Implementation of a 200 kW Adaptable Testing Platform for Experimental Research in Electrification of Aircraft Propulsion
Bibliographic record
Abstract
The continuous growth of the commercial aviation industry requires the development of novel technologies to reduce its increasing impact on climate change. Technical solutions, such as the electrification of aircraft propulsion require extensive research and exhaustive testing to reach the required levels of performance, reliability, and safety. Therefore, the development of these new technologies requires new testing infrastructure that is technically suitable for this purpose. In this report we present a fully implemented platform intended to test electrified aircraft propulsion systems at up to 1 kV and 200 kW. This platform is designed to be adaptable to multiple testing needs and system configurations, such as fully-electric, turboelectric and hybrid-electric. The adaptability and strategic value of this platform is demonstrated through experimental results and performance assessment of an electric engine under test. Results include the operational envelope where at 2700 RPM, the electric engine can provide 71.6 kW of propulsive power to an aircraft at 95% efficiency. Additionally, results for performance at 30 kW constant power and a flight mission profile with an emulated 128 Wh/Kg LiFePO4battery pack are presented. The development of tests for certification of electric engines also demonstrate the relevance of the platform.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".