Evaluation of energy efficient propulsion technologies for unmanned aerial vehicles
Bibliographic record
Abstract
The transition to cleaner, more efficient and longer-endurance aircraft is at the forefront of research and development in air vehicles. The focus of this research is to experimentally evaluate hybrid propulsion and energy harvesting systems in unmanned aerial vehicles (UAVs). Hybrid systems offer benefits over conventional gasoline and electric systems including lower environmental impacts, reduced fuel consumption, redundancy, and distributed propulsion. Additional energy efficiency can be achieved by harvesting some of the thermal energy of the exhaust gases. The development and experimental evaluation of a hybrid propulsion UAV was carried out at the University of Victoria Center for Aerospace Research (UVIC-CfAR) in the framework of the Green Aviation Research & Development Network (GARDN) grant. The work involved the development of a framework to evaluate UAV hybrid propulsion efficiency, and to predict the amount of power harvestable from thermoelectric generators (TEGs). The objective was to combine all of the components into a modular test bench that will allow the performance of the parallel hybrid system to be characterized and compared with theoretical results. Several experiments were performed to collect performance data of various components including a triple-TEG system connected to an engine, and system variables were modified to simulate flight profiles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".