Blade load parameter determination method and device, electronic equipment and storage medium

Abstract

Embodiments of the invention provide a blade load parameter determination method and apparatus, an electronic device and a storage medium. The method comprises the steps of obtaining a simulation result for a wind turbine generator blade; determining a target section of the wind turbine generator blade; the blade limit load parameter and the blade fatigue load parameter of the target section are determined according to the simulation result, so that an automatic and intelligent data acquisition, selection and calculation mechanism is introduced; according to the method, the core problem that a large amount of time is wasted and the design efficiency is affected due to manual repeated parameter setting and calculation submission in the prior art is directly and effectively solved, and therefore powerful support is provided for rapid and accurate design of the wind turbine generator.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of blade load parameter determination, and in particular to a blade load parameter determination method, a blade load parameter determination device, an electronic device, and a computer readable storage medium. BACKGROUND

[0002] Wind turbine load data processing is a core link of wind turbine design, and the progress of blade load processing directly affects the speed of blade design, and further affects the efficiency of whole machine development and design. Therefore, the processing of blade load data is the most important in wind turbine design.

[0003] According to the related art, the processing of blade load data needs to set parameters repeatedly and submit calculations repeatedly, which easily causes a large amount of time waste and affects the efficiency of wind turbine design. SUMMARY

[0004] Embodiments of the present application provide a blade load parameter determination method, device, electronic device, and computer readable storage medium to overcome the above problems or at least partially solve the above problems.

[0005] Embodiments of the present application disclose a blade load parameter determination method, comprising:

[0006] obtaining a simulation result for a wind turbine blade;

[0007] determining a target section of the wind turbine blade;

[0008] determining a blade limit load parameter and a blade fatigue load parameter of the target section through the simulation result.

[0009] Optionally, the step of obtaining the simulation result for the wind turbine blade comprises:

[0010] obtaining a working condition parameter table Excel for the simulation result; the working condition parameter table Excel comprises a first storage path of the simulation result;

[0011] obtaining a limit calculation template file and a fatigue calculation template file of the wind turbine blade based on the first storage path.

[0012] Optionally, the step of determining the target section of the wind turbine blade comprises:

[0013] receiving blade coordinate system category information input by a user;

[0014] determining a target coordinate system category based on the blade coordinate system category information;

[0015] determining an interface number of the wind turbine blade under the target coordinate system category;

[0016] determining a target section of the wind turbine blade based on the interface number.

[0017] Optionally, the step of determining the blade ultimate load parameter and the blade fatigue load parameter of the target section based on the simulation result comprises:

[0018] judging whether the simulation result contains a post-processing file of simulation software;

[0019] when it is judged that the simulation result contains the post-processing file of simulation software, calculating the blade ultimate load parameter and the blade fatigue load parameter of the target section based on the post-processing file.

[0020] Optionally, the method further comprises:

[0021] when it is judged that the simulation result does not contain the post-processing file of simulation software, obtaining working condition time sequence load data for the blade ultimate load parameter and the blade fatigue load parameter based on the simulation result;

[0022] calculating the blade ultimate load parameter and the blade fatigue load parameter of the target section based on a preset algorithm through the working condition time sequence load data.

[0023] Optionally, the step of calculating the blade ultimate load parameter and the blade fatigue load parameter of the target section based on the post-processing file comprises:

[0024] extracting time sequence result information, equivalent fatigue load information, load cycle characteristic matrix Markov and load duration distribution information LDD of the target section from the simulation result;

[0025] calculating the blade ultimate load parameter and the blade fatigue load parameter of the target section based on the post-processing file by using the time sequence result information, the equivalent fatigue load information, the load cycle characteristic matrix Markov and the load duration distribution information LDD.

[0026] Optionally, the simulation result comprises working condition time sequence load data, and the working condition time sequence load data comprises working condition time sequence ultimate load data for the blade ultimate load parameter, and the working condition time sequence ultimate load data is obtained in the following manner:

[0027] determining an original ultimate load data file of the simulation result for the target section, and a second storage path under the target coordinate system category;

[0028] extracting the original ultimate load data file based on the second storage path;

[0029] determining a target working condition, reading working condition time limit load data of the target section under the target working condition from the original limit load data file.

[0030] Optionally, the working condition time limit load data comprises working condition time fatigue load data for the blade fatigue load parameter, and the step of calculating the blade limit load parameter and the blade fatigue load parameter of the target section based on a preset algorithm through the working condition time limit load data comprises:

[0031] traversing the working condition time limit load data, and obtaining the combined moment load and force load of the target section under the target rotating angle based on a preset algorithm;

[0032] determining the blade limit load parameter of the target section based on the moment load and the force load;

[0033] traversing the working condition time fatigue load data, and performing a rainflow counting algorithm on the working condition time fatigue load data to generate a rainflow counting result of the target section under the target working condition; the rainflow counting result at least comprises a cycle number and a cycle amplitude of the target section;

[0034] calculating the blade fatigue load parameter of the target section based on a preset algorithm by using the cycle number and the cycle amplitude.

[0035] Optionally, the step of calculating the blade fatigue load parameter of the target section based on a preset algorithm by using the cycle number and the cycle amplitude comprises:

[0036] determining a load cycle characteristic matrix Markov, load duration distribution information LDD and equivalent fatigue load based on the cycle number and the cycle amplitude;

[0037] determining the load cycle characteristic matrix Markov, the load duration distribution information LDD and the equivalent fatigue load as the blade fatigue load parameter of the target section.

[0038] Optionally, the method further comprises:

[0039] determining a display graph category;

[0040] generating a graph load result of the blade limit load parameter and the blade fatigue load parameter based on the display graph category.

[0041] The embodiment of the application further discloses a blade load parameter determination device, comprising:

[0042] a simulation result acquisition module, configured to acquire a simulation result for a wind turbine blade;

[0043] a target section determination module configured to determine a target section of the wind turbine blade;

[0044] a load parameter determination module configured to determine a blade limit load parameter and a blade fatigue load parameter of the target section based on the simulation result.

[0045] The embodiment of the present application further discloses an electronic device, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus;

[0046] The memory is used for storing a computer program.

[0047] The processor is used for executing the program stored on the memory, and realizes the method as described in the embodiment of the present application.

[0048] The embodiment of the present application further discloses a computer readable storage medium, which stores instructions, and when executed by one or more processors, causes the processor to execute the method as described in the embodiment of the present application.

[0049] The embodiment of the present application has the following advantages:

[0050] The embodiment of the present application realizes the data acquisition, selection and calculation mechanism of automatic and intelligent introduction by acquiring the simulation result of the wind turbine blade, determining the target section of the wind turbine blade, and determining the blade limit load parameter and the blade fatigue load parameter of the target section based on the simulation result, directly and effectively solves the core problem of "artificial multiple repetitive parameter setting and calculation submission causing a large amount of time waste and affecting design efficiency" in the related art, and thus provides strong support for rapid and accurate design of the wind turbine. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 is a step flowchart of a blade load parameter determination method provided in the embodiment of the present application;

[0052] Figure 2 is a flowchart of a blade load parameter determination method provided in the embodiment of the present application;

[0053] Figure 3 is an equivalent fatigue calculation logic flowchart provided in the embodiment of the present application;

[0054] Figure 4 is a Markov calculation flowchart provided in the embodiment of the present application;

[0055] Figure 5is a structural block diagram of a blade load parameter determination device provided in an embodiment of the present application.

[0056] Figure 6 is a hardware structural block diagram of an electronic device provided in an embodiment of the present application.

[0057] Figure 7 is a schematic diagram of a computer readable medium provided in an embodiment of the present application. DETAILED DESCRIPTION

[0058] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0059] Referring to Figure 1 , a step flowchart of a blade load parameter determination method provided in an embodiment of the present application is shown, which can specifically include the following steps:

[0060] Step 101, obtaining simulation results for wind turbine blades;

[0061] Wind turbine blades: refers to the key component in a wind turbine that captures wind energy, usually made of composite materials, and bears complex dynamic loads.

[0062] Simulation results: refers to a large number of data files generated after numerical simulation of wind turbine blades under various working conditions by professional simulation software (such as Bladed), which contains detailed information such as force, torque, displacement of the blade at a specific time and position. It may include original time series data files, intermediate post-processing files generated by Bladed, etc.

[0063] The purpose of obtaining simulation results for wind turbine blades is to:

[0064] Provide basic data input for subsequent blade load parameter determination.

[0065] Ensure that the system can automatically and accurately identify and access all original simulation data and auxiliary files (such as template files) related to blade load analysis.

[0066] Beneficial effects:

[0067] Automation starting point: replaces the tedious process of manually searching and importing simulation results, greatly improving the efficiency of data preparation.

[0068] Reduce errors: avoid path errors or file omissions caused by manual operation, and ensure the accuracy of data sources.

[0069] Data integrity: ensure that all necessary simulation data and auxiliary template files are correctly obtained, laying a foundation for subsequent calculations.

[0070] Step 102, determining the target section of the wind turbine blade;

[0071] Target section: refers to a specific cross-section of the wind turbine blade selected for load analysis and strength / fatigue checking along its length direction. Different sections of the blade may bear different loads, so targeted analysis is needed.

[0072] The embodiments of the present application determine the target section of the wind turbine blade, aiming to:

[0073] Allow users to flexibly and accurately select the specific position of the blade for load analysis according to the actual needs of design and checking.

[0074] Focus on the key structural area of load processing, improve analysis efficiency and pertinence.

[0075] Advantages:

[0076] Analysis pertinence: ensures that load calculation and extraction are performed for key sections of interest in design, avoiding unnecessary full-blade processing.

[0077] Resource optimization: reduces calculation and data storage for unnecessary sections, saving calculation time and storage space.

[0078] User friendliness: provides an intuitive selection mechanism, simplifying user operation to specify the analysis range.

[0079] Step 103, determining the blade limit load parameters and blade fatigue load parameters of the target section through the simulation results.

