Wind turbine generator optimization design method based on tide-wave-sediment coupling simulation

Abstract

The invention relates to the technical field of data processing, and discloses a wind turbine generator optimization design method based on tidal current-wave-sediment coupling simulation. The method comprises the following steps: carrying out adaptive time step adjustment on marine environment data through a physical process intensity discrimination algorithm to obtain tidal current field, wave field and sediment transportation data; carrying out fluid-solid coupling calculation on the structural parameters of the wind turbine generator to obtain structural vibration response and ocean load distribution data, and predicting pile foundation scouring depth distribution; carrying out time-varying coefficient correction on the pile foundation bearing capacity parameters to obtain dynamic bearing capacity evaluation data; inputting a multi-objective optimization algorithm to obtain a Pareto optimal solution set; and obtaining a wind turbine generator optimization design scheme through engineering feasibility verification and screening. The technical problems that in existing offshore wind turbine generator design, tidal current-wave-sediment coupling simulation space-time scales are not matched, the pile foundation scouring prediction precision is insufficient, bearing capacity evaluation lacks time-varying characteristic consideration, and the multi-target optimization design capacity is insufficient are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a wind turbine optimization design method based on tidal flow-wave-sediment coupling simulation. BACKGROUND

[0002] The existing offshore wind turbine design method mainly adopts a separate modeling method, which usually models and analyzes tidal flow, wave and sediment three physical processes independently, and then simply superimposes the influence effect on the wind turbine. In the pile foundation design, the existing technology mainly uses empirical formula or simplified theoretical model to estimate the scour depth, and the bearing capacity calculation is based on the static safety factor, without considering the dynamic change characteristics of the marine environment. In the design optimization process, only a single target such as cost minimization or safety factor maximization is often focused on, and there is a lack of comprehensive trade-off of safety, economy and environmental friendliness.

[0003] The main deficiencies of the existing technology include: in the tidal flow-wave-sediment coupling simulation, it is difficult to match the time and space scales, and the interaction mechanism of multiple physical fields cannot be accurately captured; the fluid-structure coupling calculation precision of the wind turbine structure and the marine environment is insufficient, and the feedback effect of structure vibration on the surrounding flow field is ignored; the influence of the vortex structure behind the pile foundation on the prediction of the scour depth is not considered, resulting in low prediction accuracy; the static method is used for bearing capacity evaluation, without considering the time-varying characteristics and fatigue cumulative damage of the bearing capacity caused by scour; the design optimization lacks a multi-objective collaborative optimization mechanism, and cannot find an optimal balance point among conflicting design objectives.

[0004] Based on the analysis of the above technical deficiencies, the core technical problems to be solved in the design of offshore wind turbines are: how to establish a tidal flow-wave-sediment coupling simulation system considering the multi-time scale characteristics, realize accurate fluid-structure coupling calculation of the wind turbine structure and the marine environment, accurately predict the local scour evolution process around the pile foundation, dynamically evaluate the time-varying characteristics of the pile foundation bearing capacity under the influence of scour, and on this basis, build a multi-objective collaborative optimization algorithm to realize the comprehensive optimal design of safety, economy and environmental friendliness of the wind turbine. SUMMARY

[0005] The present application provides a wind turbine optimization design method based on tidal flow-wave-sediment coupling simulation, which is used to solve the technical problems of mismatching of time and space scales in tidal flow-wave-sediment coupling simulation, insufficient prediction accuracy of pile foundation scour, lack of consideration of time-varying characteristics in bearing capacity evaluation, and insufficient multi-objective optimization design capability in the existing offshore wind turbine design.

[0006] Firstly, this application provides a wind turbine optimization design method based on tidal current-wave-sediment coupling simulation. The method includes: adaptively adjusting the time step of marine environmental data using a physical process intensity discrimination algorithm to obtain tidal current field data, wave field data, and sediment transport data; performing fluid-structure interaction calculations on the wind turbine structural parameters based on the tidal current field data, wave field data, and sediment transport data to obtain structural vibration response data and marine load distribution data; inputting the structural vibration response data and marine load distribution data into a eddy current field calculation module for pile foundation scour depth prediction processing to obtain scour depth distribution data around the pile foundation; performing time-varying coefficient correction processing on the pile foundation bearing capacity parameters based on the scour depth distribution data around the pile foundation to obtain dynamic bearing capacity assessment data; inputting the dynamic bearing capacity assessment data into a multi-objective optimization algorithm for design parameter optimization processing to obtain a Pareto optimal solution set; and obtaining an optimized wind turbine design scheme by screening the Pareto optimal solution set through engineering feasibility verification.

[0007] The technical solution provided in this application employs a physical process intensity discrimination algorithm to adaptively adjust the time step of marine environmental data. This effectively solves the problem of difficult coupling calculations caused by the time scale differences among the three physical processes—tidal current, wave, and sediment—in existing technologies. When the wave height exceeds 3 meters or the current velocity exceeds 1.5 meters per second, a strong coupling mode is automatically triggered, reducing the wave model time step from 60 seconds to 10 seconds and the sediment model time step from 3600 seconds to 300 seconds. This ensures calculation accuracy under extreme sea conditions while avoiding the efficiency loss associated with small-step calculations throughout the entire time period. The fluid-structure interaction (FSI) calculation of wind turbine structural parameters establishes a two-way feedback mechanism between structural vibration and marine loads. By discretizing the monopile foundation into 2000 nodes and 1800 elements, and combining this with an interpolation algorithm to accurately map marine loads to the structural surface, and then updating the fluid computation domain through a mesh deformation algorithm, true FSI calculations are achieved. Compared to existing methods that treat wind turbines as static boundary conditions, this significantly improves the accuracy of structural response prediction. The eddy field calculation module performs pile foundation scour depth prediction processing. By identifying the vortex structure characteristics behind the pile foundation, it establishes a nonlinear relationship between eddy intensity and scour rate. When the eddy value exceeds 0.5 per second, the scour rate increases in a square relationship.

