Ultrahigh-temperature heavy-load self-aligning roller bearing optimization design method and system based on particle swarm optimization

Abstract

The invention discloses an ultra-high-temperature heavy-load self-aligning roller bearing optimization design method and system based on a particle swarm algorithm, belongs to the field of rolling bearing optimization design, and solves the problems that a traditional bearing design process is low in efficiency, and multi-target collaborative optimization is difficult to achieve. And the design method combining the empirical formula and the finite element model is poor in precision due to the strong coupling characteristic of the material nonlinearity, the thermal coupling effect and the bearing performance under the ultra-high-temperature heavy-load working condition. The method comprises the following steps: S1, determining a bearing parameter, a working condition parameter and a particle swarm algorithm parameter; s2, establishing an optimization model of the ultrahigh-temperature heavy-load self-aligning roller bearing: S21, establishing a target function; s22, determining a design variable; s23, establishing a constraint equation; and S3, optimizing corresponding parameters of the bearing by adopting a particle swarm algorithm to obtain an optimization result. The method is suitable for a self-aligning roller bearing optimization design scene.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of optimal design of rolling bearings, in particular to an optimal design method of super-high-temperature heavy-load self-aligning roller bearings based on a particle swarm algorithm. BACKGROUND

[0002] As the core basic part of ensuring the rotation function of equipment, rolling bearings are widely used in the fields of aerospace, mining machinery, precision manufacturing, etc., and their performance is closely related to the service life and reliability of the equipment. Among them, self-aligning roller bearings have the characteristics of automatic alignment, high load bearing performance and adaptation to shaft misalignment, and are widely used in equipment under complex working conditions such as heavy load, impact load or installation error. However, under super-high-temperature heavy-load working conditions, problems such as material thermal softening, lubrication failure, local stress concentration and abnormal wear are particularly prominent, leading to frequent problems such as early fatigue failure, sticking, jamming and even structural fracture of self-aligning roller bearings. The early failure of self-aligning roller bearings under super-high-temperature heavy-load working conditions has become one of the technical bottlenecks restricting the performance improvement of high-end equipment.

[0003] Currently, the optimal design of self-aligning roller bearings mainly relies on the method of combining empirical formulas with finite element simulation, and the optimized structure is modified through experiments, but this method has the following shortcomings: first, the traditional design process needs to repeatedly adjust the structure parameters and carry out a large number of simulation verifications, which is low in efficiency and difficult to realize multi-objective collaborative optimization; second, the strong coupling characteristics of material nonlinearity, thermal-mechanical coupling effect and bearing performance under super-high-temperature heavy-load working conditions result in poor accuracy of the design method combining empirical formulas with finite element models. Particle swarm algorithm has the characteristics of strong global search ability and fast convergence speed in complex scenes with multiple design variables and multiple nonlinear constraints, and has been applied to the field of bearing design under normal temperature conditions, but lacks adaptability improvement for super-high-temperature heavy-load working conditions. SUMMARY

[0004] The present application provides an optimal design method and system of self-aligning roller bearings based on a particle swarm algorithm, which aims to solve the problems of low efficiency and difficulty in realizing multi-objective collaborative optimization in the traditional bearing design process, and the strong coupling characteristics of material nonlinearity, thermal-mechanical coupling effect and bearing performance under super-high-temperature heavy-load working conditions, resulting in poor accuracy of the design method combining empirical formulas with finite element models.

[0005] The optimal design method of super-high-temperature heavy-load self-aligning roller bearings based on a particle swarm algorithm proposed by the present application comprises: S1: determining bearing parameters, working condition parameters and particle swarm algorithm parameters, The bearing parameters include bearing roller diameter, curvature radius, roller length, inner raceway curvature radius, outer raceway curvature radius, number of rolling elements, contact angle and material parameters; The working condition parameters include: axial force, radial force, clearance, fit, temperature and speed of the bearing; The particle swarm algorithm parameters include: inertia weight coefficient, acceleration constant, group size and number of iterations; S2: Establish an optimization model for ultra-high temperature and heavy-load spherical roller bearings, including: S21: Establish objective function; S22: Determine design variables; S23: Establish constraint equations; S3: Use particle swarm optimization to optimize the corresponding parameters of the bearing and obtain the optimization results.

[0006] Furthermore, a preferred solution is provided: the inertia weight coefficient is set to 1, and the acceleration constant is and , take the dual cognitive coefficient mode, set it to 2.05, the group size range is 20-60, the number of iterations M is preset to an initial value according to the convergence characteristics of the objective function. If the algorithm has not converged after M iterations, increase M by 20%-50% to continue to optimize.

