Flanged bearing texture design method and device based on particle swarm intelligence algorithm

Abstract

The application provides a flanging bearing texture design method and device based on a particle swarm intelligent algorithm, and the method comprises the following steps: constructing a preset group of particle swarms, the number of particles in each particle swarm is the same as the number of textures in the flanging bearing; determining the type of target design parameters of the textures, determining the initial position information corresponding to each particle in each particle swarm based on the type of the target design parameters; based on the initial position information of each particle in the particle swarm, iterative optimization is performed through the particle swarm intelligent algorithm, and when the iteration ends, the optimal position information group is determined based on the position information groups corresponding to each particle swarm in each iteration round; and the target design parameters of each texture in the flanging bearing are determined based on the optimal position information group. According to the technical scheme of the application, the global optimal solution of the design parameters of the flanging bearing texture can be obtained through the particle swarm intelligent algorithm.

Description

Technical Field

[0001] The present invention relates to the technical field of bearing design, and in particular to a method and device for designing flanged bearing texture based on a particle swarm intelligence algorithm. Background Art

[0002] The lubrication properties of the crankshaft-bearing system directly impact the engine's operational safety and lifespan. Among the various bearings in the crankshaft-bearing system, flanged bearings experience harsher lubrication conditions due to their presence in radial and axial components, and their location at the end of the crankshaft. High-temperature erosion is a common hazard, making the design of the flanged bearing texture particularly crucial.

[0003] In the process of designing flanged bearings in the prior art, the design parameters of the flanged bearing texture are often determined by an exhaustive experimental method, that is, the design parameters of the flanged bearing texture are determined through multiple experiments using simple assumed parameters. Therefore, it is difficult to obtain the global optimal solution for the design parameters of the flanged bearing texture. Summary of the Invention

[0004] The present invention provides a method and device for designing flanged bearing texture based on particle swarm intelligence algorithm, which is used to solve the defect in the existing technology that it is difficult to obtain the global optimal solution of the design parameters of the flanged bearing texture. The technical solution of the present invention adopts the particle swarm intelligence algorithm to obtain the global optimal solution of the design parameters of the flanged bearing texture.

[0005] The present invention provides a method for designing a flanged bearing texture based on a particle swarm intelligence algorithm, comprising:

[0006] Constructing a preset group of particle groups, wherein the number of particles in each of the particle groups is the same as the number of textures in the flanged bearing bush;

[0007] determining a type of target design parameter for the texture, and determining, for each particle group, initial position information corresponding to each particle in the particle group based on the type of the target design parameter;

[0008] Based on the initial position information of each particle in the particle swarm, an iterative optimization is performed using a particle swarm intelligent algorithm, and at the end of the iteration, an optimal position information group is determined based on the position information groups corresponding to each particle swarm in each iteration round;

[0009] Target design parameters of each texture in the flanged bearing are determined based on the optimal position information group.

[0010] According to a method for designing a flanged bearing texture based on a particle swarm intelligent algorithm provided by the present invention, the method performs iterative optimization based on the initial position information of each particle in the particle swarm by the particle swarm intelligent algorithm. At the end of the iteration, the method determines the optimal position information group based on the position information groups corresponding to each particle swarm in each iteration round, including:

[0011] In each iteration round, for each particle group, a target radial oil film pressure distribution and a target thrust oil film pressure distribution of the flanged bearing are determined based on preset design parameters corresponding to each texture, position information of each particle in the particle group corresponding to the current iteration round, and first structural parameters and operating condition parameters corresponding to the flanged bearing; wherein the position information corresponding to each particle in the first iteration round is the initial position information corresponding to each particle;

[0012] For each particle group, determining a target grey relational degree corresponding to the particle group in the current iteration round based on the position information group corresponding to the particle group in the current iteration round, the second structural parameter and the journal speed parameter corresponding to the flanged bearing, and the target radial oil film pressure distribution and the target thrust oil film pressure distribution of the flanged bearing;

[0013] Based on the target grey correlation degrees corresponding to the particle swarms in the historical iteration rounds and the current iteration round, the optimal solutions of the group position information corresponding to the particle swarms in the current iteration round are determined, and the optimal solution of the global position information in the current iteration round is determined according to the optimal solutions of the group position information corresponding to the particle swarms;

[0014] At the end of the iteration, the optimal solution of the global position information corresponding to the last iteration round is determined as the optimal position information group;

[0015] For each particle swarm, the position information group corresponding to the current iteration round of the particle swarm is determined based on the position information group corresponding to the previous iteration round of the particle swarm, the group position information optimal solution and the global position information optimal solution.

[0016] According to a method for designing a flanging bearing texture based on a particle swarm intelligence algorithm, the first structural parameters corresponding to the flanging bearing include: eccentricity, radial clearance between the journal and the bearing, central section offset angle, bearing position angle, inclination angle between the journal and the main bearing, angle between the journal centerline projection and the eccentricity, journal inclination angle, bearing inner diameter, and comprehensive surface roughness; the operating condition parameters corresponding to the flanging bearing include: elastic deformation parameter, thermal deformation parameter, journal speed parameter, and lubricating oil speed parameter.

[0017] The determining of a target radial oil film pressure distribution and a target thrust oil film pressure distribution of the flanged bearing based on preset design parameters corresponding to each texture, position information of a current iteration round corresponding to each particle in the particle group, and first structural parameters and operating condition parameters corresponding to the flanged bearing comprises:

[0018] S1, determining a first radial oil film pressure distribution and a first thrust oil film pressure distribution of the flanged bearing based on preset design parameters corresponding to each texture, position information of each particle in the particle group corresponding to the current iteration round, the first structural parameter, and the operating condition parameter;

[0019] S2. Determining a new elastic deformation parameter based on the first radial oil film pressure distribution and the first thrust oil film pressure distribution, updating the operating condition parameter based on the new elastic deformation parameter, and repeatedly performing steps S1 to S2 using the updated operating condition parameter as the operating condition parameter until the determined new elastic deformation parameter converges, and then determining the first operating condition parameter based on the converged new elastic deformation parameter;

[0020] S3, determining a second radial oil film pressure distribution and a second thrust oil film pressure distribution of the flanged bearing based on preset design parameters corresponding to each texture, position information of each particle in the particle swarm corresponding to the current iteration round, the first structural parameter, and the first operating condition parameter;

[0021] S4. Determine a first flanged bearing radial component temperature and a first flanged bearing thrust component temperature based on the second radial oil film pressure distribution, the second thrust oil film pressure distribution, and the lubricating oil velocity parameter;

[0022] S5. Determine a new thermal deformation parameter based on the temperature of the first flanged bearing radial component and the temperature of the first flanged bearing thrust component, update the first operating condition parameter based on the new thermal deformation parameter, and repeatedly perform steps S1 to S5 using the updated first operating condition parameter as the operating condition parameter until the determined new thermal deformation parameter converges, and then determine a second operating condition parameter based on the converged new thermal deformation parameter;

[0023] S6. Determine a third radial oil film pressure distribution and a third thrust oil film pressure distribution of the flanged bearing based on preset design parameters corresponding to each texture, position information of each particle in the particle swarm corresponding to the current iteration round, the first structural parameter, and the second operating condition parameter;

[0024] S7, determining the radial component bearing capacity of the flanged bearing based on the position information of the current iteration round corresponding to each particle in the particle swarm, the first structural parameter, and the third radial oil film pressure distribution;

[0025] S8. When the radial component bearing capacity is not equal to the preset load, a new eccentricity is determined, the first structural parameter is updated based on the new eccentricity, and steps S1 to S8 are repeated using the updated first structural parameter as the first structural parameter, until the radial component bearing capacity is equal to the preset load, the third radial oil film pressure distribution corresponding to the radial component bearing capacity is determined as the target radial oil film pressure distribution of the flanged bearing, and the third thrust oil film pressure distribution corresponding to the radial component bearing capacity is determined as the target thrust oil film pressure distribution of the flanged bearing.

[0026] According to a method for designing a flanged bearing texture based on a particle swarm intelligence algorithm provided by the present invention, determining a first radial oil film pressure distribution and a first thrust oil film pressure distribution of the flanged bearing based on preset design parameters corresponding to each texture, position information of a current iteration round corresponding to each particle in the particle swarm, the first structural parameter, and the operating condition parameter, including:

[0027] Determine the oil film thickness of the flanged bearing radial component and the oil film thickness of the flanged bearing thrust component based on the preset design parameters corresponding to each texture, the position information of the current iteration round corresponding to each particle in the particle group, the first structural parameter, and the operating condition parameter;

[0028] Constructing a first Reynolds equation for solving radial oil film pressure distribution based on the journal speed parameter and the oil film thickness of the flanged bearing radial component, and constructing a second Reynolds equation for solving thrust oil film pressure distribution based on the journal speed parameter and the oil film thickness of the flanged bearing thrust component;

[0029] The first Reynolds equation and the second Reynolds equation are iteratively solved respectively by a relaxation iterative algorithm until the respectively determined radial oil film pressure distribution and thrust oil film pressure distribution converge, and the converged radial oil film pressure distribution is determined as the first radial oil film pressure distribution, and the converged thrust oil film pressure distribution is determined as the first thrust oil film pressure distribution.

