Vehicular Aerodynamics Wind Tunnel Testing of Unmanned Aerial Multirotor Vehicles and Wall Interference Corrections
Bibliographic record
Abstract
Unmanned multirotor aerial vehicles, known commonly as drones, have become a popular form of flying system due to the versatility of their possible applications. In order to model the aerodynamics of manoeuvring multirotor vehicles, the aerodynamics of the rotors and the vehicle will be modelled separately. A DJI Matrice 210 RTK model quadcopter was reproduced using 3D printed parts and wood. The model components include the main body, four arms, two landing gear or legs, a battery, camera and gimbal, and the RTK GPS antennae, as well as accessories including a backup battery, backup antenna, and computer. Nine configurations of a combination of these components were tested in a wind tunnel at two given wind tunnel velocities and a sweep of angles of attack, sideslip angles, and roll angles. The aerodynamic forces and moments acting on the vehicle body were measured, and after accounting for tare forces and base drag, the data was corrected to account for wall and blockage effects from the wind tunnel’s closed test section. The intention of this project is to obtain wind tunnel testing results of the quadcopter model body and to setup a methodology to apply wall interference corrections. This project will support rotor aerodynamics and flight dynamics testing of the DJI Matrice 210 RTK currently in progress, which intend to improve control laws of unmanned multirotor aerial vehicles.
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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 | 0.000 | 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".