Lip seal structure optimization method, apparatus, medium, and computer program product

Abstract

The present disclosure relates to the technical field of sealing structure, and particularly provides a lip seal structure optimization method, device, medium and computer program product. The lip seal structure optimization method comprises the following steps: defining a texture parameter of a lip seal to be optimized; performing fluid-structure-thermal multi-field coupling analysis on the texture parameter of the lip seal to be optimized to obtain a sealing performance parameter; determining a plurality of to-be-optimized texture parameter intervals based on the texture parameter and the sealing performance parameter; and performing optimization on the plurality of to-be-optimized texture parameter intervals by using a genetic algorithm to obtain a target texture parameter combination. The present disclosure obtains the sealing performance parameter through fluid-structure-thermal multi-field coupling analysis, and performs parameter optimization by combining the genetic algorithm, so that the precise optimization of the lip seal texture is realized, and the sealing performance of the lip seal is improved.

Description

Technical Field

[0001] This disclosure relates to the field of sealing structure technology, and in particular to methods, equipment, media and computer program products for optimizing lip seal structures. Background Technology

[0002] Lip seals are widely used sealing devices at the interface between rotating shafts and stationary components, and their main function is to prevent leakage of liquids or gases. Lip seals are typically made of elastic materials and are designed with one or more inclined lip edges that fit tightly against the surface of the rotating shaft, achieving an effective seal through contact pressure.

[0003] In the application of textured rotary shaft lip seals, texture refers to designing specific microstructure patterns on the surface of the rotary shaft or the sealing lip. These subtle surface structures enhance the overall seal performance by improving the physical properties of the surface. Therefore, determining the impact of different lip texture designs on sealing performance through simulation analysis is particularly important. Summary of the Invention

[0004] In view of this, exemplary embodiments of the present disclosure provide a method, apparatus, medium, and computer program product for optimizing lip seal structures to solve the problems existing in the related art.

[0005] One aspect of an exemplary embodiment of this disclosure provides a method for optimizing a lip seal structure, the method comprising:

[0006] Define the texture parameters of the lip seal to be optimized;

[0007] Fluid-structure-thermal multi-field coupling analysis was performed on the texture parameters of the lip seal to be optimized to obtain sealing performance parameters;

[0008] Based on the texture parameters and the sealing performance parameters, multiple ranges of texture parameters to be optimized are determined;

[0009] A genetic algorithm is used to optimize the multiple texture parameter ranges to obtain the target texture parameter combination.

[0010] In another aspect of exemplary embodiments of this disclosure, a computer device is provided, including a memory, a processor, and a computer program stored in the memory, the processor executing the computer program to implement the methods described in exemplary embodiments of this disclosure.

[0011] In another aspect of exemplary embodiments of this disclosure, a computer-readable storage medium is provided having a computer program / instructions stored thereon that, when executed by a processor, implements the methods described in exemplary embodiments of this disclosure.

[0012] In another aspect of exemplary embodiments of this disclosure, a computer program product is provided, including a computer program / instructions that, when executed by a processor, implement the methods described in exemplary embodiments of this disclosure.

[0013] As will be described in detail below, a lip seal structure optimization method according to an embodiment of the present disclosure involves defining the texture parameters of the lip seal to be optimized; performing fluid-structure-thermal multi-field coupling analysis on the texture parameters to obtain sealing performance parameters; determining multiple texture parameter intervals to be optimized based on the texture parameters and sealing performance parameters; and using a genetic algorithm to optimize the multiple texture parameter intervals to obtain a target texture parameter combination. Therefore, the lip seal structure optimization method provided by the present disclosure obtains sealing performance parameters through fluid-structure-thermal multi-field coupling analysis and combines it with a genetic algorithm for parameter optimization, achieving precise optimization of the lip seal texture and improving the sealing performance of the lip seal. Attached Figure Description

[0014] The above and other objects, features, and advantages of this disclosure will become more apparent from the more detailed description of the embodiments thereof in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this disclosure and form part of the specification. They are used together with the embodiments of this disclosure to explain the disclosure and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0015] Figure 1 This is a flowchart illustrating an exemplary method for optimizing a lip seal structure provided in this disclosure.

[0016] Figure 2 This is a schematic diagram illustrating a common texture definition for a rotation axis provided as an example in this disclosure;

[0017] Figure 3 This is an exemplary two-dimensional triangular textured radial view provided in this disclosure;

[0018] Figure 4 This is a schematic diagram of the three-dimensional surface morphology of a triangular texture provided as an example in this disclosure;

[0019] Figure 5 This is an exemplary schematic diagram of a Pareto front solution provided in this disclosure;

[0020] Figure 6 A schematic block diagram of the functional modules of an exemplary lip seal structure optimization device provided in this disclosure;

[0021] Figure 7 This is a structural block diagram of an exemplary electronic device provided in this disclosure;

[0022] Figure 8This is a schematic diagram of a computer program product provided as an example of this disclosure. Detailed Implementation

[0023] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0024] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0025] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc., used in this disclosure are only used to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0026] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0027] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0028] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0029] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the software or hardware, such as the electronic device, application, server, or storage medium performing the operations of this disclosed technical solution, based on the prompt message.

[0030] As an optional but non-limiting implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device. It is understood that the above notification and user authorization process is merely illustrative and does not constitute a limitation on the implementation of this disclosure; other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0031] Lip seals are widely used sealing devices at the interface between rotating shafts and stationary components, and their main function is to prevent leakage of liquids or gases. Lip seals are typically made of elastic materials and are designed with one or more inclined lip edges that fit tightly against the surface of the rotating shaft, achieving an effective seal through contact pressure.

[0032] In the application of textured rotary shaft lip seals, texture refers to designing specific microstructure patterns on the surface of the rotary shaft or the sealing lip. These subtle surface structures enhance the overall seal performance by improving the physical properties of the surface. Therefore, determining the impact of different lip texture designs on sealing performance through simulation analysis is particularly important.