[0008] The time-varying coefficient correction processing for bearing capacity and the multi-objective optimization algorithm have played a crucial role in the specific application field of offshore wind turbine optimization design. The time-varying coefficient of bearing capacity quantitatively reflects the dynamic impact of seabed scour on the bearing capacity of the pile foundation by considering the nonlinear relationship between scour depth and pile diameter ratio. Combined with the superposition processing of fatigue damage factors, the bearing capacity assessment is transformed from a static safety factor to a dynamic safety assessment, providing a scientific basis for the full life cycle safety of offshore wind turbines. The improved NSGA-III algorithm, when dealing with the three-objective optimization problem of safety, economy, and environmental friendliness in wind turbine design, is particularly adapted to the characteristics of frequent extreme sea states and complex design constraints in marine engineering by introducing sea state sensitivity analysis and dynamic weight adjustment strategies. The Pareto optimal solution set obtained by the algorithm convergence can provide differentiated design scheme selection for engineering projects with different risk preferences. The feasibility verification screening of the project establishes a quantitative evaluation system that includes eighteen risk factors, comprehensively evaluating technical feasibility, construction feasibility, and operation and maintenance feasibility. This solves the technical gap between theoretical design and actual implementation of offshore wind power projects, ensures the engineering practicality and operability of the optimized design scheme, and provides a technical support system for the large-scale development of the offshore wind power industry. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a schematic diagram of an embodiment of the wind turbine optimization design method based on tidal current-wave-sediment coupling simulation in this application. Detailed Implementation

[0011] This application provides a wind turbine optimization design method based on tidal current-wave-sediment coupling simulation. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0012] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of the wind turbine optimization design method based on tidal current-wave-sediment coupling simulation in this application includes:

[0013] Step S101: Adaptive time step adjustment processing is performed on marine environmental data using a physical process intensity discrimination algorithm to obtain tidal field data, wave field data, and sediment transport data;

[0014] Step S102: Perform fluid-structure interaction calculations on the wind turbine structural parameters based on the tidal field data, wave field data, and sediment transport data to obtain structural vibration response data and marine load distribution data;

[0015] Step S103: Input the structural vibration response data and marine load distribution data into the eddy field calculation module to perform pile foundation scour depth prediction processing to obtain scour depth distribution data around the pile foundation;

[0016] Step S104: Based on the scour depth distribution data around the pile foundation, the bearing capacity parameters of the pile foundation are corrected by the time-varying coefficient to obtain dynamic bearing capacity evaluation data;

[0017] Step S105: Input the dynamic bearing capacity assessment data into a multi-objective optimization algorithm to perform design parameter optimization processing and obtain the Pareto optimal solution set;

[0018] Step S106: Obtain the optimal design scheme for the wind turbine unit by screening the Pareto optimal solution set through engineering feasibility verification.

[0019] It is understood that the executing entity of this application can be a wind turbine optimization design method system based on tidal current-wave-sediment coupling simulation, or it can be a terminal or a server; the specific implementation is not limited here. This application's embodiment uses a server as an example for illustration.

[0020] Specifically, adaptive time step adjustment is achieved through real-time monitoring and threshold judgment of raw marine environmental data. The algorithm first acquires raw marine environmental data, including wave height, current velocity, and wind speed. Then, it sets key discrimination threshold parameters. When the wave height exceeds 3 meters or the current velocity exceeds 1.5 meters per second, the algorithm automatically triggers a strongly coupled calculation mode. At this time, the basic time step of the tidal current model is dynamically reduced from the standard 600 seconds, the time step of the wave model is reduced from the normal 60 seconds to 10 seconds, and the time step of the sediment model is reduced from the basic 3600 seconds to 300 seconds. Through this dynamic adjustment mechanism, the three models' bidirectional data transmission interface can maintain the time synchronization of data exchange under different sea conditions. The tidal current model outputs tidal current field data containing current velocity distribution and water level changes based on the FVCOM calculation framework. The wave model outputs wave field data containing wave shear stress and orbital velocity based on the SWAN calculation framework. The sediment model receives the driving force data of the former two models based on the CSTM calculation framework and outputs sediment transport data containing suspended sediment concentration and bedload transport rate.

[0021] In the fluid-structure interaction calculation of wind turbine structural parameters, the finite element model of the single pile foundation discretizes the physical structure of the wind turbine into numerical calculation units. Specifically, it divides the 8-meter diameter steel pipe pile structure with a mud penetration depth of 35 meters into 2000 calculation nodes and 1800 connection elements. The interpolation algorithm is responsible for accurately mapping the marine loads from tidal and wave field data to each structural node. Through the distance weighting relationship between the calculation node coordinates and the fluid grid points, the pressure field and shear stress field in the fluid domain are converted into distributed loads acting on the structural surface. The structural dynamics solver is then used to... Based on these load distributions, the vibration response of the wind turbine tower system under marine environmental conditions is calculated, and structural vibration response data including displacement amplitude, velocity amplitude, and acceleration amplitude are output. At the same time, the mesh deformation algorithm updates the boundary conditions of the fluid computation domain according to the real-time motion state of the structure. When the difference between the structure's natural frequency and the wave's dominant frequency is detected to be less than 0.1 Hz, the system automatically starts a high-precision coupled calculation mode, further reducing the time step of fluid-structure interaction to 0.1 seconds to accurately capture the resonance phenomenon between the structure and the marine environment. Finally, marine load distribution data is output for subsequent scour depth prediction calculations.

[0022] The eddy field calculation module for predicting pile foundation scour depth first refines the computational area around the pile foundation with high-precision meshing, subdividing the original 10m×10m main computational domain mesh into a 0.1m×0.1m local mesh. Then, based on structural vibration response data and marine load distribution data, it calculates the eddy field intensity distribution of the flow field behind the pile foundation. The eddy value is obtained by calculating the curl of the velocity field. When the eddy value at a point behind the pile foundation exceeds the threshold of 0.5 s / s, the local scour rate in that area will be enhanced according to the square relationship of the eddy value. The four sediment transport mode calculation modules handle different types of sediment transport processes respectively. Suspended mass transport is calculated using the three-dimensional convection-diffusion equation. The concentration distribution and transport trajectory of sediment particles in the water body were studied. Bedload movement was calculated using the bed load formula to determine the sediment transport rate of sediment particles rolling along the seabed. For cohesive sediment, the settling velocity was corrected by considering the agglomeration effect between particles. For non-cohesive sediment, Stokes' settling velocity of a single particle in still water was calculated. The evolution of scour depth also needs to consider seasonal variations. The scour depth during typhoon season was amplified by 3.2 times the result calculated under normal sea conditions, and the scour depth during the dry season was reduced by 0.6 times. By comprehensively considering the eddy field distribution, sediment movement characteristics, and seasonal variations, the three-dimensional scour depth distribution data within a 360-degree range around the pile foundation was finally obtained.

