CN117993229A - Wind power blade leading edge coating life prediction method based on rain erosion fatigue damage - Google Patents
Wind power blade leading edge coating life prediction method based on rain erosion fatigue damage Download PDFInfo
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- 239000011248 coating agent Substances 0.000 title claims abstract description 83
- 238000000576 coating method Methods 0.000 title claims abstract description 83
- 230000003628 erosive effect Effects 0.000 title claims abstract description 78
- 238000000034 method Methods 0.000 title claims abstract description 39
- 239000002245 particle Substances 0.000 claims abstract description 90
- 238000012360 testing method Methods 0.000 claims abstract description 35
- 238000009825 accumulation Methods 0.000 claims abstract description 19
- 230000007613 environmental effect Effects 0.000 claims abstract description 9
- 238000009826 distribution Methods 0.000 claims abstract description 8
- 230000008859 change Effects 0.000 claims abstract description 7
- 230000001186 cumulative effect Effects 0.000 claims description 25
- 239000000463 material Substances 0.000 claims description 12
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 11
- 230000008569 process Effects 0.000 claims description 8
- 238000005299 abrasion Methods 0.000 claims description 7
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 claims description 4
- 238000010248 power generation Methods 0.000 claims description 4
- 238000012423 maintenance Methods 0.000 abstract description 5
- 239000011247 coating layer Substances 0.000 description 5
- 230000007774 longterm Effects 0.000 description 5
- 238000009423 ventilation Methods 0.000 description 4
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 239000002131 composite material Substances 0.000 description 2
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- 230000006872 improvement Effects 0.000 description 2
- 230000000977 initiatory effect Effects 0.000 description 2
- 229920002635 polyurethane Polymers 0.000 description 2
- 239000004814 polyurethane Substances 0.000 description 2
- 230000007480 spreading Effects 0.000 description 2
- 230000002844 continuous effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
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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
Technical Field
The invention relates to a wind power blade leading edge coating life prediction method based on rain erosion fatigue damage.
Background
The development of onshore and offshore wind power is facing various technical innovations and challenges such as unit enlargement, cost reduction and efficiency improvement. Along with the continuous increase of the single-machine capacity of the wind turbine, the blade has become a necessary trend in large size, but the reliability of the wind turbine is more and more difficult to ensure, and the failure accident of the blade is increased.
Severe service environments are one of the main causes of blade failure, and blade leading edge erosion is one of the common forms of environmental damage, which severely causes blade failure. Especially when large-scale wind-powered electricity generation blade is in the long-term operation in the more regional long-term of rainwater, receive continuous effect such as rainwater erosion, the pit phenomenon can appear on 2~3 years blade leading edge surface, if not in time maintain will further lead to damage accumulation extension such as chisel hole, base member are naked, the serious layering of condition takes place to form the crack in the junction, seriously influences blade structural stability and dynamic performance, finally causes the blade to become invalid. Meanwhile, the accumulation of damage on the surface of the blade and the increase of roughness increase the aerodynamic drag coefficient of the blade, and the aerodynamic drag coefficient has a larger influence on the power generation efficiency of the fan, and researches show that the serious blade front edge erosion causes the loss of the adult power generation capacity to reach 20 percent. Therefore, the development of wind power blade front edge accumulated damage prediction has important theoretical significance and practical value for reducing the blade maintenance cost and improving the safety of the unit, and simultaneously has obvious social and economic benefits.
Disclosure of Invention
The invention aims to provide a wind power blade front edge coating life prediction method based on rain erosion fatigue damage, which has high accuracy, reduces the maintenance cost of wind power blades and improves the safety of a unit.
The aim of the invention is achieved by the following technical measures: a wind power blade leading edge coating life prediction method based on rain erosion fatigue damage is characterized by comprising the following steps:
s1, counting distribution data of different rainfall intensities in time 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, taking a wind power blade front edge coating sample as an object, carrying out a rain erosion resistance test on the wind power blade front edge coating sample, calculating the impact speed of raindrops on each position of the front edge coating sample, and constructing an impact frequency model and a raindrop kinetic energy impact model of the wind front edge coating 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 wind power blade tip front edge rain erosion fatigue damage forming rate is related to factors such as rain drop volume, impact frequency, impact speed, material performance and the like, and the wind power blade tip front edge rain erosion fatigue damage forming rate is based on actual service environment data of wind power blades of a wind farm; secondly, establishing an association relation between the particle size of raindrops under different rainfall intensities and the rainfall intensities; then carrying out a rain erosion resistance test on the front edge coating of the blade to obtain impact resistance parameters of the front edge coating of the blade; then, constructing an impact frequency model and a raindrop kinetic energy impact model of the front edge coating of the blade in a rain field, and then constructing a correlation between accumulated impact times and impact speed in unit area and the particle size of raindrops; finally, based on palmgren-miner rule linear fatigue accumulation damage criteria, the fatigue damage life of the blade front edge coating under the condition that wind speed, rainfall intensity, raindrop particle size and impact speed in an actual environment are all non-fixed values is predicted by combining blade service environment data and various models.