[0080] Blade limit load parameters: refer to the instantaneous maximum or minimum load (such as maximum bending moment, maximum shear force, maximum tension / compression force, etc.) that the target section of the blade may bear during operation, used to check the instantaneous strength and stiffness of the blade, ensuring that no damage occurs under extreme working conditions.

[0081] Blade fatigue load parameters: refer to the material cumulative damage characteristics of the target section of the blade caused by repeated action of periodic or random loads during long-term operation, used to evaluate the fatigue life of the blade, ensuring that it will not fail due to fatigue within the design life. Common fatigue load parameters include equivalent fatigue load, load cycle characteristic matrix (such as Markov matrix), and load duration distribution (LDD), etc.

[0082] The embodiment of the application realizes the introduction of automatic and intelligent data acquisition, selection and calculation mechanism by acquiring simulation results for wind turbine blades, determining target sections of the wind turbine blades, and determining blade limit load parameters and blade fatigue load parameters of the target sections according to the simulation results, directly and effectively solves the core problem of "manual multiple repetitive parameter setting and calculation submission resulting in a large amount of time waste and affecting design efficiency" in the related art, and thus provides strong support for rapid and accurate design of wind turbine.

[0083] On the basis of the above-mentioned embodiment, a variant embodiment of the above-mentioned embodiment is proposed, and it should be noted that, in order to make the description brief, only the differences from the above-mentioned embodiment are described in the variant embodiment.

[0084] In an optional embodiment of the application, the step of acquiring simulation results for wind turbine blades comprises:

[0085] Substep: acquire a working condition parameter table Excel for the simulation results; the working condition parameter table Excel comprises a first storage path of the simulation results;

[0086] Working condition parameter table Excel: a pre-set Excel file, which is the core configuration entry of the entire data processing flow. This file not only contains various working condition parameters (such as wind speed, turbulence level, operation mode, etc.) for wind turbine load calculation, but more importantly, it explicitly specifies the storage location (i.e. the first storage path) of the Bladed simulation results and the location information of other key files (such as template files) that may be involved.

[0087] First storage path: refers to the root directory or folder path where a large number of original time series load data files generated after Bladed simulation calculation are stored.

[0088] Purpose:

[0089] Centralized configuration: all necessary input paths and basic parameters are centralized in an Excel file that is easy to edit and manage, simplifying user operation.

[0090] Automatic entry: as the first file read when the automatic program starts, the program automatically acquires the file location information required for all subsequent operations through it.

[0091] Standardized input: ensures the standardization of input data and avoids problems caused by scattered files.

[0092] Beneficial effects:

[0093] Improve configuration efficiency: Users do not need to manually search and modify paths in multiple configuration files. Only one Excel file needs to be updated.

[0094] Reduce operation complexity: Avoid the tedious and error-prone process of manually specifying a large number of simulation result file paths one by one.

[0095] Enhance process automation: Provide clear and reliable path guidance for subsequent automatic reading and processing, which is the cornerstone of the entire automation process.

[0096] Sub-step: Obtain the limit calculation template file and fatigue calculation template file of the wind turbine blade based on the first storage path.

[0097] Limit calculation template file: Refers to a pre-prepared Bladed post-processing task template file (e.g., a.pj file that defines the extraction rules for maximum torque, maximum shear, etc.) that defines how to extract blade limit loads, or a configuration file required by a custom algorithm.

[0098] Fatigue calculation template file: Refers to a pre-prepared Bladed post-processing task template file (e.g., a.pj file that defines rainflow counting, equivalent fatigue calculation parameters, etc.) that defines how to perform blade fatigue load analysis, or a configuration file required by a custom algorithm.

[0099] Objective:

[0100] Obtain processing rules: Automatically locate and load the rules or templates required for limit load and fatigue load calculation. These templates are the key to guiding how loads are calculated and extracted from raw data.

[0101] Support automated calculation: Limit calculation template file and fatigue calculation template file are prerequisites for subsequent "automated generation of calculation", whether through Bladed underlying modules or custom algorithms, these templates are needed as guidance.

[0102] Beneficial effects:

[0103] Calculation consistency: Ensure that all load calculations are based on uniform template rules, avoiding inconsistent results due to manual configuration differences.

[0104] Improve processing efficiency: Automatically obtain template files without human intervention, speeding up the start of the entire processing process.

[0105] Reduce human error: Avoid manually selecting or copying incorrect template files, ensuring the correctness of the calculation rules.

[0106] Support multiple calculation methods: whether it is a Bladed post-processing path or a custom algorithm path, you can get the corresponding template file to guide the calculation.

[0107] Example:

[0108] Get the working condition parameter table Excel for the simulation results; the working condition parameter table Excel includes the first storage path of the simulation results;

[0109] In the example, an Excel file named Config.xlsx is created, which plays the role of "working condition parameter table Excel".

[0110] In this Excel file, there is a line that records the simulation result root directory, and its value is D:\Bladed_Simulations\Project_Alpha\Results\. This path is the "first storage path of the simulation results".

[0111] The get_simulation_results_info function in the Python script first reads this Config.xlsx file and parses the root directory path from it.

[0112] Based on the first storage path, get the limit calculation template file and fatigue calculation template file of the wind turbine blade.

[0113] In Config.xlsx, the limit template file name (Blade_Limit_Section_Template.pj) and the fatigue template file name (Blade_Fatigue_Section_Template.pj) are also defined.

[0114] After the Python script gets the "simulation result root directory" (D:\Bladed_Simulations\Project_Alpha\Results\), it will be concatenated (os.path.join) with the two template file names.

[0115] Finally, the program automatically gets the complete limit calculation template file path (D:\Bladed_Simulations\Project_Alpha\Results\Blade_Limit_Section_Template.pj) and fatigue calculation template file path (D:\Bladed_Simulations\Project_Alpha\Results\Blade_Fatigue_Section_Template.pj).

[0116] In an alternative embodiment of the present application, the step of determining the target section of the wind turbine blade comprises:

[0117] Sub-step: receiving blade coordinate system category information input by a user;

[0118] In practical applications, the user can input the blade coordinate system category information through a graphical user interface (GUI) or command line interaction or other configuration methods.

[0119] Blade coordinate system category information: used to express the type of coordinate system adopted by the blade load data. In wind turbine load analysis, common blade coordinate systems include:

[0120] User coordinate system: a coordinate system defined by the user or commonly used by the user, which may be parallel to the design axis or a specific plane of the blade.

[0121] Root coordinate system: a coordinate system usually established with the root of the blade as the origin, commonly used to describe the load of the entire blade or the root.

[0122] Principal coordinate system: a blade section inertia principal axis coordinate system, whose axis coincides with the principal inertia axis direction of the section, commonly used for simplified section stress analysis.

[0123] Purpose: according to the specific needs of design and analysis, allow the user to flexibly select load data in different coordinate systems for processing. Ensure that the subsequent load extraction and analysis are based on the correct coordinate system specified by the user.

[0124] Beneficial effects:

[0125] Flexibility and adaptability: meet the needs of load coordinate systems in different design stages or different analysis purposes, for example, strength checking may prefer the Principal coordinate system, while control design may require the Root coordinate system.

[0126] User-friendly: receive user selection through intuitive interaction methods (such as drop-down menus, radio buttons), simplifying the operation process.

[0127] Sub-step: determining the target coordinate system category based on the blade coordinate system category information;

[0128] Target coordinate system category: the type of blade coordinate system determined by the user input, which is actually used for subsequent load data processing and analysis. This step is a confirmation and standardization of the user input.

[0129] Purpose:

[0130] Clear processing reference: convert the abstract information input by the user into a specific target coordinate system that can be recognized and executed by the program.

[0131] Internal Logic Conversion: Ensures that the system's internal subsequent data parsing and processing modules correctly understand and apply the selected coordinate system.

[0132] Beneficial Effects:

[0133] Internal Processing Consistency: Ensures that the entire automation process follows a unified coordinate system standard when processing data.

[0134] Error Avoidance: Avoids subsequent calculation problems caused by unclear or incorrect coordinate system information.

[0135] Sub-step: Determine the number of sections / stations of the wind turbine blade under the target coordinate system category.

[0136] Number of Sections / Stations: Refers to the total number or range of numbers of blade cross-sections defined in the Bladed model under the selected target coordinate system category. Typically, Bladed will divide the blade spanwise into multiple discrete cross-sections for load calculation.

[0137] Purpose:

[0138] Obtain Blade Structure Information: Automatically read the information about blade cross-section division in the Bladed model or simulation results to understand all available cross-sections.

[0139] Provide a basis for user selection: Based on the knowledge of the number and numbering of all available cross-sections, users can make effective selections.

[0140] Beneficial Effects:

[0141] Automatically obtain model information: Avoids manual review of model files or reports to obtain blade cross-section numbers and numbers.

[0142] Ensure data integrity: Ensure that the program can identify all potential analyzable blade cross-sections.

[0143] Improve user selection accuracy: The user interface can display a list or range of available cross-sections based on this information, reducing the risk of incorrect selection.

[0144] Sub-step: Determine the target cross-section of the wind turbine blade based on the number of sections.

[0145] Purpose:

[0146] Implement accurate analysis: According to the front-end requirements (user or configuration), accurately select one or more blade cross-sections for detailed load analysis from all available section numbers.

[0147] Filtering non-target data: Ensures that subsequent load processing is only performed on specific section data that users are interested in, avoiding the processing of irrelevant massive data.

[0148] Benefits:

[0149] Improved processing efficiency: Only processing the required section data greatly reduces the amount of calculation and data processing time.

[0150] Analysis results focus: Ensures that the output load results are for the most important sections in the design, making it easy for engineers to quickly obtain key information.

[0151] Reducing data redundancy: Avoid generating and storing a large amount of unnecessary section load data.

[0152] User control: Allows users to flexibly adjust the analysis range according to specific design review tasks.

