Linear contact elastohydrodynamic lubrication surface texture layout optimization method and system fusing global optimization strategy and local optimization strategy

Abstract

The invention discloses a method and a system for optimizing a line contact elastohydrodynamic lubrication surface texture layout by fusing global optimization and local optimization strategies, belongs to the field of mechanical engineering tribology and surface engineering, and aims to solve the problem that a systematic and automatic optimal surface texture layout design method based on a real physical field is lacked in on-line contact elastohydrodynamic lubrication. The method comprises the following steps: parameterizing a texture layout into a multi-dimensional variable set containing position, size and depth parameters; a high-fidelity elastohydrodynamic lubrication numerical model coupled with fluid dynamic pressure, elastic deformation, viscosity pressure and dense pressure effects is established and used for calculating the oil film bearing capacity; a hybrid optimization strategy that the maximum bearing capacity is taken as a target, a genetic algorithm is adopted to carry out global search and sequential quadratic programming is combined to carry out local refinement is adopted, automatic iterative optimization is carried out on position and size parameters under the condition that the area proportion constraint is met, and the optimal texture layout is output. According to the method, the texture global optimal layout automatic optimization independent of the prior hypothesis is realized.

Description

TECHNICAL FIELD

[0001] The application relates to the field of mechanical engineering tribology and surface engineering, and particularly relates to a line contact elastohydrodynamic lubrication surface texture layout optimization method and system combining global optimization and local optimization strategies. BACKGROUND

[0002] In modern mechanical systems, such as gear transmission and rolling bearings, line contact conditions are commonly encountered. Such conditions have small contact areas and concentrated loads, and the lubrication state is mostly in the elastohydrodynamic lubrication (EHL) range. In EHL, the contact zone pressure is extremely high (up to GPa level), which leads to two key effects: one is that the viscosity of the lubricant increases exponentially with pressure (viscosity-pressure effect), and the other is that the contact surface undergoes elastic deformation with a thickness comparable to the oil film. The coupling of these two effects makes the lubrication mechanism of EHL fundamentally different from traditional hydrodynamic lubrication, and the oil film pressure distribution presents typical complex characteristics such as an inlet zone, a high-pressure Hertz contact zone, and an outlet pressure peak.

[0003] To improve the lubrication performance of friction pairs, increase the load-carrying capacity, and prolong the service life, surface texture technology has been widely studied and applied. This technology designs regular micro-topography (such as pits and grooves) on the friction surface to store lubricants, generate additional dynamic pressure effects, or collect abrasive particles.

[0004] However, the application of surface texture technology in EHL conditions faces great challenges. In the complex nonlinear physical field of EHL, the introduction of texture is a "double-edged sword". If the layout is appropriate, it can effectively improve the oil film pressure and load-carrying capacity; if the layout is not appropriate, it can damage the oil film integrity, cause stress concentration, and exacerbate wear. For example:

[0005] Simply placing texture in the "inlet zone" of traditional hydrodynamic lubrication may interfere with the effective establishment of the oil film in EHL;

[0006] If the texture is placed in the center of the high-stress Hertz contact zone, it is easy to cause local rupture of the oil film and form direct contact;

[0007] Placing texture near the outlet pressure peak has a highly nonlinear influence, which is difficult to predict.

[0008] Most of the existing surface texturing design methods are based on simplified models or empirical rules, which usually assume the shape (e.g. circular pits) and location (e.g. uniform distribution or placed in the inlet region) of the texture in advance, and only take a few parameters such as the depth, diameter or area density of the texture as optimization variables. Such methods rely heavily on the prior knowledge of the designer, and because they do not fully consider the strong coupling characteristics of multi-physical fields such as the viscosity-pressure effect, elastic deformation and cavitation effect specific to elastohydrodynamic lubrication, the "optimal" design obtained is often only locally optimal, or even ineffective under actual elastohydrodynamic lubrication conditions.