[0033] Therefore, in order to solve the above problems, the exemplary embodiment of this disclosure provides a method for optimizing a lip seal structure, the specific steps of which include:

[0034] 1. Define multiple rotary axis textures. Textures for multiple rotary axes may include different shapes, sizes, and layouts. Different textures can be defined to adapt to different working environments and performance requirements.

[0035] 2. Fluid-structure-thermal multi-field coupling analysis of lip seals considering texture. In the design of rotary shaft lip seals, considering the presence of texture, fluid-structure-thermal coupling analysis is used to simulate the performance of the lip seal under actual working conditions. This step calculates the sealing parameters through numerical solution to obtain a preliminary performance evaluation value.

[0036] 3. Uncertainty Analysis of Texture Processing Errors. Monte Carlo simulation is used to analyze the uncertainty of texture processing errors and their impact on sealing performance. Under the assumption of changes in texture shape, the parameters from the previous step are reused for simulation to evaluate the impact of errors on performance and obtain a possible performance fluctuation range.

[0037] 4. Design variable optimization based on genetic methods. Finally, the design variables are optimized using a genetic algorithm. Based on the multiple performance ranges obtained in the previous step, an optimal range is found, namely the optimal combination of wear and pump suction rate, and the corresponding optimal design variables are determined.

[0038] The entire optimization process involves three levels of loops:

[0039] The innermost loop is responsible for evaluating the sealing performance of a single design state by calculating sealing parameters.

[0040] The intermediate layer uses Monte Carlo simulation to evaluate the impact of texture shape changes on performance and provides a range of performance fluctuations.

[0041] The outermost loop uses a genetic algorithm to find the optimal solution in multiple performance ranges to achieve the best sealing effect.

[0042] This hierarchical and interconnected optimization process effectively improves the design accuracy and reliability of lip seals while taking into account the complexity of actual processing errors and operating conditions.

[0043] For example, Figure 1 This is a schematic flowchart of an exemplary lip seal structure optimization method provided in this disclosure, as shown below. Figure 1 As shown, the specific steps may include:

[0044] Step S110: Define the texture parameters of the lip seal to be optimized.

[0045] When designing an uncertainty optimization method for textured rotating shaft lip seals based on genetic algorithms, it is first necessary to define the various texture types that may be used on the rotating shaft. Texture is formed on the surface of the rotating shaft through specific machining methods, and is used to improve the hydrodynamic characteristics of the sealing contact area and enhance sealing performance.

[0046] For example, the three common shape textures can include triangles, rectangles, and ellipses. Figure 2 This is a schematic diagram illustrating a common texture definition for a rotation axis provided as an example in this disclosure. For example... Figure 2 As shown, Figure 2 In the diagram, (a) represents a triangular texture, where α tr 'a' represents the angle between the base and the circumferential direction of the axis of rotation. trLet b represent the length of the base. tr c represents the length of the left side. tr Indicates the length of the right side, (h) c ,l c ) represents the coordinates of the midpoint of the base side. Figure 2 In the diagram, (b) represents a rectangular texture, and α re Indicates the circumferential angle between the base and the axis of rotation, α re Indicates the bottom edge, b re c represents the left side. re d represents the right side. re Indicates the top edge, (h) c ,l c ) represents the coordinates of the midpoint of the base side. Figure 2 In the text, (c) represents an elliptical texture, and α el Indicates the angle between the line connecting the focal points and the base, a el Indicates the semi-major axis, b el Indicates the semi-minor axis, (h c ,l c ) represents the coordinates of the midpoint of the ellipse.

[0047] For example, a triangular textured region can be represented in a Cartesian coordinate system as:

[0048]

[0049] Among them, (x tr1 ,y tr1 (x) represents the coordinates of the intersection point of the left side and the bottom side; tr2 ,y tr2 (h) represents the coordinates of the intersection point of the right side and the bottom side; c ,l c ) represents the coordinates of the midpoint of the base side; k tr k represents the slope of the base. tr1 Indicates the slope of the left side; k tr2 This indicates the slope of the right side.

[0050] In practical applications, processing errors that characterize dimensions are often used to describe texture errors. However, in the above formula (1), a triangular texture is defined by the slope. Therefore, it is necessary to convert the dimensional error into a slope error. The dimensional parameter can be converted into a slope parameter by simultaneously solving the following formulas (2) and (3):

[0051]

[0052] Among them, (x tr1 ,y tr1 (x) represents the coordinates of the intersection point of the left side and the bottom side; tr2 ,y tr2) represents the coordinates of the intersection point of the right side and the bottom side; b tr c represents the length of the left side. tr Indicates the length of the right side; k tr1 Indicates the slope of the left side; k tr2 This indicates the slope of the right side.

[0053] In this embodiment, the error in the length of the three sides of the triangle can be defined as follows: Given the existence of processing errors and the fact that these errors are approximately represented by interval variables, while neglecting changes in the angle between the base and the circumferential direction, the error can be expressed as:

[0054] η t ~U(ε i ,ε j (4)

[0055] Where, η t Indicates machining error; ε i and ε j These represent the corresponding upper and lower deviations in the tolerance, which are determined by the machining method.

[0056] Based on the characteristics of interval variables and formula (4), the length of the base considering processing errors can be obtained as:

[0057] a′ t =a t +η t (5)

[0058] Among them, a' t This represents the length of the base side, taking into account machining errors; a t η represents the length of the base side without considering machining errors. t This indicates machining error.