[0023] In the process of correcting the time-varying coefficient of pile foundation bearing capacity parameters, the effective burial depth calculation module calculates the remaining effective burial depth of the pile foundation based on the scour depth distribution data around the pile foundation. The calculation method is the initial design burial depth minus the actual scour depth. The dynamic reduction of lateral resistance bearing capacity adopts the modified β coefficient method. The β coefficient value is dynamically adjusted according to the ratio of scour depth to pile diameter. When the scour depth reaches 1.5 times the pile diameter, the β coefficient decreases from the initial design value of 0.8 to 0.6. The correction calculation of end resistance bearing capacity is based on the reduction of effective burial depth, and the end resistance component is calculated using Meyerhof bearing capacity theory. The time-varying coefficient of bearing capacity... The formula for calculating the coefficient λ(t) is 1 minus 0.15 multiplied by the 1.2 power of the ratio of scour depth to pile diameter. This coefficient quantitatively reflects the degree of weakening of the total bearing capacity of the pile foundation by different degrees of scour. The fatigue damage superposition processing module statistically analyzes the occurrence of different levels of cyclic loads in the marine environment and sums up the ratio of the actual number of cycles of each load level to the corresponding fatigue life. When the cumulative fatigue damage factor exceeds 0.8, the bearing capacity of the pile foundation needs to be reduced by 10% on the basis of the time-varying coefficient for safety. By comprehensively considering the effects of scour and fatigue damage, the final output is dynamic bearing capacity assessment data containing time-varying characteristics.

[0024] The multi-objective optimization algorithm establishes a seven-dimensional design variable optimization space, including pile diameter, mud penetration depth, pile wall thickness, unit spacing, arrangement angle, foundation elevation, and type of scour prevention measures. The safety objective function is expressed by minimizing the reciprocal of the safety factor, defined as the ratio of the pile foundation's ultimate bearing capacity to the maximum applied load. The economic objective function minimizes the weighted sum of material costs, construction costs, and operation and maintenance costs. The environmental impact objective function is constructed by quantifying the area of ​​marine ecological disturbance and the degree of change in sediment transport patterns. The improved NSGA-III algorithm builds upon traditional legacy... Based on the traditional algorithm, a sea state sensitivity analysis module is introduced. In each iteration, the extreme sea state that occurs once every 100 years is considered as a constraint. The coupling constraint relationship between design variables requires that the ratio of pile diameter to mud penetration depth must be controlled within a reasonable range of 1:4 to 1:6. The dynamic weight adjustment strategy automatically adjusts the weight coefficients of the three objective functions according to the progress of the optimization iteration. In the initial stage, the safety weight is higher, and in the later stage, the economic weight gradually increases. The algorithm uses a population size of 200 individuals to perform 100 iterations and finally converges to obtain the Pareto optimal solution set containing multiple non-dominated solutions.

[0025] The engineering feasibility verification screening process selected five representative design schemes from the Pareto optimal solution set: a conservative scheme with a safety factor of 2.5, an economical scheme with the lowest total cost, an environmentally friendly scheme with the least environmental impact, a balanced scheme with the best overall performance, and an innovative scheme using a new anti-scour technology. The construction feasibility verification module examined the matching relationship between the rated lifting capacity of the offshore lifting equipment and the weight of the wind turbine pile foundation, analyzed the number of workable days and the distribution of construction windows under different seasonal sea conditions, and assessed the impact of severe sea conditions on construction safety. The operation and maintenance feasibility verification module assessed the navigation capability of maintenance vessels under different sea conditions and checked... Whether the maximum operating water depth of the underwater inspection equipment meets the requirements for pile foundation maintenance, analyzing the reachability time and success probability of emergency repair vessels, and the engineering implementation risk quantification assessment system includes three major categories and 18 risk assessment factors: technical risk, environmental risk, and economic risk. Each factor is divided into three levels (low, medium, and high) and assigned a corresponding risk score. The comprehensive score is calculated using a weighted average method, with the weight coefficient for technical risk set at 0.5, the weight coefficient for environmental risk at 0.3, and the weight coefficient for economic risk at 0.2. By comparing the comprehensive scores of the five candidate schemes, the scheme with the highest total score is selected as the optimized design scheme for the wind turbine.

[0026] In one specific embodiment, the process of performing step S101 may specifically include the following steps:

[0027] Raw marine environmental data is processed by wave height and current velocity threshold determination to obtain physical process intensity classification results;

[0028] Based on the intensity classification results of the physical process, the basic time step of the tidal current model is dynamically adjusted to obtain the time step parameters of the tidal current field.

[0029] Based on the intensity classification results of the physical process, the time steps of the wave model and the sediment model are jointly reduced to obtain the wave field time step parameters and the sediment transport time step parameters.

[0030] The time step parameters of the tidal field, wave field, and sediment transport are input into the bidirectional data transfer interface of the three models for coupled calculation processing to obtain tidal field data, wave field data, and sediment transport data.

[0031] Specifically, the wave height and current velocity threshold judgment processing of raw marine environmental data determines the intensity level of physical processes by real-time monitoring of sea state parameters. The data acquisition module first acquires marine environmental parameters including wave height, current velocity, wind speed, and water level, and then inputs these parameters into the threshold judgment algorithm for classification processing. When the wave height value exceeds 3 meters, it is marked as a high-intensity wave process; when the current velocity value exceeds 1.5 meters per second, it is marked as a high-intensity tidal process; and when the wind speed value exceeds 15 meters per second, it is marked as a high-intensity wind and wave process. The intensity classification results of physical processes are divided into three levels: low intensity, medium intensity, and high intensity. Low intensity corresponds to normal sea state, medium intensity corresponds to severe sea state, and high intensity corresponds to extreme sea state. This classification mechanism solves the problem that existing technologies cannot distinguish the intensity of different sea states.

[0032] The dynamic adjustment of the base time step of the tidal current model is based on the intensity classification results of the physical process. The calculation time interval of the FVCOM tidal current model is adjusted according to the physical process intensity classification results. When the physical process intensity classification result is low intensity, the tidal current model maintains a base time step of 600 seconds for calculation. When the classification result is medium intensity, the time step is adjusted to 300 seconds. When the classification result is high intensity, the time step is further reduced to 150 seconds. The dynamic adjustment algorithm determines the most suitable time step parameter by comparing the relationship between the current sea state intensity and the preset threshold. During the adjustment process, the stability conditions of numerical calculation are also considered to ensure that the Courant number is always less than 1 to maintain the convergence of the calculation. The determination of the tidal current field time step parameter directly affects the accuracy and efficiency of subsequent tidal current simulation.

[0033] The joint reduction of time steps for the wave and sediment models is based on the same physical process intensity classification results, simultaneously adjusting the calculation time intervals of the SWAN wave model and the CSTM sediment model. The time step adjustment for the wave model follows these rules: 60 seconds for low intensity, reduced to 30 seconds for medium intensity, and reduced to 10 seconds for high intensity. The time step adjustment for the sediment model follows these rules: 3600 seconds for low intensity, reduced to 1800 seconds for medium intensity, and reduced to 300 seconds for high intensity. This joint reduction ensures that wave calculations and sediment transport calculations remain consistent on the time scale, avoiding time synchronization issues between different physical processes. The wave field time step parameter mainly controls the calculation accuracy of wave propagation and breaking processes, while the sediment transport time step parameter mainly controls the calculation accuracy of erosion and deposition processes.

