Icing Events Research Articles

Numerical simulations of ice accretion on wind turbine blades: are performance losses due to ice shape or surface roughness?

Abstract. Ice accretion on wind turbine blades causes both a change in the shape of its sections and an increase in surface roughness. These lead to degraded aerodynamic performances and lower power output. Here, a high-fidelity multi-step method is presented and applied to simulate a 3 h rime icing event on the National Renewable Energy Laboratory 5 MW wind turbine blade. Five sections belonging to the outer half of the blade were considered. Independent time steps were applied to each blade section to obtain detailed ice shapes. The roughness effect on airfoil performance was included in computational fluid dynamics simulations using an equivalent sand-grain approach. The aerodynamic coefficients of the iced sections were computed considering two different roughness heights and extensions along the blade surface. The power curve before and after the icing event was computed according to the Design Load Case 1.1 of the International Electrotechnical Commission. In the icing event under analysis, the decrease in power output strongly depended on wind speed and, in fact, tip speed ratio. Regarding the different roughness heights and extensions along the blade, power losses were qualitatively similar but significantly different in magnitude despite the well-developed ice shapes. It was found that extended roughness regions in the chordwise direction of the blade can become as detrimental as the ice shape itself.

Investigation of ice accretion effect on the aerodynamic characteristics of a wind turbine blade tip after a short icing event

The paper investigates the flow behavior near the NACA 64-618 airfoil profile of the blade tip section of a wind turbine for electric power production after a short icing event. The flow simulation considering the rotation speed of the wind turbine blade is performed to assess the effect of icing on the aerodynamic characteristics. Degradation of aerodynamic characteristics affects the electrical energy production of the wind turbine. The aerodynamic lift and drag coefficients are calculated for different angles of attack. The flow velocity fields near the airfoil are analyzed. The pressure coefficient distributions along the profile surface are obtained. The points of flow stall and changes of aerodynamic characteristics at different angles of attack are determined.

The effect of icing severity on wind turbine ice protection systems efficiency

In the past years, there has been a gain of interest in wind turbine Ice Protection Systems (IPS) as wind farm owners realize the extent of their ice-induced losses. More and more wind turbine manufacturers are now offering IPSs in their cold climate packages. IPS retrofits are now available to minimize icing losses on existing unheated wind turbines. With this growth in interest, questions about the efficiency of those systems arise. Field experience has shown that these systems can be ineffective in certain ambient conditions which led to the concept of IPS performance envelopes. These envelopes lay out on a wind speed vs ambient temperature map the conditions where IPSs are expected to be effective. While these envelopes can give a good overlook of the IPSs efficiency in post-icing conditions, they neglect the effect that icing severity can have on IPS effectiveness. This paper presents a simple 1D model for hot-air IPS able to generate performance envelopes considering the icing severity. This model was validated with the production data of six different icing events. It has been shown to be coherent with the observed efficiency of the hot air IPS.

Temperature Response of Metabolic Activity of an Antarctic Nematode.

Because of climate change, the McMurdo Dry Valleys of Antarctica (MCM) have experienced an increase in the frequency and magnitude of summer pulse warming and surface ice and snow melting events. In response to these environmental changes, some nematode species in the MCM have experienced steady population declines over the last three decades, but Plectus murrayi, a mesophilic nematode species, has responded with a steady increase in range and abundance. To determine how P. murrayi responds to increasing temperatures, we measured metabolic heat and CO2 production rates and calculated O2 consumption rates as a function of temperature at 5 °C intervals from 5 to 50 °C. Heat, CO2 production, and O2 consumption rates increase approximately exponentially up to 40 °C, a temperature never experienced in their polar habitat. Metabolic rates decline rapidly above 40 °C and are irreversibly lost at 50 °C due to thermal stress and mortality. Caenorhabditis elegans, a much more widespread nematode that is found in more temperate environments reaches peak metabolic heat rate at just 27 °C, above which it experiences high mortality due to thermal stress. At temperatures from 10 to 40 °C, P. murrayi produces about 6 times more CO2 than the O2 it consumes, a respiratory quotient indicative of either acetogenesis or de novo lipogenesis. No potential acetogenic microbes were identified in the P. murrayi microbiome, suggesting that P. murrayi is producing increased CO2 as a byproduct of de novo lipogenesis. This phenomenon, in conjunction with increased summer temperatures in their polar habitat, will likely lead to increased demand for carbon and subsequent increases in CO2 production, population abundance, and range expansion. If such changes are not concomitant with increased carbon inputs, we predict the MCM soil ecosystems will experience dramatic declines in functional and taxonomic diversity.