[0153] Example running process and explanation:

[0154] Receive the blade coordinate system category information input by the user:

[0155] When the program starts, a GUI window will pop up.

[0156] There is a drop-down menu in the window, showing "User", "Root", "Principal" and other coordinate system options.

[0157] The user selects, for example, the "Root" coordinate system by clicking the drop-down menu. This operation is "receiving the blade coordinate system category information input by the user".

[0158] Determine the target coordinate system category based on the blade coordinate system category information:

[0159] The program internally (self.target_coord_var.get()) obtains the value selected by the user in the drop-down menu (for example, "Root").

[0160] This selected "Root" is the "target coordinate system category", which will be used as the basis for subsequent data processing.

[0161] At the same time, the program will update the display of "available section range", for example, if the Root coordinate system has only 80 sections, it will display "1-80".

[0162] Determine the number of interfaces of the wind turbine blade in the target coordinate system category:

[0163] After the user selects the "Root" coordinate system, the program will query the pre-set self.section_count_map (in real application, this part of information will be read dynamically from the model file of Bladed or related database).

[0164] The program finds that the section numbers defined under the "Root" coordinate system are from 1 to 80. This range (1-80) is the "interface number of the wind turbine blade under the target coordinate system category".

[0165] Based on the "interface number", the target sections of the wind turbine blade are determined:

[0166] The user inputs the section numbers he wants to analyze in the text box of the GUI. For example, the user can input 10, 25, 40 (comma separated), or 50-60 (range), or even 15, 30-35, 70 (mixed input).

[0167] When the user clicks the "Confirm Target Sections" button, the determine_target_sections method in the program will be called.

[0168] This method will parse the user input string (self.parse_section_input) and compare it with the "interface number" (1-80) determined in step 3.

[0169] If the user inputs 10, 25, 85: the program will recognize that 10 and 25 are valid sections, but 85 is out of the range of the Root coordinate system 1-80, and will prompt a warning and ignore 85. The final target sections are [10, 25].

[0170] These final section number lists accepted and stored by the program (such as [10, 25]) are the "target sections of the wind turbine blade". These section numbers will be used as instructions for data processing in subsequent steps S3 / S4.

[0171] In an optional embodiment of the present application, the step of determining the target sections of the wind turbine blade based on the simulation results includes:

[0172] Sub-step: Determine whether the simulation results contain the post-processing file of the simulation software.

[0173] In wind turbine load analysis, Bladed software usually goes through two main stages:

[0174] Pre-processing and Simulation: In this stage, engineers will set up the model of the wind turbine, operating conditions (wind speed, turbulence, operating mode, etc.), control strategy, etc., and then Bladed will perform dynamic simulation to generate a large amount of raw time series data. These data are usually in the form of.out,.mbk, etc. files, containing raw information such as force, torque, displacement, speed, etc. of each component of the blade at each time step.

[0175] Post-processing: Just having raw time series data is not enough, we need to extract key design loads from these massive data, such as the maximum limit load of each section of the blade, equivalent fatigue load, statistical distribution of load (such as Markov matrix, LDD), etc. Bladed software provides built-in post-processing functions, users can create.pj (Project File) files to define these post-processing tasks, specify which type of load to extract from which raw data file, and how to perform statistical calculations (for example, choose rainflow counting algorithm to calculate fatigue cycles).

[0176] Simulation software post-processing files: Specifically refers to the files generated and used by simulation software (such as Bladed) itself for its built-in post-processing functions. These files may include:

[0177] Files defining post-processing tasks: such as Bladed's.pj (Project) file, which contains instructions on how to extract limit loads or perform fatigue statistics from raw simulation data.

[0178] Bladed processed intermediate result files: such as $ME files (containing limit load time series), $056-$073 binary files (containing fatigue counting data), $001-$018 binary files (containing Markov matrix related data), etc. The focus of this judgment is usually whether the program can find and identify these files that have been processed by Bladed itself.

[0179] Purpose:

[0180] Select the optimal processing path: identify the current available data state, decide whether to use the built-in advanced post-processing capabilities of the simulation software, or directly process the raw data with custom algorithms.

[0181] Improve processing efficiency: if the simulation software has already generated some pre-processing results, they can be directly used, avoiding repeated calculations.

[0182] Enhance flexibility of the scheme: allow the system to adapt to different Bladed simulation output habits and specific needs of users.

[0183] Benefits:

[0184] Intelligent decision-making: The program can automatically select the most appropriate load processing strategy based on the presence of files, without human intervention.

[0185] Resource optimization: If Bladed has already completed some time-consuming calculations, it can avoid repeating these calculations in custom algorithms, saving time and computational resources.

[0186] Compatibility: The solution can be compatible with both cases where Bladed has performed post-processing and where it has not.

[0187] Sub-step: When it is determined that the simulation results include a post-processing file of the simulation software, calculate the blade limit load parameters and blade fatigue load parameters of the target section based on the post-processing file.

[0188] For example, the program can automatically generate and submit a.pj file of Bladed (or directly call the API) to let the Bladed software complete the conversion from raw data to limit load, fatigue cycle, LDD, etc. statistical results.

[0189] Purpose:

[0190] Leverage Bladed's powerful built-in functions: Make full use of Bladed's mature algorithms and optimized performance in load post-processing.

[0191] Automate Bladed post-processing flow: Change Bladed post-processing from manual one-by-one setting and submission to batch, unattended automation process.

[0192] Benefits:

[0193] Greatly improve efficiency: Avoid manual repeated parameter setting and Bladed post-processing task submission, reduce the workload from several days to several hours.

[0194] Calculation accuracy and reliability: Depend on Bladed's own verified algorithms for calculation, ensure the accuracy and consistency of the results.

[0195] Reduce human error: Automated process avoids parameter setting errors or calculation omissions that may be introduced by manual operation.

[0196] Optionally, it also includes:

[0197] Substep: When it is determined that the simulation result does not contain a post-processing file of the simulation software, obtain working condition time sequence load data for the blade limit load parameter and the blade fatigue load parameter based on the simulation result;

[0198] The post-processing file not containing the simulation software refers to an intermediate result file in the simulation result for which the Bladed software has already performed its built-in post-processing, or the user wants to skip the built-in post-processing of the Bladed.

[0199] Working condition time sequence load data: refers to detailed original data sequences of forces, moments, etc. of each section of the blade changing with time under each simulation working condition. These data are the basis for calculating limit and fatigue loads.

[0200] Objective:

[0201] Provide original input for self-defined algorithms: when the built-in post-processing of Bladed is not used, the most original time sequence load data need to be directly obtained as the input of self-defined algorithms.

[0202] Ensure data traceability: directly obtaining data from original simulation results ensures the transparency and traceability of subsequent analysis.

[0203] Beneficial effects:

[0204] Strong controllability of data: the data source can be completely controlled, facilitating more flexible and in-depth self-defined analysis.

[0205] Compatibility: cope with the situation that Bladed simulation only outputs original time sequence data without subsequent post-processing.

[0206] Lay the foundation for self-defined algorithms: prepare necessary and accurate original data for the next step of self-defined algorithm processing.

[0207] Substep: calculate the blade limit load parameter and the blade fatigue load parameter of the target section based on the preset algorithm through the working condition time sequence load data.

[0208] The preset algorithm can be a program code self-written and implemented according to load analysis theory (such as extreme value extraction, rain flow counting, Markov matrix construction, LDD calculation, and equivalent fatigue load calculation formula). These algorithms directly act on original time sequence data.

[0209] Objective:

[0210] Highly customized and flexible: allow engineers to use their own professional knowledge and specific needs to design and implement more suitable load processing algorithms for the scene.

[0211] Supplement the built-in post-processing functions of simulation software: provide an alternative when the built-in post-processing functions of Bladed cannot meet specific analysis requirements.

[0212] Reduce dependence on specific software: reduce the strong dependence on Bladed post-processing modules and enhance the independence of the overall solution.

[0213] Benefits:

[0214] High flexibility: algorithms can be adjusted and optimized according to any custom requirements, such as using more complex load combination methods or new fatigue evaluation models.

[0215] Improved processing efficiency: for large amounts of raw data, custom algorithms can be optimized specifically, which may be more efficient than calling commercial software interfaces in some scenarios.

[0216] Strong controllability of results: engineers have a good understanding of each step of the algorithm, making it easy to debug and verify results.

[0217] Reduced cost: reduce the dependence on commercial software post-processing modules.

[0218] Optionally, the step of calculating the blade limit load parameters and blade fatigue load parameters of the target section based on the post-processing file comprises:

[0219] Sub-step: extract the time series result information, equivalent fatigue load information, load cycle characteristic matrix Markov, and load duration distribution information LDD of the target section from the simulation results.

[0220] Simulation results can refer to various result files generated after Bladed software performs its built-in post-processing. These files are intermediate data processed and calculated by Bladed internally, rather than the original time series data.

[0221] Time series result information: mainly refers to the data in the $ME file. This file contains envelope time series results or key load time series extracted by Bladed post-processing module for limit load analysis based on original time series data.

[0222] Equivalent fatigue load information: refers to the equivalent fatigue load results contained in the $056-$073 binary files calculated by Bladed software based on user-set S-N curve slope and other parameters.

[0223] Load cycle characteristic matrix Markov: refers to the Markov matrix data contained in the $001-$018 binary files generated by Bladed software after statistical analysis of load cycles.

[0224] Load Duration Distribution information LDD: Load duration distribution information calculated by Bladed software contained in the $001-$006 equal probability density post-processing file.

[0225] Objective:

[0226] Directly obtain data from Bladed pre-processing results: Take advantage of the complex calculations already completed by Bladed, directly read the standardized, pre-processed results generated by it.

[0227] Avoid repeated calculations: Since this information has already been calculated by Bladed, the purpose of this step is to efficiently "extract" this data rather than "calculate" it again.

[0228] Prepare for final parameter determination: Collect various load information generated by Bladed in order to perform the final integration and determination in the next step.

