Exploration of Solar Power System Integration for Sustainable Air Transportation—A Case Study for Seaplane Air Taxi Operations
Bibliographic record
Abstract
To reduce the environmental impact of airborne transportation, the aeronautic community investigates smaller aircraft with short-range operations (such as training aircraft, air taxis, or commuter aircraft) as technology incubators. This paper contributes to this effort by presenting an analysis framework and a detailed case study for integrating an auxiliary solar power system for air taxi operations. The solar power system conceptual design and analysis framework is improved to capture important effects for more realistic analysis for smaller aircraft, such as allowing the solar power system’s efficiency to be estimated as a function of aircraft mission parameters (temperature, speed, cloudiness) and providing a detailed view of the new system’s weight estimation considering potential physical integration scenarios. A detailed analysis of Harbour Air’s seaplane air taxi operations and the DHC-2 Beaver is performed using this enhanced design framework. The results show that the solar power system output exceeds the required secondary electrical power for 86% of the mission in one season; hence, it provides the potential to supplement a hybrid electric propulsion system. Secondly, the authors designed experiments to investigate the sensitivity of technology uncertainties for one critical mission. The results show that a small fuel burn reduction can be achieved with current technologies, with a promising trend of more savings with increasing system efficiency. Also, the results show that accumulated over a season’s operation, the CO2 emissions from the aircraft can be reduced. The findings indicate that integrating solar power systems can supplement traditional power sources and improve ground operations: specifically, solar energy could power a zero-emission and autonomous air-conditioning system while parked. Overall, integrating solar power into seaplane air taxi operations, even as a retrofit, presents a viable strategy for achieving more sustainable air transportation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".