Domain-invariant icing detection on wind turbine rotor blades with generative artificial intelligence for deep transfer learning

Abstract Wind energy’s ability to liberate the world from conventional sources of energy relies on lowering the significant costs associated with the maintenance of wind turbines. Since icing events on turbine rotor blades are a leading cause of operational failures, identifying icing in advance is critical. Some recent studies have utilized deep learning (DL) techniques to predict icing events with high accuracy by leveraging rotor blade images, but these studies only focus on specific wind parks and fail to generalize to unseen scenarios (e.g., new rotor blade designs). In this paper, we aim to facilitate ice prediction on the face of lack of ice images in new wind parks. We propose the utilization of synthetic data augmentation via a generative artificial intelligence technique—the neural style transfer algorithm to improve the generalization of existing ice prediction models. We also compare the proposed technique with the CycleGAN as a baseline. We show that training standalone DL models with augmented data that captures domain-invariant icing characteristics can help improve predictive performance across multiple wind parks. Through efficient identification of icing, this study can support preventive maintenance of wind energy sources by making them more reliable toward tackling climate change.

Book review. Weather – Hurricanes, Tornadoes, Ice, Lightning and Other Dramatic Weather Events in Photographs

Book review. Weather – Hurricanes, Tornadoes, Ice, Lightning and Other Dramatic Weather Events in Photographs

Modelled realistic daily variation in low winter sea-ice concentration over the Barents Sea amplifies Asian cold events

The boreal wintertime atmospheric responses, especially cold events over central Asia, to low sea-ice concentration (SIC) with and without realistic daily variation over the Barents Sea are explored with the Community Atmosphere Model version 4.0 (CAM4.0). The results show that the general atmospheric responses to approximately equal winter-mean Arctic sea-ice loss with a similar pattern but with climatological versus realistic daily variation are different. With the forcing of low SIC with climatological daily variation, Asian cold events become a little longer and stronger than in the control experiment; this mainly results from the enhancement of a 500-hPa Ural anticyclonic anomaly. However, the low SIC forcing that includes realistic daily variability greatly intensifies central Asian cold events and the cyclonic anomaly downstream of the Ural anticyclone. Further analysis reveals that Asian cold events are closely associated with Arctic deep warming at an intraseasonal time scale, which is also the strongest in the perturbed experiment forced by low SIC with realistic daily variation. This work provides a better understanding of the linkage between sea-ice variation over the Barents Sea and central Asian cold events, which may improve extreme weather prediction. It also implies that it is necessary to force air–sea coupling models and atmospheric models with realistic daily SIC in the study of the relationship between Arctic sea ice and mid-latitude cold events.

Evaluation of meteorological measurements made on the nacelle of wind turbines in cold climate

Operating wind turbines in cold climate leads to several challenges not encountered in regular climates such as frequent icing. These challenges are amplified by the difficulties associated with accurately quantifying icing without specialized instrumentation. Typically, the performance of wind turbines is monitored by comparing the turbine active power to the available power based on the wind speed measured on the nacelle. However, multiple other factors not related to cold climate can also lead to a reduction of turbine performance. Applying a temperature threshold under which ice accretion is deemed to be able to impact turbine performance is a popular tool to reduce the influence of these other factors. Currently, there is no generally accepted threshold temperature value. Using large data sets from 3 different wind farms equipped with different wind turbine models, this topic was investigated. Following this analysis, it has been found that the optimal strategy to define icing events is to consider that ice may remain on surfaces only if a minimal temperature of 1 °C in the previous 2-h window is observed. This strategy is supported by a temperature bias analysis, a turbine performance analysis and 3 case studies. An ice sensor can also be an efficient strategy to accurately identify icing events. Previous studies raised doubts on the accuracy of ice measurements made on a wind turbine nacelle due to the different conditions experienced by the blade tips. To verify this claim, the present paper provides comparisons of icing periods, defined by the wind turbine performance method, to the detection of an ice sensor installed on the nacelle. It was found that the start of 71 of the 74 identified icing events coincided with ice detection on the nacelle. The three remaining events were minor and could not be linked to the incapacity of detecting ice on the nacelle. In conclusion, sensitive nacelle-based sensors should be considered as suitable options for ice detection on wind turbine nacelle.

The impact of winter warming and more frequent icing events on reindeer herder occupational safety, health, and wellbeing.