[0229] Beneficial effects:

[0230] High efficiency: Avoid rewriting complex algorithms for limit extraction, rainflow counting, Markov matrix, and LDD calculation, and directly use the calculation results of Bladed.

[0231] Data accuracy: Rely on Bladed's own verified, industry-standard calculation results to ensure data accuracy and reliability.

[0232] Process simplification: Transfer the complexity of data processing to the internal software of Bladed, and the program only needs to focus on data reading and integration.

[0233] Compatibility: Adapt to simulation projects that have completed post-processing through Bladed's built-in post-processing.

[0234] Sub-step: Calculate the blade limit load parameters and blade fatigue load parameters of the target section based on the post-processing file using the timing result information, equivalent fatigue load information, load cycle characteristic matrix Markov, and load duration distribution information LDD.

[0235] Objective:

[0236] Final determination and output of load parameters: Organize, name, and output the various data extracted from Bladed post-processing results according to the final format required for design or verification, forming the final limit load parameter and fatigue load parameter report.

[0237] Load combination and screening: If there are additional load combination rules or screening requirements, they can be performed based on the extracted data at this step.

[0238] Report Generation: Write the determined load parameters into a report file in user-specified format (e.g., Excel).

[0239] Benefits:

[0240] Automated Report Generation: Automate the collation and report generation of complex load data, eliminating the need for manual data filtering, copying, and pasting.

[0241] Standardized Output: Ensure that load results are presented in a unified and clear format, facilitating engineers' review and use.

[0242] Efficiency in Design Decision Making: Engineers can directly use these automatically generated load parameters for subsequent structural checking and optimization design, greatly accelerating the design process.

[0243] Example Illustration:

[0244] Premise: Assume that the simulation result files of Bladed have been generated in the conventional structure D:\Bladed_Simulations\Project_Alpha\Results\Section_XX\, where XX is the section number. For example, for section 10, there will be files such as D:\Bladed_Simulations\Project_Alpha\Results\Section_10\Results_S10ME.out.

[0245] Sub-step 1: Extract the time series result information, equivalent fatigue load information, load cycle characteristic matrix, and load duration distribution information of the target section from the simulation results.

[0246] The calculate_loads_from_post_Process_files function in the script will iterate through all target sections (e.g., sections 10 and 20).

[0247] For each section, it will construct the complete path to the $ME, $056-$073, $001-$018, $001-$006, etc. files.

[0248] Then, it will call a "simulation parsing function" like simulate_parse_me_file (in a real application, this would be a call to the actual Bladed file parsing library or a custom parser) to read (extract) data from these Bladed-generated post-processing result files. The extracted data includes time series information of extreme loads, equivalent fatigue load values, statistical data of Markov matrices, and related data of LDD.

[0249] Sub-step 2: Calculate the blade limit load parameters and blade fatigue load parameters for the target section based on the post-processing file using the time series result information, the equivalent fatigue load information, the load cycle characteristic matrix, and the load duration distribution information.

[0250] The script receives these information (time_series_info, eq_fatigue_info, markov_info, ldd_info) extracted in sub-step 1.

[0251] For limit load parameters: it extracts the maximum / minimum force and moment from time_series_info, and can further combine them as needed (e.g. Combined_Load_Example in the example), finally determines the limit load parameters as output.

[0252] For fatigue load parameters: it directly organizes the data contained in eq_fatigue_info, markov_info, ldd_info as the final fatigue load parameters.

[0253] Finally, the script writes these organized limit load and fatigue load data into an Excel file with a specified name (Blade_Loads_Report_Bladed_Post_Process.xlsx), forming a structured report.

[0254] Optionally, the simulation results include working condition time series load data, and the working condition time series load data includes working condition time series limit load data for the blade limit load parameters, and the working condition time series limit load data is obtained in the following manner:

[0255] Sub-step: Determine the original limit load data file of the simulation results for the target section, the second storage path under the target coordinate system category;

[0256] Original limit load data file of simulation results: refers to the original file containing detailed time series load data of each section of the blade under all working conditions directly output after Bladed simulation is completed. These files are not subjected to any post-processing or summary processing by Bladed, including:

[0257] User coordinate system: %062-%064 (or $062-$064) file.

[0258] Root coordinate system: %041-%043 (or $041-$043) file.

[0259] Principal coordinate system: %015-%017 (or $015-$017) files.

[0260] Second storage path: refers to the specific storage location of these raw limit load data files (such as %062-%064, etc.) in the file system. This is usually a certain subfolder under the simulation result root directory, organized according to the output structure of Bladed.

[0261] Objective:

[0262] Precise data positioning: accurately identify and locate the specific file containing the required raw limit load data to ensure the correctness of subsequent data reading.

[0263] Adapt to Bladed output structure: adapt to the naming and storage conventions of Bladed software for raw data files in different coordinate systems.

[0264] Prepare for custom processing: this is the first prerequisite for using custom algorithms for limit load processing, ensuring that the algorithm can find the correct input data source.

[0265] Beneficial effects:

[0266] Clear data source: ensures that the load processing starts from the most original and unprocessed data, ensuring the transparency and traceability of data processing.

[0267] Avoid manual search: the program automatically determines the file path without the need for engineers to manually navigate to the complex Bladed output directory structure to find the file.

[0268] Reduce error rate: reduce the subsequent processing failure or result deviation caused by manual identification or input of incorrect file path.

[0269] Sub-step: extract the raw limit load data file based on the second storage path;

[0270] Objective:

[0271] Load data loading: efficiently read the raw, externally stored load data into the processing environment of the program.

[0272] Format conversion and parsing: if the original file is binary or a specific format, it needs to be parsed accordingly and converted into a data structure (such as array, DataFrame) that the program can operate.

[0273] Beneficial effects:

[0274] Automated data import: significantly reduces the time and manual operation of data loading.

[0275] Data availability: make massive raw simulation data accessible and operable in the program.

[0276] Efficiency improvement: batch and efficient reading of files is the basis for custom algorithm processing of large amounts of data.

[0277] Sub-step: Determine the target working condition, read the working condition time limit load data of the target section under the target working condition from the original limit load data file.

[0278] Target working condition: refers to the operating condition (for example, a certain wind speed, turbulence intensity, unit operation mode, etc.) that needs to be analyzed for specific load in wind turbine design and checking. Bladed simulation usually runs dozens to hundreds of different working conditions.

[0279] Working condition time limit load data: refers to the original data sequence of all load components (such as blade root bending moment, thrust, shear force, etc.) on the selected target section over time under a certain target working condition. These data are the direct input for calculating the limit value and fatigue characteristics (such as rain flow counting) under this working condition.

[0280] Purpose:

[0281] Working condition screening: accurately screen out specific working condition data that needs to be analyzed from the original file that may contain multiple working conditions, avoiding processing irrelevant working condition data.

[0282] Data isolation: ensure that the data of each working condition is processed independently, which is convenient for subsequent limit value extraction or fatigue statistics.

[0283] Provide the basis for limit value and fatigue analysis: provide direct input for custom algorithms to perform operations such as extreme value search, Combine load calculation, rain flow counting, etc.

[0284] Beneficial effects:

[0285] Fine control: allows engineers to perform independent load analysis on each target working condition to meet the design and checking requirements under different working conditions.

[0286] Resource optimization: only load and process the working condition data that needs to be analyzed, reducing memory usage and computational burden.

[0287] Processing accuracy: ensures that load calculation is based on correct, specific working condition raw time series data.

[0288] Optionally, the working condition time limit load data includes working condition time limit fatigue load data for the blade fatigue load parameter, and the working condition time limit load data is obtained by

[0289] Working condition time series load data: refers to the detailed original data sequence of the force, moment, etc. of each section of the blade changing with time under each simulation working condition. It is a broad concept, which includes both instantaneous data for limit analysis and cyclic data for fatigue analysis.

[0290] Working condition time series fatigue load data: specifically refers to the part or same data of the working condition time series load data used for fatigue analysis, emphasizing its application in fatigue analysis.

[0291] Optionally, the step of calculating the blade limit load parameter and the blade fatigue load parameter of the target section based on the preset algorithm comprises:

[0292] Sub-step: traverse the working condition time series limit load data, and obtain the combined moment load and force load of the target section at the target rotation angle based on the preset algorithm;

[0293] Target rotation angle: a specific angle of the blade in a rotation of 360 degrees, usually discretized at intervals of 10 degrees or less. At these angles, the load components borne by the blade are projected or combined.

[0294] Combined moment load and force load (Combine Load): this is an important concept in limit load analysis. Since the blade bears multiple directional forces and moments (e.g. flapping moment, edgewise moment) while rotating, in order to evaluate the maximum stress or load effect of the section, these components need to be vectorially combined or combined according to certain rules at each rotation angle (e.g. the maximum or minimum value of the combined moment and force load of each target section under all working conditions and target rotation angles), i.e. the combination for converting the load to a specific angle.

[0295] Objective:

[0296] Calculate Combine load: this is a key step in blade limit load analysis, aiming to find the worst combined load of the blade under all rotation angles and all working conditions.

[0297] Comprehensive evaluation of section strength: simply looking at the extreme value of a single load component is insufficient to fully evaluate the blade section strength. Combine load considers the coupling effect of multi-directional load.

[0298] Data refinement: convert the original time series data into more design-oriented limit load combination values.

[0299] Beneficial effects:

[0300] Improve the accuracy of limit analysis: it can identify the true maximum combined load borne by the blade during the rotation period, providing more accurate input for structural design.

[0301] Automated complex calculations: This replaces the tedious process of manually combining loads and traversing rotation angles, significantly improving efficiency.

[0302] Reduce design risks: Designing based on more comprehensive load information helps avoid structural failure under extreme conditions.

[0303] Sub-step: determining a blade limit load parameter of the target cross-section based on the moment load and the force load;

[0304] Blade limit load parameters: Refers to the maximum or minimum load values ​​of the blade under extreme operating conditions that are ultimately determined for structural verification. This may include maximum flapping bending moment, maximum shimmy bending moment, maximum resultant moment, maximum axial force, maximum shear force, etc.

