Experimental Study on Static Ice Adhesion Characteristics of Wind Turbine Blade Surfaces After Sand Erosion

Abstract

To investigate how sand erosion impacts the anti-icing performance of wind turbine blade surfaces, this study experimentally examines the individual and interactive effects of four key factors—the freezing temperature, separation temperature, surface roughness of eroded blade coatings, and loading rate on ice adhesion properties.The results from single-factor analyses reveal that the ice adhesion strength increases linearly with decreasing separation temperature. A more nuanced relationship emerges when considering the freezing temperature relative to the separation temperature: when the freezing temperature exceeds the separation temperature, the adhesion strength rises linearly as the separation temperature drops; conversely, when the freezing temperature is lower than the separation temperature, the adhesion strength decreases linearly with falling separation temperature. Higher loading rates correlate with reduced ice adhesion, while increased surface roughness induced by sand erosion leads to greater adhesion strength. Orthogonal array testing demonstrates the hierarchy of these factors’ influence on post-erosion ice adhesion, as follows: separation temperature > loading rate > freezing temperature > surface roughness of sand-eroded coatings. Notably, the separation temperature and loading rate exert the most significant effects. Furthermore, a regression equation for ice adhesion strength is established based on orthogonal test results, which can effectively predict ice adhesion strength under untested parameter combinations. These findings provide critical foundational data and a reliable theoretical tool to inform the development and optimization of practical de-icing systems in engineering applications.

1. Introduction

Against the backdrop of the accelerated global energy transition toward low-carbon solutions, wind energy has emerged as a cornerstone of sustainable energy development due to its abundance and clean, pollution-free attributes [1,2,3]. Wind turbines operating in the long term in field environments face extreme climatic conditions [4], including high winds [5], lightning [6,7], wind–sand erosion [8,9,10], and subzero temperatures [11,12,13]. While vast cold regions offer advantages for wind energy generation due to the higher air density and wind speeds in these locations [14], ice accretion on blades represents a particularly severe challenges in cold-humid zones [15]. Ice formation degrades the aerodynamic profiles of turbine blades, reducing their power output [16,17]. Severe icing events may trigger complete turbine shutdowns [7], causing widespread grid instability. Concurrently, ice-induced weight gain and imbalanced vibration [7,18], impose substantial fatigue loads on tower bases and nacelle hubs [19]. These critical issues underscore the imperative for de-icing technology research.

