Parallel Hybrid-Electric Powertrain Sizing on Regional Turboprop Aircraft with Consideration for Certification Performance Requirements
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2021-2443.vid Challenging emission reduction targets and significant growth of air transport market motivate important research of novel propulsion systems with alternative energy sources and lowered CO2 emissions. For short regional flights, hybrid-electric turboprop is seen as a promising alternative to reduce fuel burn. As conventional propulsion aircraft, hybrid powertrain aircraft must meet all certification requirements, especially one engine inoperative climbs (OEI). While classical Gas Turbine (GT) downsizing design approach could reduce fuel burn, it might not meet the OEI missed approach performance required by the certification authorities if the batteries are depleted at end of mission. Hence, a conceptual approach through mission analysis and optimization is introduced to evaluate the impact of OEI climb requirement on powertrain design and performance. The parallel hybrid-electric powertrain is sized on a retrofitted regional aircraft with 2030 near future technology using two design approaches. The first hybrid-electric design uses downsized GT while the second keeps original GT supplemented with an electric motor. The two designs are then compared to a baseline, original conventional GT propulsion system with fuel mass optimized for the same target mission. For battery pack with 200 Wh/kg and 10000lb of payload mass, hybrid electric systems show 5% fuel burn benefit compared to a conventional gas turbine propulsion system, when constraints such as energy reserves for re-routing to alternate airfields, loitering time, and missed approach OEI Climb performance, are taken into account. The downsized gas turbine solution, that needs to use battery energy during OEI climb, results in a penalty of 12.9% more fuel burn during the target mission, due to the extra weight of the unused emergency battery, resulting in a similar mission fuel burn as parallel hybrid designs that use the original full-size GT and are therefore able to perform OEI Climb without electric assistance.
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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 | 0.000 | 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) | 0.005 | 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 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".