Cylindrical roller bearing structure design method suitable for ultrahigh-temperature heavy-load working condition

Abstract

The invention relates to a cylindrical roller bearing structure design method suitable for ultrahigh-temperature and heavy-load working conditions, and aims to solve the problem that in the prior art, a multi-physical field coupling effect under ultrahigh-temperature and heavy-load conditions is not fully considered in cylindrical roller bearing structure optimization design. The cylindrical roller bearing structure design method comprises the following steps that 1, an ultrahigh-temperature and heavy-load cylindrical roller bearing multi-physical field coupling model is constructed; 2, bearing key parameter sensitivity analysis is carried out; 3, establishing a multi-objective optimization function taking the maximum contact force and the contact force unevenness as objectives, and setting constraint conditions; 4, parameter optimization based on an improved particle swarm algorithm; and 5, verifying and correcting the optimization scheme. According to the method, the thermal-structure-dynamics coupling model is established, the material performance change, thermal deformation and dynamic contact behaviors in the ultra-high-temperature environment are brought into a unified optimization framework for the first time, the limitation of traditional separated design is overcome, and the design precision is improved. The invention relates to the technical field of bearing design and manufacturing.

Description

Technical Field

[0001] This invention relates to the field of bearing design and manufacturing technology, specifically to a cylindrical roller bearing structure design method suitable for ultra-high temperature and heavy load conditions, and is particularly applicable to the design of bearing components operating in extreme environments in aerospace, power generation equipment and heavy machinery. Background Technology

[0002] Cylindrical roller bearings, as core components of mechanical transmissions, are widely used in critical equipment such as aerospace engines, gas turbines, metallurgical rolling mills, and heavy engineering machinery. In these applications, bearings often face extreme conditions of ultra-high temperatures (above 300°C) and heavy loads (above 50kN), and their reliability directly determines the operational safety of the entire machine. However, bearing failure under extreme conditions remains a persistent industry challenge, primarily manifesting as fatigue spalling in the contact area, adhesive wear, and thermal deformation seizure. Current technologies for cylindrical roller bearing structural optimization design often employ traditional empirical formulas or single-physics field analysis methods. Traditional design methods typically optimize structural parameters (such as roller diameter, length, number, cage clearance, and roller profile curves) independently, improving contact pressure distribution by adjusting geometric parameters. For example, roller profile modification can reduce edge stress concentration, and increasing the number of rollers can reduce the load on a single roller. However, these methods have significant limitations: (1) Ignoring the impact of ultra-high temperature environment on material properties. High temperature will cause a significant decrease in the elastic modulus, Poisson's ratio and other mechanical property parameters of bearing materials (such as bearing steel and high temperature alloys), and at the same time trigger the thermal softening effect of materials, which will reduce the load-bearing capacity of the contact area. Traditional design does not take into account the dynamic change law of material properties with temperature, resulting in a large deviation between the calculated contact force and the actual working conditions.

[0003] (2) The dimensional changes caused by the thermal-structural coupling effect were not considered. Under ultra-high temperature conditions, the bearing components (inner ring, outer ring, rollers) undergo uneven deformation due to the difference in their coefficients of thermal expansion. Changes in the roller geometry (length, diameter) and raceway curvature directly alter the pressure distribution in the contact area. For example, thermal elongation of the rollers may exacerbate edge contact, while thermal deformation of the raceway may lead to stress concentration. These factors all affect the formation of the lubricating film and the protective effectiveness of the surface treatment layer.

[0004] (3) Separate optimization leads to suboptimal design. Traditional methods separate structural parameter optimization from thermal effect analysis, optimizing geometric parameters first and then verifying thermal performance, or only correcting thermal effects through experience, lacking a global optimization mechanism that couples multiple physics fields. This separate optimization mode makes it difficult to balance the relationship between minimizing contact force and thermal stability, which may lead to bearings meeting performance standards at room temperature but failing prematurely under ultra-high temperature and heavy load conditions.

