Method and device for predicting contact resistance of rough surface based on semi-analytical solution

Abstract

The embodiment of the invention provides a rough surface contact resistance prediction method based on a semi-analytical solution, and relates to the technical field of electric contact, and the method comprises the steps: generating a rough surface according to the surface morphology, statistical characteristics and fractal characteristics of the rough surface, and numerical values; the surface morphology of the rough surface comprises a morphology function; the statistical characteristics comprise a height distribution rule; the fractal feature comprises a power spectral density function; determining an electric contact load condition, a material parameter and a contact parameter, and deducing a semi-analytical solution of the contact resistance; deriving a contact resistance prediction model based on the generated rough surface and the semi-analytical solution; and performing numerical prediction on the contact resistance of different rough surfaces by using the contact resistance prediction model. According to the scheme, the influence of various surface parameters on the contact resistance is considered, fitting analysis and extraction are carried out on all the parameters with large influence to obtain the contact resistance prediction model, and the rough surface contact resistance prediction efficiency and accuracy can be greatly improved.

Description

Technical Field

[0001] The present application relates to the technical field of electrical contact, and particularly to a method and device for predicting the contact resistance of a rough surface based on semi-analytical solutions. Background Art

[0002] Electrical contact is a common phenomenon in electronic and power systems. Electrical connection and signal transmission need to be achieved through electrical contact between components of modern industrial systems, between complete machines, and even between systems. The resistance between contact components is called contact resistance, which is one of the important performance indicators of contact components. The contact interface has a certain roughness and there are multi-field coupling effects of electricity-thermal-mechanics, including Joule heat generation and thermal expansion of materials, etc., thus bringing difficulties to the solution of contact resistance.

[0003] Existing research mainly focuses on the field of mechanical contact mechanics and experimental research related to contact resistance. When solving contact resistance with existing contact resistance models, the multi-field coupling effects are often ignored, and the electrical contact problem is solved as a mechanical contact problem. Moreover, it is difficult to model random rough surfaces, resulting in inaccurate prediction results of contact resistance. Therefore, it is urgent to propose an effective prediction model for the contact resistance of rough surfaces to efficiently and accurately predict the contact resistance of random rough surfaces. Summary of the Invention

[0004] The purpose of the embodiments of the present application is to provide a method for predicting the contact resistance of a rough surface based on semi-analytical solutions to improve the efficiency and accuracy of predicting the contact resistance of random rough surfaces. The specific technical solutions are as follows:

[0005] In the first aspect of the implementation of the present application, first, a method for predicting the contact resistance of a rough surface based on semi-analytical solutions is provided, including:

[0006] Numerically generate a rough surface according to the surface topography, statistical characteristics, and fractal characteristics of the rough surface; wherein, the surface topography of the rough surface includes a topography function; the statistical characteristics include the height distribution law; the fractal characteristics include: power spectral density function;

[0007] Determine the electrical contact load conditions, material parameters, and contact parameters, and derive the semi-analytical solution of the contact resistance;

[0008] Based on the generated rough surface and the semi-analytical solution, determine the contact resistance prediction model;

[0009] Use the contact resistance prediction model to numerically predict the contact resistance of different rough surfaces.

[0010] Optionally, the semi-analytical solution of the contact resistance includes the following equations and conditions:

[0011]

[0012] The constraint conditions are as follows:

[0013]

[0014] Among them, R c is the contact resistance, is the average electric potential within the contact area, I is the total current passing through the contact interface, J(k, l) represents the current density at each node on the contact surface, V(k, l) represents the electric potential distribution, and the subscripts k and l represent the node numbers of the discrete rough surface in the x and y directions, respectively satisfying 1 ≤ k ≤ N x , 1 ≤ l ≤ N y , l x and l y are the lengths of the discrete grids in the two directions respectively, and N x ×N y represents the number of discrete nodes on the rough surface.

[0015] Optionally, determining the contact resistance prediction model based on the generated rough surface and the semi-analytical solution includes:

[0016] Determining the surface parameters of the generated rough surface, where the surface parameters include root mean square roughness, Hurst exponent, and surface maximum and minimum cut-off wave numbers;

[0017] Using the surface parameters, target parameters, and the semi-analytical solution to generate corresponding contact resistance data, where the target parameters are the electrical contact load conditions, material parameters, and contact parameters corresponding to generating the contact resistance data;

[0018] Analyzing and fitting the contact resistance data to refine and obtain the contact resistance prediction model.

