The Flow Visualization CFD Studies of the Fuselage and Rolled-up Vortex Effects of the Chengdu J-10-like Fighter Canard
Bibliographic record
Abstract
Fighter aircrafts with high maneuverability and swiftness are due to fuselage effects, caused by canard-fuselage-main wing configuration. Even though the flows around fighters are highly complex, mostly they create rolled-up vortices capable to delay stalls and increase maximum lifts (Calderon, Wang & Gursul, 2012; Mitchell & Delery, 2001; Boelens, 2012; Chen, Liu, Guo & Qu, 2015). The vortex dynamics analysis method employment is introduced, in this case we focus only on the fighter canard. It characterizes the vortex core, develops the pitching moment & main wing total lift, and exploits the vortex centre visualization, the strength, negative surface pressure and its trajectory.This paper explains the influence of the fighter fuselage, it generates rolled-up vortex effects, causes the flow deflected by the fighter fuselage head, strengthen the vortex centre to become vortex core. Above the aircraft head, due to the curved contour head effect, the second vortex centers are developed makes the vortex center above the head more dynamic. Comparing with fighter without fuselage, the flow property changes, for Chengdu J-10-like model with fuselage, are concentrated at the canard leading edge, where the negative pressures are stronger, since the maximum axial velocities of the vortex centre are higher, and give more distinctive vortex breakdown locations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".