Rekuvienė Regina, Samaitis Vykintas, Jankauskas Audrius, Sadaghiani Abdolali K, Saeidiharzand Shaghayegh, Koşar Ali
Prof. K. Barsauskas Ultrasound Research Institute, Kaunas University of Technology, K. Barsausko St. 59, LT-5142 Kaunas, Lithuania.
Sabanci University Nanotechnology and Application Centre (SUNUM), Sabanci University, Istanbul 34956, Turkey.
Sensors (Basel). 2024 Apr 29;24(9):2850. doi: 10.3390/s24092850.
Ice detection poses significant challenges in sectors such as renewable energy and aviation due to its adverse effects on aircraft performance and wind energy production. Ice buildup alters the surface characteristics of aircraft wings or wind turbine blades, inducing airflow separation and diminishing the aerodynamic properties of these structures. While various approaches have been proposed to address icing effects, including chemical solutions, pneumatic systems, and heating systems, these solutions are often costly and limited in scope. To enhance the cost-effectiveness of ice protection systems, reliable information about current icing conditions, particularly in the early stages, is crucial. Ultrasonic guided waves offer a promising solution for ice detection, enabling integration into critical structures and providing coverage over larger areas. However, existing techniques primarily focus on detecting thick ice layers, leaving a gap in early-stage detection. This paper proposes an approach based on high-order symmetric modes to detect thin ice formation with thicknesses up to a few hundred microns. The method involves measuring the group velocity of the S mode at different temperatures and correlating velocity changes with ice layer formation. Experimental verification of the proposed approach was conducted using a novel group velocity dispersion curve reconstruction method, allowing for the tracking of propagating modes in the structure. Copper samples without and with special superhydrophobic multiscale coatings designed to prevent ice formation were employed for the experiments. The results demonstrated successful detection of ice formation and enabled differentiation between the coated and uncoated cases. Therefore, the proposed approach can be effectively used for early-stage monitoring of ice growth and evaluating the performance of anti-icing coatings, offering promising advancements in ice detection and prevention for critical applications.
在可再生能源和航空等领域，结冰检测面临着重大挑战，因为结冰会对飞机性能和风力发电产生不利影响。结冰会改变飞机机翼或风力涡轮机叶片的表面特性，导致气流分离，并降低这些结构的空气动力学性能。虽然已经提出了各种方法来应对结冰影响，包括化学溶液、气动系统和加热系统，但这些解决方案通常成本高昂且范围有限。为了提高防冰系统的成本效益，关于当前结冰状况的可靠信息，尤其是在早期阶段，至关重要。超声导波为结冰检测提供了一种有前景的解决方案，能够集成到关键结构中并覆盖更大区域。然而，现有技术主要侧重于检测厚冰层，在早期检测方面存在空白。本文提出了一种基于高阶对称模式的方法来检测厚度达几百微米的薄冰层形成。该方法包括在不同温度下测量S模式的群速度，并将速度变化与冰层形成相关联。使用一种新颖的群速度色散曲线重建方法对所提出的方法进行了实验验证，该方法能够跟踪结构中的传播模式。实验采用了有无特殊超疏水多尺度涂层（旨在防止结冰）的铜样品。结果表明成功检测到了结冰形成，并能够区分有涂层和无涂层的情况。因此，所提出的方法可有效地用于结冰生长的早期监测和评估防冰涂层的性能，为关键应用中的结冰检测和预防提供了有前景的进展。
