CN117993229A - A method for predicting the life of wind turbine blade leading edge coating based on rain erosion fatigue damage - Google Patents

Abstract

The invention discloses a wind power blade leading edge coating life prediction method based on rain erosion fatigue damage, which comprises the following steps of S1, counting different rainfall intensity hour distribution data aiming at the service environment of wind power blades in a wind power plant; s2, calculating the particle sizes of raindrops under different rainfall intensities and the maximum terminal dropping speeds of the raindrops with different particle sizes; s3, carrying out a rain erosion resistance test by taking a wind power blade front edge coating sample as an object, calculating impact speeds of raindrops on each position of the sample, and constructing an impact frequency model and a raindrop kinetic energy impact model of the wind sample in a rain field; s4, establishing a relation between accumulated impact times and impact speed of a unit area of a wind power blade front edge coating sample and the particle size of raindrops; s5, predicting the service life of the wind power blade front edge coating by combining the data obtained in the steps S1-S4 with a linear fatigue accumulation damage criterion based on continuous change of environmental load. The method accurately predicts the service life of the wind power blade leading edge coating, reduces the maintenance cost of the blade and improves the safety of the unit.

Description

A wind turbine blade leading edge coating life prediction method based on rain erosion fatigue damage

Technical Field

The invention relates to a method for predicting the life of a wind turbine blade leading edge coating based on rain erosion fatigue damage.

Background technique

The development of onshore and offshore wind power is facing many technical innovations and challenges such as large-scale units, cost reduction and efficiency improvement. With the continuous increase in the capacity of wind turbines, large-scale blades have become an inevitable trend, but their reliability is becoming increasingly difficult to guarantee, and blade failure accidents are increasing day by day.

Harsh service environment is one of the main causes of blade failure, and blade leading edge erosion is one of the common forms of environmental damage, which will cause blade failure in severe cases. Especially when large-scale wind turbine blades are operated for a long time in rainy areas, they will be affected by continuous rain erosion and other effects. In 2 to 3 years, pitting and pitting will appear on the leading edge of the blade. If not maintained in time, it will further lead to the accumulation and expansion of damage such as drilling and matrix exposure. In severe cases, stratification and cracks will occur at the joints, seriously affecting the stability and dynamic performance of the blade structure, and eventually causing blade failure. At the same time, the accumulation of blade surface damage and the increase in roughness will increase the aerodynamic drag coefficient of the blade, which has a great impact on the power generation efficiency of the wind turbine. Studies have shown that severe blade leading edge erosion can cause an annual power generation loss of up to 20%. Therefore, the prediction of cumulative damage to the leading edge of wind turbine blades has important theoretical significance and practical value for reducing blade maintenance costs and improving unit safety, and also contains obvious social and economic benefits.

Summary of the invention

The purpose of the present invention is to provide a method for predicting the life of a wind turbine blade leading edge coating based on rain erosion fatigue damage, which has high accuracy, reduces the maintenance cost of wind turbine blades, and improves the safety of the unit.

The purpose of the present invention is achieved by the following technical measures: A method for predicting the life of a wind turbine blade leading edge coating based on rain erosion fatigue damage, characterized in that it comprises the following steps:

S1. Count the hourly distribution data of different rainfall intensities according to the service environment of wind turbine blades in wind farms;

S2, calculate the raindrop particle size under different rainfall intensities and the maximum terminal falling speed of raindrops with different particle sizes;

S3. Take the wind turbine blade leading edge coating samples as the object, carry out rain erosion resistance test on them, calculate the impact velocity of raindrops on each position of the leading edge coating samples, and construct the impact frequency model and raindrop kinetic energy impact model of the wind turbine blade leading edge coating samples in the rain field;

S4. Establish a relationship between the cumulative number of impacts per unit area of the wind turbine blade leading edge coating sample and the impact velocity and raindrop particle size;

S5. The service life of the leading edge coating of the wind turbine blade is predicted by combining the data obtained in steps S1 to S4 with a linear fatigue cumulative damage criterion based on continuous changes in environmental loads.

