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Experimental study on high-precision detection technology for the freezing front height in brine on a horizontal cold plate surface in cold regions

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  • Shi, Han
  • Song, Mengjie
  • Narita, Fumio
  • Hosseini, Seyyed Hossein
  • Zhang, Long
  • Chao, Christopher Yu Hang

Abstract

The formation of ice on wind turbines blades or ship's hull is one of the main problems that energy and transport companies have in cold climates. To ascertain the thickness of brine ice on a horizontal low-temperature cold plate surface, an experimental system based on a capacitively coupled split-ring resonator for detecting the average height of the freezing front in brine has been devised. A static and dynamic freezing front with a 3.5 % salinity and varying heights was prepared and tested at a temperature of −20 °C. The resonant amplitude of the transmission scattering parameter for the resonator exhibited an increase from −19.9 dB to −5.0 dB as the height of the static freezing front increased from 3.2 mm to 21.5 mm. The resonant amplitude demonstrates a monotonic increase with an average sensitivity of 0.51 dB/mm and 4.584 dB/mm as the height of the dynamic freezing front increases within the range of 0–9.5 mm and 9.5–10.5 mm, respectively. The sensor displays an excellent accuracy of 87.8 % in detecting the height of saltwater freezing front in the range of 0–21.5 mm. This method represents a reference in ice detection technology and an effective solution to reduce energy loss due to icing.

Suggested Citation

  • Shi, Han & Song, Mengjie & Narita, Fumio & Hosseini, Seyyed Hossein & Zhang, Long & Chao, Christopher Yu Hang, 2025. "Experimental study on high-precision detection technology for the freezing front height in brine on a horizontal cold plate surface in cold regions," Renewable Energy, Elsevier, vol. 246(C).
    • Handle: RePEc:eee:renene:v:246:y:2025:i:c:s0960148125005907
    DOI: 10.1016/j.renene.2025.122928
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    References listed on IDEAS

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    1. Ye, Feng & Ezzat, Ahmed Aziz, 2024. "Icing detection and prediction for wind turbines using multivariate sensor data and machine learning," Renewable Energy, Elsevier, vol. 231(C).
    2. Owusu, Kwadwo Poku & Kuhn, David C.S. & Bibeau, Eric L., 2013. "Capacitive probe for ice detection and accretion rate measurement: Proof of concept," Renewable Energy, Elsevier, vol. 50(C), pages 196-205.
    3. Guo, Peng & Infield, David, 2021. "Wind turbine blade icing detection with multi-model collaborative monitoring method," Renewable Energy, Elsevier, vol. 179(C), pages 1098-1105.
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