Exploring Collaborative and Multidisciplinary Aircraft Optimization\n through the AGILE Academy Challenge -- A case study for an aircraft auxiliary\n solar power system
Bibliographic record
Abstract
Reduction in aircraft emission is a main driver for the development of more\nefficient aircraft and enabling technologies are reaching operational maturity.\nAircraft manufacturers need an efficient product development process to capture\nthese emergent technologies and develop new aircraft concepts in order to stay\ncompetitive. Presently, the aircraft development process is cross\norganizational, and harnesses distributed, heterogeneous knowledge and\nexpertise. Large scale multidisciplinary studies, involving disciplinary\nexperts and specific tools are required to evaluate different aircraft\nconcepts. These MultiDisciplinary Aircraft Optimization (MDAO) processes are\ndifficult to deploy as they involve cross-organizational collaboration and\nharmonization of processes, tools and even vocabulary. Moreover, difficulties\nin collaborative decision making, reconfiguration and integration of new\nrequirements and competencies often precludes the development of an optimal\nsolution within the available time. To address these challenges, the AGILE\nproject is an effort within the European Union funded Horizon 2020 project to\nreduce aircraft development time by developing tools and processes that enable\nefficient, collaborative aircraft design. This paper presents the work\nperformed as part of the AGILE academy challenge where students were tasked\nwith developing and solving an aircraft MDAO problem using the AGILE toolchain.\nEach team consisted of students from various universities around the globe and\nhad expertise in multiple design domains. An MDAO study is presented that\nutilizes the AGILE toolchain to investigate the feasibility of implementing an\nauxiliary solar power system on a baseline aircraft. The steps performed are:\n(1) definition of a multidisciplinary design problem, (2) development of\ncollaborative workflow and (3) optimization using surrogate models. Through the\ncase study, a novel technology concept is investigated and the efficacy of the\nAGILE toolchain in facilitating a MDAO is analyzed.\n
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".