Numerical modelling of aerodynamic response to gusts and gust effect mitigation
Bibliographic record
Abstract
Unmanned aerial vehicle (UAV) flights in urban environments are challenging due to the complex flow structures and elevated turbulence around buildings. Consequently, research has shifted towards investigating the impact of gusts on the aerodynamic stability and control of UAVs. This study focuses on enhancing gust numerical modelling capabilities to understand the aerodynamic response, specifically exploring gust mitigation strategies for UAVs operating in turbulent urban environments. The split-velocity method, originally designed for two-dimensional compressible inviscid flows, where the velocity components were decomposed into a prescribed gust velocity and the remaining velocity components, is extended to three-dimensional incompressible viscous flows. To facilitate effective gust mitigation techniques, a radial basis function is applied to the modified split-velocity method to numerically model wings in pitching motions under gust encounters. A novel strategy is proposed to correct the discretized gust velocities and ensure gust flux conservation, showing effective improvement to the numerical predictions. The computed results agreed well with the experimental data available in the public domain, confirming that wing pitch motion can effectively mitigate the effects of gusts.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".