Simulation optimization system for wind power blade mold
By using multi-dimensional data acquisition and intelligent optimization modules, the problem of inaccurate mold fitting accuracy judgment in traditional wind turbine blade mold simulation optimization systems has been solved, achieving efficient mold selection and simulation optimization, and ensuring the accuracy of simulation results and their production guidance.
Patent Information
- Application Number
- CN202511752505.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-26
- Publication Date
- 2026-02-17
AI Technical Summary
Traditional wind turbine blade mold simulation optimization systems have a single evaluation dimension and lack a comprehensive assessment of mold closing accuracy, resulting in partial compliance but overall substandard accuracy. Consequently, simulation results cannot guide real production.
Employing a multi-dimensional acquisition module and an intelligent optimization module, and connecting to a database, mold closing detection device, pressure sensor, infrared thermometer, and ANSYS software, the system acquires mold, blade, and test data, generates defect and performance indices, intelligently selects molds with high mold closing accuracy as simulation targets, dynamically simulates the molding process, and optimizes the simulation model.
It improves the efficiency of mold closing accuracy judgment, avoids the limitations of a single indicator, ensures the accuracy of simulation results and production guidance, and realizes intelligent optimization of simulation effects.
Smart Images
Figure CN121543429A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of wind power blade mold simulation, in particular to a wind power blade mold simulation optimization system. BACKGROUND
[0002] The wind power blade is the core stressed component of the wind turbine generator set and is the key carrier for converting wind energy into mechanical energy. The wind power blade is usually made of composite materials through molding, pouring and other processes and has excellent properties such as light weight, high strength, fatigue resistance, wind sand erosion resistance and salt mist corrosion resistance. The performance of the wind power blade directly determines the power generation efficiency and service life of the wind turbine generator set. The core steps of the wind power blade mold simulation include simulation modeling and geometry processing. Based on the three-dimensional design model of the blade, an entity model containing the mold body, pipelines and the like is constructed. Non-key features are simplified and high-precision grids are divided. The material and boundary conditions are defined. The thermal physical parameters of the mold and the composite material blank are input. The boundary conditions such as heating mode, cooling flow, convective heat transfer coefficient and mold clamping pressure are set. Then, the core physical field simulation is carried out, including thermal simulation of simulating temperature field uniformity, curing simulation of predicting the curing degree distribution of the blank, and structural simulation of analyzing mold deformation and stress. Finally, the results are analyzed and optimized, and the temperature curve, curing degree cloud map and the like are output. The parameters are adjusted for problems such as uneven temperature, abnormal curing and excessive deformation. The simulation is iterated until the design requirements are met.
[0003] At present, the traditional wind power blade mold simulation optimization system has single evaluation dimension and lacks comprehensive judgment of mold clamping precision, which is easy to cause the misuse of the mold with local qualification but overall precision not meeting the standard, thereby causing problems such as blade overflow and wall thickness deviation. In addition, when the simulation model simulates the adaptive relationship between the production parameters of the wind power blade, it is easy to be out of line with the actual production conditions, and the simulation results cannot guide the real production. SUMMARY
[0004] In view of the defects of the prior art, the present application provides a wind power blade mold simulation optimization system, which has the advantages of high mold clamping precision judgment efficiency and good intelligent optimization simulation effect, and solves the problems of the traditional wind power blade mold simulation optimization system, such as fuzzy mold clamping precision judgment and simulation results that cannot guide real production.
[0005] To achieve the above-mentioned purpose, the present application provides the following technical scheme: a wind power blade mold simulation optimization system, comprising a multi-dimensional acquisition module and an intelligent optimization module. The multi-dimensional acquisition module acquires the management data of all wind power blade molds, the production data of wind power blades and the simulation test data through a connection database, a mold clamping detection device, a pressure sensor, an infrared temperature measuring instrument and ANSYS software, and classifies and forms a mold data set, a blade data set and a test data set. The intelligent optimization module includes a mold evaluation unit, a simulation evaluation unit, a test evaluation unit, and an optimization management unit. The mold evaluation unit evaluates the mold closing accuracy of each mold based on the mold dataset and generates a corresponding defect index. The simulation evaluation unit sorts all wind turbine blade molds in ascending order of numerical values and selects the defect index. The smallest wind turbine blade mold is used as the simulation target, and the corresponding simulation model is constructed using ANSYS software. The simulation evaluation unit analyzes the forming process of producing each wind turbine blade based on the blade dataset, and generates the corresponding forming data set. The test evaluation unit evaluates the performance quality of the simulated wind turbine blades based on the test dataset and generates corresponding performance indices. The optimization management unit is set with a fixed numerical defect threshold. and performance threshold Combined with the defect index Molding data group and performance index It visualizes the simulation model and outputs corresponding simulation optimization suggestions.
