Development and Validation of a Propeller Slipstream Model for Unmanned Aerial Vehicles
Bibliographic record
Abstract
Recent interest in high-angle-of-attack flight, aerobatic maneuvering, vertical/short takeoff and landing, etc., of small unmanned aerial vehicles necessitates more detailed modeling of the complex aerodynamics associated with these flight regimes. This includes modeling the effect of the propeller slipstream, also called prop wash, which is the main source of airflow that helps maintain lift and control during near-zero forward-speed flight like that encountered during vertical/short takeoff and landing, as well as during high-angle-of-attack flight/aerobatic maneuvering like hovering. Propeller slipstream models based on conventional theories, such as the momentum theory, have been used extensively in the literature to predict the induced air velocity within the slipstream. However, because these conventional theories consider only acceleration of air within the slipstream and not diffusion, their applicability in regions far downstream of the propeller where diffusion is dominant, is questionable. This paper presents a propeller slipstream model that considers both acceleration and diffusion within the slipstream using simple analytical and semi-empirical equations. The proposed model is shown to be in good agreement with the experimental data for several different propellers and configurations, up to propeller diameters downstream of the propeller plane, with an rms error of in velocity. As well, the proposed model matches the simplicity of conventional models and is therefore suitable for real-time applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".