Feasibility Study of a Serial Hybrid Electric Powertrain for a Light Sports Aircraft
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2022-2050.vid A theory-based aerodynamic model was developed and applied to electrified powertrain configurations to study the feasibility of implementing fully electric and hybrid electric technologies into light sports aircraft. Overall aircraft range was chosen as the main indicator of feasibility. The aerodynamic models were created in MATLAB/Simulink utilizing proportional-integral-derivative controllers, aerodynamic specifications of a reference aircraft body, and powertrain limitations from a physical electrified powertrain test stand. Simulations that were conducted by varying mission properties, battery properties, and fuel mass provide results which can showcase the current feasibility of electrified propulsion and make predictions for the future of the industry. Results showed that aircraft powered with a fully electric powertrain were only able to achieve 5% of the range of a traditionally powered aircraft. Aircraft using a hybrid electric powertrain were able to achieve 44% of the range of a traditionally powered aircraft, but this result was found to be almost entirely dependent on the aircraft’s fuel mass. Future predictions of Lithium-Ion battery technology are expected to increase range of fully electric aircraft to 25% of traditional, and hybrid aircraft to 60% of traditional.
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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 | 0.000 |
| 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".