[0019] Optionally, the load conditions include contact force, total voltage, or total current, the material parameters include elastic modulus, Poisson's ratio, yield stress, hardening modulus, coefficient of thermal expansion, heat transfer coefficient, and resistivity, and the contact parameters include: contact conductance and contact thermal conductance.

[0020] Optionally, the height distribution law includes the probability density distribution law; the power spectral density function includes a power function form and an exponential function form;

[0021] The power function form includes:

[0022]

[0023] The exponential function form includes:

[0024]

[0025] wherein, q x and q y are the frequencies in two directions respectively, H is the Hurst exponent, q0 and q1 are the low-frequency and high-frequency cutoffs respectively, and β is the power spectral density correction parameter.

[0026] In the second aspect of the implementation of the present application, there is also provided a rough surface contact resistance prediction device based on semi-analytical solutions, including:

[0027] A numerical generation module, configured to numerically generate a rough surface according to the surface topography, statistical characteristics, and fractal characteristics of the rough surface; wherein, the surface topography of the rough surface includes a topography function; the statistical characteristics include a height distribution law; the fractal characteristics include: a power spectral density function;

[0028] A first derivation module, configured to determine the electrical contact load condition, material parameters, and contact parameters, and derive a semi-analytical solution of the contact resistance;

[0029] A second derivation module, configured to determine a contact resistance prediction model based on the generated rough surface and the semi-analytical solution;

[0030] A numerical prediction model, configured to numerically predict the contact resistance of different rough surfaces by using the contact resistance prediction model.

[0031] Optionally, the semi-analytical solution of the contact resistance includes the following equations and conditions:

[0032]

[0033] The constraint conditions are:

[0034]

[0035]

[0036] wherein, R c is the contact resistance, is the average electric potential within the contact area, I is the total current passing through the contact interface, J(k, l) represents the current density at each node on the contact surface, V(k, l) represents the electric potential distribution, and the subscripts k and l represent the node numbers of the discrete rough surface in the x and y directions, respectively satisfying 1 ≤ k ≤ N x , 1 ≤ l ≤ N y , l x and l y are the lengths of the discrete grids in two directions respectively, and N x ×N y represents the number of discrete nodes of the rough surface.

[0037] Optionally, the second derivation module is specifically configured to:

[0038] Determine the surface parameters of the generated rough surface, where the surface parameters include root mean square roughness, Hurst index, and surface maximum and minimum cut-off wave numbers;

[0039] Generate corresponding contact resistance data by using the surface parameters, target parameters, and the semi-analytical solution, where the target parameters are the electrical contact load conditions, material parameters, and contact parameters corresponding to the generated contact resistance data;

[0040] Analyze and fit the contact resistance data to extract and obtain a contact resistance prediction model.

[0041] Optionally, the load conditions include contact force, total voltage, and total current, the material parameters include elastic modulus, Poisson's ratio, yield stress, hardening modulus, thermal expansion coefficient, heat transfer coefficient, and resistivity, and the contact parameters include contact conductance and contact thermal conductance.

[0042] In another aspect of the implementation of this application, an electronic device is provided in an embodiment of this application, including a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete communication with each other through the communication bus;

[0043] The memory is used to store a computer program;

[0044] When the processor is used to execute the program stored on the memory, it implements the steps of a method for predicting the contact resistance of a rough surface based on a semi-analytical solution.

[0045] Advantages of this application:

[0046] A method for predicting the contact resistance of a rough surface based on a semi-analytical solution provided in an embodiment of this application numerically generates a rough surface according to the surface topography, statistical features, and fractal features of the rough surface. Among them, the surface topography of the rough surface includes a topography function; the statistical features include a height distribution law; the fractal features include a power spectral density function; determine the electrical contact load conditions, material parameters, and contact parameters, and derive a semi-analytical solution of the contact resistance; based on the generated rough surface and the semi-analytical solution, derive a contact resistance prediction model; use the contact resistance prediction model to numerically predict the contact resistance of different rough surfaces. In this solution, the influence of various surface parameters on the contact resistance is considered, and fitting analysis is performed on all parameters with greater influence to extract and obtain a contact resistance prediction model, which can greatly improve the prediction efficiency and accuracy of the contact resistance of the rough surface. Description of the Drawings

[0047] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art.