[0030] According to a method for designing a flanged bearing texture based on a particle swarm intelligence algorithm provided by the present invention, determining the first flanged bearing radial component temperature and the first flanged bearing thrust component temperature based on the second radial oil film pressure distribution, the second thrust oil film pressure distribution, and the lubricating oil velocity parameter, comprising:

[0031] establishing a temperature calculation boundary condition based on the second radial oil film pressure distribution, the second thrust oil film pressure distribution, and the lubricating oil speed parameter;

[0032] Determining and solving a first energy equation and a first heat conduction equation for the temperature of the radial component of the flanged bearing based on the second radial oil film pressure distribution and the lubricating oil velocity parameter;

[0033] Determining and solving a second energy equation and a second heat conduction equation for the temperature of the flanged bearing thrust component based on the second thrust oil film pressure distribution and the lubricating oil velocity parameter;

[0034] Under the temperature calculation boundary conditions, the first energy equation and the first heat conduction equation, as well as the second energy equation and the second heat conduction equation are iteratively solved respectively by a relaxation iterative algorithm until the respectively determined flanged bearing radial component temperature and flanged bearing thrust component temperature converge, and the flanged bearing radial component temperature is determined as the first flanged bearing radial component temperature, and the flanged bearing thrust component temperature is determined as the first flanged bearing thrust component temperature.

[0035] According to a flanging bearing texture design method based on a particle swarm intelligence algorithm provided by the present invention, the second structural parameter includes: a position angle of the bearing and an inner diameter of the bearing;

[0036] The determining of the target grey relational degree corresponding to the particle swarm in the current iteration round based on the position information group corresponding to the particle swarm in the current iteration round, the second structural parameter and the journal speed parameter corresponding to the flanged bearing, and the target radial oil film pressure distribution and the target thrust oil film pressure distribution of the flanged bearing comprises:

[0037] Determining the radial component friction coefficient and thrust component bearing capacity of the flanged bearing in the current iteration round based on the position information group of the current iteration round corresponding to the particle swarm, the second structural parameter, the journal speed parameter, the target radial oil film pressure distribution, and the target thrust oil film pressure distribution;

[0038] A target grey correlation degree corresponding to the particle group in the current iteration round is determined based on the radial component friction coefficient and the thrust component bearing capacity, where the target grey correlation degree represents a correlation degree between the radial component friction coefficient and the thrust component bearing capacity.

[0039] According to a method for designing a flanged bearing texture based on a particle swarm intelligence algorithm provided by the present invention, the method determines the optimal solution of the group position information corresponding to each particle swarm in the current iteration round based on the target grey correlation degree corresponding to each particle swarm in the historical iteration round and the current iteration round, and determines the optimal solution of the global position information in the current iteration round based on the optimal solution of the group position information corresponding to each particle swarm, including:

[0040] For each particle swarm, the target grey correlation degrees corresponding to the particle swarms in the historical iteration round and the current iteration round are compared, and the position information group of the particle swarm corresponding to the largest target grey correlation degree is taken as the optimal solution of the group position information corresponding to the particle swarm in the current iteration round;

[0041] The target grey correlation degrees corresponding to the optimal solutions of the group position information in the current iteration round are compared, and the optimal solution of the group position information corresponding to the largest target grey correlation degree is determined as the optimal solution of the global position information in the current iteration round.

[0042] The present invention also provides a device for designing flanged bearing texture based on a particle swarm intelligence algorithm, comprising:

[0043] A construction module is used to construct a preset group of particle groups, wherein the number of particles in each of the particle groups is the same as the number of textures in the flanged bearing bush;

[0044] a determination module, configured to determine a type of target design parameters of the texture, and determine, for each particle group, initial position information corresponding to each particle in the particle group based on the type of the target design parameters;

[0045] an iterative module, configured to perform iterative optimization based on the initial position information of each particle in the particle swarm by using a particle swarm intelligence algorithm, and determine an optimal position information group based on the position information groups corresponding to each particle swarm in each iteration round at the end of the iteration;

[0046] A target module is used to determine the target design parameters of each texture in the flanged bearing based on the optimal position information group.

[0047] The present invention also provides an electronic device comprising a memory, a processor and a computer program stored in the memory and runnable on the processor, wherein when the processor executes the program, the method for designing flanged bearing texture based on the particle swarm intelligence algorithm as described above is implemented.

[0048] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for designing a flanged bearing texture based on a particle swarm intelligence algorithm as described above is implemented.

[0049] The present invention also provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-mentioned methods for designing flanged bearing textures based on the particle swarm intelligence algorithm.

[0050] The application provides a flanging bearing texture design method and device based on a particle swarm intelligent algorithm. BRIEF DESCRIPTION OF DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0052] Figure 1 Fig. 1 is one of the flow diagrams of the flanging bearing texture design method based on the particle swarm intelligent algorithm provided by the embodiments of the application;

[0053] Figure 2 Fig. 2 is another of the flow diagrams of the flanging bearing texture design method based on the particle swarm intelligent algorithm provided by the embodiments of the application;

[0054] Figure 3 Fig. 3 is a structural diagram of the flanging bearing texture design device based on the particle swarm intelligent algorithm provided by the embodiments of the application;

[0055] Figure 4 Fig. 4 is a structural diagram of the electronic device provided by the embodiments of the application. DETAILED DESCRIPTION

[0056] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0057] In view of the above problems in the prior art, the present invention provides a method for designing flanged bearing texture based on particle swarm intelligence algorithm. Figure 1 This is one of the flow charts of the method for designing the texture of flanged bearings based on the particle swarm intelligence algorithm provided by the embodiment of the present invention. Figure 1 As shown, the flanging bearing texture design method based on particle swarm intelligence algorithm includes the following steps 110 to 140:

[0058] Step 110: Constructing a preset group of particle groups, wherein the number of particles in each particle group is the same as the number of textures in the flanged bearing.

[0059] Specifically, a predetermined group of particle groups can be constructed, for example, thirty groups of particle groups can be constructed. The number of particles in each particle group is the same as the number of textures in the flanged bearing. The texture in the flanged bearing can be, for example, a groove texture. If the number of groove textures is 10, the number of particles in each particle group is also 10.

[0060] Step 120: Determine the type of the target design parameter of the texture, and for each particle group, determine the initial position information corresponding to each particle in the particle group based on the type of the target design parameter.

[0061] Specifically, the type of target design parameter for the texture can be determined. For example, the type of target design parameter for the groove texture can be determined as the groove width. In order to optimize the design parameters of the flanged bearing texture to obtain the target design parameters, for each particle group, the initial position information corresponding to each particle in the particle group can be determined based on the type of the target design parameter, and the initial position information corresponding to each particle in the particle group can be determined according to a random function. For example, an initial groove width value can be set for each particle based on the groove width using a random function, and the initial groove width value can be determined as the initial position information of each particle.

[0062] Step 130: Based on the initial position information of each particle in the particle swarm, perform iterative optimization through a particle swarm intelligent algorithm. At the end of the iteration, determine the optimal position information group based on the position information groups corresponding to each particle swarm in each iteration round.

[0063] Specifically, the initial position information of each particle in the particle swarm can be used as a basis for iterative optimization by the particle swarm intelligence algorithm. The iteration ends when the number of iterations reaches a preset number of iterations or when the position information sets corresponding to each particle swarm in the iteration process meet a preset iteration end condition. At the end of the iteration, the optimal position information set is determined based on the position information sets corresponding to each particle swarm in each iteration round. The position information set corresponding to a particle swarm is a set of position information of each particle in the particle swarm. The preset number of iterations and the preset iteration end condition can be set as needed, and the embodiments of the present application do not make specific limitations here.

[0064] In one embodiment, Figure 2 is a flowchart of a second embodiment of the flange bush texture design method based on the particle swarm intelligence algorithm provided by the embodiments of the present application, as Figure 2 shown, the initial position information of each particle in the particle swarm is used as a basis for iterative optimization by the particle swarm intelligence algorithm. At the end of the iteration, the optimal position information set is determined based on the position information sets corresponding to each particle swarm in each iteration round. The method comprises the following steps 210 to 240:

[0065] Step 210: In each iteration round, for each particle swarm, the target radial oil film pressure distribution and the target thrust oil film pressure distribution of the flange bush are determined based on the preset design parameters corresponding to each texture, the position information of each particle in the particle swarm corresponding to the current iteration round, the first structure parameters and the operating condition parameters corresponding to the flange bush. In the first iteration round, the position information corresponding to each particle is the initial position information corresponding to each particle.