[0305] Purpose:

[0306] Finalize the ultimate loads: From the combined loads calculated under a large number of operating conditions and rotation angles, select the global extreme value (maximum or minimum) of each ultimate load parameter as the final design input.

[0307] Standardized output: The calculation results are organized into a standardized limit load report format.

[0308] Extraction of other limit loads besides Combine loads: Although this description focuses on Combine loads, the actual “blade limit load parameters” should also include the maximum / minimum values ​​of individual load components that are not Combined (such as the “Safety factor and non-safety factor limit load extraction” mentioned in the original S4), which is also determined in this sub-step.

[0309] Beneficial effects:

[0310] Provide design benchmark: Provide blade structural engineers with clear and reliable limit load values ​​for strength verification.

[0311] Report Automation: Automatically generate reports containing all key limit load parameters, saving a lot of manual compilation time.

[0312] Decision support: Clear limit load data helps engineers quickly assess the safety of design options.

[0313] Sub-step: traversing the time series fatigue load data of the working condition, and executing a rain flow counting algorithm on the time series fatigue load data of the working condition to generate a rain flow counting result of the target section under the target working condition; the rain flow counting result at least includes the number of cycles and the cycle amplitude of the target section;

[0314] Fatigue Load Time Series Data: The force and moment time series data of each section of the blade under all operating conditions for fatigue analysis, which is read from the original simulation result file (e.g., %62-%064 file). It is emphasized that these data are dynamic and contain load cycle information.

[0315] Rainflow Counting Algorithm: A standard algorithm for processing irregular stress / load time series data to identify and quantify fatigue cycles. It can extract the amplitude (peak-to-valley difference) and mean value of each complete load cycle from complex time series data and count the number of occurrences of each cycle.

[0316] Rainflow Counting Results: The output of the rainflow counting algorithm, usually presented in table form, recording the number of load cycles corresponding to different load amplitudes.

[0317] Cycle Count: The number of load cycles that occur at a specific load amplitude.

[0318] Cycle Amplitude: The difference (or half-difference) between the maximum and minimum values of a load cycle.

[0319] Objective:

[0320] Quantifying Load Cycles: Converting continuous and irregular time series load data into discrete and statistical load cycle information, which is the basis for fatigue analysis.

[0321] Preparation for Fatigue Damage Calculation: Cycle count and cycle amplitude are direct inputs for subsequent calculations of equivalent fatigue load, construction of Markov matrix and LDD.

[0322] Beneficial Effects:

[0323] Fatigue Analysis Core: Automating the execution of the complex and time-consuming rainflow counting algorithm is a key step in blade fatigue life assessment.

[0324] Data Standardization: Convert original time series data into standard load cycle data for fatigue damage accumulation calculation.

[0325] Improve Accuracy: Ensure that the data basis for fatigue analysis is accurate and meets industry standards.

[0326] Sub-step: Calculate the blade fatigue load parameters of the target section based on the preset algorithm using the cycle count and cycle amplitude.

[0327] Objective:

[0328] Comprehensive Evaluation of Fatigue Characteristics: Further derive various fatigue load parameters from rainflow counting results to evaluate the fatigue performance of the blade from different dimensions.

[0329] Meet design code requirements: These parameters are often important indicators required for checking in design codes or standards.

[0330] Benefits:

[0331] Automatic fatigue parameter calculation: Automatically generate Markov matrix, LDD and equivalent fatigue load, which are complex and time-consuming calculations, significantly improving the efficiency of fatigue analysis.

[0332] Multi-dimensional fatigue assessment: Provide multiple sets of fatigue load parameters, allowing engineers to understand the fatigue characteristics of the blade from different angles and conduct more comprehensive and reliable fatigue life assessment.

[0333] Reduce human error: Avoid errors in manual data statistics and formula calculations.

[0334] Optionally, the step of calculating the blade fatigue load parameters of the target section based on the cycle number and cycle amplitude includes:

[0335] Sub-step: Determine the load cycle characteristic matrix Markov, load duration distribution information LDD and equivalent fatigue load based on the cycle number and cycle amplitude;

[0336] Load cycle characteristic matrix Markov: A two-dimensional matrix that usually counts the number of load cycles in different load mean and load amplitude intervals. It intuitively shows the cycle characteristics and distribution of the load.

[0337] Load duration distribution information LDD: Describes the proportion of time that the load lasts at different load levels. It is usually presented in the form of a cumulative probability curve, showing the total percentage of time that a particular load value or higher load value appears.

[0338] Equivalent fatigue load: Convert irregular fatigue load sequence into an equivalent constant amplitude load with the same fatigue damage effect. It is calculated by S-N curve and Miner's rule, which is convenient for rapid estimation of fatigue life.

[0339] Objective:

[0340] Specific generation of fatigue parameters: Clearly indicates the three main fatigue load parameters to be calculated, which are the core output of fatigue analysis.

[0341] Each calculation: Emphasizes that the three parameters are independently calculated in parallel based on the rainflow counting results.

[0342] Benefits:

[0343] Clear calculation path: Clearly indicates the calculation source and target of each fatigue load parameter.

[0344] Increase automation: Ensure that the program can automatically complete all these complex fatigue parameter calculations.

[0345] Sub-step: Use the load cycle characteristic matrix Markov, the load duration distribution information LDD, and the equivalent fatigue load to determine the blade fatigue load parameters for the target section.

[0346] Objective:

[0347] Final output: Organize the calculated fatigue load information to form the final fatigue load report or data file.

[0348] Define output content: Clearly define what the "blade fatigue load parameters" presented to the user specifically include.

[0349] Beneficial effects:

[0350] Result standardization: Ensure that fatigue load parameters are presented in a consistent, clear, and complete manner.

[0351] Facilitate subsequent application: These final determined parameters can be directly used for fatigue life assessment, optimization design, and certification of blades.

[0352] Improve work efficiency: Automatically generate final reports, reducing manual sorting and checking time.

[0353] Example: Custom algorithm post-processing blade load based on Bladed raw data;

[0354] Example scenario: A new type of wind turbine blade needs to be checked for detailed limit and fatigue load. Since the built-in post-processing function in Bladed cannot fully meet its specific load combination (Combine Load) requirements, or there is no post-processing file, and it is desired to have more control over fatigue damage assessment, the required parameters are extracted from the original time series load data directly output from Bladed.

[0355] Assumptions:

[0356] Bladed simulation has been completed, and the original time series load data file is located in the D:\ProjectX\Bladed_Raw_Outputs\ directory.

[0357] Select the section 20 (20% chordwise position from the root) of the blade in the Root coordinate system for analysis.

[0358] The load components to be processed include: blade root direction bending moment (Mx) and pitch direction bending moment (My).

[0359] First part: Obtain the time series load data under working conditions;

[0360] Determine the raw load data file path:

[0361] Root coordinate system section loads are stored in corresponding %041-%043 ($041-$043) files.

[0362] The program will find the corresponding raw time series load file based on the target section number (20) and coordinate system (Root), for example, D:\ProjectX\Bladed_Raw_Outputs\Run001\Root_Section_20.out (this is an example file name, the actual Bladed file name has a specific format, but the program will locate it according to the rules).

[0363] For each simulation case (e.g. Run001, Run002...RunN), the program will traverse the corresponding raw data file.

[0364] Extract raw data:

[0365] Custom algorithms will read these files. Assume each file contains 10 minutes (600 seconds) of time series data, with a sampling frequency of 50Hz, i.e. 600s x 50Hz = 30000 data points for each case.

[0366] Extract the time series of Mx(t) and My(t) from it.

[0367] Second part: Calculate blade limit load parameters;

[0368] Objective: Calculate the maximum / minimum Combine bending moment of section 20 under all working conditions.

[0369] Sub-step: Traverse the working condition time series limit load data, and obtain the combined moment load and force load of the target section at the target rotation angle based on the preset algorithm.

[0370] 1. Load coordinate conversion and combination (Combine Load);

[0371] In blade design, the maximum combined load of the section at all rotation angles needs to be considered. Here, Mx and My are used to illustrate the calculation of bending moment.

[0372] Preset algorithm: for each time step t and each working condition:

[0373] The program traverses a preset rotation angle α (for example, from 0° to 180°, with a step of 5°).

[0374] The original Mx(t) and My(t) components are projected onto this plane of rotation angle a, to calculate the combined bending moment in this direction.

[0375] Example of combined bending moment calculation formula:

[0376] MCombine(a, t) = Mx(t) • cos(a) + My(t) • sin(a)

[0377] Formula symbols and meanings:

[0378] MCombine(a, t): The combined bending moment value at time t and rotation angle a.

[0379] Mx(t): The edgewise moment time series value at time t for the blade section in the original coordinate system.

[0380] My(t): The flapwise moment time series value at time t for the blade section in the original coordinate system.

[0381] a: The rotation angle, representing an analysis direction in the section plane (e.g., the angle from the edgewise axis clockwise). This angle is usually swept between 0° and 360° (or 0° and 180° symmetry) to find the most extreme loads.

[0382] cos(a), sin(a): The cosine and sine values of angle a, used for load component projection.

[0383] For each operating condition, the program calculates the MCombine(a, t) values for all time steps t and all rotation angles a.

[0384] Sub-step: determining blade limit load parameters for the target section based on the moment loads and the force loads.

[0385] Extracting maximum / minimum Combine loads:

[0386] For each operating condition, find the global maximum (MCombine,max) and global minimum (MC ombine,min) from all calculated MCombine(a, t) values.

[0387] Limit load parameters: These MCombine,max and MCombine,min values will be determined as part of the blade limit load parameters.

[0388] At the same time, the program will also extract the absolute maximum and minimum values from the original Mx(t) and My(t) time series (e.g. Mx,max, Mx,min, My,max, My,min), which are also important limit load parameters.