[0007] Furthermore, a preferred solution is provided: the S21 includes: establishing a pseudo-static model of the spherical roller bearing, calculating the maximum equivalent stress of the rolling element, the bearing friction coefficient, the bearing working clearance, the bearing aligning angle and the cage contact load as the objective function.

[0008] Furthermore, a preferred solution is provided: the maximum equivalent stress is obtained by calculating using the small strain thermoelasticity theory.

[0009] Furthermore, a preferred solution is provided: the pseudo-static model of the spherical roller bearing is solved using the Levenberg-Marquardt algorithm.

[0010] Furthermore, a preferred solution is provided: the constraint equations include: the contact force constraint equation between the roller and the cage pocket, the equivalent stress constraint equation, the friction coefficient constraint equation, the working clearance constraint equation, the self-aligning angle constraint equation, the rolling element number constraint equation, and the inner and outer raceway groove curvature radius coefficient constraint equation.

[0011] Furthermore, a preferred solution is provided: S3 includes: S31: Randomly generate an initial particle swarm in the solution space and define the position vector and velocity vector of each particle; S32: Calculate the optimization index evaluation results of each particle and record the current position and fitness value as the individual historical optimal , and select the global optimal ; S33: Adjusting the particle velocity and position to realize the iteration of the particle state; S34: Comparing the current fitness value of the particle with the historical optimal record, and updating if better , synchronously refreshing is the current group optimal solution; S35: When reaching the preset iteration number or the fitness convergence threshold, terminate the calculation, otherwise repeat steps S33-S35 for continuous optimization.

[0012] The application also provides a computer device, which comprises a memory and a processor, and the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes the particle swarm algorithm-based super-high-temperature heavy load self-aligning roller bearing optimization design method according to any one or more of the above-mentioned scheme combinations.

[0013] The application also provides a computer readable storage medium for storing a computer program, and the computer program executes the particle swarm algorithm-based super-high-temperature heavy load self-aligning roller bearing optimization design method according to any one or more of the above-mentioned scheme combinations.

[0014] The particle swarm algorithm-based super-high-temperature heavy load self-aligning roller bearing optimization design system provided by the application is realized based on the particle swarm algorithm-based super-high-temperature heavy load self-aligning roller bearing optimization design method according to any one or more of the above-mentioned scheme combinations, and the system comprises: a parameter determination module, which is used for determining bearing parameters, working condition parameters and particle swarm algorithm parameters, The bearing parameters comprise bearing roller diameter, curvature radius, roller length, inner raceway curvature radius, outer raceway curvature radius, number of rolling elements, contact angle and material parameters; The working condition parameters comprise axial force, radial force, clearance, fit, temperature and rotating speed of the bearing; The particle swarm algorithm parameters comprise inertia weight coefficient, acceleration constant, group size and iteration number; a super-high-temperature heavy load self-aligning roller bearing optimization model, which is used for establishing a target function, determining design variables and establishing constraint equations; a bearing optimization module, which is used for optimizing the corresponding parameters of the bearing by using the particle swarm algorithm to obtain an optimization result.

[0015] Compared with the prior art, the application has the following advantages: Under the super-high temperature heavy load working condition, the tapered roller bearing is faced with the complex alternating stress field, material performance degradation, local stress concentration and excessive wear and other problems, the optimization design method under the super-high temperature heavy load working condition needs to consider the material nonlinearity, thermal coupling effect and the strong coupling behavior of bearing performance, and needs to combine the high efficiency and fast optimization algorithm to repeatedly iterate the bearing design parameters, and then the optimal design parameters are obtained, but the optimization design method under normal temperature cannot adapt to the super-high temperature heavy load working condition, and the optimization design method of the tapered roller bearing under the super-high temperature heavy load working condition needs to comprehensively consider the above factors.

[0016] The application considers the material thermal softening and thermal coupling effect under the super-high temperature heavy load working condition, constructs a pseudo-static model of the tapered roller under the extreme working condition, selects multiple design variables and multiple nonlinear constraints to improve the particle swarm optimization algorithm, realizes the combination of the particle swarm optimization algorithm and the pseudo-static model under the super-high temperature heavy load working condition, and forms the optimization design method of the tapered roller bearing under the super-high temperature heavy load working condition, so as to provide a technical means for the performance optimization design of the tapered roller bearing under the complex working condition.