[0066] Specifically, in each iteration round, for each particle swarm, the target radial oil film pressure distribution and the target thrust oil film pressure distribution of the flange bush can be determined based on the preset design parameters corresponding to each texture, the position information of each particle in the particle swarm corresponding to the current iteration round, the first structure parameters and the operating condition parameters corresponding to the flange bush. The preset design parameters corresponding to each texture are other types of design parameters different from the target design parameters. For example, when the type of the target design parameter corresponding to the groove texture is the groove width, the preset design parameter corresponding to the groove texture can include the groove depth. It is easy to understand that the position information corresponding to each particle in the first iteration round has not changed, so the position information corresponding to each particle in the first iteration round is the initial position information corresponding to each particle.

[0067] In one embodiment, the first structure parameters corresponding to the turned-up bushing include: an eccentricity, a radius gap between a journal and the turned-up bushing, a central section offset angle, a bearing position angle, an inclination angle between the journal and the main bushing, an angle between a journal center line projection and an eccentricity, a journal inclination angle, a bearing inner diameter, and a comprehensive surface roughness; and the operating condition parameters corresponding to the turned-up bushing include: an elastic deformation parameter, a thermal deformation parameter, the journal speed parameter, and an oil speed parameter.

[0068] The target radial oil film pressure distribution and the target thrust oil film pressure distribution of the turned-up bushing are determined based on the preset design parameters corresponding to each of the textures, the position information of each particle in the particle swarm corresponding to a current iteration round, the first structure parameters and the operating condition parameters of the turned-up bushing, and include the following steps S1 to S8:

[0069] S1, determine the first radial oil film pressure distribution and the first thrust oil film pressure distribution of the turned-up bushing based on the preset design parameters corresponding to each of the textures, the position information of each particle in the particle swarm corresponding to a current iteration round, the first structure parameters and the operating condition parameters.

[0070] In one embodiment, the first radial oil film pressure distribution and the first thrust oil film pressure distribution of the turned-up bushing are determined based on the preset design parameters corresponding to each of the textures, the position information of each particle in the particle swarm corresponding to a current iteration round, the first structure parameters and the operating condition parameters, and include:

[0071] Determine the turned-up bushing radial component oil film thickness and the turned-up bushing thrust component oil film thickness based on the preset design parameters corresponding to each of the textures, the position information of each particle in the particle swarm corresponding to a current iteration round, the first structure parameters and the operating condition parameters.

[0072] Construct a first Reynolds equation for solving the radial oil film pressure distribution based on the journal speed parameter and the turned-up bushing radial component oil film thickness, and construct a second Reynolds equation for solving the thrust oil film pressure distribution based on the journal speed parameter and the turned-up bushing thrust component oil film thickness;

[0073] Iteratively solve the first Reynolds equation and the second Reynolds equation by a relaxation iteration algorithm, respectively, until the determined radial oil film pressure distribution and thrust oil film pressure distribution are both converged, and the converged radial oil film pressure distribution is determined as the first radial oil film pressure distribution, and the converged thrust oil film pressure distribution is determined as the first thrust oil film pressure distribution.

[0074] Specifically, the oil film thickness calculation domain of the texture area and the oil film thickness calculation domain of the non-texture area can be determined based on the position information of the current iteration round corresponding to each particle in the particle group. In the oil film thickness calculation domain of the texture area, the oil film thickness of the radial part of the flanged bearing and the oil film thickness of the thrust part of the flanged bearing can be determined based on the preset design parameters, first structural parameters and operating condition parameters corresponding to each texture.

[0075] For example, when the texture in the flanged bearing is a groove texture, the oil film thickness h of the radial part of the flanged bearing in the oil film thickness calculation domain is J It can be determined by the following formula:

[0076]

[0077] Where c represents the radial clearance between the journal and the bearing, ε represents the eccentricity, θ represents the position angle of the bearing, y represents the y-axis position of the bearing, V represents the axial speed of the journal in the journal speed parameter, △t represents the unit time, γ j Indicates the inclination angle between the journal and the main bearing. represents the central section deviation angle, α r Indicates the angle between the journal centerline projection and the eccentricity, δ JE Indicates the elastic deformation parameter of the radial component in the elastic deformation parameter, δ JT Indicates the radial component thermal deformation parameter in the thermal deformation parameter, h Jtex It is easy to understand that in the initial iteration round δ JE and δ JT All are zero.

[0078] In the oil film thickness calculation domain, the oil film thickness h of the flanged bearing thrust part is T It can be determined by the following formula:

[0079] h T =h0+r sinγcosθ+δ TE +δ TT +h Ttex

[0080] Among them, h0 represents the geometric oil film clearance of the thrust part, r represents the radial coordinate of the thrust component, γ represents the journal inclination angle, δ TE Indicates the elastic deformation parameter of the thrust component in the elastic deformation parameter, δ TT Indicates the thermal deformation parameter of the thrust component in the thermal deformation parameter, h Ttex Represents the preset design parameter, which is the thrust component groove depth. It is easy to understand that in the initial iteration round δ TE and δ TT All are zero.

[0081] Furthermore, the first Reynolds equation for solving the radial oil film pressure distribution can be constructed based on the journal speed parameters and the oil film thickness of the radial part of the flanged bearing, and the second Reynolds equation for solving the thrust oil film pressure distribution can be constructed based on the journal speed parameters and the oil film thickness of the thrust part of the flanged bearing.

[0082] For example, the first Reynolds equation may be expressed as follows:

[0083]

[0084] Among them, R0 represents the inner diameter of the bearing, φ θ 、φ z 、φ c 、 They represent the circumferential pressure flow factor, axial pressure flow factor, contact factor and shear flow factor respectively, η represents the viscosity of the lubricating medium, P J represents the radial oil film pressure distribution, U J It represents the circumferential linear speed of the journal in the journal speed parameters, σ represents the comprehensive surface roughness, and z represents the axial position of the bearing.

[0085] The second Reynolds equation can be expressed as the following formula:

[0086]

[0087] Among them, r T Indicates the radial position of the thrust component, P T represents the thrust oil film pressure distribution, φ r represents the radial pressure flow factor, and ω represents the journal circumferential angular velocity in the journal speed parameter.

[0088] The first and second Reynolds equations can further be iteratively solved using a relaxation iterative algorithm until the radial oil film pressure distribution and the thrust oil film pressure distribution determined respectively converge. The converged radial oil film pressure distribution is then determined as the first radial oil film pressure distribution, and the converged thrust oil film pressure distribution is determined as the first thrust oil film pressure distribution. It is readily understood that the boundary conditions during the iterative solution of the first and second Reynolds equations using the relaxation iterative algorithm can be Reynolds boundary conditions. For example, during the iterative solution of the first Reynolds equation using the relaxation iterative algorithm, the radial oil film pressure distribution obtained in the current iteration can be multiplied by the relaxation coefficient to obtain the radial oil film pressure distribution for the next iteration. This calculation is repeated until the obtained radial oil film pressure distribution converges.

[0089] In the above embodiment, the first radial oil film pressure distribution and the first thrust oil film pressure distribution of the flanged bearing are gradually determined based on the preset design parameters corresponding to each texture, the position information of the current iteration round corresponding to each particle in the particle group, the first structural parameters and the operating condition parameters, which lays the foundation for the subsequent determination of the target radial oil film pressure distribution and the target thrust oil film pressure distribution of the flanged bearing.

[0090] S2. Determine new elastic deformation parameters based on the first radial oil film pressure distribution and the first thrust oil film pressure distribution, update the operating condition parameters based on the new elastic deformation parameters, and repeat steps S1 to S2 using the updated operating condition parameters as the operating condition parameters until the determined new elastic deformation parameters converge, and then determine the first operating condition parameters based on the converged new elastic deformation parameters.

[0091] Specifically, an elastic deformation matrix can be constructed based on the first radial oil film pressure distribution and the first thrust oil film pressure distribution, and the elastic deformation matrix can be solved to determine new elastic deformation parameters. Then, the operating condition parameters can be updated based on the new elastic deformation parameters. Steps S1 to S2 are repeated using the updated operating condition parameters as the operating condition parameters until the determined new elastic deformation parameters converge. The first operating condition parameters are then determined based on the converged new elastic deformation parameters. It should be noted that during the first execution of step S1, the boundary conditions for solving the Reynolds equation are Reynolds boundary conditions. From the second execution of step S1, new boundary conditions for solving the first and second Reynolds equations can be further added based on the Reynolds boundary conditions. The added new boundary conditions can be: the oil film pressure at the interface between the radial component and the inference component satisfies the flow pressure continuity condition. The oil film pressure at the interface between the radial component and the inference component satisfies the flow pressure continuity condition and can be determined based on the oil film thickness of the flanged bearing radial component, the oil film thickness of the flanged bearing thrust component, the first radial oil film pressure distribution and the first thrust oil film pressure distribution obtained when step S1 was last executed.