[0006] Preferably, the mold dataset includes the internal gap of each wind turbine blade mold, the number of internal gap detection points, the external gap of each wind turbine blade mold, the number of external gap detection points, the surface fitting deviation, and the number of surface detection points, wherein each wind turbine blade mold has the same number of detection points.
[0007] Preferably, the blade dataset includes the injection pressure, curing temperature, and filling time for each wind turbine blade.
[0008] Preferably, the test dataset includes the maximum deformation, maximum deviation of surface curvature, maximum misalignment of parting surface, maximum curing rate, and maximum number of residual bubbles for each simulated wind turbine blade.
[0009] Preferably, the defect index The calculation process is as follows: Based on the template dataset, extract the first... The management data of the first wind turbine blade mold, and the first The internal clearance of the mold for each wind turbine blade mold is denoted as... , Indicates the first The number of gap detection points inside the wind turbine blade mold will be the number of the first... The external clearance of the mold closing mechanism for each wind turbine blade mold is denoted as... , Indicates the first The number of gap detection points on the outside of the wind turbine blade mold will be the number of the first one. The profile fit deviation of a wind turbine blade mold is denoted as , denotes the number of profile detection points inside the th wind turbine blade mold; In the formula, denotes the average value of the inside clamping gap, denotes the average value of the outside clamping gap, denotes the average value of the profile fit deviation; In the formula, denotes the inside clamping gap of the th detection point inside the th wind turbine blade mold, , denotes the standard deviation of the inside clamping gap, denotes the inside clamping gap of the th detection point outside the th wind turbine blade mold, , denotes the standard deviation of the outside clamping gap; In the formula, and denote the maximum value and the minimum value of the inside clamping gap, denotes the range of the inside clamping gap, denotes the weight of the range of the inside clamping gap, denotes the weight of the standard deviation of the inside clamping gap, and denote the maximum value and the minimum value of the outside clamping gap, denotes the range of the outside clamping gap, denotes the weight of the range of the outside clamping gap, denotes the weight of the standard deviation of the outside clamping gap, denotes the weight of the average value of the profile fit deviation, , , , and are constants, and , denotes the defect index of the th wind turbine blade mold.
[0010] Preferably, the molding data group The calculation process is as follows: Given the first A wind turbine blade mold is used as the simulation target; Based on the blade dataset, the extraction method used is the first... The wind turbine blade production data obtained from the first wind turbine blade mold will be used. The number of wind turbine blades produced by each wind turbine blade mold is denoted as . The injection pressure of each wind turbine blade is denoted as... The curing temperature of each wind turbine blade is denoted as... The filling time for each wind turbine blade is recorded as... ; In the formula, Indicates the use of the first The first wind turbine blade mold was produced. Injection pressure per wind turbine blade , Indicates the use of the first The first wind turbine blade mold was produced. The curing temperature for each wind turbine blade. Indicates the use of the first The first wind turbine blade mold was produced. Filling time for each wind turbine blade This represents the average value of the injection pressure configuration at a unit curing temperature. This represents the average value of the injection pressure configuration within a unit filling time. This represents the average value of the curing temperature configuration within a unit filling time. Indicates the first A set of molding data for a wind turbine blade mold.