[0034] The coupled computational processing of the three-model bidirectional data transfer interface applies the adjusted time step parameters to the collaborative calculation of three numerical models: tidal current, wave, and sediment. The interface first establishes the time nodes for data exchange based on the time step parameters of each model. Then, data interpolation and transfer operations are performed at each exchange node. The tidal current model transfers velocity field and water level field data to the wave model, the wave model transfers wave shear stress and orbital velocity data to the sediment model, and the sediment model feeds back bed topography change data to the tidal current and wave models. The data transfer process uses spatial interpolation algorithms to handle grid differences between different models and temporal interpolation algorithms to handle data synchronization between different time steps. The coupled computational processing finally outputs tidal current field data containing velocity distribution and pressure gradient, wave field data containing wave height distribution and wave stress, and sediment transport data containing suspended sediment concentration and bed surface changes. This adaptive time step coupled computation solves the technical problems of low computational efficiency and insufficient accuracy caused by fixed step size in the existing technology.

[0035] In one specific embodiment, the process of performing step S102 may specifically include the following steps:

[0036] The tidal field data and wave field data are input into the finite element model of the single pile foundation for structural discretization processing to obtain pile foundation node coordinate data and element connection relationship data.

[0037] Based on the pile foundation node coordinate data, the marine load distribution is mapped using an interpolation algorithm to obtain structural surface pressure distribution data and shear force distribution data.

[0038] Based on the surface pressure distribution data and shear force distribution data of the structure, the vibration response of the wind turbine tower system is calculated to obtain structural displacement field data and velocity field data.

[0039] The structural displacement field data and velocity field data are input into a mesh deformation algorithm for fluid computational domain update processing to obtain structural vibration response data and marine load distribution data.

[0040] Specifically, the structural discretization of the finite element model of a single pile foundation converts the physical structure of the wind turbine into a numerical calculation model. The discretization process first obtains the geometric parameters of the single pile foundation, including a steel pipe pile structure with a diameter of 8 meters, a mud penetration depth of 35 meters, and a wall thickness of 0.08 meters. Then, the continuous pile structure is divided into several interconnected calculation units using finite element mesh generation technology. The mesh generation algorithm divides the 35-meter length into 70 equal-length segments along the pile axis, divides the circular cross-section into 32 equal-angle segments along the circumferential direction, and divides the pile wall thickness into 4 equal-thickness layers along the radial direction. This three-dimensional mesh generation forms 2000 nodes and 1800 hexahedral elements. The pile foundation node coordinate data records the position information of each node in three-dimensional space, and the element connection relationship data records which nodes each element consists of and the topological relationship between adjacent elements. This structural discretization process solves the problem in the existing technology of treating the wind turbine as a rigid body boundary condition and ignoring structural deformation.

[0041] The interpolation algorithm mapping process accurately distributes marine loads from tidal and wave field data to each structural node based on the pile foundation node coordinate data. The mapping process first establishes the geometric correspondence between the fluid computational mesh and the structural finite element mesh. The pressure and shear stress values ​​of the fluid mesh are transferred to the nearest structural node through a distance-weighted interpolation method. The distance-weighted interpolation algorithm calculates the Euclidean distance from the fluid mesh point to the structural node, and then uses the reciprocal of the distance as a weighting coefficient to allocate the load value. When there are multiple fluid mesh points around a structural node, the load value of that node is equal to the weighted average of the load values ​​of all adjacent fluid mesh points according to the distance weight. The structural surface pressure distribution data records the magnitude and direction of the normal pressure acting on each point on the pile surface, and the shear force distribution data records the magnitude and direction of the tangential friction force acting on each point on the pile surface. The interpolation algorithm mapping process ensures that marine environmental loads can be accurately transferred from the fluid domain to the solid domain.

[0042] The vibration response calculation and processing is based on the pressure distribution data and shear force distribution data of the structural surface to solve the dynamic response of the wind turbine tower system under marine loads. The calculation process uses a structural dynamics equation solver, and the pressure and shear force distributed on the pile foundation surface are input as external loads into the finite element equations. The tower system includes the pile foundation, transition section, tower, and nacelle rotor assembly. A corresponding stiffness matrix and mass matrix are established for each component. The vibration response calculation uses the modal superposition method to decompose the complex multi-degree-of-freedom vibration problem into a linear combination of several single-degree-of-freedom vibration problems. The calculation process considers the shape of the first three modes of the structure and their corresponding natural frequencies. When the frequency of the marine load is close to the natural frequency of the structure, a resonance amplification effect will occur. The structural displacement field data records the displacement amplitude and phase information of each part of the tower in three directions, and the velocity field data records the vibration velocity vector of each part of the tower. The vibration response calculation and processing solves the static design problem that ignores the dynamic characteristics of the structure in the existing technology.

[0043] The fluid computational domain update process of the mesh deformation algorithm adjusts the boundary shape and position of the fluid computational mesh based on structural displacement field data and velocity field data. The update process first reads the displacement vectors of each node on the structural surface, and then transfers these displacements to the fluid mesh nodes adjacent to the structural surface. The mesh deformation algorithm adopts an elastic deformation method, treating the fluid computational domain as an elastic body. The displacement of the structural surface is applied to the elastic body as a boundary condition. The new position of the mesh nodes in the entire fluid domain is obtained by solving the elasticity equation. During the deformation process, the topological connection relationship of the mesh remains unchanged, and only the coordinate position of the nodes is changed. When the structural displacement is large, the algorithm automatically triggers the mesh reconstruction mechanism to prevent the mesh quality from deteriorating. The structural vibration response data comprehensively records all dynamic characteristic parameters of the structure under the action of the marine environment. The marine load distribution data updates the fluid load distribution after considering the influence of structural motion. The fluid computational domain update process solves the coupling calculation error problem caused by inaccurate fluid-solid interface processing in the prior art.

[0044] In one specific embodiment, the process of executing step S103 may specifically include the following steps:

[0045] The vibration response data of the structure is subjected to local mesh densification around the pile foundation to obtain high-precision computational mesh data;

[0046] Based on the marine load distribution data, the vorticity field intensity of the flow field behind the pile foundation is calculated to obtain the vorticity value distribution data.

[0047] Based on the vorticity distribution data, the scouring rate parameters are corrected for nonlinear relationships to obtain the local scouring rate enhancement coefficient.

[0048] The local scour rate enhancement coefficient is input into the calculation module of four sediment movement modes for scour depth evolution processing to obtain the scour depth distribution data around the pile foundation.

