Numerical Simulation of the Static and Dynamic Aerodynamics of a UAV under Wake Flows
Bibliographic record
Abstract
Frequent flight conflicts will be observed as the number of aircrafts increases, and such conflicts will cause unprecedented challenges in flight safety; thus, the flight characteristics of small aircrafts under the wake flow of a large airliner should be thoroughly analyzed. Combined with the sliding mesh technique, a computational fluid dynamics (CFD) method is proposed in this paper to simulate three wake flow patterns, i.e., wingtip vortex, jet flow, and propeller slipstream, and then, the static and dynamic derivatives that represent the stability of the fly wing under the wake flow are identified by using the least squares method. The results demonstrate that both the steady and unsteady aerodynamics of the fly wing are affected by wake flows: wingtip vortices increase the lift-to-drag ratio and considerably change the dynamic damping; jet flow reduces both the static and dynamic damping; and propeller slipstream leads to slow variations in the dynamic damping and decreases in the lift-to-drag ratio.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".