Simulation analysis method for floating fan in stormy wave environment considering atmospheric stability

Abstract

The invention relates to the field of floating type fan load simulation, in particular to a floating type fan simulation analysis method considering atmospheric stability in a stormy wave environment, which comprises the following steps of: simulating WRF wind field data; carrying out WRF-SWAN wave field simulation; calculating key parameters of wind field simulation; carrying out WRF-TurbSim wind field simulation; and carrying out OpenFAST simulation and analysis. On the basis of traditional wind field driving, a Richardson number calculation and atmospheric stability classification method is introduced, a spectrum inversion and adaptive fitting mechanism is combined, key parameters of an engineering wind speed spectrum model are extracted, and a parameter mapping relation between WRF output and TurbSim input is established. A disturbance construction module driven by Richardson numbers is developed in TurbSim, adaptive generation of wind speed disturbance under the multi-stability condition is achieved, and the physical consistency and engineering adaptability of a simulated wind speed field on the aspects of a section structure and spectrum characteristics are improved. And finally, finishing floating type fan load analysis under wind wave coupling input by combining with an OpenFAST platform, and supporting system response evaluation and structural design optimization under complex sea conditions.

Description

Technical Field

[0001] The present invention relates to the field of floating wind turbine load simulation, and in particular to a floating wind turbine simulation analysis method in a wind and wave environment taking into account atmospheric stability, which is mainly used for the dynamic response analysis of floating wind turbine structures under complex sea conditions. Background Art

[0002] Floating Offshore Wind Turbines (FOWTs) are considered the most promising technology for developing deep-sea wind energy resources. However, compared to onshore or offshore fixed wind turbines, floating wind turbines are deployed in harsher and more complex marine environments for long periods of time, and must withstand the combined effects of multiple environmental loads such as wind, waves, and currents. Their supporting platforms exhibit six degrees of freedom: translation (surge, sway, and heave) and rotation (roll, pitch, and bow). This platform motion is strongly coupled with the aeroelastic behavior of the wind turbine itself, the dynamic response of the control system, and the constraints of the mooring system. This complex multi-body dynamics problem makes accurately predicting the structural loads, motion response, fatigue damage, and ultimate limit states of floating wind turbines under actual operating conditions a highly challenging and critical technical problem.

[0003] Therefore, accurate and efficient numerical simulation analysis of the coupled dynamic response of floating wind turbines under the real and dynamic combined action of wind and waves is of vital importance for their conceptual design, performance optimization, structural safety assessment, control strategy formulation and reliable operation and maintenance throughout their life cycle. Traditional industrial analysis methods often use simplified models, such as static or uniform wind fields, regular waves or parameterized wave spectrum models, which may not be able to fully capture the spatiotemporal evolution characteristics of wind and wave fields and their complex interactions with floating platforms and wind turbines, resulting in insufficient prediction accuracy of the dynamic response of the system. In order to improve the fidelity of the simulation and increase the calculation rate as much as possible, it is necessary to develop computational methods that can effectively link high-precision environmental simulation and structural dynamics analysis, and minimize the computational cost while decoupling complex multi-dimensional dynamic problems. This usually involves the use of professional numerical models from different fields: (1) Mesoscale meteorological models (such as the Weather Research and Forecasting model, WRF) are used to generate unsteady and non-uniform wind field data with high temporal and spatial resolution.

[0004] (2) The third generation of wave models (such as Simulating Waves Nearshore, SWAN) is used to simulate the generation, propagation and evolution of wind-generated waves and obtain refined wave field parameters.

[0005] (3) Wind turbine integrated simulation tools (such as OpenFAST) are used to couple and solve the aerodynamic, hydrodynamic, control system dynamics, and structural elastic dynamics equations of wind turbines, and simulate the response of the wind turbine system to a given environmental input.

[0006] Especially under complex climate and ocean conditions, the current turbulence spectrum model used in load calculation cannot fully capture the spatio-temporal evolution characteristics; the interaction between wind and waves has an important impact on the dynamic response of floating wind turbines, but traditional models may not be able to capture this process well.

[0007] 1) For example, the Chinese patent application with the application number 201510400100.8 discloses a wind field simulation method that couples the WRF and OpenFOAM models. The coupled calculation of the WRF model and the OpenFOAM model downscales the WRF data with a horizontal resolution of several kilometers to the OpenFOAM data with a resolution of 30 meters. This method improves the wind field simulation effect under complex terrains, thereby enhancing the accuracy and operational level of wind resource assessment and wind power prediction.

[0008] 2) For example, the patent application with the application number 202410547852.6 discloses an integrated coupling calculation method for floating wind turbines based on OpenFAST-OpenFOAM. This method realizes the coupled calculation of OpenFAST and OpenFOAM, promotes the data interaction between the floating body motion data and the tower base load of floating wind turbines, significantly reduces the running time of full-scale CFD analysis, and considers the control and flexible structure deformation under normal power generation conditions.

[0009] However, the above technologies: (1) mainly focus on wind field simulation and lack in-depth consideration of wave characteristics and their interaction with the wind field. This limits the ability to evaluate the overall meteorological conditions in complex tidal environments (such as offshore wind farms), making the results may not be able to fully reflect the wind energy resources in actual situations.

[0010] (2) focus on the analysis of the floating body motion and tower base load of floating wind turbines. Although there is an improvement in dynamic response calculation, it still focuses on the local analysis of wind turbine operation and lacks a systematic analysis of the comprehensive influence of the wind field and wave field.

[0011] In summary, the existing patented technologies are not sufficient to comprehensively evaluate the wind field and wave characteristics of offshore wind farms, resulting in insufficient in-depth analysis of complex meteorological conditions. In addition, the above two patents focus on local simulations and lack high-time-resolution results of the interaction between instantaneous wind speed and waves. SUMMARY OF THE INVENTION

[0012] The object of the present invention is to overcome the deficiencies of the above-mentioned prior art and provide a simulation analysis method for floating wind turbines in a wind-wave environment considering atmospheric stability, which can finely simulate the load response of floating wind turbines in a wind-wave coupling environment on the OpenFAST-WRF-SWAN platform. Based on traditional meteorological data-driven, this method introduces a stability identification, spectrum inversion, and perturbation reconstruction mechanism to improve the adaptability and accuracy of the wind speed field under multi-stability conditions.

