An internal self-fan cooling structure optimization method, system, device, medium and product of a permanent magnet traction motor considering aerodynamic noise-temperature rise collaborative optimization
By optimizing the permanent magnet traction motor structure through multi-physics field co-simulation and orthogonal experimental design, the problem of balancing aerodynamic noise and temperature rise was solved, achieving synergistic optimization of noise and temperature rise and improving the overall performance of the motor.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- TIANJIN POLYTECHNIC UNIV
- Filing Date
- 2026-01-26
- Publication Date
- 2026-06-02
AI Technical Summary
Existing technologies struggle to simultaneously optimize aerodynamic noise and temperature rise in permanent magnet traction motors, resulting in insufficient noise prediction accuracy and poor cooling performance.
Multiple sets of structural parameters are generated using orthogonal experimental design. Through multi-physics co-simulation, combined with a flow-thermal coupling model and a non-uniform air medium field, aerodynamic noise and temperature rise are accurately predicted, and structural parameters are optimized to achieve co-optimization.
It improves the accuracy of aerodynamic noise prediction and the effect of temperature rise optimization, enhances the overall performance of permanent magnet traction motors, and balances thermal management and acoustic comfort.
Smart Images
Figure CN121562501B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of permanent magnet traction motors, and in particular to a method, system, device, medium, and product for optimizing the internal self-fan cooling structure of a permanent magnet traction motor by taking into account the coordinated optimization of aerodynamic noise and temperature rise. Background Technology
[0002] In permanent magnet traction motors, self-ventilated air cooling is a widely used internal cooling method. This type of cooling structure relies on the circulating airflow generated by the motor's own blades to drive the airflow within the cavity, effectively dissipating heat generated by copper losses, iron losses, and permanent magnet losses through an internal ventilation path. Self-ventilated air cooling systems do not require external fans, are compact in structure, and have high reliability, making them the primary heat dissipation method for enclosed permanent magnet traction motors. However, during ventilation, the interaction between blade rotation and the cavity structure generates periodic velocity pulsations and large-scale vortex structures in the airflow, resulting in significant aerodynamic noise. Furthermore, the internal ventilation path of the self-ventilated air cooling structure is complex; the size of each ventilation hole and the blade geometry significantly affect flow resistance and heat transfer performance, making it difficult to simultaneously optimize temperature rise and aerodynamic noise.
[0003] Existing technologies typically perform flow-thermal field calculations and flow-sound field calculations separately. In temperature rise analysis, flow-thermal simulation is used to obtain winding temperature, while in noise analysis, fixed air properties are often used for flow-sound simulation. However, the internal air temperature distribution of a motor is significantly uneven, and medium parameters such as sound velocity, density, and viscosity change significantly with temperature. This makes existing noise simulations unable to accurately reflect the impact of temperature on the intensity and propagation characteristics of noise sources, resulting in insufficient accuracy in aerodynamic noise prediction. Furthermore, existing optimization methods mostly optimize parameters only for either temperature rise or aerodynamic noise, lacking a synergistic optimization method that can simultaneously consider both performance aspects. Summary of the Invention
[0004] The purpose of this application is to provide a method, system, device, medium, and product for optimizing the internal self-ventilated cooling structure of a permanent magnet traction motor that takes into account the synergistic optimization of aerodynamic noise and temperature rise. This method can accurately predict aerodynamic noise and temperature rise, achieve synergistic optimization of the two, and thus improve the overall performance of the permanent magnet traction motor.
[0005] To achieve the above objectives, this application provides the following solution:
[0006] In a first aspect, this application provides a method for optimizing the internal self-ventilated cooling structure of a permanent magnet traction motor, taking into account the coordinated optimization of aerodynamic noise and temperature rise, including:
[0007] Several structural parameters and optimization objectives of the internal self-ventilated cooling structure of the permanent magnet traction motor were determined; the optimization objectives included aerodynamic noise and temperature rise.
[0008] Based on the aforementioned structural parameters, multiple combinations of structural parameters are generated using orthogonal experimental design.
[0009] For each combination of structural parameters, a multiphysics co-simulation process is executed to obtain the response value of the optimization target; the response value includes the maximum temperature rise of the winding and the maximum sound pressure level of the aerodynamic noise.