[0389] Part 3: Calculate blade fatigue load parameters for the target section.

[0390] Objective: Calculate the equivalent fatigue load, Markov matrix and load duration distribution (LDD) for section 20.

[0391] Sub-step: Traverse the fatigue load data of the working condition time series and perform rainflow counting algorithm on the fatigue load data of the working condition time series to generate rainflow counting results of the target section under the target working condition; the rainflow counting results at least include the cycle number and cycle amplitude of the target section.

[0392] Rainflow Counting: For each working condition Mx(t) and My(t) time series data, the program will apply rainflow counting algorithm.

[0393] Objective: Convert irregular load time series into a series of discrete load cycles with clear amplitude and mean value.

[0394] Output: Generate a rainflow matrix or list for each load component (e.g. Mx and My), which contains the cycle:

[0395] Cycle Amplitude (Si): The difference between the peak and valley values of the load (or half the difference).

[0396] Cycle Number (ni): The number of times the cycle with this amplitude appears in the time series.

[0397] Mean Value (mi): The average load level of the cycle.

[0398] Sub-step: Calculate the blade fatigue load parameters of the target section based on the cycle number and cycle amplitude using a pre-set algorithm. (This part is further refined: determine Markov matrix, LDD and equivalent fatigue load based on cycle number and cycle amplitude)

[0399] 1. Load Cycle Characteristic Matrix (Markov Matrix): Combine the cycle amplitude and mean value obtained from rainflow counting and the corresponding cycle number.

[0400] Objective: Provide a two-dimensional statistical view of the distribution of load cycle amplitude and mean value.

[0401] Formula / Structure Example:

[0402] ​The load amplitude and average value ranges are typically divided into several "bins" (eg, 64).

[0403] Construct a two-dimensional matrix Nbins_mean×bins_amplitude.

[0404] Each element Nij in the matrix represents the total number of cycles in which the load average falls in the i-th bin and the load amplitude falls in the j-th bin.

[0405] Symbols and meanings:

[0406] N: Load cycle characteristic matrix (Markov Matrix).

[0407] Nij: The element in the i-th row and j-th column of the matrix represents the number of cycles corresponding to the mean and amplitude intervals.

[0408] bins_mean: The number of bins for the mean.

[0409] bins_amplitude: The number of amplitude bins.

[0410] Load duration distribution information (LDD-Load Duration Distribution);

[0411] Preset algorithm: Usually based directly on load bin statistics of raw time series data or rainflow counting results.

[0412] Purpose: Displays the total proportion of time the blade is loaded at a specified load level.

[0413] Example formula / concept:

[0414] The load time series data (such as Mx(t)) is discretized and divided into multiple load levels Lk.

[0415] Calculate the total time T (L ≥ Lk) that each load level Lk (or higher than Lk) occurs.

[0416] Cumulative probability:

[0417]

[0418] Symbols and meanings:

[0419] Lk: A specific load level (such as bending moment value).

[0420] P(L≥Lk): The probability (or duration percentage) that the load value is greater than or equal to Lk.

[0421] T(L≥Lk): The total duration during which the load value is greater than or equal to Lk.

[0422] Ttotal: Total duration of the load history.

[0423] Damage Equivalent Load (DEL)

[0424] Predefined algorithm: Based on the rainflow counting results (cycle amplitudes Si, cycle numbers ni), combined with the material's S-N curve parameters (exponent m) and the load frequency f. exponent m

[0425] Purpose: To equate a complex variable amplitude load history to a single, constant amplitude load for ease of fatigue life calculation.

[0426] Formula example:

[0427]

[0428] Damage Equivalent Load (DEL). This is one of the final fatigue load parameters to be determined.

[0429] S i : Amplitude of the i-th load cycle (derived from the rainflow counting algorithm).

[0430] n i : Number of the i-th load cycle with amplitude S i (derived from the rainflow counting algorithm).

[0431] m: Material's exponent, also known as the inverse of the slope of the S-N curve. This is a material property parameter, usually determined through fatigue testing.

[0432] T: Total duration of the load history (e.g., if analyzing 10 minutes of data, T = 600 seconds).

[0433] f: Reference frequency, usually taken as 1 Hz, or the average frequency of the analyzed load components.

[0434] Sum of the contributions of all k different load cycle amplitudes to the total fatigue damage (based on the Palmgren-Miner linear damage accumulation rule).

[0435] Final determination: The program determines the load cycle characteristic matrix Markov, the load duration distribution information LDD, and the Damage Equivalent Load DEL as the blade fatigue load parameters for the target section and usually writes them into a structured report file (e.g., Excel or a custom text file).

[0436] Optionally, it also includes:

[0437] Added steps: determine the display graphics category;

[0438] Purpose:

[0439] Meeting diverse user visualization needs: Different engineers and analysis objectives may have different preferences for displaying load results. For example, some may prefer tabular data, others extreme value histograms, and still others fatigue spectra (such as Markov matrix heat maps) or LDD curves. The purpose of defining the graphics category is to allow users to select the display format that best conveys the load characteristics.

[0440] Guide graphics generation: The program needs to know what type of chart to generate (such as bar chart, curve chart, scatter plot, heat map, etc.) in order to call the corresponding drawing module and configure parameters.

[0441] Beneficial effects:

[0442] Flexibility and personalization: Users can customize output according to specific analysis needs, improving user experience.

[0443] Improve data interpretation efficiency: Generate targeted charts, allowing engineers to quickly understand load characteristics and make decisions, eliminating the need to search for key information in large amounts of raw data.

[0444] An additional step is to generate graphic load results of the blade limit load parameter and the blade fatigue load parameter based on the display graphic category.

[0445] Purpose:

[0446] Intuitively present complex load information: Blade load parameters (especially fatigue load parameters such as Markov matrices and LDD) are often multidimensional or in a curve format. Simply viewing numerical tables makes it difficult to quickly understand their patterns and trends. Converting this data into visual charts can significantly improve the efficiency of information transmission.

[0447] Assisted design decision-making and reporting: Graphical load results are an important part of design review, external communication and report writing, and can clearly demonstrate the load characteristics and safety of the design solution.

[0448] Beneficial effects:

[0449] Significantly improve analysis efficiency: Engineers can quickly identify load trends, extreme points, critical frequencies, fatigue damage distribution, etc. through graphics, which is several times more efficient than reading table data.

[0450] Enhanced data insights: Graphics can reveal hidden patterns and anomalies in the data, helping engineers gain a deeper understanding of load behavior.

[0451] Improve communication effectiveness: clear and professional charts help engineers communicate effectively within the team, with customers or certification agencies, reducing the cost of understanding.

[0452] Automatic report generation: avoids the tediousness and error-prone of manual drawing, ensuring the standardization and accuracy of report charts.

[0453] In order for those skilled in the art to better understand the embodiments of the present application, the following will use an example to describe the embodiments of the present application.

[0454] Reference Figures 2-4 , Figure 2 is a flowchart of a blade load parameter determination method provided in the embodiments of the present application; Figure 3 is a schematic diagram of an equivalent fatigue calculation logic flow provided in the embodiments of the present application; Figure 4 is a Markov calculation flowchart provided in the embodiments of the present application;

[0455] S1. Automatically obtain the load result working condition file path and template file;

[0456] Read the given load calculation Excel template, automatically identify the full working condition data file path calculated by Bladed, the limit calculation template file and the fatigue calculation template file corresponding to a section of the blade, and the output file path.

[0457] S2. Automatically obtain all sections of the blade and select the required section;

[0458] Under normal circumstances, the blade coordinate system has User, Root, Principal three coordinate systems, and the user can select different coordinate systems according to the required design load, obtain the interface number under different coordinate systems, and then select one or more blade section numbers according to the front-end requirements.

[0459] S3. Limit and fatigue post-processing file automatic generation calculation;

[0460] Before extracting the limit and fatigue load of each section of the blade, there are usually two algorithms to choose from, one is to call the post-processing algorithm based on the Bladed software post-processing file for calculation, and the other is to directly write an algorithm to post-process the data according to the working condition calculation result file.

[0461] If the first method is selected, the limit and fatigue files of the selected section need to be automatically generated and calculated in batches, the steps are as follows: first, according to the limit and fatigue files of the first kind as template files, replace the corresponding parameters in the files according to the selected section, realize the automatic generation of the blade section batch files, and then call the Bladed post-processing bottom module for calculation, and the calculation result is used for result extraction.

[0462] If the results calculated based on the limit fatigue post-processing file, the limit load of the blade is directly extracted through the timing results in the $ME file of the calculation file, and the fatigue load is mainly equivalent fatigue load, Markov matrix and LDD load extraction, etc. The equivalent fatigue result is obtained through $056-073 binary, the Markov is extracted through $001-018 binary file, and the LDD is extracted through the probability density post-processing file $001-006.

[0463] S4. Automatic extraction of blade limit load and fatigue load;

[0464] If the algorithm is written based on the Bladed load full working condition calculation result file to directly post-process the data, then the limit and fatigue load data of each section of the blade are processed as follows:

[0465] The limit load of each section of the user coordinate system is stored in the corresponding %062-%064 ($062-$064) file;

[0466] The load of each section of the root coordinate system is stored in the corresponding %041-%043 ($041-$043) file;

[0467] The load of each section of the principal coordinate system is stored in the %015-%017 ($015-$017) file;

[0468] According to the code in the script file, the limit load of the safety factor and the non-safety factor is extracted and written into the corresponding Excel. In addition, for the limit Combine load (at intervals of a certain rotation angle range from 0° to 360°, with a value interval of 10°), blade load coordinate conversion is needed here, and the conversion formula is:

[0469]

[0470] Formula symbols and meaning explanation:

[0471]

[0472] Meaning: It usually represents the combined moment or the rotation angle of the principal axis direction in the Mx and My plane.

[0473] In formula (1), it is calculated by Mx and My, which represents the angle of the combined moment relative to a certain reference axis (usually the My axis). In practical application, in order to find the worst combined load, this may be traversed (for example, from 0° to 180° or 360°) to find the maximum value.

