Numerical simulation method of floating wind turbine based on cross-scale typhoon modeling algorithm

Abstract

The application discloses a kind of numerical simulation method of floating wind turbine based on cross-scale typhoon modeling algorithm, to solve the problem that prior art cannot accurately simulate typhoon unsteady wind field and its influence on floating wind turbine coupled dynamic response.This method first divides the whole process of typhoon according to its meteorological characteristics into multiple typical stages;For each stage, a parameterized mesoscale typhoon model is used to calculate the time-varying average wind field, and a three-dimensional turbulence component generated based on a microscale turbulence model is embedded to generate a high-fidelity stage wind field file;Use splicing algorithm to integrate each stage wind field into a complete and continuous unsteady typhoon wind field input file;Finally, the wind field file is input into the aerodynamic-hydrodynamic-servo-elastic coupling simulation platform OpenFAST to analyze the full coupling dynamic response of floating wind turbine.The application improves the prediction accuracy of floating wind turbine system load and motion response under typhoon extreme conditions, and provides a reliable method for typhoon-resistant design and safety evaluation of floating wind turbine.

Description

Technical Field

[0001] This invention relates to the field of wind power engineering technology, and in particular to a numerical simulation method for floating wind turbines based on a cross-scale typhoon modeling algorithm. Background Technology

[0002] Floating offshore wind power is a key technology for developing deep-sea wind energy resources. However, the southeastern coastal waters of my country are frequently hit by typhoons, and typhoon wind fields exhibit complex characteristics such as high unsteadiness, strong wind shear, strong turbulence, and abrupt changes in wind direction as the typhoon structure evolves. The extreme wind and wave environment brought by typhoons poses a severe challenge to the survival and safety of floating wind turbines. Floating wind turbine systems have a wide frequency response range to wind excitation: their large-scale platform movements, such as pitching and rolling, are sensitive to low-frequency wind excitation; while the elastic modes of their blades, towers, and other structures are sensitive to high-frequency turbulent fluctuations. Accurately predicting the dynamic response and ultimate load of floating wind turbines during typhoons is a core prerequisite for ensuring their structural safety and reliable operation. The key bottleneck to achieving high-precision prediction lies in obtaining wind field input data that can simultaneously reproduce the mesoscale unsteady evolution and microscale high-frequency turbulent characteristics of typhoons.

[0003] However, existing wind field modeling techniques suffer from the following fundamental limitations when addressing this cross-scale requirement: The limitations of mesoscale meteorological models: Models such as the WRF (Weather Research and Forecasting) model or the parameterized YM (Yan Meng) model can effectively simulate typhoon paths, intensity evolution, and mesoscale mean wind field structures. However, these models typically have kilometer-scale resolutions and cannot resolve the microscale turbulent fluctuations that are crucial for generating high-frequency fatigue and ultimate loads on wind turbine blades and towers. The limitations of microscale turbulence simulators: Stochastic turbulence simulators such as TurbSim can generate three-dimensional microscale turbulent fields that conform to specific spectral and spatial coherence and are widely used for simulating aeroelastic loads on wind turbines. However, the theoretical basis of these methods is the assumption of a steady-state process. They cannot reproduce the non-steady-state evolution process of a typhoon that lasts for several hours, such as the sharp increase in wind speed, the sudden drop in wind speed when passing through the eye of the typhoon, and the large deflection of wind direction. This prevents them from being used to simulate the entire non-steady-state response of a typhoon.

[0004] In summary, high-precision simulation of floating wind turbines urgently requires a novel wind field modeling algorithm. This algorithm must be able to achieve cross-scale fusion, reproducing both the mesoscale unsteady evolution process of typhoons (macroscopic characteristics) and analyzing the microscale high-frequency turbulent fluctuations (microscopic characteristics) that are crucial to structural loads. Currently, how to efficiently and physically consistently fuse wind field characteristics at these two scales is a key technical challenge that urgently needs to be solved in this field.