[0013] To achieve the above object, the present invention adopts the following technical solutions: A simulation analysis method for floating wind turbines in a wind-wave environment considering atmospheric stability, comprising: (1). WRF wind field data simulation; A multi-layer nested grid structure is established, and WRF simulation is carried out with the location of the wind farm as the center. The resolution of the innermost grid is 1 km. A two-way nesting and multi-physical parameterization scheme is adopted, and the simulation deviation is corrected by combining ground-based wind measurement tower or radar data. High-resolution, stratified wind speed data and temperature profile data are output.

[0014] (2). WRF-SWAN wave field simulation; After quality control of the wind field data output by WRF, it is used as the SWAN driving term. Multi-spectrum and multi-directional resolution settings are introduced, and the Westhuysen whitening model and Komen dissipation mechanism are used to model the wave process. Wave height, period, and directional parameters are output, and finally, a wave file compatible with OpenFAST is generated.

[0015] (3) Calculation of key parameters for wind field simulation; In this part, based on the output results of the WRF model, a key physical quantity extraction and processing process for synthesizing the wind speed field is constructed. By introducing the Richardson number calculation and atmospheric stability classification method, the identification of the atmospheric state at different height layers is realized. Combining the fast Fourier transform (FFT) of the time series, the wind speed power spectral density (PSD) is inverted, and the key parameters of spectral types such as Kaimal and von Karman are extracted through spectral function adaptive fitting technology. Further, the wind field information of the local area where the wind turbine is located can be extracted by using a spatial mask, and a high-resolution wind speed profile can be obtained through vertical interpolation, or a wind speed profile can be constructed through an empirical function. The above process establishes a parameter mapping relationship between the WRF simulated wind field and the engineering wind speed spectrum model, providing basic data support for the construction of the perturbation profile and the generation of input files.

[0016] In summary, this link forms a closed-loop system from multi-source WRF variables to wind field structure identification, stability judgment, and spectral parameter inversion, establishing a traceable and physically driven data basis for TurbSim wind speed synthesis.

[0017] (4) WRF-TurbSim Wind Field Simulation; Based on the TurbSim source code framework, modular extension functions driven by WRF output are developed. By reconstructing the main program structure and input file parsing logic, automatic reading and dynamic setting of parameters such as wind speed spectrum model selection, wind speed profile calculation, and perturbation superposition are realized. The newly added perturbation construction module supports users to input Richardson number-driven perturbation terms, which are used to superimpose turbulence characteristics representing different atmospheric stability states on the existing mean profile. In addition, the adjusted output program can generate a wind speed field file in.bts format compatible with OpenFAST, ensuring the physical consistency of the generated data in terms of time series, profile structure, and spectral characteristics. This module significantly improves the linkage simulation efficiency of WRF-TurbSim and expands the applicability and engineering generality of TurbSim under non-standard input conditions.

[0018] (5) OpenFAST Simulation and Analysis; Configure the required wind and wave file paths in OpenFAST, set them to binary TurbSim format, and run the simulation. Conduct subsequent data analysis through MATLAB or PyDatView to support the design optimization of wind power projects.

[0019] The beneficial effects of the present invention are as follows: The present invention first calculates the wind field through WRF, then uses the wind field results of WRF to drive SWAN to calculate the wave field, and finally inputs the wind field information of WRF and the wave field information of SWAN into OpenFAST together for the full-system coupled dynamic response analysis of floating wind turbines. The purpose of this method is to provide a more accurate and reliable technical means for the refined performance evaluation and design verification of floating wind turbines in complex wind and wave environments by integrating professional numerical models in different physical fields and establishing specific data transfer paths. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a flow chart of the present invention; Figure 2 is a structural block diagram of the present invention; Figure 3 is a schematic diagram of using WRF for nested domains in the present invention. d01, d02, and d03 respectively represent the numbers of nested domains in WRF. WFA is Wind Farm A assumed in the present invention, which is used to describe the nested domain settings of WRF; Figure 4 is a comparison of power spectral densities of different wind spectrum models under different atmospheric stabilities in the present invention; Figure 5 is a comparison of different wind profiles and perturbation models under different atmospheric stabilities in the present invention; Figure 6 Schematic diagram of the perturbation function used in the present invention; Figure 7 Velocity comparison in the u-component direction using different wind spectrum models in the present invention; Figure 8 Velocity comparison in the v-component direction using different wind spectrum models in the present invention; Figure 9 Velocity comparison in the w-component direction using different wind spectrum models in the present invention; Figure 10 Power generation comparison using different wind spectrum models at the above wind speeds in the present invention; Figure 11 Rotor speed comparison using different wind spectrum models at the above wind speeds in the present invention; Figure 12 Comparison of the fore-and-aft displacement of the nacelle using different wind spectrum models at the above wind speeds in the present invention; Figure 13 Comparison of the out-of-plane displacement of the blade using different wind spectrum models at the above wind speeds in the present invention; Figure 14 Comparison of the in-plane displacement of the blade using different wind spectrum models at the above wind speeds in the present invention; Figure 15 Comparison of the surge of the floating platform using different wind spectrum models at the above wind speeds in the present invention; Figure 16 Comparison of the pitch of the floating platform using different wind spectrum models at the above wind speeds in the present invention; Figure 17 Comparison of the yaw of the floating platform using different wind spectrum models at the above wind speeds in the present invention. Detailed implementation manners

[0021] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0022] The structures, ratios, sizes, etc. shown in the drawings of this specification are only used to cooperate with the content disclosed in the specification for those skilled in this technology to understand and read, and are not used to limit the limiting conditions for the implementation of the present invention. Therefore, they do not have a substantial technical meaning. Any modification of the structure, change of the proportional relationship, or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope covered by the technical content disclosed in the present invention. At the same time, the terms such as "upper", "lower", "left", "right", "middle", and "one" cited in this specification are only for the convenience of clear narration and are not used to limit the scope for the implementation of the present invention. The change or adjustment of their relative relationships, without substantial change in the technical content, should also be regarded as the scope for the implementation of the present invention.

[0023] Such asFigures 1 - 17 As shown, a simulation analysis method for a floating wind turbine in a wind and wave environment considering atmospheric stability includes: 1. WRF wind field data simulation; (1). Nesting domain setting; Step 1. Define the target area: Determine the location of the wind farm and obtain the precise longitude and latitude of this location. For example, if the location is in a specific city or area, its longitude and latitude information can be extracted through map services (such as Google Maps, AutoNavi Maps, etc.).

