Method and device for analyzing reliability of bearing capacity of offshore wind turbine

Abstract

The invention belongs to the technical field of offshore wind turbine bearing capacity analysis, and discloses an offshore wind turbine bearing capacity reliability analysis method and device. According to the method, an offshore wind turbine simulation model is obtained; constructing an autocorrelation function between adjacent unit bodies in the three-dimensional random field; based on the autocorrelation function, adopting a matrix decomposition algorithm to generate cohesive force and internal friction angle data; dividing soil blocks to obtain a plurality of soil blocks, calculating a plurality of groups of offshore wind turbine simulation models under cohesive force and friction angle data according to a set sample number by utilizing the finite element model after load introduction, and performing automatic batch calculation on the plurality of groups of offshore wind turbine simulation models to obtain calculated data; according to the result of finite element batch calculation, the failure probability under the set sample number is calculated, and the reliability of the bearing capacity of the offshore wind turbine is judged according to the failure introduction. According to the invention, more reliable indexes are provided for long-term monitoring, design and construction of offshore wind power.

Description

Technical Field

[0001] The present invention belongs to the technical field of bearing capacity analysis of offshore wind turbines, and particularly relates to a method and device for reliability analysis of bearing capacity of offshore wind turbines. Background Art

[0002] Offshore wind turbines are often built in the near sea, where the marine environment is very complex. The wind turbines are subjected to environmental loads such as wind, waves and wave currents. In the field of offshore wind turbines, the normal service limit state of the wind turbine structure is an important index for evaluating the long-term stability of the wind turbine. Moreover, the offshore soil is often soft soil, and the influence of the soil on the stability of the wind turbine is also great. In most cases, in order to simplify the calculation and analysis process, the seabed soil is set as homogeneous soil and the soil parameters cohesion and internal friction angle are set as fixed values. However, in actual situations, the cohesion and internal friction angle of the seabed soil often have spatial variability. This spatial variability may be caused by the complexity of geological conditions, the action of ocean currents, the impact of waves and other environmental factors. Therefore, the traditional homogeneous assumption may not accurately reflect the true situation of the wind turbine foundation in the safety analysis of the actual wind turbine foundation and the prediction of the normal service limit state.

[0003] Since the cohesion and internal friction angle are important control parameters for the stability of offshore wind turbine foundations, the non-uniformity of their spatial distribution has a direct impact on the stability analysis of wind turbine foundations. When traditional methods calculate the reliability of wind turbine foundations and determine the bearing capacity, it is easy to ignore the spatial variability of soil parameters, which may make the calculation results too conservative in some cases. This error will affect the reliability of wind turbine design, and may increase unnecessary design measures or potential instability risks that cannot be predicted.

[0004] In addition, when traditional methods calculate the bearing capacity of offshore wind turbine foundations, they often use simplified assumptions and models for analysis, ignoring the non-homogeneous characteristics of the seabed soil and complex marine geological conditions. This may not only lead to deviations in bearing capacity prediction, but also affect the accuracy of normal service limit state judgment, thus bringing uncertainties in the evaluation of wind turbine foundation bearing capacity and engineering design. Especially in areas with complex marine geological conditions, the results of traditional methods may have obvious differences from the actual situation, affecting their application value in complex offshore wind turbine projects. Summary of the Invention

[0005] In order to overcome the problems existing in the related technologies, the disclosed embodiments of the present invention provide a method and device for reliability analysis of bearing capacity of offshore wind turbines, specifically relating to a method and device for reliability analysis of bearing capacity of offshore wind turbines considering the uncertainty of soil parameters.

[0006] The technical solution is as follows: A method for reliability analysis of bearing capacity of offshore wind turbines includes the following steps: S1. Perform simulation modeling based on the geometry of the offshore wind turbine and the pile foundation parameters of the turbine tower to obtain an offshore wind turbine simulation model; S2. Conduct univariate analysis on the cohesion and internal friction angle, select the optimal marginal distribution, and construct the autocorrelation function between adjacent elements in the three-dimensional random field; Based on the autocorrelation function, use the matrix decomposition algorithm to generate cohesion and internal friction angle data; S3. Divide the soil mass of the obtained offshore wind turbine simulation model to obtain several soil masses, import the random field of cohesion and friction angle, determine the distribution of cohesion and friction angle in each soil mass, and batch generate the random field and export the data through the command stream; S4. Use the finite element model after importing the load, calculate the offshore wind turbine simulation model under several groups of cohesion and friction angle data according to the set number of samples, and perform automated batch calculations on several groups of offshore wind turbine simulation models to obtain the calculated data; S5. According to the results of the finite element batch calculation, calculate the failure probability under the set number of samples, and judge the bearing capacity reliability of the offshore wind turbine based on the failure probability.

