Rotor structure optimization method and device, and storage medium

Abstract

Disclosed in the embodiments of the present application are a rotor structure optimization method and device and a storage medium. The rotor structure optimization method comprises: on the basis of an initial structure parameter of a rotor, establishing a parameterized two-dimensional planar model and a parameterized three-dimensional model, wherein the two-dimensional planar model is a stress calculation model, and the three-dimensional model is a dynamics calculation model; respectively performing trial calculations on the two-dimensional planar model and the three-dimensional model to obtain corresponding output parameters; modeling the initial structure parameter and the output parameters by means of a genetic algorithm aggregation response surface methodology to generate a corresponding response surface; and optimizing the response surface by means of a multi-objective optimization algorithm, and determining an optimized target structure parameter.

Description

Rotor structure optimization method, device and storage medium

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application is based on the Chinese patent application with application number 202310913060.1 and application date of July 24, 2023, and claims the priority of the Chinese patent application. The entire content of the Chinese patent application is hereby incorporated into this application by introduction. Technical Field

[0003] The present application relates to the technical field of high-speed motors, and in particular to a rotor structure optimization method, device, and storage medium. Background Art

[0004] High-speed motors feature small size, can be directly connected to high-speed loads, reduce system noise, and improve system transmission efficiency. High-speed permanent magnet motors (PMMs) offer advantages over squirrel-cage rotor motors, wound rotor motors, and switched reluctance motors, offering higher efficiency and power density. They have become a research hotspot in the motor field both domestically and internationally. Due to the high rotor speeds, non-magnetic alloy sheaths are often used in engineering to protect the permanent magnets. Hollow shafts are typically hollow because they reduce weight, conserve raw materials, and lower the moment of inertia, making them more suitable for high-speed operation. Designing a rotor structure that meets both strength and dynamic requirements is key to ensuring reliable operation of high-speed PMM motors. To ensure reliable rotor operation at high speeds, the interference fit between the sheath and the PMs should ensure that the pressure between the PMs and the shaft, and between the PMs and the shaft, generates a certain amount of friction under operating conditions to ensure torque transfer from the PMs to the shaft. Furthermore, the stresses in the sheath, PMs, and shaft must meet strength requirements, and the rotor as a whole must meet dynamic requirements to prevent mechanical damage and excessive vibration. From the perspective of assembly, the interference fit between the permanent magnet and the sleeve cannot be too large, and from the perspective of torque transmission, the interference fit between the permanent magnet and the sleeve cannot be too small; from the perspective of reducing eddy current loss, the sleeve thickness cannot be too large, and from the perspective of stress, the sleeve thickness cannot be too small; from the perspective of rigidity, the shaft aperture cannot be too large, and from the perspective of lightweight and reducing moment of inertia, the hollow hole cannot be too small.

[0005] Therefore, how to optimize the rotor structure to meet the strength and dynamic requirements has become an urgent issue to be considered.

[0006] Summary of the Invention

[0007] The embodiments of the present application hope to provide a rotor structure optimization method, device and storage medium.

[0008] The technical solution of this application is achieved as follows:

[0009] An embodiment of the first aspect of the present application provides a rotor structure optimization method, comprising:

[0010] Based on the initial structural parameters of the rotor, a parameterized two-dimensional plane model and a three-dimensional model are established; the two-dimensional plane model is a stress calculation model, and the three-dimensional model is a dynamic calculation model;

[0011] Performing trial calculations on the two-dimensional plane model and the three-dimensional model respectively to obtain corresponding output parameters;

[0012] Modeling the initial structural parameters and the output parameters using a genetic algorithm aggregate response surface method to generate a corresponding response surface;

[0013] The response surface is optimized using a multi-objective optimization algorithm to determine the optimized target structural parameters.

[0014] Optionally, the initial structural parameters of the rotor include rotor shaft diameter, hollow hole diameter, permanent magnet thickness, sleeve thickness, and interference between the permanent magnet and the sleeve.

[0015] Optionally, performing trial calculations on the two-dimensional plane model and the three-dimensional model respectively to obtain corresponding output parameters includes:

[0016] A stress calculation is performed on the two-dimensional plane model to obtain the sheath stress, permanent magnet stress, shaft stress, pressure between the sheath and the permanent magnet, and pressure between the permanent magnet and the shaft.

