Hybrid-Electric Aircraft Testbed: Electric Propulsion System and Energy Storage System Qualification Testing
Bibliographic record
Abstract
Aircraft electrification is one pathway the Canadian aviation industry is pursuing in order to meet its net-zero greenhouse gas emission targets by 2050. To ensure the National Research Council of Canada (NRC) developed the capabilities to support industry in this emerging field, the Hybrid-Electric Aircraft Testbed (HEAT) started development in 2019 and subsequently had its first flight in February 2022. The aim of the project was to gain practical experience in the process of installing an electric powertrain onto an aircraft. A Cessna 337G – a push-pull configuration aircraft – was procured and the rear engine replaced with a fully-electric powertrain; this included the installation and commissioning of an electric motor and a high voltage (800 V) battery pack and associated systems. Prior to flight testing, the entire electric propulsion system was setup and tested at the NRC Gas Turbine Laboratory (GTL). The purpose of this testing was to fully qualify the safety systems and performance of the individual components and overall system in a controlled test cell environment where rapid troubleshooting and modifications could be conducted. This paper will provide details of the experimental setup at the facility, several key experimental results obtained, and lessons learned from testing high voltage electric propulsion systems.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".