Ground effect experiments and model validation with Draganflyer X8 rotorcraft
Bibliographic record
Abstract
Ground effect on rotary aircraft has been studied for many decades. Although a large body of research results is now available for conventional helicopters, this topic is just beginning to receive attention in the unmanned aerial vehicle community, particularly for small size UAVs. The objective of this paper is to assess the applicability of a widely-used ground effect model, developed in the middle of last century, for predicting the ground effect on a small rotary UAV. The particular vehicle employed in this work, the Draganflyer X8, is actuated by 8 propellers arranged in 4 coaxial pairs in a quadrotor configuration and is currently employed for the development of autonomous landing capabilities for UAVs. The aforementioned ground effect model gives an explicit relationship between the thrust produced by a single propeller while operating in-ground-effect and the thrust out-of-ground effect, as a function of the normalized propeller height above ground. A series of experiments was conducted with the propellers of the vehicle on a test stand, and with the X8 vehicle in flight, from which we obtained the in-ground and out-of-ground thrusts. Juxtaposition of our results against the theoretical model points to a stronger ground effect on the X8 vehicle than predicted by theory. Discussion of the assumptions underlying the theory and the experimental procedures and their implications on the results obtained is also included in the paper.
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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 | 0.001 |
| 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 | 0.001 | 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".