[0010] Determine the optimal combination of structural parameters based on the response value;
[0011] The internal self-ventilated cooling structure of the permanent magnet traction motor is optimized based on the optimal combination of structural parameters.
[0012] Secondly, this application provides an optimized internal self-ventilated cooling structure system for a permanent magnet traction motor that considers aerodynamic noise-temperature rise co-optimization, comprising:
[0013] The structural parameter and optimization target determination module is used to determine multiple structural parameters and optimization targets of the internal self-ventilated cooling structure of the permanent magnet traction motor; the optimization targets include aerodynamic noise and temperature rise.
[0014] The structural parameter combination generation module is used to generate multiple sets of structural parameter combinations based on the structural parameters using orthogonal experimental design method;
[0015] The co-simulation module is used to execute a multi-physics co-simulation process for each combination of structural parameters to obtain the response value of the optimization target; the response value includes the maximum temperature rise of the winding and the maximum sound pressure level of aerodynamic noise;
[0016] An optimal structural parameter combination determination module is used to determine the optimal structural parameter combination based on the response value;
[0017] The optimization module is used to optimize the internal self-ventilated air-cooling structure of the permanent magnet traction motor based on the optimal combination of structural parameters.
[0018] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for optimizing the internal self-ventilated cooling structure of a permanent magnet traction motor that takes into account aerodynamic noise-temperature rise co-optimization.
[0019] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for optimizing the internal self-fan cooling structure of a permanent magnet traction motor, taking into account aerodynamic noise-temperature rise co-optimization.
[0020] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-mentioned method for optimizing the internal self-ventilated cooling structure of a permanent magnet traction motor, taking into account aerodynamic noise and temperature rise.
[0021] According to the specific embodiments provided in this application, this application has the following technical effects:
[0022] This application sets both aerodynamic noise and temperature rise as optimization objectives, employs orthogonal experimental design to generate multiple combinations of structural parameters, and performs multiphysics co-simulation on each combination, including the highest temperature rise of the winding and the highest sound pressure level of aerodynamic noise. This yields response values that balance the two optimization objectives, ultimately determining the optimal combination of structural parameters. By considering the influence of the temperature field on aerodynamic noise through multiphysics co-simulation, this application can accurately predict aerodynamic noise and temperature rise, achieving synergistic optimization of these two aspects. This, in turn, improves the overall performance of the permanent magnet traction motor, balancing thermal management and acoustic comfort. Attached Figure Description
[0023] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0024] Figure 1 A flowchart illustrating an embodiment of this application provides a method for optimizing the internal self-ventilated cooling structure of a permanent magnet traction motor, taking into account the coordinated optimization of aerodynamic noise and temperature rise.
[0025] Figure 2 A detailed flowchart illustrating an optimization method for the internal self-ventilated cooling structure of a permanent magnet traction motor that takes into account the coordinated optimization of aerodynamic noise and temperature rise, provided for an embodiment of this application;
[0026] Figure 3 A cross-sectional schematic diagram of the internal self-ventilated cooling structure of a permanent magnet traction motor;
[0027] Figure 4 A schematic diagram showing the layout of external noise monitoring points for a permanent magnet traction motor.
[0028] Figure 5 Temperature rise distribution cloud maps of permanent magnet traction motor windings before and after optimization; where (a) is the temperature rise distribution cloud map of permanent magnet traction motor windings before optimization, and (b) is the temperature rise distribution cloud map of permanent magnet traction motor windings after optimization.
[0029] Figure 6The images show the sound power level contours before and after optimization; where (a) is the sound power level contour before optimization and (b) is the sound power level contour after optimization. Detailed Implementation
[0030] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0031] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0032] In one exemplary embodiment, such as Figures 1-2 As shown, a method for optimizing the internal self-ventilated cooling structure of a permanent magnet traction motor that takes into account the coordinated optimization of aerodynamic noise and temperature rise is provided. This method is executed by a computer device, which can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In this embodiment, the method is applied to a server as an example for illustration, including the following steps S1 to S5.
[0033] S1: Determine multiple structural parameters and optimization objectives of the self-ventilated air-cooling structure inside the permanent magnet traction motor; the optimization objectives include aerodynamic noise and temperature rise.
