Multi-parameter thermal analysis method for ultrahigh-speed bearing design
By using a multi-parameter thermal analysis method combined with one-dimensional and three-dimensional models, we have achieved efficient and accurate thermal performance evaluation of ultra-high-speed bearing design. This solves the problem of predicting thermal failure risk in ultra-high-speed bearing design using traditional methods, optimizes bearing design, and is applicable to extreme operating conditions such as main bearings of gas turbine engines and bearings of high-speed centrifugal compressors.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-03
- Publication Date
- 2026-03-24
AI Technical Summary
Existing technologies are insufficient to effectively predict the risk of thermal failure in the design of ultra-high speed bearings. Traditional methods lack systematic and quantitative requirements for the complex interaction of internal structural parameters of bearings, and their calculation accuracy and efficiency are insufficient, making them unsuitable for ultra-high speed and extreme working conditions.
By employing a multi-parameter thermal analysis method, combining a one-dimensional system thermal network model and a three-dimensional fine thermal analysis model, and through real-time data exchange and parametric simulation, key thermal influencing factors are identified, and bearing design is optimized.
It enables efficient and accurate thermal performance evaluation during the design phase of ultra-high-speed bearings, identifies key thermal performance bottlenecks, shortens the R&D cycle, reduces testing costs, and is applicable to extreme operating conditions such as main bearings of gas turbine engines and bearings of high-speed centrifugal compressors.
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Figure CN121723905A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of thermal management technology for high-speed rotating machinery, and more specifically to a multi-parameter thermal analysis method for the design of ultra-high-speed bearings. Background Technology
[0002] With the increasing demands on the performance of rotating machinery in energy, power, and other fields, the speed, load, and power density of bearings are constantly rising, entering the realm of ultra-high-speed operation. Under such extreme conditions, the internal frictional heat generation of the bearing increases dramatically, while heat dissipation conditions deteriorate, leading to a significant increase in bearing temperature. Excessively high temperatures not only accelerate lubricant aging and failure, reducing oil film carrying capacity, but also cause serious failures such as material annealing, thermal deformation, and even bearing seizure, becoming a key factor restricting the reliability and lifespan of ultra-high-speed bearings. Traditional bearing anti-galling design mainly relies on empirical formulas, simplified calculations, and extensive bench tests for verification. Common methods include: selecting materials with good anti-galling properties (such as high-temperature bearing steel and surface-modified materials), optimizing heat treatment processes, improving lubrication (such as using extreme pressure additive lubricating oil and increasing oil supply), and controlling operating temperature and load. However, these methods often focus on adjusting external factors or single parameters, lacking a systematic and quantitative approach to the complex interactions between internal bearing structural parameters (such as raceway geometry, matching of rolling element size and quantity, cage design details, and surface microstructure) and their impact on galling failure.
[0003] Traditional bearing thermal analysis methods mainly include:
[0004] Empirical formula method: This method calculates the average temperature rise or frictional power consumption based on empirical formulas with simplified assumptions. While fast, this method has limited accuracy, struggles to reflect complex structures, non-uniform temperature fields, and localized hot spots, and has poor adaptability to new operating conditions such as ultra-high speeds and extreme loads.
[0005] Finite element thermal analysis can simulate the temperature distribution and thermal deformation of solid domains, but it is not capable of handling complex flow, heat transfer and gas-liquid two-phase problems of lubricating fluids, and it is difficult to accurately obtain convective heat transfer boundaries and frictional heat sources.
[0006] Single CFD / CHT simulation: It can simulate the coupled processes of fluid flow, heat transfer and solid heat conduction in detail, but the calculation cost of the full three-dimensional model is high, especially for system-level analysis involving multiple bearings and complex flow channels, which is difficult to meet the efficiency requirements of design iteration.
[0007] To address the above shortcomings, this invention proposes a thermal analysis method for ultra-high-speed bearings that balances computational efficiency and accuracy, effectively couples multiple physics fields, and supports parametric design and optimization. Summary of the Invention
[0008] To address the challenge of predicting thermal failure risks in existing ultra-high-speed bearing designs, this invention proposes a multi-parameter thermal analysis method for ultra-high-speed bearing design. This method enables efficient and accurate evaluation and optimization of bearing thermal performance during the design phase.