[0059] Similarly, other side lengths considering processing errors can be obtained. Based on the side lengths considering processing errors, the intersection points of different side lengths can be determined. Substituting the side lengths and intersection points considering processing errors into formulas (2) and (3), the slope considering processing errors is obtained, and based on formula (1), the expression of the triangular texture considering processing errors in the rectangular coordinate system is obtained:

[0060]

[0061] Where, k′ tr1 , k′ tr2 These represent the slopes of the left and right sides, respectively, after considering machining errors; (x′ tr1 ,y′ tr1 ), (x′ tr2 ,y′ tr2 ) represent the intersection points of the left and right sides with the bottom edge after considering machining errors; (h c ,lc ) represents the coordinates of the midpoint of the base side; k tr This represents the slope of the base.

[0062] For example, a rectangular textured region can be represented in a Cartesian coordinate system as:

[0063]

[0064] Among them, (h c ,l c (x) represents the coordinates of the midpoint of the base; re1 ,y re1 (x) represents the coordinates of the intersection point of the left side and the top side; re2 ,y re2 (h) represents the coordinates of the intersection point of the right side and the top edge; re1 ,l re1 ) represents the coordinates of the midpoint of the top edge; k re k represents the slope of the base. re1 Indicates the slope of the left side; k re2 Indicates the slope of the right side; k re3 This represents the slope of the top edge.

[0065] In this embodiment, the error of the four sides of the rectangle can be defined as follows: there is a processing error and the error is approximately represented by an interval variable. At the same time, the change of the angle between the bottom side and the circumferential direction is not considered. The four sides follow the interval variable of the above formula (4). The four sides can be represented by the above formula (5). Based on this, the four sides can be represented by the rectangular texture area in a point-slope manner:

[0066]

[0067] Among them, (h c ,l c (x') represents the coordinates of the midpoint of the base side; re1 ,y' re1 (x') represents the coordinates of the intersection point of the left side and the top side after considering machining errors; re2 ,y' re2 ) represents the coordinates of the intersection point of the right side and the top edge after considering machining errors; k re k′ represents the slope of the base. re1 This represents the slope of the left side after considering machining errors; k′ re2 This represents the slope of the right side after considering machining errors; k′ re3 (h1,l1) represents the slope of the top edge after considering machining errors; (h1,l1) represents the coordinates of the midpoint of the top edge after considering machining errors.

[0068] For example, key parameters of an ellipse include: focal length, (semi-)major axis, (semi-)minor axis, and the angle between the line connecting the two foci and the circumferential axis of rotation (x-axis). Figure 2 As shown in (c), α el Indicates the angle between the line connecting the focal points and the base, a el Indicates the semi-major axis, b el Indicates the semi-minor axis and (h) c ,l c The coordinates of the midpoint of the ellipse are used as a parameter to define the elliptical texture. The texture shape is circular if and only if the major axis is equal to the minor axis.

[0069] For example, an elliptical textured region can be represented in a Cartesian coordinate system as:

[0070] A el (xh c ) 2 +B el (xh c (yl) c )+C el (yl c ) 2 ≤1 (9)

[0071] in, α represents the angle between the line connecting the foci and the base. el Indicates the semi-major axis, b el Indicates the semi-minor axis; (h c ,l c ) represents the coordinates of the midpoint of the ellipse.

[0072] In this embodiment, the machining error for an ellipse can be defined as: with the semi-major axis a el With semi-minor axis b el As the object of error definition, and with the texture simplified, α is not considered in error analysis. el To reduce machining errors, and simultaneously fix the center of the ellipse (h) c ,l c The machining error follows the interval variable of the above formula (4). The semi-major axis and semi-minor axis considering the machining error can be obtained from the characteristics of the interval variable and formula (5).

[0073] Based on this, the above equation (5) and α el and the center point coordinates (h) c ,l c Substituting into formula (9) yields an elliptical texture region that takes into account processing errors:

[0074] A el (xh c ) 2 +B el(xh c (yl) c )+C el (yl c ) 2 ≤1 (10)

[0075] in, α represents the angle between the line connecting the foci and the base, a' el b' represents the semi-major shaft after considering machining errors. el This represents the semi-minor shaft after accounting for machining errors; (h) c ,l c ) represents the coordinates of the midpoint of the ellipse.

[0076] Step S120: Perform fluid-structure-thermal multi-field coupling analysis on the texture parameters of the lip seal to be optimized to obtain sealing performance parameters.

[0077] For example, a hybrid elastohydrodynamic lubrication model can be used to perform fluid-structure-thermal multi-field coupling analysis of textured lip seals to simulate the sealing performance of lip seals under operating conditions.

[0078] Specifically, the three-dimensional microscopic surface morphology of the lip seal to be optimized is constructed using three-dimensional fractal theory;

[0079] The sealing performance parameters and temperature of the three-dimensional micro-surface morphology under preset operating parameters were evaluated using a pre-constructed hybrid elastohydrodynamic lubrication model.

[0080] After the pressure and temperature iterative convergence, the sealing performance parameters are output.

[0081] Among them, the hybrid elastohydrodynamic lubrication model is a model used to evaluate sealing performance under lubrication conditions. It combines elastic deformation and hydrodynamic effects and can evaluate sealing performance under preset operating parameters.

[0082] Input basic operating parameters, which may include: rotational speed U, ambient temperature T0, viscosity η, ambient pressure P0, and sealing pressure P. s The parameters include: shaft radius R, friction coefficient f, sealing ring elastic modulus E, oil density ρ, oil specific heat capacity c, and wear coefficient K.

[0083] Step S121: Calculate the hydrodynamic oil film thickness.

[0084] The oil film thickness equation and the oil film pressure equation are solved together to determine the oil film pressure value.

[0085] Step S122: Solve the elastic deformation equation.

[0086] Calculate the elastic deformation of the lip and update the oil film thickness based on the deformation.

[0087] Step S123: Define the convergence criterion for oil film thickness using the point-by-point over-relaxation iterative method. Iterate to determine whether the oil film thickness and pressure have converged. If converged, calculate relevant parameters such as pump suction rate and friction force; if not converged, repeat steps S121 to S123 until the convergence condition is met.