The method can accurately predict the service life of the wind power blade front edge coating, is suitable for predicting the rain erosion damage caused by erosion and abrasion due to frequent high-speed impact between the front edge of the blade tip and rain drops in the long-term service process of the wind power blade at a certain place, can reduce the maintenance cost of the blade, improves the safety of a unit, and has obvious economic and social benefits.
In the step S2, the invention changes the rainfall intensity of small/medium rain, namely the rainfall intensityThe particle size of the raindrops corresponding to the raindrops is calculated when the particle size is smaller than or equal to 1mm/h：
Formula (1)
For medium/heavy rain intensity in low latitude areas, i.e. intensity of rainfallThe method comprises the following steps: 1< R <5mm/h, and calculating the particle size of the raindrops corresponding to the particle size：
Formula (2)
For heavy/extra heavy rain in low latitude areas, i.e. intensity of rainfallCalculating the particle size of raindrops corresponding to 5mm/h：
Formula (3)
For various rainfall intensity in middle and high latitude areas, namely rainfall intensity1Mm/h, and calculating the particle size of raindrops corresponding to the particle size：
Formula (4)
According to the particle size of the raindrops, calculating the maximum terminal falling speed of the raindrops with different particle sizes under different rainfall intensities：
Formula (5)
In the step S3, according to the positions and the corresponding time of abrasion of the wind power blade front edge coating samples from outside to inside, the impact speed of each position is calculated.
In the step S3, the water drops in the rain field are assumed to be spherical and have uniform particle diameters, and the number of impact times F occurring in unit area per unit time is:
Formula (6)
The wind power blade wind power generation system is an impact frequency model, wherein V t is the passing speed of the wind power blade in a rain field;
the cumulative impact number per unit area N Ei is:
Formula (7)
Wherein t is time, s;
The energy E k of a single impact is:
Formula (8)
The model is the raindrop kinetic energy impact model.
In the step S4, a relation between the accumulated impact times and the impact speed of a unit area of a wind power blade front edge coating sample and the particle size of raindrops, namely a rain erosion fatigue damage accumulation model, is obtained by combining a formula (8):
Formula (9)
Where c and m are constants related to the properties of the front material.
In the step S4, the invention obtains the relationship between the accumulated impact times and the accumulated rainfall in the unit area according to the relationship between the rainfall intensity and the particle size of the raindrops.
In the step S5, the present invention is based on the linear fatigue cumulative damage criterion that the environmental load continuously changes:
Formula (10)
Wherein i is the load grade number, ni is the number of cycles when i is the load grade, ni is the number of cycles when failure occurs under the load grade in the test process, and j is the number of load grade;
when M.gtoreq.1, the material subjected to the fatigue cycle reaches the expected fatigue life, and is judged to be failed.
In the step S1, the invention counts the distribution data of different rainfall intensity hours for at least about 3 years, and preferably, the invention performs statistical analysis on the rainfall environment of a certain wind field for about 5 years and more.
In the step S1, the invention counts the distribution data of different rainfall intensities over more than 5 years.
Compared with the prior art, the invention has the following remarkable effects:
The method can accurately predict the service life of the wind power blade front edge coating, is suitable for predicting the rain erosion damage caused by erosion and abrasion due to frequent high-speed impact between the front edge of the blade tip and rain drops in the long-term service process of the wind power blade at a certain place, can reduce the maintenance cost of the blade, improves the safety of a unit, and has obvious economic and social benefits.
Drawings
The invention will now be described in further detail with reference to the drawings and to specific examples.
FIG. 1 is a graph of the relationship between the occurrence time of leading edge erosion and the impact velocity in example 1 of the present invention;
FIG. 2 is a graph of impact number per unit area versus single impact energy for initiating a coating failure in example 1 of the present invention;
FIG. 3 is a graph showing the S-N curve for different raindrop particle sizes in example 1 of the present invention;
FIG. 4 is a graph of impact velocity versus expected cumulative rainfall that causes coating failure for different rain drop particle sizes in example 1 of the present invention;
FIG. 5 is a graph of the relationship between the occurrence time of leading edge erosion and the impact velocity in example 2 of the present invention;
FIG. 6 is a graph of impact number per unit area versus single impact energy for initiating a coating failure in example 2 of the present invention;
FIG. 7 is a graph showing the S-N curve for different raindrop particle sizes in example 2 of the present invention;
FIG. 8 is a graph of impact velocity versus expected cumulative rainfall that causes coating failure for different rain drop particle sizes in example 2 of the present invention.