[0017] Optionally, performing trial calculations on the two-dimensional plane model and the three-dimensional model respectively to obtain corresponding output parameters includes:

[0018] Dynamic calculations are performed on the three-dimensional model to obtain the first-order critical speed and rotor mass.

[0019] Optionally, before modeling the initial structural parameters and the output parameters using a genetic algorithm aggregate response surface methodology, the method further comprises:

[0020] The initial structural parameters and the output parameters are simulated and screened to determine initial modeling parameters; the number of the initial modeling parameters is less than a first preset threshold.

[0021] Optionally, the modeling of the initial structural parameters and the output parameters using a genetic algorithm aggregate response surface method to generate a corresponding response surface comprises:

[0022] Performing an experimental design on the initial modeling parameters to determine target modeling parameters; the target modeling parameters are evenly distributed in the design space, and the position range of the target modeling parameters is consistent with the value range of the initial modeling parameters;

[0023] The target modeling parameters are modeled using a genetic algorithm aggregate response surface method to generate a corresponding response surface.

[0024] Optionally, the adopting a multi-objective optimization algorithm to optimize the response surface and determine the optimized target structural parameters includes:

[0025] A multi-objective optimization algorithm is used to optimize the response surface to obtain multiple Pareto frontier points;

[0026] Determine the optimal solution set corresponding to the multiple Pareto frontier points based on preset constraints;

[0027] The optimal solution set is used to determine the optimized target structural parameters.

[0028] Optionally, it also includes:

[0029] If the target structural parameters do not meet the preset requirements, modeling is performed again to generate a new response surface;

[0030] The new response surface is optimized to determine the optimized target structural parameters until the target structural parameters meet the preset requirements.

[0031] An embodiment of a second aspect of the present application provides a rotor structure optimization device, comprising:

[0032] A first modeling module is configured to establish a parameterized two-dimensional plane model and a three-dimensional model based on initial structural parameters of the rotor; the two-dimensional plane model is a stress calculation model, and the three-dimensional model is a dynamic calculation model;

[0033] a calculation module configured to perform calculations on the two-dimensional plane model and the three-dimensional model respectively to obtain corresponding output parameters;

[0034] a second modeling module configured to model the initial structural parameters and the output parameters using a genetic algorithm aggregate response surface method to generate a corresponding response surface;

[0035] The optimization module is configured to optimize the response surface using a multi-objective optimization algorithm to determine the optimized target structural parameters.

[0036] An embodiment of the third aspect of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the processor performs the steps of the method described in the first aspect.

[0037] The embodiment of the present application provides a rotor structure optimization method, device and storage medium, wherein the rotor structure optimization method includes: establishing a parameterized two-dimensional plane model and a three-dimensional model based on the initial structural parameters of the rotor; the two-dimensional plane model is a stress calculation model, and the three-dimensional model is a dynamic calculation model; performing trial calculations on the two-dimensional plane model and the three-dimensional model respectively to obtain corresponding output parameters; using a genetic algorithm aggregate response surface method to model the initial structural parameters and the output parameters to generate a corresponding response surface; using a multi-objective optimization algorithm to optimize the response surface to determine the optimized target structural parameters. The technical solution of the present application is adopted to screen the parameters by constructing a two-dimensional plane model and a three-dimensional model, and using a genetic algorithm to model the selected adopted numbers to generate a response surface, and then optimizing the response surface, thereby achieving fast and effective forward optimization design and obtaining more accurate target structural parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] FIG1 is a schematic flow chart of a rotor structure optimization method provided in an embodiment of the present application;

[0039] FIG2a is a schematic diagram of a two-dimensional plane model provided in an embodiment of the present application;

[0040] FIG2 b is a schematic diagram of a three-dimensional model provided in an embodiment of the present application;

[0041] FIG3 is a schematic structural diagram of a rotor structure optimization device provided in an embodiment of the present application;

[0042] FIG4 is a schematic structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0044] In addition, the accompanying drawings are merely schematic illustrations of the present application and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the blocks shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0045] The flowcharts shown in the accompanying drawings are merely exemplary and do not necessarily include all steps. For example, some steps may be decomposed, while some steps may be combined or partially combined, so the actual execution order may change according to actual circumstances.

[0046] The purpose of the terms used herein is only to describe specific embodiments and is not intended to limit the present application. When used herein, the singular forms "a", "an", and "the" are intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the terms "comprising" and / or "including", when used in this specification, determine the presence of the features, integers, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, parts and / or groups. When used herein, the term "and / or" includes any and all combinations of the relevant listed items.

