Circular Flight-Path Optimization for a Solar-Powered UAV Flying in Horizontal Winds
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-1210.vid The loiter radius with the highest chargerate for a solar-powered aircraft flying circuits over a fixed point on the ground is determined. The aircraft is assumed to be flying in horizontal atmospheric winds. Chargerate is calculated as the difference between the average solar-power input and power required to fly the circular loiter for each wind scenario. Model validation was performed using flight test data from the CREATeV solar-powered aircraft, with comparisons found to be within a 10% difference. A parametric sweep was performed to determine the optimum loiter at different sun elevations for various wind conditions. It was found that, for the solar-powered aircraft under consideration, in most scenarios, the chargerate is maximized by flying a large loiter radius. The exceptions to this case are when: (1) The sun elevation is higher than 20 degrees, and there is a high windspeed with a large component of the wind vector perpendicular to the sun vector, and (2) The sun elevation is lower than 20 degrees, and the wind vector is not aligned with the sun vector. Overall, flying with a changing loiter radius based on wind conditions and sun position can lead to a power gain of up to 11 W. Although these results are specific to the CREATeV solar-powered aircraft, the model discussed in this paper can be modified to apply to any solar-powered aircraft.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".