A method and system for predicting bearing varnish evolution under high thermal load

Abstract

This invention discloses a method and system for predicting the evolution of bearing varnish under high heat load. The method includes: reading input parameters; initializing relevant variables; solving for the current bearing state variables, film pressure and film temperature; solving for the varnish formation rate; calculating the current time step based on the varnish formation rate; calculating the change in varnish deposition height at the current time step; updating the varnish deposition height array based on the change in varnish deposition height and calculating the film thickness change; based on the deposition height calculation result and the total time step, determining whether the maximum deposition height or the total simulation time of the bearing is greater than the planned height or time. If not, updating the film thickness and iterating the relevant variables, then returning; if satisfied, determining the final deposition height, film thickness, and film pressure, and outputting the results. The system includes a reading module, an initialization module, a calculation module, and a judgment module. This invention solves the problems of difficult characterization of bearing varnish evolution and large prediction errors under high heat load.

Description

Technical Field

[0001] This invention belongs to the field of sliding bearings, and specifically relates to a method and system for predicting the evolution of bearing varnish under high thermal load. Background Technology

[0002] Sliding bearings are widely used in critical equipment operating under high heat loads and harsh conditions, such as large nuclear power equipment, aerospace, nuclear power, and railways, directly affecting the reliability and lifespan of mechanical equipment. Over time and with changes in harsh operating conditions and high heat loads, a varnish film may form on the high-heat-load areas of the bearing's inner surface. This can lead to changes in the thickness and distribution of the oil film, resulting in insufficient lubrication and increased wear risk. Varnish film evolution is a dynamic process, exhibiting phenomena such as continuous accumulation, scraping and peeling, and even continuous cycles. Therefore, accurately predicting varnish film evolution is a significant challenge.

[0003] Currently, the formation and evolution of varnish film are mainly attributed to lubricant degradation, with various metal ions acting as catalysts to further promote this degradation. As degradation increases, varnish film continuously deposits, leading to deposition peaks. Therefore, the establishment of a deposition model has a decisive impact on the accuracy of simulation prediction results. Currently, the Arrhenius equation is primarily used to establish the model, which characterizes the relationship between the rate constant of a chemical reaction and temperature. The varnish film deposition rate model established using this equation shows deposition occurring at all temperatures, and the deposition rate is essentially the same at different operating temperatures. Therefore, the establishment of deposition models still faces challenges in characterizing varnish film evolution and in predicting large errors. There is an urgent need for a computationally efficient and accurate model to characterize the varnish film evolution of bearings under high thermal loads. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for predicting the evolution of bearing varnish under high heat load. To address the difficulties in characterizing the evolution of bearing varnish under high heat load and the large errors in prediction results, this invention proposes a method for predicting the evolution of bearing varnish under high heat load based on activation functions.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A method for predicting bearing varnish evolution under high thermal load includes the following steps:

[0007] S1. Read input parameters, including bearing properties, initial state, and current wear parameters;

[0008] S2. Initialize relevant variables based on input parameters, including oil film thickness, deposition height array, and time step array;

[0009] S3. Based on the initialized relevant variables, the current state variables of the bearing, membrane pressure and membrane temperature, are obtained by solving the control equations.

[0010] S4. Based on the current state variables of the bearing, such as membrane pressure and membrane temperature, determine the actual bearing working conditions, operating cycle and analysis requirements, and select the Arrhenius model or activation function model to solve for the coating film formation rate.

[0011] S5. Calculate the current time step based on the paint film formation rate.

[0012] S6. Based on the paint film formation rate and time step, calculate the change in paint film deposition height at the current time step;

[0013] S7. Update the paint film deposition height array based on the change in paint film deposition height and calculate the film thickness change;

[0014] S8. Based on the deposition height calculation results and the total time step, determine whether the maximum deposition height or the total simulation time of the bearing is greater than the planned height or time. If not, update the film thickness h and iterate the relevant variables, then return to S2. If satisfied, determine the final deposition height, film thickness and film pressure, and output the results.

[0015] A further improvement of the present invention is that step S3 specifically includes the following steps:

[0016] The state variables of the bearing, namely membrane pressure and membrane temperature, are obtained by solving the Reynolds equation and energy equation respectively through the control equation. The solution method adopts the general numerical solution method of finite element method or finite difference method.