[0024] Step 2. Set the boundaries of the domain: Determine the boundaries of the innermost domain. Usually, when setting the boundaries of the domain, the range that may be affected by the wind farm should be considered. Adopt an extended setting. Assume that the radius of the wind farm is 20 km, then the boundaries of the innermost domain can be set as the central longitude and latitude plus or minus 0.18°. Step 3. Define other nested domains: Set the middle and outer domains. The area of each layer of the domain should overlap appropriately with the innermost domain to ensure the effectiveness of nesting. Inner domain (1 km resolution), middle domain (3 km resolution), outer domain (9 km resolution). Taking the wind farm location as the center, the innermost is the domain where the wind farm is located, with a resolution of 1 km. Establish a resolution ratio of 3:1 between each sub-domain.

[0025] Step 4. Set the resolution and nesting attributes of each domain by configuring the namelist.input file of the WRF model.

[0026] Each layer of the domain is identified using parent_id and grid_id. For example: Outer domain: grid_id = 1, parent_id = 0. Middle domain: grid_id = 2, parent_id = 1. Inner domain: grid_id = 3, parent_id = 2. Each layer of the domain is identified using parent_id (parent domain serial number) and grid_id (grid serial number) through e_we and e_sn representing the number of horizontal and vertical grid points of each domain. For example: Outer domain: grid_id = 1, parent_id = 0. Middle domain: grid_id = 2, parent_id = 1. Inner domain: grid_id = 3, parent_id = 2. e_we and e_sn represent the number of horizontal and vertical grid points of each domain.

[0027] Step 5. Two-way nesting setting: After ensuring nesting, check the parameter settings of two-way nesting. In WRF, the feedback (nesting feedback) function is used to realize the influence of the target area on the outer domain, so as to improve the simulation accuracy. Enable two-way nesting to ensure that the simulation results in the inner domain can be fed back to the outer domain and affect its simulation results.

[0028] Step 6. Conduct grid division and verification: Use NCL or Python libraries (such as matplotlib) to plot the grid settings and key areas of each domain to ensure the coverage and nesting accuracy of the domain.

[0029] (2). WRF preprocessing; Step 7. Collect high-resolution terrain data: Download high-resolution data suitable for WRF from the WRF official website, such as USGS, GTOPO30, etc. (in running formats). If high-precision terrain data for China or a specific region is required, you can access the Digital Elevation Model (DEM) data center of Tsinghua University (such as THUDEM).

[0030] Step 8. Determine the data format: Terrain data is usually in NetCDF format. Ensure that the dataset meets the requirements of WRF. It is necessary to check the valid coordinate system (such as WGS84) and ensure that the data covers the entire simulation area.

[0031] Step 12. Collect high-resolution meteorological data: Download high-resolution meteorological data (such as ERA5, NARR, GFS, GDAS, etc.) from the website of the National Center for Atmospheric Research (NCAR). Download the data according to the simulation time period to ensure that the data frequency is consistent with the simulation requirements (such as every hour or every three hours).

[0032] Step 15. Meteorological data processing: Use the ungrib tool to process the downloaded meteorological data to ensure that the data is converted into a format that WRF can recognize. The following command-line operations can be used to decompress the meteorological data:. / ungrib.exe <input_data.

[0033] Step 18. Use the WPS tool: The WRF preprocessing stage includes multiple tools, including geogrid.exe, metgrid.exe, and ungrib.exe. Among them, use geogrid.exe to process the terrain data, which will generate high-resolution terrain and ocean data, and the output file is geo_em.d0*.nc; use ungrib.exe to convert the meteorological data into the WRF format, with the configuration file ungrib.cfg; synthesize the initial meteorological data with the processed terrain data through metgrid.exe to generate the wrfinput_d0* and wrfbdy_d0* files.

[0034] Step 12. Data check after preprocessing: Check the generated wrfinput (initial field input) and wrfbdy (boundary field input) files. Use tools such as ncdump or ncview to view the content of the generated files to ensure that the terrain and meteorological data are accurate. If data problems are found (such as NaN missing values or unreasonable terrain heights), go back to the original data source to ensure that there are no errors in the data download and processing process.

[0035] (3). Sensitivity test; Step 13. Determine the purpose of the sensitivity test: Before conducting the final WRF simulation, a sensitivity test needs to be carried out to determine the best parameterization scheme. This step ensures that the selected parameters are suitable for the wind farm area being simulated, thereby improving the simulation accuracy. The purpose of this method test is to determine the parameterization scheme combination that has the greatest impact on the wind farm simulation, including planetary boundary layer parameterization, microphysical process parameterization, and deep convective cumulus parameterization. By verifying the simulation results of different schemes, identify the optimal combination to reduce the deviation between simulation results such as wind speed and wind direction and the observed data.

[0036] Step 14. Select test parameterization schemes: In the sensitivity test, multiple alternative parameterization schemes need to be selected. The following are the key schemes to consider: Planetary Boundary Layer (PBL) schemes. Commonly used PBL schemes include YM, MYJ, YSU, etc. The optimal scheme is selected by evaluating their impact on the wind field at different height levels; Microphysical schemes. Microphysical process schemes include WSM6, Thompson, Ferrier, etc. By changing the precipitation simulation effect, evaluate its impact on atmospheric stability and wind speed; Cumulus schemes, including Kain-Fritsch, Betts-Miller-Janjic, etc. By analyzing the performance of these schemes under deep convective conditions, understand their impact on the wind field simulation.

[0037] Step 15. Experimental design: Select experimental combinations based on different parameterization schemes. For each test combination, set the same initial and boundary conditions to ensure the comparability of the results. For example, 5 combinations of different schemes can be designed, namely: PBL: YSU, microphysics: WSM6, cumulus: Kain-Fritsch; PBL: MYJ, microphysics: Thompson, cumulus: Betts-Miller-Janjic; PBL: YSU, microphysics: Ferrier, cumulus: Kain-Fritsch; PBL: MYJ, microphysics: WSM6, cumulus: Betts-Miller-Janjic; PBL: YSU, microphysics: Thompson, cumulus: Ferrier. The time range is selected with an initial time of 12 - 24 hours to capture the dynamic characteristics of the wind field evolution.

[0038] Step 16. Set the NAMELIST configuration: Configure different parameterization schemes in the WRF input file namelist.input for calling when running WRF.

[0039] Step 17. Start the WRF simulation: For each designed experimental combination, call the WRF main program for simulation.

