Hybrid Electric Aircraft Testbed: Ground and Flight Testing of an Electric Propulsion System and Energy Storage System
Bibliographic record
Abstract
Aircraft electrification is one pathway that the Canadian aviation industry is taking to meet its net zero greenhouse gas emissions targets by 2050. To ensure that the National Research Council of Canada developed the capabilities to support industry in this emerging field, the Hybrid-Electric Aircraft Testbed started development in 2019 and subsequently had its first flight in February 2022. The purpose of the project was to gain practical experience in the process of installing an electric powertrain in an aircraft. A Cessna 337G, a push-pull configuration aircraft, was purchased and the rear engine was replaced with a fully electric powertrain; this included the installation and commissioning of an electric motor and a high-voltage (800 V) battery stack, and associated systems. The ground testing of the aircraft began on August 5th, 2021 following the successful completion of the facility ground testing at the Gas Turbine Laboratory. Throughout ground testing, 85 test points were completed before the electric propulsion system was considered flightworthy. Flight testing of the aircraft began on February 7th, 2022, and is ongoing. Throughout the more than 8 hours of flights performed thus far, significant data have been collected on various topics, including whirl mode, acoustic noise, and endurance flying. This paper concludes by providing some lessons learned during the commissioning process, which will help inform industry and regulators as Canada's aviation industry transitions to net zero.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".