Detailed Description
The invention is further illustrated by the following description of specific embodiments, which are not intended to be limiting, and various modifications or improvements can be made by those skilled in the art in light of the basic idea of the invention, but are within the scope of the invention as long as they do not depart from the basic idea of the invention.
Example 1
The wind power blade front edge coating life prediction method based on rain erosion fatigue damage is suitable for rain erosion damage prediction of erosion wear caused by frequent high-speed impact of the front edge of a blade tip and raindrops in a long-term service process of a wind power blade at a certain place. In the embodiment, fatigue damage prediction of the wind power blade front edge coating is performed by taking a Qingzhou wind field as an example, and the method specifically comprises the following steps of:
s1, counting distribution data of different rainfall intensities in time aiming at the service environment of wind power blades in a wind power plant;
The main reason of rain erosion failure of the wind power blade front edge coating is that fatigue damage is finally formed on the surface of the coating by high-speed high-frequency impact of raindrops, the performance form is erosion abrasion, and the rate of blade abrasion is closely related to rainfall intensity besides the performance of the material of the blade.
The rainfall hours data of a certain wind field in Qingzhou is used for carrying out the analysis of the severity of the rain erosion environment, wherein the rainfall hours of different intensities of the wind field in 2018-2022 are distributed and accumulated in the same time as shown in a table 1, so that 0-1mm/h (small rain-medium rain) can be obtained, and the annual average accumulation is 659 hours; 1-2mm (medium rain-heavy rain), and the annual average accumulation lasts 272 hours; 2-5mm (heavy rain to heavy rain), and the annual accumulation time is 131 hours; 5-10mm (heavy rain to heavy rain) and is accumulated for 35 hours in each year; 10-20mm (heavy storm to extra heavy storm) and 8.8 hours of annual accumulation.
(Table 1)
S2, calculating the particle sizes of raindrops under different rainfall intensities and the maximum terminal dropping speeds of the raindrops with different particle sizes;
the relation between the rainfall raindrop particle size D and the rainfall intensity R is established through the following formula, and the raindrop particle size corresponding to each rainfall intensity is calculated:
For the global small/medium rain intensity (+.1mm/h), the corresponding raindrop particle size is calculated using equation (1):
Formula (1)
For medium/large/heavy rain rainfall intensity (1 < R <5 mm/h) in low latitude areas, calculating the corresponding raindrop particle size by adopting a formula (2):
Formula (2)
For the rainfall intensity (> 5 mm/h) of heavy rain/extra heavy rain in low latitude areas, the corresponding raindrop particle size is calculated by adopting the formula (3):
Formula (3)
Optionally, for each type of rainfall intensity (> 1 mm/h) in the middle-high latitude area, calculating the corresponding raindrop particle size by adopting a formula (4):
Formula (4)
In this embodiment, the Qingzhou wind farm belongs to a low latitude area, so that the particle sizes of raindrops corresponding to different rainfall intensities are calculated according to formulas (1) - (3), and the calculation results are shown in table 2:
| rainfall intensity (mm/h) | Raindrop particle diameter (mm) |
| 1 | 1.3 |
| 2 | 1.6 |
| 5 | 1.8 |
| 10 | 1.9 |
| 20 | 2.1 |
| 50 | 2.4 |
(Table 2)
According to the particle size of the raindrops, calculating the maximum terminal falling speed of the raindrops with different particle sizes under different rainfall intensitiesWhen the raindrops fall from the high altitude, the raindrops can be acted by the acting force of surrounding flow fields, the falling speed from the high altitude to the stable moment is the maximum terminal falling speed of the raindrops, and the expression is as follows:
Formula (5)
In this embodiment, through the relationship between the raindrop particle size and the maximum end drop velocity, the relationship between different rainfall intensities and the maximum end drop velocity is constructed by combining the raindrop particle sizes corresponding to different rainfall intensities of the Qingzhou wind field, and the results are shown in table 3:
| rainfall intensity (mm/h) | Raindrop particle diameter (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, taking a wind power blade front edge coating sample as an object, carrying out a rain erosion resistance test on the wind power blade front edge coating sample, calculating impact speeds of raindrops on all positions of the wind power blade front edge coating sample, and constructing an impact frequency model and a raindrop kinetic energy impact model of the wind power blade front edge coating sample in a rain field;
The service life of the wind power blade front edge coating is related to the service environment and the performance of the material, a cantilever type rain erosion device is adopted to carry out a rain erosion test by taking a blade front edge coating sample as an object, the impact resistance parameter of the blade front edge coating is obtained, the impact resistance parameter is the position and the corresponding time of the front edge coating sample from outside to inside when abrasion occurs, and the impact speed of raindrops on each position of the front edge coating sample is calculated.