[0040] Step 18. Output results: Record the simulation results of each test combination, including wind speed, wind direction, and other key meteorological parameters. Generate data at the specified output frequency (e.g., hourly output).

[0041] Step 19. Data comparison and result analysis: Compare the wind field results simulated by WRF with the data from anemometers or other meteorological observation stations. Statistical process control can be used, such as indicators like root mean square error and correlation coefficient for evaluation. Calculate the deviation of each group of experimental results, find the minimum root mean square error and other correlation indicators, and identify the optimal parameterization combination. Record the performance indicators of each scheme combination, compare and determine the best-performing parameter combination. Ensure that the finally selected scheme is recorded in the namelist.input input file.

[0042] Table 1. Sample sensitivity test scheme adopted in the present invention (pbl2_mp6_cu11_fdda2);

[0043] (4). WRF simulation domain data output settings.

[0044] Step 20 Integrate the parameterization scheme: Before conducting WRF atmospheric model simulations, it is first necessary to integrate the optimal parameterization scheme determined in the sensitivity tests. This will ensure that the simulations follow the verified settings and improve the accuracy of the results.

[0045] Step 21 Start WRF simulation: Start the WRF simulation using the prepared initialization and boundary condition data to obtain the atmospheric data and sea surface wind field data for the target area. Load the wrfinput (initial field input) and wrfbdy (boundary field input) files, ensuring that the filenames of the initial conditions and boundary conditions are correctly set in namelist.input so that the model can read them. Set the simulation time and output frequency: Set the total duration of the simulation and the frequency of output results (e.g., every hour). Navigate to the run directory of the WRF model in the terminal and call the WRF main program wrf.exe to perform the model simulation.

[0046] Step 22 Extract output data: After the WRF simulation is completed, it is necessary to extract the sea surface wind field data and other atmospheric parameters and conduct subsequent data analysis. Confirm the file format of the output results. The WRF default output is in NetCDF format (such as wrfout_d01_YYYY - MM - DD_HH:MM:SS), ensuring that it can be directly compatible with subsequent models (such as SWAN). Extract data at specific heights: Extract the wind speeds at 10 meters above the sea surface (U10, V10 components) and the wind speed information at other height levels. The xarray library in Python can be used for data reading and extraction. To obtain more accurate wind field simulation results, it is necessary to interpolate the WRF simulation output data. For example, for spatial interpolation, based on the simulation results, interpolate the wind speed data to a higher spatial resolution (such as 0.1 km) using the interpolation function in the scipy library. Secondly, there is also time interpolation, resample the data to a finer time scale, such as every 10 minutes, through the resampling function of xarray. The calculation methods for some key variables can be as follows: For example, the average wind speed at height z in the target area is , where N is the total number of sampling times, and u(z,t i ) and v(z,t i ) are the u - component and v - component of the velocity at height z and time step t i respectively.

[0047] The calculation of the turbulence intensity σ u wrf (z) in WRF first requires calculating the wind speed perturbation u'(z,ti ) : , wherein, u(z,t i ) is the wind speed at height z at time t, i and is the average wind speed at height z, u'(z,t i ) is the wind speed perturbation.

[0048] 2. WRF-SWAN wave field simulation; Step 1: Perform quality verification on the wind field data, remove outliers, and use spatial interpolation (such as bilinear interpolation) to match the SWAN grid when necessary.

[0049] Step 2: Define the SWAN calculation domain; First, set the grid range (using longitude and latitude or UTM coordinates in WRF) and resolution (recommended ≤1 km, nearshore resolution ≤500 m), and configure the time parameters: total simulation duration (recommended 3600 s or longer), time step (where the wind field driving step is synchronized with the WRF output, such as 10 minutes. The wave calculation step is recommended to be 60 - 300 seconds to meet the CFL condition). The selection of grid type can refer to the following method: preferentially use the Curvilinear grid (curved grid) in the nearshore area, and define the curved coastline fitting through the CGRIDCURV command; in the deep sea area, the Rectangular grid can be used, and the calculation can be simplified through CGRID REG.

[0050] Step 3: Configure physical processes; Activate the Westhuysen nonlinear wave interaction model (GEN3 WESTHUYSEN) to simulate wave energy transfer. Specifically, the Komen white cap dissipation model (WCAP KOMEN) can be referred to, set the shallow water wave breaking rate, and use the Collins bottom friction formula (FRICTION COLLINS).

[0051] Step 4: Data correction; It can be referred to that a correction factor of 1.05 is applied to the WRF wind speed to compensate for the underestimation of the ocean wind speed. Add a 5° wind direction offset to calibrate the systematic deviation of the WRF wind direction.

[0052] Step 5: Set boundary conditions; For the incident wave boundary condition, JONSWAP wave spectrum simulation can be considered. Extract the hydrological parameters of the location of the wind farm to define the wave spectrum parameters (such as wave height, peak period, etc.) to reflect the local ocean conditions. For strong wind and wave conditions, the Pierson-Moskowitz wave spectrum is adopted. The open boundary condition can also be set according to the situation to reduce the reflection and influence on the waves, or the reflection boundary condition can be used to simulate the behavior of nearshore waves.

[0053] Step 5: Output parameter setting; Select the significant wave height (HSIGN), peak period (TPS), mean wave direction (DIR), wavelength (WLEN), phase propagation, etc. as output parameters. Set the output format as NetCDF for OpenFAST to read.

[0054] 3. Calculation of key parameters for wind field simulation: Step 1: Open the WRF output file.

[0055] Use the netCDF4 library to open the output file generated by the WRF model to read the required meteorological variables. This step ensures that the file is opened in read-only mode for subsequent safe data reading.

[0056] Step 2: Set the time range.

[0057] Define the data period to be extracted by setting the time index slice. For example, multiple time stages can be selected, such as 36 to  44 to meet specific engineering requirements.

[0058] Step 3: Define meteorological variables.

[0059] Extract the key meteorological variables in the WRF output, including potential temperature (theta), vertical wind speed, horizontal wind speed (u and v components), ground height (z), surface wind speed, planetary boundary layer height (PBLH), etc. for subsequent analysis.

[0060] Step 4: Set the geographical range.

[0061] Set the geographical boundaries of the analysis area, and determine the area of interest by specifying the minimum and maximum latitudes and longitudes. This process can be achieved through spatial masking operations using geographical grid data.

[0062] Step 5: Interpolation calculation.