The essence of wind turbine de-icing lies in disrupting ice-substrate interactions through physical, chemical, or thermodynamic means [20], enabling safe and efficient ice-turbine separation. Ice adhesion primarily originates from mechanical interlocking (the embedment of ice onto surface with microscale roughness) and intermolecular forces (van der Waals forces, and hydrogen bonding) [21]. Understanding the ice adhesion properties at the blade interface is pivotal for developing deicing systems, as this informs the selection of de-icing methods and technical pathways based on the quantitative characteristics of adhesive strength. Understanding these properties also enable the structural parameters of mechanical, thermal, and hydrodynamic de-icing devices to be optimized, validates the de-icing efficacy and reliability under extreme operating condition simulations, and underpins the formulation of industry standards and optimized design practices [22,23]. Consequently, extensive research has been conducted: Mu et al. systematically analyzed the ice adhesion strength under varying salt concentrations, ambient temperatures, and wind speeds via wind tunnel tests [24]; Piscitelli et al. focused on measuring ice adhesion measurement with superhydrophobic coatings [25]; Shi experimentally investigated the icing and adhesion characteristics of NACA0018 and S809 airfoils, quantifying the wind speed’s effects on the ice adhesion strength [26]; and Shen et al. performed systematic experiments using an icing wind tunnel and a custom adhesion measurement apparatus to quantify the impacts of the temperature, material, airfoil geometry, and angle of attack on ice accretion patterns and adhesion performance [27]. However, under real-world conditions, painted blade surfaces subjected to prolonged wind–sand erosion develop minute pits, protrusions, and scratches [28]. Zou et al. Samples with high roughness were prepared using sandblasting technology. It was found that compared with the rougher sandblasted surfaces, the smoother sample surfaces at the time of reception had lower ice adhesion strength [29]. Hassan et al. used a forced vibration method of a cantilever composite beam at 10.0 Hz was used to study the interfacial fracture at the gold–ice interface. Low-cost strain gauges were used instead of the piezoelectric PVDF sensors employed in other reported studies for adhesion strength measurements. It was found that increasing surface roughness leads to higher interfacial bonding strength [30]. Schulz et al. used finite element method tools to analyze the stress state at the ice-substrate interface under different loading conditions, aiming to gain an in-depth understanding of stress distribution. It was found that stress and peeling stress concentrations occur at the points where stress is transmitted through the interface. Ice under rapid loading has lower strength because ice reacts in a more brittle manner [31]. Sivakumar et al. experimentally evaluated the ice adhesion on aluminum and polyurethane with three different surface roughnesses and two different surface sizes, and found that surface roughness significantly increased the adhesion [32]. Aoyama et al. studied the influence of the arithmetic mean height and arithmetic mean gradient of solid surfaces on the adhesive shear strength between pure ice and copper plates. It was found from the geometric model that the gradient is more important than the adhesion height, because the area expansion rate and force components depend on the gradient [33]. These features alter the intrinsic adhesion strength through mechanical interlocking during ice formation. Quantitative studies in this domain remain scarce. Specifically, this study aims to clarify: the individual and interactive effects of key factors such as freezing temperature, separation temperature, surface roughness caused by sand erosion, and strain rate on the ice adhesion strength of wind turbine blade surfaces after sand erosion, as well as the quantitative characteristics of these effects. The quantitative research results can provide critical foundational reference data for the development of deicing systems, helping to optimize the structural parameters of deicing devices, select appropriate deicing principles and technical pathways, and provide data support for verifying the deicing efficacy and reliability under extreme operating conditions.

In this study, we first fabricated wind turbine blade test specimens with polyurethane primer and topcoat using a spin-coating apparatus. These specimens were then subjected to sand erosion via a custom-built wind–sand erosion simulation setup, with varying erosion durations, to obtain samples with different surface roughness ($Ra$) values. Subsequently, static tangential ice adhesion tests were conducted to examine the effects of key factors—freezing temperature, separation temperature, loading rate, and sand erosion-induced surface roughness—on ice adhesion properties. Through both single-factor analyses and multi-factor coupling experiments (orthogonal array tests), we quantitatively characterized the static ice adhesion characteristics of sand-eroded wind turbine blade surfaces, aiming to provide critical foundational data for the development of practical de-icing systems in engineering applications.

2. Test Devices and Methods

2.1. Coating Preparation

Wind turbines operate for long periods of time in field environments exposed to weathering effects, necessitating protective coatings; these come into contact with ice that forms on wind turbine surfaces. To replicate this process, specimens for ice adhesion testing were prepared using the spin-coating apparatus shown in Figure 1. The apparatus consists of a support base, a high-speed motor, and a specimen fixture. Before spin-coating, the test piece was installed and fixed on the test piece fixing device, and a commercial wind turbine coating was dripped onto the center test piece using a dropper; During spin-coating, the high-speed motor rotated the fixed parts of the test piece, using centrifugal force to evenly spread the coating; During the coating preparation process, the laboratory indoor environment was selected, with an indoor temperature of 25 °C and an environmental humidity of 35%. After spin-coating, the test piece was placed in a drying oven (model 202-00S) for curing, with a curing time of 24 h and a drying temperature of 65 °C. The coating thickness was measured using a coating thickness gauge to form a film layer similar to that of a wind turbine blade coating on the surface of the test piece. This process forms coating films on specimens that simulate wind turbine blade coatings, facilitating related performance studies. In this research, $30 \mathrm{mm}\times 30 \mathrm{mm}\times 1 \mathrm{mm}$, stainless steel 304 specimens substituted wind turbine blades fabricated from glass-fiber-reinforced polymer composites, with a $200 \mathsf{\mu }\mathrm{m}$ thick polyurethane primer and $40 \mathsf{\mu }\mathrm{m}$ thick polyurethane topcoat onto specimen surfaces. The rationale for this selection lies in the fact that during actual wind turbine icing, the ice layer primarily interacts with the protective coating on the blade surface. Thus, the core factor influencing ice adhesion strength is the performance of the coating rather than the substrate itself. When the same thickness of dedicated wind turbine protective coating is applied via spin coating, both 304 stainless steel and GFRP substrates can accurately characterize ice adhesion properties. Additionally, the coating thickness gauge utilized in this experiment is exclusively compatible with metal substrates, and 304 stainless steel facilitates precise control over the thickness of the spin-coated coating. For these reasons, 304 stainless steel specimens were chosen as substitutes for GFRP-based wind turbine blades.