[0017] The application is suitable for the optimization design scene of the tapered roller bearing. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 The flowchart of the super-high temperature heavy load tapered roller bearing optimization design method based on the particle swarm optimization algorithm is described in the specific embodiment one of the application. DETAILED DESCRIPTION

[0019] In the following description, specific details are set forth such as particular system configurations, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. However, persons skilled in the art will understand that the present application can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

[0020] It should be understood that, when used in the specification and the appended claims, the term "comprising" indicates the presence of the described features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0021] It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.

[0022] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the present application specification. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work under the premise that the embodiments in the present application are within the scope of protection of the present application.

[0023] In the following description, a large number of specific details are set forth in order to facilitate a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of the present application, therefore the present application is not limited by the specific embodiments disclosed below.

[0024] Embodiment one: With reference to Figure 1 The present embodiment is described.

[0025] A super-high-temperature heavy-load self-aligning roller bearing optimization design method based on a particle swarm algorithm, the method comprising: S1: determining the type of the self-aligning roller bearing, thereby determining the bearing parameters, the working condition parameters and the particle swarm algorithm parameters, The bearing parameters include: bearing roller diameter , curvature radius , roller length , inner raceway curvature radius , outer raceway curvature radius , number of rolling elements , contact angle and material parameters; The working condition parameters include: axial force , radial force , clearance, fit, temperature, speed; The particle swarm algorithm parameters include: inertia weight coefficient, acceleration constant, population size and iteration number, specifically: the parameter configuration of the particle swarm algorithm needs to be finely adjusted according to the specific optimization problem, the core control parameters include inertia weight coefficient , acceleration constant and , population size and iteration number , etc. Set to 1 to maintain particle motion inertia; acceleration constant With Generally take the dual recognition coefficient mode, set to 2.05, through random number With Introduce disturbance, ensure the balance of individual experience and group cooperation; group size Experience value range is 20-60, iteration number According to the convergence characteristics of the objective function, preset the initial value, if the algorithm still does not converge after times of iteration, increase by 20%-50% to continue optimization. The above data provides data for subsequent establishment of super-high temperature heavy load angular ball bearing optimization model and application of particle swarm algorithm optimization.

[0026]

[0027] S2: Establishing a super-high temperature heavy load angular ball bearing optimization model, including: S21: Establishing an objective function, including: establishing a quasi-static model of the angular ball bearing, the quasi-static model of the angular ball bearing is solved by Levenberg-Marquardt algorithm, calculating the contact load of the angular ball bearing under high temperature and heavy load working condition, according to the contact load, calculating the maximum equivalent stress of the rolling body, the bearing friction coefficient, the bearing working clearance, the bearing alignment angle and the cage contact load, as the objective function; S22: Determining the design variables, there are 7 variables directly affecting the objective function, respectively, the bearing roller diameter , curvature radius , roller length , inner raceway curvature radius , outer raceway curvature radius , number of rolling bodies , contact angle , selecting the above 7 parameters as the design variables of the method, the expression is as follows: ; S23: Establishing constraint equations, including roller and cage hole contact force constraint equation, equivalent stress constraint equation, friction coefficient constraint equation, working clearance constraint equation, alignment angle constraint equation, rolling body number constraint equation, inner and outer raceway groove curvature radius coefficient constraint equation; S3: Using particle swarm algorithm to optimize the corresponding parameters of the bearing, obtaining the optimization results, including: S31: Randomly generate initial particle swarm in solution space, define the position vector and velocity vector of each particle; ​S32: Calculate the optimization index evaluation result of each particle, record the current position and fitness value as the individual historical optimal , and screen the global optimal of the population ; S33: Adjust the particle velocity and position to realize the iteration of the particle state; S34: Compare the current fitness value of the particle with the historical optimal record, if better, update , and refresh synchronously as the current population optimal solution; S35: When the preset iteration number or the fitness convergence threshold is reached, terminate the calculation, otherwise repeat steps S33-S35 for continuous optimization.

[0028] Embodiment two: This embodiment is a further illustration of S21 in the super-high-temperature heavy-load self-aligning roller bearing optimization design method based on the particle swarm algorithm described in embodiment one.

[0029] This step calculates the maximum equivalent stress of the rolling body, the bearing friction coefficient, the bearing working clearance, the bearing self-aligning angle and the cage contact load of the self-aligning roller bearing by establishing a quasi-static model of the self-aligning roller bearing, as the objective function.