[0088] Step S124: Input the initial temperature and calculate the friction force and pumping rate using the hybrid elastohydrodynamic lubrication model. Calculate the new temperature value by combining the energy conservation and heat conduction equations. Update the viscosity using the viscosity-temperature equation and iterate the new viscosity back to the model for recalculation until the temperature distribution error is less than 0.01.

[0089] If the temperature distribution error is less than 0.01, the temperature field is considered to have converged, and the output parameters of the simulation results after iteration are output. If the temperature distribution error is not less than 0.01, the temperature field is considered to have not converged, and steps S121 to S124 are repeated until the result meets the convergence condition.

[0090] Output parameters may include: half-moon oil film pressure, output viscosity, pump suction rate, friction force, tangential torque, average oil film thickness, temperature, average contact load, contact load ratio, and wear rate, etc.

[0091] Specifically, the expressions for the above parameters are as follows:

[0092] For example, the microscopic surface of a lip seal can be represented by a Weierstrass-Mandelbrot fractal surface of dimension D:

[0093]

[0094] Among them, C n A represents an independent random number that follows a standard normal distribution; n B n Let represent independent random numbers uniformly distributed on [0, 2π]; λ represents a constant greater than 1; D represents the fractal dimension of the surface; and n is the number of natural sequences.

[0095] The static oil film thickness characterizes the hydrostatic pressure of the fluid when the shaft is stationary. To better understand the influence of different texture structures on the static oil film thickness and thus simulate the sealing effect of different texture structures, the static oil film thickness when the rotating shaft is in an inactive state can be defined as follows:

[0096]

[0097] Among them, h s δ represents the static oil film thickness; h0 represents the initial average oil film pressure; δ represents the texture depth; z represents the WM fractal surface with dimension D.

[0098] Furthermore, the Reynolds equation for cavitation can be used to characterize the influence of different texture size parameters on the sealing mechanism:

[0099]

[0100] in, Indicates the aspect ratio of the region; X represents the dimensionless radial sliding speed; Y represents the dimensionless circumferential length; H represents the dimensionless axial width; P represents the dimensionless initial oil film thickness; and P represents the dimensionless oil film pressure.

[0101] as well as:

[0102]

[0103] The liquid film integrity region refers to a continuous liquid film region formed on a solid surface, where the liquid thickness is sufficient to prevent direct contact between solid surfaces.

[0104] Furthermore, due to the load pressure and initial interference under operating conditions, the oil film is subjected to a certain oil film pressure during operation. This oil film pressure further acts on the lip of the lip seal, producing a certain amount of elastic deformation. The elastic deformation equation can be expressed as:

[0105]

[0106] Where E represents the elastic modulus; υ represents Poisson's ratio; x k Represents the additional coordinates corresponding to x; y l y represents the additional coordinate corresponding to y; p represents the oil film pressure value at that point.

[0107] Furthermore, friction and pump suction rate can be selected as important sealing performance parameters.

[0108] The tangential friction force can be obtained by integrating the friction force on the lip surface of the lip seal:

[0109]

[0110] Among them, F f η represents the tangential friction force; b represents the axial width of the region; l represents the length of the contact region; h represents the oil film thickness under convergence conditions obtained by solving the Reynolds equation; p represents the oil film pressure under convergence conditions obtained by solving the Reynolds equation; η represents the viscosity of the lubricating oil; and U represents the rotational speed of the rotating shaft.

[0111] Pump suction rate can be expressed as:

[0112]

[0113] Where Q represents the pump suction rate; l represents the contact area length; h represents the oil film thickness under convergence conditions obtained by solving the Reynolds equation; p represents the oil film pressure under convergence conditions obtained by solving the Reynolds equation; and η represents the lubricating oil viscosity.

[0114] Furthermore, when evaluating the sealing performance of lip seals, it is necessary to consider the heat generated by friction and the heat transferred or retained through different mechanisms (such as the pumping effect).

[0115] According to the principle of energy conservation, the heat generated by the friction between the lip of the lip seal and the rotating shaft is equal to the heat "carried away" by the lubricating oil through the "pumping" effect and the heat retained in the sealing area:

[0116] fpV=Qρc(T-T0)+α2πRL(T-T0) (18)

[0117] Where f represents the coefficient of friction; p represents the oil film pressure; Q represents the pump suction rate; ρ represents the density; c represents the specific heat capacity; T represents the actual temperature; T0 represents the ambient temperature, i.e., the initial temperature value; α represents the heat exchange coefficient of the lubricating oil; R represents the shaft radius; and L represents the shaft length.

[0118] Since the viscosity of lubricating oil changes with temperature, after obtaining the actual temperature, the viscosity of the lubricating oil needs to be updated and then iterated back into the mixed elastohydrodynamic lubrication model to obtain a new temperature distribution. The Reynolds viscosity-temperature equation can be used to describe the relationship between fluid viscosity and temperature.

[0119]

[0120] Where η0 represents the viscosity at T0; α T T represents the undetermined coefficient; T represents the actual temperature; T0 represents the ambient temperature, i.e., the initial temperature value.

[0121] For example, the Arcard wear formula can be used to calculate the amount of wear on the lip:

[0122]

[0123] Where f represents the wear amount on the lip surface; K represents the parameter related to the probability of particle wear generated by the two contacting rough peaks; N represents the pressure acting on the contact surface between the sealing lip and the rotating shaft; and H represents the material hardness of the rotating shaft.

[0124] Based on this, a precise three-dimensional microscopic surface morphology of the lip was constructed by applying three-dimensional fractal theory, and a fluid-solid-thermal multi-field coupling analysis was performed on the working environment of the lip to accurately simulate the lip seal under working conditions.

[0125] Step S130: Determine multiple ranges of texture parameters to be optimized based on texture parameters and sealing performance parameters.