2.2. Sand Erosion Wear Simulation Device

To obtain specimens with varying surface roughnesses after wind–sand erosion, erosion tests were conducted on the spin-coated specimens using the wind-sand erosion simulation apparatus shown in Figure 2. The procedure was as follows: A glass funnel containing test sand particles was connected vertically to the vertical section of a T-shaped tee. Sand particles fell into the vertical section of the T-shaped tee under gravity. By adjusting the rotation angle of the flow control valve on the vertical pipe, the quantity of sand was regulated. air of an adjustable flow rate and pressure entered the horizontal section of the T-shaped tee pipe. This airflow carried sand particles through the horizontal outlet into a mixed flow tube. Particles were accelerated within the tube before impacting the vertically mounted specimens to achieve erosion. Employing gravity, the post-impact sand particles fell into a collection sandbox by gravity while loss of air flow was lost through the sandbox’s opening.

In this study, 0.25–0.35 mm test sand particles were selected to conduct erosion tests on spin-coated specimens under an impact airflow velocity of 12 m/s and sandblasting rate of 0.517 g/s. Specimens with progressively increasing surface roughness $Ra$ were obtained after applying varying erosion durations. Surface roughness measurements were conducted using a roughness tester (TR200), with which five random points within a 10 mm diameter central area were measured, and the average of the results was given as the final $Ra$ value. Five repeated tests were performed for each $Ra$ level. Figure 3 illustrates the distribution law of surface roughness for test pieces under varying erosion times. Figure 3a statistically presents roughness $Ra$ data corresponding to different erosion durations via box plots, revealing the discreteness and central tendency of roughness during the erosion process. As the erosion time increases, the average value of $Ra$ gradually rises from $Ra=0.133 \mathsf{\mu }\mathrm{m}$, and the interquartile range (IQR) expands, reflecting the aggravation of differences in surface undulations. Through this calibration, test pieces with $Ra$ values of 0.2, 0.4, 0.6, and 0.8 were obtained for subsequent experimental research. The contour curves in Figure 3(b-1–b-5) correspond to typical test results with $Ra=0.1, 0.2, 0.4, 0.6, 0.8 \mathsf{\mu }\mathrm{m}$. The fluctuation amplitude of the curves increases significantly with the increase in $Ra$, demonstrating the shaping effect of sandblasting erosion on the surface micro-profile. When the $Ra$ value is low, the surface is relatively flat; when the $Ra$ value is high, due to the continuous impact and cutting of the abrasive, deeper and more irregular peak–valley structures are formed. The SEM images in Figure 3(c-1–c-5,d-1–d-5) confirm this change from the microscopic morphology: when the $Ra$ value is low, only fine scratches and shallow pits remain on the surface. After the erosion time is prolonged and the $Ra$ value increases, the mechanical action of the abrasive causes more dense pits and gullies to appear on the surface of the paint layer. Even local coatings may crack and peel off due to impact stress, revealing that sandblasting gradually constructs a rough structure over time through the mechanism of “abrasive kinetic energy impact-surface material peeling/plastic deformation”.

2.3. Method for Testing Tangential Adhesive Force of Ice Coating

For ice adhesion testing, frozen specimens were prepared using the method illustrated in Figure 4. The test piece was secured to the fixed block with fastening bolts and nuts. A silicone block with a cylindrical cavity was positioned onto the test piece. The sample was pre-cooled in a low-temperature chamber to the target freezing temperature $T_0$. Distilled water at 25 °C as injected into the cavity using a dropper. After the water completely froze at $T_0$, the silicone block was removed to obtain a frozen ice cylinder–plate specimen. The resulting cylindrical ice measuring $\varphi 10 \mathrm{mm}\times 20 \mathrm{mm}$.