[0009] The technical solution adopted by the present invention to solve the above-mentioned technical problems is as follows:
[0010] A multi-parameter thermal analysis method for ultra-high speed bearing design includes the following steps:
[0011] Step 1: Determine the multi-parameter set: The multi-parameter set includes bearing structural parameters, material thermophysical property parameters, operating load parameters, lubricant characteristic parameters, and thermal boundary condition parameters;
[0012] Step 2: Construct a one-dimensional system thermal network model: Based on the thermal network method, establish a one-dimensional thermal network model of the bearing system, including the inner and outer rings of the bearing, rolling elements, cage, lubricant and adjacent structures, for rapid calculation of system-level temperature distribution and heat flow dynamics;
[0013] Step 3: Construct a detailed three-dimensional thermal analysis model of the component: Based on the principles of computational fluid dynamics and solid heat transfer, a detailed three-dimensional thermal analysis model is established for the key friction pair area of the bearing to accurately simulate the lubrication flow field, heat conduction, convective heat transfer and thermal deformation.
[0014] Step 4: Establish a model coupling and real-time data exchange mechanism: Through the co-simulation interface, realize bidirectional real-time data exchange between the one-dimensional system thermal network model in Step 2 and the three-dimensional component fine thermal analysis model in Step 3. The one-dimensional model provides system boundary conditions for the three-dimensional model, and the three-dimensional model provides local fine heat sources and heat transfer coefficients for the one-dimensional model.
[0015] Step 5: Perform parametric simulation and sensitivity analysis: Based on the multi-parameter set in Step 1, drive the coupled model to perform parametric simulation, analyze the influence of different parameter changes on the temperature field, thermal deformation and thermal stress of the bearing system, identify key thermal influence factors, and perform sensitivity analysis on the parameter set or detailed engineering scheme.
[0016] Step Six: Output Thermal Analysis Results and Optimization Suggestions: Based on the simulation results, output the temperature, thermal gradient, thermal deformation cloud map of key bearing components, and sensitivity ranking of key parameters. Optimize the parameter set or detailed engineering scheme analysis results to provide a basis for bearing structure optimization, material selection, lubrication scheme, and cooling strategy design.
[0017] Furthermore, in step one, the bearing structural parameters include the bearing pitch circle diameter, roller diameter, effective roller length, number of rollers, radial clearance, and bearing cavity geometry.
[0018] The thermophysical properties of materials include the material density, specific heat capacity, and thermal conductivity of bearing rings and rolling elements; the density, specific heat capacity, thermal conductivity, and kinematic viscosity of lubricants; and the material density, specific heat capacity, and thermal conductivity of cages.
[0019] Operating load parameters include bearing speed, radial load, and axial load;
[0020] Lubricant characteristic parameters include lubricant type, oil supply pressure, oil supply temperature, and oil supply flow rate;
[0021] Thermal boundary condition parameters include ambient temperature, convective heat transfer coefficient, and contact thermal resistance at the installation interface.
[0022] Furthermore, in step two, the method for establishing a one-dimensional model of the bearing system based on the thermal network method includes the following steps:
[0023] The first step is to discretize the bearing inner and outer rings, rolling elements, cage, lubricant, and adjacent structures into nodes;
[0024] The second step involves connecting nodes using thermal conductivity, convection, and contact thermal resistance.
[0025] Step 3: Input the frictional heat source and boundary conditions;
[0026] The fourth step is to solve the nodal temperature equations to obtain the system-level temperature distribution and heat flow path.
[0027] Furthermore, in step three, the method for establishing a three-dimensional fine thermal analysis model for the key friction pair region of the bearing includes the following steps:
[0028] The first step is to use digital modeling software to create a detailed geometric model;
[0029] The second step is to divide the grid and densify the key areas;
[0030] Step 3: Set up the computational domain: The fluid domain uses a multiphase flow model to simulate oil-gas mixing and solves the energy equation, taking turbulence into account; the solid domain solves the heat conduction equation.
