A Systems Engineering Approach for Enabling Research and Development in the Vertical Lift Autonomy Flight Sciences Domain
Bibliographic record
Abstract
The objective of the joint National Research Council of Canada (NRC) and The Boeing Company Technology Development Program (TDP) entitled 'Canadian Vertical Lift Autonomy Demonstration' (CVLAD) is to evaluate automated and supervised autonomous flight systems on NRC Bell 412 Advanced Systems Research Aircraft (ASRA) and Royal Canadian Air Force Boeing CH-147F Chinook demonstrators. Boeing technologies such as Degraded Visual Environment Pilotage System and Advanced Vehicle Management System form the foundation of an autonomy solution that aims to satisfy Royal Canadian Air Force, US Army, and other Armed Service branch end-use objectives for force multiplication, tactical advantage, pilot assistance, reduced crew operations, and enhanced fleet productivity. The Boeing Company engaged NRC under a Cooperative Research Agreement since 2016 as part of a number of strategies to upgrade Medium-Heavy Lift H-47 Chinook capabilities prior to long-term aircraft replacement in the 2030 to 2060 timeframe. A recent achievement of the CVLAD TDP by its Boeing Phantom Works, Boeing Chinook Program, Aurora Flight Sciences, and NRC Flight Research Laboratory team was the development of Automated Flight Guidance methods addressing system safety and performance. Design and evaluation activities occurred in Boeing Software-/Hardware in-loop facilities as well as on the NRC Bell 412 ASRA. The CVLAD team is using a blend of traditional Systems Engineering 'V-Shaped' Life Cycle Model, System of Systems, and Model-Based processes to develop a cyber-physical system that aims to meet end-user concept of operations and requirements. Significant benefits of virtual development tools such as component-vehicle digital twins and surrogate inflight simulation facilities are achieved as they promote effective collaboration, efficient design, and relevant verification/validation methodologies. Business models can be made more robust by phasing the introduction of technology where effective automation provides users with near-term benefits, while providing a foundation for safe, reliable, and trusted autonomous capabilities for long-term production.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".