[0474] Unit: radian (rad) or degree (°).

[0475] arctan (arctangent): inverse tangent function, used to calculate the angle of a given tangent value. Here, it calculates the angle corresponding to the relationship between Mx and My.

[0476] Mx: usually represents the edgewise bending moment (Edgewise Bending Moment) on the blade section or the bending moment component perpendicular to the blade spanwise and parallel to the edgewise direction in some coordinate system.

[0477] Unit: Newton-meter (N·m) or kilo-Newton-meter (kN·m), etc.

[0478] My: usually represents the flapwise bending moment (Flapwise Bending Moment) on the blade section or the bending moment component perpendicular to the blade spanwise and parallel to the flapwise direction in some coordinate system.

[0479] Unit: Newton-meter (N·m) or kilo-Newton-meter (kN·m), etc.

[0480] represents the combined bending moment in the direction of the new coordinate axis after rotation The formula is the combined component obtained by projecting the two orthogonal components Mx and My onto the new rotated axis. In load checking, different values are usually traversed to find the maximum (or minimum) combined bending moment that the section is subjected to.

[0481] Unit: Newton-meter (N·m) or kilo-Newton-meter (kN·m), etc.

[0482] (cosine phi): cosine value of angle , used for projection of load components.

[0483] (sine phi): sine value of angle , used for projection of load components.

[0484] Mxy: represents the modulus value (or amplitude value of radial bending moment) of the combined bending moment of the blade section in the x-y plane. It is the absolute size of the vector sum of Mx and My. It represents the size of the bending moment vector that the blade section is subjected to at that point in time, regardless of its direction.

[0485] Unit: Newton-meter (N·m) or kilo-Newton-meter (kN·m), etc.

[0486] Equation (1) provides the instantaneous direction angle of the combined bending moment vector with respect to the My axis at a certain time point.

[0487] Equation (2) calculates the bending moment component in a certain direction When performing limit load analysis, it is common to calculate the combined bending moment at each time step by traversing all possible angles (e.g. 0° to 360°, every 5° or 10°) and Then the maximum value is found from all the calculated results as the maximum combined bending moment under this working condition.

[0488] Equation (3) calculates the magnitude of the combined bending moment, which does not consider the direction and only represents the size of the bending moment vector in the two-dimensional plane.

[0489] Based on the above formulas, the combined torque load and force load of each cross-section of the blade under different rotation angles are obtained by Combine, and written into the corresponding Excel.

[0490] For fatigue load, first, for all working conditions, the rainflow calculation algorithm is used for rainflow calculation, and the time parameters and frequency parameters used by the rainflow algorithm are given in the given load calculation Excel template. The time sequence load of each working condition is obtained by the rainflow counting algorithm to get the cycle number and cycle amplitude and average value important parameters, and the load is distinguished and divided into 64 bins from small to large. The first column is the load mean, the second column is the load amplitude, and according to mathematical statistics, the number of times corresponding to the mean amplitude is obtained by Tongji, which is written into a 64*64 matrix. Then edit the script file and save it to the predefined Excel template. The LDD load is obtained by calculating the probability of the occurrence of each load bin in the total time, and the load of each bin is taken as an absolute value and arranged from large to small and the probability of occurrence is accumulated. When a certain value is reached, the corresponding load value is taken out as the load corresponding to the probability of the corresponding time, and saved to the predefined Excel template. The equivalent fatigue load is based on the slope m (usually 3-18) and frequency f of the selected S-N curve of the blade material, and is obtained by fatigue analysis. The formula for calculating the equivalent fatigue is:

[0491]

[0492] Seq: meaning: equivalent fatigue load (Equivalent Fatigue Load), also commonly known as damage equivalent load (Damage Equivalent Load, DEL). It refers to a constant amplitude load that, if applied to the material within a specified number of cycles (or total time), will cause the same fatigue damage as the actual irregular variable amplitude load history. Single with the original load S i ​Same (e.g. N*m, kN*m, N, kN, MPa, etc.).

[0493] Si: Cycle amplitude of the i-th load cycle. Also extracted from the original load time history through algorithms like Rainflow Counting, it represents half of the difference between the peak and valley values of the cycle (or sometimes the peak-to-valley difference directly).

[0494] ni: Cycle number of the i-th load cycle. This is obtained by processing the original load time history data through algorithms like Rainflow Counting, it represents the number of occurrences of a cycle with a specific amplitude Si in the entire load history.

[0495] m: Material's Exponent, also known as the inverse of the slope of the S-N curve. This is a material property parameter, usually determined through fatigue tests.

[0496] T: Total duration of the load time history (e.g. if analyzing 10 minutes of data, T = 600 seconds).

[0497] f: Reference frequency, usually taken as 1 Hz, or the average frequency of the analyzed load components.

[0498] ∑i: Summation symbol, representing the accumulation over all different load cycles. The index i here runs through all the load cycles identified through Rainflow Counting algorithm or other cycle counting methods.

[0499] The core of this formula is based on the Palmgren-Miner linear fatigue damage accumulation rule, which transforms a complex, irregular variable-amplitude load history (composed of a series of different amplitudes Si: and cycle numbers ni: ) into a single, equivalent constant-amplitude load Seq that has the same fatigue damage effect.

[0500] In wind turbine blade design, the Seq calculated through this formula can greatly simplify the fatigue life assessment process. Engineers can compare the calculated Seq with the material's allowable fatigue load to quickly judge the fatigue safety of the blade design, without the need to analyze each complex load cycle individually.

[0501] Final determination: The program determines the calculated load cycle characteristic matrix Markov, load duration distribution information LDD, and equivalent fatigue load as the blade fatigue load parameters of the target section, and usually writes them into a structured report file (such as Excel or a custom text file).

[0502] S5. Output load time history viewing load time history bar chart;

[0503] For the selected blade section, the time sequence diagram of fatigue working condition is needed in the detailed checking process of wind turbine design, therefore this module mainly reads the fatigue load calculation working condition result, obtains the time sequence load under each small working condition according to the selected blade section in the corresponding coordinate system, saves into binary value and saves into the corresponding file according to the calculation working condition naming, meanwhile, the working condition corresponding viewing function is also provided, and the column chart is displayed, the higher, the greater the load value, so as to judge whether the calculation load is accurate.

[0504] S6. modifying the path and outputting the result;

[0505] For the limit load output and fatigue load output in the graphical user interface (GUI), the corresponding paths are modified and run respectively, and the load result of the required blade section can be obtained.

[0506] It should be noted that for the method embodiments, in order to simply describe, they are all expressed as a series of action combinations, but those skilled in the art should know that the embodiments of the present application are not limited by the action sequence described, because according to the embodiments of the present application, certain steps can be performed in other order or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions involved are not necessarily the necessary of the embodiments of the present application.

[0507] Referring to Figure 5 , a structural block diagram of a blade load parameter determination device provided in the embodiments of the present application is shown, which can specifically include the following modules:

[0508] A simulation result acquisition module 301 is configured to acquire a simulation result for a wind turbine blade.

[0509] A target section determination module 302 is configured to determine a target section of the wind turbine blade.

[0510] A load parameter determination module 303 is configured to determine a blade limit load parameter and a blade fatigue load parameter of the target section through the simulation result.

[0511] For the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the related parts can be referred to the part of the method embodiments.

[0512] In addition, the embodiments of the present application also provide an electronic device, which includes a processor, a memory, a computer program stored on the memory and executable on the processor, the computer program implements each process of the above-mentioned blade load parameter determination method embodiments when executed by the processor, and can achieve the same technical effect, to avoid repetition, here is no longer repeated.

[0513] The embodiment of the present application further provides a computer readable storage medium, and the computer readable storage medium stores a computer program. The computer program is executed by a processor to realize each process of the blade load parameter determination method embodiment and achieve the same technical effects. To avoid repetition, details are not described herein. The computer readable storage medium includes a read-only memory (ROM), a random access memory (RAM), a magnetic disk, an optical disk, and the like.

[0514] Figure 6 A hardware structure schematic diagram of an electronic device for implementing various embodiments of the present application.

[0515] The electronic device 400 includes, but is not limited to, a radio frequency unit 401, a network module 402, an audio output unit 403, an input unit 404, a sensor 405, a display unit 406, a user input unit 407, an interface unit 408, a memory 409, a processor 410, and a power supply 411, and the like. Those skilled in the art can understand that the electronic device 400 can include more or less components, or combine some components, or arrange different components. In the embodiments of the present application, the electronic device includes, but is not limited to, a mobile phone, a tablet computer, a notebook computer, a palm computer, a vehicle terminal, a wearable device, and a pedometer, and the like. Figure 4

[0516] It should be understood that, in the embodiments of the present application, the radio frequency unit 401 can be used for receiving and sending signals in the process of information transmission or communication. Specifically, after receiving the downlink data from the base station, the processor 410 processes the data. In addition, the uplink data is sent to the base station. Generally, the radio frequency unit 401 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like. In addition, the radio frequency unit 401 can also communicate with the network and other devices through a wireless communication system.

[0517] The electronic device provides wireless broadband Internet access for users through the network module 402, such as helping users to send and receive emails, browse web pages, and access streaming media, and the like.

[0518] The audio output unit 403 can convert audio data received by the radio frequency unit 401 or the network module 402 or stored in the memory 409 into an audio signal and output as a sound. Moreover, the audio output unit 403 can also provide audio output related to a specific function performed by the electronic device 400 (for example, a call signal receiving sound, a message receiving sound, and the like). The audio output unit 403 includes a speaker, a buzzer, a receiver, and the like. ​

[0519] The input unit 404 is configured to receive audio or video signals. The input unit 404 can include a graphics processor (GPU) 4041 and a microphone 4042. The graphics processor 4041 processes image data of a still picture or a video obtained by an image capture device (e.g., a camera) in a video capture mode or an image capture mode. Processed image frames can be displayed on the display unit 406. Processed image frames can be stored in the memory 409 (or other storage medium) or transmitted via the radio frequency unit 401 or the network module 402. The microphone 4042 can receive sound and can process such sound as audio data. Processed audio data can be converted into a format that can be transmitted to a mobile communication base station via the radio frequency unit 401 in a telephone call mode.