[0009] In recent years, although some research has attempted to introduce optimization algorithms (such as genetic algorithms) into texture design, they usually use simplified analytical models or alternative models with smaller computational cost (such as neural network fitting models) to approximate the lubrication process in order to reduce the optimization calculation cost. This "optimization of approximate model" rather than "optimization of real physical model" strategy, although it improves the calculation efficiency, but sacrifices the physical fidelity, the reliability and universality of the optimization result is questionable.

[0010] Therefore, it has become an urgent technical need to develop a surface texturing optimization design method that can directly based on a high-fidelity elastohydrodynamic lubrication physical model, without preassuming the texture location and shape, and can automatically, efficiently and reliably search for the globally optimal layout. This not only is a difficulty in the field of surface engineering and tribology, but also is a key link to realize the self-design of high-end equipment. SUMMARY

[0011] In order to solve the problem of lack of systematic, automatic and real physical field based optimal surface texture layout design method in line contact elastohydrodynamic lubrication, the present application provides a line contact elastohydrodynamic lubrication surface texture layout optimization method which combines global optimization and local optimization strategies, and performs full parameterization joint optimization on texture position-size-depth.

[0012] In one aspect of the present application, a line contact elastohydrodynamic lubrication surface texture layout optimization method which combines global optimization and local optimization strategies is provided, comprising the following steps:

[0013] S1, parameterization definition: the surface texture layout in the solution domain is characterized as a set of multi-dimensional optimization parameters, the parameter set includes a position parameter for determining the spatial position of the texture in the lubricant entrainment direction, a size parameter for determining the characteristic size of the texture in the entrainment direction, and a texture depth parameter; wherein the position parameter and the size parameter are taken as free variables to be optimized;

[0014] S2, physical model construction and objective function definition: a high-fidelity numerical model of line contact elastohydrodynamic lubrication is established for calculating the oil film pressure distribution and film thickness distribution under the texture layout defined by the parameter set; the oil film carrying capacity of the system is obtained by integrating the pressure distribution, and maximizing the oil film carrying capacity is defined as the optimization goal;

[0015] S3, hybrid optimization solution: a hybrid optimization strategy combining global optimization algorithm and local optimization algorithm is adopted, the optimization goal is used as the guide, the position parameters and size parameters are used as free variables to be optimized, and a constraint condition containing the total area ratio of the texture in the solution domain is introduced, automatic iterative optimization is performed, and the optimal texture layout parameter group that maximizes the oil film carrying capacity is output.

[0016] Preferably, the hybrid optimization strategy in step S3 is specifically a two-stage optimization performed in sequence:

[0017] Global search stage: the global optimization algorithm is used to search in the entire feasible domain of the multi-dimensional optimization parameter set, and one or more global candidate parameter groups are obtained;

[0018] Local refinement stage: the candidate parameter group obtained in the global search stage is used as the initial point, the local optimization algorithm is used to perform fine search in its neighborhood, and the optimal texture layout parameter group is obtained.

[0019] Preferably, in step S3, the global optimization algorithm is a genetic algorithm, and the local optimization algorithm is a sequential quadratic programming algorithm.

[0020] Preferably, in step S1, the multi-dimensional optimization parameter set includes four combinations of the position parameters and size parameters, and one texture depth parameter.

[0021] Preferably, the constraint condition of the total area ratio is that the total area of all textures does not exceed times the area of the solution domain, wherein is a preset limit ratio constant, and .

[0022] Preferably, in step S3, the constraint condition further includes boundary constraints on the position parameters and size parameters, for ensuring that the texture is completely located within the solution domain.

[0023] Preferably, in step S2, the high-fidelity numerical model of line contact elastohydrodynamic lubrication is a multi-physics coupling model which couples fluid dynamic pressure lubrication control equation, contact surface elastic deformation equation, and takes into account the viscosity-pressure effect and density-pressure effect of the lubricant.