[0007] In step S2, conduct univariate analysis on the cohesion and internal friction angle, select the optimal marginal distribution, including: Collect soil parameter data, and conduct univariate analysis on the soil parameter cohesion and internal friction angle . To select the appropriate marginal distribution function for cohesion and internal friction angle, based on the AIC criterion, select the optimal marginal distribution from the four marginal distribution models of truncated normal distribution, lognormal distribution, truncated Gumbel distribution, and Weibull distribution.

[0008] Construct the autocorrelation function between adjacent elements in the three-dimensional random field, and the expression is: ; In the formula, is the soil autocorrelation coefficient, indicating the autocorrelation degree between different positions and . The value of is between 0 and 1, and the larger the value, the stronger the autocorrelation; is the distance between point and point in the direction, is the distance between point and point in the direction, is the distance between point and point in the direction, is the cohesion and the internal friction angle The correlation lengths of the random field in the x, y, and z directions, is the serial number of adjacent soil blocks; Based on the autocorrelation function, the matrix decomposition algorithm is used to generate the cohesion and internal friction angle data, including: Using the kl series expansion method in the matrix decomposition algorithm, a set of cohesion and internal friction angle data is generated. The specific steps are as follows: Step 1, construct the corresponding distance autocorrelation matrix according to the autocorrelation function. The expression is: ; In the formula, is the autocorrelation matrix of, indicating that there are sample points; is the sample point and the sample point The autocorrelation function between them reflects the degree of correlation between them, , is the number of samples; Step 2, construct the coefficient cross-correlation matrix based on the cross-correlation coefficient between the cohesion and the internal friction angle. The expression is: ; In the formula, is the coefficient cross-correlation matrix of, is the cross-correlation coefficient between the internal friction angle in the vertical column and the cohesion , is the cross-correlation coefficient between the cohesion in the horizontal column and the internal friction angle , reflecting the strength and direction of the linear relationship between the two; When, it means that the mutual relationship between the two parameters is symmetric Step 3, apply the matrix decomposition algorithm to decompose the autocorrelation matrix and the coefficient cross-correlation matrix to obtain the corresponding triangular matrix; Step 4, use the triangular matrix for calculation to obtain the parameter matrix of the unit body, and extract a set of cohesion and internal friction angle data through this parameter matrix.

[0009] Furthermore, in Step 3, the autocorrelation matrix and the coefficient cross-correlation matrix are decomposed by the matrix decomposition algorithm to obtain the corresponding triangular matrix under the decomposition. The expression is as follows: ; ; In the formula, are all lower triangular matrices, is the transpose matrix; ; ; In step 4, the parameter matrix of the element body is obtained, and the expression is: ; In the formula, is the parameter matrix, which is used to describe the cohesion and the internal friction angle of the soil, and is used to receive the introduced lognormal distribution matrix , so that contains uncertainty and randomness, enabling more accurate modeling of the physical properties of soil materials. By analyzing , the statistical properties of cohesion and internal friction angle, such as mean value, standard deviation, and correlation coefficient, are obtained; is the linear transformation matrix, which transforms the set lognormal distribution into a standard form for calculation; is the lognormal distribution cumulative function, is the lognormal distribution random matrix with a dimension of , , representing cohesion and internal friction angle respectively.

[0010] In step S3, the soil body of the wind turbine simulation model is discretized and meshed to obtain several element bodies and discrete soil blocks. The random fields of cohesion and internal friction angle are generated using the above-selected optimal marginal distributions, and are imported into the offshore wind turbine simulation model through the PLAXIS 3D API interface to determine the distribution of cohesion and internal friction angle of each soil block; Control the automation through the API interface to obtain the material parameters of the tower barrel and pile of each element body, the material parameters of the soil, the mass and height parameters of the wind turbine blades and the upper tower barrel, the blade radius and power parameters in the wind turbine simulation model; as well as the ID number of the soil block, the corresponding coordinates, and the values of different cohesion and internal friction angles corresponding to the soil block; After obtaining the cohesion and internal friction angle data through the parameter matrix, assign values to the cohesion and internal friction angle of the soil block in the offshore wind turbine simulation model; According to the set number of samples, obtain several groups of cohesion and internal friction angle data through the offshore wind turbine simulation model, and then obtain several groups of cohesion and internal friction angle data for subsequent calculation of the bearing capacity reliability and serviceability limit state of the wind turbine simulation model under different cohesion and internal friction angles.