[0047] In some embodiments, please refer to FIG1 , which is a flow chart of a rotor structure optimization method provided in an embodiment of the present application. The rotor structure optimization method includes:

[0048] Step S110 , based on the initial structural parameters of the rotor, a parameterized two-dimensional plane model and a three-dimensional model are established; the two-dimensional plane model is a stress calculation model, and the three-dimensional model is a dynamic calculation model.

[0049] In this embodiment, the initial structural parameters of the rotor include the rotor shaft diameter, hollow hole diameter, permanent magnet thickness, sheath thickness, and the interference between the permanent magnet and the sheath. According to the required performance indicators of the motor, the approximate rotor outer diameter, permanent magnet thickness, and rotor core length range can be obtained through preliminary electromagnetic calculations, and then the approximate shaft diameter range can be obtained through torque calculations. Here, the initial structural parameters of the rotor are structural parameters that affect the rotor structural strength and rotor dynamics, including but not limited to the rotor shaft diameter, hollow hole diameter, permanent magnet thickness, sheath thickness, and the interference between the permanent magnet and the sheath. A parameterized two-dimensional plane model and three-dimensional model are established based on the initial structural parameters of the rotor. The radial structural parameters in the three-dimensional model are inherited from the two-dimensional parameters, and the axial structure such as the core length can be set according to the preliminary electromagnetic calculation results or as needed.

[0050] In step S120 , calculations are performed on the two-dimensional plane model and the three-dimensional model respectively to obtain corresponding output parameters.

[0051] In this embodiment, based on the finite element analysis software, the two-dimensional plane model is used as the stress calculation model, and a preliminary calculation is performed. The sheath stress, permanent magnet stress, shaft stress, pressure between the sheath and the permanent magnet, and pressure between the permanent magnet and the shaft in the result can be set as output parameters; the three-dimensional model is used as the rotor dynamics calculation model, and a preliminary calculation is performed once to obtain the first few critical speeds of the rotor, which are compared with the speed range of the motor's required performance indicators. The critical speeds close to the motor speed range and within the motor speed range are set as output parameters. The input conditions such as materials, loads, bearing support stiffness, and damping in the two calculation models can also be set as input parameters as needed. The structural parameter range is set according to the preliminary electromagnetic calculation, the material parameter range is set according to the optional materials, and the load parameter range is set according to the motor performance indicators.

[0052] In some embodiments, step S120, performing trial calculations on the two-dimensional plane model and the three-dimensional model to obtain corresponding output parameters includes:

[0053] The stress calculation of the two-dimensional plane model was performed to obtain the sheath stress, permanent magnet stress, shaft stress, pressure between the sheath and permanent magnet, and pressure between the permanent magnet and shaft.

[0054] In one example, see Figure 2a, which is a schematic diagram of a two-dimensional planar model provided in an embodiment of the present application; wherein the outermost portion is a sheath 210, the middle portion is a permanent magnet 220, and the innermost portion is a shaft 230. The two-dimensional planar model is used as a stress calculation model. Acceleration and temperature calculations are performed on the two-dimensional planar model, and the sheath stress, permanent magnet stress, shaft stress, pressure between the sheath and permanent magnet, and pressure between the permanent magnet and shaft are set as output parameters.

[0055] In some embodiments, step S120 performs trial calculations on the two-dimensional plane model and the three-dimensional model to obtain corresponding output parameters, including:

[0056] The dynamic calculation of the three-dimensional model is performed to obtain the first-order critical speed and rotor mass.

[0057] In one example, please refer to Figure 2b, which is a schematic diagram of a three-dimensional model provided in an embodiment of the present application; wherein, the three-dimensional model includes, in sequence, a sheath 210, a permanent magnet 220, and a shaft 230. The three-dimensional model is used as a rotor dynamics calculation model, and a rotor dynamics trial calculation is performed on the three-dimensional model, and the critical speed close to and within the motor speed range and the rotor mass are set as output parameters. Here, the critical speed close to and within the motor speed range only includes the first-order critical speed.

[0058] In one example, the parameter types and their ranges for inputting a 2D plane model and a 3D model are shown in Table 1:

[0059] Table 1

[0060] Step S130 , using a genetic algorithm aggregate response surface method to model the initial structural parameters and output parameters, and generate a corresponding response surface.