The ice adhesion strength was tested using a quasi-static method as shown in Figure 5. The frozen specimen was secured to a fixed platform using fastening bolts. During testing, a stepper motor drove a traverse sled horizontally along linear rails. A dynamometer mounted on the sled was connected to a pusher rod. As the sled advanced, the rod pushed the cylindrical ice tangentially off the plate component at the push rod speed v, The dynamometer recorded the real-time force applied to the ice during de-icing. The pusher rod measured $\varphi 8 \mathrm{mm}$ in diameter, with its center line positioned 4.2 mm from the plate surface. The entire experimental system was housed within a low-temperature chamber set to the separation temperature $T_1$, or was placed in the chamber at least one hour before testing to ensure the thermal equilibrium of both the ice and plate. The de-icing process was simultaneously recorded on video. When the contact area between the ice and plate is A and the measured force is Fτ, tangential adhesion strength satisfies Equation (1):

$$\tau ={\displaystyle \frac{F_{\tau }}{A}}$$

(1)

3. Results and Analysis

3.1. Influence of Separation Temperature $T_1$ on Ice Adhesion Strength

Investigating the effect of the separation temperature $T_1$ on the ice adhesion strength $\tau$ elucidates the challenge of ice detachment in varying low-temperature environments. Tests were conducted with $T_1$ was set to −3 °C, −6 °C, −9 °C, and −12 °C, with the following fixed parameters: freezing temperature $T_0$= −9 °C, loading rate v = 1 mm/s, and specimen surface roughness $Ra=0.264 \mathsf{\mu }\mathrm{m}$. Figure 6 illustrates the relationship between $T_1$ and $\tau$. As the separation temperature decreased, the ice adhesion strength increased monotonically, consistent with existing studies [21,24,34]. Lower temperatures facilitate denser crystalline structures at ice–substrate interfaces, enhancing the interfacial molecular interactions and thereby strengthening adhesion.

3.2. Influence of Freezing Temperature $T_0$ on Ice Adhesion Strength

Measuring the effect of freezing temperature $T_0$ on the ice adhesion strength $\tau$ is significant as it enables an exploration of the variation law of the ice adhesion strength after water droplets impinge on wind turbine blades and freeze under different low-temperature conditions. Tests were conducted at $T_0$ = −3 °C, −6 °C, −9 °C, and −12 °C, with the following fixed parameters: separation temperature $T_1$ = −9 °C, loading rate v = 1 mm/s, and specimen surface roughness $Ra=0.264 \mathsf{\mu }\mathrm{m}$. The relationship between $T_0$ and $\tau$ is shown in Figure 7. The ice adhesion strength exhibited an initial increase followed by a decrease as the freezing temperature declined, peaking at −9 °C. Since $T_1$ was fixed at −9 °C in this single-factor test, When $T_1$ < $T_0$, the adhesion strength increased monotonically (with a mechanism identical to that described in Section 2.1). When $T_1$ > $T_0$, differential thermal expansion at the ice–substrate interface induced partial bond failure and ice cracking, reducing the adhesion strength. This phenomenon underscores the coupling effect between freezing and separation temperatures [20,21].

3.3. Influence of Loading Rate on Ice Adhesion Strength

Investigating the effect of the loading rate on the ice adhesion strength $\tau$ elucidates the challenges of ice detachment under varying loading rates. Here, the loading rate is characterized by the push rod speed v, the tests conducted in this study employed v = 0.5 mm/s, 1.0 mm/s, 1.5 mm/s, and 2.0 mm/s with the following fixed parameters: separation temperature $T_1$ = −9 °C, freezing temperature $T_0$ = −9 °C, and specimen surface roughness $Ra=0.264 \mathsf{\mu }\mathrm{m}$. As shown in Figure 8, the ice adhesion strength decreased with increasing push rod speed (i.e., higher loading rate). This reduction occurs because the wind–sand-eroded surfaces develop irregular interfaces that concentrate stress. At higher loading rates, the intermolecular bonds at the ice–substrate interfaces break before their full development. These mechanisms prevent effective interfacial bonding establishment, manifesting macroscopically as reduced adhesion strength [35].