[0034] S2: Based on the structural parameters, multiple combinations of structural parameters are generated using orthogonal experimental design.
[0035] S3: For each combination of structural parameters, execute a multi-physics co-simulation process to obtain the response value of the optimization target; the response value includes the maximum temperature rise of the winding and the maximum sound pressure level of aerodynamic noise.
[0036] S4: Determine the optimal combination of structural parameters based on the response value.
[0037] S5: Optimize the internal self-ventilated cooling structure of the permanent magnet traction motor based on the optimal combination of structural parameters.
[0038] By implementing steps S1 to S5 above, this application achieves synergistic optimization of the self-fan cooling structure in terms of heat dissipation and noise control, effectively solving the problem that temperature rise and aerodynamic noise are difficult to balance in traditional optimization, and improving the overall performance of the permanent magnet traction motor cooling structure.
[0039] In a specific embodiment, step S1 includes: determining multiple structural parameters based on the airflow organization and noise generation mechanism of the self-ventilated cooling structure inside the permanent magnet traction motor; and determining optimization targets based on the design requirements of the self-ventilated cooling structure inside the permanent magnet traction motor.
[0040] Based on the airflow organization and noise generation mechanism of the internal self-ventilated cooling structure of the permanent magnet traction motor, representative structural parameters for internal flow, heat exchange and pressure pulsation characteristics are selected, namely, fan blade width w, fan blade height h, stator ventilation hole diameter r and radial ventilation hole width d, and their value ranges are set.
[0041] Based on the design requirements of the internal self-ventilated cooling structure of the permanent magnet traction motor, aerodynamic noise and temperature rise are taken as optimization targets.
[0042] In this embodiment, the value ranges of each structural parameter are as follows:
[0043] .
[0044] In a specific embodiment, step S2 includes: discretizing each structural parameter within the range of values of each structural parameter, selecting an orthogonal array according to the number of structural parameters and the number of levels, and constructing an experimental design matrix; mapping each row in the experimental design matrix to a set of structural parameter combinations to obtain multiple sets of structural parameter combinations.
[0045] This embodiment uses L16 (4 4 The orthogonal array is used to uniformly sample the four structural parameters and their four levels, generating a total of 16 combinations of structural parameters.
[0046] In one specific embodiment, the multiphysics co-simulation process includes:
[0047] (1) Construct a flow-thermal coupling model and solve it to obtain a three-dimensional temperature distribution field. Extract the maximum temperature rise of the winding from the three-dimensional temperature distribution field.
[0048] Figure 3 This is a cross-sectional schematic diagram of the self-ventilated cooling structure inside the permanent magnet traction motor. The self-ventilated cooling structure includes a front cover 7, a rear cover 1, a housing 6, a stator 2, windings, a rotor 5, a shaft 4, and fan blades 3 arranged inside the permanent magnet traction motor. The fan blades 3 rotate with the rotor 5, creating a self-circulating airflow inside the permanent magnet traction motor cavity to improve the heat dissipation capacity inside the permanent magnet traction motor.
[0049] based on Figure 3The structure shown is used to establish a complete geometric model including fan blades, internal air ducts in the end cover, stator core, stator windings, rotor, and heat dissipation structure, and then mesh it. Internal heat sources and boundary conditions are applied to the geometric model: heat sources such as copper loss, iron loss, permanent magnet eddy current loss, and mechanical friction loss are all applied to their corresponding components according to rated operating conditions; a velocity inlet is set, and a pressure outlet is used; rotor rotation is achieved by adding rotational wall conditions to the rotor ventilation holes and upper and lower end faces, and the rotational speed is set to the motor's rated speed; a conjugate heat transfer model is used between the solid and fluid, thus completing the fluid-thermal coupling model. The fluid-thermal coupling model is solved to obtain the three-dimensional temperature distribution field inside the permanent magnet traction motor, and the highest temperature rise Tw of the winding is extracted as the response value of the temperature rise.
[0050] (2) Construct a non-uniform air medium field based on the three-dimensional temperature distribution field.