[0031] Step 4: Set boundary conditions: inlet, outlet, thermal coupling interface, convection / radiation boundary;
[0032] Step 5: Configure the solver, time step, and convergence criteria.
[0033] Furthermore, in step four, the method for achieving bidirectional real-time data exchange between the one-dimensional system thermal network model in step two and the three-dimensional component fine thermal analysis model in step three through a co-simulation interface includes the following steps:
[0034] Step 1: Real-time data exchange from one-dimensional to three-dimensional: providing system-level background temperature field, lubricant inlet flow rate and temperature;
[0035] The second step is real-time data exchange from three dimensions to one dimension: providing localized refined calculations of frictional heat power and localized convective heat transfer coefficients.
[0036] Furthermore, in step five, the method for conducting sensitivity analysis on the parameter set or detailed engineering scheme includes the following steps:
[0037] The first step is to automatically drive the coupled model to perform multiple simulations based on a multi-parameter set using scripts or optimization software.
[0038] The second step is to use experimental design methods to change the parameter combinations;
[0039] The third step is to analyze the sensitivity of the output results to the input parameters.
[0040] The fourth step is to calculate the sensitivity index Si to identify the key parameters that have the greatest impact on thermal performance.
[0041] Furthermore, in step six, the method for optimizing the parameter set or detailed engineering scheme analysis results includes the following steps:
[0042] The first step is to generate temperature cloud maps, thermal gradient distribution maps, and thermal deformation cloud maps for key components.
[0043] The second step is to output the system-level temperature profile and heat flux distribution.
[0044] The third step is to provide a sensitivity ranking report for key parameters.
[0045] The fourth step is to propose optimization suggestions based on the results, including adjusting the clearance, optimizing the oil supply position / flow rate, selecting high thermal conductivity materials, and improving the cooling structure.
[0046] The beneficial effects of this invention compared to the prior art are:
[0047] This invention provides a method for use in ultra-high speed bearings (typically referring to bearings with a DN value ≥ 3 × 10⁻⁶). 6 This invention employs a multi-parameter thermal analysis method (mm·r / min) for thermal performance evaluation and optimization during the design phase. This method integrates rapid one-dimensional system thermal network analysis, three-dimensional local fine-grained thermofluid simulation, and multi-parameter sensitivity analysis, making it suitable for thermal safety design and performance prediction of bearings under extreme operating conditions, such as those in gas turbine engine main shaft bearings, high-speed centrifugal compressor bearings, and high-speed motor bearings. The significant advantages of this invention are specifically reflected in the following aspects:
[0048] 1. Balance between accuracy and efficiency: One-dimensional models ensure system-level analysis efficiency, three-dimensional models ensure the accuracy of prediction of key local hot spots and thermal deformation, and the coupling mechanism achieves overall accuracy improvement.
[0049] 2. Deep coupling of multiple physics fields: It effectively integrates tribology, fluid mechanics and solid heat transfer, and truly reflects the complex thermal-fluid-solid coupling behavior of ultra-high speed bearings.
[0050] 3. Parametric-driven optimization: Systematic parameter sensitivity analysis quickly identifies thermal performance bottlenecks, providing a clear direction for design optimization and shortening the R&D cycle.
[0051] 4. For ultra-high speed characteristics: The model can capture key phenomena such as centrifugal oil effect, complex turbulence, and oil-gas two-phase flow at ultra-high speed.
[0052] 5. High versatility: The methodology is applicable to the thermal analysis of various rolling bearings (ball bearings, roller bearings), especially for DN values ≥ 3 × 10⁻⁶. 6 Ultra-high speed scenarios at mm·r / min.
[0053] 6. Reduced testing costs: Thermal performance can be predicted during the design phase, reducing expensive bench tests and the risk of failure. Attached Figure Description
[0054] Figure 1 This is a schematic diagram of a multi-parameter thermal analysis method for ultra-high speed bearing design in this invention. Detailed Implementation
[0055] To make the technical problems solved, the technical solutions, and the beneficial effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention.