[0049] Specifically, the local mesh refinement process around the pile foundation identifies key areas requiring detailed calculations based on structural vibration response data. The refinement process first analyzes the displacement gradient and stress concentration areas in the structural vibration response data, identifying the pile-seabed interface, the pile's upstream face, and the wake region behind the pile as key calculation areas. Then, the calculation mesh for these areas is progressively refined. The main calculation domain's mesh size is gradually refined from the original 10m×10m to 5m×5m, then 2m×2m, ultimately forming a high-precision 0.1m×0.1m calculation mesh around the pile foundation. The mesh refinement algorithm uses a quadtree subdivision method, dividing a single mesh cell into four sub-cells at each subdivision. During subdivision, the size ratio of adjacent mesh cells is kept below 2:1 to maintain the stability of the numerical calculation. The high-precision calculation mesh data includes the coordinate information of all mesh nodes within the refined area, cell connection relationships, and boundary condition settings. This local refinement process solves the problem of lost near-field flow details in pile foundations due to insufficient mesh resolution in existing technologies.

[0050] The vortex field intensity calculation and processing method calculates the vortex structure characteristics of the flow field behind the pile foundation based on the velocity field information in the ocean load distribution data. The calculation and processing first extracts three components of the velocity field: the flow velocity in the x-direction, y-direction, and z-direction. Then, the spatial gradient of the velocity field is calculated using the finite difference method. The vortex value is obtained through the curl of the velocity field. The specific calculation process includes calculating the partial derivative of the x-direction velocity in the y-direction and the partial derivative of the y-direction velocity in the x-direction. The difference between the two is used to obtain the vortex component in the z-direction. Similarly, the vortex components in the x-direction and y-direction are calculated. The magnitude of the vortex value reflects the rotation intensity of the fluid micro-elements. Due to the fluid movement around the pile foundation, alternating vortex structures are formed behind the pile foundation. The intensity and frequency of these vortices directly affect the scouring intensity around the pile foundation. The vortex value distribution data records the vortex vector and vortex intensity scalar of each grid point in the computational domain. When the vortex value exceeds the preset threshold of 0.5 seconds, it is marked as a strong vortex region. The vortex field intensity calculation and processing method solves the problem that existing technologies cannot accurately identify the complex vortex structure around the pile foundation.

[0051] The nonlinear relationship correction process dynamically adjusts the scour rate parameter based on vorticity distribution data. This correction establishes a nonlinear mapping relationship between vorticity intensity and scour enhancement effect. When the vorticity value in a certain region is below 0.2 s / s, the scour rate remains at its baseline value. When the vorticity value is between 0.2 and 0.5 s / s, the scour rate increases linearly. When the vorticity value exceeds 0.5 s / s, the scour rate increases sharply according to a quadratic relationship. The mathematical expression of the nonlinear relationship uses a piecewise function. The enhancement coefficient for the low vorticity range is 1.0; for the medium vorticity range, it is 1.0 plus 0.5 times the difference between the vorticity value and 0.2; and for the high vorticity range, it is 1.15 plus 2.0 times the square of the difference between the vorticity value and 0.5. The local scour rate enhancement coefficient reflects the amplification effect of vortices of different intensities on the seabed sediment erosion capacity. This coefficient is directly used as a correction factor for subsequent sediment transport calculations. The nonlinear relationship correction process solves the problem of weak correlation between scour rate and flow field characteristics in existing technologies.

[0052] The scour depth evolution processing of the four sediment transport model calculation modules applies the local scour rate enhancement coefficient to the transport calculation of different types of sediment. The evolution processing includes four sub-modules: suspended sediment transport model, bedload transport model, viscous sediment agglomeration model, and non-viscous sediment dispersion model. The suspended sediment transport model calculates the suspension transport process of fine-grained sediment in water bodies; the bedload transport model calculates the rolling and sliding process of coarse-grained sediment along the seabed; the viscous sediment agglomeration model considers the influence of the adhesion between silt particles on the settling velocity; and the non-viscous sediment dispersion model... The independent motion characteristics of particles such as sand and gravel are treated by the sediment dispersion model. Each motion model is corrected by the local scour rate enhancement coefficient. When the enhancement coefficient is greater than 1.0, it indicates that the scour effect in the area is enhanced, the sediment initiation threshold is reduced and the transport capacity is increased. The evolution of scour depth is calculated stepwise by the time step method to calculate the change of seabed elevation. The seabed elevation is updated according to the net value of sediment transport in each time step. The scour depth distribution data around the pile foundation records the seabed scour depth values ​​at different azimuth angles and different radial distances.

[0053] In one specific embodiment, the process of executing step S104 may specifically include the following steps:

[0054] The effective burial depth calculation is performed on the scour depth distribution data around the pile foundation to obtain the remaining burial depth parameters of the pile foundation;

[0055] Based on the remaining burial depth parameters of the pile foundation, the lateral resistance bearing capacity coefficient is dynamically reduced to obtain the corrected lateral resistance bearing capacity data.

[0056] Based on the remaining burial depth parameters of the pile foundation, the end bearing capacity parameters are effectively corrected by burial depth to obtain corrected end bearing capacity data.

[0057] The modified side resistance bearing capacity data and the modified end resistance bearing capacity data are input into the bearing capacity time-varying coefficient calculation module for fatigue damage superposition processing to obtain dynamic bearing capacity evaluation data.

[0058] Specifically, the effective burial depth calculation process determines the actual remaining burial length of the pile foundation in the seabed based on the scour depth distribution data around the pile foundation. The calculation process first reads the scour depth values ​​at different azimuth angles from the scour depth distribution data, then divides the area around the pile foundation into several fan-shaped regions. The maximum scour depth in each fan-shaped region is taken as the representative scour depth of that region. The effective burial depth calculation adopts a geometric subtraction operation, subtracting the scour depth value of the corresponding region from the initial design burial depth of the pile foundation. When the scour depth around the pile foundation is uneven, it is necessary to calculate the remaining burial depth length of the pile body at different depths. The parameters of the remaining burial depth of the pile foundation include the average remaining burial depth, the minimum remaining burial depth, and the remaining burial depth distribution of different depth layers. The average remaining burial depth is obtained by weighted averaging of the remaining burial depth values ​​at all azimuth angles, and the minimum remaining burial depth is taken as the minimum value of the remaining burial depth among all azimuth angles. The effective burial depth calculation process solves the problem of ignoring the uneven spatial distribution of scour depth in the prior art.