[0092] For example, the new elastic deformation parameter δ E (θ,z) can be determined by the following formula:

[0093]

[0094] Among them, COL is the number of grids in the circumferential direction, ROW is the number of grids in the axial direction, represents the elastic deformation matrix.

[0095] S3, determine the second radial oil film pressure distribution and the second thrust oil film pressure distribution of the flanged bushing based on the preset design parameters corresponding to each of the textures, the position information of the current iteration round corresponding to each particle in the particle group, the first structure parameter and the first operating condition parameter.

[0096] Specifically, the second radial oil film pressure distribution and the second thrust oil film pressure distribution of the flanged bushing can be determined based on the preset design parameters corresponding to each texture, the position information of the current iteration round corresponding to each particle in the particle group, the first structure parameter and the first operating condition parameter. The way of determining the second radial oil film pressure distribution and the second thrust oil film pressure distribution can be the same as that of determining the first radial oil film pressure distribution and the first thrust oil film pressure distribution in step S1.

[0097] S4, determine the first flanged bushing radial component temperature and the first flanged bushing thrust component temperature based on the second radial oil film pressure distribution, the second thrust oil film pressure distribution and the oil speed parameter.

[0098] In one embodiment, the determination of the first flanged bushing radial component temperature and the first flanged bushing thrust component temperature based on the second radial oil film pressure distribution, the second thrust oil film pressure distribution and the oil speed parameter comprises:

[0099] constructing a temperature calculation boundary condition based on the second radial oil film pressure distribution, the second thrust oil film pressure distribution and the oil speed parameter;

[0100] determining a first energy equation and a first heat conduction equation for solving the flanged bushing radial component temperature based on the second radial oil film pressure distribution and the oil speed parameter;

[0101] determining a second energy equation and a second heat conduction equation for solving the flanged bushing thrust component temperature based on the second thrust oil film pressure distribution and the oil speed parameter;

[0102] under the temperature calculation boundary condition, iteratively solving the first energy equation and the first heat conduction equation, and the second energy equation and the second heat conduction equation by a relaxation iteration algorithm until the determined flanged bushing radial component temperature and the flanged bushing thrust component temperature are both converged, and then determining the flanged bushing radial component temperature as the first flanged bushing radial component temperature and the flanged bushing thrust component temperature as the first flanged bushing thrust component temperature.

[0103] Specifically, the temperature calculation boundary condition can be constructed based on the second radial oil film pressure distribution, the second thrust oil film pressure distribution and the oil speed parameter.

[0104] For example, the temperature calculation boundary condition can be shown as the following formula:

[0105]

[0106] Among them, k b Indicates the heat transfer coefficient of the bearing, h e represents the thermal convection coefficient, k l Indicates the heat transfer coefficient of lubricating oil, k J represents the journal heat transfer coefficient, T l Indicates the oil film temperature, T b Indicates the bearing temperature, T ∞ represents the ambient temperature, and n represents the bearing normal direction.

[0107] The first energy equation and the first heat conduction equation for solving the temperature of the radial component of the flanged bearing can also be determined based on the second radial oil film pressure distribution and the lubricating oil velocity parameter.

[0108] For example, the first energy equation may be shown as follows:

[0109]

[0110] Where ρ represents the oil density, C p represents the specific heat capacity at constant pressure, T J represents the radial component temperature of the flanged bearing, α represents the thermal expansion coefficient of the lubricating oil, and v J Indicates the circumferential speed of the radial component lubricating oil in the lubricating oil speed parameter, w J Indicates the axial speed of the radial component lubricating oil in the lubricating oil speed parameter, u J Indicates the radial speed of the radial component oil in the oil speed parameter, P J2 Represents the second radial oil film pressure distribution.

[0111] The first heat conduction equation can be expressed as the following formula:

[0112]

[0113] Where x represents the x-position of the bearing.

[0114] The second energy equation and the second heat conduction equation for solving the temperature of the flanged bearing thrust component can also be determined based on the second thrust oil film pressure distribution and the lubricating oil velocity parameter.

[0115] For example, the second energy equation may be shown as follows:

[0116]

[0117] Among them, U r Indicates the radial velocity of the thrust component oil in the oil velocity parameter, Uθ Indicates the circumferential speed of the thrust component oil in the oil speed parameter, T T Indicates the temperature of the thrust part of the flanged bearing, P T2 Represents the second thrust oil film pressure distribution.

[0118] The second heat conduction equation can be expressed as the following formula:

[0119]

[0120] Furthermore, under the temperature calculation boundary conditions, the first energy equation and the first heat conduction equation can be iteratively solved by a relaxation iterative algorithm, and the second energy equation and the second heat conduction equation can be iteratively solved by a relaxation iterative algorithm, until the flanged bearing radial component temperature and the flanged bearing thrust component temperature determined respectively converge, the flanged bearing radial component temperature is determined as the first flanged bearing radial component temperature, and the flanged bearing thrust component temperature is determined as the first flanged bearing thrust component temperature. Exemplarily, in the process of iteratively solving the first energy equation and the first heat conduction equation by a relaxation iterative algorithm, the flanged bearing radial component temperature obtained in the current iteration round can be multiplied by the relaxation coefficient to obtain the flanged bearing radial component temperature of the next iteration round, and the calculation is repeated until the obtained flanged bearing radial component temperature converges.

[0121] In the above embodiment, the first flanged bearing radial component temperature and the first flanged bearing thrust component temperature are determined based on the second radial oil film pressure distribution, the second thrust oil film pressure distribution and the lubricating oil speed parameter, which lays the foundation for the subsequent determination of the target radial oil film pressure distribution and the target thrust oil film pressure distribution of the flanged bearing.

[0122] S5. Determine new thermal deformation parameters based on the temperature of the first flanged bearing radial component and the temperature of the first flanged bearing thrust component, update the first operating condition parameters based on the new thermal deformation parameters, and repeat steps S1 to S5 using the updated first operating condition parameters as the operating condition parameters until the determined new thermal deformation parameters converge, and determine the second operating condition parameters based on the converged new thermal deformation parameters.

[0123] Specifically, a thermal deformation matrix can be constructed based on the temperature of the radial component of the first flanged bearing and the temperature of the thrust component of the first flanged bearing, the thermal deformation matrix can be solved to determine new thermal deformation parameters, the first operating condition parameters can be updated based on the new thermal deformation parameters, and steps S1 to S5 can be repeated using the updated first operating condition parameters as the operating condition parameters until the determined new thermal deformation parameters converge, and the second operating condition parameters are determined based on the converged new thermal deformation parameters.

[0124] For example, the new thermal deformation parameter can be δ T (θ,z) is determined by the following formula:

[0125]

[0126] in, represents the thermal deformation matrix.

[0127] S6. Determine the third radial oil film pressure distribution and the third thrust oil film pressure distribution of the flanged bearing based on the preset design parameters corresponding to each of the textures, the position information of the current iteration round corresponding to each particle in the particle group, the first structural parameters and the second operating condition parameters.

[0128] Specifically, the third radial oil film pressure distribution and the third thrust oil film pressure distribution of the flanged bearing can be determined based on the preset design parameters corresponding to each texture, the position information of the current iteration round corresponding to each particle in the particle group, the first structural parameter and the second operating condition parameter. The method of determining the third radial oil film pressure distribution and the third thrust oil film pressure distribution can be the same as the method of determining the first radial oil film pressure distribution and the first thrust oil film pressure distribution in step S1.

[0129] S7. Determine the radial component bearing capacity of the flanged bearing based on the position information of the current iteration round corresponding to each particle in the particle group, the first structural parameter, and the third radial oil film pressure distribution.

[0130] Specifically, the calculation domain can be determined based on the position information of the current iteration round corresponding to each particle in the particle group, and then the radial component bearing capacity of the flanged bearing can be determined in the calculation domain based on the first structural parameter and the third radial oil film pressure distribution.

[0131] For example, the radial component load capacity W of the flanged bearing can be determined by the following formula 1: J :

[0132] W J =∫∫ Ω P J3 R0dθdz

[0133] Where Ω represents the computational domain, P J3 Represents the third radial oil film pressure distribution.