[0520] The electronic device 400 also includes at least one sensor 405, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor includes an ambient light sensor and a proximity sensor, wherein the ambient light sensor can adjust the brightness of the display panel 4061 according to the brightness of ambient light, and the proximity sensor can turn off the display panel 4061 and / or the backlight when the electronic device 400 is moved to the ear. As one of the motion sensors, the accelerometer sensor can detect the magnitude of acceleration in each direction (generally three axes), and when at rest, can detect the magnitude and direction of gravity, and can be used to identify the electronic device posture (such as screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, knock), and the like. The sensor 405 can also include a fingerprint sensor, a pressure sensor, an iris sensor, a molecular sensor, a gyroscope, a barometer, a hygrometer, a thermometer, an infrared sensor, and the like, which will not be described here.

[0521] The display unit 406 is configured to display information input by a user or information provided to the user. The display unit 406 can include a display panel 4061, which can be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like.

[0522] The user input unit 407 can be used to receive inputted digital or character information, and to generate key signal input related to user settings of the electronic device and control of functions. Specifically, the user input unit 407 includes a touch panel 4071 and other input devices 4072. The touch panel 4071, also called a touch screen, can collect a user's touch operation (such as a user's operation on or near the touch panel 4071 using a finger, a stylus, or any suitable object or accessory) on or near it. The touch panel 4071 can include two parts, a touch detection device and a touch controller. The touch detection device detects the user's touch position and detects a signal caused by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into touch coordinates, and sends it to the processor 410, receives commands from the processor 410 and executes them. In addition, the touch panel 4071 can be implemented in various types such as resistive, capacitive, infrared, and surface acoustic wave. In addition to the touch panel 4071, the user input unit 407 can also include other input devices 4072. Specifically, the other input devices 4072 can include, but are not limited to, a physical keyboard, function keys (such as volume control buttons, on / off buttons, etc.), trackballs, mice, joysticks, and the like, which will not be described here.

[0523] Further, the touch panel 4071 can be overlaid on the display panel 4061, and when the touch panel 4071 detects a touch operation on or near it, it transmits to the processor 410 to determine the type of touch event, and then the processor 410 provides corresponding visual output on the display panel 4061 according to the type of touch event. Although in the Figure 4 In some embodiments, the touch panel 4071 and the display panel 4061 can be integrated to realize the input and output functions of the electronic device, which is not limited here.

[0524] The interface unit 408 is an interface for connecting external devices to the electronic device 400. For example, the external devices can include a wired or wireless headset port, an external power supply (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device having an identification module, an audio input / output (I / O) port, a video I / O port, an earphone port, and the like. The interface unit 408 can be used to receive input (e.g., data information, power, etc.) from external devices and transmit the received input to one or more elements within the electronic device 400, or can be used to transmit data between the electronic device 400 and external devices.

[0525] The memory 409 can be used to store software programs and various data. The memory 409 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, application programs required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; and the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), and the like. In addition, the memory 409 can include a high-speed random access memory, and can also include a nonvolatile memory, for example, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state memory device.

[0526] The processor 410 is the control center of the electronic device, connects all parts of the electronic device through various interfaces and lines, executes various functions of the electronic device and processes data by running or executing software programs and / or modules stored in the memory 409 and calling data stored in the memory 409, and thus monitors the whole electronic device. The processor 410 can include one or more processing units; preferably, the processor 410 can integrate an application processor and a modem processor, wherein the application processor mainly processes an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communication. It can be understood that the above-mentioned modem processor can also not be integrated into the processor 410.

[0527] The electronic device 400 can also include a power supply 411 (such as a battery) for supplying power to various components; preferably, the power supply 411 can be logically connected to the processor 410 through a power management system, so as to realize the functions of managing charging, discharging, and power consumption management, etc. through the power management system.

[0528] In addition, the electronic device 400 includes some functional modules which are not shown and will not be described here.

[0529] It should be noted that in this document, the term "comprising" or "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article, or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such a process, method, article, or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of additional identical elements in the process, method, article, or device including the element.

[0530] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better embodiment. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as RoM / RAM, disk, CD), including a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, air conditioner, or network equipment, etc.) to execute the methods described in each embodiment of the present invention.

[0531] like Figure 7 As shown, in another embodiment provided by the present invention, a computer-readable storage medium 501 is also provided, in which instructions are stored. When the computer-readable storage medium 501 is run on a computer, the computer executes the blade load parameter determination method described in the above embodiment.

[0532] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are protected by the present invention.

[0533] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the embodiments of the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0534] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0535] In the embodiments of the present application, it should be understood that the disclosed apparatus and method can be implemented in other manners. For example, the described apparatus embodiments are merely schematic. The units as divided can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0536] The units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments of the present application.

[0537] In addition, each functional unit in the various embodiments of the present application can be integrated in one processing unit, or each unit can be a physically independent unit, or two or more units can be integrated in one unit.

[0538] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the parts that make contributions to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes various media that can store program codes, such as U disk, mobile hard disk, ROM, RAM, magnetic disk or optical disk, etc.

[0539] The above description is merely specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for determining blade load parameters, characterized in that: include: Obtain simulation results for wind turbine blades; determining a target cross-section of the wind turbine blade; The blade limit load parameters and blade fatigue load parameters of the target cross section are determined based on the simulation results.

2. The method according to claim 1, characterized in that The step of obtaining simulation results for wind turbine blades includes: Obtaining an operating condition parameter table for the simulation result; the operating condition parameter table includes a first storage path of the simulation result; The limit calculation template file and the fatigue calculation template file of the wind turbine blade are acquired based on the first storage path.

3. The method according to claim 1, characterized in that The step of determining the target cross-section of the wind turbine blade comprises: receiving blade coordinate system category information input by a user; determining a target coordinate system category based on the blade coordinate system category information; Determining the number of interfaces of the wind turbine blade in the target coordinate system category; A target cross-section of the wind turbine blade is determined based on the number of interfaces.

4. The method according to claim 3, characterized in that The step of determining the blade limit load parameters and blade fatigue load parameters of the target cross-section through the simulation results includes: Determining whether the simulation result includes a post-processing file of the simulation software; When it is determined that the simulation result includes a post-processing file of the simulation software, the blade limit load parameters and the blade fatigue load parameters of the target section are calculated based on the post-processing file.

5. The method according to claim 4, characterized in that Also includes: When it is determined that the simulation result does not include a post-processing file of the simulation software, obtaining working condition time series load data for the blade limit load parameter and the blade fatigue load parameter based on the simulation result; The blade limit load parameters and blade fatigue load parameters of the target section are calculated based on the working condition time series load data and a preset algorithm.

6. The method according to claim 4, characterized in that The step of calculating the blade limit load parameters and blade fatigue load parameters of the target section based on the post-processing file includes: Extracting timing result information, equivalent fatigue load information, load cycle characteristic matrix and load duration distribution information of the target cross section from the simulation results; The blade limit load parameters and blade fatigue load parameters of the target section are calculated based on the post-processing file using the timing result information, the equivalent fatigue load information, the load cycle characteristic matrix and the load duration distribution information.

7. The method according to claim 5, characterized in that The simulation results include working condition time series load data, and the working condition time series load data includes working condition time series limit load data for the blade limit load parameter. The working condition time series limit load data is obtained as follows: Determine a second storage path of an original limit load data file of a simulation result for the target cross section under the target coordinate system category; extracting the original extreme load data file based on the second storage path; A target working condition is determined, and working condition time series limit load data of the target cross section under the target working condition is read from the original limit load data file.

8. The method according to claim 7, characterized in that The operating condition time series load data includes operating condition time series fatigue load data for the blade fatigue load parameter, and the step of calculating the blade limit load parameter and the blade fatigue load parameter of the target cross section based on a preset algorithm using the operating condition time series load data includes: Traversing the time series limit load data of the working condition, and obtaining the combined moment load and force load corresponding to the target cross section at the target rotation angle based on a preset algorithm; determining a blade limit load parameter of the target cross-section based on the moment load and the force load; Traversing the working condition time series fatigue load data, and executing a rainflow counting algorithm on the working condition time series fatigue load data to generate a rainflow counting result of the target section under the target working condition; the rainflow counting result at least includes the number of cycles and the cycle amplitude of the target section; The blade fatigue load parameter of the target cross section is calculated based on a preset algorithm using the cycle number and cycle amplitude.

9. The method according to claim 8, characterized in that The step of calculating the blade fatigue load parameter of the target cross section using the cycle number and cycle amplitude based on a preset algorithm includes: Determining a load cycle characteristic matrix, load duration distribution information, and equivalent fatigue load based on the number of cycles and the cycle amplitude; The load cycle characteristic matrix, the load duration distribution information and the equivalent fatigue load are used to determine the blade fatigue load parameters of the target section.

10. The method according to claim 1, characterized in that Also includes: Determine the display graphics category; Graphic load results of the blade limit load parameter and the blade fatigue load parameter are generated based on the display graphic category.

11. A blade load parameter determination device, characterized in that: include: A simulation result acquisition module, used to obtain simulation results for wind turbine blades; A target cross-section determination module, configured to determine a target cross-section of the wind turbine blade; The load parameter determination module is used to determine the blade limit load parameter and blade fatigue load parameter of the target section through the simulation results.

12. An electronic device, characterized in that: comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; The memory is used to store computer programs; The processor is configured to implement the method according to any one of claims 1 to 10 when executing a program stored in the memory.

13. A computer-readable storage medium having instructions stored thereon, which, when executed by one or more processors, cause the processors to perform the method according to any one of claims 1 to 10.

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