Ice Detection Research Articles

Early-Stage Ice Detection Utilizing High-Order Ultrasonic Guided Waves.

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 S1 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.

The U-net-based ice pore parameter extraction method for establishing the SAW icing sensing mechanism

The porous ice layer formed upon the SAW (surface acoustic wave) propagation path will produce transient acoustic attenuation, and a new icing sensing method can be constructed based on this feature. Obtaining the pore characteristics of an ice layer is a prerequisite and basis for deriving the sensing mechanism of SAW icing. Employing the U-net network, a segmentation model of frozen pores is developed by collecting frozen images and calibrating sample data sets for training in this work. The MIOU and the MPA of the model reach 92.16 % and 95.15 % respectively. Then, the watershed algorithm and post-processing were used to accurately obtain the pore characteristic parameters (porosity, pore number, and average pore area) of the ice layer. On this basis, applying the Biot's theory for two-phase porous media, a theoretical model of the SAW icing sensing mechanism was established, and the acoustic sensing response under different icing types was theoretically simulated. To prove the accuracy of the theoretical simulation, a SAW sensor using a waveguide structure of SU-8/ST-90 °X quartz operating at 80 MHz was constructed and evaluated experimentally. The results demonstrate that the acoustic sensing responses of different ice types are completely consistent with the theoretical results. This fully verifies the accuracy of the ice pore parameters extracted in this work and also confirms the feasibility of using pore microscopic information to achieve ice detection and ice type discrimination.

AIDER: Aircraft Icing Potential Area DEtection in Real-Time Using 3-Dimensional Radar and Atmospheric Variables

Aircraft icing refers to the accumulation of ice on the surface and components of an aircraft when supercooled water droplets collide with the aircraft above freezing levels (at altitudes at which the temperature is below 0 °C), which requires vigilant monitoring to avert aviation accidents attributable to icing. In response to this imperative, the Weather Radar Center (WRC) of the Korea Meteorological Administration (KMA) has developed a real-time icing detection algorithm. We utilized 3D dual-polarimetric radar variables, 3D atmospheric variables, and aircraft icing data and statistically analyzed these variables within the icing areas determined by aircraft icing data from 2018–2022. An algorithm capable of detecting icing potential areas (icing potential) was formulated by applying these characteristics. Employing this detection algorithm enabled the classification of icing potential into three stages: precipitation, icing caution, and icing warning. The algorithm was validated, demonstrating a notable performance with a probability of detection value of 0.88. The algorithm was applied to three distinct icing cases under varying environmental conditions—frontal, stratiform, and cumuliform clouds—thereby offering real-time observable icing potential across the entire Korean Peninsula.

Analysis method and experimental study of ice accumulation detection signal based on Lamb waves

A quantitative identification method for in-flight icing has the capability to significantly enhance the safety of aircraft operations. Ultrasonic guided waves have the unique advantage of detecting icing in a relatively large area, but quantitative identification of ice layers is a challenge. In this paper, a quantitative identification method of ice accumulation based on ultrasonic guided waves is proposed. Firstly, a simulation model for the wave dynamics of piezoelectric coupling in three dimensions is established to analyze the propagation characteristics of Lamb waves in a structure consisting of an aluminum plate and an ice layer. The wavelet transform method is utilized to extract the Time of Flight (ToF) or Time of Delay (ToD) of S0/B1 mode waves, which serves as a characteristic parameter to precisely determine and assess the level of ice accumulation. Then, an experimental system is developed to evaluate the feasibility of Lamb waves-based icing real-time detection in the presence of spray conditions. Finally, a combination of the Hampel median filter and the moving average filter is developed to analyze ToF/ToD signals. Numerical simulation results reveal a positive correlation between geometric dimensions (length, width, thickness) of the ice layer and ToF/ToD of B1 mode waves, indicating their potential as indicators for quantifying ice accumulation. Experimental results of real-time icing detection indicate that ToF/ToD will reach greater peak values with the growth of the arbitrary-shaped ice layer until saturation to effectively predict the simulation results. This study lays a foundation for the practical application of quantitative icing detection via ultrasonic guided waves.