[0134] S8. When the radial component bearing capacity is not equal to the preset load, a new eccentricity is determined, the first structural parameter is updated based on the new eccentricity, and steps S1 to S8 are repeated using the updated first structural parameter as the first structural parameter, until the radial component bearing capacity is equal to the preset load, the third radial oil film pressure distribution corresponding to the radial component bearing capacity is determined as the target radial oil film pressure distribution of the flanged bearing, and the third thrust oil film pressure distribution corresponding to the radial component bearing capacity is determined as the target thrust oil film pressure distribution of the flanged bearing.

[0135] Specifically, a new eccentricity can be determined when the radial component load capacity is not equal to the preset load, and the first structural parameter can be updated based on the new eccentricity. Steps S1 to S8 are repeated using the updated first structural parameter as the first structural parameter until the radial component load capacity is equal to the preset load, and the third radial oil film pressure distribution corresponding to the radial component load capacity is determined as the target radial oil film pressure distribution of the flanged bearing, and the third thrust oil film pressure distribution corresponding to the radial component load capacity is determined as the target thrust oil film pressure distribution of the flanged bearing. The preset load can be pre-set as needed, and this is not specifically limited in this embodiment of the present invention.

[0136] In the above embodiment, the target radial oil film pressure distribution and the target thrust oil film pressure distribution of the flanged bearing are determined based on steps S1 to S8, which lays the foundation for the subsequent determination of the target grey correlation degree corresponding to each particle group in the current iteration round.

[0137] Step 220: For each of the particle groups, based on the position information group of the current iteration round corresponding to the particle group, the second structural parameter and journal speed parameter corresponding to the flanged bearing, the target radial oil film pressure distribution and the target thrust oil film pressure distribution of the flanged bearing, determine the target grey correlation degree corresponding to the particle group in the current iteration round.

[0138] In one embodiment, the second structural parameter includes: a position angle of the bearing and an inner diameter of the bearing;

[0139] The determining of the target grey relational degree corresponding to the particle swarm in the current iteration round based on the position information group corresponding to the particle swarm in the current iteration round, the second structural parameter and the journal speed parameter corresponding to the flanged bearing, and the target radial oil film pressure distribution and the target thrust oil film pressure distribution of the flanged bearing comprises:

[0140] Determining the radial component friction coefficient and thrust component bearing capacity of the flanged bearing in the current iteration round based on the position information group of the current iteration round corresponding to the particle swarm, the second structural parameter, the journal speed parameter, the target radial oil film pressure distribution, and the target thrust oil film pressure distribution;

[0141] A target grey correlation degree corresponding to the particle group in the current iteration round is determined based on the radial component friction coefficient and the thrust component bearing capacity, where the target grey correlation degree represents a correlation degree between the radial component friction coefficient and the thrust component bearing capacity.

[0142] Specifically, for each particle group, the calculation domain can be determined based on the position information group of the current iteration round corresponding to the particle group, and then the radial component friction coefficient and thrust component bearing capacity of the flanged bearing in the current iteration round can be determined in the calculation domain based on the second structural parameter, the target radial oil film pressure distribution and the target thrust oil film pressure distribution.

[0143] For example, the radial component friction coefficient μ can be determined by the following formula:

[0144]

[0145] Where Ω represents the computational domain, P Jm represents the target radial oil film pressure distribution, h Jm Indicates the oil film thickness of the radial component of the flanged bearing corresponding to the target radial oil film pressure distribution.

[0146] Thrust component bearing capacity W T It can be determined by the following formula:

[0147] W T =∫∫ Ω P Tm rdθdr

[0148] Among them, P Tm Represents the target thrust oil film pressure distribution.

[0149] Furthermore, the target grey correlation degree corresponding to the particle swarm in the current iteration round can be determined based on the radial component friction coefficient and the thrust component bearing capacity, and the target grey correlation degree represents the correlation degree between the radial component friction coefficient and the thrust component bearing capacity.

[0150] For example, the target grey relational degree γ i It can be determined by the following formula:

[0151]

[0152] Among them, W k Represented by entropy E kDetermine the entropy weight of the kth indicator. W1 represents the entropy weight corresponding to the friction coefficient of the radial component, W2 represents the entropy weight corresponding to the bearing capacity of the thrust component, and i represents the number corresponding to the particle group. The above parameters can be determined by the following formula:

[0153]

[0154]

[0155] Among them, ξ i (k) represents the grey relational coefficient, f kj Determine the entropy E k The intermediate quantity, n represents the evaluation index, specifically 2, n p Indicates the evaluation object, the number of specific particle swarms, E k represents the entropy of the kth indicator, represents the standardized evaluation index, represents the optimal evaluation index after standardization, represents the radial component friction coefficient, Indicates the bearing capacity of the thrust component.

[0156] In the above embodiment, for each particle group, the radial component friction coefficient and the thrust component bearing capacity are determined based on the position information group of the current iteration round corresponding to the particle group, the second structural parameter corresponding to the flanged bearing, the target radial oil film pressure distribution of the flanged bearing, and the target thrust oil film pressure distribution. Then, the target gray correlation degree corresponding to the particle group of the current iteration round is determined, and the particle group is optimized based on the target gray correlation degree. Since the radial component friction coefficient of the flanged bearing and the thrust component bearing capacity are taken into consideration, the texture can be designed in a targeted manner according to the radial friction reduction requirements and axial bearing requirements.

[0157] Step 230: Based on the target grey correlation degrees corresponding to the particle swarms in the historical iteration rounds and the current iteration round, determine the optimal solutions of the group position information corresponding to the particle swarms in the current iteration round, and determine the optimal solution of the global position information in the current iteration round based on the optimal solutions of the group position information corresponding to the particle swarms.

[0158] In one embodiment, the method of determining the optimal solution of the group position information corresponding to each particle swarm in the current iteration round based on the target grey correlation degree corresponding to each particle swarm in the historical iteration round and the current iteration round, and determining the optimal solution of the global position information in the current iteration round based on the optimal solution of the group position information corresponding to each particle swarm, includes:

[0159] For each particle swarm, the target grey correlation degrees corresponding to the particle swarms in the historical iteration round and the current iteration round are compared, and the position information group of the particle swarm corresponding to the largest target grey correlation degree is taken as the optimal solution of the group position information corresponding to the particle swarm in the current iteration round;

[0160] The target grey correlation degrees corresponding to the optimal solutions of the group position information in the current iteration round are compared, and the optimal solution of the group position information corresponding to the largest target grey correlation degree is determined as the optimal solution of the global position information in the current iteration round.

[0161] Specifically, for each particle swarm, the target gray correlation degrees corresponding to the particle swarm in the historical iteration round and the current iteration round can be compared, and the position information group of the particle swarm corresponding to the largest target gray correlation degree is used as the optimal solution for the group position information corresponding to the particle swarm in the current iteration round. For example, for particle swarm A, the current iteration round is the tenth iteration round. The target gray correlation degrees corresponding to particle swarm A in the ten iteration rounds are compared. If it is determined that the target gray correlation degree corresponding to particle swarm A in the fifth iteration round is the largest target gray correlation degree, the position information group of particle swarm A in the fifth iteration round is used as the optimal solution for the group position information corresponding to particle swarm A in the tenth iteration round.

[0162] Furthermore, the target grey correlation degrees corresponding to the optimal solutions of the group position information in the current iteration round can be compared, and the optimal solution of the group position information corresponding to the largest target grey correlation degree can be determined as the optimal solution of the global position information in the current iteration round. For example, the target grey correlation degrees corresponding to the optimal solution of the group position information of particle swarm A, the target grey correlation degrees corresponding to the optimal solution of the group position information of particle swarm B, and the target grey correlation degrees corresponding to the optimal solution of the group position information of particle swarm C in the current iteration round are compared, and if the target grey correlation degree corresponding to the optimal solution of the group position information of particle swarm C is determined to be the largest target grey correlation degree, then the optimal solution of the group position information of particle swarm C can be determined as the optimal solution of the global position information in the current iteration round.

[0163] In the above embodiment, the optimal solution of the group position information corresponding to each particle swarm in the current iteration round is determined based on the target gray correlation degree corresponding to each particle swarm in the historical iteration round and the current iteration round, and the optimal solution of the global position information in the current iteration round is determined based on the optimal solution of the group position information corresponding to each particle swarm, which lays the foundation for determining the optimal position information group.

[0164] Step 240: At the end of the iteration, the optimal global position information solution corresponding to the last iteration round is determined as the optimal position information group;

[0165] Wherein, for each particle swarm, the position information group corresponding to the current iteration round of the particle swarm is determined based on the position information group corresponding to the previous iteration round of the particle swarm, the group position information optimal solution and the global position information optimal solution.