The formation rate of rain erosion fatigue damage on the leading edge of the tip of a wind turbine blade is related to factors such as raindrop volume, impact frequency, impact velocity and material properties. The present invention is based on the actual service environment data of wind turbine blades in wind farms. First, the rainfall intensity distribution is statistically analyzed to obtain the annual average distribution data of hours of different rainfall intensities, and the severity classification of the rain erosion environment is completed; secondly, the correlation between raindrop particle size and rainfall intensity under different rainfall intensities is established; then, a rain erosion resistance test is carried out on the leading edge coating of the blade to obtain the impact resistance performance parameters of the leading edge coating of the blade; then, an impact frequency model and a raindrop kinetic energy impact model of the leading edge coating of the blade in the rain field are constructed, and then a correlation between the cumulative number of impacts per unit area and the impact velocity and raindrop particle size is established; finally, based on the palmgren-miner rule linear fatigue cumulative damage criterion, combined with the blade service environment data and each model, the fatigue damage life of the leading edge coating of the blade in the actual environment where the wind speed, rainfall intensity, raindrop particle size and impact velocity are all non-constant is predicted.

The present invention can accurately predict the life of the coating on the leading edge of a wind turbine blade. It is suitable for predicting rain erosion damage caused by erosion and wear caused by frequent high-speed collisions between the leading edge of the blade tip and raindrops during the long-term service of a wind turbine blade at a certain location. It can reduce the maintenance cost of the blade, improve the safety of the unit, and has obvious economic and social benefits.

In the step S2 of the present invention, for the global light/moderate rain intensity, that is, the rainfall intensity ≦1mm/h, calculate the corresponding raindrop size :

Formula 1)

For moderate/heavy/torrential rainfall in low latitudes, that is, rainfall intensity 1<R<5mm/h, calculate the corresponding raindrop size :

Formula (2)

For heavy rain/extremely heavy rain in low latitudes, the rainfall intensity is >5mm/h, calculate the corresponding raindrop size :

Formula (3)

For various types of rainfall intensity in mid- and high-latitude regions, that is, rainfall intensity >1mm/h, calculate the corresponding raindrop size :

Formula (4)

Then, according to the raindrop particle size, the maximum terminal falling speed of raindrops of different particle sizes under different rainfall intensities is calculated. :

Formula (5)

In the step S3 of the present invention, the impact velocity at each position is calculated according to the position where the wind turbine blade leading edge coating sample is worn from the outside to the inside and the corresponding time.

In the step S3 of the present invention, it is assumed that the water droplets in the rain field are spherical and have a uniform particle size and are evenly distributed, and the number of impacts F occurring per unit area per unit time is:

Formula (6)

That is the impact frequency model, where _Vt is the speed of the wind turbine blade in the rain field;

The cumulative number of impacts per unit area N _Ei is:

Formula (7)

Where, t is time, s;

The energy E _k of a single impact is:

Formula (8)

This is the raindrop kinetic energy impact model.

In the step S4 of the present invention, the relationship between the cumulative number of impacts per unit area of the wind turbine blade leading edge coating sample and the impact velocity and raindrop particle size is obtained by combining formula (8), that is, the rain erosion fatigue damage accumulation model:

Formula (9)

Where c and m are constants related to the leading edge material properties.

In the step S4 of the present invention, the relationship between the cumulative number of impacts per unit area and the cumulative rainfall is obtained according to the relationship between the rainfall intensity and the raindrop particle size.

In the step S5, the present invention uses the linear fatigue cumulative damage criterion based on the continuous change of environmental load:

Formula (10)

Where i is the load level number, ni is the number of cycles at load level i, Ni is the number of cycles when failure occurs at load level i during the test, and j is the load level number;

When M≧1, the material that has undergone fatigue cycles has reached the expected fatigue life and is judged to have failed.

In the step S1 of the present invention, the hourly distribution data of different rainfall intensities in at least the past three years are statistically analyzed. Preferably, the rainfall environment of a certain wind farm in the past five years or more is statistically analyzed.

In the step S1 of the present invention, hourly distribution data of different rainfall intensities over the past five years or more are collected.

Compared with the prior art, the present invention has the following significant effects:

The present invention can accurately predict the life of the coating on the leading edge of a wind turbine blade. It is suitable for predicting rain erosion damage caused by erosion and wear caused by frequent high-speed collisions between the leading edge of the blade tip and raindrops during the long-term service of a wind turbine blade at a certain location. It can reduce the maintenance cost of the blade, improve the safety of the unit, and has obvious economic and social benefits.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments.