[0048] Figure 1 It is a flowchart of a method for predicting the contact resistance of a rough surface based on semi-analytical solutions in the present application.

[0049] Figure 2 It is the influence of 9 groups of different rough surface parameters on the contact resistance-contact force curve in the present application;

[0050] Figure 3 It is the influence law of each surface parameter on the contact resistance-contact force curve in the present application;

[0051] Figure 4 It is a physical diagram of the experimental instrument and specimen used in the present application;

[0052] Figure 5 It is the true surface topography of the specimen used in the present application;

[0053] Figure 6 It is the height probability density distribution diagram and power spectral density curve diagram of the true surface topography of the specimen in the present application;

[0054] Figure 7 It is an example diagram of a rough surface profile generated according to the statistical characteristics and fractal characteristic values of the true rough surface in the present application;

[0055] Figure 8 It is a comparison diagram of the calculation results and experimental results in the present application. Specific embodiments

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0057] To solve the problems in the prior art, the present application provides a method for predicting the contact resistance of a rough surface based on semi-analytical solutions to improve the efficiency and accuracy of predicting the contact resistance of a random rough surface.

[0058] It should be noted that a method for predicting the electrical contact behavior of a rough surface based on the boundary element method provided in the embodiments of the present application can be applied to electronic devices. In practical applications, the electronic device can be: a smart phone, a tablet computer, a laptop computer, etc., which are all reasonable.

[0059] To better illustrate the embodiments in this application, exemplarily, assume that a rigid body with a rough surface compresses a deformable elastoplastic block B under the action of a contact force. The height of the block is h0 and the nominal area is L x ×L y . Apply a total current I (or total voltage ΔV), and there is current flowing through each contacting rough peak (current density is J). Due to the effect of Joule heat, surface heat flux Q will be generated s , and this heat flux is evenly distributed between the upper and lower surfaces. Therefore, this electrical contact process is a problem of multi-field coupling of electricity, heat, and force, and the main heat generation part is on the contact surface.

[0060] First, a method for predicting the contact resistance of a rough surface based on semi-analytical solutions provided by the embodiments of this application will be introduced below.

[0061] As Figure 1 shown, a method for predicting the contact resistance of a rough surface based on semi-analytical solutions provided by the embodiments of this application may include the following steps:

[0062] S101, numerically generate a rough surface according to the surface topography, statistical characteristics, and fractal characteristics of the rough surface.

[0063] Among them, the surface topography of the rough surface includes a topography function; the statistical characteristics include the height distribution law; the fractal characteristics include: the power spectral density function. In numerical calculations, the rough surface is characterized as a set of discrete nodes.

[0064] Exemplarily, in one implementation, numerically generating a rough surface according to the surface topography, statistical characteristics, and fractal characteristics of the rough surface may include: using a statistical distribution to determine the characteristics of the surface height distribution law, the power spectral density function to determine the fractal characteristics, numerically generating the rough surface, and simultaneously determining the height h0 of the block and the nominal area L of the contact surface x ×L y .

[0065] Exemplarily, in one implementation, the height distribution law includes a probability density distribution law, and this probability density distribution law may include Gaussian distribution and Weibull distribution, etc.; the power spectral density function includes a power function form and an exponential function form;

[0066] The power function form includes:

[0067]

[0068] The exponential function form includes:

[0069]

[0070] Among them, q x and q y are the frequencies in two directions respectively, H is the Hurst exponent, q0 and q1 are the low-frequency and high-frequency cutoffs respectively, and β is the power spectral density correction parameter.

[0071] S102. Determine the electrical contact load conditions, material parameters, and contact parameters, and derive the semi-analytical solution of the contact resistance;

[0072] It should be noted that in order to obtain the contact resistance of a rough surface, relevant conditions or parameters can be determined first, such as electrical contact load conditions, material parameters, and contact parameters. Among them, the electrical contact load conditions can include: contact force and total current (or total voltage), and the material parameters can include elastic modulus, Poisson's ratio, plastic parameters, heat transfer coefficient, thermal expansion coefficient, and resistivity; the contact parameters can include contact conductance and contact thermal conductance. In addition, in order to obtain the electrical contact pressure distribution under different materials and contact states, the settings of material parameters and contact parameters may vary, which are all reasonable.