[0005] To address the aforementioned issues, there is an urgent need for a cylindrical roller bearing structural optimization design method that can comprehensively consider the thermal-structural-dynamic coupling effects under ultra-high temperature and heavy load conditions. By synergistically optimizing key bearing parameters, precise control of contact force can be achieved, thereby improving the reliability and service life of the bearing in extreme environments. Summary of the Invention

[0006] To address the shortcomings of existing cylindrical roller bearing structural optimization designs that do not fully consider the multi-physics coupling effects under ultra-high temperature and heavy load conditions, this invention proposes a cylindrical roller bearing structural design method suitable for ultra-high temperature and heavy load conditions.

[0007] The technical solution adopted by the present invention to solve the above problems is as follows: This invention proposes a structural design method for ultra-high temperature heavy-duty cylindrical roller bearings, comprising the following steps: Step 1: Construct a multiphysics coupling model for ultra-high temperature heavy-duty cylindrical roller bearings; Step 2: Select the design parameters that affect the contact force of the bearing as optimization variables, and screen the core optimization variables through sensitivity coefficient analysis; Step 3: Establish a multi-objective optimization function with the maximum contact force and contact force non-uniformity as objectives, and set constraints; Step 4: Use the improved particle swarm optimization algorithm to solve the optimization function and obtain the global optimal parameter vector; Step 5: Verify the optimization results. If the design requirements are met, output the optimization solution; otherwise, return to step 4 to adjust the parameters and re-optimize.

[0008] Furthermore, the model described in step 1 includes: A sub-model for the change of material properties with temperature, used to describe the functional relationship between elastic modulus, coefficient of thermal expansion, and yield strength as a function of temperature; The heat conduction sub-model calculates the temperature field based on the transient heat conduction equation, taking into account frictional heat generation power, rolling contact line velocity, and thermal boundary conditions. A structural deformation sub-model is used to calculate thermal deformation based on temperature field results. The dynamic sub-model analyzes the roller skew angle, Hertzian line contact force, and composite elastic modulus by establishing multibody dynamic equations.

[0009] Furthermore, in step 2, the optimization variables include: Structural parameters: Number of rollers N (6~15) Roller diameter Roller length: 5~20mm L For 10~50mm shaping parameters: crown coefficient k The ratio of the shaping length is 0.001 to 0.01.l The value is 0.1~0.3; Dynamic parameters: tilt angle i 0.1°~1°, cage clearance c It is 0.01~0.1mm.

[0010] Furthermore, the constraints in step 3 include: contact stress constraints, thermal deformation constraints, skew angle constraints, and parameter boundary approximations.

[0011] Furthermore, the algorithm in step 4 employs a hybrid particle swarm optimization-simulated annealing algorithm, where the particle velocity is updated as follows: (twenty one) in, For the first i The speed of each particle For location, w For inertial weights, c 1. c 2 is the learning factor. r 1. r 2 is a random number. p best,i , g best These are the individual and global optimal solutions, respectively. Simulated annealing acceptance probability P for: P = exp (-Δ E / T )(twenty two) Where Δ E The increment of the objective function, T This is the current temperature.

[0012] Furthermore, in step 5, the verification indicators include: optimization magnitude, calculating the percentage reduction in maximum contact force and contact force non-uniformity; fatigue life prediction, estimating bearing life based on rated dynamic load and equivalent dynamic load.

[0013] The beneficial effects of this invention are: 1. Considering the multi-physics coupling effect: This invention establishes a thermal-structure-dynamic coupling model, and for the first time incorporates the material performance changes, thermal deformation and dynamic contact behavior under ultra-high temperature environment into a unified optimization framework, overcoming the limitations of traditional separate design and improving design accuracy.

[0014] 2. Multi-parameter and multi-objective optimization scheme: Based on determining the influence weights of key parameters such as the number of rollers, shaping parameters, and skew angle, this invention takes multiple constraints such as contact stress, roller skew, and thermal deformation as objectives and adopts an improved particle swarm optimization algorithm to achieve multi-objective parameter collaborative optimization, thereby achieving the maximum contact force in the contact area (reduced by 20%~30% after optimization) and the contact force non-uniformity (reduced by 15%~25% after optimization).