[0024] Preferably, the high-fidelity numerical model also employs JFO boundary conditions to handle the cavitation effect of the lubrication film.

[0025] In another aspect of the present application, a system for optimizing the layout of surface texture in line contact elastohydrodynamic lubrication by combining global optimization and local optimization strategies is provided, comprising:

[0026] A parameterization module for defining and outputting a set of multi-dimensional optimization parameters;

[0027] An elastohydrodynamic lubrication physical calculation module for implementing the high-fidelity numerical model, receiving the parameter set and calculating the corresponding oil film carrying capacity;

[0028] A target and constraint module for setting the optimization target and the constraint condition;

[0029] A hybrid optimization solution module for executing the hybrid optimization strategy, driving iteration and outputting the optimal texture layout parameter group.

[0030] The beneficial effects of the present application are:

[0031] (1) Overall optimal layout: The present application does not make pre-assumptions about the position and shape of the texture using traditional methods, but instead considers both the position X and size L of the texture as optimization variables. First, a global systematic search is conducted with the help of a genetic algorithm, and then the parameter combination obtained by the global optimization algorithm is used as the initial value for local optimization search using sequential quadratic programming. This method can automatically explore the best texture layout in the complex pressure field of elastohydrodynamic lubrication, making up for the shortcomings of traditional heuristic rules.

[0032] (2) The hybrid solution strategy of the present application combines the globality of genetic algorithm and the local efficiency of sequential quadratic programming, overcoming the defects of pure sequential quadratic programming in falling into local optimum and the defects of genetic algorithm in slow convergence and low precision. For the optimization strategy that has been used in related fields by combining genetic algorithm and neural network, the optimization result obtained by the optimization strategy of the present application is the global optimal solution of the real physical model, rather than the approximate physical model optimal solution obtained by the genetic algorithm and neural network optimization strategy, which has higher fidelity.

[0033] (3) The strategy of combining genetic algorithm and sequential quadratic programming used in the present application provides an effective way to achieve the best balance among global optimality, convergence efficiency and physical precision for elastohydrodynamic lubrication optimization problems. Compared with other texture parameter optimization methods, the present application focuses on the field of line contact elastohydrodynamic lubrication, and provides a precise and efficient solution for texture design in high-performance line contact elastohydrodynamic lubrication by exploring the optimal layout that maximizes the carrying capacity, which has positive significance for improving the carrying capacity of mechanical systems and prolonging their service life. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 Flow chart of hybrid optimization solution of fusion genetic algorithm and sequential quadratic programming of the present application.

[0035] Figure 2 Schematic diagram for definition of texture position parameter X and size parameter L in solution domain, wherein, Figure 2 (a) is a schematic diagram for definition of position parameter X in solution domain, Figure 2 (b) is a schematic diagram for definition of size parameter L in solution domain.

[0036] Figure 3 Schematic diagram for setting boundary condition of solution domain in elastohydrodynamic lubrication numerical model (periodic boundary condition is adopted), wherein, Figure 3 (a) is XOY plane boundary, Figure 3 (b) is XOZ plane boundary.

[0037] Figure 4 Schematic diagram for composition of total film thickness in elastohydrodynamic lubrication.

[0038] Figure 5 Coupling solution logic block diagram of high-fidelity elastohydrodynamic lubrication numerical model.

[0039] Figure 6 Two-dimensional schematic diagram of a texture layout optimized by the method of the present application (area ratio =0.3).

[0040] Figure 7 Figure 6 Three-dimensional topography schematic diagram corresponding to the texture layout.

[0041] Figure 8 Distribution curve of elastohydrodynamic lubrication line contact pressure and film thickness along entrainment direction after optimal texture layout is adopted.

[0042] Figure 9 Three-dimensional pressure distribution cloud chart under optimal texture layout.

[0043] Figure 10 Three-dimensional film thickness distribution cloud chart under optimal texture layout.