In this embodiment, a composite polyurethane coated board sample was prepared, a rain erosion resistance test was performed using a cantilever type rain erosion device, and test conditions were set: the rainfall intensity is 34 mm/h, the particle size of the water drops is 2 mm, and the impact speed is 110-150 m/s from the near end to the far end of the sample. The test time was 6 hours, the sample surface was inspected once per hour, and the data such as the coating breakage spreading position was measured, and the rain erosion test results are shown in fig. 1.
From fig. 1, it can be seen that the time at which the leading edge erosion occurs increases stepwise from the outside of the sample to the inside of the sample. The outermost side is assumed to be 0mm, the rotating speed is 150 m/s, the innermost side is assumed to be 250mm, and the rotating speed is about 110 m/s. After the rain erosion test is carried out for 1h, the accumulated impact damage occurs on a 60mm sample, and the rotating speed of the sample is 141 m/s; after the rain erosion test 2h, the sample at the position of 129mm has accumulated impact damage, and the rotating speed at the position is 131 m/s; after the rain erosion test 3h, the sample at the position of 155mm has accumulated impact damage, and the rotating speed at the position is 127 m/s; after the rain erosion test 4h, the sample at the position of 172mm has accumulated impact damage, and the rotating speed at the position is 124 m/s; after the rain erosion test 5 h, the sample at the position of 198mm has accumulated impact damage, and the rotating speed at the position is 120 m/s; after the rain erosion test 6 h, the sample at 232mm had accumulated impact damage, at a rotational speed of 115 m/s.
In the rain erosion resistance test process, the water drops in the rain field are assumed to be spherical, the particle sizes are consistent, namely the drop speeds of the water drops are the same, and an impact frequency model and a raindrop kinetic energy impact model of an object in the rain field are constructed.
In this embodiment, collisions of the surface with rain drops occur randomly as the blade travels in the rain field. The rain drops in the rain field are assumed to have the same particle size and uniform distribution, and the falling speed of the rain drops is far smaller than the running speed of the blades, so that neglected treatment is carried out.
The data obtained by the rain erosion resistance test are shown in a table 4 (input parameters) and the rainfall intensity R is 34mm/h, the particle size D of the rain drops is 2mm, the tail speed v r of the rain drops is about 6m/s, the passing speed of the blades in a rain field is v t (110-150 m/s), the time t when the damage occurs at each position of a sample is substituted into the following formula, and the impact frequency F of unit area, the accumulated impact frequency N Ei of unit area and the energy E k of single impact are obtained:
Formula (6)
Formula (7)
Formula (8)
The calculation results are shown in table 4 (output result). As is clear from Table 4, the coating layer was broken only by the 141m/s water drops of 18890 times at the outer 60mm of the sample, and the coating layer was broken only by the 115m/s water drops of 91500 times at the inner 232 mm.
(Table 4)
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;
And (3) combining the formula (8) to obtain the relation between the cumulative impact times and the impact speed of the unit area of the wind power blade front edge coating sample and the particle size of the raindrops, namely, a rain erosion fatigue damage cumulative model:
Formula (9)
Where c and m are constants related to the properties of the front material.
In the present embodiment, the impact kinetic energy and the accumulated impact times are subjected to power function fitting, and coefficients c and m of the rain erosion fatigue damage accumulation model (formula 9) are obtained.
In this example, fig. 2 shows the relationship between the number of impacts that cause the failure of the coating layer per unit area and the single impact energy, and the cumulative model coefficient c of rain erosion fatigue damage is 0.416 and m is 3.42 obtained by fitting a power function.
The data in Table 4 is used as model input data, and the relation between the cumulative impact times and the impact speed in unit area when the particle size of the raindrops is 1.5 and 2.5mm is further calculated, and the result is shown in FIG. 3, namely, when the raindrops are increased, the curve moves leftwards, which shows that the larger the raindrops are, the less the impact times in unit area are required for causing the same-level damage; when the raindrops become smaller, the curve moves to the right, indicating that the smaller the raindrops, the more impact times per unit area are required to cause the same level of damage. Further, the relationship between the cumulative impact number per unit area and the cumulative rainfall is obtained by combining the relationship between the rainfall intensity and the particle size of the raindrops (formulas 1 to 3), and the result is shown in fig. 4.