[0063] At the set height levels (such as 90 m - the hub height of the wind turbine and 300 m - the top layer of the wind farm grid), calculate the values of the extracted meteorological variables at this height through interpolation methods. This step can ensure that the required meteorological parameters are obtained at specific heights for wind energy simulation in OpenFAST. Linear interpolation is used for interpolation:

[0064] Among them, p(z) is the value of the atmospheric pressure to be calculated at height z, p bottom is the variable value corresponding to the bottom z of a specific height layer bottom ; p top is the variable value corresponding to the top z of a specific height layer, and z is the specific height for interpolation, between two known heights set. top

[0065] Step 6: Apply the spatial mask.

[0066] Perform spatial masking on the extraction results to focus on the meteorological parameters in the region of interest and eliminate the values outside the specified region. This can be achieved through the method of Boolean masking. Suppose there is a meteorological parameter P (here taking atmospheric pressure as an example, and it can also be variables such as temperature and humidity) in a two-dimensional array, and the dimension of this array is (i,j ), corresponding to the rows and columns of the model grid. Define a Boolean mask M, which is also an array of the same dimension, indicating whether each point is in the region of interest. The mask M(i,j) and the extracted atmospheric pressure value P after applying the mask masked (i,j) are respectively: .

[0067] Step 7: Calculate important meteorological parameters.

[0068] Using the extracted meteorological parameters, calculate the gradient Richardson number and the important index of pulsating stress; the gradient Richardson number is used to evaluate the atmospheric stability, while the pulsating stress is used to analyze the influence of wind speed pulsation on meteorological processes; the specific calculations include: The formula of the gradient Richardson number is applied to a specific height to evaluate the airflow characteristics, and the formula is as follows: , Among them, g is the acceleration of gravity; θ is the neutral temperature at the measurement height, in units of K, which can be derived from T, P, and PB in WRF, where T is the perturbed potential temperature, P is the perturbed atmospheric pressure, and PB is the basic state atmospheric pressure; u,v are the east-west and north-south wind speeds respectively, in units of m / s; z is the height, obtained from PH and PHB in WRF, where PH is the perturbed geopotential and PHB is the basic state geopotential; and , ​is the neutral temperature, the rate of change of the velocity component with height, calculated by central difference approximation; The pulsating stress (such as PC_UW) is obtained by calculating the pulsating values of each wind speed component and calculating its mean square value. The formula is as follows:

[0069] Among them, PC_UW is the u' , w' average Reynolds stress desired at the wind turbine hub, with the unit of m 2 / s 2 , u' j,correlated is j at the point u correlation value of the wind speed in the direction, and its independent wind speed in the u direction u' j,independen data are equal; v' j,correlated is j at the point v correlation value of the wind speed in the direction, through u, v, w in the direction of the independent wind speed u' j,independen , v' j,independen and w' j,independen data are combined and calculated; similarly, w' j,correlated is j at the point w correlation value of the wind speed in the direction, by the independent wind speed u' j,independen and w' j,independen data are combined and calculated, a uv 、a vw 、a uw is the correlation coefficient to ensure the correlation between different wind speed variables, U' hub,correlated and W' hub,correlated refers to the correlation value corresponding to the u and w components of the velocity at the wind turbine hub.

[0070] Step 8: Classification criteria for atmospheric stability status; Based on the standards of a large number of LES simulations and field observations, the present invention adopts the following classification scheme:

[0071] Step 9: Wind spectrum back-calculation and parameter fitting; Method for Inverting and Constructing Wind Speed Spectrum Based on WRF Wind Field Output. By performing a Fast Fourier Transform (FFT) on the WRF simulation results, the spectral characteristics of local wind speed perturbations are extracted, and this spectral information is embedded into the TurbSim input parameters, thereby realizing the generation of user-defined wind speed spectra different from the standard IEC spectrum and improving the simulation accuracy of floating wind turbines under non-IEC wind conditions.

[0072] Step 9.1: Extract WRF wind speed time series data; Select the central grid of the target wind farm area (or several representative grid points within the area), and extract the horizontal wind speed at the hub height (such as 120 m) u(t) time series, with a sampling time interval of Δ t , and a total of N sample points are obtained, forming a discrete wind speed sequence: , U 0 ,u 1 ,…,U N-1 represent the wind speeds at different time steps; The sampling frequency is: , corresponding to the maximum frequency (Nyquist frequency) of .

[0073] Step 9.2. Calculate the wind speed power spectral density (PSD) by FFT; Perform a discrete Fourier transform on the wind speed sequence at a certain height u(n),(n = 0, 1, …, N - 1) : , where k is the frequency index, and the actual frequency , f s is the sampling frequency, N is the number of sample points.

[0074] Then calculate its unilateral power spectral density (Power Spectral Density, PSD): , Step 9.3: Spectrum model selection and fitting; According to the "local stability state", initially select a spectrum model, for example: In a high-turbulence state, such as S1~S2, adopt the von karmon spectrum.

[0075] , where Su (f) is the longitudinal wind speed power spectral density, σ u spec is the standard deviation in the wind spectrum, f is the frequency, L u is the integral scale length, which will be fitted through WRF results later, U is the mean wind speed.

[0076] In relatively neutral and weak atmospheric states, such as S3 - S4, the Kaimal spectrum is adopted.

[0077] , where, S u (f) is the longitudinal wind speed power spectral density, σ u spec is the standard deviation in the wind spectrum, f is the frequency, L u is the integral scale length, which will be fitted through WRF results later, U is the mean wind speed.

[0078] In a strongly stable atmospheric state, such as S5, a smoothed spectrum is adopted, in the form as follows: , This method can adaptively smooth the changing trend of the actual PSD without forcibly aligning with the standard spectrum model. The spectral parameter a is the amplitude coefficient of the power - law spectrum, and its dimension varies with b and b is the slope exponent (dimensionless) of the power - law spectrum. The above - mentioned parameters can be obtained through least - squares fitting or Bayesian optimization.

[0079] For the wind spectrum with stable stratification, stable wind can be directly used, assuming there is no turbulence.

[0080] Step 9.4. Least - squares fitting of the wind spectrum; Based on the above steps, create an error function ε(θ) to describe the error between the power spectral density PSD of WRF and the initial selected model.

[0081] , where, r i (θ) is the residual at the frequency point, and the error function is the sum of the squares of all residuals. S u WRF ( f i) is the PSD estimated from WRF data, S u ( f i ; θ) is the value of the parameterized spectral model. θ = (σ u , L u ) is the set of parameters to be fitted.