[0073] Exemplarily, the semi-analytical solution of the contact resistance can include the following equations and conditions:

[0074]

[0075] The constraint conditions can include:

[0076]

[0077] Among them, R c is the contact resistance, is the average electric potential within the contact area, I is the total current passing through the contact interface, J(k, l) represents the current density at each node on the contact surface, and V(k, l) represents the electric potential distribution. The subscripts k and l represent the node numbers of the discrete rough surface in the x and y directions respectively, satisfying 1 ≤ k ≤ N x , 1 ≤ l ≤ N y , l x and l y are the lengths of the discrete grids in two directions respectively, and N x ×N y represents the number of discrete nodes on the rough surface.

[0078] S103. Based on the generated rough surface and the semi-analytical solution, determine the contact resistance prediction model;

[0079] Exemplarily, in one implementation, based on the generated rough surface and the semi-analytical solution, determining the contact resistance prediction model can include:

[0080] Determine the surface parameters of the generated rough surface, where the surface parameters include root mean square roughness, Hurst exponent, and surface maximum and minimum cut-off wave numbers;

[0081] Using the surface parameters, target parameters, and the semi-analytical solution, with emphasis on the influence of surface parameters on contact resistance, generate corresponding contact resistance data, where the target parameters are the electrical contact load conditions, material parameters, and contact parameters corresponding to the generated contact resistance data;

[0082] Analyze and fit the contact resistance data, considering the surface parameters with greater influence, and refine to obtain a contact resistance prediction model.

[0083] Considering the influence of different surface parameters on contact resistance, when deriving the contact resistance prediction model, multiple surface parameters can be comprehensively considered, such as: the height distribution of the rough surface topography, root mean square roughness, Hurst exponent (or fractal dimension), and surface maximum and minimum cut-off wave numbers, etc.

[0084] Considering that different surface parameters and target parameters can generate different contact resistance data, then, in order to improve the prediction efficiency and accuracy of the contact resistance prediction model, as well as the applicable range of the prediction model, multiple different sets of surface parameters and target parameters can be selected to generate contact resistance data.

[0085] In the embodiments of the present application, the electrical contact problem can be solved by existing theoretical or numerical methods. In addition, multiple sets of contact resistance data can be generated using multiple sets of surface parameters and semi-analytical solutions, and combined with the influence law of contact resistance by different rough surface parameters, fitting analysis is performed on all parameters with greater influence, and a contact resistance prediction model is derived to numerically predict the contact resistance of different rough surfaces.

[0086] S104, use the contact resistance prediction model to numerically predict the contact resistance of different rough surfaces.

[0087] The contact resistance prediction model of the present application is a simple and easy-to-use contact resistance prediction model formed after analyzing and fitting the rough surface parameters with greater influence, and it needs to be used when the material parameters and surface parameters are known, without the need to specify contact parameters or cooperate with other algorithms.

[0088] In this application, a rough surface is numerically generated based on the surface topography, statistical characteristics, and fractal characteristics of the rough surface; wherein, the surface topography of the rough surface includes a topography function; the statistical characteristics include the height distribution law; the fractal characteristics include: a power spectral density function; the electrical contact load conditions, material parameters, and contact parameters are determined, and a semi-analytical solution of the contact resistance is derived; based on the generated rough surface and the semi-analytical solution, a contact resistance prediction model is derived; using the contact resistance prediction model, the contact resistance of different rough surfaces is numerically predicted. In this solution, the influence of various surface parameters on the contact resistance is considered, and fitting analysis is performed on all parameters with a greater influence, and a contact resistance prediction model is derived, which can greatly improve the prediction efficiency and accuracy of the contact resistance of the rough surface.

[0089] To further illustrate the solution of this application, the following takes specific examples to introduce a method for predicting the contact resistance of a rough surface based on a semi-analytical solution of this application.