[0011] Preferably, the performance index The calculation process is as follows: The simulated wind turbine blade output from the simulation model is denoted as... ; Based on the test dataset, extract simulated wind turbine blades. The test data will be used to simulate wind turbine blades. The maximum deformation is denoted as Simulated wind turbine blades The maximum deviation of the surface curvature is denoted as Simulated wind turbine blades The maximum misalignment of the parting surface is denoted as Simulated wind turbine blades The maximum curing rate is denoted as Simulated wind turbine blades the maximum value of the bubble residual quantity is denoted as ; In the formula, represents a standard value for measuring the maximum deformation amount, represents a conversion coefficient of the maximum deformation amount standard value and the maximum deformation amount ratio, represents a weight of the maximum deformation amount standard value and the maximum deformation amount ratio, represents a standard value for measuring the maximum deviation of the profile curvature, represents a conversion coefficient of the maximum deviation of the profile curvature standard value and the maximum deviation of the profile curvature ratio, represents a weight of the maximum deviation of the profile curvature standard value and the maximum deviation of the profile curvature ratio, represents a standard value for measuring the maximum misplacement of the split surface, represents a conversion coefficient of the maximum misplacement of the split surface standard value and the maximum misplacement of the split surface ratio, represents a weight of the maximum misplacement of the split surface standard value and the maximum misplacement of the split surface ratio, represents a standard value for measuring the maximum value of the curing rate, represents a conversion coefficient of the maximum value of the curing rate standard value and the maximum value of the curing rate ratio, represents a weight of the maximum value of the curing rate standard value and the maximum value of the curing rate ratio, represents a standard value for measuring the maximum value of the bubble residual quantity, represents a conversion coefficient of the maximum value of the bubble residual quantity standard value and the maximum value of the bubble residual quantity ratio, represents a weight of the maximum value of the bubble residual quantity standard value and the maximum value of the bubble residual quantity ratio, , , , and are constants, and , represents a performance index of the simulated wind turbine blade generated by the th wind turbine blade mold model.
[0012] Preferably, when the defect index ≥ defect threshold value , it indicates that the mold closure accuracy of the corresponding mold does not meet the standard, triggering measures including prohibiting the use of the corresponding mold as a simulation target and timely correcting and repairing.
[0013] Preferably, the optimization management unit takes the molding data set as the hyperparameters of the simulation model, for dynamically simulating the molding process of the wind turbine blade.
[0014] Preferably, the performance index ≤ performance threshold , indicating that the simulation wind turbine blade performance quality generated by the corresponding simulation model is not up to standard, triggering measures including refining the simulation modeling process and optimizing the error compensation mechanism.
[0015] Compared with the prior art, the wind turbine blade mold simulation optimization system provided by the present application has the following beneficial effects: 1. The present application connects the database, mold closing detection device, pressure sensor, infrared thermometer and ANSYS software through the multi-dimensional acquisition module to obtain the management data of all wind turbine blade molds, production data of wind turbine blades and simulation test data, and classify them into mold data sets, blade data sets and test data sets. The intelligent optimization module evaluates the mold closing accuracy of each mold according to the mold data set, and generates the corresponding defect index , avoiding the limitation of a single index, sorting all wind turbine blade molds in order from low to high, and screening out the wind turbine blade mold with the smallest defect index as the simulation target. ANSYS software is used to build the corresponding simulation model, and the mold with substandard accuracy is prohibited as the simulation target in time, so that the mold closing accuracy judgment efficiency is high.
[0016] 2. The present application generates the corresponding forming data group by analyzing the forming process of each wind turbine blade produced by the bionic target through the intelligent optimization module according to the blade data set , quantifies the adaptive relationship between key process parameters, and uses the forming data group as the hyperparameters of the simulation model to dynamically simulate the forming process of the wind turbine blade. The intelligent optimization module evaluates the performance quality of the simulation wind turbine blade according to the test data set, generates the corresponding performance index , visualizes the simulation model, and outputs the corresponding simulation optimization suggestion, avoiding the simulation optimization failure caused by the substandard local performance, and the intelligent optimization simulation effect is good.
[0017] Figure 1 The present application is a system flowchart. DETAILED DESCRIPTION
[0018] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.