[0056] Specific implementation method one: Combining Figure 1 This embodiment describes a multi-parameter thermal analysis method for ultra-high-speed bearing design, which includes the following steps:
[0057] Step 1: Determine the multi-parameter set: The multi-parameter set includes bearing structural parameters, material thermophysical property parameters, operating load parameters, lubricant characteristic parameters, and thermal boundary condition parameters;
[0058] Step 2: Construct a one-dimensional system thermal network model: Based on the thermal network method, establish a one-dimensional thermal network model of the bearing system, including the inner and outer rings of the bearing, rolling elements, cage, lubricant and adjacent structures, for rapid calculation of system-level temperature distribution and heat flow dynamics;
[0059] Step 3: Construct a detailed three-dimensional thermal analysis model of the component: Based on the principles of computational fluid dynamics and solid heat transfer, a detailed three-dimensional thermal analysis model is established for the key friction pair area of the bearing to accurately simulate the lubrication flow field, heat conduction, convective heat transfer and thermal deformation.
[0060] Step 4: Establish a model coupling and real-time data exchange mechanism: Through the co-simulation interface, realize bidirectional real-time data exchange between the one-dimensional system thermal network model in Step 2 and the three-dimensional component fine thermal analysis model in Step 3. The one-dimensional model provides system boundary conditions for the three-dimensional model, and the three-dimensional model provides local fine heat sources and heat transfer coefficients for the one-dimensional model.
[0061] Step 5: Perform parametric simulation and sensitivity analysis: Based on the multi-parameter set in Step 1, drive the coupled model to perform parametric simulation, analyze the influence of different parameter changes on the temperature field, thermal deformation and thermal stress of the bearing system, identify key thermal influence factors, and perform sensitivity analysis on the parameter set or detailed engineering scheme.
[0062] Step Six: Output Thermal Analysis Results and Optimization Suggestions: Based on the simulation results, output the temperature, thermal gradient, thermal deformation cloud map of key bearing components, and sensitivity ranking of key parameters. Optimize the parameter set or detailed engineering scheme analysis results to provide a basis for bearing structure optimization, material selection, lubrication scheme, and cooling strategy design.
[0063] Specific Implementation Method Two: Combining Figure 1 This embodiment describes the bearing structural parameters in step one, including the bearing pitch circle diameter, roller diameter, effective roller length, number of rollers, radial clearance, and bearing cavity geometry.
[0064] The thermophysical properties of materials include the material density, specific heat capacity, and thermal conductivity of bearing rings and rolling elements; the density, specific heat capacity, thermal conductivity, and kinematic viscosity of lubricants; and the material density, specific heat capacity, and thermal conductivity of cages.
[0065] Operating load parameters include bearing speed, radial load, and axial load;
[0066] Lubricant characteristic parameters include lubricant type, oil supply pressure, oil supply temperature, and oil supply flow rate;
[0067] Thermal boundary condition parameters include ambient temperature, convective heat transfer coefficient, and contact thermal resistance at the installation interface.
[0068] The undisclosed technical features in this embodiment are the same as those in Specific Embodiment 1.
[0069] In step one, a multi-parameter set is established: parameters affecting the thermal performance of the bearing are summarized and defined, including but not limited to:
[0070] (1) Structural parameters: bearing pitch circle diameter D m Roller diameter D r Effective roller length L, number of rollers Z, radial clearance h, bearing cavity geometry, etc.
[0071] (2) Material properties: density ρ of bearing rings and rolling elements s Specific heat capacity Cp s Thermal conductivity k s Lubricant density ρ1, specific heat capacity Cp1, thermal conductivity k1, kinematic viscosity ν; cage material density ρ c Specific heat capacity Cp c Thermal conductivity k c wait.
[0072] (3) Operating loads: bearing speed n, radial load F r Axial load F a .
[0073] (4) Lubrication parameters: type of lubricating oil, oil supply pressure P oil Oil supply temperature T oil Oil supply flow rate Q oil .