[0011] ​In step S4, loads are input into the offshore wind turbine model in the PLAXIS 3D software. The loads include wind loads and wave loads. Among them, the wind load is the thrust acting on the fan blades and the distributed load acting on the tower. The calculation formula for the thrust acting on the fan blades is: ; In the formula, is the thrust, is the air density, ; is the thrust coefficient, is the swept area of the fan blades, is the average wind speed at the hub; For the load calculation formula acting on the tower is: ; In the formula, is the load force acting on the tower, is the wind speed at height, is the height coefficient. For the tower height range of 0 - 100 meters, its variation range is 1 to 1.58; is the shape coefficient. For a cylindrical cross-section, ; is the outer diameter of the tower; The calculation formula of ; In the formula, is the average wind speed at a height of 10 m above sea level, is the wind speed at a certain height of the wind turbine, is the height of the wind turbine at 10 m above sea level, is the wind speed profile exponent, ; The wave force on the monopile of the wind turbine is calculated using the Morison equation. The wave force is expressed as: ; In the formula, is the inertial force, is the drag force, is the inertial force coefficient, ; is the seawater density, ; is the pile diameter, is the drag force coefficient, ; are the velocity and acceleration of the water particle respectively, is the wave load force received by the monopile of the wind turbine, is the water depth, For derivative calculation.

[0012] In step S5, the expression of the failure probability is: ; In the formula, is the failure probability, is the number of failures, is the sampling quantity.

[0013] Another object of the present invention is to provide an offshore wind turbine bearing capacity reliability analysis device, which implements the offshore wind turbine bearing capacity reliability analysis method. The device includes: A model construction module, which is used to perform simulation modeling according to the geometric shape of the offshore wind turbine and the parameters of the wind turbine tower pile foundation to obtain an offshore wind turbine simulation model; A cohesion and friction angle processing module, which is used to perform univariate analysis on the cohesion and internal friction angle, select the optimal marginal distribution, and construct the autocorrelation function between adjacent unit bodies in the three-dimensional random field; based on the autocorrelation function, use the matrix decomposition algorithm to generate cohesion and friction angle data; A random field generation and data acquisition module, which is used to divide the soil blocks of the obtained offshore wind turbine simulation model to obtain several soil blocks, import the random field of cohesion and friction angle, determine the distribution of cohesion and friction angle in each soil block body, and batch generate the random field and export the data through the command stream; A finite element model batch automatic calculation module, which uses the finite element model after importing the load, calculates the offshore wind turbine simulation model under several groups of cohesion and friction angle data according to the set sample quantity, and performs automatic batch calculation on several groups of offshore wind turbine simulation models to obtain the calculated data; A failure mode solving module, which is used to calculate the failure probability under the set sample quantity according to the results of the finite element batch calculation, and judge the reliability of the offshore wind turbine bearing capacity according to the failure probability.

[0014] Combining all the above technical solutions, the beneficial effects of the present invention are as follows: First, the present invention takes into account the inhomogeneous characteristics of the seabed soil and complex marine geological conditions, and based on the spatial variability of soil parameters, avoids the over-conservatism of the calculation results of offshore wind turbines. It proposes a method and device for solving the bearing capacity of offshore wind turbine foundations and the normal service limit state. Based on the reliability of wind turbine design, it helps to reasonably design measures for wind turbines and predict potential instability risks in advance, and increases the accuracy of judging the normal service limit state. The method for solving the bearing capacity of offshore wind turbine foundations and the normal service limit state based on the randomness of soil parameters and the matching device of the present invention can consider the real marine geological environment and soil inhomogeneous characteristics of offshore wind turbines, which is different from the traditional method for solving homogeneous soil, and a device is proposed corresponding to the method.

[0015] Second, this technology can optimize the basic design, reduce unnecessary over-design, and lower material and construction costs. Reduce maintenance costs: By accurately evaluating the bearing capacity of the foundation, it is possible to improve the safety of the foundation while reducing the frequency of subsequent maintenance and inspections, thereby reducing long-term operating costs.

[0016] Third, the present invention embodies the innovation of the method for evaluating the randomness of soil parameters. In the design of offshore wind turbine foundations, it mainly relies on deterministic soil parameters and rarely considers the spatial specificity between soil parameters, lacking in-depth analysis of the randomness and spatial specificity of soil parameters. This method often leads to inaccurate evaluation results of the bearing capacity of the foundation, affecting the safety and economy of the wind turbine. The present invention establishes a corresponding bearing capacity evaluation model by comprehensively considering the randomness and spatial specificity of soil parameters. This model can more realistically reflect the true characteristics of the soil and systematically reflect the behavior of offshore wind turbine foundations under dynamic loads.

[0017] Fourth, the present invention provides a new technical solution that can accurately evaluate the influence of the randomness and spatial variability of soil parameters on the bearing capacity of the foundation. This method comprehensively applies random analysis, probability theory, and soil mechanics principles, successfully combining theory with the needs of actual engineering. With the help of advanced mathematical modeling and numerical analysis techniques, the present invention can achieve the evaluation of the bearing capacity of the foundation under various uncertain conditions, overcoming the limitations of traditional methods that can only rely on experience and deterministic models.