FIG1 is a graph showing the relationship between the time of occurrence of leading edge erosion and the impact velocity in Example 1 of the present invention;

FIG2 is a graph showing the relationship between the number of impacts per unit area that cause coating failure and the energy of a single impact in Example 1 of the present invention;

FIG3 is a graph of S-N curves at different raindrop particle sizes in Example 1 of the present invention;

FIG4 is a graph showing the impact velocity at different raindrop particle sizes versus the expected cumulative rainfall that causes coating failure in Example 1 of the present invention;

5 is a graph showing the relationship between the time of occurrence of leading edge erosion and the impact velocity in Example 2 of the present invention;

6 is a graph showing the relationship between the number of impacts per unit area that cause coating failure and the energy of a single impact in Example 2 of the present invention;

FIG. 7 is a graph of S-N curves at different raindrop particle sizes in Example 2 of the present invention;

8 is a graph showing the impact velocity at different raindrop particle sizes versus the expected cumulative rainfall that causes coating failure in Example 2 of the present invention.

Detailed ways

The present invention is further illustrated below through the description of specific implementation methods, but this is not a limitation of the present invention. Those skilled in the art can make various modifications or improvements based on the basic idea of the present invention, but as long as they do not deviate from the basic idea of the present invention, they are all within the protection scope of the present invention.

Example 1

The present invention provides a method for predicting the life of the leading edge coating of a wind turbine blade based on rain erosion fatigue damage, which is suitable for predicting rain erosion damage caused by frequent high-speed collisions between the leading edge of the blade tip and raindrops during the long-term service of the wind turbine blade at a certain location. This embodiment takes a wind farm in Qingzhou as an example to predict the fatigue damage of the leading edge coating of a wind turbine blade, and specifically includes the following steps:

S1. Count the hourly distribution data of different rainfall intensities according to the service environment of wind turbine blades in wind farms;

The main reason for the rain erosion failure of the leading edge coating of wind turbine blades is that the high-speed and high-frequency impact of raindrops eventually causes fatigue damage on the coating surface, which manifests itself as erosion and wear. The rate of blade wear is closely related to the rainfall intensity in addition to the performance of the material itself.

The rainfall hourly data of a wind farm in Qingzhou from 2018 to 2022 were used to analyze the severity of the rain erosion environment. The hourly distribution and cumulative time of rainfall of different intensities in the wind farm from 2018 to 2022 are shown in Table 1. It can be seen that 0-1mm/h (light rain-moderate rain) has an average annual accumulation of 659 hours; 1-2mm (moderate rain-heavy rain) has an average annual accumulation of 272 hours; 2-5mm (heavy rain to torrential rain) has an average annual accumulation of 131 hours; 5-10mm (torrential rain to heavy rain) has an average annual accumulation of 35 hours; 10-20mm (heavy rain to extremely heavy rain) has an average annual accumulation of 8.8 hours.

(Table 1)

S2, calculate the raindrop particle size under different rainfall intensities and the maximum terminal falling speed of raindrops with different particle sizes;

The relationship between the raindrop size D and the rainfall intensity R is established through the following formula, and the raindrop size corresponding to each rainfall intensity is calculated:

For light/moderate rainfall intensity (≤1 mm/h) in the global range, the corresponding raindrop size is calculated using formula (1):

Formula 1)

For moderate/heavy/torrential rainfall intensities (1<R<5mm/h) in low-latitude areas, the corresponding raindrop size is calculated using formula (2):

Formula (2)

For heavy rain/extremely heavy rain intensities (≧5 mm/h) in low-latitude areas, the corresponding raindrop size is calculated using formula (3):

Formula (3)

Optionally, for each type of rainfall intensity (>1 mm/h) in mid- and high-latitude regions, the corresponding raindrop size is calculated using formula (4):

Formula (4)

In this embodiment, the Qingzhou wind farm belongs to a low-latitude area, so the raindrop particle sizes corresponding to different rainfall intensities are calculated according to formulas (1) to (3). The calculation results are shown in Table 2:

Rainfall intensity (mm/h) Raindrop size (mm) 1 1.3 2 1.6 5 1.8 10 1.9 20 2.1 50 2.4

(Table 2)

Then, according to the raindrop particle size, the maximum terminal falling speed of raindrops of different particle sizes under different rainfall intensities is calculated. When a raindrop falls from a high altitude, it will be affected by the surrounding flow field. The speed at which it falls from a high altitude to the stable moment is the maximum terminal falling speed of the raindrop, and its expression is:

Formula (5)

In this embodiment, the relationship between the raindrop particle size and the maximum terminal falling speed is constructed by combining the raindrop particle size corresponding to different rainfall intensities in the Qingzhou wind farm. The results are shown in Table 3:

Rainfall intensity (mm/h) Raindrop size (mm) Falling speed(m/s) 1 1.3 4.6 2 1.6 5.2 5 1.8 5.6 10 1.9 5.7 20 2.1 6.1

(table 3)

S3. Carry out rain erosion resistance test on the wind turbine blade leading edge coating samples, calculate the impact velocity of raindrops on each position of the wind turbine blade leading edge coating samples, and construct the impact frequency model and raindrop kinetic energy impact model of the wind turbine blade leading edge coating samples in the rain field;

The life of the leading edge coating of wind turbine blades is not only related to the service environment, but also to the performance of the material itself. Taking the leading edge coating samples of blades as the objects, a cantilever rain erosion test is carried out using cantilever rain erosion equipment to obtain the impact resistance parameters of the leading edge coating of the blades. The impact resistance parameters are the positions and corresponding times where wear occurs from the outside to the inside of the leading edge coating samples. The impact speed of raindrops on each position of the leading edge coating samples is calculated.

In this example, a composite material polyurethane coating plate sample was prepared, and a rain erosion test was carried out using a cantilever rain erosion device. The test conditions were set as follows: rainfall intensity of 34 mm/h, water droplet diameter of 2 mm, and impact velocity from the near end to the far end of the sample of 110-150 m/s. The test time was 6 hours, the sample surface was checked once an hour, and the data such as the coating damage extension position was measured. The rain erosion test results are shown in Figure 1.

As shown in Figure 1, the time for the front edge erosion to occur increases step by step from the outside to the inside of the sample. Assuming that the outermost side is 0 mm, the rotation speed is 150 m/s, and the innermost side is 250 mm, the rotation speed is about 110 m/s. After 1 hour of rain erosion test, the sample at 60 mm has cumulative impact damage, and the rotation speed is 141 m/s; after 2 hours of rain erosion test, the sample at 129 mm has cumulative impact damage, and the rotation speed is 131 m/s; after 3 hours of rain erosion test, the sample at 155 mm has cumulative impact damage, and the rotation speed is 127 m/s; after 4 hours of rain erosion test, the sample at 172 mm has cumulative impact damage, and the rotation speed is 124 m/s; after 5 hours of rain erosion test, the sample at 198 mm has cumulative impact damage, and the rotation speed is 120 m/s; after 6 hours of rain erosion test, the sample at 232 mm has cumulative impact damage, and the rotation speed is 115 m/s.

During the rain erosion resistance test, it is assumed that the water droplets in the rain field are spherical and have the same particle size, that is, the water droplets fall at the same speed, and the impact frequency model of the object in the rain field and the raindrop kinetic energy impact model are constructed.

In this embodiment, when the blade passes through the rain field, the collision between the surface and the raindrops occurs randomly. Assuming that the raindrops in the rain field have the same particle size and are evenly distributed, and the falling speed of the raindrops is much smaller than the running speed of the blade, they are ignored.

Substitute the data results of the rain erosion test in Table 4 (input parameters), rainfall intensity R of 34 mm/h, raindrop diameter D of 2 mm, raindrop terminal velocity v _r of about 6 m/s, blade travel speed in the rain field v _t (110-150 m/s), and the time t at which damage occurs at each position of the sample into the following formula to obtain the impact frequency per unit area F, the cumulative number of impacts per unit area _NEi , and the energy of a single impact E _k :

Formula (6)

Formula (7)

Formula (8)

The calculation results are shown in Table 4 (output results). As can be seen from Table 4, only 18,890 water droplets at a speed of 141 m/s are needed to hit the coating at 60 mm on the outside of the sample for the coating to be damaged, and 91,500 water droplets at a speed of 115 m/s are needed to hit the coating at 232 mm on the inside for the coating to be damaged.