[0030] Specifically: (1) Considering the influence of temperature, assembly stress, roller skew, etc., a quasi-static model of the self-aligning roller bearing is constructed, and the formula is as follows: (1) (2) (3) (4) In the formula, represents the resultant force acting on the roller; represents the resultant force acting on the inner ring; represents the resultant moment acting on the roller; represents the resultant moment acting on the inner ring; represents the resultant force between the roller and the rib; represents the tangential force of the inner raceway acting on the roller generated by sliding; represents the tangential force of the outer raceway acting on the roller generated by sliding; Fy represents the tangential force on the roller from the lubrication film between the inner raceway and the roller; Fy represents the tangential force on the roller from the lubrication film between the outer raceway and the roller; Fn represents the normal force on the roller from the lubrication film between the inner raceway and the roller; Fn represents the normal force on the roller from the lubrication film between the outer raceway and the roller; Fy represents the tangential force on the inner raceway from the lubrication film between the inner raceway and the roller; Fy represents the vector of the inner raceway contact point; Fy represents the vector of the outer raceway contact point; Fy represents the vector of the roller rib contact point; F represents the force between the roller and the cage; Fy represents the vector of the cage contact point; R represents the inner raceway contact radius; Fy represents the vector of the contact point in the inner ring coordinate system.

[0031] The contact load calculation of the rolling body and the cage pocket is performed: the Levenberg-Marquardt algorithm is used to solve the quasi-static model, and the contact force between the roller and the cage pocket is obtained.

[0032] (2) Calculate the equivalent stress: Under the condition of ultra-high temperature and heavy load, the equivalent stress calculation formula of the self-aligning roller bearing is: (5) In the formula, S represents the deviatoric stress tensor under high temperature working condition.

[0033] (3) Calculate the friction coefficient of the self-aligning roller bearing: (6) In the formula, F represents the friction torque of the self-aligning roller bearing; d represents the inner diameter of the self-aligning roller bearing; D represents the outer diameter of the self-aligning roller bearing; P represents the equivalent load of the self-aligning roller bearing.

[0034] (4) Calculate the working clearance of the self-aligning roller bearing: (7) wherein: represents the change of the clearance caused by the inner ring fit under high temperature environment; represents the change of the clearance caused by the outer ring fit under high temperature environment; represents the change of the clearance caused by the thermal deformation of the bearing ring under high temperature environment; represents the change of the clearance caused by the external load.

[0035] (5) Calculation of the alignment angle of the self-aligning roller bearing: Considering the influence of the shaft deflection deformation under high temperature, the calculation formula of the alignment angle of the self-aligning roller bearing under thermal coupling is as follows: (8) wherein: represents the inclination angle caused by the shaft deflection under high temperature environment; represents the inclination angle of the inner ring caused by the installation error; represents the inclination angle of the inner ring caused by the load under high temperature environment.

[0036] From the above, the objective function obtained in this step is: (9) Embodiment three: This embodiment is a further illustration of S23 in the particle swarm algorithm-based optimization design method for super-high-temperature heavy-load self-aligning roller bearings according to Embodiment one.

[0037] The constraint equations include: roller-retainer pocket contact force constraint equation, equivalent stress constraint equation, friction coefficient constraint equation, working clearance constraint equation, alignment angle constraint equation, number of rolling elements constraint equation, inner and outer raceway groove curvature radius coefficient constraint equation.

[0038] Specifically: (1) The roller-retainer pocket contact force should be less than the allowable value, i.e.: (10) wherein: represents the allowable roller-retainer pocket contact force.

[0039] (2) Equivalent stress constraint equation: The maximum equivalent stress of the inner and outer raceways and the rib of the rolling element is less than the yield strength, i.e.: (11) In the formula: represents the yield strength considering the temperature effect.

[0040] (3) Friction coefficient constraint equation: (12) In the formula: represents the allowable friction coefficient of the self-aligning roller bearing.

[0041] (4) Working clearance constraint equation: (13) In the formula: represents the minimum allowable working clearance; represents the maximum allowable working clearance.

[0042] (5) Self-aligning angle constraint equation: The self-aligning angle of the self-aligning roller bearing should be less than the design value, thus the constraint equation is: (14) In the formula: represents the allowable inclination angle of the bearing.

[0043] (6) Number of rolling elements constraint equation: The empirical value range of the number of rollers is: (15) In the formula, ; ; represents the coefficient related to the bearing series.