[0005] It should be noted that the information disclosed in the background section above is only for understanding the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The technical problem to be solved by this invention is "how to provide a high-fidelity, high-efficiency, non-steady-state typhoon wind field generation method, and combine it with a fully coupled dynamic model of floating wind turbines to achieve accurate simulation of the dynamic response of floating wind turbines throughout the entire process of typhoon passage". To this end, this application provides a floating wind turbine numerical simulation method based on a cross-scale typhoon modeling algorithm to solve the above-mentioned technical problem.

[0007] The technical solution adopted in this application to solve the above-mentioned technical problems is as follows.

[0008] This application provides a numerical simulation method for floating wind turbines based on a cross-scale typhoon modeling algorithm, comprising the following steps:

[0009] S1. Divide the non-steady-state time history of the typhoon's passage into N consecutive time periods;

[0010] S2. For each of the N time periods, use the parameterized mesoscale typhoon model to calculate the corresponding set of mesoscale physical parameters for that time period.

[0011] S3. For each of the N time periods, a microscale stochastic turbulence simulator is used, with the mesoscale physical parameter set as the driving input, to generate turbulent wind field data consistent with the physical characteristics of that time period.

[0012] S4. The generated N turbulent wind field data are spliced ​​together in time sequence, and a boundary smoothing algorithm is used at the connection point of adjacent turbulent wind field data to reconstruct a continuous long-term unsteady typhoon wind field data.

[0013] S5. The unsteady typhoon wind field data is used as the wind field input and imported into the fully coupled numerical simulation software for floating wind turbines to perform time-domain simulation in order to obtain the dynamic response of the floating wind turbines throughout the typhoon process.

[0014] In some embodiments, in step S2, the parameterized mesoscale typhoon model is the Yan Meng wind field model, which solves the mesoscale physical parameter set based on the Holland pressure field model and the atmospheric boundary layer momentum equation.

[0015] In some embodiments, in step S2, the Yan Meng wind field model considers sea surface roughness based on wind speed variations when solving the atmospheric boundary layer momentum equation.

[0016] In some embodiments, the sea surface roughness based on wind speed variation is calculated using the Charnock expression.

[0017] In some embodiments, in step S2, the set of mesoscale physical parameters includes: mean wind profile, friction velocity, and atmospheric stability parameters.

[0018] In some embodiments, step S3, in which the mesoscale physical parameters drive the microscale stochastic turbulence simulator, includes the following steps:

[0019] A turbulence spectrum model that is sensitive to atmospheric stability was selected;

[0020] The friction velocity is used as the input parameter of the microscale random turbulence simulator;

[0021] The atmospheric stability parameters are used as input parameters for the microscale stochastic turbulence simulator.

[0022] In some embodiments, the microscale stochastic turbulence simulator is Turbsim, the turbulence spectrum model sensitive to atmospheric stability corresponds to the Turbsim GP_LLJ model, the friction velocity input parameter corresponds to Turbsim UStar, and the atmospheric stability input parameter corresponds to Turbsim RICH_NO.

[0023] In some embodiments, in step S3, the average wind profile is also input as a user-defined wind profile into the microscale random turbulence simulator.

[0024] In some embodiments, in step S4, the boundary smoothing algorithm is a raised cosine weighted function algorithm, which performs a weighted average within an overlapping time region set between two adjacent turbulent wind field data.

[0025] In some embodiments, in step S5, the fully coupled numerical simulation software is OpenFAST; the typhoon wind field data is read in through the InflowWind module of OpenFAST.

[0026] In a second aspect, this application provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method of this application.