[0126] Since certain processing errors are unavoidable in the actual production of seals, these errors may cause deviations between the sealing performance and the predictions of the theoretical model. Therefore, in order to more accurately assess the impact of these uncertainties on the texture sealing performance, step S130 uses the input parameters from step S120 as input to perform multiple simulation analyses to obtain the possible variation range of the sealing performance parameters, and then determines multiple texture parameter ranges to be optimized.

[0127] Specifically, multiple simulations are performed based on the input parameters in step S120. In each simulation, the dimensional processing error of the texture shape is assumed to be a random variable in the interval [0,1]. This random variable represents the range of possible error variation from minimum to maximum.

[0128] Each simulation employs the analysis method described in step S120, using randomly generated texture dimension processing error values ​​as one of the input parameters. This approach allows for the evaluation of sealing performance at different error levels, including key performance indicators such as oil film thickness, pressure distribution, friction, and temperature.

[0129] By conducting multiple simulations, the sealing performance parameters obtained from each simulation are collected, and these data are then analyzed to determine the range of performance variation and the corresponding statistical characteristics. This includes calculating the mean (midpoint) and standard deviation (radius) of the target variable.

[0130] The results of step S130 will be directly used in the subsequent optimization iteration process of step S140. Each optimization iteration will be based on the interval (midpoint and radius) of the target variable obtained from the Monte Carlo simulation to ensure that the optimization strategy can take into account the uncertainties introduced by machining errors.

[0131] Therefore, uncertainty analysis based on Monte Carlo simulation is a crucial step in understanding and predicting the behavior of lip seals under conditions of machining errors. By simulating different error scenarios, the stability and reliability of the seal performance can be predicted more comprehensively.

[0132] Step S140: Use a genetic algorithm to optimize multiple texture parameter ranges to obtain the target texture parameter combination.

[0133] Step S140 is the process of optimizing the design variables of the lip seal assembly. It uses a genetic algorithm (GA) to handle the uncertainty of the seal texture parameters and find the Pareto front solution under given constraints.

[0134] Step S141: Define the interval variable, the midpoint of the interval, and the radius of the interval.

[0135] First, based on the results of the Monte Carlo method (step S130), an interval for each texture parameter to be optimized is defined. The midpoint of the interval represents the expected average performance index, while the interval radius reflects the potential impact of processing errors or other uncertainties on performance.

[0136] For example, an interval variable can be expressed mathematically as follows:

[0137] A I =[A L A R ] (twenty one)

[0138] Among them, A I Represent the interval A; A L Denotes the lower bound of interval A; A R This represents the upper bound of interval A.

[0139] The midpoint and radius of an interval are often used as important parameters for examining the interval, and can be expressed as:

[0140]

[0141] Among them, A c This represents the midpoint of interval A.

[0142]

[0143] Among them, A w This represents the radius of interval A.

[0144] Step S142: Determine the constraints and thresholds for optimization.

[0145] For example, the constraints and thresholds for the size parameters during optimization can be expressed as:

[0146]

[0147] Among them, h t Indicates texture depth; h max α1 represents the limit of machining depth; α2 represents the gas side angle; α3 represents the liquid side angle.

[0148] Step S143: Solve for the optimization interval and find the target texture parameter combination.

[0149] Uncertainty analysis of the sealing texture parameters is performed using the Monte Carlo method in step S130. For each set of machining error uncertainties, the target variables (wear amount, pump suction rate) will generate a range. Under constraints, ensuring that operating parameters such as rotational speed and the parameters of the shaft and the seal itself (elastic modulus, friction coefficient, wear coefficient) remain constant, the optimal performance range is found from several ranges of texture parameters to be optimized based on the machining error uncertainties, and the corresponding texture parameter combination is returned. Under given operating parameters, the decision variables are selected as: α1, α2, h. t The corresponding texture size parameters (defined in step S110) and the decision variables are the target texture parameter combinations, which are the final outputs.

[0150] Since pump suction rate and frictional torque are both important sealing performance parameters, and wear and frictional torque are positively correlated with increasing wear time, pump suction rate and wear amount can be used as target variables for optimization, while frictional torque can be considered as a constraint condition for sealing. Therefore, to ensure that the sealing assembly designed after considering the uncertainty factors of texture processing errors can meet the torque requirements, the constraint condition should consider the worst case and ensure that it does not exceed a predetermined threshold:

[0151] T e R ≤T e D (25)

[0152] Among them, T e R T represents the maximum torque. e D This indicates the torque threshold.

[0153] In addition to friction torque constraints, constraints on dimensional parameters should also be considered. The constraint expressions are given in formula (24) above.

[0154] Under the condition that the operating parameters remain constant, considering only the sealing texture processing error and applying the nth Monte Carlo method for multi-field coupled simulation, the wear amounts f1, f2, ..., f are obtained. n And pump suction rates Q1, Q2, ..., Q n There must exist a maximum and a minimum value. Using the midpoint and radius representation methods in interval mathematics, the wear rate and pump suction rate can be expressed as:

[0155] f = [f m ,f w (26)

[0156] Q = [Q] m Q w (27)

[0157] Among them, f m Indicates the midpoint of the wear range; f w Indicates the radius of the wear range; Q m Q represents the midpoint of the pump suction rate range; w This indicates the radius of the pump suction rate range.

[0158] After m iterations of optimization, m intervals of wear amount and pump suction rate as shown in equations (26) and (27) can be obtained. In order to identify the solution set that simultaneously satisfies the minimization of wear amount and the maximization of pump suction rate, the objective function of optimization can be expressed as:

[0159]

[0160] Among them, f m Indicates the midpoint of the wear range; f w Indicates the radius of the wear range; Q m Q represents the midpoint of the pump suction rate range; w T represents the radius of the pump suction rate range; e R T represents the maximum torque. e D Indicates the torque threshold; h t Indicates texture depth; h max α1 represents the limit of machining depth; α2 represents the gas side angle; α3 represents the liquid side angle.