[0018] Fifth, the present invention overcomes the technical bias of traditional engineering methods that overly rely on deterministic models: In the evaluation of soil parameters and the design of foundations, traditional engineering practices often rely on experience and deterministic models, lacking consideration of the randomness of soil parameters. This technical bias may lead to underestimation or overestimation of the bearing capacity of the foundation, thus affecting engineering safety and economy. Conservatism in traditional design: When making designs, engineers usually tend to adopt conservative methods and analyze using the most unfavorable conditions to ensure safety. However, this overly conservative method often results in the use of expensive materials and unnecessary cost expenditures. Introduction of randomness analysis: The present invention establishes a bearing capacity evaluation model for offshore wind turbine foundations by introducing the randomness of soil parameters. This model not only considers the uncertainty of the soil but also, through probability analysis methods, realistically reflects the performance of the soil under different load and environmental conditions, and takes into account the spatial variability of soil parameters and studies the relationships between soil parameters. Description of the Drawings

[0019] The drawings herein are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure; Figure 1It is the flowchart of the method for analyzing the bearing capacity reliability of an offshore wind turbine provided by an embodiment of the present invention; Figure 2 It is the rendering of the hardware structure of the offshore wind turbine of the present invention; Figure 3 It is the simulation diagram of the offshore wind turbine of the present invention; Figure 4 It is the cohesion edge distribution diagram of the present invention; Figure 5 It is the internal friction angle edge distribution diagram of the present invention; Figure 6 It is the simulation diagram of the random field model of the present invention; Figure 7 It is the displacement nephogram of the offshore wind turbine model of the present invention; Specific embodiments

[0020] To make the above objects, features, and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description in order to fully understand the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0021] The innovation of the present invention lies in: The present invention systematically considers the influence of the randomness and spatial variability of soil parameters on the foundation bearing capacity of offshore wind turbines under complex environments. By introducing the analysis and comprehensive evaluation of the randomness and spatial variability of soil parameters, a more scientific and accurate bearing capacity evaluation method and device are provided, significantly improving the safety and economy of the foundation design of offshore wind turbines.

[0022] Example 1, as Figure 1 shown, the method for analyzing the bearing capacity reliability of an offshore wind turbine provided by an embodiment of the present invention is a method for solving the foundation bearing capacity and serviceability limit state of an offshore wind turbine based on the randomness of soil parameters; specifically includes the following steps: S1, perform simulation modeling according to the geometric shape of the offshore wind turbine and the pile foundation parameters of the wind turbine tower to obtain an offshore wind turbine simulation model; S2, perform univariate analysis on cohesion and internal friction angle, select the optimal edge distribution, and construct the autocorrelation function between adjacent unit bodies in the three-dimensional random field; based on the autocorrelation function, use the matrix decomposition algorithm to generate cohesion and internal friction angle data; S3, divide the soil blocks of the obtained offshore wind turbine simulation model to obtain several soil blocks, import the random fields of cohesion and friction angle, determine the distribution of cohesion and friction angle in each soil block body, and batch generate random fields and export data through command flow; S4. Using the finite element model after importing the load, calculate the simulation models of offshore wind turbines under several groups of cohesion and friction angle data according to the set number of samples, and perform automated batch calculations on several groups of offshore wind turbine simulation models to obtain the calculated data. S5. According to the results of the finite element batch calculation, calculate the failure probability under the set number of samples, and judge the bearing capacity reliability of the offshore wind turbine based on the failure probability.

[0023] Exemplarily, step S1 specifically includes: establishing a model of the offshore wind turbine to obtain a simulation model of the wind turbine, determining the shapes of the wind turbine and the marine soil mass, simplifying the nacelle of the wind turbine into a mass block, and then collecting relevant parameters and using the numerical simulation software PLAXIS 3D to establish a simulation model of the offshore wind turbine.

[0024] The relevant parameters include: material parameters of the tower and pile, material parameters of the soil, mass and height of the wind turbine blades and the upper tower, blade radius and power.

[0025] Exemplarily, step S2 specifically includes: S201. Collect soil parameter data, and perform univariate analysis on the soil parameters of cohesion c and internal friction angle φ. To select the appropriate marginal distribution functions for cohesion and internal friction angle, select the optimal marginal distribution under different marginal distribution models based on the AIC criterion.

[0026] S202. Construct the autocorrelation function between adjacent soil mass blocks in the three-dimensional random field; based on the autocorrelation function, use the kl series expansion method in the matrix decomposition algorithm and generate a set of cohesion and internal friction angle data. In the three-dimensional space, these data consider the spatial correlation under the actual geological conditions. Therefore, the generated random field of soil parameters is closer to the real situation, effectively solving the problem that the results are not realistic due to the use of a homogeneous model in the traditional method. The expression of the autocorrelation function between adjacent soil mass blocks in the three-dimensional random field is: ; In the formula, is the soil autocorrelation coefficient, indicating the autocorrelation degree between different positions and . The value of is between 0 and 1, and the larger the value, the stronger the autocorrelation; is the distance between point and point in the direction, is the distance between point and point in the direction, is the distance between point and point The distance in the direction, is the cohesion and the internal friction angle The correlation lengths of the random field in the x, y, and z directions, is the serial number of adjacent soil blocks.

