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 ∼ 105) 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/m3) 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 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".