[0082] Here, the initial values of the parameter set θ 0 = (σ (0) u , L (0) u ) , σ (0) u is the initial value of the standard deviation in the wind spectrum, L (0) u is the initial value of the integral scale length; where σ (0) u has a value consistent with the standard deviation σ WRF u obtained in WRF.

[0083] Among them, ， ， the value of the integral scale can be obtained from the above formula, is the integral scale parameter and can be described by the following formula: , for the error function ε( θ ) solving, optimization algorithms such as Levenberg - Marquardt, trust region method, BFGS, etc. can be used to solve the parameters that minimize the error. In the present invention, taking the evenberg–Marquardt (LM) algorithm as an example, because of its characteristics of non - linear least squares, low - dimensional parameter space, and faster convergence, it is more suitable.

[0084] LM is a balanced method between the Gauss - Newton method and the gradient descent method. Its core update formula: , Among them, J is the Jacobian matrix of the error term with respect to the parameters, T represents transposing the matrix, I is the identity matrix, r is the residual vector, and λ is the damping coefficient, which is used to adaptively balance the convergence direction and step size during the iteration process.

[0085] By setting the maximum number of iterations, the error tolerance, and repeating the iteration until the error converges or the maximum number of steps is reached, the optimal fitting parameters can be obtained: , σ * u is the standard deviation in the wind spectrum, L * u is the integral scale length.

[0086] Step 10: Profile perturbation coupling spectrum driving module; In this part, for the convenience of engineering applications, according to the different resolutions of WRF, the present invention provides two feasible methods for reconstructing the profile perturbation.

[0087] 10.1. Matching the wind profile function according to the state; When the resolution of WRF is insufficient, the method in this section can be used for profile reconstruction.

[0088] According to different atmospheric states, a suitable profile function is selected for the mean wind speed distribution: For S1~S2, the logarithmic wind profile can be used: , where, is the mean wind speed at height z; u * is the friction velocity, representing the influence of surface friction on the wind speed; k is the von Kármán constant (usually about 0.4), which is an empirical constant in the logarithmic wind profile theory; z0 is the surface roughness length, representing the parameter of surface irregularity and roughness in the model.

[0089] For the stable boundary layer, the stable modified Monin-Obukhov wind profile is adopted: , where, ψ is the stable correction function, L is the Obukhov length, a positive value indicates the current atmosphere is stable, and a negative value indicates instability, estimated from the WRF output. is the mean wind speed at height z ; u * friction velocity, representing the influence of surface friction on the wind speed; κ is the von Kármán constant (usually about 0.4), which is an empirical constant in the logarithmic wind profile theory; z0 is the surface roughness length, representing the parameter of surface irregularity and roughness in the model.

[0090] For the case of strong perturbation, , wherein, is the average wind speed at height z U hub is the wind speed at the hub of the wind turbine, Z hub is the height at the hub of the wind turbine, α is the wind speed profile power exponent, and the mathematical expression of the disturbance term ε(z) is: , wherein, f i is the frequency point uniformly sampled in the unilateral PSD, A i (z) is determined by the spectral density S( f i ) and the integral height, Φi is the random phase, taken from [0, 2π], t is the total time. Specifically, A i the expression of (z) is: , S (f i ) is the longitudinal wind speed power spectral density, ∆f is the discrete frequency interval, and G(z) is expressed as a vertical scale function (such as exponential decay or amplitude retention), reflecting the weakening or enhancement of the turbulence amplitude with height; , Z is the height to be determined, z ref is the reference height, such as the hub height; z ABL is the boundary layer thickness, which can be provided by WRF; γ and β are modulation coefficients, depending on the stability state setting (for example, for S1, γ = 0.3 and β = 0.1; for S5, γ = -0.5 and β = 1.0. This function ensures that: in the unstable layer, the disturbance intensity increases with height; in the stable layer, the disturbance decays rapidly with height).

[0091] 10.2 Directly adopt the vertical wind speed distribution of WRF (such as 90 m, etc.), perform interpolation fitting, and then superimpose the disturbance term. This belongs to a data-driven completion method for scenarios where the WRF data granularity is sufficient.

[0092] 4. TurbSim code modification and operation; TurbSim currently only has a few fixed wind speed spectrum models, such as the Kaimal spectrum, von Karman spectrum, etc. If you want to apply the wind spectrum model based on atmospheric stability proposed in this method (for example, different wind spectra corresponding to the S1 - S5 states), you need to add a new wind spectrum model to the TurbSim source code.

[0093] Step 1: Modify the source code to support custom wind spectra and wind profiles Locate the existing wind spectrum model section and find the part in the TurbSim code that defines the wind speed spectrum model. Add the wind spectrum models based on atmospheric stability corresponding to this method (such as von Karman, Kaimal, etc.) and the wind profile function to TurbSim. Corresponding calculation formulas need to be written for each state type.

[0094] Step 2: Modify the wind speed spectrum selection logic: In the main program of TurbSim, ensure that the corresponding wind speed spectrum is selected according to the given Ri_g (gradient Richardson number) and altitude layer.

[0095] Step 3: Add a matching function: Verify the matching of the wind spectrum model with the Richardson number.

[0096] To ensure that the selected wind spectrum model matches the given Ri_g, a verification function needs to be added to the code to check whether the wind spectrum model meets the requirements. If it does not match, the program should report an error. During the running process, this function is called before the wind spectrum model is officially applied.

[0097] Step 4: Set the wind speed - related input files; When passing the calculated wind speed field data and atmospheric state information to TurbSim, it is first necessary to ensure that all relevant input files are ready. The key operations include: configuring the TurbSim input files. In the inp input file of TurbSim, set WrADFF to 'TRUE', which means outputting the full-field time series data in the form of TurbSim / AeroDyn; TurbModel should be set to USRVKM. This configuration tells TurbSim that the generation of the wind speed field no longer depends on the default IEC wind speed spectrum, but uses a custom von Karman spectrum for wind speed field simulation. The von Karman spectrum is widely used to describe the characteristics of turbulence, so it can reflect the changes in wind speed under dynamic and intense meteorological conditions; specifying the wind speed profile data: in the configuration file, the ProfileFile parameter needs to be specified. This file should contain the wind speed profile data output by the WRF model. Specifically, ProfileFile needs to include meteorological parameters such as the wind speed, wind direction, and standard deviation of the wind speed (i.e., turbulence intensity) at each altitude layer. Each line in the file should be organized in ascending order of altitude to ensure that TurbSim can correctly read and apply the wind speed field data at each altitude layer. For example, a line of data in the file may contain the following content: altitude: indicating the altitude of the data point (unit: meter); wind speed: the average wind speed at this altitude layer (unit: m / s); wind direction: the wind direction at this altitude layer (unit: degree); standard deviation of wind speed: the standard deviation used to represent the turbulence intensity (unit: m / s); length scale: a parameter used to represent the size of the turbulent vortex, reflecting the spatial extent of turbulence in the wind speed field. This data format will help TurbSim generate the corresponding wind speed spectrum for each specified point at different altitude layers.