[0027] Exemplarily, based on the autocorrelation function, the KL series expansion method in the matrix decomposition algorithm is adopted, and a set of cohesion and internal friction angle data is generated. The specific steps are as follows: Step 1, construct the corresponding distance autocorrelation matrix according to the autocorrelation function. The expression is: ; In the formula, is the autocorrelation matrix, indicating that there are sample points; is the sample point and the sample point The autocorrelation function between them reflects the degree of correlation between them. , is the number of samples; Step 2, construct the coefficient cross-correlation matrix based on the cross-correlation coefficient between cohesion and internal friction angle. The expression is: ; In the formula, is the coefficient cross-correlation matrix, is the cross-correlation coefficient between the internal friction angle in the vertical column and the cohesion , is the cross-correlation coefficient between the cohesion in the horizontal column and the internal friction angle , reflecting the strength and direction of the linear relationship between the two; When, it means that the mutual relationship between the two parameters is symmetric Step 3, apply the matrix decomposition algorithm to decompose the autocorrelation matrix and the coefficient cross-correlation matrix to obtain the corresponding triangular matrix; The expression is as follows: ; ; In the formula, are all lower triangular matrices, is the transposed matrix; ; ; Step 4: Calculate using a triangular matrix to obtain the parameter matrix of the element body, and extract a set of cohesion and internal friction angle data through this parameter matrix; the expression is: ; In the formula, is the parameter matrix, which is used to describe the cohesion and internal friction angle of the soil, and its statistical characteristics and uncertainties, and is used to receive the introduced lognormal distribution matrix , so that contains uncertainties and randomness, enabling more accurate modeling of the physical properties of soil materials. By analyzing , the statistical characteristics of cohesion and internal friction angle, such as mean value, standard deviation, and correlation coefficient, are obtained; is the linear transformation matrix, which transforms the set lognormal distribution into a standard form for calculation; is the lognormal distribution cumulative function, is the lognormal distribution random matrix with a dimension of , , which respectively represent cohesion and internal friction angle .

[0028] Exemplarily, in step S3, since the cohesion and internal friction angle are not uniformly distributed and have spatial variability, and the random field has the ability to understand and process complex data with spatial correlation, the soil body of the wind turbine simulation model is discretized and meshed to obtain several element bodies and discrete soil blocks. The random fields of cohesion and internal friction angle are generated using the above-selected optimal marginal distributions, and are imported into the offshore wind turbine simulation model through the PLAXIS 3D API interface to determine the distribution of cohesion and internal friction angle of each soil block.

[0029] Therefore, after discretizing the model soil body, by generating the random fields of cohesion and internal friction angle, the distribution of cohesion and internal friction angle in each soil block can be intuitively obtained. At the same time, through the API interface, the relevant parameters of each element body in the wind turbine simulation model are automatically obtained, including but not limited to the ID number of the soil block, the corresponding coordinates, and the values of different cohesion and internal friction angles corresponding to the soil block.

[0030] After obtaining the cohesion and internal friction angle data through the parameter matrix, the cohesion and internal friction angle of the soil block can be assigned in the offshore wind turbine simulation model; According to the set number of samples, several groups of cohesion and internal friction angle data are obtained through the offshore wind turbine simulation model, and then several groups of cohesion and internal friction angle data are obtained for subsequent calculation of the bearing capacity reliability and serviceability limit state of the wind turbine simulation model under different cohesion and internal friction angles.

[0031] Exemplarily, in step S4, next, load input is performed on the generated offshore wind turbine simulation model. In the PLAXIS 3D software, loads are input into the offshore wind turbine model. The loads include wind loads and wave loads. Among them, the action of the wind load on the structure is the thrust acting on the fan blades and the distributed load acting on the tower. The thrust acting on the fan blades can be calculated by the following formula: ; In the formula, is the thrust, is the air density, ; is the thrust coefficient, is the fan blade swept area, is the average wind speed at the hub; The calculation formula for the load acting on the tower is: ; In the formula, is the load force acting on the tower, is the wind speed at height, is the height coefficient. For the tower height range of 0 - 100 meters, its variation range is 1 to 1.58; is the shape coefficient. For a cylindrical cross-section, ; is the outer diameter of the tower; The calculation formula of ; In the formula, is the average wind speed at a height of 10 m above sea level, is the wind speed at a certain height of the wind turbine, is the wind turbine height at 10 m above sea level, is the wind speed profile index, ; The wave force on the monopile of the wind turbine is calculated using the Morison equation. The wave force is expressed as: ; In the formula, is the inertial force, is the drag force, is the inertial force coefficient, ; is the seawater density, ; is the pile diameter, is the drag force coefficient, ; are respectively the velocity and acceleration of the water particle, is the wave load force received by the wind turbine monopile, is the water depth, is the derivative.