[0017] The specific equation form is as follows:

[0018] The Reynolds equation is as follows:

[0019]

[0020] The above equation takes into account the three-dimensional viscosity distribution of the liquid film under varying temperatures in three spatial directions:

[0021]

[0022] In addition, R represents the bearing radius; θ represents the circumferential angle; x and y represent the circumferential and axial coordinates, respectively; U represents the journal linear velocity; η, ρ, and A represent the viscosity, density, and density ratio of the lubricating oil, respectively; p and h represent the oil film pressure and thickness, respectively; R q The root mean square surface roughness; The pressure flow factor represents the ratio of the average pressure flow of the oil film on a rough surface to the pressure flow on a smooth surface. The shear flow factor represents the additional transport flow rate carried away by the valleys and peaks of roughness due to relative surface sliding.

[0023] The energy equation is as follows:

[0024]

[0025] In the formula, ρ is the density of the medium, and Q is... p Let be the specific heat capacity, u, v, w be the velocity components in the x, y, and z directions, T be the temperature, and k be the thermal conductivity coefficient. e U is the thermal conductivity in the y-direction. e This is the equivalent viscosity.

[0026] A further improvement of the present invention is that step S4 specifically includes the following steps:

[0027] Based on the current state variables of the bearing, the actual working conditions, operating cycle, and analysis requirements of the bearing are determined, and a suitable model is manually selected to solve for the varnish formation rate. Optional models include the Arrhenius model and the activation function model. The selection criteria are: the Arrhenius model exhibits deposition at all temperatures, and the deposition rate varies little at operating temperatures, making it suitable for varnish deposition simulations with short operating cycles; the activation function model characterizes the relationship between lubricating oil temperature and varnish formation rate through three parameters: activation reaction temperature, saturation reaction temperature, and saturation reaction rate, making it suitable for long-cycle deposition processes that can reach the saturation stage.

[0028] The specific parameters of the model are as follows:

[0029] The Arrhenius model: This model represents the relationship between the chemical reaction rate constant and temperature, here characterizing the relationship between the rate of lubricant degradation and the rate of varnish film deposition and temperature; the specific calculation equation is as follows:

[0030]

[0031] Where k B Calculate using the following formula:

[0032]

[0033] In the formula, Γ0 is the initial generation rate, and ΔG act For activation energy difference, k B Here, T is the Boltzmann constant, A is the pre-exponential factor / Arrhenius constant, and E is the temperature in the absolute temperature scale. a Let R be the activation energy of the reaction, and R be the gas constant.

[0034] Activation function model: By setting the activation reaction temperature, saturation reaction temperature, and saturation reaction rate, the relationship between paint film deposition rate and lubricating oil temperature is characterized. The specific calculation equation is as follows:

[0035]

[0036] In the formula, S0 is the initial generation rate, T0 is the activation reaction temperature, T1 is the saturation reaction temperature, and T is the lubricating oil temperature. It is a dimensionless temperature constant.

[0037] A further improvement of the present invention is that step S5 specifically includes the following steps:

[0038] The time step is automatically calculated based on the current film thickness, and the maximum time step is obtained using the current minimum film thickness. The time step calculation formula is as follows:

[0039]

[0040] In the formula, d t v represents the film thickness grown within a specific time period. g This represents the paint film growth rate, corresponding to Γ in the Arrhenius model. growth rate In the activation model, S(x) ∈ is a stable term in the denominator.

[0041] A further improvement of the present invention is that step S6 specifically includes the following steps:

[0042] The coating deposition height is calculated based on the coating growth rate and the current time step, using the following formula:

[0043] h g =-v g ·t i

[0044] In the formula, v g Indicates the film growth rate, t i For time step.

[0045] A further improvement of the present invention is that step S7 specifically includes the following steps:

[0046] The coating film deposition height array is updated based on the deposition height change rate, and the film thickness change is calculated. The film thickness calculation formula is as follows:

[0047] h(θ,z)=C p +h groove (θ,z)+u j (θ,z)+u p (θ,z)+h g

[0048] In the formula, θ is the circumferential coordinate, z is the axial coordinate, and h groove For oil tanks and geometric shaping, u p For the force and heat deformation of the tile, u j For the force and thermal deformation of the journal, C ph is the machining radius clearance of the bearing bush. g This refers to the height of the bearing varnish deposition.