[0090] Study the root mean square roughness h of the rough surface according to the above steps rms , Hurst exponent H, and minimum cut-off wave number on the contact resistance. The value range of each parameter is shown in Table 1. First, take 9 groups of rough surface parameters within the used parameter range to study the influence of rough surface parameters on the contact resistance. As Figure 2 shown, it can be seen that in the double logarithmic coordinate, the contact resistance and the contact force are close to a linear relationship. Therefore, assume that the relationship between the contact resistance and the contact force (i.e., an example of the contact resistance prediction model in this application) is:

[0091]

[0092] where C1 and C2 are undetermined parameters, which are mainly related to surface parameters and material parameters, and R * is the dimensionless contact resistance, and the calculation method is R * =R c / (ρh rms / A0), ρ is the resistivity of the material, A0 is the nominal contact area, and P * is the dimensionless contact force, and the calculation method is E * is the comprehensive elastic modulus, is the root-mean-square gradient of the rough surface (▽h represents the gradient of the rough surface profile, and the symbol <·> represents taking the mathematical expectation of the parameters inside it), which can usually be obtained using surface topography data or power spectral density function. The remaining material parameters are shown in Table 2. The following focuses on the influence of the above three rough surface parameters on C1 and C2. When studying the effect of each surface parameter, 20 rough surfaces are generated for each set of parameters to eliminate the influence of surface randomness on the results. When each surface parameter varies within a given range, the values of the other two parameters are fixed respectively. For example, when studying h rms , fix H = 0.7, When studying H, fix h rms = 1μm, When studying , h rms = 1μm, H = 0.7. The influence rules of the three parameters on C1 and C2 are as Figure 3 shown. According to the above research method, the fitting formulas of C1 and C2 for the three surface parameters can be obtained as

[0093]

[0094] Combined with the relationship between the dimensionless resistance and the dimensionless contact force, substituting the above two parameters, that is, the effective contact resistance prediction model of the present invention. Next, the present invention is verified using an experimental method.

[0095] Table 1 Rough surface parameters

[0096]

[0097] Table 2 Material parameters

[0098]

[0099] The experimental method is used to compare and verify with the present invention. A cube with dimensions of 10mm×10mm×10mm is made as the rough surface indenter, and a cuboid with dimensions of 20mm×20mm×10mm is made as the elastoplastic block. Both specimens are made of copper alloy material. The surface topography, statistical characteristics, and fractal characteristics of the rough surface are obtained by analyzing using a Zegage Plus, Zygo, USA white light interferometer. Further, a Keithley 6221 current source, a Keithley 2182A nanovoltmeter, and a Shimadzu universal testing machine equipped with a 10kN force sensor are used, as Figure 4 shown. According to Ohm's law, the contact resistance is measured using the four-wire method. The normal contact force data is obtained by applying a compressive displacement at a constant speed using the force sensor. After the experiment, the results of the contact force and the contact resistance are extracted and compared with the results determined using the present invention.

[0100] In this embodiment, a white light interferometer is used to measure a real rough surface, and its real surface topography is as shown in Figure 5 . This rough surface is obtained by sandblasting on a smooth surface. According to the measurement data of the real surface topography, its height probability density distribution and power spectral density curve are obtained, as shown in Figure 6 . Further analyzing the statistical characteristics and fractal characteristics of this rough surface, it is known that the surface topography of this rough surface is close to a Gaussian distribution, with a root mean square roughness h rms = 4.5 μm, a Hurst exponent H = 0.65, and a minimum cut-off wave number . A rough surface is generated according to the obtained parameters, and an example of one rough surface is as shown in Figure 7 . Through the analysis and fitting of the contact resistance data of the random rough surface in this application, a contact resistance prediction model is formed, and the prediction results include a contact resistance-contact force curve, as shown in Figure 8 , which is in good agreement with the experimental results. Through comparison, the effectiveness of a method for predicting the contact resistance of a rough surface based on a semi-analytical solution in this application is proven.

[0101] Compared with the embodiments of the above method, the embodiment of this application provides a device for predicting the contact resistance of a rough surface based on a semi-analytical solution. The device may include:

[0102] A numerical generation module, configured to numerically generate a rough surface according to the surface topography, statistical characteristics, and fractal characteristics of the rough surface; wherein, the surface topography of the rough surface includes a topography function; the statistical characteristics include a height distribution law; the fractal characteristics include: a power spectral density function;

[0103] A first derivation module, configured to determine the electrical contact load condition, material parameters, and contact parameters, and derive a semi-analytical solution of the contact resistance;

[0104] A second derivation module, configured to determine a contact resistance prediction model based on the generated rough surface and the semi-analytical solution;

[0105] A numerical prediction model, configured to numerically predict the contact resistance of different rough surfaces by using the contact resistance prediction model.

