How Industry Concepts of Concurrent Engineering Enhance Aircraft Design Education
Bibliographic record
Abstract
Two student projects are described including the intended goals, the approaches taken, the tools used, and what was learned from the exercises. An international collaborative teaching protocol between Ecole Polytechnique de Montreal and the Royal Institute of Technology (KTH) was exercised in aircraft design education. Poignantly, a novel instructive design process using the analogue of contemporary concurrent engineering practices in industry was implemented. The idea was to strategically assign multi-disciplinary design tasks to each Partner University in accordance with their respective competencies. The university-industry coupling was initiated by request for proposals and corresponding marketing requirements and objectives produced by Bombardier Aerospace in Montreal, Canada. Two MATLAB TM-based tools were prominent in facilitating the capstone aircraft design projects. They included: Quick Conceptual Aircraft Research and Design, a computer-aided conceptual design engineering system; and TORNADO, a Vortex-Lattice code for computing aerodynamic characteristics. The result of the two exercises was found to benefit the participating industry, the educational establishments involved, and the students carrying out the projects.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".