[0049] A bearing varnish evolution prediction system under high thermal load includes:

[0050] The reading module reads input parameters, including bearing properties, initial state, and current wear parameters;

[0051] The initialization module initializes relevant variables based on the input parameters, including oil film thickness, deposition height array, and time step array.

[0052] The first calculation module, based on the initialized relevant variables, uses the control equations to solve for the current state variables of the bearing, namely membrane pressure and membrane temperature.

[0053] The second calculation module determines the actual bearing working conditions, operating cycle and analysis requirements based on the current state variables of the bearing, such as membrane pressure and membrane temperature, and selects the Arrhenius model or activation function model to solve for the coating film formation rate.

[0054] The third calculation module calculates the current time step based on the paint film formation rate.

[0055] The fourth calculation module calculates the change in coating film deposition height at the current time step based on the coating film formation rate and time step.

[0056] The fifth calculation module updates the paint film deposition height array based on the change in paint film deposition height and calculates the change in film thickness.

[0057] The judgment module, based on the deposition height calculation result and the total time step, determines whether the maximum deposition height or the total simulation time of the bearing is greater than the planned height or time. If not, it updates the film thickness h and iterates the relevant variables, then returns to the initialization module. If the conditions are met, it determines the final deposition height, film thickness, and film pressure, and outputs the results.

[0058] Compared with the prior art, the present invention has at least the following beneficial technical effects:

[0059] 1) This invention provides a method for predicting the evolution of bearing varnish under high heat load, which can realize the prediction of the evolution of bearing varnish with operating conditions, time and temperature under high heat load and the calculation of bearing performance.

[0060] 2) This invention proposes an activation function model, which characterizes the relationship between paint film deposition rate and lubricating oil temperature by setting the activation reaction temperature, saturation reaction temperature and saturation reaction rate.

[0061] 3) The design method of the present invention has the advantages of simplicity and efficiency. It can quickly and intuitively obtain the bearing deposition rate by simply using the relationship between the coating deposition rate and the lubricating oil temperature, and then calculate the coating deposition thickness, which saves tedious calculation steps and is more efficient. Attached Figure Description

[0062] Figure 1 This is a temperature fluctuation diagram showing the evolution of the coating on a nuclear power plant under high heat load.

[0063] Figure 2 This is a flowchart of a method for predicting the evolution of bearing varnish under high thermal load.

[0064] Figure 3 This is a schematic diagram showing the relationship between the dimensionless paint film deposition rate and the lubricating oil temperature in the Arrhenius model.

[0065] Figure 4 This is a schematic diagram illustrating the relationship between the dimensionless paint film deposition rate and the lubricating oil temperature in the activation function model.

[0066] Figure 5 This is a comparison graph showing the relationship between deposition rate and lubricating oil temperature for two deposition rate models.

[0067] Figure 6 This is a schematic diagram illustrating the relationship between paint film deposition height and time during paint film evolution calculated using the Arrhenius model. Figure 6 (a) shows the relationship between deposition height and time after 20 hours of operation. Figure 6 (a) shows the relationship between deposition height and time after 80 hours of operation, where Figure 6 (a) shows the relationship between deposition height and time after 155 hours of operation.

[0068] Figure 7 This is a schematic diagram illustrating the relationship between film pressure and time during coating film evolution calculated using the Arrhenius model. Figure 7 (a) shows the relationship between membrane pressure and time after 20 hours of operation, where Figure 7 (a) shows the relationship between membrane pressure and time after 80 hours of operation, where Figure 7 (a) shows the relationship between membrane pressure and time after 155 hours of operation.

[0069] Figure 8 This is a schematic diagram illustrating the relationship between paint film deposition height and time during paint film evolution calculated using the activator model. Figure 8 (a) shows the relationship between deposition height and time after 20 hours of operation. Figure 8 (a) shows the relationship between deposition height and time after 80 hours of operation, where Figure 8 (a) shows the relationship between deposition height and time after 155 hours of operation.

