AGILE the Next Generation of Collaborative MDO: Achievements and Open Challenges
Bibliographic record
Abstract
The EU funded AGILE project is developing the next generation of aircraft Multidisciplinary Design and Optimization processes, which target significant reductions in aircraft development costs and time to market, leading to more cost-effective and greener aircraft solutions. 19 industry, research and academia partners from Europe, Canada and Russia are developing solutions to cope with the challenges of collaborative design and optimization of complex products. In order to accelerate the deployment of large-scale, collaborative multidisciplinary design and optimization (MDO), a novel methodology, the so-called AGILE Paradigm, has been developed. The AGILE Paradigm is a “blueprint for MDO”, guiding the deployment and the execution of collaborative “MDO systems” for complex products practiced by cross-organizational design teams, distributed multi-site, and with heterogeneous expertise. A set of technologies has been developed by the AGILE consortium in order to enable the implementation of the AGILE Paradigm, and to support the design and the optimization of novel aircraft configurations. The AGILE Paradigm ambition is reduce the lead time of 40% with respect to the current state-of-the-art. This paper addresses the MDO challenges tackled by the AGILE Paradigm. An overview of the main AGILE Paradigm’s underlying architecture is described. The paper presents a preliminary assessment of the AGILE Paradigm application, and provides an overview of the main achievements enabled by its implementation for the solution of selected aircraft design and optimization use cases. The paper concludes with an overview of the challenges still open and an outlook of the AGILE Paradigm.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".