[0027] The present invention has the following beneficial effects:

[0028] Compared with the prior art, the present invention has the following significant advantages:

[0029] This invention divides the unsteady time history of a typhoon's passage into multiple consecutive time periods and calculates the mesoscale physical parameter set for each time period using a parameterized mesoscale typhoon model (such as the Yan Meng wind field model), ensuring an accurate description of the macroscopic evolution characteristics of the typhoon. Furthermore, this mesoscale parameter set drives a microscale stochastic turbulence simulator to generate turbulent wind field data for each time period, achieving a physical consistency fusion between mesoscale evolution and microscale turbulence. This fundamentally overcomes the shortcomings of mesoscale models being unable to analyze turbulence and microscale simulators being unable to reproduce unsteady processes, thereby generating a high-fidelity unsteady typhoon wind field.

[0030] Furthermore, this invention combines a parametric mesoscale model with a microscale stochastic turbulence simulator (such as TurbSim) widely used in engineering, avoiding the high computational cost of full-scale computational fluid dynamics simulation, significantly improving wind field generation efficiency, and ensuring physical rationality.

[0031] Furthermore, by employing a boundary smoothing algorithm (such as a raised cosine weighted function) at the connection points of adjacent turbulent wind field data, this invention effectively avoids abrupt changes in wind speed and acceleration at the splicing points, prevents the introduction of non-physical high-frequency components, and ensures the numerical stability and reliability of subsequent dynamic simulation results.

[0032] Furthermore, this invention uses the reconstructed unsteady typhoon wind field data as input data and imports it into a fully coupled numerical simulation software for floating wind turbines (such as OpenFAST) for time-domain simulation. This can accurately capture the dynamic response and ultimate load of floating wind turbines under extreme wind and wave coupling during the passage of a typhoon, and realize the accurate simulation of the dynamic response of floating wind turbines throughout the entire process of a typhoon.

[0033] In summary, this invention achieves a deep integration of cross-scale wind field modeling and floating wind turbine dynamics simulation through an organically linked technical chain: "typhoon time period division—mesoscale parameter calculation—microscale turbulence generation—wind field stitching—fully coupled simulation." The mesoscale model provides the physical drive, the turbulence simulator generates high-frequency details, the boundary smoothing algorithm ensures data continuity, and the fully coupled simulation platform solves the system response. The close cooperation of each component ensures high fidelity, high efficiency, and engineering practicality in the dynamic response simulation of floating wind turbines under typhoon conditions.

[0034] Other beneficial effects of the present invention will be further described below. Attached Figure Description

[0035] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0036] Figure 1This is a flowchart illustrating the overall technical process of the present invention.

[0037] Figure 2 This is a schematic diagram of the quasi-static time period division of the typhoon process in step one of the present invention;

[0038] Figure 3 This is a schematic diagram of the cross-scale physical parameter fusion in steps two and three of this invention;

[0039] Figure 4 The following is an example of a three-dimensional typhoon wind field modeling based on the present invention (which involves a multi-stage typhoon wind speed time history stitched diagram (FOVS→FEWS→TES→BEWS→BOVS), with smoothed boundaries: raised cosine weighted, and an overlap time of 30.0s (1.5 times the natural period)).

[0040] Figures 5(a), 5(b), and 5(c) are time history curves of the wind field at the wind turbine hub based on the fusion algorithm modeling in this invention. Among them, Figure 5(a) is a slice of the spatiotemporal distribution of the longitudinal wind speed (U), Figure 5(b) is a slice of the spatiotemporal distribution of the lateral wind speed (V), and Figure 5(c) is a slice of the spatiotemporal distribution of the wind field based on the longitudinal wind speed (W).

[0041] Figure 6 The figure shows an example of numerical simulation of typhoon sea conditions for a floating wind turbine based on the present invention (which involves the time history curves of the six degrees of freedom motion response of the floating wind turbine (BOVS→BEWS→TES→FEWS→FOVS)). Detailed Implementation

[0042] The embodiments of the present invention will be described in detail below. It should be emphasized that the following description is merely exemplary and is not intended to limit the scope and application of the present invention.

[0043] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of embodiments of the present invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0044] The embodiments of this application will be further described below with reference to the accompanying drawings.