Environmental Chamber Characterization of an Ice Detection Sensor for Aviation Using Graphene and PEDOT:PSS.

In the context of improving aircraft safety, this work focuses on creating and testing a graphene-based ice detection system in an environmental chamber. This research is driven by the need for more accurate and efficient ice detection methods, which are crucial in mitigating in-flight icing hazards. The methodology employed involves testing flat graphene-based sensors in a controlled environment, simulating a variety of climatic conditions that could be experienced in an aircraft during its entire flight. The environmental chamber enabled precise manipulation of temperature and humidity levels, thereby providing a realistic and comprehensive test bed for sensor performance evaluation. The results were significant, revealing the graphene sensors' heightened sensitivity and rapid response to the subtle changes in environmental conditions, especially the critical phase transition from water to ice. This sensitivity is the key to detecting ice formation at its onset, a critical requirement for aviation safety. The study concludes that graphene-based sensors tested under varied and controlled atmospheric conditions exhibit a remarkable potential to enhance ice detection systems for aircraft. Their lightweight, efficient, and highly responsive nature makes them a superior alternative to traditional ice detection technologies, paving the way for more advanced and reliable aircraft safety solutions.

A Dual-Mode Surface Acoustic Wave Delay Line for the Detection of Ice on 64°-Rotated Y-Cut Lithium Niobate.

Ice accumulation on infrastructure poses severe safety risks and economic losses, necessitating effective detection and monitoring solutions. This study introduces a novel approach employing surface acoustic wave (SAW) sensors, known for their small size, wireless operation, energy self-sufficiency, and retrofit capability. Utilizing a SAW dual-mode delay line device on a 64°-rotated Y-cut lithium niobate substrate, we demonstrate a solution for combined ice detection and temperature measurement. In addition to the shear-horizontal polarized leaky SAW, our findings reveal an electrically excitable Rayleigh-type wave in the X+90° direction on the same cut. Experimental results in a temperature chamber confirm capability for reliable differentiation between liquid water and ice loading and simultaneous temperature measurements. This research presents a promising advancement in addressing safety concerns and economic losses associated with ice accretion.

Fault diagnosis of wind turbine blade icing based on feature engineering and the PSO-ConvLSTM-transformer

In winter, wind turbines often face the risk of blade icing, and traditional methods – which mostly rely on manual observation and sensors – are limited by the experience of testers and costs; therefore, they are not widely used in practice. With the development of artificial intelligence, data-driven methods have garnered increased attention. However, it is challenging to mine the effective hidden information within data. In this regard, this paper proposes a wind turbine blade icing detection method based on feature engineering and the PSO-ConvLSTM-Transformer. Firstly, feature engineering is carried out with the help of expert experience to construct the mechanism variables related to icing; secondly, the ConvLSTM-Transformer prediction model is constructed to mine the timing information between supervisory control and data acquisition (SCADA) data, and finally, the model hyper-parameters are optimized by using the particle swarm optimization (PSO) algorithm to improve the diagnostic performance and generalizability of the model. The proposed model is evaluated using two wind turbine blade icing datasets. The proposed PSO-ConvLSTM-Transformer has significant advantages over multiple baseline models. This study can inform wind farm operations in terms of optimizing maintenance strategies, ensuring the safe and efficient operation of wind turbines in cold weather conditions.