[0051] After solving the flow-thermal field, the three-dimensional temperature distribution field is mapped to the flow-acoustic simulation domain. During this process, based on the variation of air properties with temperature, the medium properties of each grid cell are updated point-by-point, ensuring that the medium density, dynamic viscosity, and local sound velocity accurately reflect the temperature level at the corresponding location. By redistributing the physical property parameters, the flow-acoustic simulation domain forms a non-uniform air medium field that perfectly matches the temperature distribution, thereby significantly improving the acoustic solution's ability to capture physical phenomena such as the decrease in gas density, the increase in local sound velocity, and the enhancement of turbulent structures in high-temperature regions.
[0052] This approach ensures that the flow-heat calculation results can realistically drive the flow-sound solution, realizing a collaborative simulation mechanism that allows air properties to change synchronously with temperature. This is the key innovation of this application in terms of noise prediction accuracy.
[0053] (3) Solve the unsteady flow field in the non-uniform air medium field and extract the aerodynamic sound source.
[0054] After updating the medium property field, unsteady flow field solutions were performed based on the non-uniform air medium field. To this end, the DES-SST turbulence model was used to simulate the complex large-scale vortex structure and periodic velocity fluctuations induced by the fan blade rotation, thus realistically reproducing the unsteady evolution process of airflow within the permanent magnet traction motor between the fan blades, ventilation holes, and other structures. In this unsteady calculation, periodic pressure fluctuations caused by the geometric characteristics of the fan blades and ventilation structures were recorded, and a time-varying pressure fluctuation sequence was obtained on the control surfaces of the envelope blades and internal air ducts. This pressure fluctuation sequence accurately reflects the real aerodynamic sound sources generated during the internal flow of the permanent magnet traction motor, providing a necessary input basis for subsequent aerodynamic noise solutions.
[0055] (4) Based on the aerodynamic sound source, the highest sound pressure level of aerodynamic noise is calculated using the FW-H model.
[0056] After completing the flow-sound field calculation, the pressure pulsation sequence obtained in the previous step is introduced into the FW-H acoustic model as an aerodynamic sound source to further solve the sound field distribution outside the permanent magnet traction motor.
[0057] Therefore, such as Figure 4 As shown, noise monitoring points H1 and H2 are arranged at the end of the permanent magnet traction motor to obtain the sound pressure level of the end noise; at the same time, monitoring points H3 to H6 are set around the permanent magnet traction motor. Figure 3 The model is a 1 / 4 scale model, containing only one quadrant region. Therefore, only monitoring point H3 needs to be placed in this quadrant (H4 to H6 can be derived from the circular symmetry of the model and are not labeled separately). This model is used to characterize the average sound pressure level of the circumferential region. Subsequently, the signal obtained from the acoustic solution is processed in the frequency domain. The main noise frequencies and their amplitudes are extracted by Fast Fourier Transform, thereby obtaining the highest sound pressure level of aerodynamic noise (the highest total sound pressure level of end noise SPL1 and the highest total sound pressure level of circumferential noise SPL2).
[0058] The FW-H model expression is as follows:
[0059]
[0060] In the formula, The local velocity of sound varies with temperature. This is a sound pressure disturbance. and Let i be the spatial coordinate components of the measuring point (i,j=1,2,3). Represents the generalized spatial or temporal derivative operator. The air density varies with temperature. Normal velocity, The thermodynamic temperature of the flow field, as a function of spatial position, determines the local physical properties; and These are the Dirac function and the Heaviside function, respectively. Spatial description function of the sound source surface ( (Indicates the boundary of the sound source). For compressive stress tensor, it usually includes hydrostatic disturbance and viscous stress terms; Let be the components of the unit outward normal vector of the sound source surface. Let the Lighthill stress tensor be... For the observation time. Since this application obtained the three-dimensional temperature distribution field inside the motor in the flow-thermal field and mapped the three-dimensional temperature distribution field to the flow-acoustic simulation domain, therefore... , And the viscosity updates synchronously with temperature, making the unsteady flow field... and The solution process is also indirectly affected by temperature, thus enabling the sound source term in the FW-H model to accurately reflect the flow and acoustic characteristics under thermal conditions. This application therefore achieves co-simulation of temperature rise and aerodynamic noise, improving the accuracy of aerodynamic noise prediction.
