Bibliographic record
Abstract
Purpose – The purpose of this paper is to develop a physics-based model for UAV propellers that is capable of predicting all aerodynamic forces and moments in any general forward flight condition such as no flow, pure axial flow and pure side flow etc. Design/methodology/approach – The methodology adopted in this paper is the widely used Blade Element Momentum Theory (BEMT) for propeller model development. The difficulty arising from the variation of induced flow with blade’s angular position is overcome by supplementing the BEMT with the inflow model developed by Pitt and Peters. More so, high angle of attack aerodynamics is embedded in the simulation as it is likely for the blades to stall in general forward flight, for example during extreme aerobatics/maneuvers. Findings – The validity of the model is demonstrated via comparison with experiments as well as with other existing models. It is found that one of the secondary forces is negligible while the other is one order of magnitude less than the primary static thrust, and as such may be neglected depending on the level of accuracy required. On the other hand, both secondary moments must be considered as they are of similar order of magnitude as the primary static torque. Research limitations/implications – The paper does not consider the swirl component of the induced flow under the assumption that it is negligible compared to the axial component. Originality/value – This paper fulfills the identified need of a propeller model for general forward flight conditions, and aims to fill this void in the existing literature pertaining to UAVs.
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".