[0032] Exemplarily, in step S5, after establishing the wind turbine simulation model and the random field model, batch calculations are performed through the PLAXIS 3D API interface using the command stream, the model is calculated and data is exported. By exporting the rotation angle data at the mudline, the mudline rotation angles under hundreds of models are calculated, and the reliability of the offshore wind turbine bearing capacity is judged by calculating its failure probability. The expression of the failure probability is: ; In the formula, is the failure probability, is the number of failures, is the number of samples.

[0033] According to the solved failure probability of the offshore wind turbine under the uncertainty of soil parameters, which is closer to the actual geological conditions and actual working conditions in reality, by judging the reliability of the offshore wind turbine bearing capacity, through the method provided by the present invention, it is possible to provide guidance for engineering stability evaluation and engineering design, improve the safety during the use of the wind turbine and reduce costs.

[0034] Embodiment 2. The device for analyzing the reliability of the bearing capacity of an offshore wind turbine provided by the embodiment of the present invention includes: a model construction module, a random field generation and data acquisition module, a cohesion and friction angle processing module, a finite element model batch automatic calculation module, and a failure mode solution module, where: The model construction module is used to perform simulation modeling according to the geometric shape of the offshore wind turbine and the pile foundation parameters of the wind turbine tower to obtain an offshore wind turbine simulation model; The cohesion and friction angle processing module is used to perform univariate analysis on the cohesion and internal friction angle, select the optimal marginal distribution, and construct the autocorrelation function between adjacent unit bodies in the three-dimensional random field; based on the autocorrelation function, the matrix decomposition algorithm is used to generate cohesion and friction angle data; The random field generation and data acquisition module is used to divide the soil blocks of the obtained offshore wind turbine simulation model to obtain several soil blocks, import the random field of cohesion and friction angle, determine the distribution of cohesion and friction angle in each soil block body, and generate the random field and export data in batches through the command stream; The finite element model batch automatic calculation module uses the finite element model after importing the load to calculate the offshore wind turbine simulation model under several groups of cohesion and friction angle data according to the set number of samples, and performs automatic batch calculations on several groups of offshore wind turbine simulation models to obtain the calculated data; The failure mode solving module is used to calculate the failure probability under a set number of samples based on the results of batch finite element calculations, and to judge the bearing capacity reliability of the offshore wind turbine according to the failure probability.

[0035] In this embodiment, the specific working processes and working principles of the model construction module, the random field generation and data acquisition module, the cohesion and friction angle processing module, the batch automatic calculation module of the finite element model, and the failure mode solving module are the same as those of the above method. Each unit module can be implemented by software, hardware, or a combination thereof. These modules can be integrated into the processor of the computer device in the form of hardware, or exist as independent hardware. In addition, they can also be stored in the memory of the computer in the form of software, so as to facilitate the processor to call and execute the corresponding functions.

[0036] To further illustrate the related effects of the embodiments of the present invention, the following experiments are carried out.

[0037] First, determine the geometry of the offshore wind turbine, as shown in Figure 2 the hardware structure effect diagram of the offshore wind turbine; And based on the geometry of the wind turbine, obtain the material parameters of the wind turbine as shown in Table 1. Then, use the PLAXIS 3D numerical simulation calculation software to construct a wind turbine simulation model. The offshore wind turbine simulation model is as shown in Figure 3 shown.

[0038] Then, according to the data of soil cohesion and internal friction angle, and perform univariate analysis on the soil parameters of cohesion , internal friction angle . To select the appropriate marginal distribution functions for cohesion and internal friction angle, based on the AIC criterion, select the optimal marginal distributions of four marginal distribution models of truncated normal distribution, lognormal distribution, truncated Gumbel distribution, and Weibull distribution. Finally, the appropriate distribution is selected as shown in Figure 4 , Figure 5 . It can be seen that the lognormal distribution is selected as the optimal marginal distribution. Among them, the marginal distribution of cohesion is as shown in Figure 4 , and the marginal distribution of internal friction angle is as shown in Figure 5 shown.