Noncontact estimation of the onset of ice accretion in turbofan stators

AbstractThe problem of crystal ice formation inside aircraft turbine engines is well‐documented, and poses a significant risk to safety. The problem is not only one of power loss in flight, but the very real possibility that a flame‐out event could occur due to ice accretion on compressor stators, with potentially catastrophic outcomes. Although many instrumentation systems have been developed for wing ice detection, incipient formation of crystal ice is somewhat more difficult to detect. This is compounded by the need for a noncontact sensor which is robust to in‐flight conditions. This paper proposes an approach to the detection of ice formation based on microwave transmission characteristics across the first and possibly the second stage of the compressor stator. It is shown that noncontact detection is feasible under realistic conditions. The contribution of this paper is twofold. First, the microwave transmission approach is motivated using wind tunnel measurements, and appropriate frequency bands are determined. Next, a signal processing approach involving higher‐order analysis of time‐frequency distribution characteristics is then put forward. Experimental results are presented to support the hypothesis that multiband detection offers a workable approach to the incipient crystal‐ice detection problem.

Simulation study of the lunar water ice detection based on a small neutron generator

Lunar water ice is an important resource to support the construction of lunar base and other deep space activities, and it is crucial to detect lunar water ice. In this paper, we studied the detection of lunar water ice based on a small neutron generator through Monte-Carlo simulation. The schematic of the detection system and the principle are explained. The simulation results demonstrate that the change of thermal neutron flux with the increase of water content is most obvious and clear. There is a positive correlation between thermal neutron counting and water content in lunar soil. Using thermal neutron counting, the water content can be obtained by inversion.

Sea ice detection network for icebreakers in polar environments with attention-based deeplabv3+ architecture

Shipborne sea ice detection aboard icebreakers plays a paramount role in polar navigation. The continuous evolution of deep learning semantic segmentation networks has promoted the advancement of sea ice detection tasks. At this stage, there are relatively few studies on shipboard sea ice detection, and the accuracy of polar sea ice detection will be reduced due to problems such as blurred sea fog and indistinct boundaries. In this study, a shipboard sea ice detection dataset is constructed, and a sea ice detection method that combines multi-branch attention feature alignment and multi-scale feature extraction is proposed. The heterogeneous receptive field enhancement atrous spatial convolution pooling pyramid module is designed, and the feature alignment module based on the attention mechanism is constructed, which strengthens the model’s extraction of sea ice features and elevates representation performance. Experimental results underscore the heightened precision of our approach in sea ice detection, to some extent alleviating the issue of missed detections in new ice. It constitutes a positive contribution towards advancing shipborne sea ice detection in polar environments.

Sea Ice Detection Method Using the Dependence of the Radar Cross-Section on the Incidence Angle

The method for sea ice detection using the data from the Dual-frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) satellite data is suggested. The approach is based on the analysis of the shape of normalized radar cross-section dependence on the incidence angle. The coefficient of kurtosis of surface slopes probability density function is introduced as a parameter to distinguish between open water and ice cover. The approach was validated using the data on sea ice concentration from the AMSR-2 radiometer in the Antarctic region.

Water Ice Resources on the Shallow Subsurface of Mars: Indications to Rover-Mounted Radar Observation

The planet Mars is the most probable among the terrestrial planets in our solar system to support human settlement or colonization in the future. The detection of water ice or liquid water on the shallow subsurface of Mars is a crucial scientific objective for both the Chinese Tianwen-1 and United States Mars 2020 missions, which were launched in 2020. Both missions were equipped with Rover-mounted ground-penetrating radar (GPR) instruments, specifically the RoPeR on the Zhurong rover and the RIMFAX radar on the Perseverance rover. The in situ radar provides unprecedented opportunities to study the distribution of shallow subsurface water ice on Mars with its unique penetrating capability. The presence of water ice on the shallow surface layers of Mars is one of the most significant indicators of habitability on the extraterrestrial planet. A considerable amount of evidence pointing to the existence of water ice on Mars has been gathered by previous researchers through remote sensing photography, radar, measurements by gamma ray spectroscopy and neutron spectrometers, soil analysis, etc. This paper aims to review the various approaches utilized in detecting shallow subsurface water ice on Mars to date and to sort out the past and current evidence for its presence. This paper also provides a comprehensive overview of the possible clues of shallow subsurface water ice in the landing area of the Perseverance rover, serving as a reference for the RIMFAX radar to detect water ice on Mars in the future. Finally, this paper proposes the future emphasis and direction of rover-mounted radar for water ice exploration on the Martian shallow subsurface.