[0070] Figure 9 This is a schematic diagram illustrating the relationship between film pressure and time during coating film evolution calculated using the activator model. Figure 9 (a) shows the relationship between membrane pressure and time after 20 hours of operation, where Figure 9 (a) shows the relationship between membrane pressure and time after 80 hours of operation, where Figure 9 (a) shows the relationship between membrane pressure and time after 155 hours of operation.

[0071] Figure 10 This is a structural block diagram of a bearing varnish evolution prediction system under high heat load according to the present invention. Detailed Implementation

[0072] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0073] The flowchart of a bearing varnish evolution prediction method under high heat load according to an embodiment of the present invention specifically includes: inputting parameters, determining parameters, solving for film pressure and film thickness according to the control equation, extracting current state variables, selecting an Arrhenius model or activation function model to calculate the varnish formation rate according to the operating conditions, automatically calculating the time step, calculating the deposition height, updating the deposition height array and calculating the film thickness change, determining whether the maximum deposition height of the bearing or the total simulation time is greater than the planned height or time, and determining the final deposition height, film thickness, film pressure, and other information. This design method can overcome the difficulties of bearing varnish characterization and large result errors under high heat load. At the same time, this design method has the advantages of simplicity and efficiency, saving a lot of tedious design steps.

[0074] like Figure 1 The figure shown is a temperature fluctuation diagram of the coating evolution of a nuclear power equipment under high heat load.

[0075] like Figure 2 The diagram shown is a flowchart for predicting the evolution of bearing varnish under high thermal load, including the following steps:

[0076] The technical solution of the present invention is as follows: A method for predicting the evolution of bearing varnish under high thermal load, comprising the following steps:

[0077] Step 1: Read the input parameters

[0078] Input parameters, including bearing properties, initial state, and current wear state, into the system.

[0079] Step 2: Initialize relevant variables

[0080] Based on the input parameters or iterative return parameters, initialize the relevant variables required for calculation, such as the deposition height array, time step array, and film thickness.

[0081] Step 3: Based on the initialized relevant variables, solve for the current state variables, membrane pressure and membrane thickness, according to the governing equations.

[0082] State variables such as membrane pressure and membrane temperature are obtained by solving the Reynolds equation and the energy equation respectively through the control equation. The solution method can be a general numerical solution method such as the finite element method and the finite difference method.

[0083] The specific equation form is as follows:

[0084] The Reynolds equation is as follows:

[0085]

[0086] The above equation takes into account the three-dimensional viscosity distribution of the liquid film under varying temperatures in three spatial directions.

[0087]

[0088] In addition, R represents the bearing radius; θ represents the circumferential angle; x and y represent the circumferential and axial coordinates, respectively; U represents the journal linear velocity; η, ρ, and A represent the viscosity, density, and density ratio of the lubricating oil, respectively; p and h represent the oil film pressure and thickness, respectively; R q The root mean square surface roughness; The pressure flow factor represents the ratio of the average pressure flow of the oil film on a rough surface to the pressure flow on a smooth surface. The shear flow factor represents the additional transport flow carried away by the valleys and peaks of roughness due to relative surface sliding.

[0089] The energy equation is as follows:

[0090]

[0091] In the formula, ρ is the density of the medium, and Q is... p Let be the specific heat capacity, u, v, w be the velocity components in the x, y, and z directions, T be the temperature, and k be the thermal conductivity coefficient. e U is the thermal conductivity in the y-direction. e Equivalent viscosity

[0092] Step 4: Based on the current state variables of the bearing, determine the actual bearing operating conditions and analysis requirements, and select either the Arrhenius model or the activation function model to solve for the varnish formation rate.

[0093] The appropriate model is manually selected to solve for the varnish formation rate based on the actual bearing operating conditions and analytical requirements; available models include the Arrhenius model and the activation function model. Selection criteria: The Arrhenius model is the relationship between the chemical reaction rate constant and temperature, as shown here... Figure 3 The figure shows the relationship between the dimensionless paint film deposition rate and the lubricating oil temperature. It can be seen from the figure that deposition occurs at all temperatures using the Arrhenius model, and the deposition rate varies little at the operating temperature. This model is chosen for lacquer film deposition simulation in short operating cycles. Figure 4 The figure shows the relationship between the dimensionless paint film deposition rate and the lubricating oil temperature in the activation function model. The relationship between the lubricating oil temperature and the paint film formation rate is characterized by three parameters: activation reaction temperature, saturation reaction temperature, and saturation reaction rate. This model is selected for long-cycle deposition processes that can reach the saturation stage.