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.
In the actual service environment of the wind power blade, wind speed, rainfall intensity, raindrop particle size and impact speed are all non-fixed, and the fatigue damage life of the front edge coating of the wind power blade is predicted based on palmgren-miner rule linear fatigue accumulation damage criteria and by combining the environmental data of the steps S1-S4 and each model.
The specific process is as follows: firstly, the cumulative time of the annual average hours of different rainfall intensities is given as model rainfall time input in table 1, the droplet particle size is determined by utilizing the association relation between the droplet particle size and the rainfall intensity (table 2), the maximum end drop speed of the droplet is determined by utilizing the relation between the droplet particle size and the drop speed of the droplet (table 3), the droplet particle size parameters under the actual different rainfall intensities are input into a droplet kinetic energy impact model, the specific parameters are shown in table 4, and the linear fatigue cumulative damage criterion based on continuous change of the load is obtained by combining palmgren-miner law (formula 11), so that 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 the i load level, ni is the number of cycles at which failure occurs at the i load level during the test, and j is the number of load levels.
When M.gtoreq.1, the material subjected to the fatigue cycle reaches the expected fatigue life, and is judged to be failed.
Model input parameters and predictions Table 5 shows that the blade leading edge life is about 1.5 years.
(Table 5)
Example 2
The invention relates to a wind power blade leading edge coating life prediction method based on rain erosion fatigue damage, which takes a fan serving in a south ventilation wind farm as an example to predict the blade leading edge coating fatigue damage, and specifically comprises the following steps:
s1, counting distribution data of different rainfall intensities in time aiming at the service environment of wind power blades in a wind power plant;
The rainfall hours data of a south China ventilation wind field 2018-2022 are used for carrying out the severity analysis of the rain erosion environment, the distribution and accumulation time of different rainfall hours of the wind field in 2018-2022 are shown in a table 6, as can be seen from the table 6, the total rainfall amounts of the region in 2018-2022 are 1123, 874, 1289, 1205 and 813mm respectively, the annual average rainfall amount is about 1061mm, wherein 0-1 mm/h (light rain-medium rain) is adopted, and 733 hours are accumulated annually; 1-2mm (medium rain-heavy rain), and the annual average accumulation time is 209 hours; 2-5mm (heavy rain to heavy rain), and the annual average time is 103 hours; 5-10mm (heavy rain to heavy rain) and 15 hours of annual accumulation; 10-20mm (heavy storm to extra heavy storm) and 0.4 hours of annual accumulation.
(Table 6)
S2, calculating the particle sizes of raindrops under different rainfall intensities and the maximum terminal dropping speeds of the raindrops with different particle sizes;
and (3) establishing a relation between the rainfall droplet particle sizes Dm and the rainfall intensities R through formulas (1) - (4) (the same as in the embodiment 1), and calculating the droplet particle sizes corresponding to the rainfall intensities.
The south ventilation field of the embodiment belongs to the latitude and longitude regions, so that the particle size of raindrops corresponding to the rainfall intensity can be calculated according to formulas (1) and (4), and the result is shown in table 7;
| rainfall intensity (mm/h) | Raindrop particle diameter (mm) |
| 1 | 1.3 |
| 2 | 1.5 |
| 5 | 1.7 |
| 10 | 2.0 |
| 20 | 2.2 |
| 50 | 2.6 |
(Table 7)
And establishing a relation between the dropping speed of the raindrops and the particle size of the raindrops, and converting the particle size of the raindrops into the dropping speed of the raindrops.
According to the particle size of the raindrops, calculating the maximum terminal falling speed of the raindrops with different particle sizes under different rainfall intensities：
Formula (5)
In this embodiment, through the relationship between the raindrop particle size and the maximum end falling speed, in combination with the raindrop particle sizes corresponding to different rainfall intensities in the south ventilation field, the relationship between the rainfall intensity and the falling speed is constructed, and the results are shown in table 8:
| rainfall intensity (mm/h) | Raindrop particle diameter (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, taking a wind power blade front edge coating sample as an object, carrying out a rain erosion resistance test on the wind power blade front edge coating sample, calculating impact speeds of raindrops on all positions of the wind power blade front edge coating sample, and constructing an impact frequency model and a raindrop kinetic energy impact model of the wind power blade front edge coating sample in a rain field;
The service life of the front edge coating of the blade is related to the service environment and the performance of the material, a cantilever type rain erosion device is adopted to carry out a rain erosion resistance test by taking a sample of the front edge coating of the blade as an object, and the impact resistance parameter of the front edge coating of the blade is obtained.