[0074] (5) Thermal boundary: ambient temperature T am b. Convection heat transfer coefficient h am b. Thermal resistance Rc at the installation interface, etc.
[0075] Specific implementation method three: Combining Figure 1 This embodiment describes a method for establishing a one-dimensional model of the bearing system based on the thermal network method in step two, which includes the following steps:
[0076] The first step is to discretize the bearing inner and outer rings, rolling elements, cage, lubricant, and adjacent structures into nodes;
[0077] The second step involves connecting nodes using thermal conductivity, convection, and contact thermal resistance.
[0078] Step 3: Input the frictional heat source and boundary conditions;
[0079] The fourth step is to solve the nodal temperature equations to obtain the system-level temperature distribution and heat flow path.
[0080] The undisclosed technical features in this embodiment are the same as those in Specific Embodiment 1.
[0081] This model is fast and suitable for scheme selection and system preliminary thermal analysis.
[0082] In the first step, the lubricant is considered to have concentrated heat capacity.
[0083] Specific implementation method four: Combination Figure 1 This embodiment describes a method for establishing a three-dimensional fine thermal analysis model for the critical friction pair region of the bearing, which includes the following steps in step three:
[0084] The first step is to use digital modeling software to create a detailed geometric model;
[0085] The second step is to divide the grid and densify the key areas;
[0086] Step 3: Set up the computational domain: The fluid domain uses a multiphase flow model to simulate oil-gas mixing and solves the energy equation, taking turbulence into account; the solid domain solves the heat conduction equation.
[0087] Step 4: Set boundary conditions: inlet, outlet, thermal coupling interface, convection / radiation boundary;
[0088] Step 5: Configure the solver, time step, and convergence criteria.
[0089] The undisclosed technical features in this embodiment are the same as those in Specific Embodiment 1.
[0090] In this embodiment, the key friction pair areas of the bearing include, for example, the roller-raceway and the roller end face-shoulder.
[0091] In the fourth step, boundary conditions are set: inlet (flow / mass inlet, conditions from the one-dimensional model), outlet (pressure outlet), thermal coupling interface (fluid-solid), and convection / radiation boundary.
[0092] In the fifth step, configure the solver (transient / steady-state), time step, and convergence criterion.
[0093] Specific Implementation Method Five: Combining Figure 1 This embodiment describes a method for achieving bidirectional real-time data exchange between the one-dimensional system thermal network model in step two and the three-dimensional component fine thermal analysis model in step three through a co-simulation interface. The method includes the following steps:
[0094] Step 1: Real-time data exchange from one-dimensional to three-dimensional: providing system-level background temperature field, lubricant inlet flow rate and temperature;
[0095] The second step is real-time data exchange from three dimensions to one dimension: providing localized refined calculations of frictional heat power and localized convective heat transfer coefficients.
[0096] The undisclosed technical features in this embodiment are the same as those in Specific Embodiment 1.
[0097] By connecting one-dimensional and three-dimensional models through a co-simulation interface, bidirectional real-time data exchange is achieved, improving the overall model accuracy.
[0098] The data exchange ranges from one-dimensional to three-dimensional: providing a system-level background temperature field (as the far-field boundary of the three-dimensional model), lubricant inlet flow rate, and temperature.
[0099] Data exchange from three dimensions to one dimension: providing locally refined calculations of frictional heat power (more accurately reflecting heat generation in the contact area) and local convective heat transfer coefficients (reflecting the actual flow state).
[0100] Specific Implementation Method Six: Combination Figure 1 This embodiment describes a method for performing sensitivity analysis on parameter sets or detailed engineering schemes in step five, which includes the following steps:
[0101] The first step is to automatically drive the coupled model to perform multiple simulations based on a multi-parameter set using scripts or optimization software.
[0102] The second step is to use experimental design methods to change the parameter combinations;
[0103] The third step is to analyze the sensitivity of the output results to the input parameters.
