Evaluation Of Energy Efficient Propulsion Technologies For Unmanned Aerial Vehicles
Bibliographic record
Abstract
The transition to cleaner, more efficient and longerendurance aircraft is at the forefront of current research and development in air transportation systems.The focus of this research is to experimentally evaluate Hybrid Propulsion and Energy Harvesting Systems in Unmanned Aerial Vehicles (UAV).Hybrid systems offer several potential benefits over more conventional gasoline and electric systems including lower environmental impacts, reduced fuel consumption, longer endurance, redundancy and distributed propulsion.Additional energy efficiency can be achieved by harvesting some of the thermal energy of the exhaust gases.By using the Seebeck effect, the temperature gradient between ambient air and the exhaust can be used to generate electric power, making it possible to eliminate costly mechanical systems such as alternators and reduce fuel consumption.The development and experimental evaluation of a hybridpropulsion 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, as well as to predict the amount of power harvestable from thermoelectric generators (TEG).The hybrid propulsion framework was used to investigate the trade-offs between different hybrid architectures against conventional electric and internal combustion propulsion systems.The energy harvesting module was designed to evaluate the trade-off between energy harvested, implementation costs and weight.In order to validate the computational results, experimental testing was performed.First, an apparatus was designed to collect performance data of a triple-TEG system connected to a 4-stroke Saito internal combustion engine.Thermal performance of the system was evaluated at eleven different test points, and a number of variables were modified to simulate real flight profiles.Next, another apparatus was designed to characterize the performance of a parallel hybrid-electric propulsion system in a UAV.This apparatus allows for different mission profiles that closely match the flight test data from other propulsion types.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".