Aerodynamics and Flowfield of Distributed Electric Propulsion Tiltwing During Transition With Deflected Trailing-Edge Flap
Bibliographic record
Abstract
Abstract The aerodynamics and flowfield of a rectangular semiwing equipped with four four-bladed propellers and a 40%-chord full-span plain trailing-edge flap were investigated by using force balance and particle image velocimetry (PIV). The distributed electric propulsion (DEP) wing was tilted from zero to 90-deg angle of attack. The maximum lift coefficient, lift-curve slope, and stall angle of the DEP wing were found to increase significantly with increasing propeller rotation. The DEP wing also exhibited a gradual stall in contrast to the sudden stall of the baseline wing. The lift coefficient of the DEP wing positioned vertically at 90 deg was also found to be greatly increased with increasing propeller rotation. Regardless of the magnitude of propeller rotation, the general pattern and behavior of the lift curve were consistent. The deployment of the flap led to a further increase in the maximum lift coefficient and lift-curve slope but an earlier stall and an increased drag of the DEP wing as compared to the unflapped wing. The flap deflection also led to a lowered lift coefficient in the poststall angle-of-attack regime as compared to the unflapped DEP wing. Gurney flap was also employed to further increase the lift generation of the DEP wing. The lift augmentation produced by the propeller slipstream was supplemented by the PIV flowfield measurements.
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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 | 0.000 | 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".