[0044] Figure 11 Overall logic and system module flow chart of the optimization method of the present application. DETAILED DESCRIPTION

[0045] ​The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0047] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but this is not intended to limit the scope of the invention.

[0048] Specific Implementation Method 1: The following is combined with... Figures 1 to 11 This embodiment describes an optimization method for the texture layout of a line contact elastohydrolubrication surface that integrates global and local optimization strategies, comprising the following steps:

[0049] S1. Parametric Definition: The surface texture layout in the solution domain is characterized as a multidimensional optimization parameter set, which includes position parameters for determining the spatial position of the texture in the lubricant entrainment direction, size parameters for determining the characteristic size of the texture in the entrainment direction, and texture depth parameters; wherein, the position parameters and size parameters are free variables to be optimized.

[0050] S2. Physical Model Construction and Objective Function Definition: A high-fidelity numerical model for one-line contact elastohydrolubrication is established to calculate the oil film pressure distribution and film thickness distribution under the textured layout defined by the parameter set; the oil film bearing capacity of the system is obtained based on the integral of the pressure distribution, and maximizing the oil film bearing capacity is defined as the optimization objective.

[0051] S3. Hybrid Optimization Solution: A hybrid optimization strategy combining global and local optimization algorithms is adopted. Guided by the optimization objective, the position and size parameters are used as free variables to be optimized. Constraints including the proportion of texture in the total area of ​​the solution domain are introduced to perform automatic iterative optimization and output the optimal texture layout parameter set that maximizes the oil film bearing capacity.

[0052] The following is a detailed explanation:

[0053] Step S1, Parameter Definition

[0054] like Figure 2 As shown, to achieve free optimization of the texture layout, a parametric model of the texture must first be established. This implementation represents a single texture structure within the solution domain as a 9-dimensional optimization vector. :

[0055]

[0056] The first eight parameters Represents four sets of position and size parameters ; ; ; ;

[0057] Indicates the first The dimensionless coordinate position of the center of each texture sub-unit in the entrainment direction (relative to the zero point of the solution domain) is called the position parameter.

[0058] Indicates the first The characteristic length of a texture subunit in the suction direction is called a dimensional parameter.

[0059] The ninth parameter : The depth of the texture is called the depth parameter.

[0060] In this embodiment, position parameters and dimensional parameters As a free variable to be optimized, the depth parameter It can be preset to a fixed value according to process requirements. By... and All variables are set as free variables. This method can explore the optimal layout of texture in any possible location, such as the entrance region, Hertz contact region, and exit region, breaking through the prior assumptions of traditional methods regarding texture location.

[0061] To better define the details of the texture shape, the first three sets of parameters are expanded and symmetrically distributed along the third region division line, and the texture depth value is added to the parameters. Finally, a total of 15 parameters are involved in the pressure and film thickness calculations, satisfying the following table:

[0062]

[0063] Step S2: Physical Model Construction and Objective Function Definition

[0064] This step establishes a high-fidelity elastohydrodynamic (EHL) numerical model to evaluate the performance of any given texture layout. First, the spatial coordinates within the solution domain are defined. :set up The direction along which the lubricant is entrained indicates the main flow direction or the load direction. The axis is perpendicular to the direction of the entrainment velocity and represents the lateral or width direction of the contact area. Axis perpendicular The plane represents the direction of texture depth or oil film thickness. This model forms the physical basis of the optimization process, and its high fidelity is reflected in the simultaneous coupling of multiple physical fields, including hydrodynamic pressure, surface elastic deformation, lubricant viscosity and pressure-density effects, and cavitation effects. The core equations of the model are as follows:

[0065] (1) Reynolds equation: The two-dimensional steady-state Reynolds equation is adopted:

[0066]

[0067]