[0015] 3. Strong adaptability of the design: The optimization process of this invention fully considers the dynamic characteristics of ultra-high temperature and heavy load conditions. The optimized bearing structure can maintain stable contact performance in the temperature range of 300°C to 600°C and the load range of 50kN to 200kN, which significantly improves the reliability and service life of the bearing (expected to extend the service life by more than 30%). Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a design method for a cylindrical roller bearing structure suitable for ultra-high temperature and heavy-load conditions according to the present invention. Detailed Implementation

[0017] Combination Figure 1 This embodiment describes a design method for cylindrical roller bearing structures suitable for ultra-high temperature and heavy-load conditions, implemented through the following steps: Step 1. Construct a multiphysics coupling model for ultra-high temperature heavy-duty cylindrical roller bearings: Based on the actual operating parameters of the bearing, including ambient temperature T 0. Axial load F a radial load F r and rotational speed n An analytical model incorporating thermal-structural-dynamic coupling was established. The model includes: (1). Sub-model of material properties changing with temperature: The properties of bearing materials (inner ring, outer ring, rollers) under temperature variations are obtained experimentally. Establish a functional relationship for performance parameters within the temperature range of 20°C to 600°C: elastic modulus E ( T )for: E ( T ) = E 0•(1- k E •Δ T (1) in E 0 represents the elastic modulus at room temperature. k E Δ is the temperature coefficient.T For temperature rise changes ,ΔT = T -20°C; coefficient of thermal expansion α ( T )for: α ( T )= α 0•(1+ k α •Δ T (2) in α 0 is the coefficient of thermal expansion at room temperature. k α It is the coefficient of thermal expansion and temperature coefficient. Yield strength s s ( T )for: ( T )= s s0 •e^(- k σ •Δ T (3) in s s0 The yield strength at room temperature k σ The intensity attenuation coefficient; (2). Thermal conductivity sub-model: The temperature field is calculated using the transient heat conduction equation: r • c p • T / t = ▽•( k •▽ T ) + q (4) in r For material density, c p For specific heat capacity, k Thermal conductivity, q Density due to frictional heat generation; Frictional heat generation power Q for: Q = m k •P c •v (5) in m k The coefficient of friction, P c For contact pressure, v The rolling contact linear velocity; Rolling contact linear velocity v for: v = π• D r •n / 60 (6) in D r Where is the diameter of the roller. n Rotational speed; Thermal boundary conditions k for: k • T / n = h •( T s - T 0)(7) in h The convective heat transfer coefficient is... T s Surface temperature, n For the boundary normal vector, T 0 represents the ambient temperature; (3). Structural Deformation Sub-model: Calculation of thermal deformation based on temperature field results: Linear expansion Δ L : Δ L = L 0•∫α( T ) d T (8) in L 0. Initial length of bearing components at room temperature; Finite element equilibrium equations: K•U = F r + F t (9) in K Here is the stiffness matrix. U It is a displacement vector. F r For external load vector, F t This is the thermal load vector; Corrected roller diameter D ᵣ': D r ' = D r + Δ D (10) Where Δ D r This is the thermal expansion of the roller; (4). Dynamics Submodel Establish the equations of multibody dynamics: Roller skew angle influence coefficient: C θ = 1 + k θ • i 2 (11) in k θ The skewness effect coefficient is... i It is a skew angle; Hertzian contact force: F = (π• L • E' • R' ) 1 / 2 • 3 / 2 (12) in L For contact length, E' For composite elastic modulus, R' The radius of curvature is the composite radius of curvature. Composite elastic modulus: 1 / E ' = (1 - m 1 2 ) / E 1+ (1 - m 2 2 ) / E 2 (13) in m 1. m 2 represents the Poisson's ratio of the roller and the raceway, respectively. E 1. E 2 represents the corresponding elastic modulus; Step 2. Conduct sensitivity analysis of key bearing parameters: Key design parameters affecting contact force are selected as optimization variables, and the sensitivity coefficient is calculated using the controlled variable method. Sensitivity coefficient: S = (Δ F max / Fmax0 ) / (Δ P / P 0)(14) in F max0 The initial maximum contact force, P 0 is the initial value of the parameter; Non-uniformity of contact force: (15) in Standard deviation, The mean; Optimization variables include: Structural parameters: Number of rollers N (6~15) Roller diameter D r (5~20mm), roller length L r (10~50mm); Shape modification parameters: Convexity coefficient k (0.001~0.01), shaping length ratio l (0.1~0.3); Dynamic parameters: tilt angle i (0.1°~1°) Cage clearance c (0.01~0.1mm).

