Multilocation Microwave Ice Sensor for Integrated, Zone-Based Deicing
Bibliographic record
Abstract
Atmospheric icing during flight poses a significant risk to smaller autonomous aerial vehicles, drones, and electric vertical take-off and landing crafts. In-flight icing negatively affects their aerodynamics and increases weight, thus restricting their operational envelopes and reducing seasonal reliability. Here a smart ice detection and removal system utilizing multifrequency microwave split-ring-resonator sensors for location-specific impact ice sensing is introduced. The sensor is integrated onto an airfoil with a multizone electrothermal deicing system and a deicing coating for a zone-based ice protection system. Experimental testing is conducted within a refrigerated icing wind tunnel under low-Reynolds number conditions (Re ∼ 10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup>) at airspeeds (20 and 40 m/s) relevant to urban air mobility. The sensor's response to distinct accretion characteristics at −5 °C, −10 °C, and −20 °C and at varying liquid water contents (0.3, 0.5, and 0.8 g/m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>) is investigated. The sensor exhibits a high sensitivity of 179 MHz/mm and data postprocessing enables precise monitoring of icing rates under various conditions and with different types of ice accretions for prediction of ice thickness. This proof-of-concept system shows significant potential for use in green energy and aviation sectors, offering prompt and efficient ice protection to maintain the safety and effectiveness of aerospace vehicles.
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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".