Sea Ice Extent Retrieval Using CSCAT 12.5 km Sampling Data

Polar sea ice extent exhibits a highly dynamic nature. This paper investigates the sea ice extent retrieval on a fine (6.25 km) grid based on the 12.5 km sampling data from the China France Ocean Satellite Scatterometer (CSCAT), which is generated by an adapted Bayesian sea ice detection algorithm. The CSCAT 12.5 km sampling data are analyzed, a corresponding sea ice GMF model is established, and the important calculation procedures and parameter settings of the adapted Bayesian algorithm for CSCAT 12.5 km sampling data are elaborated on. The evolution of the sea ice edge and extent based on CSCAT 12.5 km sampling data from 2020 to 2022 is introduced and quantitatively compared with sea ice extent products of Advanced Microwave Scanning Radiometer 2 (AMSR2) and the Advanced Scatterometer onboard MetOp-C (ASCAT-C). The results suggest the sea ice extent of CSCAT 12.5 km sampling data has good consistency with AMSR2 at 15% sea ice concentration. The sea ice edge accuracy between them is about 7 km and 10 km for the Arctic and Antarctic regions, and their sea ice extent difference is 0.25 million km2 in 2020 and 0.5 million km2 in 2021 and 2022. Compared to ASCAT-C 12.5 km sampling data, the sea ice edge Euclidean distance (ED) of CSCAT 12.5 km data is 14 km (2020 and 2021) and 12.5 km (2022) for the Arctic region and 14 km for the Antarctic region. The sea ice extent difference between them is small except for January to May 2020 and 2021 for the Arctic region. There are significant deviations in the sea ice extents of CSCAT 12.5 km and 25 km sampling data, and their sea ice extent difference is 0.3–1.0 million km2.

Sea Ice Detection from RADARSAT-2 Quad-Polarization SAR Imagery Based on Co- and Cross-Polarization Ratio

Sea Ice Detection from RADARSAT-2 Quad-Polarization SAR Imagery Based on Co- and Cross-Polarization Ratio

Wind Tunnel Characterization of a Graphene-Enhanced PEDOT:PSS Sensing Element for Aircraft Ice Detection Systems.

This study details the development and validation of a graphene-based ice detection system, designed to enhance flight safety by monitoring ice accumulation on aircraft surfaces. The system employs a semiconductive polymer (PEDOT:PSS) with graphene electrodes, interpreting resistance changes to detect water impact and ice formation in real time. The sensor's performance was rigorously tested in a wind tunnel under various temperature and airflow conditions, focusing on resistance signal dependency on air temperature and phase change. The results demonstrate the sensor's ability to distinguish water droplet impacts from ice formation, with a notable correlation between resistance signal amplitude and water droplet impacts leading to ice accretion. Further analysis shows a significant relationship between air temperature and the resistance signal amplitude, particularly at lower temperatures beneficial to ice formation. This underlines the sensor's precision in varied atmospheric conditions. The system's compact design and accurate detection highlight its potential for improving aircraft ice monitoring, offering a path toward a robust and reliable ice detection system.

An image augmentation‐based ice monitoring method for safe navigation of polar ships