[0018] Step 3. Establish a multi-objective optimization function: With the goal of maximizing contact force and force non-uniformity, the constraints include: Objective function: min f (X) = [ w 1• F max (X), w 2• d (X)](16) in w 1. w 2 is the weighting coefficient, which satisfies w 1+ w 2 = 1; Contact stress constraint: s h = √( F • E ' / (π• L • R ')) ≤ [ s h (17) Thermal deformation constraint: Δ = | D r ' - D r0 '| ≤ 0.05mm (18) in D r0 'This refers to the roller diameter under design baseline conditions; Skew angle constraint: i ≤ 0.5° (19) Parameter boundary constraints: X ∈[ X min , X max (20) in X min The minimum value vector of the core optimization variables. X max This is the vector of maximum values ​​for the core optimization variables; Step 4. Parameter optimization based on the improved particle swarm optimization algorithm: A hybrid algorithm of particle swarm optimization and simulated annealing is adopted: Particle velocity update: (twenty one) in, For the first i The speed of each particle For location, w For inertial weights, c 1. c 2 is the learning factor. r 1. r 2 is a random number. p best,i , g best These are the individual and global optimal solutions, respectively.

[0019] Simulated annealing acceptance probability: P = exp (-Δ E / T )(twenty two) Where Δ E The increment of the objective function, T The current temperature; Step 5. Optimization and Revision of the Solution: Verification index calculation formula: Optimization extent: (twenty three) in F 0 is the initial value. For optimization values; Fatigue life prediction: L 10 = ( C / P )^ e (twenty four) in C For the basic rated dynamic load, P For equivalent dynamic load, e Life expectancy index; Example: Taking a high-temperature, heavy-duty cylindrical roller bearing for an aero-engine as the research object, its initial design parameters are as follows: Basic parameters: bearing inner diameter d =80mm, bearing outer diameter D =120mm, width B =28mm; Roller parameters: quantity N =10, diameter D r =12mm, length L r =25mm, convexity coefficient k =0.005; Operating parameters: Ambient temperature T 0=400°C, radial load F r =80kN, rotational speed n =3000r / min.

[0020] (1). Establishment and verification of the coupling model A coupled model was constructed, using the high-temperature alloy GH4169 as the material. Its performance parameters change with temperature as follows: elastic modulus E ( T ) = 210 - 0.12 T (GPa) (T is temperature, in °C); coefficient of thermal expansion α ( T = 11.8 + 0.005 T (×10 -6 / °C); Allowable contact stress s h ]=1800-1.5 T (MPa).

[0021] A three-dimensional model of the bearing was created using finite element software. The mesh was generated using hexahedral elements, with the mesh in the contact area refined to 0.1 mm. The thermal boundary conditions were set as follows: [The text abruptly ends here, so the translation stops as well.] h =50W / (m 2 •K), ambient temperature 400°C. Dynamic analysis was performed using multibody dynamics software, with the contact friction coefficient between the roller and cage set to 0.002 and the raceway contact friction coefficient set to 0.0015.

[0022] Simulation results show that the maximum contact force of the initial design is 12.5kN, the contact force non-uniformity is 0.25, the highest temperature in the contact area is 452°C, and there is edge stress concentration.