[0019] EMBODIMENT Referring to Figure 1 , Table 1 defect index experimental data and Table 2 performance index experimental data, the application provides a wind power blade mold simulation optimization system, comprising a multidimensional acquisition module and an intelligent optimization module; The multidimensional acquisition module acquires management data, production data and simulation test data of all wind power blade molds through connection of a database, a mold closing detection device, a pressure sensor, an infrared temperature measuring instrument and ANSYS software, and classifies and forms a mold data set, a blade data set and a test data set; The mold data set comprises mold closing internal clearance, internal clearance detection point number, mold closing external clearance, external clearance detection point number, surface fitting deviation and surface detection point number of each wind power blade mold, wherein each wind power blade mold is distributed with the same number of detection points; The blade data set comprises pouring pressure, curing temperature and filling time of each wind power blade; The test data set comprises maximum deformation, maximum surface curvature deviation, maximum split surface misplacement, maximum curing rate and maximum bubble residue number of each simulation wind power blade; The intelligent optimization module comprises a mold evaluation unit, a simulation evaluation unit, a test evaluation unit and an optimization management unit, the mold evaluation unit evaluates the mold closing accuracy of each mold according to the mold data set, and generates the corresponding defect index , and the calculation process is as follows: According to the mold data set, the management data of the first wind power blade mold is extracted, and the mold closing internal clearance of the first wind power blade mold is recorded as , represents the internal clearance detection point number of the first wind power blade mold, the mold closing external clearance of the first wind power blade mold is recorded as , represents the external clearance detection point number of the first wind power blade mold, the surface fitting deviation of the first wind power blade mold is recorded as , represents the internal surface detection point number of the first wind power blade mold; In the formula, represents the average value of the mold closing internal clearance, represents the average value of the mold closing external clearance, an average value of the profile fit deviation; in the formula, represents the inside gap of the mold closure at the th detection point inside the wind turbine blade mold, , represents the standard deviation of the inside gap of the mold closure, represents the inside gap of the mold closure at the th detection point outside the wind turbine blade mold, , represents the standard deviation of the outside gap of the mold closure; in the formula, and represent the maximum and minimum values of the inside gap of the mold closure, represents the range of the inside gap of the mold closure, represents the weight of the range of the inside gap of the mold closure, represents the weight of the standard deviation of the inside gap of the mold closure, and represent the maximum and minimum values of the outside gap of the mold closure, represents the range of the outside gap of the mold closure, represents the weight of the range of the outside gap of the mold closure, represents the weight of the standard deviation of the outside gap of the mold closure, represents the weight of the average value of the profile fit deviation, , , , and are constants, and , represents the defect index of the th wind turbine blade mold; Specifically, the mold closing gap of wind turbine blade molds directly affects the amount of glue overflow at the blade edge and the wall thickness deviation. Among them, the internal gap is mostly measured at the web plate installation area. It can be obtained indirectly by measuring the displacement of the cylindrical pin in the measuring device, or by placing clay in the internal mold closing seam area, removing it after mold closing, and measuring its thickness. Alternatively, a pressure body with conductive contacts can be placed directly into the internal mold closing seam area, and the gap size of the mold closing seam in that area can be accurately obtained by the on / off signal of the conductive contacts after mold closing. The external gap is mostly concentrated at key edge parts such as mold flange, leading edge, and trailing edge. Usually, the detection points are set at easily operable surface positions such as the outer wall of the flange of the upper or lower mold. The elevation difference between the reference point and these measuring points can be used to obtain the external gap of the mold closing. The surface fitting deviation refers to the deviation change between the theoretical surface and the actual fitting surface, which directly reflects the actual fitting degree of the upper and lower mold surfaces after mold closing. The following are the experimental data for the defect index, as shown in Table 1: Table 1: Experimental Data of Defect Index Table 1 shows the experimental data for the defect index. Wind turbine blade mold A was selected as the experimental target, with the following weights: , , , , ; The optimization management unit is set with a fixed defect threshold value. This is used to measure whether the mold closing accuracy of each mold meets the standard. In the defect index experimental data in Table 1, the defect threshold is... The preferred value is set to 0.03. Based on the assessment, the defect index of wind turbine blade mold A is... >Defect threshold This indicates that the mold closing accuracy of wind turbine blade mold A is not up to standard. The triggering measures include prohibiting the use of wind turbine blade mold A