[0068] in For oil film pressure, For cavitation pressure, For oil film thickness, The dynamic viscosity of the lubricating oil. For the density of lubricating oil, The suction speed is the speed at which the suction is drawn in. For is the fluid film density With the density of the sealing medium The ratio;

[0069] (2) Equation of film thickness at a certain cross section along the direction of entrainment velocity (e.g.) Figure 4 (as shown)

[0070]

[0071] Since it only represents a cross-section along the entrainment direction, the y-coordinate on this cross-section remains unchanged, in the formula, The initial central film thickness, The equivalent radius of curvature, This represents the surface elastic deformation. For texture depth, Surface roughness is defined by the areas to which each part belongs; these areas can be viewed. Figure 4 .

[0072] (3) Elastic deformation equation (line contact assumption):

[0073] First solve for the line load. :

[0074]

[0075] in, for Additional coordinates of the axis, representing arbitrary linear loads. Distance from the origin; and Indicates load The coordinates of the start and end points along the direction perpendicular to the suction direction.

[0076]

[0077] in, The combined elastic modulus of the two contact surfaces. and respectively load The coordinates of the starting and ending points;

[0078] Then the one-dimensional deformation obtained from the line contact Extended to two-dimensional deformation .

[0079] (4) Constitutive equation, dynamic viscosity of lubricating oil and lubricating oil density With oil film pressure The equations vary, and the Roelands viscous compression equation and the Dowson-Higginson compaction equation are used.

[0080] Viscosity-pressure equation:

[0081]

[0082] in, For temperature ,pressure The dynamic viscosity of the lubricating oil at that time The pressure viscosity coefficient, , The coefficients in the viscosity-pressure formula are... .

[0083] Pressure Compressibility Equation:

[0084]

[0085] Among them, oil film pressure The unit is .

[0086] (5) Boundary conditions

[0087] JFO Boundary Condition: To accurately simulate the cavitation effect of the oil film in the outlet region, this embodiment employs the JFO boundary condition. This condition ensures that the pressure is simultaneously satisfied at the oil film rupture point. and pressure gradient .

[0088] in, This represents the direction of the normal to the free boundary at the point where the oil film breaks.

[0089] Periodic boundaries (e.g.) Figure 3 ):

[0090] The direction along the suction speed ( (in the direction of) and perpendicular to the entrainment velocity direction ( The solution domain of the Reynolds equations for all directions uses periodic boundary conditions, i.e.:

[0091]

[0092]

[0093] in The row containing a certain pressure point. The column containing a certain pressure point. The number of meshes for the solution domain is [number], both along and perpendicular to the entrainment velocity direction. ; Indicates the solution domain right boundary along the entrainment direction. The pressure point on the right side of the line. Indicates the first [unclear] on the left boundary of the solution domain along the entrainment direction. The pressure point of the line, Indicates the left boundary of the solution domain along the entrainment direction. The pressure point on the left side of the line. Indicates the first... The pressure point of the line, Indicates the upper right boundary of the solution domain along the perpendicular entrainment direction. The upper pressure point of the column, Indicates the first [unclear] on the lower boundary of the solution domain along the perpendicular entrainment direction. The pressure points of the column, Indicates the solution domain lower boundary along the perpendicular entrainment direction. The lower pressure point of the column, Indicates the first step on the upper boundary of the solution domain along the perpendicular entrainment direction. The pressure points of the column.

[0094] The above equations constitute a strongly coupled nonlinear system. This implementation method uses a multigrid method combined with over-relaxation iteration for numerical solution (see flowchart). Figure 5 The final output is given. Pressure distribution below and film thickness distribution , Figure 5 Elastic flow lubrication is abbreviated as EHL.

[0095] The objective function is defined as oil film bearing capacity. This is obtained by integrating the pressure field:

[0096]

[0097] in, The spatial region representing the integral.

[0098] The optimization objective is to maximize. In optimization algorithms, this is often transformed into minimizing the negative bearing capacity.