[0044] (7) Inner and outer raceway groove curvature radius coefficient constraint equation: According to the design manual, the long axis of the roller contact ellipse is less than or equal to 1.5 times the effective length of the roller, so the model needs to satisfy the following constraint equation: (16) (17) (18) In the formula: represents the coefficient related to the elliptic function, represents the maximum load on the roller; The total curvature expression of the outer raceway is: (19) The total curvature expression of the inner raceway is: (20) (21) In summary, the optimized design model of the super-high-temperature heavy load self-aligning roller bearing is: (22) Embodiment four: The embodiment is a further illustration of S3 in the super-high-temperature heavy load self-aligning roller bearing optimized design method based on the particle swarm algorithm according to the embodiment one.

[0045] The S3 comprises: Randomly generate an initial particle swarm in the solution space, and the state of each particle in the d dimensional space is defined by its position vector and . In the iteration process, each particle updates its state through a double guidance mechanism: on the one hand, it refers to its historical optimal position , which records the best solution obtained by the particle in the search process; on the other hand, it tracks the current global optimal position of the group. At each iteration, the particle adjusts its speed and spatial coordinates according to the two guidance factors through the following equation: (23) (24) The steps of the particle swarm optimization algorithm are as follows: (1) Randomly generate an initial particle swarm in the solution space, and define the position vector and the velocity vector of each particle; (2) Calculate the optimization index evaluation results of each particle, record the current position and fitness value as the individual historical optimal , and select the global optimal of the group; (3) Adjust the particle speed and position according to equation (23) and equation (24) to realize the iteration of the particle state; (4) Compare the current fitness value of the particle with the historical optimal record, if it is better, update ; synchronously refresh as the current global optimal solution; (5) When the preset iteration number or the fitness convergence threshold is reached, terminate the calculation, otherwise repeat steps (3)-(5) for continuous optimization.

[0046] Embodiment five: A super-high-temperature heavy load self-aligning roller bearing optimized design system based on a particle swarm algorithm, the system is realized based on a super-high-temperature heavy load self-aligning roller bearing optimized design method based on a particle swarm algorithm as described in the above embodiment, the system comprises: Parameter determination module: used for determining bearing parameters, working condition parameters and particle swarm algorithm parameters, The bearing parameters include bearing roller diameter, curvature radius, roller length, inner raceway curvature radius, outer raceway curvature radius, number of rolling elements, contact angle and material parameters; The working condition parameters include axial force, radial force, clearance, fit, temperature and rotating speed of the bearing; The particle swarm algorithm parameters include inertia weight coefficient, acceleration constant, population size and iteration number; The super-high-temperature heavy-load self-aligning roller bearing optimization model is used for establishing a target function, determining design variables and establishing constraint equations; The bearing optimization module is used for optimizing the corresponding parameters of the bearing by using the particle swarm algorithm to obtain an optimization result.

[0047] Embodiment six: In this embodiment, a certain type of self-aligning roller bearing is selected as an example, the working condition is that the load is 3KN, the rotating speed is 1000r / min, the initial clearance is 15um, the temperature is 800℃, and the bearing structure parameters and the target function values before and after optimization are shown in Tables 1-4. The parameters of the particle swarm algorithm are as follows: the inertia weight coefficient is 1, the acceleration constant is 2.05, the random number is 0.25, the random number is 0.4, the population size is 30, and the iteration number is 400.

[0048] According to the results in Tables 2 and 4, after the optimized bearing design parameters are used, the maximum equivalent stress of the bearing is reduced by 10.48%, the friction coefficient of the bearing is reduced by 25.00%, the working clearance and the self-aligning angle of the bearing are within the allowable range, the working clearance of the bearing is within the allowable range of ±20um, the self-aligning angle is within the allowable range of ±3°, the contact load of the retainer is reduced by 40.00%, and the optimization design method provided by the present application has a good improvement effect on the performance of the bearing.

[0049] Table 1 original bearing structure parameters

[0050] Table 2 target function values before optimization

[0051] Table 3 bearing structure parameters after optimization

[0052] Table 4 target function values after optimization ​​​​​​​

[0053] It can be seen that the method applies the particle swarm algorithm to the optimal design of high-temperature heavy load self-aligning roller bearings, maximizes the bearing life under the condition that the bearing meets certain load strength, and has the advantages of high accuracy, strong reliability, fast calculation speed, etc. It is a practical and effective optimal design method for self-aligning roller bearings.