as a simulation target and timely correction and repair. The simulation evaluation unit sorted all wind turbine blade molds in ascending order of numerical values and selected the defect indices. The smallest wind turbine blade mold is used as the simulation target, and the corresponding simulation model is built using ANSYS software. The simulation evaluation unit analyzes the forming process of producing each wind turbine blade based on the blade dataset, and generates the corresponding forming data set. The calculation process is as follows: Given the first A wind turbine blade mold is used as the simulation target; Based on the blade dataset, the extraction method used is the first... The wind turbine blade production data obtained from the first wind turbine blade mold will be used. The number of wind turbine blades produced by each wind turbine blade mold is denoted as . The injection pressure of each wind turbine blade is denoted as... The curing temperature of each wind turbine blade is denoted as... The filling time for each wind turbine blade is recorded as... ; In the formula, Indicates the use of the first The first wind turbine blade mold was produced. Injection pressure per wind turbine blade , Indicates the use of the first The first wind turbine blade mold was produced. The curing temperature for each wind turbine blade. Indicates the use of the first The first wind turbine blade mold was produced. Filling time for each wind turbine blade This represents the average value of the injection pressure configuration at a unit curing temperature. This represents the average value of the injection pressure configuration within a unit filling time. This represents the average value of the curing temperature configuration within a unit filling time. Indicates the first A set of molding data for a wind turbine blade mold; The optimized management unit will form data groups As a hyperparameter of the simulation model, it is used to dynamically simulate the forming process of wind turbine blades; The testing and evaluation unit evaluates the performance quality of simulated wind turbine blades based on the test dataset and generates corresponding performance indices. The calculation process is as follows: The simulated wind turbine blade output from the simulation model is denoted as... ; Based on the test dataset, extract simulated wind turbine blades. The test data will be used to simulate wind turbine blades. The maximum deformation is denoted as Simulated wind turbine blades The maximum deviation of the surface curvature is denoted as Simulated wind turbine blades The maximum misalignment of the parting surface is denoted as Simulated wind turbine blades The maximum curing rate is denoted as Simulated wind turbine blades The maximum number of residual bubbles is denoted as ; In the formula, represents a standard value for measuring the maximum deformation amount, represents a conversion coefficient of the maximum deformation amount standard value and the maximum deformation amount ratio, represents a weight of the maximum deformation amount standard value and the maximum deformation amount ratio, represents a standard value for measuring the maximum deviation of the profile curvature, represents a conversion coefficient of the maximum deviation of the profile curvature standard value and the maximum deviation of the profile curvature ratio, represents a weight of the maximum deviation of the profile curvature standard value and the maximum deviation of the profile curvature ratio, represents a standard value for measuring the maximum misalignment amount of the split surface, represents a conversion coefficient of the maximum misalignment amount of the split surface standard value and the maximum misalignment amount of the split surface ratio, represents a weight of the maximum misalignment amount of the split surface standard value and the maximum misalignment amount of the split surface ratio, represents a standard value for measuring the maximum value of the solidification rate, represents a conversion coefficient of the maximum value of the solidification rate standard value and the maximum value of the solidification rate ratio, represents a weight of the maximum value of the solidification rate standard value and the maximum value of the solidification rate ratio, represents a standard value for measuring the maximum value of the number of bubble residues, represents a conversion coefficient of the maximum value of the number of bubble residues standard value and the maximum value of the number of bubble residues ratio, represents a weight of the maximum value of the number of bubble residues standard value and the maximum value of the number of bubble residues ratio, , , , and are constants, and , represents a performance index of a simulated wind turbine blade generated from the th wind turbine blade mold model; The following is the performance index experimental data, as shown in Table 2: Table 2: Performance index experimental data Table 2: Performance index experimental data In the performance index experimental data of Table 2, the simulated wind turbine blade B generated by the wind turbine blade mold A is selected as the experimental target, and the weight , , , , ; and The optimization management unit is provided with a fixed numerical performance threshold value , for measuring the performance quality of the simulated wind turbine blade, in the performance index experimental data in Table 2, the performance threshold The preferred value of the performance threshold is set to 1.5, and it is judged that the performance index < Performance threshold , indicating that the performance quality of the simulated wind turbine blade B generated by the wind turbine blade mold A does not meet the standard, triggering measures including refining the simulation modeling process and optimizing the error compensation mechanism.