[0059] The dynamic reduction of the lateral bearing capacity coefficient is based on the remaining burial depth parameter of the pile foundation to correct the pile's side skin friction. The reduction process employs a layered calculation method, dividing the pile body into several calculation segments along the depth direction. The lateral bearing capacity coefficient of each segment is dynamically adjusted according to the remaining burial depth of that segment. When a segment is completely exposed within the scour pit, its lateral bearing capacity coefficient drops to zero. When a segment is partially exposed, the lateral bearing capacity coefficient is linearly reduced according to the exposure ratio. The algorithm for reducing the lateral bearing capacity coefficient uses... The β coefficient correction method determines the original β coefficient value based on soil properties and pile-soil contact conditions. The corrected β coefficient is equal to the original β coefficient multiplied by the burial depth correction factor, which is equal to the ratio of the actual remaining burial depth to the initial design burial depth. When the scour depth reaches 1.5 times the pile diameter, the β coefficient decreases from the initial value of 0.8 to 0.6. The corrected lateral bearing capacity data includes the corrected β coefficient value of each layer and the corresponding lateral bearing capacity contribution. The dynamic reduction process solves the problem that the lateral bearing capacity calculation in the existing technology does not consider the scour effect.

[0060] The effective burial depth correction process for end bearing capacity parameters adjusts the calculation parameters of pile end bearing capacity based on the remaining burial depth parameters of the pile foundation. The correction process first determines the remaining burial depth value at the depth of the pile end, and then corrects the burial depth-related parameters in the end bearing capacity calculation formula based on this remaining burial depth value. The end bearing capacity is calculated using the Meyerhof bearing capacity theory. The effective burial depth parameter in the calculation formula directly affects the magnitude of the end bearing capacity. The effective burial depth correction algorithm replaces the original pile end burial depth value with the actual burial depth value after considering the scour effect. When scour occurs around the pile end, the self-weight stress of the soil overlying the pile end decreases, and the corresponding end bearing capacity also decreases. The correction calculation also needs to consider the additional influence of the stability of the scour pit sidewall on the end bearing capacity. When the scour pit is deep, the soil near the pile end will have a lateral unloading effect, further reducing the end bearing capacity. The corrected end bearing capacity data includes the end bearing capacity value after considering the scour effect and the corresponding safety factor. The effective burial depth correction process solves the problem of ignoring the scour effect in the calculation of end bearing capacity in the existing technology.

[0061] The fatigue damage superposition processing of the bearing capacity time-varying coefficient calculation module comprehensively analyzes the corrected lateral resistance bearing capacity data and the corrected end resistance bearing capacity data. The superposition processing first calculates the total bearing capacity of the pile foundation, which is equal to the sum of the corrected lateral resistance bearing capacity and the corrected end resistance bearing capacity. Then, a fatigue damage factor is introduced to further correct the bearing capacity. The fatigue damage factor is calculated by statistically analyzing the frequency and duration of different levels of cyclic loads in the marine environment. Damage accumulation adopts Miner's linear cumulative damage theory, which sums the ratio of the number of cycles of each load level to the corresponding fatigue life. When the cumulative damage factor exceeds 0.8, the bearing capacity needs to be reduced by an additional safety factor. The time-varying coefficient calculation considers the decay law of bearing capacity over time. The time-varying coefficient is equal to 1 minus 0.15 times the ratio of scour depth to pile diameter to the power of 1.2. This coefficient reflects the weakening effect of scour on bearing capacity. The dynamic bearing capacity assessment data includes the pile foundation bearing capacity value after considering the dual effects of scour and fatigue, the corresponding safety factor, and the bearing capacity time-varying curve. The fatigue damage superposition processing solves the problem that the bearing capacity assessment in the prior art does not consider long-term cumulative damage.

[0062] In one specific embodiment, the process of executing step S105 may specifically include the following steps:

[0063] The dynamic bearing capacity assessment data is processed by constructing a safety objective function to obtain the objective of minimizing the reciprocal of the safety factor;

[0064] Based on the design variable range of the wind turbine, the total cost of the economic objective function is calculated to obtain the objective of minimizing material, construction and operation and maintenance costs.

[0065] Based on the assessment of marine ecological disturbance, the objective function of environmental impact is quantified to obtain the objective of minimizing environmental impact;

[0066] The objectives of minimizing the reciprocal of the safety factor, minimizing material construction and maintenance costs, and minimizing environmental impact are input into the improved NSGA-III algorithm for multi-objective parallel optimization to obtain the Pareto optimal solution set.

[0067] Specifically, the safety objective function construction process establishes a quantitative evaluation index for the safety of wind turbine pile foundations based on dynamic bearing capacity assessment data. The process first extracts the ultimate bearing capacity and maximum load values ​​of the pile foundations from the dynamic bearing capacity assessment data, and then calculates the safety factor, which equals the ultimate bearing capacity divided by the maximum load. The safety objective function is expressed as the reciprocal of the safety factor, i.e., the objective function value equals the maximum load divided by the ultimate bearing capacity. This reciprocal form design allows the optimization algorithm to maximize the safety factor by minimizing the objective function value during the optimization process. The minimization of the reciprocal safety factor considers load variations and bearing capacity attenuation under different sea conditions. The objective function also includes a weighted average of time-varying safety factors. The time-varying weights are determined according to the probability of occurrence of different sea conditions: 0.6 for normal sea conditions, 0.3 for severe sea conditions, and 0.1 for extreme sea conditions. This safety objective function construction process solves the problem of the lack of quantitative indicators in existing safety assessment technologies.

[0068] The total cost calculation for the economic objective function establishes a full life-cycle cost model based on the design variables of the wind turbine. The calculation decomposes the total cost into three main components: material cost, construction cost, and operation and maintenance cost. Material cost calculation is based on design variables such as pile diameter, wall thickness, and burial depth. Steel usage equals pile volume multiplied by steel density, and material cost equals steel usage multiplied by steel unit price. Construction cost calculation considers the special characteristics of offshore construction, including transportation costs, crane rental costs, construction vessel costs, and labor costs. Construction costs are positively correlated with pile weight and construction difficulty. Operation and maintenance cost calculation covers periodic inspection costs, corrosion protection maintenance costs, and emergency repair costs. Operation and maintenance costs are related to the pile surface area and the severity of the marine environment. The total cost calculation formula weights and sums the three types of costs. The weighting coefficients are determined based on actual engineering experience: material cost has a weight of 0.5, construction cost has a weight of 0.3, and operation and maintenance cost has a weight of 0.2. The goal of minimizing material, construction, and operation and maintenance costs is achieved by optimizing design variables to reduce the total investment and operating costs of the wind turbine.