[0166] Specifically, at the end of the iteration, the global position information optimal solution corresponding to the last iteration round can be determined as the optimal position information group. For each particle swarm, the position information group corresponding to the current iteration round of the particle swarm is determined based on the position information group corresponding to the previous iteration round of the particle swarm, the group position information optimal solution, and the global position information optimal solution.

[0167] For example, during the iteration process, the velocity vector and position vector of each particle in the particle swarm can be determined by the following formula:

[0168]

[0169]

[0170] Represents the velocity vector of the particle in the current iteration round, Represents the position vector of the particle in the current iteration round, represents the velocity vector of the particle in the last iteration round, represents the position vector of the particle in the last iteration, i represents the number of the particle group, d represents the number of the particle, c1 and c2 represent learning factors, r1 and r2 are random numbers between 0 and 1, Indicates the optimal solution of the group position information of the previous iteration round corresponding to particle swarm i, gbest F represents the optimal solution of global position information in the previous iteration round, ω i represents the inertia weight, where the inertia weight ω i It can be determined by the following formula:

[0171]

[0172] Among them, ω max represents the maximum inertia weight, ω min represents the minimum inertia weight, F represents the number of previous iterations, and F max Indicates the preset number of iterations.

[0173] Step 140: Determine target design parameters for each texture in the flanged bearing based on the optimal position information group.

[0174] Specifically, the target design parameters of each texture in the flanged bearing shell can be determined based on the optimal position information group.

[0175] The present invention provides a method for designing the texture of a flanged bearing based on a particle swarm intelligent algorithm. First, a preset group of particle swarms is constructed, and the number of particles in each particle swarm is the same as the number of textures in the flanged bearing. Then, the type of the target design parameter of the texture is determined. For each particle swarm, the initial position information corresponding to each particle in the particle swarm is determined based on the type of the target design parameter. Then, based on the initial position information of each particle in the particle swarm, an iterative optimization is performed using a particle swarm intelligent algorithm. At the end of the iteration, the optimal position information group is determined based on the position information group corresponding to each particle swarm in each iteration round. Finally, the target design parameters of each texture in the flanged bearing are determined based on the optimal position information group. The technical solution of the present invention determines the initial position information corresponding to each particle in the particle swarm based on the type of the target design parameter, further uses a particle swarm intelligent algorithm to iteratively optimize the initial position information of each particle in the particle swarm, determines the optimal position information group, and finally determines the target design parameters of each texture in the flanged bearing based on the optimal position information group, so as to obtain the global optimal solution of the design parameters of the flanged bearing texture.

[0176] The following describes the flanged bearing texture design device based on the particle swarm intelligence algorithm provided by the present invention. The flanged bearing texture design device based on the particle swarm intelligence algorithm described below and the flanged bearing texture design method based on the particle swarm intelligence algorithm described above can be referenced to each other.

[0177] Figure 3 Schematic diagram of the structure of the flanged bearing texture design device based on the particle swarm intelligence algorithm provided by the embodiment of the present invention. Figure 3 As shown, the flanged bearing texture design device 300 based on the particle swarm intelligence algorithm includes:

[0178] A construction module 310 is used to construct a preset group of particle groups, wherein the number of particles in each of the particle groups is the same as the number of textures in the flanged bearing shell;

[0179] a determination module 320 for determining a type of target design parameter for the texture, and determining, for each particle group, initial position information corresponding to each particle in the particle group based on the type of the target design parameter;

[0180] An iterative module 330 is configured to perform iterative optimization based on the initial position information of each particle in the particle swarm using a particle swarm intelligence algorithm, and at the end of the iteration, determine an optimal position information group based on the position information groups corresponding to each particle swarm in each iteration round;

[0181] The target module 340 is used to determine the target design parameters of each texture in the flanged bearing based on the optimal position information group.

[0182] In one embodiment, the iteration module 330 is specifically configured to:

[0183] In each iteration round, for each particle group, a target radial oil film pressure distribution and a target thrust oil film pressure distribution of the flanged bearing are determined based on preset design parameters corresponding to each texture, position information of each particle in the particle group corresponding to the current iteration round, and first structural parameters and operating condition parameters corresponding to the flanged bearing; wherein the position information corresponding to each particle in the first iteration round is the initial position information corresponding to each particle;

[0184] For each particle group, determining a target grey relational degree corresponding to the particle group in the current iteration round based on the position information group corresponding to the particle group in the current iteration round, the second structural parameter and the journal speed parameter corresponding to the flanged bearing, and the target radial oil film pressure distribution and the target thrust oil film pressure distribution of the flanged bearing;

[0185] Based on the target grey correlation degrees corresponding to the particle swarms in the historical iteration rounds and the current iteration round, the optimal solutions of the group position information corresponding to the particle swarms in the current iteration round are determined, and the optimal solution of the global position information in the current iteration round is determined according to the optimal solutions of the group position information corresponding to the particle swarms;

[0186] At the end of the iteration, the optimal solution of the global position information corresponding to the last iteration round is determined as the optimal position information group;

[0187] Wherein, for each particle swarm, the position information group corresponding to the current iteration round of the particle swarm is determined based on the position information group corresponding to the previous iteration round of the particle swarm, the group position information optimal solution and the global position information optimal solution.

[0188] In one embodiment, the first structural parameters corresponding to the flanged bearing include: eccentricity, radial clearance between the journal and the bearing, central section offset angle, bearing position angle, inclination angle between the journal and the main bearing, angle between the journal centerline projection and the eccentricity, journal inclination angle, bearing inner diameter, and comprehensive surface roughness; the operating condition parameters corresponding to the flanged bearing include: elastic deformation parameter, thermal deformation parameter, the journal speed parameter, and lubricating oil speed parameter. The iteration module 330 is further specifically configured to:

[0189] S1, determining a first radial oil film pressure distribution and a first thrust oil film pressure distribution of the flanged bearing based on preset design parameters corresponding to each texture, position information of each particle in the particle group corresponding to the current iteration round, the first structural parameter, and the operating condition parameter;

[0190] S2. Determining a new elastic deformation parameter based on the first radial oil film pressure distribution and the first thrust oil film pressure distribution, updating the operating condition parameter based on the new elastic deformation parameter, and repeatedly performing steps S1 to S2 using the updated operating condition parameter as the operating condition parameter until the determined new elastic deformation parameter converges, and then determining the first operating condition parameter based on the converged new elastic deformation parameter;

[0191] S3, determining a second radial oil film pressure distribution and a second thrust oil film pressure distribution of the flanged bearing based on preset design parameters corresponding to each texture, position information of each particle in the particle swarm corresponding to the current iteration round, the first structural parameter, and the first operating condition parameter;

[0192] S4. Determine a first flanged bearing radial component temperature and a first flanged bearing thrust component temperature based on the second radial oil film pressure distribution, the second thrust oil film pressure distribution, and the lubricating oil velocity parameter;

[0193] S5. Determine a new thermal deformation parameter based on the temperature of the first flanged bearing radial component and the temperature of the first flanged bearing thrust component, update the first operating condition parameter based on the new thermal deformation parameter, and repeatedly perform steps S1 to S5 using the updated first operating condition parameter as the operating condition parameter until the determined new thermal deformation parameter converges, and then determine a second operating condition parameter based on the converged new thermal deformation parameter;

[0194] S6. Determine a third radial oil film pressure distribution and a third thrust oil film pressure distribution of the flanged bearing based on preset design parameters corresponding to each texture, position information of each particle in the particle swarm corresponding to the current iteration round, the first structural parameter, and the second operating condition parameter;

[0195] S7, determining the radial component bearing capacity of the flanged bearing based on the position information of the current iteration round corresponding to each particle in the particle swarm, the first structural parameter, and the third radial oil film pressure distribution;

[0196] S8. When the radial component bearing capacity is not equal to the preset load, a new eccentricity is determined, the first structural parameter is updated based on the new eccentricity, and steps S1 to S8 are repeated using the updated first structural parameter as the first structural parameter, until the radial component bearing capacity is equal to the preset load, the third radial oil film pressure distribution corresponding to the radial component bearing capacity is determined as the target radial oil film pressure distribution of the flanged bearing, and the third thrust oil film pressure distribution corresponding to the radial component bearing capacity is determined as the target thrust oil film pressure distribution of the flanged bearing.