Northern Finland, like the rest of the Arctic, has experienced increases in mean annual temperature, the number of winter rains, the number of thaw-freeze days, the number of extremely warm weather events, and a shortened snow season. These changes have produced numerous problems for reindeer herders whose livelihoods rely on a healthy ecosystem with predictable weather patterns. We performed a scoping literature review to assess how climate change induced extreme weather has negatively impacted reindeer herding as well as the health and wellbeing of reindeer herders. Late snow cover negatively impacts reindeer herding through a more widely dispersed herd that increases the work to gather reindeer, leads to reduced calf weight, and results in less meat for sale. This increased labor, especially in extreme cold conditions, can also negatively impact reindeer herder health. Icing due to thaw-freeze and rain-on-snow events makes it impossible for reindeer to dig through the snow to access lichens, increasing the need for reindeer herders to keep the herd in winter enclosures and provide supplemental feed. Climate change induced weather events such as late snow cover and icing increase reindeer herder efforts and expenses, put their livelihood at risk, and put their health at risk.

An integrated ice tracking and mitigation system on the stagnation line of a cylindrical surface based on thermal imaging and electro-thermal elements

This work aims to research and develop an integrated ice tracking and mitigation technique based on thermal imaging and heat elements along a stagnation line of a cylindrical surface. It tracks and measures ice accumulation during an ice event and ice decrement during de-icing using sequential thermal images and image processing algorithms. It also employs an infrared camera to monitor the processes of ice buildup, de-icing, and relaxation by measuring the average temperature of the target surface. Thermal imaging measurements are validated using an optical camera. The measurements suggest that the average uncertainty of the ice thickness determined from thermal and optical images during ice buildup is about 0.16 mm, and about 0.1 mm during ice mitigation. The suggested method provides proper evaluation for a wide range of cold environment applications.

Validation of a numerical ice accretion model on a wind turbine with high-resolution field data

With the increase of wind power capacity in cold climate, an improved understanding of the effects of ice accretion on wind turbine blades would prove to be useful to better estimate the power losses due to icing. Currently, computational fluid dynamics (CFD) is used in conjunction with blade element momentum (BEM) theory to provide power losses estimations. However, most studies so far do not line up with the experimental observations by generally underestimating icing losses. An accurate ice accretion model is a crucial part of the icing estimation process. This paper proposes a method to improve the modelling of ice accretion on a wind turbine nacelle by combining variable high-resolution atmospheric data with the LEWICE software. To validate the methodology, 8 icing events were selected over the winter of 2018–2019, in a Canadian wind farm. Atmospheric data were obtained by a Meteorological Conditions Monitoring Station (MCMS) and images were provided by a camera, both installed on a wind turbine nacelle. The models were compared with experimental data provided by camera images of a reference cylinder. This paper shows that the usage of the LEWICE model improves the concordance with the camera observations compared to the Makkonen model. It was found that the wind orientation on the nacelle could vary significantly during an event and caused conditions outside of the validation range of LEWICE. It was observed that the choice of the meteorological conditions averaging time has a significant impact on the final ice shape. This paper provides valuable insights on the use of high-resolution data and on the LEWICE ice accretion model for ice modelling on the nacelle structures. It becomes a first step before transposing ice accretion to the blades to provide better performance predictions.

Harmonization and Verification of Three National European Icing Forecast Models Using Pilot Reports

The Single European Sky Air Traffic Management Research (SESAR) program aims at modernizing and harmonizing the European airspace, which currently has a strongly fragmented character. Besides turbulence and convection, in-flight icing is part of SESAR and can be seen as one of the most important meteorological phenomena, which may lead to hazardous flight conditions for aircraft. In this study, several methods with varying complexities are analyzed for combining three individual in-flight icing forecasts based on numerical weather prediction models from Deutscher Wetterdienst, Météo-France, and Met Office. The optimal method will then be used to operate one single harmonized in-flight icing forecast over Europe. As verification data, pilot reports (PIREPs) are used, which provide information about hazardous weather and are currently the only direct regular measure of in-flight icing events available. In order to assess the individual icing forecasts and the resulting combinations, the probability of detection skill score is calculated based on multicategory contingency tables for the forecast icing intensities. The scores are merged into a single skill score to give an overview of the quality of the icing forecast and enable comparison of the different model combination approaches. The concluding results show that the most complex combination approach, which uses iteratively optimized weighting factors for each model, provides the best forecast quality according to the PIREPs. The combination of the three icing forecasts results in a harmonized icing forecast that exceeds the skill of each individual icing forecast, thus providing an improvement to in-flight icing forecasts over Europe.