[0094] The specific parameters of the model are as follows:

[0095] 1) Arrhenius model:

[0096] Where k B Calculate using the following formula:

[0097]

[0098] In the formula, Γ0 is the initial generation rate, and ΔG act For activation energy difference, k B Here, T is the Boltzmann constant, A is the pre-exponential factor / Arrhenius constant, and E is the temperature in the absolute temperature scale. a R is the activation energy of the reaction, and R is the gas constant.

[0099] 2) Activation function model:

[0100] In the formula, S0 is the initial generation rate, T0 is the activation reaction temperature, T1 is the saturation reaction temperature, and T is the lubricating oil temperature. It is a dimensionless temperature constant.

[0101] like Figure 5 The figure shows a comparison of the relationship between deposition rate and lubricating oil temperature for two deposition rate models. It can be seen that the Arrhenius model can be used when solving the paint film thickness in a short time, but the activation function model is more suitable when solving for a long time or when higher solution accuracy is required.

[0102] Step 5: Automatically calculate the current time step based on the paint film formation rate.

[0103] The time step is automatically calculated using the current film thickness, and the maximum time step is obtained using the current minimum film thickness. The time step calculation formula is as follows:

[0104]

[0105] In the formula, d t v represents the film thickness grown within a specific time period. g This represents the paint film growth rate, corresponding to Γ in the Arrhenius model. growth rate And in the activation model, S(x), ∈ is the stable term in the denominator, when v g When t is 0, i Approximately infinity.

[0106] Step 6: Based on the paint film formation rate and time step, calculate the change in paint film deposition height at the current time step.

[0107] The formula for calculating the paint film deposition height is as follows:

[0108] h g =-v g ·t i

[0109] In the formula, v g Indicates the film growth rate, t i For time step.

[0110] Step 7: Update the coating film deposition height array and calculate the film thickness change based on the change in deposition height.

[0111] Update the paint film deposition height array and calculate the film thickness variation. The film thickness calculation formula is as follows:

[0112] h(θ,z)=C p +h groove (θ,z)+u j (θ,z)+u p (θ,z)+h g

[0113] In the formula, θ is the circumferential coordinate, z is the axial coordinate, and h groove For oil tanks and geometric shaping, u p For the force and heat deformation of the tile, u j For the force and thermal deformation of the journal, C p h is the machining radius clearance of the bearing bush. g Bearing varnish deposition height

[0114] Step 8: Determine if the maximum deposition height of the bearing or the total simulation time meets the requirements.

[0115] Based on the calculated deposition height and the total time step, determine whether the maximum deposition height or total simulation time of the bearing is greater than the planned height or simulation time. If the conditions are met, determine the final deposition height, film thickness, film pressure, film temperature, and other information, and output the results. If the conditions are not met, update the film thickness h and iterate the relevant variables, then return to S2 for looping.

[0116] The coating film evolution model described in this invention includes the Arrhenius model and the activator factor model. Here, a numerical example is used to analyze and calculate using both models, as follows: Figure 6 , Figure 7 The results of the Arrhenius model analysis are as follows. Figure 8 , Figure 9 The results are from the activator model analysis.

[0117] like Figure 6 The figure shows the relationship between paint film buildup height and time during paint film evolution calculated using the Arrhenius model, where (a), (b), and (c) represent the relationship between paint film buildup height and time at different time points, respectively. Figure 6 As shown, in the Arrhenius model, the paint film buildup height is linearly related to time. As time increases, the paint film gradually builds up, and the minimum film thickness decreases accordingly.

[0118] like Figure 7 The figure shows the relationship between film pressure and time during coating film evolution calculated using the Arrhenius model, where (a), (b), and (c) represent the relationship between film pressure and time at different times, respectively. Figure 7 As shown, in the Arrhenius model, the highest oil film pressure decreases slowly over time, while the highest temperature increases due to the accumulation of the paint film.