[0045] This invention provides a numerical simulation method for floating wind turbines based on a cross-scale typhoon modeling algorithm, achieving accurate simulation of the dynamic response of floating wind turbines throughout the entire typhoon process. The method includes the following steps:

[0046] Step 1: Quasi-static Time Period Division of the Typhoon Process. Based on the meteorological structural characteristics of the typhoon, the entire process of its passage and impact on wind turbines is dynamically divided into N consecutive quasi-static time periods. The division process first identifies five typical meteorological stages as the basic framework based on the relative positions of the typhoon center and the wind turbines. These include: Outer Vortex Stage (FOVS), Foreeye Wall Stage (FEWS), Eye Stage (TES), Backeye Wall Stage (BEWS), and Outer Vortex Stage (BOVS). Subsequently, key meteorological parameters are set for each stage, including duration, typhoon center location, movement speed, central pressure, maximum wind speed radius, and Holland B parameters.

[0047] Within each stage, dynamic subdivisions are performed based on the time-varying rate of change of key wind field parameters (10-minute moving average wind speed at hub height). When this rate of change exceeds a preset threshold (15%), the current stage is subdivided to ensure that the evolution of the macroscopic wind field within each final time period i is sufficiently slow, satisfying the quasi-static assumption. The physical basis of this assumption is that the evolution timescale of the typhoon's macroscopic wind field (tens of minutes to hours) is much larger than the pulsation period of microscale turbulence (seconds to minutes). Therefore, within a reasonably divided time period T_i (typically 10 minutes to 1 hour), the background average wind field can be considered stable, allowing the use of constant mesoscale parameters to drive turbulence generation. The determination of time period T_i must satisfy the condition that its length is greater than the shortest time required for turbulence to reach statistical stationarity (typically greater than 1-2 minutes), while being less than the characteristic time when the macroscopic wind field undergoes significant changes.

[0048] Step 2: Mesoscale physical parameter evolution and wind field generation. For each time period... Using a parametric mesoscale typhoon model (Yan Meng wind field model, or YM model for short), based on the typhoon track and intensity data at that moment, the average physical parameter field for that period is calculated. This physical parameter field includes:

[0049] (a) Mean wind speed profile and mean wind direction profile ;

[0050] (b) Friction velocity of the atmospheric boundary layer ;

[0051] (c) Atmospheric stability parameter, namely Richardson number .

[0052] The YM model is solved based on the Holland pressure field (Equation 1) and the atmospheric boundary layer momentum equation (Equation 2):

[0053]

[0054]

[0055] in, Let r be the air pressure at a distance r from the center. For reference air pressure, Due to air pressure difference, The maximum distance referenced in barometric pressure calculations is given by r, where r is the distance from a reference point to the current point, and B is the attenuation factor. For the rate of change of velocity, For convection terms, For density, For pressure gradient, Coriolis force influencing factor For the friction term, decoupling yields... and .

[0056] The YM model considers sea surface roughness based on wind speed variations when solving the atmospheric boundary layer momentum equation. It adopts the Charnock expression recommended by the International Electrotechnical Commission standard IEC 61400-3:2009. Perform parameterized calculations.

[0057] Step 3: Physically driven generation of microscale turbulent fields. For each time period... Using the TurbSim stochastic turbulence simulator and taking the mesoscale physical parameters from step two as input, a quasi-static microscale turbulent wind field file consistent with the physical characteristics of this typhoon phase is generated. Specifically, configure the stochastic turbulence simulator:

[0058] (a) Select the turbulence spectrum model GP_LLJ, which is sensitive to atmospheric physical parameters;

[0059] (b) The friction velocity obtained in step two As input parameter UStar;

[0060] (c) The Richardson number obtained in step two RICH_NO is used as an input parameter;

[0061] (d) The average wind profile obtained in step two This is a custom wind profile (set WindProfileType = "USR"). The resulting turbulence spectrum is generated in this step. The following functional relationship is satisfied to achieve physics-driven implementation:

[0062]

[0063] Step 4: Full-process unsteady-state wind field time series reconstruction. Reconstruct the N quasi-static turbulent wind field files generated in Step 3. The files are stitched together chronologically. To prevent numerical divergence caused by discontinuities in wind speed at file connections, adjacent files are stitched together. and Set an overlapping time region at the connection point. Within this overlapping region, a boundary smoothing algorithm (raised cosine weighted function) is used to smooth the wind speed time history, with the overlap time length... The selection principle is that its value must be greater than the period of the main structural modes of the floating wind turbine (such as platform pitch and tower first-order frequency). It is usually taken as 1-2 times the lowest natural vibration period of the system to ensure that smooth operation does not filter out the wind field change frequency that the wind turbine structure may respond to, thereby avoiding underestimation of critical loads. As shown in formulas (4), (5) and (6):

[0064]

[0065]

[0066]

[0067] Where t is the relative time within the overlapping region. For absolute time, and These are the wind speed vectors from the two separate files. They are ultimately combined into a single, continuous, long-term, unsteady, full-process wind field file. The advantage of the raised cosine function is that its first derivative is zero at both the start and end points of the overlap region. This means that the spliced ​​wind speed time history signal is not only continuous, but its rate of change is also smooth and continuous. This effectively avoids abrupt acceleration changes and ensures that the splicing process does not introduce non-physical high-frequency components into the wind field, thereby guaranteeing the numerical stability and reliability of subsequent dynamic simulations.

[0068] Step 5: Fully Coupled Response Simulation of Floating Wind Turbine. The fully coupled dynamic model of the floating wind turbine (including modules for aerodynamics, hydrodynamics, servo systems, mooring, and structural elasticity) is established in the fully coupled numerical simulation software OpenFAST. The unsteady-state full-process wind field file generated in Step 4 is then used. This is the input file for InflowWind, the wind field input module of the simulation software. Run a fully coupled time-domain simulation to solve the fully coupled dynamic equations of the floating wind turbine system (Equation 7):

[0069]

[0070] in For the system's generalized coordinates, These are the mass, damping, and stiffness matrices, respectively. External load, and aerodynamic load It is a fusion wind field The function is then used. Finally, time history data of the loads (such as blade root bending moment and mooring chain tension) and motion responses (such as the six-degree-of-freedom motion of the platform) of the floating wind turbine are obtained throughout the typhoon process.

[0071] The above steps will be described in further detail below.

[0072] like Figure 1 As shown in step S100, the quasi-static time period of the typhoon process is divided as follows: Figure 2 As shown, the target typhoon's parameter path data is obtained, including time, latitude and longitude, and minimum central pressure. Maximum wind speed radius , etc. Based on the fixed latitude and longitude of the floating wind turbine, calculate the change in the relative position between the typhoon center and the wind turbine over time. Divide the entire process of the typhoon's impact into... A continuous quasi-static time period Meanwhile, based on relative location, the physical stage of the typhoon to which each time period belongs is marked. Specifically, shorter time periods (e.g., 10-20 minutes) are taken in the eyewall region (FEWS, BEWS) where the wind field changes drastically, while longer time periods (e.g., 30-60 minutes) can be taken in the typhoon eye (TES) or outer region (FOVS, BOVS) where the wind field is relatively stable.

[0073] like Figure 1 As shown in step S200, the mesoscale physical parameter evolution field generation is performed for each time period. Execute the parametric mesoscale typhoon model (YM model). Input Typhoon parameters at any time , and the relative position of the wind turbine The pressure gradient field is calculated using formula (1). The solution takes into account the friction term. The boundary layer momentum equation (2) is calculated using the YM model for this time period. The corresponding steady-state solution is used as the average physical parameters for that time period. A set of physical parameters is output. ,Include:

[0074] (1) Wind line: average wind speed With altitude The data is changing.