3.4. Influence of Roughness $Ra$ on Ice Adhesion Strength

Investigating the effect of the surface roughness $Ra$ on the ice adhesion strength $\tau$ enables the elucidates of the impact of wind-sand erosion-induced roughness on ice detachment from wind turbine blades. In this study, we employed $Ra$ values of $0.264 \mathsf{\mu }\mathrm{m}$, $0.454 \mathsf{\mu }\mathrm{m}$, $0.650 \mathsf{\mu }\mathrm{m}$, and $0.862 \mathsf{\mu }\mathrm{m}$ (many scholars have studied the effect of this blade surface roughness on the performance of wind turbines) [36] in our tests with the following fixed parameters: separation temperature $T_1$ = −9 °C, freezing temperature $T_0$ = −9 °C, and loading rate v = 1 mm/s. As shown in Figure 9, the ice adhesion strength increased with rising surface roughness, consistent with the findings of existing studies [37]. This enhancement occurs because the increased roughness enlarges the actual contact area between the ice and substrate beyond the apparent contact area, while simultaneously enabling mechanical interlocking when ice embeds into surface asperities during freezing, significantly strengthening the interfacial adhesion [38].

3.5. Coupling Effects on Ice Adhesion Strength

To investigate the coupled effects of the separation temperature $T_1$, freezing temperature $T_0$, loading rate v, and roughness $Ra$ on the adhesion strength between ice and wind turbine blades, an orthogonal array test design using the L16 (4⁵) orthogonal table was adopted. Four levels were selected for each factor, with the adhesion strength (Y) as the evaluation index. The experimental factors, levels, orthogonal test design, and results are shown in

Table 1. Factor levels.

Factor	Level 1	Level 2	Level 3	Level 4
Separation temperature $A{/}^{\circ }\mathrm{C}$	−3	−6	−9	−12
Freezing temperature $B{/}^{\circ }\mathrm{C}$	−3	−6	−9	−12
Loading rate $C/\left(\mathrm{mm}/\mathrm{s}\right)$	0.5	1	1.5	2
Roughness $D/\mathsf{\mu }\mathrm{m}$	0.264	0.454	0.650	0.862

and

Table 2. Orthogonal experimental design.

No.	Separation Temperature A/°C	Freezing Temperature B/°C	Loading Rate C/(mm/s)	Roughness D/μm	Adhesion Strength (Y)/(kPa)
1	−3	−3	0.5	0.264	61.2
2	−3	−6	1	0.454	54.8
3	−3	−9	1.5	0.650	57.3
4	−3	−12	2	0.862	53.5
5	−6	−3	1	0.650	61.2
6	−6	−6	0.5	0.862	63.7
7	−6	−9	2	0.264	54.8
8	−6	−12	1.5	0.454	62.4
9	−9	−3	1.5	0.862	63.7
10	−9	−6	2	0.650	58.6
11	−9	−9	0.5	0.454	72.6
12	−9	−12	1	0.264	66.2
13	−12	−3	2	0.454	70.1
14	−12	−6	1.5	0.264	66.2
15	−12	−9	1	0.862	82.8
16	−12	−12	0.5	0.650	87.3
$K_1$	56.700	64.050	71.200	62.100
$K_2$	60.525	60.825	66.250	64.975
$K_3$	65.275	66.875	62.400	66.100
$K_4$	76.600	67.350	59.250	65.925
R	19.900	6.525	11.950	4.000

. Table 2 presents the results of the range analysis table of the orthogonal test, where $K_i$ (i = 1, 2, 3, 4) represents the mean value for each level, and R is the difference between the maximum and minimum $K_i$ values of a factor. A larger R value indicates a more significant influence of the factor on the adhesion strength. As shown in the table, $R_A$ = 19.900, $R_B$ = 6.525, $R_C$ = 11.950, and $R_D$ = 4.000. Therefore, the order of influence of each factor on the adhesion strength is as follows: separation temperature > loading rate > freezing temperature > paint surface roughness.