[0119] like Figure 8 The figure shows the relationship between paint film buildup height and time during paint film evolution calculated using the activation factor model. (a), (b), and (c) represent the relationship between paint film buildup height and time at different time points, respectively. Figure 8 As shown, the activator model analysis of (a) and (b) shows that the deposition rate of the paint film deposition height gradually increases with time, and the rate of change of the minimum film thickness also changes accordingly; the activator model analysis of (b) and (c) shows that the paint film deposition rate has a significant turning point and decreases, while the minimum film thickness has a significant increase.

[0120] like Figure 9 The figure shows the relationship between film pressure and time during coating film evolution calculated using the activation factor model. (a), (b), and (c) represent the relationship between film pressure and time at different times, respectively. Figure 9As shown, the activation factor model analysis of (a) and (b) shows that the maximum oil film pressure decreases continuously with the increase of the paint film accumulation height, while the maximum oil film temperature increases with the accumulation of heat in the paint film. The activation factor model analysis of (b) and (c) shows that the rate of decrease of the maximum oil film pressure fluctuates, and the pressure peak gradually changes from one to two. This is because the model predicts that the paint film will be slightly scratched. The maximum oil film temperature first increases with the accumulation of heat in the paint film, and then decreases due to the scratching of the accumulated paint film.

[0121] This invention provides a method for predicting the evolution of bearing varnish under high thermal load. By setting the activation reaction temperature, saturation reaction temperature, and saturation reaction rate, the relationship between the varnish deposition rate and the lubricating oil temperature is characterized, thereby enabling the prediction of bearing varnish evolution under heavy load.

[0122] like Figure 10 As shown, the present invention provides a bearing varnish evolution prediction system under high thermal load, comprising:

[0123] The reading module reads input parameters, including bearing properties, initial state, and current wear parameters;

[0124] The initialization module initializes relevant variables based on the input parameters, including oil film thickness, deposition height array, and time step array.

[0125] The first calculation module, based on the initialized relevant variables, uses the control equations to solve for the current state variables of the bearing, namely membrane pressure and membrane temperature.

[0126] The second calculation module determines the actual bearing working conditions, operating cycle and analysis requirements based on the current state variables of the bearing, such as membrane pressure and membrane temperature, and selects the Arrhenius model or activation function model to solve for the coating film formation rate.

[0127] The third calculation module calculates the current time step based on the paint film formation rate.

[0128] The fourth calculation module calculates the change in coating film deposition height at the current time step based on the coating film formation rate and time step.

[0129] The fifth calculation module updates the paint film deposition height array based on the change in paint film deposition height and calculates the change in film thickness.

[0130] The judgment module, based on the deposition height calculation result and the total time step, determines whether the maximum deposition height of the bearing or the total simulation time is greater than the planned height or time. If not, it updates the film thickness and iterates the relevant variables, then returns to the initialization module. If the conditions are met, it determines the final deposition height, film thickness, and film pressure, and outputs the results.

[0131] The present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method for evaluating the dynamic aerodynamic load of a wind turbine generator set.

[0132] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0133] This application is described with reference to flowchart illustrations and / or block diagrams of methods, systems, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0134] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0135] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0136] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the scope of the invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0137] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for predicting the evolution of bearing varnish under high thermal load, characterized in that, Includes the following steps: S1. Read input parameters, including bearing properties, initial state, and current wear parameters; S2. Initialize relevant variables based on input parameters, including oil film thickness, deposition height array, and time step array; S3. Based on the initialized relevant variables, the current state variables of the bearing, membrane pressure and membrane temperature, are obtained by solving the control equations. S4. Based on the current state variables of the bearing, such as membrane pressure and membrane temperature, determine the actual bearing working conditions, operating cycle and analysis requirements, and select the Arrhenius model or activation function model to solve for the coating film formation rate. S5. Calculate the current time step based on the paint film formation rate; S6. Based on the paint film formation rate and time step, calculate the change in paint film deposition height at the current time step; S7. Update the paint film deposition height array based on the change in paint film deposition height and calculate the film thickness change; S8. Based on the deposition height calculation results and the total time step, determine whether the maximum deposition height or the total simulation time of the bearing is greater than the planned height or time. If not, update the film thickness and iterate the relevant variables, then return to S2. If satisfied, determine the final deposition height, film thickness and film pressure, and output the results.