Characteristics of Arctic Summer Inversion and Its Correlation with Extreme Sea Ice Anomalies

Low tropospheric temperature inversion is very common in the Arctic region. Based on the hyperspectral Atmospheric Infrared Sounder (AIRS) profiles from 2002 to 2020, this study provides a comprehensive analysis of the characteristics and anomalies for low tropospheric inversions in the entire Arctic, especially during the summer period. Three types of inversion are classified here, representing the inversions under the clear-sky condition (“clear” inversion), under the cloudy condition with clouds under the inversion layer top (“cloud-I” inversion), and without clouds under the inversion layer top (“cloud-II” inversion). Obvious seasonality is revealed in these three types of inversion, which is stronger in winter than in summer, as per previous studies. We further found that a “summer” peak of inversions occurs in the Arctic, notably in July. Averaged over the study region (60−90° N, 180° W−180° E), the frequencies of “cloud-I” and “cloud-II” inversions peak in July with values of about 22.1% and 34.6%, respectively. Moreover, the three inversion types all display a small “July” peak of inversion strength, ranging from 2.14 to 3.19 K. The result reveals that when the frequency and strength of summer inversions are both with high positive anomalies, there would be a drop in sea ice concentration in September. This implied that the high positive anomalies, both in inversion frequency and strength in summer, might be a predicted signal for the extreme low sea ice event in September. It is also noted that during the extreme low sea ice events in 2007 and 2020, the summer inversion has a strong positive anomaly. However, the summer inversion in 2012, when the sea ice extent also broke the low record, was not extreme as in 2007 and 2020. Further study needs to be supported by follow-up models and observations to evaluate the impact of the inversions on the sea ice.

The Ability of the ICE-T Microphysics Scheme in HARMONIE-AROME to Predict Aircraft Icing

AbstractIn-cloud icing is a major hazard for aviation traffic and forecasting of these events is an important task for weather agencies worldwide. A common tool utilized by aviation forecasters is an icing intensity index based on supercooled liquid water from numerical weather prediction models. We seek to validate the modified microphysics scheme, ICE-T, in the HARMONIE-AROME numerical weather prediction model with respect to aircraft icing. Icing intensities and supercooled liquid water derived from two 3-month winter season simulations with the original microphysics code, CTRL, and ICE-T are compared with pilot reports of icing and satellite retrieved values of liquid and ice water content fromCloudSat–CALIPSOand liquid water path from AMSR-2. The results show increased supercooled liquid water and higher icing indices in ICE-T. Several different thresholds and sizes of neighborhood areas for icing forecasts were tested out, and ICE-T captures more of the reported icing events for all thresholds and nearly all neighborhood areas. With a higher frequency of forecasted icing, a higher false alarm ratio cannot be ruled out, but is not possible to quantify due to the lack of no-icing observations. The increased liquid water content in ICE-T shows a better match with the retrieved satellite observations, yet the values are still greatly underestimated at lower levels. Future studies should investigate this issue further, as liquid water content also has implications for downstream processes such as the cloud radiative effect, latent heat release, and precipitation.

The Development of Green Olympic Games: A Comparison of CO2 Emission Reduction Strategies between PyeongChang 2018 and Beijing 2022

The Olympic Games are striking a balance between economic and ecological benefits. Taking the 5 ice event venues of the 2018 PyeongChang Winter Olympic Games as the research object, the CO2 emissions under the 2018 PyeongChang Low-carbon scenario and 2022 Beijing Low-carbon scenario are compared. The empirical results show that, in terms of venue operation, the Beijing strategies can reduce CO2 emissions by 715.7 t compared with the PyeongChang strategies. In terms of the refrigerant escape, Beijing's adoption of CO2 refrigerant can reduce the emission of CO2 by 1202.2 t compared with the R404 refrigerant used in PyeongChang. This paper confirms the progress of the Green Winter Olympic Games, and may provide implications for the future host countries as well as the cities which in the green transformation.

Wind Turbine Blade Icing Detection Based on Random Forest

In this paper, a multimodal random forest method is proposed to solve the problem that the traditional linear method has low accuracy in predicting the icing of generator blade. This paper uses the random forest algorithm to model and analyze the icing event of wind turbine blades, and describes in detail the process of using the C4.5 algorithm to generate a decision tree, and then randomly selecting samples and sample features to train to generate a random forest. The effectiveness of the method in this paper is experimentally verified by SCADA data, and the results show that the multi-modal random forest algorithm reduces the error rate to 1.97% in wind turbine blade icing prediction, which is more accurate than the traditional method.