[0106] In this application, a rough surface is numerically generated according to the surface topography, statistical characteristics, and fractal characteristics of the rough surface; wherein, the surface topography of the rough surface includes a topography function; the statistical characteristics include the height distribution law; the fractal characteristics include: a power spectral density function; an electrical contact load condition, material parameters, and contact parameters are determined, and a semi-analytical solution of the contact resistance is derived; based on the generated rough surface and the semi-analytical solution, a contact resistance prediction model is derived; and the contact resistance prediction model is used to numerically predict the contact resistance of different rough surfaces. In this solution, the influence of various surface parameters on the contact resistance is considered, fitting analysis is performed on all parameters with a large influence, and a contact resistance prediction model is derived, which can greatly improve the prediction efficiency and accuracy of the contact resistance of the rough surface.

[0107] Optionally, the semi-analytical solution of the contact resistance includes the following equations and conditions:

[0108]

[0109] The constraint conditions are:

[0110]

[0111] Wherein, R c is the contact resistance, is the average electric potential within the contact area, I is the total current passing through the contact interface, J(k, l) represents the current density at each node on the contact surface, V(k, l) represents the electric potential distribution, and the subscripts k and l represent the node numbers of the discrete rough surface in the x and y directions, respectively satisfying 1 ≤ k ≤ N x , 1 ≤ l ≤ N y , l x and l y are the lengths of the discrete grids in the two directions, respectively, and N x ×N y represents the number of discrete nodes of the rough surface.

[0112] Optionally, the second derivation module is specifically configured to:

[0113] Determine the surface parameters of the generated rough surface, wherein the surface parameters include root mean square roughness, Hurst exponent, and surface maximum and minimum cut-off wave numbers;

[0114] Using the surface parameters, target parameters, and the semi-analytical solution, with emphasis on the influence of the surface parameters on the contact resistance, corresponding contact resistance data is generated, and the target parameters are the electrical contact load conditions, material parameters, and contact parameters corresponding to the generated contact resistance data;

[0115] Analyze and fit the contact resistance data, consider the surface parameters with greater influence, and refine to obtain a contact resistance prediction model.

[0116] Optionally, the load conditions include contact force, total voltage, and total current, the material parameters include elastic modulus, Poisson's ratio, yield stress, hardening modulus, coefficient of thermal expansion, heat transfer coefficient, and resistivity, and the contact parameters include contact conductance and contact thermal conductance.

[0117] An embodiment of the present application also provides an electronic device, including a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete communication with each other through the communication bus.

[0118] The memory is used to store a computer program.

[0119] When the processor is used to execute the program stored in the memory, it implements a method for predicting the contact resistance of a rough surface based on a semi-analytical solution provided by an embodiment of the present application.

[0120] The communication bus mentioned in the above terminal may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of representation, only a thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0121] The communication interface is used for communication between the above terminal and other devices.

[0122] The memory may include a Random Access Memory (RAM), or may also include a non-volatile memory, such as at least one disk memory. Optionally, the memory may also be at least one storage device located far from the aforementioned processor.

[0123] The above-mentioned processor may be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it may also be a digital signal processor (DSP for short), an application specific integrated circuit (ASIC for short), a field-programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0124] In another embodiment provided by the present application, there is also provided a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the rough surface contact resistance prediction method based on the semi-analytical solution described in any one of the above embodiments is implemented.

[0125] In another embodiment provided by the present application, there is also provided a computer program product containing instructions, which when running on a computer, causes the computer to execute the rough surface contact resistance prediction method based on the semi-analytical solution described in any one of the above embodiments.

[0126] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that the computer can access, or a data storage device such as a server or data center that includes one or more integrated available media. The available medium may be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk (SSD)).

[0127] It should be noted that, in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the said element.

[0128] Each embodiment in this specification is described in a related manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the apparatus embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and reference can be made to the relevant parts of the method embodiments for the related content.

[0129] The above are only the preferred embodiments of the present application and are not intended to limit the protection scope of the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application are all included in the protection scope of the present application.

Claims

1. A method for predicting the contact resistance of a rough surface based on semi-analytical solutions, characterized in that, Including: Numerically generate a rough surface according to the surface topography, statistical features, and fractal features of the rough surface; wherein, the surface topography of the rough surface includes a topography function; the statistical features include the height distribution law; the fractal features include: a power spectral density function; Determine the electrical contact load conditions, material parameters, and contact parameters, and deduce the semi-analytical solution of the contact resistance; Based on the generated rough surface and the semi-analytical solution, determine the contact resistance prediction model; Use the contact resistance prediction model to numerically predict the contact resistance of different rough surfaces.

