Proof-of-concept analysis of a supplemental solar power system for aircraft
Bibliographic record
Abstract
Purpose This paper aims to analyze the viability of a solar power system as a supplemental power source for commercial and business aircraft. Design/methodology/approach First, a model is established to estimate the potential available power from suitable aircraft surfaces for various meteorological conditions, ground and flight mission characteristics. A proposed aircraft system architecture and an associated parametric conceptual sizing model are presented. This supplemental solar power system sizing model is integrated into an aircraft multidisciplinary design optimization environment to evaluate the aircraft-level impact on mission fuel burn. A parametric study for a business jet aircraft is performed to analyze various solar cell types and power densities for converters. Trade-off studies are performed between efficiency and weight. Findings Considering today’s efficiency and power-to-weight ratio of the system components, overall fuel burn reduction can be achieved. Therefore, the technology development work can start now to target short to mid-term applications. In addition, promising system integration scenarios are identified, such as the use of solar power for autonomous operation of the air conditioning system on ground, which yield potential further benefit. In conclusion, a supplemental solar power system seems a promising candidate for more efficient aircraft operation. Originality/value The presented novel supplemental solar power system architecture concept and its foreseen aircraft integration show potential benefits for near term applications. The results show that the break even for this technology is already reached and therefore build the foundation to further investigate the technology integration challenges. Clear directions for future research and development are outlined enabling the advancement of the technology readiness level.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.007 | 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".