Digital Displacement Hydrostatic Transmission for Rotorcraft and Distributed Propulsion
Bibliographic record
Abstract
This paper introduces a new series-hybrid digital transmission technology for distributed propulsion systems offering a lower cost, a lighter weight and more environmental tolerance than series-electric-hybrid powertrains. In particular, this concept can be used on large multicopters for applications requiring long range, heavy payloads and continuous hover, such as offshore search and rescue, surveillance and logistics. The concept, design principles and analysis are described to allow component sizing and calculation of operating point under steady state and dynamic conditions. A design is described for a quadcopter with maximum take-off weight of 380kg, including a Rotax 915 engine, a Digital Displacement® hydraulic pump and bent-axis motors. The components of the propulsion system are described and compared to an equivalent electric system, showing lower cost and weight, and similar efficiency. A computationally efficient model is described of the rotor behavior allowing rapid simulation with accurate rotor loads. A 6 DOF vehicle simulation is described, including dynamics of the proposed system, coupled to a flight controller. Results show that such a standard flight controller can fly the proposed system under a range of conditions including at the engine power limit. The expected flight endurance of the 380kg quadcopter is over 6 hours and range over 1000km depending on payload. Test rigs are described, including at full scale for the 380 kg study, which demonstrate stable control of hover. Other applications for distributed propulsion are discussed.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".