Dynamic Modelling of Multi-Body Unmanned Airship with a Slung-Payload
Bibliographic record
Abstract
Unmanned aerial vehicles (UAV) provide a conve-nient way to conduct experiments and simulations at a low cost. After a long period of being relinquished, interest in airships has resurged owing to stringent CO2 emissions regulations and the increasing market demand on air cargo. This paper discusses the application of Udwadia-Kalaba technique to model the dynamics of a reconfigurable unmanned airship. The investigated multi-body consists of an airship, a gondola, and a payload. The investigated airship is designed to have a movable gondola. This novel design overcomes a major issue that conventional airships encountered which is a mechanism to improve the manoeuvrability of the airship. Modelling of multi-body system can be difficult when non-holonomic constraints are present. Three constraints were identified for the investigated system. Position and orientation constraints between the airship and gondola were first derived. In addition, a length constraint between the gondola and slung-payload was enforced. Udwadia-Kalaba method was used to model the multi-body system. The equations of motion are solved numerically for a test case where a step side force is applied for 1 second. The resulting trajectory over a period of 7 seconds was presented and analysed. Physical modes such as pendulum-like behaviour of slung-payload and coupling between the multi-body components were captured in the simulations and discussed.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".