[0104] The fourth step is to calculate the sensitivity index Si to identify the key parameters that have the greatest impact on thermal performance.
[0105] The undisclosed technical features in this embodiment are the same as those in Specific Embodiment 1.
[0106] The second step involves using experimental design methods (such as full factorial, partial factorial, and Latin hypercube sampling) to change the combination of parameters.
[0107] The third step involves analyzing the output results (such as the highest bearing temperature T). max cage temperature T c Raceway thermal deformation δ, maximum oil film temperature T oilmax Sensitivity to input parameters.
[0108] In the fourth step, the sensitivity index Si is calculated to identify the key parameters that have the greatest impact on thermal performance (such as rotational speed n and fuel supply Q). oil Radial clearance h, thermal conductivity k s The calculation method is Si = (ΔXi / Xi) / (ΔY / Y), where ΔXi is the change in parameter Xi (the change in the i-th input parameter), Xi is the parameter baseline value (the original or reference value of the i-th input parameter); ΔY is the change in output Y (the change in the model output result due to parameter changes), and Y is the output baseline value (the original or reference value of the model).
[0109] Specific implementation method seven: Combining Figure 1 This embodiment describes a method for optimizing the parameter set or detailed engineering scheme analysis results in step six, which includes the following steps:
[0110] The first step is to generate temperature cloud maps, thermal gradient distribution maps, and thermal deformation cloud maps for key components.
[0111] The second step is to output the system-level temperature profile and heat flux distribution.
[0112] The third step is to provide a sensitivity ranking report for key parameters.
[0113] The fourth step is to propose optimization suggestions based on the results, including adjusting the clearance, optimizing the oil supply position / flow rate, selecting high thermal conductivity materials, and improving the cooling structure.
[0114] The undisclosed technical features in this embodiment are the same as those in Specific Embodiment 1.
[0115] The first step generates temperature cloud maps, thermal gradient distribution, and thermal deformation cloud maps (from the 3D model) for key components.
[0116] The second step outputs the system-level temperature curve and heat flux distribution (from the one-dimensional model).
[0117] The fourth step proposes optimization suggestions based on the results, such as adjusting the clearance h and optimizing the fuel supply position / flow rate Q. oil Select high thermal conductivity materials (to increase kJ / m³) s Improved cooling structure (affecting h) am b) etc.
[0118] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.
Claims
1. A multi-parameter thermal analysis method for ultra-high speed bearing design, characterized in that: Includes the following steps: Step 1: Determine the multi-parameter set: The multi-parameter set includes bearing structural parameters, material thermophysical property parameters, operating load parameters, lubricant characteristic parameters, and thermal boundary condition parameters; Step 2: Construct a one-dimensional system thermal network model: Based on the thermal network method, establish a one-dimensional thermal network model of the bearing system, including the inner and outer rings of the bearing, rolling elements, cage, lubricant and adjacent structures, for rapid calculation of system-level temperature distribution and heat flow dynamics; Step 3: Construct a detailed three-dimensional thermal analysis model of the component: Based on the principles of computational fluid dynamics and solid heat transfer, a detailed three-dimensional thermal analysis model is established for the key friction pair area of the bearing to accurately simulate the lubrication flow field, heat conduction, convective heat transfer and thermal deformation. Step 4: Establish a model coupling and real-time data exchange mechanism: Through the co-simulation interface, realize bidirectional real-time data exchange between the one-dimensional system thermal network model in Step 2 and the three-dimensional component fine thermal analysis model in Step 3. The one-dimensional model provides system boundary conditions for the three-dimensional model, and the three-dimensional model provides local fine heat sources and heat transfer coefficients for the one-dimensional model. Step 5: Perform parametric simulation and sensitivity analysis: Based on the multi-parameter set in Step 1, drive the coupled model to perform parametric simulation, analyze the influence of different parameter changes on the temperature field, thermal deformation and thermal stress of the bearing system, identify key thermal influence factors, and perform sensitivity analysis on the parameter set or detailed engineering scheme. Step Six: Output Thermal Analysis Results and Optimization Suggestions: Based on the simulation results, output the temperature, thermal gradient, thermal deformation cloud map of key bearing components, and sensitivity ranking of key parameters. Optimize the parameter set or detailed engineering scheme analysis results to provide a basis for bearing structure optimization, material selection, lubrication scheme, and cooling strategy design.