(Table 4)

S4. Establish the relationship between the cumulative impact times per unit area of the wind turbine blade leading edge coating sample and the impact velocity and raindrop particle size;

Combining formula (8), the relationship between the cumulative number of impacts per unit area of the wind turbine blade leading edge coating sample and the impact velocity and raindrop particle size is obtained, that is, the rain erosion fatigue damage accumulation model:

Formula (9)

Where c and m are constants related to the leading edge material properties.

In this implementation case, the impact kinetic energy and the cumulative number of impacts are fitted with a power function to obtain the coefficients c and m of the rain erosion fatigue damage accumulation model (Formula 9).

In this embodiment, FIG. 2 shows the relationship between the number of impacts per unit area that cause coating failure and the energy of a single impact. The rain erosion fatigue damage accumulation model coefficient c is 0.416 and m is 3.42 obtained by power function fitting.

Using the data in Table 4 as the model input data, the relationship between the cumulative number of impacts per unit area and the impact velocity when the raindrop size is 1.5 and 2.5 mm is further calculated. The results are shown in Figure 3. That is, when the raindrop size increases, the curve shifts to the left, indicating that the larger the raindrop, the fewer the number of impacts per unit area required to cause the same level of damage; when the raindrop size decreases, the curve shifts to the right, indicating that the smaller the raindrop, the more the number of impacts per unit area required to cause the same level of damage. Furthermore, the relationship between the cumulative number of impacts per unit area and the cumulative rainfall is obtained by combining the relationship between rainfall intensity and raindrop size (Formulas 1 to 3). The results are shown in Figure 4.

S5. The service life of the leading edge coating of the wind turbine blade is predicted by combining the data obtained in steps S1 to S4 with a linear fatigue cumulative damage criterion based on continuous changes in environmental loads.

In the actual service environment of wind turbine blades, wind speed, rainfall intensity, raindrop size and impact velocity are all non-constant values. Based on the palmgren-miner rule linear fatigue cumulative damage criterion, combined with the environmental data of steps S1~S4 and various models, the fatigue damage life of the leading edge coating of wind turbine blades is predicted.

The specific process is as follows: First, the cumulative time of the annual average hours of different rainfall intensities given in Table 1 is used as the rainfall time input of the model, and the droplet size is determined by using the correlation between the raindrop size and the rainfall intensity (Table 2). The maximum terminal droplet falling velocity is determined by using the relationship between the raindrop size and the raindrop falling velocity (Table 3). The droplet size parameters under different actual rainfall intensities are input into the raindrop kinetic energy impact model. The specific parameters are shown in Table 4. Combined with the Palmgren-Miner law (Formula 11), the linear fatigue cumulative damage criterion based on the continuous change of load is used to predict the service life of the blade leading edge coating:

Formula (10)

Where i is the load level number, ni is the number of cycles at load level i, Ni is the number of cycles when failure occurs at load level i during the test, and j is the load level number.

When M≧1, the material that has undergone fatigue cycles has reached the expected fatigue life and is judged to have failed.

The model input parameters and prediction results are shown in Table 5. The leading edge life of the blade is about 1.5 years.

(table 5)

Example 2

The present invention provides a method for predicting the life of a wind turbine blade leading edge coating based on rain erosion fatigue damage. This embodiment takes a wind turbine in service in a wind farm in Nantong as an example to predict the fatigue damage of the blade leading edge coating, and specifically includes the following steps:

S1. Count the hourly distribution data of different rainfall intensities according to the service environment of wind turbine blades in wind farms;

The rainfall hourly data of a wind farm in Nantong from 2018 to 2022 were used to analyze the severity of the rain erosion environment. The hourly distribution and cumulative time of rainfall of different intensities in the wind farm from 2018 to 2022 are shown in Table 6. It can be seen from Table 6 that the total rainfall in the area from 2018 to 2022 is 1123, 874, 1289, 1205, and 813 mm, respectively, and the annual average rainfall is about 1061 mm, of which 0-1 mm/h (light rain-moderate rain) has an average annual accumulation of 733 hours; 1-2 mm (moderate rain-heavy rain) has an average annual accumulation of 209 hours; 2-5 mm (heavy rain to torrential rain) has an average annual accumulation of 103 hours; 5-10 mm (torrential rain to heavy rain) has an average annual accumulation of 15 hours; 10-20 mm (heavy rain to extremely heavy rain) has an average annual accumulation of 0.4 hours.