[0061] In one specific embodiment, step S4 includes: calculating the mean and variance of the response values of each group of structural parameter combinations; and determining the optimal structural parameter combination based on the mean and variance.
[0062] The highest winding temperature rise Tw, the highest total sound pressure level SPL1 at the end of the winding, and the highest total sound pressure level SPL2 of the circumferential noise corresponding to each combination of structural parameters obtained in step S3 are used as performance indicators. All simulation output results are filled into L16(4) according to the corresponding combination of structural parameters. 4 The orthogonal experimental tables are shown in Table 1.
[0063] Table 1
[0064]
[0065] Based on this, mean analysis was performed on the average values at each structural parameter level to determine the influence trend of different structural parameters on Tw, SPL1, and SPL3. Then, using analysis of variance (ANOVA), the significance of each structural parameter for different optimization objectives was quantified. Based on the optimal level indicated by the mean trend and the key variables identified by ANOVA, the improvement of the three performance indicators was comprehensively compared, and the optimal combination of structural parameters that can simultaneously take into account temperature rise and aerodynamic noise performance was selected from multiple combinations of structural parameters in the orthogonal experiment.
[0066] Based on the mean trend and variance contribution rate, the optimal combination of structural parameters that can simultaneously improve temperature rise and aerodynamic noise performance is obtained: , , , . To achieve the optimal blade width, For optimal fan blade height, To determine the optimal stator ventilation hole diameter, The optimal radial ventilation hole width.
[0067] In a specific embodiment, after step S5, the method further includes: substituting the optimal combination of structural parameters obtained in step S4 back into the multiphysics co-simulation process of step S3 for verification, to obtain the maximum winding temperature rise Tw, the maximum total sound pressure level SPL1 of the end noise, and the maximum total sound pressure level SPL2 of the circumferential noise under optimized conditions. By comparing the optimized results with the three performance indicators of the original structure, it is determined whether the combination of structural parameters has simultaneously improved in terms of temperature rise reduction and aerodynamic noise suppression, thereby verifying the effectiveness of the optimization results. When the verification results meet the expected performance improvement requirements, the combination of structural parameters can be used as the final optimized scheme for the internal self-ventilated air-cooling structure of the permanent magnet traction motor; if the improvement is insufficient, the range of structural parameters can be adjusted according to the synergistic law, and the multiphysics co-simulation process can be re-executed.
[0068] like Figure 5 As shown in (a) and (b), the winding with the structure before optimization has a higher maximum temperature rise and obvious local hot spots. After adopting the optimal combination of structural parameters, the overall temperature distribution of the winding area is more uniform, and the maximum temperature rise is reduced by 12.73%, indicating that the airflow organization is improved and the self-fan cooling effect is enhanced.
[0069] At the same time, such as Figure 6 As shown in (a) and (b), the high sound pressure region in the optimized sound power level cloud map is significantly reduced, the overall noise radiation intensity is reduced, and the highest sound pressure level of the end noise and the highest sound pressure level of the circumferential noise are reduced by 8.45 dB and 7.9 dB, respectively. This indicates that the optimal structure effectively weakens the periodic pressure pulsation induced by blade rotation and the resulting aerodynamic noise without increasing losses and weight.
[0070] Therefore, by comparing the temperature rise distribution and sound pressure level distribution before and after optimization, it can be confirmed that the optimal combination of structural parameters obtained in step S4 can simultaneously improve the heat dissipation performance and aerodynamic noise performance of the self-fan cooling structure inside the motor, thereby verifying the effectiveness of the collaborative simulation and collaborative optimization method of this application.
[0071] The beneficial effects of this application are as follows:
[0072] (1) Improve the accuracy of aerodynamic noise prediction: This application introduces the temperature field obtained from the flow-thermal simulation into the flow-acoustic calculation and corrects the air property parameters point by point, so that the acoustic calculation medium is consistent with the real thermal environment when the motor is running. The flow-acoustic co-simulation under this non-uniform medium can more accurately capture the pressure pulsation caused by blade rotation and ventilation structure, thereby significantly improving the prediction accuracy of aerodynamic noise.