[0069] The quantification of the environmental impact objective function is based on the assessment of marine ecological disturbance to establish an environmentally friendly evaluation system. The quantification process first identifies the main influencing factors of wind turbine construction and operation on the marine environment, including suspended sediment diffusion during construction, changes in the hydrodynamic field during operation, changes in seabed topography, and disturbance of marine habitats. The impact of suspended sediment diffusion is quantified by calculating the concentration and diffusion range of suspended sediment generated during the pile foundation sinking process. The impact of changes in the hydrodynamic field is assessed by comparing the differences in tidal current and wave characteristics before and after wind farm construction. The impact of changes in seabed topography is quantified by statistically analyzing the scour range and scour volume around the pile foundation. The impact of disturbance of marine habitats is quantified by assessing the degree of change in the benthic community structure. The environmental impact objective function uses a weighted scoring method, summing the quantification results of various influencing factors according to their degree of impact. The weight for suspended sediment diffusion is 0.3, the weight for changes in the hydrodynamic field is 0.25, the weight for changes in seabed topography is 0.25, and the weight for disturbance of marine habitats is 0.2. The environmental impact minimization objective aims to reduce the negative impact of wind turbines on the marine ecological environment by optimizing design parameters.

[0070] The improved NSGA-III algorithm's multi-objective parallel optimization process collaboratively optimizes three conflicting objective functions. The optimization process employs a reference-point-based multi-objective evolutionary algorithm framework. The algorithm first uniformly distributes reference points in the objective space, then generates new individual solutions through genetic operations. During individual evaluation, the distance between each solution and the reference point is calculated, and the selection mechanism prioritizes solutions closer to the reference point. The improved NSGA-III algorithm incorporates three key improvements tailored to the characteristics of offshore wind power engineering: firstly, it introduces a sea state sensitivity analysis module, considering the constraints of once-in-a-century extreme sea states in each iteration. Secondly, engineering constraints are established between design variables, and the ratio of pile diameter to burial depth is controlled within a reasonable range. Thirdly, a dynamic weight adjustment strategy is adopted to automatically adjust the relative importance of the three objectives according to the optimization process. The algorithm uses a population size of 200 individuals and runs 100 iterations. In each iteration, the objective function values ​​of all individuals are evaluated in parallel. The crossover probability is set to 0.9 and the mutation probability is set to 0.1. The multi-objective parallel optimization process eventually converges to obtain a Pareto optimal solution set containing multiple non-dominated solutions. Each solution in this solution set represents a trade-off between safety, economy and environmental friendliness.

[0071] In one specific embodiment, the process of executing step S106 may specifically include the following steps:

[0072] Representative schemes were selected from the Pareto optimal solution set to obtain five typical design schemes;

[0073] Based on the five typical design schemes, the construction feasibility parameters were verified for lifting capacity and construction window period to obtain construction risk assessment data.

[0074] Based on the construction risk assessment data, the operation and maintenance feasibility parameters are verified for navigation capability and maintenance depth to obtain operation and maintenance risk assessment data.

[0075] The construction risk assessment data and operation and maintenance risk assessment data are input into the engineering implementation risk quantification assessment system for comprehensive scoring and processing to obtain the optimized design scheme of the wind turbine.

[0076] Specifically, the representative scheme selection process selects five typical design schemes based on the distribution characteristics of the objective function values ​​of different solutions in the Pareto optimal solution set. The selection process first performs cluster analysis on all solutions in the Pareto optimal solution set according to the three objective function values. The clustering algorithm uses the K-means method to divide the solution set into five different categories, each representing a specific design tendency. During the selection process, the solution with the highest safety coefficient is selected as the conservative scheme, the solution with the lowest total cost is selected as the economic scheme, the solution with the least environmental impact is selected as the environmentally friendly scheme, the solution with balanced objective function values ​​is selected as the balanced scheme, and the solution using innovative anti-scour technology is selected as the innovative scheme. These five typical design schemes correspond to different engineering needs and risk preferences. The conservative scheme is suitable for critical sea areas with extremely high safety requirements, the economic scheme is suitable for commercial projects seeking investment returns, the environmentally friendly scheme is suitable for ecologically sensitive sea areas, the balanced scheme is suitable for conventional projects that comprehensively consider multiple factors, and the innovative scheme is suitable for technology verification and demonstration projects. The representative scheme selection process solves the problem that the optimization results in existing technologies are too complex to be practically applied.

[0077] The feasibility parameters for construction, including lifting capacity and construction window verification, assess the technical feasibility of offshore construction based on the specific parameters of five typical design schemes. The verification process first calculates the weight and dimensional parameters of the pile foundation in each scheme, where the pile foundation weight equals the steel volume multiplied by the steel density. Then, it compares the weight with the lifting capacity of existing offshore construction equipment. Lifting capacity verification considers limitations such as the rated lifting capacity of the crane vessel, lifting radius, and sea state level. When the pile foundation weight exceeds the lifting equipment's capacity, it is necessary to assess whether this can be resolved through segmented construction or equipment replacement. The construction window verification process analyzes sea state statistics for different seasons, calculating the number of effective days in each month that meet construction conditions. Construction conditions include wave height less than 2 meters, wind speed less than 12 meters per second, and visibility greater than 1 kilometer. The construction window analysis also considers the impact of tidal conditions on the accuracy of pile foundation placement. Construction risk assessment data includes equipment compatibility score, construction difficulty level, expected construction period, and cost risk level. The lifting capacity and construction window verification processes address the problem of insufficient feasibility studies in existing technologies.

[0078] The verification process for navigation capability and maintenance depth of operation and maintenance feasibility parameters further evaluates the maintenance and support capabilities of wind turbines during operation based on construction risk assessment data. The verification process first assesses the navigation capability of the operation and maintenance vessel, considering factors such as the matching relationship between vessel size and pile spacing, vessel handling safety under different sea conditions, and the feasibility of berthing operations. When the pile spacing is too small, it will restrict the passage of large operation and maintenance vessels, requiring the use of smaller vessels, which increases operation and maintenance costs and risks. The verification process for maintenance depth assesses the operational capabilities of underwater inspection and maintenance equipment. Maintenance depth includes the inspection depth of the pile penetration section, the inspection depth of the anti-corrosion layer, and the scour monitoring depth. When the scour depth exceeds the capabilities of conventional inspection equipment, specialized deep-water operation equipment is required. The verification process for maintenance depth also considers the safety depth limits and operational efficiency of divers. The operation and maintenance risk assessment data includes navigation safety level, maintenance accessibility score, emergency response time, and operation and maintenance cost risk level. The verification process for navigation capability and maintenance depth solves the problem of neglecting the technical constraints of the operation and maintenance phase in existing technologies.

[0079] The comprehensive scoring process of the project implementation risk quantification assessment system unifies the risk assessment data of construction and operation and maintenance. The scoring process establishes an assessment system containing 18 risk factors, categorized into three main types: technical risk, environmental risk, and economic risk. Technical risks include six factors: equipment compatibility, construction difficulty, maintenance accessibility, structural safety, corrosion resistance and durability, and foundation stability. Environmental risks include six factors: sea state impact, ecological disturbance, suspended sediment diffusion, noise impact, landscape impact, and fishery impact. Economic risks include six factors: investment overrun, construction delay, operation and maintenance costs, electricity price risk, technical risk, and market risk. Each risk factor is classified into three levels—low, medium, and high—corresponding to risk scores of 1, 2, and 3 points, respectively. The comprehensive score is calculated using a weighted average method, with a weighting coefficient of 0.5 for technical risk, 0.3 for environmental risk, and 0.2 for economic risk. The comprehensive scoring process ultimately selects the scheme with the lowest total score as the recommended optimized design scheme for the wind turbine, which offers the best comprehensive risk control effect while ensuring technical feasibility.