[0039] Through the API interface in PLAXIS 3D, use the command stream to control the output of the ID numbers, corresponding coordinates, etc. of each soil block in the wind turbine simulation model. Since the geology of the offshore wind turbine is greatly affected by the soil cohesion and internal friction angle, using the above marginal distributions, generate the random field parameters of cohesion and internal friction angle through Cholesky decomposition, and generate the random field as shown in Figure 6 the random field model; And a set of cohesion is given and the internal friction angle The distribution in the fan simulation model. During the calculation of this set of parameters, The cohesion obtained has a mean value of 20 KPa and a variance of 0.3; the internal friction angle has a mean value of 25 and a variance of 0.2. Then, the cohesion and the internal friction angle in the soil blocks in the fan simulation model are assigned values. Table 2 shows other parameters of the soil body.

[0040] Table 1 Tower Barrel and Pile Material Property Table

[0041] Table 2 Soil Body Parameter Table

[0042] Then, the load is input into the established model. The sample quantity is set to 1000. Through the command stream to control the PLAXIS 3D API interface for batch modeling, 1000 groups of cohesion and internal friction angle data are generated by the above method and imported into the fan model to obtain 1000 fan simulation models (the model sizes are the same, and the data distributions of cohesion c and internal friction angle φ are different), and batch calculations are carried out. Figure 7 is the displacement nephogram of a calculated offshore fan model. The rotation angle at the mud line of the fan can be obtained from this. By calculating 1000 models, the values of the mud surface rotation angle in Table 3 are obtained, and its failure probability is calculated to judge the reliability of the fan foundation bearing capacity. The failure probability is 0.2%.

[0043] Table 3 Calculation and Distribution of Mud Surface Rotation Angle

[0044] As mentioned above, it is only a relatively optimal specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for analyzing the reliability of offshore wind turbine bearing capacity, characterized in that: The method comprises the following steps: S1, performing simulation modeling according to the geometric shape of the offshore wind turbine and the parameters of the wind turbine tower pile foundation to obtain an offshore wind turbine simulation model; S2, univariate analysis of cohesion and internal friction angle is performed, the optimal edge distribution is selected, and the autocorrelation function between adjacent units in the three-dimensional random field is constructed; based on the autocorrelation function, the matrix decomposition algorithm is used to generate the cohesion and internal friction angle data; S3, dividing the obtained offshore wind turbine simulation model into soil blocks to obtain a number of soil blocks, importing the random fields of cohesion and friction angle, determining the distribution of cohesion and friction angle in each soil block, and batch generating random fields and exporting data through command streams; S4, using the finite element model after the load is imported, calculating several sets of offshore wind turbine simulation models under cohesion and friction angle data according to the set sample quantity, performing automatic batch calculation on several sets of offshore wind turbine simulation models, and obtaining calculated data; S5, based on the results of finite element batch calculation, calculate the failure probability under the set sample number, and judge the reliability of the offshore wind turbine bearing capacity based on the failure overview.

2. The offshore wind turbine bearing capacity reliability analysis method according to claim 1, characterized in that: In step S2, univariate analysis is performed on the cohesion and internal friction angle to select the optimal edge distribution, including: Collect soil parameter data and analyze soil parameter cohesion , internal friction angle Univariate analysis was performed to select the appropriate marginal distribution function for cohesion and internal friction angle. Based on the AIC criterion, the optimal marginal distribution of four marginal distribution models, namely truncated normal distribution, lognormal distribution, truncated Gumbel distribution and Weibull distribution, was selected.

3. The offshore wind turbine bearing capacity reliability analysis method according to claim 1, characterized in that: In step S2, the autocorrelation function between adjacent unit cells in the three-dimensional random field is constructed, and the expression is: ； In the formula, is the soil autocorrelation coefficient, For point and Point exist The distance of the direction, For point and Point exist The distance of the direction, For point and Point exist The distance of the direction, Cohesion and internal friction angle The correlation lengths of the random field in the x, y, and z directions, It is the serial number of adjacent soil blocks.

4. The offshore wind turbine bearing capacity reliability analysis method according to claim 1, characterized in that: In step S2, based on the autocorrelation function, a matrix decomposition algorithm is used to generate cohesion and internal friction angle data, including: The KL series expansion method in the matrix decomposition algorithm is used to generate a set of cohesion and internal friction angle data. The specific steps are as follows: Step 1: Construct the corresponding distance autocorrelation matrix based on the autocorrelation function, expressed as: ； In the formula, for The autocorrelation matrix of For sample points and sample points The autocorrelation function between , is the number of samples; Step 2: Based on the correlation coefficient between cohesion and internal friction angle, construct the coefficient correlation matrix, which is expressed as: ； In the formula, for The coefficient cross-correlation matrix of is the friction angle within the longitudinal column Cohesion The mutual correlation coefficient of For the horizontal cohesion Angle of internal friction The mutual correlation coefficient of Step 3, apply the matrix decomposition algorithm to decompose the autocorrelation matrix and the coefficient cross-correlation matrix to obtain the corresponding triangular matrix; Step 4: Use the triangular matrix to calculate and obtain the parameter matrix of the unit body, and extract a set of data on cohesion and internal friction angle through the parameter matrix.