[0073] (2) Constructing a collaborative optimization system based on multi-physics response: This application utilizes orthogonal experimental samples to cover the design space, and identifies key structural parameters and their optimal levels from multi-physics response data through mean analysis and variance analysis, so that optimization decisions are truly based on the comprehensive performance of temperature rise and noise. Compared with traditional optimization methods that rely solely on geometric relationships or single physical quantity evaluations, the collaborative optimization process of this application can more accurately reflect the real influence of design variables, thereby obtaining an optimized structure with practical engineering significance.
[0074] (3) The overall method is highly versatile and can be widely applied: This application forms a system method framework of "multi-physics field collaborative simulation + multi-objective collaborative optimization", which is applicable to the design of internal air-cooling system of permanent magnet traction motor with different speeds and different structural forms, and provides reliable technical means and theoretical basis for the integrated thermal-acoustic design of high power density motors.
[0075] Based on the same inventive concept, this application also provides a system for implementing the aforementioned method for optimizing the internal self-ventilated cooling structure of a permanent magnet traction motor that considers the coordinated optimization of aerodynamic noise and temperature rise. The solution provided by this system is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the system for optimizing the internal self-ventilated cooling structure of a permanent magnet traction motor that considers the coordinated optimization of aerodynamic noise and temperature rise provided below can be found in the limitations of the method for optimizing the internal self-ventilated cooling structure of a permanent magnet traction motor that considers the coordinated optimization of aerodynamic noise and temperature rise described above, and will not be repeated here.
[0076] In one exemplary embodiment, a self-ventilated air-cooling structure optimization system for a permanent magnet traction motor that takes into account aerodynamic noise-temperature rise co-optimization is provided, including the following modules.
[0077] The structural parameter and optimization target determination module is used to determine multiple structural parameters and optimization targets of the internal self-ventilated air-cooling structure of the permanent magnet traction motor; the optimization targets include aerodynamic noise and temperature rise.
[0078] The structural parameter combination generation module is used to generate multiple sets of structural parameter combinations based on the structural parameters using orthogonal experimental design.
[0079] The co-simulation module is used to perform a multi-physics co-simulation process for each combination of structural parameters to obtain the response value of the optimization target; the response value includes the maximum temperature rise of the winding and the maximum sound pressure level of the aerodynamic noise.
[0080] The optimal structural parameter combination determination module is used to determine the optimal structural parameter combination based on the response value.
[0081] The optimization module is used to optimize the internal self-ventilated air-cooling structure of the permanent magnet traction motor based on the optimal combination of structural parameters.
[0082] In one specific embodiment, the co-simulation module includes the following units.
[0083] The model building and solving unit is used to build a flow-thermal coupling model and solve it to obtain a three-dimensional temperature distribution field, and extract the maximum temperature rise of the winding from the three-dimensional temperature distribution field.
[0084] A non-uniform air medium field construction unit is used to construct a non-uniform air medium field based on the three-dimensional temperature distribution field.
[0085] The aerodynamic sound source extraction unit is used to solve the unsteady flow field in the non-uniform air medium field and extract the aerodynamic sound source.
[0086] The aerodynamic noise maximum sound pressure level calculation unit is used to calculate the aerodynamic noise maximum sound pressure level based on the aerodynamic sound source using the FW-H model.
[0087] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments. The computer device may be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (I / O), and a communication interface. The processor, memory, and I / O are connected via a system bus, and the communication interface is connected to the system bus via the I / O interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device stores data to be processed. The I / O interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communicating with an external terminal via a network connection. When the computer program is executed by the processor, it implements the steps in the above-described method embodiments.
[0088] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.
[0089] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.
[0090] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.
[0091] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).
[0092] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.
[0093] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0094] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A method for optimizing the internal self-ventilated cooling structure of a permanent magnet traction motor, considering the coordinated optimization of aerodynamic noise and temperature rise, characterized in that... include: Several structural parameters and optimization objectives of the internal self-ventilated cooling structure of the permanent magnet traction motor were determined; the optimization objectives included aerodynamic noise and temperature rise. Based on the aforementioned structural parameters, multiple combinations of structural parameters are generated using orthogonal experimental design. For each combination of structural parameters, a multiphysics co-simulation process is executed to obtain the response value of the optimization target; The response values include the highest temperature rise of the winding and the highest sound pressure level of the aerodynamic noise. The multiphysics co-simulation process includes: constructing a flow-thermal coupling model and solving it to obtain a three-dimensional temperature distribution field; extracting the maximum temperature rise of the winding from the three-dimensional temperature distribution field; constructing a non-uniform air medium field based on the three-dimensional temperature distribution field; solving the unsteady flow field in the non-uniform air medium field and extracting aerodynamic sound sources; and calculating the maximum sound pressure level of aerodynamic noise using the FW-H model based on the aerodynamic sound sources. Determine the optimal combination of structural parameters based on the response value; The internal self-ventilated cooling structure of the permanent magnet traction motor is optimized based on the optimal combination of structural parameters.