(Table 6)

S2, calculate the raindrop particle size under different rainfall intensities and the maximum terminal falling speed of raindrops with different particle sizes;

By using formulas (1) to (4) (same as in Example 1), the relationship between the raindrop particle size Dm and the rainfall intensity R is established, and the raindrop particle size corresponding to each rainfall intensity is calculated.

The wind farm in the south of this embodiment belongs to the mid-latitude region, so the raindrop particle size corresponding to the rainfall intensity can be calculated according to formulas (1) and (4), and the results are shown in Table 7;

Rainfall intensity (mm/h) Raindrop size (mm) 1 1.3 2 1.5 5 1.7 10 2.0 20 2.2 50 2.6

(Table 7)

The relationship between the falling speed of raindrops and the size of raindrops is established, and the size of raindrops is converted into the falling speed of raindrops.

According to the raindrop particle size, calculate the maximum terminal falling speed of raindrops of different particle sizes under different rainfall intensities :

Formula (5)

In this embodiment, the relationship between the raindrop particle size and the maximum terminal falling speed is combined with the raindrop particle size corresponding to different rainfall intensities in the Nantong wind farm to construct the relationship between the rainfall intensity and the falling speed. The results are shown in Table 8:

Rainfall intensity (mm/h) Raindrop size (mm) Falling speed(m/s) 1 1.3 4.8 2 1.5 5.4 5 1.7 5.9 10 2.0 6.5 20 2.2 6.9

(Table 8)

S3. Carry out rain erosion resistance test on the wind turbine blade leading edge coating samples, calculate the impact velocity of raindrops on each position of the wind turbine blade leading edge coating samples, and construct the impact frequency model and raindrop kinetic energy impact model of the wind turbine blade leading edge coating samples in the rain field;

The life of the blade leading edge coating is not only related to the service environment, but also to the performance of the material itself. Taking the blade leading edge coating samples as the objects, a cantilever rain erosion equipment was used to carry out rain erosion resistance tests to obtain the impact resistance performance parameters of the blade leading edge coating.

In this example, a composite material high-performance polyurethane coating plate sample was prepared, and a rain erosion test was carried out using a cantilever rain erosion device. The test conditions were set as follows: rainfall intensity of 34 mm/h, water droplet diameter of 2 mm, and impact speed from the near end to the far end of the sample of 110-150 m/s. The test time was 6 hours, the sample surface was checked once an hour, and the data such as the coating damage extension position was measured. The rain erosion test results are shown in Figure 5 and Table 9.

As shown in Figure 5, the time for the front edge erosion to occur increases step by step from the outside of the sample to the inside of the sample. Assuming that the outermost side is 0mm, the rotation speed is 150m/s, and the innermost side is 250mm, the rotation speed is about 110m/s. After 2 hours of rain erosion test, the sample at 12mm has cumulative impact damage, and the rotation speed is 148m/s; after 3 hours of rain erosion test, the sample at 42mm has cumulative impact damage, and the rotation speed is 143m/s; after 4 hours of rain erosion test, the sample at 72mm has cumulative impact damage, and the rotation speed is 138m/s; after 5 hours of rain erosion test, the sample at 86mm has cumulative impact damage, and the rotation speed is 136m/s; after 6 hours of rain erosion test, the sample at 99mm has cumulative impact damage, and the rotation speed is 134m/s.

Test time Erosion front length Accumulated rainfall Impact speed t(h) (mm) (mm) v _t (m/s) 2 12 74 145 3 42 114 140 4 72 154 138 5 86 194 136 6 99 234 134

(Table 9)

The data results obtained from the rain erosion test in Table 9 (input parameters) and the rainfall intensity R is 34 mm/h, the raindrop particle size D is 2 mm, the raindrop terminal velocity v _r is about 6 m/s, the blade travel speed in the rain field is v _t (110~150 m/s), and the time t when the damage occurs at each position of the sample is substituted into formula (6)~formula (8) (the same as Example 1) to obtain the impact frequency per unit area F, the cumulative number of impacts per unit area _NEi and the energy of a single impact _Ek .