In this embodiment, a composite high-performance polyurethane coated board sample was prepared, a rain erosion resistance test was performed using a cantilever type rain erosion device, and test conditions were set: the rainfall intensity is 34mm/h, the particle size of water drops is 2mm, and the impact speed is 110-150 m/s from the near end to the far end of the sample. The test time was 6 hours, the sample surface was inspected once per hour, and the data such as the coating breakage spreading position was measured, and the rain erosion test results are shown in fig. 5 and table 9.
From fig. 5, it can be seen that the time at which the leading edge erosion occurs increases stepwise from the outside of the sample to the inside of the sample. The outermost side was assumed to be 0mm, the rotational speed was 150m/s, the innermost side was assumed to be 250mm, and the rotational speed was about 110 m/s. After the rain erosion test is carried out for 2 hours, the accumulated impact damage occurs on a sample at the position of 12mm, and the rotating speed at the position is 148 m/s; after a rain erosion test for 3 hours, accumulated impact damage occurs to a sample at a position of 42mm, and the rotating speed at the position is 143m/s; after a rain erosion test for 4 hours, accumulated impact damage occurs to a sample at a position of 72mm, and the rotating speed at the position is 138m/s; after 5 hours of the rain erosion test, the sample at the position of 86mm has accumulated impact damage, and the rotating speed at the position is 136m/s; after 6 hours of the rain erosion test, the sample at 99mm had accumulated impact damage, and the rotational speed at this point was 134m/s.
| Test time | Erosion front length | Accumulated rainfall | Impact velocity |
| t(h) | (mm) | (mm) | vt(m/s) |
| 2 | 12 | 74 | 145 |
| 3 | 42 | 114 | 140 |
| 4 | 72 | 154 | 138 |
| 5 | 86 | 194 | 136 |
| 6 | 99 | 234 | 134 |
(Table 9)
And substituting the data obtained by the rain erosion resistance test into a formula (6) to a formula (8) (the same as in the embodiment 1) from the data result table 9 (input parameters) and the rainfall intensity R, wherein the rain drop particle size D is 2mm, the rain drop tail speed v r is about 6m/s, the travel speed of the blade in a rain field is v t (110-150 m/s), and the time t when the damage occurs at each position of the sample into the formula (6) to the formula (8), so as to obtain the impact frequency F of unit area, the accumulated impact frequency N Ei of unit area and the energy E k of single impact.
As shown in Table 10 (output), it is clear from Table 10 that the water drop impact coating layer of 39140 times 148m/s was broken at the outer side 12mm of the sample, and the water drop impact coating layer of 108500 times 134m/s was broken at the 99 mm.
| Kinetic energy of impact | Impact frequency per unit area | Number of impacts per unit area |
| Ek(J) | F(cm-2s-1) | NEi(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)
And S4, establishing a relation between the accumulated impact times and the impact speed of the unit area of the wind power blade front edge coating sample and the particle size of the raindrops.
And (3) combining the formula (8) to obtain the relation between the cumulative impact times and the impact speed of the unit area of the wind power blade front edge coating sample and the particle size of the raindrops, namely, a rain erosion fatigue damage cumulative model:
Formula (9)
Where c and m are constants related to the properties of the front material.
In this embodiment, fig. 6 shows the relationship between the number of impacts that cause the coating failure in a unit area and the single impact energy, and the cumulative model coefficient c of rain erosion fatigue damage obtained by fitting a power function is 2.3×10 -6, and m is 7.516.
Using the data of tables 9 and 10 as model input data, calculating the relationship between the cumulative impact number and impact speed per unit area when the particle size of the raindrops is 1.5 and 2.5mm, and the result is shown in fig. 7, that is, when the raindrops are increased, the curve moves left, which shows that the larger the raindrops are, the fewer the impact number per unit area is required to cause the same level damage; when the raindrops become smaller, the curve moves to the right, indicating that the smaller the raindrops, the more impact times per unit area are required to cause the same level of damage. In addition, the model extends the area beyond the impact speed of 134-145 m/s, and the overall result is that the larger the impact speed is, the smaller the number of times of impact per unit area is required.
Further, the relationship between the cumulative impact number per unit area and the cumulative rainfall was obtained by combining the relationship between the rainfall intensity and the particle size of the raindrops (equations 1 and 4), and the result is shown in fig. 8.