[0197] In one embodiment, the iteration module 330 is further configured to:

[0198] determine the oil film thickness of the radial component of the flanged bushing and the oil film thickness of the thrust component of the flanged bushing based on the preset design parameters corresponding to each of the textures, the position information of each particle in the particle swarm corresponding to the current iteration round, the first structure parameter, and the operating condition parameter;

[0199] construct a first Reynolds equation for solving the radial oil film pressure distribution based on the journal speed parameter and the oil film thickness of the radial component of the flanged bushing, and construct a second Reynolds equation for solving the thrust oil film pressure distribution based on the journal speed parameter and the oil film thickness of the thrust component of the flanged bushing;

[0200] iteratively solve the first Reynolds equation and the second Reynolds equation by a relaxation iteration algorithm, respectively, until the determined radial oil film pressure distribution and thrust oil film pressure distribution, respectively, are converged, and determine the converged radial oil film pressure distribution as the first radial oil film pressure distribution and determine the converged thrust oil film pressure distribution as the first thrust oil film pressure distribution.

[0201] In one embodiment, the iteration module 330 is further configured to:

[0202] construct a temperature calculation boundary condition based on the second radial oil film pressure distribution, the second thrust oil film pressure distribution, and the oil speed parameter;

[0203] determine a first energy equation and a first heat conduction equation for solving the temperature of the radial component of the flanged bushing based on the second radial oil film pressure distribution and the oil speed parameter;

[0204] determine a second energy equation and a second heat conduction equation for solving the temperature of the thrust component of the flanged bushing based on the second thrust oil film pressure distribution and the oil speed parameter;

[0205] under the temperature calculation boundary condition, iteratively solve the first energy equation and the first heat conduction equation, and the second energy equation and the second heat conduction equation by a relaxation iteration algorithm, respectively, until the determined temperature of the radial component of the flanged bushing and the temperature of the thrust component of the flanged bushing are both converged, and determine the temperature of the radial component of the flanged bushing as the first temperature of the radial component of the flanged bushing and determine the temperature of the thrust component of the flanged bushing as the first temperature of the thrust component of the flanged bushing.

[0206] In one embodiment, the second structure parameter includes the position angle of the bearing and the inner diameter of the bearing, and the iteration module 330 is further configured to:

[0207] Determining the radial component friction coefficient and thrust component bearing capacity of the flanged bearing in the current iteration round based on the position information group of the current iteration round corresponding to the particle swarm, the second structural parameter, the journal speed parameter, the target radial oil film pressure distribution, and the target thrust oil film pressure distribution;

[0208] A target grey correlation degree corresponding to the particle group in the current iteration round is determined based on the radial component friction coefficient and the thrust component bearing capacity, where the target grey correlation degree represents a correlation degree between the radial component friction coefficient and the thrust component bearing capacity.

[0209] In one embodiment, the iteration module 330 is further configured to:

[0210] For each particle swarm, the target grey correlation degrees corresponding to the particle swarms in the historical iteration round and the current iteration round are compared, and the position information group of the particle swarm corresponding to the largest target grey correlation degree is taken as the optimal solution of the group position information corresponding to the particle swarm in the current iteration round;

[0211] The target grey correlation degrees corresponding to the optimal solutions of the group position information in the current iteration round are compared, and the optimal solution of the group position information corresponding to the largest target grey correlation degree is determined as the optimal solution of the global position information in the current iteration round.

[0212] The present invention provides a device for designing flanged bearing textures based on a particle swarm intelligent algorithm. First, a preset group of particle swarms is constructed, and the number of particles in each particle swarm is the same as the number of textures in the flanged bearing. Then, the type of target design parameters of the texture is determined. For each particle swarm, the initial position information corresponding to each particle in the particle swarm is determined based on the type of target design parameters. Then, based on the initial position information of each particle in the particle swarm, an iterative optimization is performed using a particle swarm intelligent algorithm. At the end of the iteration, the optimal position information group is determined based on the position information group corresponding to each particle swarm in each iteration round. Finally, the target design parameters of each texture in the flanged bearing are determined based on the optimal position information group. The technical solution of the present invention determines the initial position information corresponding to each particle in the particle swarm based on the type of target design parameters, further uses a particle swarm intelligent algorithm to iteratively optimize the initial position information of each particle in the particle swarm, determines the optimal position information group, and finally determines the target design parameters of each texture in the flanged bearing based on the optimal position information group, thereby obtaining a global optimal solution for the design parameters of the flanged bearing texture.

[0213] Figure 4 An example of a physical structure diagram of an electronic device is shown below. Figure 4As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other via the communication bus 440. The processor 410 may call the logic instructions in the memory 430 to execute the flanging bearing texture design method based on the particle swarm intelligence algorithm, which includes:

[0214] Constructing a preset group of particle groups, wherein the number of particles in each of the particle groups is the same as the number of textures in the flanged bearing bush;

[0215] determining a type of target design parameter for the texture, and determining, for each particle group, initial position information corresponding to each particle in the particle group based on the type of the target design parameter;

[0216] Based on the initial position information of each particle in the particle swarm, an iterative optimization is performed using a particle swarm intelligent algorithm, and at the end of the iteration, an optimal position information group is determined based on the position information groups corresponding to each particle swarm in each iteration round;

[0217] Target design parameters of each texture in the flanged bearing are determined based on the optimal position information group.

[0218] In addition, the logic instructions in the above-mentioned memory 430 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0219] On the other hand, the present invention further provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the flanged bearing texture design method based on the particle swarm intelligence algorithm provided by the above methods, which includes:

[0220] Constructing a preset group of particle groups, wherein the number of particles in each of the particle groups is the same as the number of textures in the flanged bearing bush;

[0221] determining a type of target design parameter for the texture, and determining, for each particle group, initial position information corresponding to each particle in the particle group based on the type of the target design parameter;

[0222] Based on the initial position information of each particle in the particle swarm, an iterative optimization is performed using a particle swarm intelligent algorithm, and at the end of the iteration, an optimal position information group is determined based on the position information groups corresponding to each particle swarm in each iteration round;

[0223] Target design parameters of each texture in the flanged bearing are determined based on the optimal position information group.

[0224] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for designing a flanged bearing texture based on a particle swarm intelligence algorithm provided by the above methods is implemented. The method includes:

[0225] Constructing a preset group of particle groups, wherein the number of particles in each of the particle groups is the same as the number of textures in the flanged bearing bush;

[0226] determining a type of target design parameter for the texture, and determining, for each particle group, initial position information corresponding to each particle in the particle group based on the type of the target design parameter;

[0227] Based on the initial position information of each particle in the particle swarm, an iterative optimization is performed using a particle swarm intelligent algorithm, and at the end of the iteration, an optimal position information group is determined based on the position information groups corresponding to each particle swarm in each iteration round;

[0228] Target design parameters of each texture in the flanged bearing are determined based on the optimal position information group.

[0229] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0230] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0231] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for designing flanged bearing texture based on particle swarm intelligence algorithm, characterized in that: include: Constructing a preset group of particle groups, wherein the number of particles in each of the particle groups is the same as the number of textures in the flanged bearing bush; determining a type of target design parameter for the texture, and determining, for each particle group, initial position information corresponding to each particle in the particle group based on the type of the target design parameter; Based on the initial position information of each particle in the particle swarm, an iterative optimization is performed using a particle swarm intelligent algorithm, and at the end of the iteration, an optimal position information group is determined based on the position information groups corresponding to each particle swarm in each iteration round; Target design parameters of each texture in the flanged bearing are determined based on the optimal position information group.

2. The method for designing flanged bearing texture based on particle swarm intelligence algorithm according to claim 1, characterized in that: The iterative optimization is performed based on the initial position information of each particle in the particle swarm by a particle swarm intelligent algorithm, and at the end of the iteration, the optimal position information group is determined based on the position information groups corresponding to each particle swarm in each iteration round, including: In each iteration round, for each particle group, a target radial oil film pressure distribution and a target thrust oil film pressure distribution of the flanged bearing are determined based on preset design parameters corresponding to each texture, position information of each particle in the particle group corresponding to the current iteration round, and first structural parameters and operating condition parameters corresponding to the flanged bearing; wherein the position information corresponding to each particle in the first iteration round is the initial position information corresponding to each particle; For each particle group, determining a target grey relational degree corresponding to the particle group in the current iteration round based on the position information group corresponding to the particle group in the current iteration round, the second structural parameter and the journal speed parameter corresponding to the flanged bearing, and the target radial oil film pressure distribution and the target thrust oil film pressure distribution of the flanged bearing; Based on the target grey correlation degrees corresponding to the particle swarms in the historical iteration rounds and the current iteration round, the optimal solutions of the group position information corresponding to the particle swarms in the current iteration round are determined, and the optimal solution of the global position information in the current iteration round is determined according to the optimal solutions of the group position information corresponding to the particle swarms; At the end of the iteration, the optimal solution of the global position information corresponding to the last iteration round is determined as the optimal position information group; For each particle swarm, the position information group corresponding to the current iteration round of the particle swarm is determined based on the position information group corresponding to the previous iteration round of the particle swarm, the group position information optimal solution and the global position information optimal solution.