The results of the variance analysis on the influence of various factors on adhesion strength are shown in

Table 3. Variance analysis table.

Factor	Sum of Squares	Degree of Freedom	Mean Square	F	Significance (p)
Corrected Model	1361.655	12	113.471	19.002	0.017
Intercept	67,132.810	1	67,132.810	11,241.888	<0.001
Separation Temperature	893.395	3	297.798	49.869	0.005
Freezing Temperature	108.675	3	36.225	6.066	0.086
Loading rate	318.490	3	106.163	17.778	0.021
Roughness	41.095	3	13.698	2.294	0.257
Error	17.915	3	5.972
Total	68,512.380	16
Corrected Total	1379.570	15
$R^2=0.987$ (adjusted $R^2=0.935$)

. In hypothesis testing, a smaller significance p-value indicates a more significant influence of the factor on the result. The physical meaning of the p-value is: the probability of observing the current experimental result under the assumption that the factor has no influence on the result. A smaller p-value means a lower probability of the coincidence that extreme results occur while the factor has no influence. Therefore, we can be more confident that the factor has a real impact on the result, that is, the significance is stronger. In this study, the p-value of the separation temperature is 0.005 (less than 0.01), indicating that its influence on the adhesion strength reaches an extremely significant level in statistics. The order of significance of each factor judged by the p-values is: separation temperature (0.005) > loading rate (0.021) > freezing temperature (0.086) > roughness (0.257). The results of variance analysis and range analysis mutually verify each other and show a consistent trend.

In exploring the establishment of a predictive model for ice adhesion strength, it should be noted that while this study has quantitatively measured the adhesion strength under limited test conditions and clarified the influence weights of various factors, the development of deicing systems will inevitably encounter parameter combinations beyond the scope of existing tests. To address this issue, a regression equation for ice adhesion strength was fitted based on the orthogonal test results. The equation is as follows:

$$T=59.395-0.008x_1^3+0.001x_1{x}_2-2.362x_3^2+0.140x_1{x}_2{x}_4$$

(2)

Among them, $x_1$ represents the separation temperature, $x_2$ represents the freezing temperature, $x_3$ represents the loading rate, and $x_4$ represents the roughness. By selecting the results of the single—factor experiment as the verification data for the multiple regression model, Figure 10 is a comparison chart of the measured values and regression-fitted values of the adhesion strength. It can be seen from the figure that the measured values and fitted values of the adhesion strength match well. This equation can effectively predict the ice adhesion strength under untested parameter combinations, providing a reliable theoretical tool for the development and optimization of deicing systems in engineering practice.

4. Conclusions

In this study, we have investigated the influence laws of the separation temperature, freezing temperature, loading rate, and surface roughness on the static ice adhesion characteristics of wind turbine blade surfaces after sand erosion. The obtained data can directly provide crucial fundamental data support for the development of practical de-icing systems in the engineering field. The main conclusions of this paper are as follows:

(1)

As the separation temperature decreases, the ice adhesion strength increases linearly. When the freezing temperature exceeds the separation temperature, the adhesion strength increases linearly with the decreasing in the separation temperature; when the freezing temperature is lower than the separation temperature, the adhesion strength decreases linearly as the separation temperature drops. An increase in the loading rate leads to a decrease in the ice adhesion strength, while an increase in the surface roughness after sand erosion results in increased ice adhesion strength.

(2)

The orthogonal experiments have shown that the order of influence of various factors on the ice adhesion force is as follows separation temperature > loading rate > freezing temperature > surface roughness. Here, the first two factors exerting the most significant impacts.

(3)

A regression equation for ice adhesion strength was established based on the orthogonal test results, which can effectively predict the ice adhesion strength under untested parameter combinations. The good agreement between the measured values from single-factor experiments and the fitted values from the regression model verifies its reliability, providing a theoretical tool for the development and optimization of de-icing systems in engineering practice.