Bibliographic record
Abstract
The OpenFOAM CFD package was used to initially investigate the aerodynamic effects of vortex-ring state of a quadrotor, then to study various quadrotor flight maneuvers to minimize the thrust loses of vortex-ring state and followed by possible detection methods for a drone entering vortex-ring state. Vortex-ring state is characteristic of a decrease in the effective angle of attack of incoming airflow due to a rotor descending into its downwash. This causes significant loses in the thrust of the rotor, which typically leads to severe flight upsets for rotorcraft. A quadrotor was studied at varying descent velocities to investigate wake roll-up at the rotor tips and the subsequent effects on rotor thrust and power. The quadrotor was then subjected to non-vertical descent angles to investigate thrust loss mitigation approaches due to vortex-ring state. A method of detecting the on-set of vortex-ring state is proposed using various differential pressure measurements on the quadrotor. It has been shown that by monitoring the pressure difference between the top of the quadrotor body and the bottom of one of the quadrotor legs, a pressure drop can be seen just prior to the on-set of vortex-ring state. This pressure drop was shown to occur during descending flight regimes and may prove to be an effective pre-vortex-ring state warning system.
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) | 0.000 | 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".