[0099] Step S3: Hybrid optimization solution

[0100] This step employs a two-stage hybrid strategy combining genetic algorithm (GA) and sequential quadratic programming (SQP) for optimization, as follows: Figure 1 As shown in the figure. This strategy balances global search capability with local convergence accuracy.

[0101] 1. Constraint settings: Optimization must satisfy the following nonlinear constraints:

[0102] (1) Position constraints (ensuring the texture is within the solution domain):

[0103]

[0104] There are a total of 8 positional constraints. Let be the length of the solution domain along the entrainment direction.

[0105] (2) Area constraints (limiting the proportion of total area) =0.3):

[0106]

[0107] (3) Depth constraint:

[0108]

[0109] 2. First stage: Global search (genetic algorithm)

[0110] Configure GA parameters: population size, maximum number of generations, maximum number of stagnant generations, number of elites, crossover and mutation factors, convergence accuracy, etc. Specific settings are as follows:

[0111]

[0112] Encapsulate the high-fidelity EHL model into a fitness function. :

[0113] .

[0114] The GA calls the model in parallel and performs extensive random searches within the parameter space to locate the region where the global optimum is located.

[0115] Output a high-quality global candidate solution. The purpose of this step is to find the correct range of optimal solutions, that is, the parameter combination domain in which the global optimal solution is located. Genetic algorithms have difficulty finding the global optimal solution quickly and accurately, and usually can only find a globally better solution.

[0116] 3. Second stage: Local refinement (sequential quadratic programming)

[0117] To address the low convergence accuracy of genetic algorithms, a second phase is initiated, employing sequential quadratic programming for local refinement:

[0118] The high-quality global candidate solutions obtained in the first stage are used as the initial points for SQP.

[0119] Sequential quadratic programming still uses the fitness function To optimize the objective function.

[0120] Configure SQP parameters: maximum number of iterations, maximum number of calls, step size tolerance, and function value tolerance; specific settings are as follows:

[0121]

[0122] Execute sequential quadratic programming, which will call the high-fidelity elastohydrolubrication physical calculation model. function gradient from Starting with this set of parameters, the sequential quadratic programming algorithm converges accurately and quickly to the optimal parameters within this region. The initial computation point of the sequential quadratic programming is already guaranteed by the genetic algorithm to be highly likely to lie in the global optimum, so the local optimum reached by the sequential quadratic programming is actually the global optimum. .

[0123] The sequential quadratic programming algorithm avoids getting trapped in a globally optimal solution, thus enabling it to find the truly globally optimal texture layout parameters.

[0124] By executing the above optimization solution module, parameterization module, elastohydrolubrication physics calculation module, and objective function module, the area constraint is obtained. The optimal pattern with a value of 0.3, its texture layout, and its two-dimensional and three-dimensional morphologies are as follows: Figure 6 and Figure 7 As shown, a two-dimensional schematic diagram of the distribution of line contact pressure and film thickness along the direction of entrainment motion is as follows. Figure 8 As shown, the three-dimensional pressure distribution and the three-dimensional film thickness distribution are respectively as follows: Figure 9 and Figure 10 As shown, the overall elastohydrolubrication solution logic block diagram is as follows: Figure 11 As shown, the results indicate that this method can automatically find complex asymmetric texture shapes and significantly improve the oil film bearing capacity.

[0125] While the invention has been described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the invention. Therefore, it should be understood that many modifications can be made to the exemplary embodiments, and other arrangements can be designed without departing from the spirit and scope of the invention as defined by the appended claims. It should be understood that different dependent claims and features described herein can be combined in ways different from those described in the original claims. It is also understood that features described in conjunction with individual embodiments can be used in other described embodiments.

