Vehicular Aerodynamics Wind Tunnel Testing of Unmanned Aerial Multirotor Vehicles and Wall Interference Corrections
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>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. </p>
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it