[0161] By solving the four-variable optimization problem of equation (28) using a genetic algorithm (GA), Pareto front solutions can be obtained. The analytic hierarchy process (AHP) can then be applied to select the optimal solution from these front solutions through trade-offs. After obtaining the optimal solution, the correctness of the optimization theory can be verified by re-simulating the multi-field coupling and wear model in step S120.

[0162] Based on the above embodiments, in another embodiment provided in this disclosure, an experimental embodiment taking triangular texture as an example is also provided.

[0163] First, step S110 is performed to establish the internal region of the triangular texture based on the above formula (1).

[0164] For example, Figure 3 This is an exemplary two-dimensional triangular textured radial view provided in this disclosure, such as... Figure 3 As shown, red indicates textured areas and green indicates non-textured areas.

[0165] Furthermore, based on the above formula (11), a three-dimensional surface morphology diagram of the triangular texture is obtained. Figure 4 This is a schematic diagram of a three-dimensional surface morphology of a triangular texture provided as an example of the present disclosure.

[0166] In step S110, the input parameters are shown in Table 1:

[0167] Table 1 Input parameters for step S110

[0168] Fractal dimension D 2.3 Angle between the base and the circumferential direction (°) 10 Left slope 1 right slope -1 Texture depth (μm) 4 <![CDATA[Initial oil film thickness h0 (μm)]]> 5

[0169] Next, step S120 is executed, and the input parameters are shown in Table 2:

[0170] Table 2 Input parameters for step S120

[0171]

[0172]

[0173] Step S121: The thickness of the hydrodynamic oil film can be obtained by using the above formula (12), and the oil film thickness equation and the oil film pressure equation are combined to obtain the oil film pressure value.

[0174] Step S122: Solve the elastic deformation equation to obtain the elastic deformation of the lip, and update the oil film thickness based on the elastic deformation.

[0175] Execution step S123: Apply the point-by-point over-relaxation iterative method. The convergence criterion for oil film thickness is defined as:

[0176]

[0177] Where ER is 0.01.

[0178] The oil film thickness and pressure of this iteration are determined by the above formula (29). If they converge, the pump suction rate and friction force related to the sealing and lubrication of the lip seal can be calculated by the oil film thickness and oil film pressure. If they do not converge, steps S121-S123 are repeated until the result is satisfactory.

[0179] Step S124: The hybrid thermo-elastohydrodynamic lubrication model of the lip seal mainly considers the influence of temperature on the viscosity of the lubricating oil. The initial temperature is input, and the corresponding friction force and pumping rate are calculated by the hybrid thermo-elastohydrodynamic lubrication model (step S123). The new temperature value is obtained by thermal analysis. The new temperature value is substituted into the viscosity-temperature equation to obtain the new lubricating oil viscosity. Then it is iterated back to the original hybrid thermo-elastohydrodynamic lubrication model for recalculation until the temperature distribution error is less than the limit value.

[0180] After inputting the initial temperature, the relevant parameters of lubrication and sealing are obtained through the hybrid elastohydrodynamic lubrication model. The temperature at that time is solved by the energy conservation and heat conduction equations. Then, the viscosity is updated using the viscosity-temperature equation. The obtained viscosity is iterated back to the original hybrid elastohydrodynamic lubrication model for calculation until the temperature error is less than 0.01.

[0181] For example, the convergence criterion for the temperature field is:

[0182]

[0183] Where T1 represents the temperature value obtained from the last calculation; T2 represents the temperature at which the viscosity value was last calculated.

[0184] If the temperature field converges, output the corresponding parameters. If the temperature field does not converge, repeat steps S121-S124 until the result meets the convergence condition. The output parameters are shown in Table 3.

[0185] Table 3 Output parameters of step S120

[0186] Half-moon oil film pressure (MPa) 0.01108 Output viscosity (Pas) 0.02 Pump suction rate (ml / h) 5.836 Frictional force (N) 0.029 Tangential torque (Nm) 0.0483 Average oil film thickness (μm) 5.63 Temperature (°C) 20.05 Average contact load (MPa) 0.1024 Contact load ratio 1.025 Wear rate 0.2048

[0187] Then, step S130 is executed, setting the sample size to 100 and the processing error interval variable to [0,1] (the expression for the processing error interval variable is given in formula (4) above). Step S130, combined with the Monte Carlo simulation method, performs uncertainty analysis on the processing error of the texture size. Based on steps S110 and S120, it only needs to set the number of simulations (sample size) and treat step S120 as each simulation of step S130. Step S130 provides the midpoint and radius of the interval necessary for analysis and optimization in step S140. For each optimization iteration, the result will produce a corresponding target variable interval (midpoint and radius).

[0188] Next, step S140 is executed. The design variables may include α1 (airside angle), α2 (liquidside angle), texture processing depth, and texture size, where the airside angle, liquidside angle, and texture processing depth are given in the form of upper and lower limits. Thresholds and simulation conditions are set, and the Pareto front solution is obtained, thereby determining the target texture parameter combination.

[0189] The texture parameter values ​​in step S140 are shown in Table 4:

[0190] Table 4 shows the texture parameter values ​​for step S140.

[0191] Lower limit of lateral angle (°C) 30 Upper limit of air lateral angle (°C) 33 Lower limit of liquid side angle (°C) 45 Lower limit of liquid side angle (°C) 48 Lower limit of texture processing depth (μm) 2 Upper limit of texture processing depth (μm) 3 Left slope 1 right slope -1

[0192] For example, Figure 5 This is an exemplary schematic diagram of a Pareto front solution provided in this disclosure, such as... Figure 5 As shown, black dots represent solutions obtained in each iteration, and ⊙ represents the frontier solution.