2. The multi-parameter thermal analysis method for ultra-high speed bearing design according to claim 1, characterized in that: In step one, the bearing structural parameters include the bearing pitch circle diameter, roller diameter, effective roller length, number of rollers, radial clearance, and bearing cavity geometry. The thermophysical properties of materials include the material density, specific heat capacity, and thermal conductivity of bearing rings and rolling elements; the density, specific heat capacity, thermal conductivity, and kinematic viscosity of lubricants; and the material density, specific heat capacity, and thermal conductivity of cages. Operating load parameters include bearing speed, radial load, and axial load; Lubricant characteristic parameters include lubricant type, oil supply pressure, oil supply temperature, and oil supply flow rate; Thermal boundary condition parameters include ambient temperature, convective heat transfer coefficient, and contact thermal resistance at the installation interface.
3. The multi-parameter thermal analysis method for ultra-high speed bearing design according to claim 1, characterized in that: Step two, the method for establishing a one-dimensional model of the bearing system based on the thermal network method, includes the following steps: The first step is to discretize the bearing inner and outer rings, rolling elements, cage, lubricant, and adjacent structures into nodes; The second step involves connecting nodes using thermal conductivity, convection, and contact thermal resistance. Step 3: Input the frictional heat source and boundary conditions; The fourth step is to solve the nodal temperature equations to obtain the system-level temperature distribution and heat flow path.
4. The multi-parameter thermal analysis method for ultra-high speed bearing design according to claim 1, characterized in that: Step three involves establishing a three-dimensional fine thermal analysis model for the critical friction pair region of the bearing, including the following steps: The first step is to use digital modeling software to create a detailed geometric model; The second step is to divide the grid and densify the key areas; Step 3: Set up the computational domain: The fluid domain uses a multiphase flow model to simulate oil-gas mixing and solves the energy equation, taking turbulence into account; the solid domain solves the heat conduction equation. Step 4: Set boundary conditions: inlet, outlet, thermal coupling interface, convection / radiation boundary; Step 5: Configure the solver, time step, and convergence criteria.
5. The multi-parameter thermal analysis method for ultra-high speed bearing design according to claim 1, characterized in that: In step four, the method for achieving bidirectional real-time data exchange between the one-dimensional system thermal network model in step two and the three-dimensional component fine thermal analysis model in step three through a co-simulation interface includes the following steps: Step 1: Real-time data exchange from one-dimensional to three-dimensional: providing system-level background temperature field, lubricant inlet flow rate and temperature; The second step is real-time data exchange from three dimensions to one dimension: providing localized refined calculations of frictional heat power and localized convective heat transfer coefficients.
6. The multi-parameter thermal analysis method for ultra-high speed bearing design according to claim 1, characterized in that: Step five, the method for conducting sensitivity analysis on the parameter set or detailed engineering scheme, includes the following steps: The first step is to automatically drive the coupled model to perform multiple simulations based on a multi-parameter set using scripts or optimization software. The second step is to use experimental design methods to change the parameter combinations; The third step is to analyze the sensitivity of the output results to the input parameters. The fourth step is to calculate the sensitivity index Si to identify the key parameters that have the greatest impact on thermal performance.
7. The multi-parameter thermal analysis method for ultra-high speed bearing design according to claim 1, characterized in that: Step six involves optimizing the parameter set or detailed engineering scheme analysis results, including the following steps: The first step is to generate temperature cloud maps, thermal gradient distribution maps, and thermal deformation cloud maps for key components. The second step is to output the system-level temperature profile and heat flux distribution. The third step is to provide a sensitivity ranking report for key parameters. Step 4: Based on the results, propose optimization suggestions, including adjusting clearance, optimizing oil supply location / flow, selecting high thermal conductivity materials, and improving the cooling structure.