2. The method for predicting bearing varnish evolution under high thermal load according to claim 1, characterized in that, Step S3 specifically includes the following steps: The state variables of the bearing, namely membrane pressure and membrane temperature, are obtained by solving the Reynolds equation and energy equation respectively through the control equation. The solution method adopts the general numerical solution method of finite element method or finite difference method. The specific equation form is as follows: The Reynolds equation is as follows: The above equation takes into account the three-dimensional viscosity distribution of the liquid film under varying temperatures in three spatial directions: also, x and y These represent the circumferential and axial coordinates, respectively. U Indicates the journal linear velocity; η , ρ and A These represent the viscosity, density, and density ratio of the lubricating oil, respectively. p and h These represent oil film pressure and thickness, respectively. The root mean square surface roughness; , The pressure flow factor represents the ratio of the average pressure flow of the oil film on a rough surface to the pressure flow on a smooth surface. The shear flow factor represents the additional transport flow rate carried away by the valleys and peaks of roughness due to relative surface sliding. The energy equation is as follows: In the formula, ρ For the density of the medium, For specific heat capacity, u , v , w for x , y , z velocity components in the direction, T For temperature, k The thermal conductivity coefficient, for y Thermal conductivity in the direction, This is the equivalent viscosity.

3. The method for predicting bearing varnish evolution under high thermal load according to claim 2, characterized in that, Step S4 specifically includes the following steps: Based on the current state variables of the bearing, the actual working conditions, operating cycle, and analysis requirements of the bearing are determined, and a suitable model is manually selected to solve for the varnish formation rate. Optional models include the Arrhenius model and the activation function model. The selection criteria are: the Arrhenius model exhibits deposition at all temperatures, and the deposition rate varies little at operating temperatures, making it suitable for varnish deposition simulations with short operating cycles; the activation function model characterizes the relationship between lubricating oil temperature and varnish formation rate through three parameters: activation reaction temperature, saturation reaction temperature, and saturation reaction rate, making it suitable for long-cycle deposition processes that can reach the saturation stage. The specific parameters of the model are as follows: The Arrhenius model: This model represents the relationship between the chemical reaction rate constant and temperature, here characterizing the relationship between the rate of lubricant degradation and the rate of varnish film deposition and temperature; the specific calculation equation is as follows: in Calculate using the following formula: In the formula, The initial generation rate, Because of poor activation energy, Boltzmann's constant, T Temperature under the absolute temperature scale A The pre-exponential factor / Arrhenius constant, The activation energy of the reaction. R It is the gas constant; Activation function model: By setting the activation reaction temperature, saturation reaction temperature, and saturation reaction rate, the relationship between paint film deposition rate and lubricating oil temperature is characterized. The specific calculation equation is as follows: In the formula, The initial generation rate, To activate the reaction temperature, The saturation reaction temperature For lubricating oil temperature, It is a dimensionless temperature constant.

4. The method for predicting bearing varnish evolution under high thermal load according to claim 1, characterized in that, Step S5 specifically includes the following steps: The time step is automatically calculated based on the current film thickness, and the maximum time step is obtained using the current minimum film thickness. The time step calculation formula is as follows: In the formula, The thickness of the paint film within a specific time period. This represents the paint film growth rate, corresponding to the Arrhenius model. and activation model , It is a stable term in the denominator.

5. The method for predicting bearing varnish evolution under high thermal load according to claim 1, characterized in that, Step S6 specifically includes the following steps: The coating deposition height is calculated based on the coating growth rate and the current time step, using the following formula: In the formula, Indicates the paint film growth rate. For time step.

6. The method for predicting bearing varnish evolution under high thermal load according to claim 1, characterized in that, Step S7 specifically includes the following steps: The coating film deposition height array is updated based on the deposition height change rate, and the film thickness change is calculated. The film thickness calculation formula is as follows: In the formula, θ Circumferential coordinates z For axial coordinates, For oil tanks and geometric shaping, For the force and heat deformation of the tile, For the force and thermal deformation of the journal, For the bearing bush machining radius clearance, This refers to the height of the bearing varnish deposition.