[0193] The target texture parameter combinations output in step S140 are shown in Table 5:

[0194] Table 5 Target Texture Parameter Combinations

[0195] Processing depth (μm) 3.796 Texture dimensional parameters 1 1.023 Texture dimension parameter 2 -0.8359 Air lateral angle (°C) 31.35 Liquid side angle (°C) 46.2

[0196] One or more technical solutions provided in the exemplary embodiments of this disclosure first comprehensively define the lip seal texture, covering key parameters such as texture morphology and orientation, and accurately describing the specific characteristics of the texture in a Cartesian coordinate system. This detailed definition helps ensure the accuracy and reliability of subsequent simulation and optimization processes, providing a solid foundation for seal design.

[0197] Secondly, the three-dimensional fractal theory was applied to construct the three-dimensional microscopic surface morphology of the lip. Furthermore, a fluid-solid-thermal multi-field coupling analysis was performed on the working environment of the lip to accurately simulate the performance of the lip seal under actual working conditions.

[0198] Furthermore, a comprehensive optimization of the macro- and micro-design parameters of the lip seal was achieved by applying a genetic algorithm combined with Monte Carlo statistical methods. This approach not only addresses and optimizes individual parameters but also considers the interactions between various design parameters and their impact on sealing performance, thereby obtaining the Pareto front solution and providing an effective solution strategy for multi-objective optimization problems.

[0199] Therefore, the lip seal structure optimization method provided in the exemplary embodiments of this disclosure can seek the optimal combination of target texture parameters through Monte Carlo simulation and genetic algorithm, thereby ensuring that the rotary shaft lip seal can achieve the best sealing effect under actual working conditions.

[0200] The foregoing primarily describes the solutions provided by exemplary embodiments of this disclosure. It is understood that, in order to achieve the above functions, the electronic device includes corresponding hardware structures and / or software modules for performing each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this disclosure can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0201] The exemplary embodiments of this disclosure can divide the electronic device into functional units according to the above method examples. For example, each function can be divided into a separate functional module, or two or more functions can be integrated into a single processing module. The integrated module can be implemented in hardware or as a software functional module. It should be noted that the module division in the exemplary embodiments of this disclosure is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0202] In the case of dividing each functional module according to its respective functions, an exemplary embodiment of this disclosure provides a lip seal structure optimization device, which can be a server or a chip applied to a server. Figure 6 This is a schematic block diagram illustrating the functional modules of an exemplary lip seal structure optimization device provided in this disclosure. Figure 6 As shown, the lip seal structure optimization device 600 includes:

[0203] The data acquisition module 610 is used to define the texture parameters of the lip seal to be optimized.

[0204] The data processing module 620 is used to perform fluid-structure-thermal multi-field coupling analysis on the texture parameters of the lip seal to be optimized, and obtain sealing performance parameters.

[0205] The data processing module 620 is also used to determine multiple ranges of texture parameters to be optimized based on the texture parameters and the sealing performance parameters.

[0206] The data processing module 620 is further configured to use a genetic algorithm to optimize the multiple texture parameter ranges to obtain a target texture parameter combination.

[0207] In another embodiment provided in this disclosure, the data processing module 620 is further configured to define the texture region of the lip seal to be optimized based on a Cartesian coordinate system; define a processing error range; and determine the texture parameters of the lip seal to be optimized based on the texture region and the processing error range.

[0208] In another embodiment provided in this disclosure, the data processing module 620 is further configured to construct the three-dimensional microscopic surface morphology of the lip seal to be optimized using three-dimensional fractal theory; evaluate the sealing performance parameters and temperature of the three-dimensional microscopic surface morphology under preset operating parameters using a pre-constructed hybrid elastohydrodynamic lubrication model; and output the sealing performance parameters when the temperature meets the preset conditions.

[0209] In another embodiment provided in this disclosure, the data processing module 620 further includes: the sealing performance parameters include tangential friction force and pump suction rate; the tangential friction force is expressed as:

[0210]

[0211] Among them, F f η represents the tangential friction force; b represents the axial width of the region; l represents the length of the contact region; h represents the oil film thickness under convergence conditions obtained by solving the Reynolds equation; p represents the oil film pressure under convergence conditions obtained by solving the Reynolds equation; η represents the viscosity of the lubricating oil; U represents the rotational speed of the rotating shaft.

[0212] The pump suction rate is expressed as:

[0213]

[0214] Where Q represents the pump suction rate; l represents the contact area length; h represents the oil film thickness under convergence conditions obtained by solving the Reynolds equation; p represents the oil film pressure under convergence conditions obtained by solving the Reynolds equation; and η represents the lubricating oil viscosity.

[0215] In another embodiment provided in this disclosure, the data processing module 620 is further configured to repeatedly extract random variables in the processing error range and determine multiple texture parameters considering processing errors; sequentially simulate the multiple texture parameters considering processing errors through Monte Carlo simulation to obtain corresponding sealing performance parameters considering processing errors; and determine multiple texture parameter ranges to be optimized based on the multiple sealing performance parameters considering processing errors.

[0216] In another embodiment provided in this disclosure, the data processing module 620 is further configured to define an objective function for optimization; the genetic algorithm evaluates multiple texture parameter ranges to be optimized through the objective function to obtain corresponding frontier solutions; and the analytic hierarchy process is applied to optimize multiple frontier solutions to obtain the target texture parameter combination.

[0217] In another embodiment provided in this disclosure, the data processing module 620 further includes: the objective function is expressed as:

[0218] min{f m ,f w ,-Q m ,-Q w}

[0219]

[0220] Among them, f m Indicates the midpoint of the wear range; f w Indicates the radius of the wear range; Q m Q represents the midpoint of the pump suction rate range; w T represents the radius of the pump suction rate range; e R T represents the maximum torque. e D Indicates the torque threshold; h t Indicates texture depth; h max α1 represents the limit of machining depth; α2 represents the gas side angle; α3 represents the liquid side angle.

[0221] Exemplary embodiments of this disclosure also provide an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to cause the electronic device to perform a method according to an embodiment of this disclosure.