[0061] A genetic algorithm (GA) is a computational model that simulates the biological evolutionary process of natural selection and genetics. It is a method for searching for optimal solutions by simulating the natural evolutionary process. A response surface can be a surface in a three-dimensional model that represents the functional mapping relationship between a response variable and a set of input variables.

[0062] In one example, the output parameters and errors of the response surface are shown in Table 2:

[0063] Table 2

[0064] In some embodiments, before using the genetic algorithm aggregated response surface methodology to model the initial structural parameters and output parameters in step S130, the method further includes:

[0065] The initial structural parameters and the output parameters are simulated and screened to determine the initial modeling parameters; the number of the initial modeling parameters is less than a first preset threshold.

[0066] In this embodiment, when there are more than 10 input parameters, the subsequent design space point sampling (DOE) data will increase dramatically, and the analysis will become increasingly difficult. Therefore, the first preset threshold value can be 10. Here, LHS (Latin Hypercube Sampling) can be used to generate sample points to perform a series of simulation calculations. Correlation calculation methods such as Spearman's Rank Correlation and Pearson's Linear Correlation are used to obtain a correlation matrix to identify unimportant input parameters. Such input parameters can be ignored in subsequent DOE to reduce unnecessary sampling points. The correlation between the LHS input parameters is less than or equal to 5%, and the input parameter values ​​of any two sample points are different. The steps for implementing this method are as follows:

[0067] For k variables X1, X2, ..., X k Perform N simple random samplings, variable X k The cumulative distribution function (CDF) of (1≤k≤K) is Y k =F k (X k ), the value range of this distribution function [0, 1] is divided into N sub-intervals, and a random number Y is randomly drawn from each interval according to a uniform distribution k, by the inverse function of CDF Find X k , each subinterval is not repeatedly extracted.

[0068] In some embodiments, step S130, modeling the initial structural parameters and the output parameters using a genetic algorithm aggregate response surface method to generate a corresponding response surface includes:

[0069] Conducting experimental design on the initial modeling parameters to determine the target modeling parameters; the target modeling parameters are evenly distributed in the design space, and their position range is consistent with the value range of the initial modeling parameters;

[0070] The genetic algorithm aggregate response surface method is used to model the target modeling parameters and generate the corresponding response surface.

[0071] In this embodiment, the initial modeling parameters after the value range is determined can be subjected to DOE using methods such as Optimal Space-Filling Design or Central Composite Design. It is required that the sampling points are arranged evenly, the position range of the sample points in the design space is consistent with the value range of the input parameters, and the number of sample points is at least twice the number of input parameters. After the sample points in the DOE are calculated, the genetic algorithm aggregate response surface method is used to analyze and process the sample data set. This method uses a genetic algorithm to generate multiple response surface model populations and solve them, which can achieve the optimal integration of the response surface model. The mathematical description is as follows:

[0072] Where, is the predicted value of the integrated response surface model, is the predicted value of the ith response surface model, N M is the number of response surface metamodels, N M ≥1; w i is the weight of the ith response surface model, which satisfies:

[0073] And w i ≥0, 1≤i≤N.

[0074] The response surface can be improved by combining the predicted results of the response surface with the calculated data of the sampling points. On the one hand, the error of the sheath stress, permanent magnet stress, and the pressure between the permanent magnet and the shaft can be controlled, and on the other hand, the sampling surface can be improved by adding optimization points.

[0075] In one example, the output parameters and maximum prediction errors of the improved response surface are shown in Table 3:

[0076] Table 3

[0077] In an optional embodiment, the response surface model can be constructed using the coefficient of determination R 2 To evaluate, R 2 The closer to 1, the higher the model accuracy. 2 ≥0.9. If the standard is not met, it is necessary to add verification points and use them as refinement points to re-form the locally improved response surface. The response surface can also be optimized by controlling the prediction error within a certain range. 2 The mathematical description is as follows:

[0078] In the formula, m is the total number of samples, is the response surface model for sample y i The estimated value of is the average value of m samples.

[0079] In one example, the output parameters and determination coefficients of the improved response surface are shown in Table 4:

[0080] Table 4

[0081] Step S140 , optimizing the response surface using a multi-objective optimization algorithm to determine optimized target structural parameters.