The calculation results are shown in Table 10 (output results). It can be seen from Table 10 that at 12 mm outside the sample, 39,140 water droplets at a speed of 148 m/s are needed to impact the coating before it is damaged, while at 99 mm, 108,500 water droplets at a speed of 134 m/s are needed to impact the coating before it is damaged.

Impact kinetic energy Impact frequency per unit area Impact times/unit area E _k (J) F(cm ^-2 s ^-1 ) N _Ei (cm ^-2 ) 0.044 5.44 3.914E+04 0.041 5.25 5.668E+04 0.040 5.17 7.449E+04 0.039 5.10 9.177E+04 0.038 5.02 1.085E+05

(Table 10)

S4. Establish the relationship between the cumulative number of impacts per unit area of the wind turbine blade leading edge coating sample and the impact velocity and raindrop particle size.

Combining formula (8), the relationship between the cumulative number of impacts per unit area of the wind turbine blade leading edge coating sample and the impact velocity and raindrop particle size is obtained, that is, the rain erosion fatigue damage accumulation model:

Formula (9)

Where c and m are constants related to the leading edge material properties.

In this embodiment, FIG6 shows the relationship between the number of impacts per unit area that cause coating failure and the energy of a single impact. The rain erosion fatigue damage accumulation model coefficient c is 2.3*10 ^-6 and m is 7.516 obtained by power function fitting.

Using the data in Tables 9 and 10 as model input data, the relationship between the cumulative number of impacts per unit area and the impact velocity when the raindrop diameter is 1.5 and 2.5 mm is calculated. The results are shown in Figure 7. That is, when the raindrop size increases, the curve shifts to the left, indicating that the larger the raindrop, the fewer impacts per unit area are required to cause the same level of damage; when the raindrop becomes smaller, the curve shifts to the right, indicating that the smaller the raindrop, the more impacts per unit area are required to cause the same level of damage. In addition, the model also extends the area outside the impact velocity of 134~145m/s. The overall result is that the larger the impact velocity, the fewer impacts per unit area are required.

Furthermore, the relationship between the cumulative number of impacts per unit area and the cumulative rainfall is obtained by combining the relationship between rainfall intensity and raindrop size (Formulas 1 and 4), and the results are shown in Figure 8.

S5. The service life of the leading edge coating of the wind turbine blade is predicted by combining the data obtained in steps S1 to S4 with a linear fatigue cumulative damage criterion based on continuous changes in environmental loads.

The specific process is as follows: First, the cumulative time of the annual average hours of different rainfall intensities given in Table 6 is used as the rainfall time input of the model, and the droplet size is determined by using the correlation between the raindrop size and the rainfall intensity (Table 7). The maximum velocity of the droplet end is determined by using the relationship between the raindrop size and the raindrop falling speed (Table 8). The droplet size parameters under actual different rainfall intensities are input into the raindrop kinetic energy impact model. The specific parameters and results are shown in Tables 9 and 10. Based on the S-N curve fitting formula 10, combined with the Palmgren-Miner law (Formula 11), the service life of the blade leading edge coating is predicted.

Formula (10)

Where i is the load level number, ni is the number of cycles at load level i, Ni is the number of cycles when failure occurs at load level i during the test, and j is the load level number.

When M≧1, the material that has undergone fatigue cycles has reached the expected fatigue life and is judged to have failed.

The model input parameters and prediction results are shown in Table 11. The leading edge life of the blade is about 3.8 years.

(Table 11)

The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present invention. Therefore, all equivalent modifications or changes made by a person with ordinary knowledge in the technical field without departing from the spirit and technical ideas disclosed by the present invention shall still be covered by the claims of the present invention.