[0075] (2) Friction speed: .

[0076] (3) Gradient Richardson Number: .

[0077] like Figure 1As shown in step S300, the physically driven microscale turbulent field is generated, such as... Figure 3 As shown, for the first During a specific time period, the TurbSim V2.00 program is invoked, including:

[0078] (1) Dynamically generate input file: Create a TurbSim input file slice_i.inp.

[0079] (2) Configure core parameters:

[0080] ① In the “Meteorological Boundary Conditions” section: TurbModel = "GP_LLJ" because it is sensitive to stability parameters. WindProfileType = "USR". ProfileFile = "slice_i_profile.dat".

[0081] ② In the “Non-IEC Meteorological Boundary Conditions” section: Enter the Richardson number RICH_NO calculated by S200. And friction speed UStar= .

[0082] ③ In the “Turbine / Model Specifications” section: Define the total duration AnalysisTime, UsableTime = "ALL".

[0083] ④ In the “Runtime Options” section: To ensure the repeatability of the entire typhoon process simulation and to make the turbulence fields of each stage statistically independent, RandSeed1 is set using a deterministic rule based on the time period index i (e.g., RandSeed1 = 100000 + i), thereby generating a unique random seed for each slice, ensuring that the turbulence of each slice is not repeated, and that the entire simulation process is completely reproducible.

[0084] (3) Execution and Output: The TurbSim program runs based on the input physical parameters. and Automatically adjust turbulence spectrum And combined with the input average wind profile Generate a physically consistent turbulent wind field file containing the entire duration of the typhoon's passage. .

[0085] (4) Repeat this step for all time periods to generate... arrive There are N files in total.

[0086] like Figure 1 As shown in step S400, the entire process of unsteady wind field time sequence reconstruction is performed, and wind field splicing is executed. Figure 4 To accurately capture the phased changes in wind speed during typhoon passage using the wind speed time history curve at the turbine hub height obtained using this method, the macroscopic structure of the mesoscale typhoon model and the fine turbulent fluctuations generated by the microscale turbulence simulator were physically integrated. Compared to single-scale models, the wind field generated by this method can not only reproduce the overall evolution trend of the typhoon passage but also analyze the microscale turbulent details that are crucial to the dynamic response of the floating turbine blades and tower. This provides realistic and reliable input conditions for high-precision fully coupled simulation and proves its effectiveness in reproducing the unsteady evolution of the entire typhoon process from a time perspective. Figures 5(a), 5(b), and 5(c) are three-dimensional wind field slices of the turbine computational domain obtained using this method. The slices reveal the fine vortex structure generated by the microscale turbulence simulation through subtle changes in color and vectors. These high-frequency fluctuations are crucial to the aerodynamic load on the turbine blades and the fatigue damage to the tower, and the spatial dimension verifies the ability of this method to integrate mesoscale structures and microscale turbulence. Both of these results demonstrate that the wind field generated by this method can not only reproduce the overall evolution trend of typhoon passage, but also analyze the details that are crucial to the dynamic response of floating wind turbines, providing realistic and reliable input conditions for high-precision fully coupled simulation.

[0087] (1) Set the overlap area: Set an overlap time area. .

[0088] (2) Loop splicing: Let each time period be... The duration is Process the first slice ( ): Read Extract its front The duration data is written to a new file. Processing intermediate slices ( arrive ): Read After Duration data Read The former Duration data In this Within the overlapping region, a weighted average is calculated by applying formulas (4), (5), and (6) at each time step to obtain the smooth transition segment. .Will The middle part (i.e.) arrive (Time period) data and fused data Data concatenation and appending Process the last slice ( ): Read After Duration data, appended to .

[0089] (3) Update the file header: Finally, modify... The file header information, including the total duration. The data is updated to the sum of all spliced ​​time periods, and other relevant parameters are adjusted. The final result is a continuous and smooth non-steady-state typhoon wind field file with a total duration. .