[0082] In this embodiment, the multi-objective optimization algorithm (MOGA) process involves randomly generating an initial population, calculating the objective function value at each point, and ranking each individual in the population based on the concept of Pareto optimality. The ranking method is to associate each member of the population with the number of other individuals in the population that dominate it. All individuals on the Pareto front are ranked as level 1, and the remaining members are ranked according to the number of members that outperform the individual.

[0083] Optimization setting constraints include but are not limited to the following:

[0084] n≤0.7n1 or 1.3n cr ≤n≤0.7n cr+1

[0085] Among them, P sleeve-m is the pressure between the sheath and the permanent magnet; P m-shaft is the pressure between the permanent magnet and the shaft; σ sleeve is the sheath Mises stress; [σ sleeve ] s is the yield strength of the sheath material; σ mag-pull is the tensile strength of the permanent magnet; σmag-compress is the radial compressive stress of the permanent magnet, [σ mag ]bc is the compressive strength of the permanent magnet; σ shsft is the Mises stress of the rotation axis; [σ shsft ] s is the yield strength of the shaft material. n is the rated speed, n1 is the first critical speed, n cr is a critical speed of a certain order, n cr+1 is the lower critical speed of a certain critical speed.

[0086] The settings include but are not limited to the following objectives:

[0087] Among them, T sleeve is the thickness of the jacket; Mass is the mass of the rotor. Importance levels can be set for constraints and objectives as needed.

[0088] In some embodiments, step S140, optimizing the response surface using a multi-objective optimization algorithm to determine optimized target structural parameters, includes:

[0089] The response surface is optimized using a multi-objective optimization algorithm to obtain multiple Pareto frontier points;

[0090] Determine the optimal solution set corresponding to multiple Pareto frontier points based on preset constraints;

[0091] The optimal solution set is used to determine the optimized target structure parameters.

[0092] In this embodiment, the Pareto front can be obtained after MOGA optimization. Design points are selected in the Pareto front for verification as needed, and the response surface is modified by repeatedly considering the verification points. Multiple optimization analyses are performed and repeated iterations are performed to obtain a high-fidelity response surface model, and the most suitable structural parameters are obtained through MOGA optimization.

[0093] In some embodiments, it further includes:

[0094] If the target structural parameters do not meet the preset requirements, the model is re-modeled to generate a new response surface;

[0095] The new response surface is optimized to determine the optimized target structure parameters until the target structure parameters meet the preset requirements.

[0096] In one example, an appropriate Pareto frontier point was selected for verification, resulting in the input parameters for a sample point and its predicted and actual response surface values, as shown in Tables 5 and 6. Table 5 shows the input parameters for the verification point, while Table 6 shows the predicted and actual values ​​for the verification point.

[0097] Table 5

[0098] Table 6

[0099] According to the data in Tables 5 and 6, if the error between the response surface predicted value and the actual value meets the preset requirements, the target structure parameters can be directly determined by the above-mentioned response surface. If the error between the response surface predicted value and the actual value does not meet the preset requirements, a new response surface can be generated by modeling again, and repeated iterations can be performed until a high-fidelity response surface model is obtained to obtain the most suitable target structure parameters. Here, the preset requirements can be determined according to the accuracy requirements, and there is no restriction on this.

[0100] This embodiment screens parameters by constructing a two-dimensional plane model and a three-dimensional model, and uses a genetic algorithm to model the selected adoption numbers to generate a response surface, and then optimizes the response surface, thereby achieving fast and effective forward optimization design and obtaining more accurate target structural parameters.

[0101] In some embodiments, the present application provides a rotor structure optimization device. Please refer to FIG3 , which is a schematic structural diagram of a rotor structure optimization device provided in an embodiment of the present application. The rotor structure optimization device 300 includes:

[0102] A first modeling module 310 is configured to establish a parameterized two-dimensional plane model and a three-dimensional model based on the initial structural parameters of the rotor; the two-dimensional plane model is a stress calculation model, and the three-dimensional model is a dynamic calculation model;

[0103] A calculation module 320 is configured to perform calculations on the two-dimensional plane model and the three-dimensional model respectively to obtain corresponding output parameters;

[0104] A second modeling module 330 is configured to model the initial structural parameters and the output parameters using a genetic algorithm aggregate response surface method to generate a corresponding response surface;

[0105] The optimization module 340 is configured to optimize the response surface using a multi-objective optimization algorithm to determine optimized target structural parameters.

[0106] In some embodiments, the initial structural parameters of the rotor include the rotor shaft diameter, the hollow hole diameter, the permanent magnet thickness, the sleeve thickness, and the interference between the permanent magnet and the sleeve.