5. The offshore wind turbine bearing capacity reliability analysis method according to claim 4 is characterized in that: In step 3, the autocorrelation matrix and the coefficient cross-correlation matrix are decomposed by the matrix decomposition algorithm to obtain the corresponding triangular matrix under decomposition, which is expressed as follows: ； ； In the formula, are lower triangular matrices, is the transposed matrix; ； 。 6. The offshore wind turbine bearing capacity reliability analysis method according to claim 4, characterized in that: In step 4, the parameter matrix of the unit cell is obtained, which is expressed as: ； In the formula, is the parameter matrix, is the linear transformation matrix, is the lognormal cumulative function, The dimension is A log-normal random matrix of , respectively represent the cohesion and internal friction angle .

7. The offshore wind turbine bearing capacity reliability analysis method according to claim 1, characterized in that: In step S3, the wind turbine simulation model is discretized and meshed to obtain a number of unit bodies and discrete soil blocks, and the random fields of cohesion and internal friction angle are generated by using the above-selected optimal edge distribution. The fields are then imported into the offshore wind turbine simulation model through the PLAXIS 3D API interface to determine the distribution of cohesion and internal friction angle of each soil block; The API interface is used to automatically obtain the material parameters of the tower and piles of each unit in the wind turbine simulation model, the material parameters of the soil, the mass and height parameters of the wind turbine blades and the upper tower, the blade radius and power parameters; as well as the ID number of the soil block, the corresponding coordinates, and the values ​​of different cohesion and internal friction angles corresponding to the soil block; After obtaining the cohesion and internal friction angle data through the parameter matrix, the cohesion and internal friction angle of the soil block are assigned in the offshore wind turbine simulation model; According to the set number of samples, several groups of cohesion and internal friction angle data are obtained through the offshore wind turbine simulation model, and then several groups of cohesion and internal friction angle data are obtained for the subsequent calculation of the bearing capacity reliability and normal use limit state of the wind turbine simulation model under different cohesion and internal friction angles.

8. The offshore wind turbine bearing capacity reliability analysis method according to claim 1, characterized in that: In step S4, the load is input into the offshore wind turbine model in the PLAXIS 3D software. The load includes wind load and wave load. The wind load is the thrust acting on the wind turbine blades and the distributed load acting on the tower. The thrust calculation formula acting on the blades is: ； In the formula, For thrust, is the air density, is the thrust coefficient, is the fan blade swept area, is the average wind speed at the hub; The calculation formula for the load acting on the tower is: ； In the formula, is the load acting on the tower, for Wind speed at height, is the height coefficient, is the shape factor, is the outer diameter of the tower; The calculation formula is: ； In the formula, is the average wind speed at a height of 10 m above sea level, is the wind speed at a certain height of the wind turbine, is the height of the wind turbine at 10 m above sea level, is the wind speed profile index; The wave force on the wind turbine pile is calculated using the Morrison equation, and the wave force is expressed as: ； In the formula, is the inertial force, is the drag force, is the inertia coefficient, is the density of seawater, is the pile diameter, is the drag force coefficient, are the velocity and acceleration of the water particle, is the wave load force received by the wind turbine pile, is the depth of water, To seek guidance.

9. The offshore wind turbine bearing capacity reliability analysis method according to claim 1, characterized in that: In step S5, the expression of failure probability is: ； In the formula, is the failure probability, is the number of failures, is the sampling quantity.

10. An offshore wind turbine bearing capacity reliability analysis device, characterized in that: The device implements the offshore wind turbine bearing capacity reliability analysis method as claimed in any one of claims 1 to 9, and the device comprises: A model building module is used to perform simulation modeling based on the geometric shape of the offshore wind turbine and the parameters of the wind turbine tower pile foundation to obtain an offshore wind turbine simulation model; The cohesion and friction angle processing module is used to perform univariate analysis on cohesion and internal friction angle, select the optimal edge distribution, and construct the autocorrelation function between adjacent units in the three-dimensional random field; based on the autocorrelation function, the matrix decomposition algorithm is used to generate cohesion and internal friction angle data; The random field generation and data acquisition module is used to divide the obtained offshore wind turbine simulation model into soil blocks, obtain a number of soil blocks, import the random fields of cohesion and friction angle, determine the distribution of cohesion and friction angle in each soil block, and generate random fields and export data in batches through command streams; The finite element model batch automatic calculation module uses the finite element model after the load is imported to calculate the offshore wind turbine simulation model under several sets of cohesion and friction angle data according to the set number of samples, and performs automatic batch calculations on several sets of offshore wind turbine simulation models to obtain the calculated data; The failure mode solving module is used to calculate the failure probability under a set sample size based on the results of finite element batch calculations, and to determine the reliability of the offshore wind turbine bearing capacity based on the failure overview.

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