Experimental Evaluation of Multi-rotor UAV Operation under Icing Conditions
Bibliographic record
Abstract
Unmanned aerial vehicles (UAV) with multi-rotor configurations have become a popular platform for aerial surveillance and and are forecast for use in delivery of packages. During these missions, UAVs can be exposed to icing conditions. In this paper, two icing experiments on representative UAV configurations are discussed. The object of this paper is to investigate the effects of ice accretion on the operation of a UAV. The first experiment is to measure degradation in thrust and increase in power requirements of a co-axial rotor configuration in an icing wind tunnel. The second experimental set-up tested a commercial UAV (DJI Mavic Pro) in a forward flight regime in an artificial icing environment. These co-axial UAV rotors are controlled by two algorithms. The first algorithm keeps a constant rpm during 60 seconds in an icing cloud. The second algorithm maintains thrust until ice shed from rotors. In addition, a DJI Mavic Pro icing flight test was conducted inside an icing chamber. Both experiments demonstrate that ice accretion on UAV rotors can lead to an intimidating deficit in thrust and abrupt increase in power consumption that could result in catastrophic failure. When rpm is fixed, increase in power and decrease in thrust are linearly correlated with icing time. When thrust is constant, rpm and power could be modeled as a linear function of time before ice sheds. Power change rates under varying icing conditions were compared and discussed. In addition, ice shedding events were observed in the wind tunnel test. A shedding event could result in abrupt change in thrust and power affecting the capability of the motor control algorithm to operate. In the DJI icing flight test, motor current exceeded the current safety limit of the motor within 30 seconds of ice exposure. The motor coils heated during the ice accretion process melting the insulation on wires. The test confirmed the almost immediate damage to small UAV (under 50 Kg.) motors and batteries during icing process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".