[0090] like Figure 1 As shown in step S500, the fully coupled response simulation of the floating wind turbine was performed using OpenFASTV4.1.2 developed by NREL.

[0091] (1) Establish the FOWT model: The NREL 5MW floating wind turbine model is adopted. The model includes modules such as ElastoDyn (structure), AeroDyn (aerodynamics), HydroDyn (hydraulics), MAP++ (mooring), and ServoDyn (control).

[0092] (2) Configure InflowWind: In the InflowWind.dat input file: Set WindType = 3 (indicating the use of Bladed-style binary files). Set FileName = (Specify the wind field file generated by S400).

[0093] (3) Run the simulation: Execute the OpenFAST fully coupled time domain simulation.

[0094] Analysis Results: After the simulation is completed, key data time histories are extracted from the output file, such as... Figure 6 The figure shows the 6-DOF motion response curves of the wind turbine, including pitch and roll. The curves clearly reveal the strong correlation between the platform motion and different physical stages of the typhoon. In the foreeye wall (FEWS) and backeye wall (BEWS) stages, when the typhoon's influence is strongest, the pitch angle amplitude reaches its maximum value. In the eye of the typhoon (TES) stage, due to the sudden drop in wind speed and the reduction in wind load, the pitch and roll motions exhibit low-frequency, small-amplitude free decay oscillations. The oscillation frequency is close to the platform's natural pitch / roll frequency, reflecting the platform's dynamic relaxation process after unloading. Figure 6The response time histories demonstrate that this method successfully simulates the complex dynamic behavior of a floating wind turbine under extreme unsteady typhoon conditions. The motion response not only accurately captures the transient impact effects caused by drastic wind speed changes but also reveals the dominant motion modes and load characteristics of the platform at different typhoon stages. These high-fidelity response data provide crucial information for evaluating the ultimate tension of the mooring system, structural fatigue damage, and validating control system strategies.

[0095] Based on the above disclosure, it can be understood that the present invention has the following significant advantages compared with the prior art:

[0096] Physical consistency and high fidelity: This invention overcomes the shortcomings of the prior art, which separates mesoscale and microscale simulations. By driving the microscale turbulence spectrum with mesoscale physical parameters, it ensures that the generated turbulence is physically consistent with the unsteady evolution stages of a typhoon (such as strong convection in the eyewall and stability of the typhoon eye).

[0097] Full-process unsteady-state simulation: This invention reconstructs multiple steady-state wind field files into a continuous unsteady-state wind field through "quasi-static slicing" and "smooth stitching". It can simulate the response of floating wind turbines during the entire process of typhoon passage and capture the transient impact load caused by drastic changes in wind speed and direction (such as passing eye), which is impossible to achieve with traditional single steady-state simulation.

[0098] Engineering practicality and efficiency: This invention ingeniously integrates the computationally efficient parametric model (YM) with the widely recognized turbulence simulator TurbSim and simulation platform OpenFAST, avoiding the massive computational resources required for full-scale CFD simulation, and providing a high-precision and practical numerical simulation tool for the typhoon-resistant design and certification of floating wind turbines.

[0099] This invention also provides a computer-readable storage medium storing program instructions thereon, which, when executed by a processor, implement the steps of the method of this invention.

[0100] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0101] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0102] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0103] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0104] The background section of this invention may include background information about the problems or environment in which the invention is being developed, and is not necessarily a description of prior art. Therefore, the content included in the background section does not constitute an admission of prior art by the applicant.

[0105] The above description provides a further detailed explanation of the present invention in conjunction with specific / preferred embodiments, and it should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various substitutions or modifications can be made to these described embodiments without departing from the concept of the present invention, and all such substitutions or modifications should be considered within the scope of protection of the present invention. In the description of this specification, the reference to terms such as "an embodiment," "some embodiments," "preferred embodiment," "example," "specific example," or "some examples," etc., indicates that the specific features, structures, materials, or characteristics described in connection with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any suitable manner in one or more embodiments or examples. Without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification and the features of different embodiments or examples. Although the embodiments of the present invention and their advantages have been described in detail, it should be understood that various changes, substitutions, and modifications can be made herein without departing from the scope of protection of the patent application.