아이스하키 경기에서 효율적인 공격지역 진입을 위한 전술에 관한 연구

The purpose of this study is to present basic data on how to enter the offensive zone for ice hockey coaches and players. Kick-out is the tactics for making Blue Line Entry to attack the offensive zone. When a center kick-out the puck to both side wingers, the center has to invite and bite opponent defense. After that, the center has to go the net which is the middle drive. That will help the puck carrier to attack the net. After the pass to the winger, the center must skate between the opposing defenders and skating strongly to the net. Blue Line Entry Options include Seam Pass, Delay, Bump Back, Ladder Play, and Rebound. Puck Carrier Shooting, Crease Cross. Dump & Chase is also widely used as a tactic to enter the offensive zone. The coaches must continuously teach players to communicate effectively in the on ice practice and events. Finally, every coaches should study and make the their own playbook for players and team. It will eventually come out with good results.

River ice phenology and thickness from satellite altimetry: potential for ice bridge road operation and climate studies

Abstract. River ice is a key component of the cryosphere. Satellite monitoring of river ice is a rapidly developing area of scientific enquiry, which has wide-ranging implications for climate, environmental and socioeconomic applications. Spaceborne radar altimetry is widely used for monitoring river water regimes; however, its potential for the observation of river ice processes and properties has not been demonstrated yet. Using Ku-band backscatter measurements from the Jason-2 and Jason-3 satellite missions (2008–2019), we demonstrate the potential of radar altimetry for the retrieval of river ice phenology dates and ice thickness for the first time. The altimetric measurements were determined to be sensitive enough to detect the first appearance of ice and the beginning of thermal breakup on the lower Ob River (Western Siberia). The uncertainties in the retrieval of ice event timing were within the 10 d repeat cycle of Jason-2 and Jason-3 in 88 %–90 % of the cases analysed. The uncertainties in the river ice thickness retrievals made via empirical relations between the satellite backscatter measurements and in situ observations, expressed as the root mean square error (RMSE), were of 0.07–0.18 m. A novel application of radar altimetry is the prediction of ice bridge road operations, which is demonstrated herein. We established that the dates of ferry closing and ice road opening and closing in the city of Salekhard can be predicted with an accuracy (expressed as RMSE) of 3–5 d.

Towards standards in the analysis of wind turbines operating in cold climate – Part A: Power curve modeling and rotor icing detection

Analyzing the performance of wind turbines operating in cold climate conditions is a complex matter. The lack of any widely used standards adds to the challenge of comparing turbine performance between and within a wind farm. It proves to be an even bigger issue when alternative operational strategies, including ice protection systems (IPS) are implemented and their performance has to be assessed. Before making any comparison between turbines, it is important to build robust power curve models that are not influenced by secondary factors (e.g. temperature and wind orientation). An accurate power curve model allows to detect more precisely any ice formed on the rotor. This paper details a process that allows to build a simple power curve model while minimizing the influence of temperature and wind direction. The piecewise function used to represent the power curve yields a precise parametric representation while being more robust to outliers. Different tools being used to evaluate the performance of the model are presented. With these tools it was possible to observe that standard air density corrections were not able to completely compensate the effect of temperature on the power output. The analysis also provides comprehensive definitions of the different periods composing an icing event. A simple and efficient ice detection threshold using only the power curve is proposed and validated. Using this threshold, algorithms defining significant production loss periods and icing events are presented. The different tools introduced in this paper can decrease significantly the uncertainty in the available power estimation, which proves to be crucial when comparing the performance of multiple turbines.

Comparison of the WRF and HARMONIE models ability for mountain wave warnings

Mountain lee waves usually involve aircraft icing and turbulence events. These weather phenomena, in turn, are a threat to aviation safety. For this reason, mountain lee waves are an interesting subject of study for the scientific community. This paper analyses several mountain lee waves events in the south-east of the Guadarrama mountain range, near the Adolfo Suarez Madrid-Barajas airport (Spain), using the Weather Research and Forecasting (WRF) and the HARMONIE-AROME high-resolution numerical models. For this work, simulated brightness temperature from the optimum WRF parametrization schemes and from the HARMONIE are validated using satellite observations to evaluate the performance of the models in reproducing the lenticular clouds associated to mountain lee waves. The brightness temperature probability density shows interesting differences between both models. Following, a mountain wave characterization is performed simulating some atmospheric variables (wind direction, wind speed, atmospheric stability, liquid water content and temperature) in several grid points located in the leeward, windward and over the summit of the mountains. The characterization results are compared for both numerical models and a decision tree is developed for each to forecast and warn the mountain lee waves, lenticular clouds and icing events with a 24 to 48 h lead time. These warnings are validated using several skill scores, revealing similar results for both models.