[0222] Exemplary embodiments of this disclosure also provide a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform a method according to embodiments of this disclosure.

[0223] Figure 7 The present disclosure provides an exemplary structural block diagram of an electronic device. The structural block diagram of an electronic device 700 that can serve as a server or client of the present disclosure is now described, and it is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0224] like Figure 7 As shown, the electronic device 700 includes a computing unit 701, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. The RAM 703 may also store various programs and data required for the operation of the electronic device 700. The computing unit 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0225] Multiple components in electronic device 700 are connected to I / O interface 705, including: input unit 706, output unit 707, storage unit 708, and communication unit 709. Input unit 706 can be any type of device capable of inputting information to electronic device 700. Input unit 706 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of electronic device. Output unit 707 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 708 may include, but is not limited to, disk and optical disk. Communication unit 709 allows electronic device 700 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth™ devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0226] The computing unit 701 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 701 performs the various methods and processes described above. The various methods described above can all be implemented as computer software programs, which are tangibly contained in a machine-readable medium, such as storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 700 via ROM 702 and / or communication unit 709.

[0227] Figure 8 The present disclosure provides an exemplary computer program product, and an exemplary embodiment of the present disclosure also provides a computer program product 800, including a computer program 801, wherein the computer program 801, when executed by a computer's processor, is used to cause the computer to perform a method according to an embodiment of the present disclosure.

[0228] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0229] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0230] As used in this disclosure, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0231] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0232] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0233] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other.

[0234] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of this disclosure are performed, in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a terminal, a user equipment, or other programmable device. The computer program or instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center integrating one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; it can also be an optical medium, such as a digital video disc (DVD); or it can be a semiconductor medium, such as a solid-state drive (SSD).

[0235] Although this disclosure has been described in conjunction with specific features and embodiments, it will be apparent that various modifications and combinations can be made thereto without departing from the spirit and scope of this disclosure. Accordingly, this specification and drawings are merely illustrative descriptions of the disclosure as defined by the appended claims and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of this disclosure. It is obvious that those skilled in the art can make various alterations and modifications to this disclosure without departing from its spirit and scope. Thus, this disclosure is also intended to include any such modifications and modifications that fall within the scope of the claims of this disclosure and their equivalents.

Claims

1. A method for optimizing a lip-shaped sealing structure, characterized in that, The method includes: Define the texture parameters of the lip seal to be optimized; Fluid-structure-thermal multi-field coupling analysis was performed on the texture parameters of the lip seal to be optimized to obtain sealing performance parameters; Based on the texture parameters and the sealing performance parameters, multiple ranges of texture parameters to be optimized are determined; A genetic algorithm is used to optimize the multiple texture parameter ranges to obtain the target texture parameter combination; The process of defining the texture parameters of the lip seal to be optimized includes: defining the texture region of the lip seal to be optimized based on a Cartesian coordinate system; defining the processing error range; and determining the texture parameters of the lip seal to be optimized based on the texture region and the processing error range. The step of determining multiple texture parameter ranges to be optimized based on the texture parameters and the sealing performance parameters includes: repeatedly sampling random variables within the processing error range and determining multiple texture parameters considering processing errors; sequentially simulating the multiple texture parameters considering processing errors using Monte Carlo simulation to obtain the corresponding sealing performance parameters considering processing errors; and determining multiple texture parameter ranges to be optimized based on the multiple sealing performance parameters considering processing errors.

2. The method according to claim 1, characterized in that, The process involves performing a fluid-structure-thermal multi-field coupling analysis on the texture parameters of the lip seal to be optimized to obtain sealing performance parameters, including: The three-dimensional microscopic surface morphology of the lip seal to be optimized was constructed using three-dimensional fractal theory; The sealing performance parameters and temperature of the three-dimensional microscopic surface morphology under preset operating parameters were evaluated using a pre-constructed hybrid elastohydrodynamic lubrication model. After the pressure and temperature iterative convergence, the sealing performance parameters are output.

3. The method according to claim 2, characterized in that, The sealing performance parameters include tangential friction force and pump suction rate; The tangential friction force is expressed as: in, F f This represents tangential frictional force; b Indicates the axial width of the region; l Indicates the length of the contact area; h This represents the oil film thickness under convergence conditions obtained by solving the Reynolds equation; p This represents the oil film pressure under convergence conditions obtained by solving the Reynolds equation; η Indicates the viscosity of the lubricating oil; U Indicates the rotational speed of the shaft; τ x This represents the circumferential shear stress on the lip surface of a lip seal. dA This represents the area of ​​the contact region within the lip seal; The pump suction rate is expressed as: in, Q Indicates pump suction rate; l Indicates the length of the contact area; h This represents the oil film thickness under convergence conditions obtained by solving the Reynolds equation; This represents the oil film pressure under convergence conditions obtained by solving the Reynolds equation; This indicates the viscosity of the lubricating oil.

4. The method according to claim 1, characterized in that, The step of using a genetic algorithm to optimize the multiple texture parameter ranges to obtain the target texture parameter combination includes: Define the objective function for optimization; The genetic algorithm evaluates multiple intervals of texture parameters to be optimized using an objective function, and obtains the corresponding frontier solutions. The target texture parameter combination is obtained by optimizing multiple frontier solutions using the applied analytic hierarchy process.

5. The method according to claim 4, characterized in that, The objective function is expressed as: in, f m Indicates the midpoint of the wear range; f w Indicates the radius of the wear range; Q m Indicates the midpoint of the pump suction rate range; Q w Indicates the radius of the pump suction rate range; T e R Indicates the maximum torque. T e D Indicates the torque threshold; h t Indicates texture depth; h max Indicates the maximum machining depth; Indicates the lateral angle; Indicates the liquid side angle.

6. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the method of claim 1.

7. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instruction is executed by the processor, it implements the method of claim 1.

8. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the method of claim 1.

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