Wind Tunnel Investigation of the Icing of a Drone Rotor in Forward Flight
Bibliographic record
Abstract
The Bell Textron APT70 is a UAV concept developed for last mile delivery and other usual applications. It performs vertical takeoff and transition into aircraft mode for forward flight. It includes four rotor each with four rotating blades. A test campaign has been performed to study the effects of ice accretion on rotor performance through a parametric study of different parameters, namely MVD, LWC, rotor speed, and pitch angle. This paper presents the last experimentations of this campaign for the drone rotor operating in forward flight under simulated icing conditions in a refrigerated, closed-loop wind tunnel. Results demonstrated that the different parameters studied greatly impacted the collection efficiency of the blades and thus, the resulting ice accretion. Smaller droplets were more easily influenced by the streamlines around the rotating blades, resulting in less droplets impacting the surface and thus slower ice accumulations. Higher rotation speeds and pitch angles generated more energetic streamlines, which again transported more droplets around the airfoils instead of them impacting on the surface, which also led to slower accumulation. Slower ice accumulation resulted in slower thrust losses, since the loss in performances can be directly linked to the amount of ice accreted. This research has not only allowed the obtainment of very insightful results on the effect of each test parameter on the ice accumulation, but it has also conducted the development of a unique test bench for UAV propellers. The new circular test sections along with the new instrumentation installed in and around the tunnel will allow the laboratory to be able to generate icing on various type of UAV in forward flight under representative atmospheric conditions.
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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".