[0080] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A wind turbine optimization design method based on tidal current-wave-sediment coupling simulation, characterized in that, The method includes: The marine environmental data were processed by an adaptive time step adjustment algorithm based on the physical process intensity to obtain tidal field data, wave field data, and sediment transport data. Based on the tidal field data, wave field data, and sediment transport data, the structural parameters of the wind turbine are processed by fluid-structure interaction calculation to obtain structural vibration response data and marine load distribution data. The structural vibration response data and marine load distribution data are input into the eddy field calculation module to perform pile foundation scour depth prediction processing, and the scour depth distribution data around the pile foundation is obtained. Based on the scour depth distribution data around the pile foundation, the bearing capacity parameters of the pile foundation are corrected by the time-varying coefficient to obtain dynamic bearing capacity evaluation data. The dynamic bearing capacity assessment data is input into a multi-objective optimization algorithm to optimize the design parameters and obtain the Pareto optimal solution set. The optimal design scheme for wind turbine units is obtained by screening the Pareto optimal solution set through engineering feasibility verification.

2. The wind turbine optimization design method based on tidal current-wave-sediment coupling simulation according to claim 1, characterized in that, The process involves adaptively adjusting the time step of marine environmental data using a physical process intensity discrimination algorithm to obtain tidal field data, wave field data, and sediment transport data, including: Raw marine environmental data is processed by wave height and current velocity threshold determination to obtain physical process intensity classification results; Based on the intensity classification results of the physical process, the basic time step of the tidal current model is dynamically adjusted to obtain the time step parameters of the tidal current field. Based on the intensity classification results of the physical process, the time steps of the wave model and the sediment model are jointly reduced to obtain the wave field time step parameters and the sediment transport time step parameters. The time step parameters of the tidal field, wave field, and sediment transport are input into the bidirectional data transfer interface of the three models for coupled calculation processing to obtain tidal field data, wave field data, and sediment transport data.

3. The wind turbine optimization design method based on tidal current-wave-sediment coupling simulation according to claim 1, characterized in that, The process of performing fluid-structure interaction calculations on the wind turbine structural parameters based on the tidal field data, wave field data, and sediment transport data to obtain structural vibration response data and marine load distribution data includes: The tidal field data and wave field data are input into the finite element model of the single pile foundation for structural discretization processing to obtain pile foundation node coordinate data and element connection relationship data. Based on the pile foundation node coordinate data, the marine load distribution is mapped using an interpolation algorithm to obtain structural surface pressure distribution data and shear force distribution data. Based on the surface pressure distribution data and shear force distribution data of the structure, the vibration response of the wind turbine tower system is calculated to obtain structural displacement field data and velocity field data. The structural displacement field data and velocity field data are input into a mesh deformation algorithm for fluid computational domain update processing to obtain structural vibration response data and marine load distribution data.

4. The wind turbine optimization design method based on tidal current-wave-sediment coupling simulation according to claim 1, characterized in that, The structural vibration response data and marine load distribution data are input into the eddy field calculation module for pile foundation scour depth prediction processing to obtain scour depth distribution data around the pile foundation, including: The vibration response data of the structure is subjected to local mesh densification around the pile foundation to obtain high-precision computational mesh data; Based on the marine load distribution data, the vorticity field intensity of the flow field behind the pile foundation is calculated to obtain the vorticity value distribution data. Based on the vorticity distribution data, the scouring rate parameters are corrected for nonlinear relationships to obtain the local scouring rate enhancement coefficient. The local scour rate enhancement coefficient is input into the calculation module of four sediment movement modes for scour depth evolution processing to obtain the scour depth distribution data around the pile foundation.

5. The wind turbine optimization design method based on tidal current-wave-sediment coupling simulation according to claim 1, characterized in that, The process of correcting the bearing capacity parameters of the pile foundation based on the scour depth distribution data around the pile foundation using the bearing capacity time-varying coefficient correction method yields dynamic bearing capacity assessment data, including: The effective burial depth calculation is performed on the scour depth distribution data around the pile foundation to obtain the remaining burial depth parameters of the pile foundation; Based on the remaining burial depth parameters of the pile foundation, the lateral resistance bearing capacity coefficient is dynamically reduced to obtain the corrected lateral resistance bearing capacity data. Based on the remaining burial depth parameters of the pile foundation, the end bearing capacity parameters are effectively corrected by burial depth to obtain corrected end bearing capacity data. The modified side resistance bearing capacity data and the modified end resistance bearing capacity data are input into the bearing capacity time-varying coefficient calculation module for fatigue damage superposition processing to obtain dynamic bearing capacity evaluation data.

6. The wind turbine optimization design method based on tidal current-wave-sediment coupling simulation according to claim 1, characterized in that, The step of inputting the dynamic bearing capacity assessment data into a multi-objective optimization algorithm for design parameter optimization to obtain the Pareto optimal solution set includes: The dynamic bearing capacity assessment data is processed by constructing a safety objective function to obtain the objective of minimizing the reciprocal of the safety factor; Based on the design variable range of the wind turbine, the total cost of the economic objective function is calculated to obtain the objective of minimizing material, construction and operation and maintenance costs. Based on the assessment of marine ecological disturbance, the objective function of environmental impact is quantified to obtain the objective of minimizing environmental impact; The objectives of minimizing the reciprocal of the safety factor, minimizing material construction and maintenance costs, and minimizing environmental impact are input into the improved NSGA-III algorithm for multi-objective parallel optimization to obtain the Pareto optimal solution set.

7. The wind turbine optimization design method based on tidal current-wave-sediment coupling simulation according to claim 1, characterized in that, The process of obtaining the optimal wind turbine design scheme by screening the Pareto optimal solution set through engineering feasibility verification includes: Representative schemes were selected from the Pareto optimal solution set to obtain five typical design schemes; Based on the five typical design schemes, the construction feasibility parameters were verified for lifting capacity and construction window period to obtain construction risk assessment data. Based on the construction risk assessment data, the operation and maintenance feasibility parameters are verified for navigation capability and maintenance depth to obtain operation and maintenance risk assessment data. The construction risk assessment data and operation and maintenance risk assessment data are input into the engineering implementation risk quantification assessment system for comprehensive scoring and processing to obtain the optimized design scheme of the wind turbine.

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