[0107] In some embodiments, the calculation module 320 is specifically configured as follows:

[0108] The stress calculation of the two-dimensional plane model was performed to obtain the sheath stress, permanent magnet stress, shaft stress, pressure between the sheath and permanent magnet, and pressure between the permanent magnet and shaft.

[0109] In some embodiments, the calculation module 320 is further configured to:

[0110] The dynamic calculation of the three-dimensional model is performed to obtain the first-order critical speed and rotor mass.

[0111] In some embodiments, the rotor structure optimization device further includes a screening module, and the screening module is specifically configured as follows:

[0112] The initial structural parameters and the output parameters are simulated and screened to determine the initial modeling parameters; the number of the initial modeling parameters is less than a first preset threshold.

[0113] In some embodiments, the screening module is further configured to:

[0114] Conducting experimental design on the initial modeling parameters to determine the target modeling parameters; the target modeling parameters are evenly distributed in the design space, and their position range is consistent with the value range of the initial modeling parameters;

[0115] The genetic algorithm aggregate response surface method is used to model the target modeling parameters and generate the corresponding response surface.

[0116] In some embodiments, the optimization module 340 is specifically configured to:

[0117] The response surface is optimized using a multi-objective optimization algorithm to obtain multiple Pareto frontier points;

[0118] Determine the optimal solution set corresponding to multiple Pareto frontier points based on preset constraints;

[0119] The optimal solution set is used to determine the optimized target structure parameters.

[0120] In some embodiments, the optimization module 340 is further configured to:

[0121] If the target structural parameters do not meet the preset requirements, the model is re-modeled to generate a new response surface;

[0122] The new response surface is optimized to determine the optimized target structure parameters until the target structure parameters meet the preset requirements.

[0123] This embodiment screens parameters by constructing a two-dimensional plane model and a three-dimensional model, and uses a genetic algorithm to model the selected adoption numbers to generate a response surface, and then optimizes the response surface, thereby achieving fast and effective forward optimization design and obtaining more accurate target structural parameters.

[0124] It should be noted that the description of the rotor structure optimization apparatus embodiment described above is similar to the description of the rotor structure optimization method embodiment described above, and has similar beneficial effects as the rotor structure optimization method embodiment. For technical details not disclosed in the rotor structure optimization apparatus embodiment of this application, please refer to the description of the rotor structure optimization method embodiment of this application for understanding, and will not be repeated here.

[0125] In some embodiments, the present application provides an electronic device. Please refer to Figure 4. Figure 4 is a structural schematic diagram of an electronic device provided in an embodiment of the present application. The electronic device includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor executes the steps of the above-mentioned rotor structure optimization method.

[0126] The electronic device may be a terminal, a server, or a similar computing device. The electronic device may vary greatly due to different configurations or performances, and may include one or more central processing units (CPUs), including but not limited to microprocessors (MCUs) or programmable logic devices (FPGAs), memories for storing data, and one or more storage media for storing applications or data (e.g., one or more mass storage devices). The memories and storage media may be temporary or permanent storage. The program stored in the storage medium may include one or more modules, each of which may include a series of instruction operations on the electronic device. Furthermore, the central processing unit may be configured to communicate with the storage medium and execute a series of instruction operations in the storage medium on the electronic device. The electronic device may also include one or more power supplies, one or more wired or wireless network interfaces, one or more input and output interfaces, and / or one or more operating systems, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. The input and output interfaces may be used to receive or send data via a network. Specific examples of the above-mentioned network may include wireless networks provided by the communication provider of the electronic device.

[0127] In one embodiment, the input / output interface includes a network interface controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In another exemplary embodiment, the input / output interface can be a radio frequency (RF) module configured to communicate with the Internet wirelessly.

[0128] Those skilled in the art will appreciate that the structure shown in FIG4 is merely illustrative and does not limit the structure of the electronic device. For example, the electronic device may include more or fewer components than shown in FIG4 , or have a configuration different from that shown in FIG4 .

[0129] In some embodiments, the embodiments of the present application provide a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned rotor structure optimization method are implemented.

[0130] It should be noted that the descriptions of the electronic device and storage medium embodiments described above are similar to the description of the rotor structure optimization method embodiment described above, and have similar beneficial effects as the rotor structure optimization method embodiment. For technical details not disclosed in the electronic device and storage medium embodiments of this application, please refer to the description of the rotor structure optimization method embodiment of this application for an understanding, and will not be repeated here.