7. A bearing varnish evolution prediction system under high thermal load, characterized in that, include: The reading module reads input parameters, including bearing properties, initial state, and current wear parameters; The initialization module initializes relevant variables based on the input parameters, including oil film thickness, deposition height array, and time step array. The first calculation module, based on the initialized relevant variables, uses the control equations to solve for the current state variables of the bearing, namely membrane pressure and membrane temperature. The second calculation module determines the actual bearing working conditions, operating cycle and analysis requirements based on the current state variables of the bearing, such as membrane pressure and membrane temperature, and selects the Arrhenius model or activation function model to solve for the coating film formation rate. The third calculation module calculates the current time step based on the paint film formation rate; The fourth calculation module calculates the change in coating film deposition height at the current time step based on the coating film formation rate and time step. The fifth calculation module updates the paint film deposition height array based on the change in paint film deposition height and calculates the change in film thickness. The judgment module, based on the deposition height calculation result and the total time step, determines whether the maximum deposition height of the bearing or the total simulation time is greater than the planned height or time. If not, it updates the film thickness and iterates the relevant variables, then returns to the initialization module. If the conditions are met, it determines the final deposition height, film thickness, and film pressure, and outputs the results.

8. The bearing varnish evolution prediction system under high thermal load according to claim 7, characterized in that, In the first calculation module: The state variables of the bearing, namely membrane pressure and membrane temperature, are obtained by solving the Reynolds equation and energy equation respectively through the control equation. The solution method adopts the general numerical solution method of finite element method or finite difference method. The specific equation form is as follows: The Reynolds equation is as follows: The above equation takes into account the three-dimensional viscosity distribution of the liquid film under varying temperatures in three spatial directions: also, x and y These represent the circumferential and axial coordinates, respectively. U Indicates the journal linear velocity; η , ρ and A These represent the viscosity, density, and density ratio of the lubricating oil, respectively. p and h These represent oil film pressure and thickness, respectively. The root mean square surface roughness; , The pressure flow factor represents the ratio of the average pressure flow of the oil film on a rough surface to the pressure flow on a smooth surface. The shear flow factor represents the additional transport flow rate carried away by the valleys and peaks of roughness due to relative surface sliding. The energy equation is as follows: In the formula, ρ For the density of the medium, For specific heat capacity, u , v , w for x , y , z velocity components in the direction, T For temperature, k The thermal conductivity coefficient, for y Thermal conductivity in the direction, This is the equivalent viscosity.

9. The bearing varnish evolution prediction system under high thermal load according to claim 7, characterized in that, In the second calculation module: Based on the current state variables of the bearing, the actual working conditions, operating cycle, and analysis requirements of the bearing are determined, and a suitable model is manually selected to solve for the varnish formation rate. Optional models include the Arrhenius model and the activation function model. The selection criteria are: the Arrhenius model exhibits deposition at all temperatures, and the deposition rate varies little at operating temperatures, making it suitable for varnish deposition simulations with short operating cycles; the activation function model characterizes the relationship between lubricating oil temperature and varnish formation rate through three parameters: activation reaction temperature, saturation reaction temperature, and saturation reaction rate, making it suitable for long-cycle deposition processes that can reach the saturation stage. The specific parameters of the model are as follows: The Arrhenius model: This model represents the relationship between the chemical reaction rate constant and temperature, here characterizing the relationship between the rate of lubricant degradation and the rate of varnish film deposition and temperature; the specific calculation equation is as follows: in Calculate using the following formula: In the formula, The initial generation rate, Because of poor activation energy, Boltzmann's constant, T Temperature under the absolute temperature scale A The pre-exponential factor / Arrhenius constant, The activation energy of the reaction. R It is the gas constant; Activation function model: By setting the activation reaction temperature, saturation reaction temperature, and saturation reaction rate, the relationship between paint film deposition rate and lubricating oil temperature is characterized. The specific calculation equation is as follows: In the formula, The initial generation rate, To activate the reaction temperature, The saturation reaction temperature For lubricating oil temperature, It is a dimensionless temperature constant.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the bearing varnish evolution prediction method under high thermal load as described in any one of claims 1-6.

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