Claims (9)

1. A method for predicting the life of a wind turbine blade leading edge coating based on rain erosion fatigue damage, characterized by comprising the following steps: S1. Count the hourly distribution data of different rainfall intensities according to the service environment of wind turbine blades in wind farms; S2, calculate the raindrop particle size under different rainfall intensities and the maximum terminal falling speed of raindrops with different particle sizes; S3. Take the wind turbine blade leading edge coating samples as the object, carry out rain erosion resistance test on them, calculate the impact velocity of raindrops on each position of the leading edge coating samples, and construct the impact frequency model and raindrop kinetic energy impact model of the leading edge coating samples in the rain field; S4. Establish the relationship between the cumulative number of impacts per unit area of the wind turbine blade leading edge coating sample and the impact velocity and raindrop particle size; S5. The service life of the leading edge coating of the wind turbine blade is predicted by combining the data obtained in steps S1 to S4 with a linear fatigue cumulative damage criterion based on continuous changes in environmental loads. 2. The method for predicting the life of the leading edge coating of a wind turbine blade based on rain erosion fatigue damage according to claim 1 is characterized in that: in step S2, for the global light/moderate rain intensity, that is, the rainfall intensity ≦1mm/h, calculate the corresponding raindrop diameter D: Formula 1) For moderate/heavy/torrential rainfall in low latitudes, that is, rainfall intensity 1<R<5mm/h, calculate the corresponding raindrop diameter D: Formula (2) For heavy rain/extremely heavy rain in low latitudes, the rainfall intensity is >5mm/h, calculate the corresponding raindrop diameter D: Formula (3) For various types of rainfall intensity in mid- and high-latitude regions, that is, rainfall intensity >1mm/h, calculate the corresponding raindrop diameter D: Formula (4) Then, according to the raindrop particle size, the maximum terminal falling speed of raindrops of different particle sizes under different rainfall intensities is calculated. : Formula (5). 3. The method for predicting the life of the leading edge coating of a wind turbine blade based on rain erosion fatigue damage according to claim 2 is characterized in that: in the step S3, the impact velocity at each position is calculated according to the position and corresponding time where the wear of the leading edge coating sample of the wind turbine blade occurs from the outside to the inside. 4. The method for predicting the life of the leading edge coating of a wind turbine blade based on rain erosion fatigue damage according to claim 3 is characterized in that: in the step S3, it is assumed that the water droplets in the rain field are spherical and have a uniform particle size and are evenly distributed, and the number of impacts F occurring per unit area per unit time is: Formula (6) That is the impact frequency model, where _Vt is the speed of the wind turbine blade in the rain field; The cumulative number of impacts per unit area N _Ei is: Formula (7) Where, t is time, s; The energy E _k of a single impact is: Formula (8) This is the raindrop kinetic energy impact model, where: is the density of water. 5. The method for predicting the life of the leading edge coating of a wind turbine blade based on rain erosion fatigue damage according to claim 4 is characterized in that: in the step S4, the relationship between the cumulative number of impacts per unit area of the leading edge coating sample of the wind turbine blade and the impact velocity and raindrop particle size is obtained in combination with formula (8), that is, the rain erosion fatigue damage accumulation model: Formula (9) Where c and m are constants related to the leading edge material properties; _E0 is the unit energy, which is 1. 6. The method for predicting the life of the leading edge coating of a wind turbine blade based on rain erosion fatigue damage according to claim 5 is characterized in that: in the step S4, the relationship between the cumulative number of impacts per unit area and the cumulative rainfall is obtained according to the relationship between the rainfall intensity and the raindrop particle size. 7. The method for predicting the life of the leading edge coating of a wind turbine blade based on rain erosion fatigue damage according to claim 6 is characterized in that: in the step S5, based on the linear fatigue cumulative damage criterion of the continuous change of the environmental load: Formula (10) Where i is the load level number, ni is the number of cycles at load level i, Ni is the number of cycles when failure occurs at load level i during the test, and j is the load level number; When M≧1, the material that has undergone fatigue cycles has reached the expected fatigue life and is judged to have failed. 8. The method for predicting the life of the leading edge coating of a wind turbine blade based on rain erosion fatigue damage according to claim 7 is characterized in that: in the step S1, hourly distribution data of different rainfall intensities in at least the past three years are statistically analyzed. 9. The method for predicting the life of the leading edge coating of a wind turbine blade based on rain erosion fatigue damage according to claim 8 is characterized in that: in the step S1, hourly distribution data of different rainfall intensities over the past five years or more are statistically analyzed.