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 specific process is as follows: firstly, the cumulative time of annual average hours of different rainfall intensities is given as model rainfall time input in table 6, the droplet particle size is determined by utilizing the association relation between the droplet particle size and the rainfall intensity (table 7), the maximum speed of the tail end of the droplet is determined by utilizing the relation between the droplet particle size and the falling speed of the droplet (table 8), the droplet particle size parameters under the actual different rainfall intensities are input into a raindrop kinetic energy impact model, the specific parameters and the results are shown in tables 9 and 10, and the service life of the blade front edge coating is predicted by combining palmgren-miner law (formula 11) based on an S-N curve fitting formula 10.
Formula (10)
Where i is the load level number, ni is the number of cycles at the i load level, ni is the number of cycles at which failure occurs at the i load level during the test, and j is the number of load levels.
When M.gtoreq.1, the material subjected to the fatigue cycle reaches the expected fatigue life, and is judged to be failed.
The model input parameters and predictions are shown in Table 11, and the blade leading edge life is approximately 3.8 years.
(Table 11)
The above embodiments are merely illustrative of the principles of the present invention and its effectiveness, and are not intended to limit the invention. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the invention, and it is intended that the appended claims be interpreted as covering all equivalent modifications and variations as fall within the true spirit and scope of the invention.
Claims (9)
1. A wind power blade leading edge coating life prediction method based on rain erosion fatigue damage is characterized by comprising the following steps:
s1, counting distribution data of different rainfall intensities in time 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, taking a wind power blade front edge coating sample as an object, carrying out a rain erosion resistance test on the wind power blade front edge coating sample, calculating the impact speed of raindrops on each position of the front edge coating sample, and constructing an impact frequency model and a raindrop kinetic energy impact model of the front edge coating 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.
2. The wind power blade leading edge coating life prediction method based on rain erosion fatigue damage according to claim 1, wherein the method comprises the following steps: in said step S2, for global small/medium rain intensity, i.e. rainfall intensityAnd (3) calculating the particle size D of the raindrops corresponding to the particle size D of less than or equal to 1 mm/h:
Formula (1)
For medium/heavy rain intensity in low latitude areas, i.e. intensity of rainfallThe method comprises the following steps: 1< R <5mm/h, and calculating the corresponding raindrop particle diameter D:
Formula (2)
For heavy/extra heavy rain in low latitude areas, i.e. intensity of rainfallCalculating the particle size D of raindrops corresponding to the particle size D of 5 mm/h:
Formula (3)
For various rainfall intensity in middle and high latitude areas, namely rainfall intensity1Mm/h, and calculating the corresponding raindrop particle diameter D:
Formula (4)
According to the particle size of the raindrops, calculating the maximum terminal falling speed of the raindrops with different particle sizes under different rainfall intensities：
Equation (5).
3. The wind power blade leading edge coating life prediction method based on rain erosion fatigue damage according to claim 2, wherein the method comprises the following steps: in the step S3, the impact speed of each position is calculated according to the position and the corresponding time of abrasion of the wind power blade front edge coating sample from outside to inside.
4. The wind power blade leading edge coating life prediction method based on rain erosion fatigue damage according to claim 3, wherein the wind power blade leading edge coating life prediction method based on rain erosion fatigue damage is characterized by comprising the following steps of: in the step S3, it is assumed that the water droplets in the rain field are spherical and have uniform particle diameters, and the number of impacts F occurring per unit area per unit time is:
Formula (6)
The wind power blade wind power generation system is an impact frequency model, wherein V t is the passing speed of the wind power blade in a rain field;
The cumulative impact number per unit area N Ei is:
Formula (7)
Wherein t is time, s;
The energy E k of a single impact is:
Formula (8)
Namely a raindrop kinetic energy impact model, in which,Is the density of water.
5. The wind power blade leading edge coating life prediction method based on rain erosion fatigue damage according to claim 4, wherein the wind power blade leading edge coating life prediction method based on rain erosion fatigue damage is characterized by comprising the following steps of: in the step S4, a relation between the cumulative impact times per unit area of the wind power blade leading edge coating sample, the impact speed and the rain drop particle size, namely a rain erosion fatigue damage cumulative model, is obtained by combining the formula (8):
Formula (9)
Wherein c and m are constants related to the properties of the front material; e 0 is unit energy and has a value of 1.
6. The wind power blade leading edge coating life prediction method based on rain erosion fatigue damage according to claim 5, wherein the wind power blade leading edge coating life prediction method based on rain erosion fatigue damage is characterized by comprising the following steps of: in the step S4, a relationship between the cumulative impact number per unit area and the cumulative rainfall is obtained from the relationship between the rainfall intensity and the particle size of the raindrops.