2. The method for optimizing the internal self-ventilated cooling structure of a permanent magnet traction motor considering aerodynamic noise and temperature rise as described in claim 1, characterized in that, Determine multiple structural parameters and optimization objectives for the internal self-ventilated cooling structure of the permanent magnet traction motor, specifically including: Based on the airflow organization and noise generation mechanism of the internal self-fan cooling structure of the permanent magnet traction motor, multiple structural parameters were determined. The optimization target was determined based on the design requirements of the internal self-ventilated air-cooling structure of the permanent magnet traction motor.
3. The method for optimizing the internal self-ventilated cooling structure of a permanent magnet traction motor considering aerodynamic noise and temperature rise as described in claim 1, characterized in that, Based on the aforementioned structural parameters, multiple combinations of structural parameters are generated using orthogonal experimental design, specifically including: Within the range of values for each structural parameter, each structural parameter is discretized, and an orthogonal array is selected based on the number of structural parameters and the number of levels to construct the experimental design matrix. Each row in the experimental design matrix is mapped to a set of structural parameter combinations, resulting in multiple sets of structural parameter combinations.
4. The method for optimizing the internal self-ventilated cooling structure of a permanent magnet traction motor considering aerodynamic noise and temperature rise as described in claim 1, characterized in that, Determining the optimal combination of structural parameters based on the response value specifically includes: Calculate the mean and variance of the response values for each combination of structural parameters; The optimal combination of structural parameters is determined based on the mean and variance.
5. A self-ventilated air-cooling structure optimization system for a permanent magnet traction motor that considers aerodynamic noise-temperature rise co-optimization, characterized in that, include: The structural parameter and optimization target determination module is used to determine multiple structural parameters and optimization targets of the internal self-ventilated cooling structure of the permanent magnet traction motor; the optimization targets include aerodynamic noise and temperature rise. The structural parameter combination generation module is used to generate multiple sets of structural parameter combinations based on the structural parameters using orthogonal experimental design method; The co-simulation module is used to execute a multi-physics co-simulation process for each combination of structural parameters to obtain the response value of the optimization target. The response value includes the maximum temperature rise of the winding and the maximum sound pressure level of aerodynamic noise. The co-simulation module includes: a model building and solving unit, used to build a flow-thermal coupling model and solve it to obtain a three-dimensional temperature distribution field, and extract the maximum temperature rise of the winding from the three-dimensional temperature distribution field; a non-uniform air medium field construction unit, used to build a non-uniform air medium field based on the three-dimensional temperature distribution field; an aerodynamic sound source extraction unit, used to solve the unsteady flow field in the non-uniform air medium field and extract the aerodynamic sound source; and a maximum sound pressure level calculation unit, used to calculate the maximum sound pressure level of aerodynamic noise based on the aerodynamic sound source using the FW-H model. An optimal structural parameter combination determination module is used to determine the optimal structural parameter combination based on the response value; The optimization module is used to optimize the internal self-ventilated air-cooling structure of the permanent magnet traction motor based on the optimal combination of structural parameters.
6. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method for optimizing the internal self-ventilated cooling structure of a permanent magnet traction motor, taking into account the aerodynamic noise-temperature rise co-optimization, as described in any one of claims 1-4.
7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for optimizing the internal self-fan cooling structure of the permanent magnet traction motor that takes into account the aerodynamic noise-temperature rise co-optimization as described in any one of claims 1-4.
8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for optimizing the internal self-fan cooling structure of the permanent magnet traction motor that takes into account the aerodynamic noise-temperature rise co-optimization as described in any one of claims 1-4.