[0098] Step 5: Set non-IEC meteorological boundary conditions; In the TurbSim configuration file, further set the non-IEC meteorological boundary conditions related to the boundary layer state. The specific operations include Setting the gradient Richardson number (Ri_g): The gradient Richardson number Ri_g is used to characterize the stability of the meteorological layer. In the TurbSim configuration file, the gradient Richardson number value needs to be provided for each time step according to the meteorological data output by WRF. This parameter helps to describe the stability state of the boundary layer, thus affecting the fluctuation characteristics of the wind speed field. For example, under strong unstable conditions, the Ri_g value is small, the turbulence is stronger, and the wind speed changes more violently. In the stable layer, the Ri_g value is large, the turbulence is suppressed, and the wind speed changes smoothly; Set the pulsating stress at hub height: Pulsating stress is an important parameter to describe the turbulence intensity in the wind speed field. TurbSim needs to use the pulsating stress at hub height to adjust the turbulence characteristics of the wind speed field. The pulsating stress at a specific height (such as hub height) can be specified by setting the corresponding parameters in the inp file. Usually, this value is closely related to the turbulence intensity and needs to be set according to the TKE output of WRF or field measurement data.

[0099] Spectrum correction for custom boundary conditions: According to the selected wind spectrum model (such as Kaimal or von Karman), it is necessary to adjust each frequency band of the wind field to ensure that the generated wind speed field under different atmospheric conditions conforms to the actual situation. The spectrum correction here can select different wind spectrum types according to the atmospheric conditions (such as strongly stable, weakly stable, etc.) and adjust the turbulence intensity and integral scale parameters.

[0100] Step 6: Run TurbSim to generate the wind file; Start TurbSim using the configured inp input file. TurbSim will generate the target wind speed field based on the input wind spectrum data, meteorological boundary conditions, and perturbation model. During the running process, TurbSim will dynamically calculate the wind speed field at each time step according to the preset wind speed model, spectrum type, and perturbation characteristics, and finally output a.bts file that meets the requirements of OpenFAST. The output.bts file contains the three-dimensional wind speed field data at each moment, which is suitable for OpenFAST to perform subsequent wind turbine load simulations. Each time point in the file contains information such as wind speed components, turbulence intensity, and wind speed standard deviation. OpenFAST can read and utilize these data to calculate the structural response of the wind turbine and simulate the loads and dynamic responses of the wind turbine under different atmospheric conditions.

[0101] Step 7: Check the results; After generating the wind file, the generated.bts file can be checked through MATLAB or the pyDatView tool to ensure that the wind speed field data meets the expectations and can be correctly read in OpenFAST. If necessary, the input parameters or wind spectrum model can be adjusted for further optimization.

[0102] 5. OpenFAST simulation: Step 1: Set the wind file to be used; In the InflowWind INPUT FILE of OpenFAST, set wind type to 3 = binary TurbSimFF. And add the path of the bts file in the Parameters for Binary TurbSim Full-Field files.

[0103] Step 2: Set the wave file to be used; In hydrodyn.dat of OpenFAST, set WaveMod to 6 and set the wave NetCDF file path in WvKinFile.

[0104] Step 3: Run OpenFAST; Open a terminal in the file path and run OpenFAST. Subsequent loads can use matlab or PyDatView for data analysis.

[0105] Although the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, it is not a limitation of the protection scope of the present invention. Those skilled in the art should understand that based on the technical solution of the present invention, various modifications or deformations that can be made by those skilled in the art without creative efforts are still within the protection scope of the present invention.

Claims

1. A simulation analysis method for floating wind turbines in a wind-wave environment considering atmospheric stability, characterized in that, Including: (1) WRF wind field data simulation; Nested domain setting. By establishing three layers of nested regions with the wind farm location as the center, the innermost layer is the domain where the wind farm is located, and the resolution of the innermost layer domain is 1 km. The two-way nesting method is adopted; high-resolution terrain and meteorological data preprocessing are used, and the parameterization scheme is optimized in combination with sensitivity tests to ensure that the simulation results are consistent with the data of the anemometer tower; The simulation results are interpolated to obtain sea surface wind speeds and data at different altitude layers with high time resolution; (2). WRF-SWAN wave field simulation; After the wind field data passes quality verification and outlier rejection, the SWAN model is used for wave field simulation; the computational domain and grid parameters are defined, and a modern physical process model is used to simulate wave energy transfer; by setting the corresponding wave spectra and boundary conditions, suitable wave outputs are generated for subsequent reading by OpenFAST; (3). WRF-TurbSim wind field simulation; Based on the WRF simulation results, TurbSim is used to generate a wind file; Meteorological variables are extracted, and interpolation and spatial masking processing are performed to calculate key parameters such as the gradient Richardson number and pulsating stress to evaluate the wind field stability; The meteorological parameters are integrated into the TurbSim input file, and the binary wind file is generated for OpenFAST by running; (4). OpenFAST simulation and analysis; Configure the required wind and wave file paths in OpenFAST, set them to the binary TurbSim format, and run the simulation; subsequent data analysis is performed through MATLAB or PyDatView to support the design optimization of the wind power project.

2. The simulation analysis method of a floating wind turbine under a wind-wave environment considering atmospheric stability as described in claim 1, characterized in that, In the nested domain setting in (1) above, the resolution ratio between each sub-domain is 3:1, that is, the resolution between each layer is 9 km:3 km:1 km.

3. The simulation analysis method of a floating wind turbine under a wind-wave environment considering atmospheric stability as described in claim 1, characterized in that, In (1) above, by configuring the namelist.input file of the WRF model, the resolution and nesting attributes of each domain are set, and each layer domain is identified using parent_id and grid_id.