7. The wind power blade leading edge coating life prediction method based on rain erosion fatigue damage according to claim 6, wherein the method comprises the following steps: in said step S5, a linear fatigue cumulative damage criterion based on the continuous change of the environmental load:
Formula (10)
Wherein i is the load grade number, ni is the number of cycles when i is the load grade, ni is the number of cycles when failure occurs under the load grade in the test process, and j is the number of load grade;
when M.gtoreq.1, the material subjected to the fatigue cycle reaches the expected fatigue life, and is judged to be failed.
8. The wind power blade leading edge coating life prediction method based on rain erosion fatigue damage according to claim 7, wherein the wind power blade leading edge coating life prediction method based on rain erosion fatigue damage is characterized by comprising the following steps of: in the step S1, data are distributed for different rainfall intensity hours for at least 3 years.
9. The wind power blade leading edge coating life prediction method based on rain erosion fatigue damage according to claim 8, wherein the method comprises the following steps: in the step S1, data are distributed by counting different rainfall intensity hours over more than 5 years.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2018185171A (en) * | 2017-04-24 | 2018-11-22 | 株式会社東芝 | Fatigue life analysis apparatus for windmill power generator, wind power generation system, and fatigue life analysis method for windmill power generator |
| KR102099614B1 (en) * | 2018-11-08 | 2020-04-10 | 군산대학교산학협력단 | Method and apparatus for fatigue life evaluation of wind turbine composite blade |
| CN113011109A (en) * | 2021-01-15 | 2021-06-22 | 浙江大学 | Fatigue analysis method for wind driven generator blade coating considering raindrop erosion |
| CN114239168A (en) * | 2021-12-14 | 2022-03-25 | 浙江大学 | Wind driven generator coating fatigue life optimization design method considering raindrop erosion |
| CN114547718A (en) * | 2022-03-12 | 2022-05-27 | 北京工业大学 | Rain erosion performance simulation analysis method for protective film of front edge of wind turbine blade |
| CN117685177A (en) * | 2023-12-29 | 2024-03-12 | 东方电气风电股份有限公司 | Method for reducing rain erosion of blade and monitoring equipment |
-
2024
- 2024-04-03 CN CN202410401071.6A patent/CN117993229A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2018185171A (en) * | 2017-04-24 | 2018-11-22 | 株式会社東芝 | Fatigue life analysis apparatus for windmill power generator, wind power generation system, and fatigue life analysis method for windmill power generator |
| KR102099614B1 (en) * | 2018-11-08 | 2020-04-10 | 군산대학교산학협력단 | Method and apparatus for fatigue life evaluation of wind turbine composite blade |
| CN113011109A (en) * | 2021-01-15 | 2021-06-22 | 浙江大学 | Fatigue analysis method for wind driven generator blade coating considering raindrop erosion |
| CN114239168A (en) * | 2021-12-14 | 2022-03-25 | 浙江大学 | Wind driven generator coating fatigue life optimization design method considering raindrop erosion |
| CN114547718A (en) * | 2022-03-12 | 2022-05-27 | 北京工业大学 | Rain erosion performance simulation analysis method for protective film of front edge of wind turbine blade |
| CN117685177A (en) * | 2023-12-29 | 2024-03-12 | 东方电气风电股份有限公司 | Method for reducing rain erosion of blade and monitoring equipment |
Non-Patent Citations (7)
| Title |
|---|
| C. HASAGER: "Assessment of the rain and wind climate with focus on wind turbine blade leading edge erosion rate and expected lifetime in Danish Seas", RENEWABLE ENERGY, vol. 149, 30 April 2020 (2020-04-30) * |
| JAKOB ILSTED BECH等: "Extending the life of wind turbine blade leading edges by reducing the tip speed during extreme precipitation events", WIND ENERGY SCIENCE, vol. 3, no. 2, 19 October 2018 (2018-10-19), pages 729 - 744 * |
| JAVIER CONTRERAS LÓPEZ等: "A wind turbine blade leading edge rain erosion computational framework", RENEWABLE ENERGY, vol. 203, 28 February 2023 (2023-02-28) * |
| LUIS BARTOLOMÉ等: "Methodology for the energetic characterisation of rain erosion on wind turbine blades using meteorological data: A case study for The Netherlands", WIND ENERGY, vol. 24, no. 7, 7 January 2021 (2021-01-07) * |
| 朱显漠: "黄土高原土壤与农业", 31 May 1989, 北京：农业出版社, pages: 415 * |
| 纪永祥，赵新: "引信试验鉴定工程与实践", 31 August 2021, 北京：北京理工大学出版社, pages: 206 * |
| 陈炜镒 等: "考虑雨滴侵蚀的风力发电机叶片涂层疲劳寿命优化设计方法", 机械工程学报, vol. 58, no. 17, 16 April 2022 (2022-04-16) * |
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