Claims

1. An optimization method for the surface texture layout of line contact elastohydrolubrication that integrates global and local optimization strategies, characterized in that, Includes the following steps: S1. Parametric Definition: The surface texture layout in the solution domain is characterized as a multidimensional optimization parameter set, which includes position parameters for determining the spatial position of the texture in the lubricant entrainment direction, size parameters for determining the characteristic size of the texture in the entrainment direction, and texture depth parameters; wherein, the position parameters and size parameters are free variables to be optimized. S2. Physical Model Construction and Objective Function Definition: A high-fidelity numerical model for one-line contact elastohydrolubrication is established to calculate the oil film pressure distribution and film thickness distribution under the textured layout defined by the parameter set; the oil film bearing capacity of the system is obtained based on the integral of the pressure distribution, and maximizing the oil film bearing capacity is defined as the optimization objective. S3. Hybrid Optimization Solution: A hybrid optimization strategy combining global and local optimization algorithms is adopted. Guided by the optimization objective, the position and size parameters are used as free variables to be optimized. Constraints including the proportion of texture in the total area of ​​the solution domain are introduced to perform automatic iterative optimization and output the optimal texture layout parameter set that maximizes the oil film bearing capacity.

2. The optimization method for the surface texture layout of line contact elastohydrolubrication, which integrates global and local optimization strategies according to claim 1, is characterized in that... The hybrid optimization strategy in step S3 is specifically a two-stage optimization performed sequentially: Global search phase: The global optimization algorithm is used to search the entire feasible domain of the multidimensional optimization parameter set to obtain one or more global candidate parameter groups; Local refinement stage: Using the candidate parameter set obtained in the global search stage as the initial point, the local optimization algorithm is used to perform a refined search in its neighborhood to obtain the optimal texture layout parameter set.

3. The optimization method for the surface texture layout of line contact elastohydrolubrication, which integrates global and local optimization strategies according to claim 2, is characterized in that... In step S3, the global optimization algorithm is a genetic algorithm, and the local optimization algorithm is a sequential quadratic programming algorithm.

4. The optimization method for the surface texture layout of line contact elastohydrolubrication, which integrates global and local optimization strategies according to claim 1, is characterized in that... In step S1, the multidimensional optimization parameter set includes four combinations of the position parameters and size parameters, as well as one texture depth parameter.

5. The optimization method for the surface texture layout of line contact elastohydrolubrication as described in claim 1, which integrates global and local optimization strategies, is characterized in that... The constraint on the total area percentage is: the total area of ​​all textures does not exceed the area of ​​the solution domain. times, of which The preset limiting ratio constant, and .

6. The optimization method for the surface texture layout of line contact elastohydrolubrication based on the integration of global and local optimization strategies according to claim 1 or 5, characterized in that, In step S3, the constraints also include boundary constraints on the position and size parameters to ensure that the texture is completely within the solution domain.

7. The optimization method for the surface texture layout of line contact elastohydrolubrication as described in claim 1, which integrates global and local optimization strategies, is characterized in that... In step S2, the high-fidelity numerical model of line contact elastohydrodynamic lubrication is a multi-physics coupling model, which couples the hydrodynamic lubrication control equation and the contact surface elastic deformation equation, and takes into account the viscosity-pressure effect and density-pressure effect of the lubricant.

8. The optimization method for the surface texture layout of line contact elastohydrolubrication according to claim 7, which integrates global and local optimization strategies, is characterized in that... The high-fidelity numerical model also employs JFO boundary conditions to handle the cavitation effect of the lubricating film.

9. A surface texture layout optimization system for line contact elastohydrolubrication integrating global and local optimization strategies, used to implement the method of any one of claims 1 to 8, characterized in that, include: The parameterization module is used to define and output the multidimensional optimization parameter set; The elastohydrodynamic lubrication physical calculation module is used to implement the high-fidelity numerical model, receive the parameter set, and calculate the corresponding oil film bearing capacity. The objective and constraint module is used to set the optimization objective and the constraint conditions; The hybrid optimization solution module is used to execute the hybrid optimization strategy, drive iteration, and output the optimal texture layout parameter set.

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