environment compiles system information within a system architecture specification, acting as the backbone and visual support for each stage in the systems architecting process. MDAO is used for the evaluation of system architectures. This paper presents a case study as part of the EU-funded AGILE4.0, in which the specific link between model specification in the MBSE tool Capella and a system-level MDAO workflow is explored. Overall, this paper presents a practical contribution to linking MBSE and MDAO and paves the way for better integration of MBSE into the aircraft design process, thereby enabling MBSE implementation from conceptual design onwards. Furthermore, this will enable more detailed system analysis, such as safety analysis, starting in conceptual design, based on architecture models.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".