Claims

1. A particle swarm optimization algorithm based design method for ultra-high temperature and heavy-load spherical roller bearings, characterized in that: The method comprises: S1: Determine the bearing parameters, working condition parameters and particle swarm algorithm parameters, The bearing parameters include: bearing roller diameter, curvature radius, roller length, inner raceway curvature radius, outer raceway curvature radius, number of rolling elements, contact angle and material parameters; The working condition parameters include: axial force, radial force, clearance, fit, temperature and speed of the bearing; The particle swarm algorithm parameters include: inertia weight coefficient, acceleration constant, group size and number of iterations; S2: Establish an optimization model for ultra-high temperature and heavy-load spherical roller bearings, including: S21: Establish objective function; S22: Determine design variables; S23: Establish constraint equations; S3: Use particle swarm optimization to optimize the corresponding parameters of the bearing and obtain the optimization results.

2. The particle swarm optimization algorithm-based design method for ultra-high temperature and heavy-load spherical roller bearings according to claim 1, characterized in that: The inertia weight coefficient is set to 1, and the acceleration constant is and , take the dual cognitive coefficient model, set it to 2.05, the group size range is 20-60, the number of iterations M According to the convergence characteristics of the objective function, the initial value is preset. M After iterations, it still fails to converge. M Improve by 20%-50% and continue to seek optimization.

3. The method for optimizing the design of ultra-high temperature and heavy-load spherical roller bearings based on particle swarm optimization according to claim 1, characterized in that: The S21 includes: establishing a pseudo-static model of the spherical roller bearing, and calculating the maximum equivalent stress of the rolling element, the bearing friction coefficient, the bearing working clearance, the bearing aligning angle and the cage contact load as the objective function.

4. The method for optimizing the design of ultra-high temperature and heavy-load spherical roller bearings based on particle swarm optimization according to claim 3, characterized in that: The maximum equivalent stress is obtained by calculating the small strain thermoelasticity theory.

5. The method for optimizing the design of ultra-high temperature and heavy-load spherical roller bearings based on particle swarm optimization according to claim 3, characterized in that: The pseudo-static model of the spherical roller bearing is solved using the Levenberg-Marquardt algorithm.

6. The method for optimizing the design of ultra-high temperature and heavy-load spherical roller bearings based on particle swarm optimization according to claim 1, characterized in that: The constraint equations include: the contact force constraint equation between the roller and the cage pocket, the equivalent stress constraint equation, the friction coefficient constraint equation, the working clearance constraint equation, the self-aligning angle constraint equation, the rolling element number constraint equation, and the inner and outer raceway groove curvature radius coefficient constraint equation.

7. The method for optimizing the design of ultra-high temperature and heavy-load spherical roller bearings based on particle swarm optimization according to claim 1, characterized in that: The S3 includes: S31: Randomly generate an initial particle swarm in the solution space and define the position vector and velocity vector of each particle; S32: Calculate the optimization index evaluation results of each particle and record the current position and fitness value as the individual historical optimal , and select the global optimal ; S33: Adjust particle speed and position to achieve particle state iteration; S34: Compare the particle's current fitness value with the historical best record, and update if it is better. , synchronous refresh The optimal solution for the current group; S35: When the preset number of iterations or the fitness convergence threshold is reached, the calculation is terminated, otherwise steps S33-S35 are repeated for continuous optimization.

8. A computer device, characterized in that: The computer device includes a memory and a processor, wherein a computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes an ultra-high temperature and heavy-load spherical roller bearing optimization design method based on a particle swarm algorithm according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium is used to store a computer program, and the computer program executes an ultra-high temperature and heavy-load spherical roller bearing optimization design method based on a particle swarm algorithm according to any one of claims 1 to 7.

10. An optimization design system for ultra-high temperature and heavy-load spherical roller bearings based on particle swarm optimization, characterized in that: The system is implemented based on an optimization design method for ultra-high temperature and heavy-load spherical roller bearings based on a particle swarm algorithm according to any one of claims 1 to 7, and the system includes: Parameter determination module: used to determine bearing parameters, operating parameters and particle swarm algorithm parameters, The bearing parameters include: bearing roller diameter, curvature radius, roller length, inner raceway curvature radius, outer raceway curvature radius, number of rolling elements, contact angle and material parameters; The working condition parameters include: axial force, radial force, clearance, fit, temperature and speed of the bearing; The particle swarm algorithm parameters include: inertia weight coefficient, acceleration constant, group size and number of iterations; Ultra-high temperature heavy-load spherical roller bearing optimization model is used to: establish the objective function; determine the design variables; establish the constraint equations; Bearing optimization module: used to optimize the corresponding parameters of the bearing using particle swarm algorithm to obtain the optimization results.

Images