4. The simulation analysis method of a floating wind turbine under a wind-wave environment considering atmospheric stability according to claim 1, characterized in that, In (2) above, when defining the computational domain, first set the grid range and resolution, and configure the time parameters: total simulation duration, time step; grid type selection method: the Curvilinear grid is preferably used in the nearshore area, and the curved coastline fitting is defined through the CGRID CURV command; the Rectangular grid is used in the deep sea area, and the calculation is simplified through CGRID REG.

5. The simulation analysis method of a floating wind turbine under a wind-wave environment considering atmospheric stability according to claim 1, characterized in that, In (2) above, the modern physical process model adopts a non-linear wave interaction model.

6. The simulation analysis method of a floating wind turbine in a wind-wave environment considering atmospheric stability according to claim 1, characterized in that, In (3) above, the specific steps of the key parameters of the WRF-TurbSim wind field simulation are as follows: Step 1: Open the WRF output file, Use the netCDF4 library to open the output file generated by the WRF model to read the required meteorological variables; this step ensures that the file is opened in read-only mode for subsequent safe data reading; Step 2: Set the time range, Define the data period to be extracted by setting the time index slice; Step 3: Define meteorological variables, Extract key meteorological variables from WRF output, including potential temperature, vertical wind speed, horizontal wind speed, ground height, surface wind speed, and boundary layer height, for subsequent analysis; Step 4: Set the geographical range, Define the geographical boundaries of the analysis area and determine the region of interest by specifying the minimum and maximum latitudes and longitudes; Step 5: Interpolation calculation, At the set altitude levels, calculate the values of the extracted meteorological variables at that altitude through interpolation methods; this step ensures obtaining the required meteorological parameters at specific altitudes for wind energy simulation in OpenFAST; Step 6: Apply spatial masking, Perform spatial masking on the extraction results to focus on the meteorological parameters in the region of interest and eliminate values outside the specified area; Step 7: Calculate important meteorological parameters, Using the extracted meteorological parameters, calculate important indicators such as the gradient Richardson number and fluctuating stress; the gradient Richardson number is used to evaluate atmospheric stability, while fluctuating stress is used to analyze the impact of wind speed fluctuations on meteorological processes; Step 8: Determine the classification criteria for stability states based on the standards of a large number of LES simulations and field observations; Step 9: Wind spectrum inversion and parameter fitting, Through fast Fourier transform of the WRF simulation results, extract the spectral characteristics of local wind speed perturbations and embed this spectral information into the TurbSim input parameters to achieve the generation of user-defined wind speed spectra different from the standard IEC spectrum, improving the simulation accuracy of floating wind turbines under non-IEC wind conditions; Step 10: Provide two methods for profile perturbation reconstruction according to different WRF resolutions; one is, when the WRF resolution is insufficient, according to the state-matching wind profile function; the second is to directly adopt the vertical wind speed distribution of WRF through interpolation fitting and then superimpose the perturbation terms for scenarios where the WRF data granularity is sufficient; Step 11: TurbSim code modification and operation, Modify the source code to support custom wind spectra and wind profiles, modify the wind spectrum selection logic, and add a matching function: check the matching of the wind spectrum model with the Richardson number. In the TurbSim input file inp, set WrADFF to 'TRUE', TurbModel to USRVKM, and the wind field type to USR; it is necessary to combine the relevant atmospheric parameter files calculated by WRF; specify the Profiler file, which should contain the wind speed and related meteorological parameters obtained from the WRF output; add the wind speed, wind direction, and wind speed standard deviation parameters for each altitude level in ascending order of altitude; Step 12: Set non-IEC meteorological boundary conditions, In the TurbSim configuration file, set the gradient Richardson number of the target area and the fluctuating stress parameters at the hub height; Step 13: Run TurbSim to generate a wind file, Run TurbSim and generate a bts file that OpenFAST can read.

7. The method for simulating and analyzing a floating wind turbine in a wind-wave environment considering atmospheric stability as described in claim 6, wherein The interpolation in step 5 uses linear interpolation: ， Among them, p(z) is the value of the air pressure to be calculated at altitude z, p bottom is the variable value corresponding to the bottom z of a specific altitude layer bottom , p top is the variable value corresponding to the top z of a specific altitude layer top , and z is the specific altitude at which interpolation is required, between two known altitudes set.

8. The simulation analysis method of a floating wind turbine under a wind-wave environment considering atmospheric stability as described in claim 6, characterized in that, The specific calculations in step 7 include: The formula for the gradient Richardson number is as follows: ， where g is the acceleration due to gravity; θ is the neutral temperature at the measurement height, in K, which can be derived from T, P, and PB in WRF, where T is the perturbed potential temperature, P is the perturbed pressure, and PB is the basic-state pressure; u, v are the east-west and north-south wind speeds, in m / s; z is the height, obtained from PH and PHB in WRF, where PH is the perturbed geopotential and PHB is the basic-state geopotential; and , are the rates of change of the neutral temperature and velocity components with height, calculated by central-difference approximation; The formula for fluctuating stress is as follows: ， ， Among them, PC_UW is the u' , w' average Reynolds stress desired to be obtained at the wind turbine hub, with the unit of m 2 / s 2 , u' j,correlated is the j correlation value of the wind speed in the u direction at the u point, and its sum is u' j,independen equal to the data of the independent wind speed in the v' j,correlated is the j correlation value of the wind speed in the v direction at the u, v, w point, obtained by combining and calculating the independent wind speeds in the u' j,independen , v' j,independen and w' j,independen data; similarly, w' j,correlated is the j correlation value of the wind speed in the w direction at the u' j,independen and w' j,independen data; a uv 、a vw 、a uw is the correlation coefficient to ensure the correlation between different wind speed variables, U' hub,correlated and W' hub,correlated refer to the correlation values corresponding to the u and w components of the velocity at the wind turbine hub.

9. The method for simulating and analyzing a floating wind turbine in a wind-wave environment considering atmospheric stability as described in claim 1, wherein, The OpenFAST simulation and analysis include the following steps: Step 1: Set the wind file to be used. In the InflowWind INPUT FILE of OpenFAST, set wind type to 3 = binary TurbSim FF; and add the path of the bts file in the Parameters for Binary TurbSim Full-Field files. Step 2: Set the wave file to be used. In hydrodyn.dat of OpenFAST, set WaveMod to 6, and set the wave NetCDF file path in WvKinFile. Step 3: Run OpenFAST. Open a terminal in the file path and run OpenFAST; for subsequent loads, use matlab or PyDatView for data analysis.

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