[0131] In the several embodiments provided in this application, it should be understood that the disclosed rotor structure optimization method, device, equipment and storage medium can be implemented in other ways. The above-described method, device, equipment and storage medium embodiments are merely illustrative.

[0132] The rotor structure optimization method, device, equipment and storage medium described in the embodiments of this application are only taken as an example of the embodiments described in this application, but are not limited to this. As long as it involves the rotor structure optimization method, device, electronic equipment and storage medium, it is within the scope of protection of this application.

[0133] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned serial numbers of the embodiments of the present application are for description only and do not represent the advantages and disadvantages of the embodiments.

[0134] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0135] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A rotor structure optimization method, comprising: Based on the initial structural parameters of the rotor, a parameterized two-dimensional plane model and a three-dimensional model are established; The two-dimensional plane model is a stress calculation model, and the three-dimensional model is a dynamic calculation model; Performing trial calculations on the two-dimensional plane model and the three-dimensional model respectively to obtain corresponding output parameters; Modeling the initial structural parameters and the output parameters using a genetic algorithm aggregate response surface method to generate a corresponding response surface; The response surface is optimized using a multi-objective optimization algorithm to determine the optimized target structural parameters.

2. The rotor structure optimization method according to claim 1, wherein: The initial structural parameters of the rotor include the rotor shaft diameter, the hollow hole diameter, the permanent magnet thickness, the sleeve thickness, and the interference between the permanent magnet and the sleeve.

3. The rotor structure optimization method according to claim 1, wherein: The two-dimensional plane model and the three-dimensional model are respectively calculated to obtain corresponding output parameters, including: The two-dimensional plane model is subjected to stress calculation to obtain the sheath stress, the permanent magnet stress, the shaft stress, the pressure between the sheath and the permanent magnet, and the pressure between the permanent magnet and the shaft.

4. The rotor structure optimization method according to claim 1, wherein: The two-dimensional plane model and the three-dimensional model are respectively calculated to obtain corresponding output parameters, including: The three-dimensional model is subjected to dynamic calculation to obtain the first-order critical speed and rotor mass.

5. The rotor structure optimization method according to claim 1, wherein: Before modeling the initial structural parameters and the output parameters using a genetic algorithm aggregate response surface method, the method further includes: The initial structural parameters and the output parameters are simulated, calculated and screened to determine initial modeling parameters; the number of the initial modeling parameters is less than a first preset threshold.

6. The rotor structure optimization method according to claim 5, wherein: The method of using a genetic algorithm aggregate response surface method to model the initial structural parameters and the output parameters to generate a corresponding response surface includes: Performing an experimental design on the initial modeling parameters to determine target modeling parameters; the target modeling parameters are evenly distributed in the design space, and the position range of the target modeling parameters is consistent with the value range of the initial modeling parameters; The target modeling parameters are modeled using a genetic algorithm aggregate response surface method to generate a corresponding response surface.

7. The rotor structure optimization method according to claim 1, wherein: The method of optimizing the response surface using a multi-objective optimization algorithm to determine the optimized target structural parameters includes: The response surface is optimized by using a multi-objective optimization algorithm to obtain multiple Pareto frontier points; Determine the optimal solution set corresponding to the multiple Pareto frontier points based on preset constraints; The optimal solution set is used to determine the optimized target structural parameters.

8. The rotor structure optimization method according to claim 7, wherein: Also includes: If the target structural parameters do not meet the preset requirements, modeling is performed again to generate a new response surface; The new response surface is optimized to determine the optimized target structure parameters until the target structure parameters meet the preset requirements.

9. A rotor structure optimization device, comprising: A first modeling module is configured to establish a parameterized two-dimensional plane model and a three-dimensional model based on initial structural parameters of the rotor; The two-dimensional plane model is a stress calculation model, and the three-dimensional model is a dynamic calculation model; A calculation module is configured to perform calculations on the two-dimensional plane model and the three-dimensional model respectively to obtain corresponding output parameters; A second modeling module is configured to model the initial structural parameters and the output parameters using a genetic algorithm aggregate response surface method to generate a corresponding response surface; The optimization module is configured to optimize the response surface using a multi-objective optimization algorithm to determine the optimized target structural parameters.

10. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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