[0020] In this embodiment, the mold screening, simulation modeling and optimization decision-making process form a closed loop in three dimensions of the quality of the wind turbine blade mold itself, the adaptability of the blade production process and the effectiveness of the simulation results. The intelligent optimization module comprehensively covers quality parameters such as mold closing internal gap, mold closing external gap and surface fitting deviation, evaluates the mold closing accuracy of each mold, and generates a corresponding defect index , avoids the limitations of a single indicator, and timely prohibits molds with substandard accuracy from being used as simulation targets. The intelligent optimization module analyzes the molding process of each wind turbine blade produced by the bionic target, generates a corresponding molding data set , quantifies the adaptive relationship between key process parameters, and uses the molding data set as the hyperparameters of the simulation model to dynamically simulate the molding process of the wind turbine blade. The intelligent optimization module evaluates the performance quality of the simulated wind turbine blade based on the test data set, generates a corresponding performance index , avoids simulation optimization failure caused by substandard local performance, and makes optimization decisions more data-driven.
[0021] The size of the threshold is set to facilitate comparison. The size of the threshold depends on the number of sample data and the base number set by the person skilled in the art for each set of sample data; as long as it does not affect the proportional relationship between the parameters and the quantized values.
[0022] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value. The coefficients in the formula are set by the person skilled in the art according to the actual situation. The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can make equivalent substitutions or changes to the technical solutions and inventive concepts of the present application within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application.
Claims
1. A wind turbine blade mould simulation optimisation system, characterised in that: The application relates to a multi-dimensional acquisition and intelligent optimization system for wind power blade molds. The multi-dimensional acquisition module acquires management data, production data and simulation test data of all wind power blade molds through connection with a database, a mold closing detection device, a pressure sensor, an infrared temperature detector and ANSYS software, and classifies and forms mold data sets, blade data sets and test data sets. The intelligent optimization module includes a mold evaluation unit, a simulation evaluation unit, a test evaluation unit, and an optimization management unit. The mold evaluation unit evaluates the mold closing accuracy of each mold based on the mold dataset and generates a corresponding defect index. The simulation evaluation unit sorts all wind turbine blade molds in ascending order of numerical values and selects the defect index. The smallest wind turbine blade mold is used as the simulation target, and the corresponding simulation model is constructed using ANSYS software. The simulation evaluation unit analyzes the forming process of producing each wind turbine blade based on the blade dataset, and generates the corresponding forming data set. The test evaluation unit evaluates the performance quality of the simulated wind turbine blades based on the test dataset and generates corresponding performance indices. The optimization management unit is set with a fixed defect threshold value. and performance threshold Combined with the defect index Molding data group and performance index It visualizes the simulation model and outputs corresponding simulation optimization suggestions.
2. A wind turbine blade mould simulation optimization system according to claim 1, characterized in that: The mold data set includes an internal mold closing gap, an internal gap detection point number, an external mold closing gap, an external gap detection point number, a surface fitting deviation and a surface detection point number of each wind power blade mold.
3. A wind turbine blade mould simulation optimisation system according to claim 2, wherein: The blade data set includes a pouring pressure, a curing temperature and a filling time length of each wind power blade.
4. A wind turbine blade mould simulation optimisation system according to claim 3, wherein: The test data set includes a maximum deformation amount, a maximum surface curvature deviation, a maximum split surface displacement amount, a maximum curing rate and a maximum bubble residual number of each simulation wind power blade.
5. A wind turbine blade mould simulation optimisation system according to claim 4, wherein: the defect index The calculation proceeds as follows: Based on the template dataset, extract the first... The management data of the first wind turbine blade mold, and the first The internal clearance of the mold for each wind turbine blade mold is denoted as... , Indicates the first The number of gap detection points inside the wind turbine blade mold will be the number of the first... The external clearance of the mold closing mechanism for each wind turbine blade mold is denoted as... , Indicates the first The number of gap detection points on the outside of the wind turbine blade mold will be the number of the first one. The surface fitting deviation of a wind turbine blade mold is denoted as . , Indicates the first The number of surface inspection points inside each wind turbine blade mold; In the formula, represents the average value of the inside clearance of the mold, represents the average value of the outside clearance of the mold, represents the average value of the profile fit deviation; In the formula, represents the inside gap of the mold of the wind turbine blade mold at the first detection point, represents the inside gap of the mold of the wind turbine blade mold at the first detection point, , represents the standard deviation of the inside gap of the mold, represents the inside gap of the mold of the wind turbine blade mold at the first detection point, , represents the standard deviation of the outside gap of the mold. In the formula, and This indicates the maximum and minimum values of the internal clearance of the mold. This indicates the range of the internal clearance of the mold. The weight representing the range of internal clearance difference in mold closing is... The weights representing the standard deviation of the internal clearance of the mold are: and This indicates the maximum and minimum values of the external clearance of the mold. This represents the range of the external clearance of the mold. The weight representing the range of external clearance in mold closing. The weights representing the standard deviation of the external clearance of the mold are: The weight of the average deviation of the surface fit is indicated. , , , and All are constants, and , Indicates the first Defect index of a wind turbine blade mold.