Claims

1. A numerical simulation method for floating wind turbines based on a cross-scale typhoon modeling algorithm, characterized in that, Includes the following steps: S1. Divide the non-steady-state time history of the typhoon's passage into N consecutive time periods; S2. For each of the N consecutive time periods, use the parameterized mesoscale typhoon model to calculate the corresponding set of mesoscale physical parameters for that time period. S3. For each of the N consecutive time periods, a microscale stochastic turbulence simulator is used, with the mesoscale physical parameter set as the driving input, to generate turbulent wind field data consistent with the physical characteristics of that time period. S4. The generated N turbulent wind field data are spliced ​​together in time sequence, and the boundary smoothing algorithm is used at the connection point of adjacent turbulent wind field data to reconstruct a continuous long-term unsteady typhoon wind field data. S5. The unsteady typhoon wind field data is used as the wind field input and imported into the fully coupled numerical simulation software for floating wind turbines to perform time-domain simulation in order to obtain the dynamic response of the floating wind turbines during the entire typhoon process. The time period division is based on the meteorological structural characteristics of the typhoon, including the outer periphery of the vortex, the eyewall stage, the eye stage, the back eyewall stage, and the outer periphery of the vortex. The boundary smoothing algorithm uses a raised cosine weighted function algorithm to perform a weighted average within an overlapping time region set between adjacent turbulent wind field data. The length of the overlapping time region is 1-2 times the minimum natural vibration period of the floating wind turbine system. The mesoscale physical parameter set includes the mean wind profile, friction velocity, and atmospheric stability parameters. The microscale stochastic turbulence simulator uses the friction velocity and atmospheric stability parameters as driving inputs.

2. The numerical simulation method for floating wind turbines based on a cross-scale typhoon modeling algorithm according to claim 1, characterized in that, In step S2, the parameterized mesoscale typhoon model is the Yan Meng wind field model, which solves the mesoscale physical parameter set based on the Holland pressure field model and the atmospheric boundary layer momentum equation.

3. The numerical simulation method for floating wind turbines based on a cross-scale typhoon modeling algorithm according to claim 2, characterized in that, In step S2, the Yan Meng wind field model considers sea surface roughness based on wind speed variation when solving the atmospheric boundary layer momentum equation.

4. The numerical simulation method for floating wind turbines based on a cross-scale typhoon modeling algorithm according to claim 3, characterized in that, The sea surface roughness based on wind speed variation is calculated using the Charnock expression.

5. The numerical simulation method for floating wind turbines based on a cross-scale typhoon modeling algorithm according to claim 1, characterized in that, In step S3, a turbulence spectrum model that is sensitive to atmospheric stability is selected.

6. The numerical simulation method for floating wind turbines based on a cross-scale typhoon modeling algorithm according to claim 5, characterized in that, The microscale stochastic turbulence simulator is Turbsim, and the turbulence spectrum model sensitive to atmospheric stability corresponds to the Turbsim GP_LLJ model; the friction velocity input parameter corresponds to Turbsim UStar; and the atmospheric stability input parameter corresponds to Turbsim RICH_NO.

7. The numerical simulation method for floating wind turbines based on a cross-scale typhoon modeling algorithm according to claim 1, characterized in that, In step S3, the average wind profile is also input as a user-defined wind profile into the microscale random turbulence simulator.

8. The numerical simulation method for floating wind turbines based on a cross-scale typhoon modeling algorithm according to claim 1, characterized in that, In step S5, the fully coupled numerical simulation software for the floating wind turbine is OpenFAST; the typhoon wind field data is read in through the InflowWind module of OpenFAST.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1 to 8.

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