AbstractThe navigation safety of polar ships depends on accurate monitoring of ice conditions along the route. Complex polar environments but scarce ice datasets pose challenges for deep learning‐based object detection methods. To address this issue, we propose an ice monitoring method based on polar environment adaptive image enhancement. This approach adaptively augments the ice dataset by simulating polar environments such as snowflakes and aurora to improve the training precision of ice detection models in harsh polar environments and meet the needs of safe navigation of polar ships. First, an image augmentation method based on randomly generated snowflake textures is proposed to address the difficulty of covering polar storm scenarios in existing ice datasets, which leads to weak generalization of ice monitoring models. Second, targeting the problem that the boundary between ice and background is confused by aurora phenomena, which severely reduces the ice monitoring accuracy, we propose an image augmentation method based on aurora simulations to improve the ice monitoring performance of trained models under aurora. Finally, an adaptive image augmentation strategy based on the brightness‐contrast threshold is proposed to optimize the snowflake density and aurora intensity in the augmented images to improve the discrimination of ice boundaries. Experimental results demonstrate the effectiveness of the proposed method, which improves the ice state detection accuracy from 0.640 to 0.950, thus providing a reliable guarantee for the safe navigation of polar ships.

Phase-transition-induced phosphorescence: A novel icing detection strategy for airplane

Icing has been proven to be one of the main factors causing aviation accidents. Therefore, the development of convenient and intelligent icing detection technology for airplanes in high-cold areas before takeoff is of great significance to ensure its flight safety. Herein, a novel B-based phosphorescent material (h-PA/BA) was simply fabricated by heat treatment of boric acid (BA) and phenylboric acid (PA) precursors. The obtained h-PA/BA emits blue fluorescence (FL) and green phosphorescence (PL) with an observable time for 15 s (PL lifetime 1.203 s). More interestingly, the h-PA/BA can be dispersed well in aqueous solution with quenched PL, but a stronger PL can then be regenerated due to the phase transition from solution to solid (icing) by inhibiting the non-radiative process. The mechanism of PL generation-quenching-recovery for h-PA/BA under different states is studied in detail. By introducing h-PA/BA into commercial deicing liquid (DL), the novel DL developed with an ice warning function (i-DL) can successfully determine whether the airplane is icing according to the changes in FL and PL. This work opens the door to developing novel specific PL materials, which can expand their applications in airplane safety based on the phase-transition-induced phosphorescence during icing.

An Unsupervised Approach to Wind Turbine Blade Icing Detection Based on Beta Variational Graph Attention Autoencoder

Supervised deep-learning methods are data-driven and widely used for wind turbine blade icing detection (ID). Data driven methods generally require a complete dictionary of labeled sensor data. However, labeling sensor data increases engineering costs and can introduce costly errors such as incorrect data labels. Additionally, the reported data-driven approaches ignore the contribution of structural properties of multivariate sensor data to failure patterns identification. To address these shortcomings, the current study proposes a Beta variational graph attention autoencoder (β-VGATAE) for blade ID. The β-VGATAE model employs a Beta variational autoencoder (β-VAE) architecture to achieve unsupervised learning. A graph attention network is used as a spatial feature extractor within the β-VAE architecture since it considers the spatial structure of the sensor data. Actual sensor data from supervisory control and data acquisition systems were used to validate the proposed model. Specifically, we verified the rationality of designing each component in the β-VGATAE. Experimental results show that the highest levels of accuracy achieved were 90.9% and 93.4% for the respective scenarios involving two wind turbines; the β-VGATAE detection model has high accuracy and excellent generalization ability.

Research on Icing Classification Method Based on Wind Tunnel Test with Double Impact Surface Probe

Facing supercooled large water droplet environment, an effective ice detection method is a prerequisite to implement the avoidance strategy and get out of the icing environment of SLD as soon as possible. Fiber-optic icing sensors were arranged on the double impact surface probe. The probe was used for icing wind tunnel test. Different machine learning algorithms were used to establish the classification method of icing conditions based on multi-sensor ice thickness information fusion. An appropriate algorithm was selected for the classification method to detect icing conditions. The icing classification method based on SVM could effectively distinguish the conventional water droplet icing condition from the SLD icing condition, and it has significant potential on aviation industry application.

Electromechanical Deicing of Helicopter Structures with Integrated Ice Detection by Impedance Measurement

Electromechanical Deicing of Helicopter Structures with Integrated Ice Detection by Impedance Measurement