A hybrid propulsion system for a high-endurance UAV: configuration selection, aerodynamic study, and gas turbine bench tests
Bibliographic record
Abstract
In recent years, renewed interest in the development of unmanned aerial vehicles (UAVs) has led to a wide range of interesting applications in reconnaissance and surveillance. In these missions, the noise produced by propeller-driven UAVs is a major drawback, which can be partially solved by installing an electric motor to drive the propeller. While the evolution of high performance brushless motors makes electric propulsion particularly appealing, at least for small and medium UAVs, all electric propulsion systems developed to date are penalized by the limited range and endurance that can be provided by a reasonably sized battery pack. In this paper we propose a hybrid propulsion system based on a recently developed ultramicro gas–turbine (UMGT), which can be used to power an electric generator, providing a significant range and (or) mission time extension. The UMGT is undergoing operational testing in our laboratory, to identify the most suitable configuration and to improve performance: a new compact regenerative combustion chamber was developed and several tests are being carried out to reduce its weight and size so as to increase, all other things being equal, the vehicle payload. This paper aims to propose a high endurance UAV, by a preliminary configuration selection and aerodynamic study of its performance.
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 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 | 0.000 |
| 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) | 0.001 | 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".