6. A wind turbine blade mould simulation optimisation system according to claim 5, wherein: The shaped data set The calculation proceeds as follows: A first wind turbine blade mould is known as a simulation target; According to the blade data set, extract the wind turbine blade production data produced by the first wind turbine blade mold, and record the number of wind turbine blades produced by the first wind turbine blade mold as , record the infusion pressure of each wind turbine blade as , record the curing temperature of each wind turbine blade as , and record the filling duration of each wind turbine blade as ; In the formula, Indicates the use of the first The first wind turbine blade mold was produced. Injection pressure per wind turbine blade , Indicates the use of the first The first wind turbine blade mold was produced. The curing temperature for each wind turbine blade. Indicates the use of the first The first wind turbine blade mold was produced. Filling time for each wind turbine blade This represents the average value of the injection pressure configuration at a unit curing temperature. This represents the average value of the injection pressure configuration within a unit filling time. This represents the average value of the curing temperature configuration within a unit filling time. Indicates the first A set of molding data for a wind turbine blade mold.
7. A wind turbine blade mould simulation optimisation system according to claim 6, wherein: The performance index The calculation proceeds as follows: The simulation wind turbine blade outputted by the simulation model is denoted as ; Based on the test dataset, extract simulated wind turbine blades. The test data will be used to simulate wind turbine blades. The maximum deformation is denoted as Simulated wind turbine blades The maximum deviation of the surface curvature is denoted as Simulated wind turbine blades The maximum misalignment of the parting surface is denoted as Simulated wind turbine blades The maximum curing rate is denoted as Simulated wind turbine blades The maximum number of residual bubbles is denoted as ; In the formula, This represents the standard value used to measure the maximum deformation. This represents the conversion factor between the standard value of the maximum deformation and the ratio of the maximum deformation. The weight represents the ratio of the standard value of the maximum deformation to the maximum deformation. This represents the standard value used to measure the maximum deviation of the surface curvature. The conversion factor representing the ratio of the maximum deviation of the surface curvature to the standard value of the maximum deviation of the surface curvature. The weight representing the ratio of the standard value of the maximum deviation of the surface curvature to the maximum deviation of the surface curvature. This represents the standard value used to measure the maximum misalignment at the parting line. This represents the conversion factor between the standard value of the maximum misalignment at the parting line and the ratio of the maximum misalignment at the parting line. This represents the weight of the standard value of the maximum misalignment at the parting line and the ratio of the maximum misalignment at the parting line. This represents a standard value used to measure the maximum curing rate. This represents the conversion factor between the standard value of the maximum curing rate and the ratio of the maximum curing rate. The weight representing the ratio of the standard value of the maximum curing rate to the maximum curing rate. This represents the standard value used to measure the maximum number of residual bubbles. This represents the conversion factor between the maximum standard value of residual bubbles and the ratio of the maximum number of residual bubbles. This represents the weight of the ratio of the maximum number of residual bubbles to the standard value. , , , and All are constants, and , Indicates the first Simulated wind turbine blades generated from a wind turbine blade mold model The performance index.
8. A wind turbine blade mould simulation optimisation system according to claim 7, wherein: the defect index ≥ defect threshold When the defect index is greater than the defect threshold, it indicates that the clamping accuracy of the corresponding mold is not up to standard, and measures triggered include prohibiting the use of the corresponding mold as a simulation target and timely correction and repair.
9. A wind turbine blade mould simulation optimisation system according to claim 8, wherein: The optimization management unit assembles the shaping data sets as hyperparameters of the simulation model for dynamically simulating the shaping process of a wind turbine blade.
10. A wind turbine blade mould simulation optimisation system according to claim 9, wherein: the performance index ≤ performance threshold , indicating that the simulation wind turbine blade performance quality generated by the corresponding simulation model does not meet the standard, triggering measures including refining the simulation modeling process and optimizing the error compensation mechanism.