3. The method for designing flanged bearing texture based on particle swarm intelligence algorithm according to claim 2, characterized in that: The first structural parameters corresponding to the flanged bearing include: eccentricity, radial clearance between the journal and the bearing, central section offset angle, bearing position angle, tilt angle between the journal and the main bearing, angle between the journal centerline projection and eccentricity, journal tilt angle, bearing inner diameter, and comprehensive surface roughness; the operating condition parameters corresponding to the flanged bearing include: elastic deformation parameter, thermal deformation parameter, journal speed parameter, and lubricating oil speed parameter; The determining of a target radial oil film pressure distribution and a target thrust oil film pressure distribution of the flanged bearing based on preset design parameters corresponding to each texture, position information of a current iteration round corresponding to each particle in the particle group, and first structural parameters and operating condition parameters corresponding to the flanged bearing comprises: S1, determining a first radial oil film pressure distribution and a first thrust oil film pressure distribution of the flanged bearing based on preset design parameters corresponding to each texture, position information of each particle in the particle swarm corresponding to the current iteration round, the first structural parameter, and the operating condition parameter; S2. Determining a new elastic deformation parameter based on the first radial oil film pressure distribution and the first thrust oil film pressure distribution, updating the operating condition parameter based on the new elastic deformation parameter, and repeatedly performing steps S1 to S2 using the updated operating condition parameter as the operating condition parameter until the determined new elastic deformation parameter converges, and then determining the first operating condition parameter based on the converged new elastic deformation parameter; S3, determining a second radial oil film pressure distribution and a second thrust oil film pressure distribution of the flanged bearing based on preset design parameters corresponding to each texture, position information of each particle in the particle swarm corresponding to the current iteration round, the first structural parameter, and the first operating condition parameter; S4, determining a first flanged bearing radial component temperature and a first flanged bearing thrust component temperature based on the second radial oil film pressure distribution, the second thrust oil film pressure distribution, and the lubricating oil velocity parameter; S5. Determine a new thermal deformation parameter based on the temperature of the first flanged bearing radial component and the temperature of the first flanged bearing thrust component, update the first operating condition parameter based on the new thermal deformation parameter, and repeatedly perform steps S1 to S5 using the updated first operating condition parameter as the operating condition parameter until the determined new thermal deformation parameter converges, and then determine a second operating condition parameter based on the converged new thermal deformation parameter; S6. Determining a third radial oil film pressure distribution and a third thrust oil film pressure distribution of the flanged bearing based on preset design parameters corresponding to each texture, position information of each particle in the particle swarm corresponding to the current iteration round, the first structural parameter, and the second operating condition parameter; S7, determining the radial component bearing capacity of the flanged bearing based on the position information of the current iteration round corresponding to each particle in the particle swarm, the first structural parameter, and the third radial oil film pressure distribution; S8. When the radial component bearing capacity is not equal to the preset load, a new eccentricity is determined, the first structural parameter is updated based on the new eccentricity, and steps S1 to S8 are repeated using the updated first structural parameter as the first structural parameter, until the radial component bearing capacity is equal to the preset load, the third radial oil film pressure distribution corresponding to the radial component bearing capacity is determined as the target radial oil film pressure distribution of the flanged bearing, and the third thrust oil film pressure distribution corresponding to the radial component bearing capacity is determined as the target thrust oil film pressure distribution of the flanged bearing.

4. The method for designing flanged bearing texture based on particle swarm intelligence algorithm according to claim 3, characterized in that: The determining of a first radial oil film pressure distribution and a first thrust oil film pressure distribution of the flanged bearing based on preset design parameters corresponding to each texture, position information of a current iteration round corresponding to each particle in the particle group, the first structural parameter, and the operating condition parameter includes: Determine the oil film thickness of the flanged bearing radial component and the oil film thickness of the flanged bearing thrust component based on the preset design parameters corresponding to each texture, the position information of the current iteration round corresponding to each particle in the particle group, the first structural parameter, and the operating condition parameter; Constructing a first Reynolds equation for solving radial oil film pressure distribution based on the journal speed parameter and the oil film thickness of the flanged bearing radial component, and constructing a second Reynolds equation for solving thrust oil film pressure distribution based on the journal speed parameter and the oil film thickness of the flanged bearing thrust component; The first Reynolds equation and the second Reynolds equation are iteratively solved respectively by a relaxation iterative algorithm until the respectively determined radial oil film pressure distribution and thrust oil film pressure distribution converge, and the converged radial oil film pressure distribution is determined as the first radial oil film pressure distribution, and the converged thrust oil film pressure distribution is determined as the first thrust oil film pressure distribution.

5. The method for designing flanged bearing texture based on particle swarm intelligence algorithm according to claim 3, characterized in that: The determining of the first flanged bearing radial component temperature and the first flanged bearing thrust component temperature based on the second radial oil film pressure distribution, the second thrust oil film pressure distribution, and the lubricating oil velocity parameter comprises: establishing a temperature calculation boundary condition based on the second radial oil film pressure distribution, the second thrust oil film pressure distribution, and the lubricating oil speed parameter; Determine and solve a first energy equation and a first heat conduction equation for the temperature of the radial component of the flanged bearing based on the second radial oil film pressure distribution and the lubricating oil velocity parameter; Determining and solving a second energy equation and a second heat conduction equation for the temperature of the flanged bearing thrust component based on the second thrust oil film pressure distribution and the lubricating oil velocity parameter; Under the temperature calculation boundary conditions, the first energy equation and the first heat conduction equation, as well as the second energy equation and the second heat conduction equation are iteratively solved respectively by a relaxation iterative algorithm until the respectively determined flanged bearing radial component temperature and flanged bearing thrust component temperature converge, and the flanged bearing radial component temperature is determined as the first flanged bearing radial component temperature, and the flanged bearing thrust component temperature is determined as the first flanged bearing thrust component temperature.

6. The method for designing flanged bearing texture based on particle swarm intelligence algorithm according to any one of claims 2 to 5, characterized in that: The second structural parameters include: a position angle of the bearing and an inner diameter of the bearing; The determining of the target grey relational degree corresponding to the particle swarm in the current iteration round based on the position information group corresponding to the particle swarm in the current iteration round, the second structural parameter and the journal speed parameter corresponding to the flanged bearing, and the target radial oil film pressure distribution and the target thrust oil film pressure distribution of the flanged bearing comprises: Determining the radial component friction coefficient and thrust component bearing capacity of the flanged bearing in the current iteration round based on the position information group of the current iteration round corresponding to the particle swarm, the second structural parameter, the journal speed parameter, the target radial oil film pressure distribution, and the target thrust oil film pressure distribution; A target grey correlation degree corresponding to the particle group in the current iteration round is determined based on the radial component friction coefficient and the thrust component bearing capacity, where the target grey correlation degree represents a correlation degree between the radial component friction coefficient and the thrust component bearing capacity.

7. The method for designing flanged bearing texture based on particle swarm intelligence algorithm according to any one of claims 2 to 5, characterized in that: The method of determining the optimal solution of the group position information corresponding to each particle swarm in the current iteration round based on the target grey correlation degree corresponding to each particle swarm in the historical iteration round and the current iteration round, and determining the optimal solution of the global position information in the current iteration round based on the optimal solution of the group position information corresponding to each particle swarm, includes: For each particle swarm, the target grey correlation degrees corresponding to the particle swarms in the historical iteration round and the current iteration round are compared, and the position information group of the particle swarm corresponding to the largest target grey correlation degree is taken as the optimal solution of the group position information corresponding to the particle swarm in the current iteration round; The target grey correlation degrees corresponding to the optimal solutions of the group position information in the current iteration round are compared, and the optimal solution of the group position information corresponding to the largest target grey correlation degree is determined as the optimal solution of the global position information in the current iteration round.

8. A device for designing flanged bearing texture based on particle swarm intelligence algorithm, characterized in that: include: A construction module is used to construct a preset group of particle groups, wherein the number of particles in each of the particle groups is the same as the number of textures in the flanged bearing bush; a determination module, configured to determine a type of target design parameters of the texture, and determine, for each particle group, initial position information corresponding to each particle in the particle group based on the type of the target design parameters; an iterative module, configured to perform iterative optimization based on the initial position information of each particle in the particle swarm by using a particle swarm intelligence algorithm, and determine an optimal position information group based on the position information groups corresponding to each particle swarm in each iteration round at the end of the iteration; A target module is used to determine the target design parameters of each texture in the flanged bearing based on the optimal position information group.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method for designing flanged bearing texture based on particle swarm intelligence algorithm as described in any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for designing flanged bearing texture based on particle swarm intelligence algorithm as described in any one of claims 1 to 7 is implemented.

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