2. The method according to claim 1, characterized in that, The semi-analytical solution of the contact resistance includes the following equations and conditions: The constraint conditions are: wherein, R c is the contact resistance, is the average electric potential within the contact area, I is the total current passing through the contact interface, J(k, l) represents the current density at each node on the contact surface, V(k, l) represents the electric potential distribution, and the subscripts k, l represent the node numbers of the discrete rough surface in the x and y directions, respectively satisfying 1 ≤ k ≤ N x , 1 ≤ l ≤ N y , l x and l y are the lengths of the discrete grids in the two directions respectively, and N x × N y represents the number of discrete nodes of the rough surface.

3. The method according to claim 1 or 2, characterized in that, The determining the contact resistance prediction model based on the generated rough surface and the semi-analytical solution includes: Determine the surface parameters of the generated rough surface, wherein the surface parameters include the root mean square roughness, Hurst exponent, and surface maximum and minimum cut-off wave numbers; Use the surface parameters, target parameters, and the semi-analytical solution to generate corresponding contact resistance data, where the target parameters are the electrical contact load conditions, material parameters, and contact parameters corresponding to generating the contact resistance data; Analyze and fit the contact resistance data to refine and obtain the contact resistance prediction model.

4. The method according to claim 1 or 2, characterized in that, The load conditions include contact force, total voltage, or total current, the material parameters include elastic modulus, Poisson's ratio, yield stress, hardening modulus, thermal expansion coefficient, heat transfer coefficient, and resistivity, and the contact parameters include: contact conductance and contact thermal conductance.

5. The method according to claim 1 or 2, characterized in that, The height distribution law includes the probability density distribution law; the power spectral density function includes a power function form and an exponential function form; The power function form includes: The exponential function form includes: where q x and q y are the frequencies in two directions respectively, H is the Hurst exponent, q0 and q1 are the low-frequency and high-frequency cutoffs respectively, and β is the power spectral density correction parameter.

6. A rough surface contact resistance prediction device based on semi-analytical solutions, characterized in that, Including: A numerical generation module for numerically generating a rough surface according to the surface topography, statistical features, and fractal features of the rough surface; wherein, the surface topography of the rough surface includes a topography function; the statistical features include the height distribution law; the fractal features include: a power spectral density function; A first derivation module for determining the electrical contact load conditions, material parameters, and contact parameters, and deducing the semi-analytical solution of the contact resistance; A second derivation module for determining the contact resistance prediction model based on the generated rough surface and the semi-analytical solution; A numerical prediction model for numerically predicting the contact resistance of different rough surfaces using the contact resistance prediction model.

7. The device according to claim 6, characterized in that, The semi-analytical solution of the contact resistance includes the following equations and conditions: The constraint conditions are: Among them, R c is the contact resistance, is the average electric potential within the contact area, I is the total current passing through the contact interface, J(k, l) represents the current density at each node on the contact surface, and V(k, l) represents the electric potential distribution. The subscripts k and l represent the node numbers of the discrete rough surface in the x and y directions, respectively, satisfying 1 ≤ k ≤ N x , 1 ≤ l ≤ N y , l x and l y are the lengths of the discrete grid in the two directions, respectively, and N x ×N y represents the number of discrete nodes of the rough surface.

8. The device according to claim 6 or 7, characterized in that, The second derivation module is specifically used for: Determine the surface parameters of the generated rough surface, wherein the surface parameters include the root mean square roughness, Hurst exponent, and surface maximum and minimum cut-off wave numbers; Use the surface parameters, target parameters, and the semi-analytical solution to generate corresponding contact resistance data, where the target parameters are the electrical contact load conditions, material parameters, and contact parameters corresponding to generating the contact resistance data; Analyze and fit the contact resistance data to refine and obtain the contact resistance prediction model.

9. The device according to claim 6 or 7, characterized in that, The load conditions include contact force, total voltage, and total current, the material parameters include elastic modulus, Poisson's ratio, yield stress, hardening modulus, coefficient of thermal expansion, heat transfer coefficient, and resistivity, and the contact parameters include contact conductance and contact thermal conductance.

10. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete mutual communication through the communication bus; The memory is used to store computer programs; The processor is used to implement the method steps described in any one of claims 1-5 when executing the programs stored on the memory.

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