[0023] (2). Parameter sensitivity analysis Sensitivity analysis was performed on the following 7 design parameters, and the results are shown in Table 1: Table 1 Sensitivity coefficients of design parameters

[0024] Based on the results in Table 1, the top 5 parameters in terms of sensitivity (roller diameter, number, length, convexity coefficient, and skew angle) were selected as the core optimization variables.

[0025] (3). Optimization process and results The optimization algorithm parameters are set as follows: particle count 50, iteration count 100, simulated annealing initial temperature 100, and cooling coefficient 0.95. After optimization using the improved particle swarm optimization algorithm in step S4, the optimal parameters are as follows: Number of rollers N=12, diameter D r =13mm, length L r =26mm, convexity coefficient k=0.006, skew angle θ=0.3°. Performance comparison before and after optimization is shown in Table 2: Table 2 Comparison of bearing performance parameters before and after optimization

[0026] (4). Verification results Substituting the optimized parameters into the coupled model for verification, the results show that the uniformity of contact force distribution is significantly improved, the edge stress concentration phenomenon is eliminated, the contact area temperature is reduced by 14°C, and all performance parameters meet the constraints. Bench tests verify that the optimized bearing achieves a fatigue life of 850 hours under 400°C and 80kN load, a 30.8% improvement compared to the initial scheme (650 hours), thus verifying the effectiveness of the method of this invention.

[0027] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent substitutions, and improvements made to the above embodiments without departing from the scope of the present invention, based on the technical essence of the present invention and within the spirit and principles of the present invention, shall still fall within the protection scope of the present invention.

Claims

1. A structural design method for ultra-high temperature heavy-duty cylindrical roller bearings, characterized in that, Includes the following steps: Step 1: Construct a multiphysics coupling model for ultra-high temperature heavy-duty cylindrical roller bearings; Step 2: Select the design parameters that affect the contact force of the bearing as optimization variables, and screen the core optimization variables through sensitivity coefficient analysis; Step 3: Establish a multi-objective optimization function with the maximum contact force and contact force non-uniformity as objectives, and set constraints; Step 4: Use the improved particle swarm optimization algorithm to solve the optimization function and obtain the global optimal parameter vector; Step 5: Verify the optimization results. If the design requirements are met, output the optimization solution; otherwise, return to step 4 to adjust the parameters and re-optimize.

2. The design method for an ultra-high temperature heavy-duty cylindrical roller bearing structure according to claim 1, characterized in that, The model described in step 1 includes: A sub-model for the change of material properties with temperature, used to describe the functional relationship between elastic modulus, coefficient of thermal expansion, and yield strength as a function of temperature; The heat conduction sub-model calculates the temperature field based on the transient heat conduction equation, taking into account frictional heat generation power, rolling contact line velocity, and thermal boundary conditions. A structural deformation sub-model is used to calculate thermal deformation based on temperature field results. The dynamic sub-model analyzes the roller skew angle, Hertzian line contact force, and composite elastic modulus by establishing multibody dynamic equations.

3. The method for designing an ultra-high temperature heavy-duty cylindrical roller bearing structure according to claim 1, characterized in that, In step 2, the optimization variables include: Structural parameters: Number of rollers N 6-15 rollers, roller diameter D ᵣ is 5~20mm, roller length L ᵣ is 10~50mm; shaping parameter: convexity coefficient k The ratio of the shaping length is 0.001 to 0.

01. λ The value is 0.1~0.3; Dynamic parameters: tilt angle θ 0.1°~1°, cage clearance c It is 0.01~0.1mm.

4. The method for designing an ultra-high temperature heavy-duty cylindrical roller bearing structure according to claim 1, characterized in that, The constraints in step 3 include: contact stress constraints, thermal deformation constraints, skew angle constraints, and parameter boundary approximations.

5. The structural design method for an ultra-high temperature heavy-duty cylindrical roller bearing according to claim 1, characterized in that, Place The algorithm in step 4 uses a hybrid particle swarm optimization-simulated annealing algorithm, where the particle velocity is updated as follows: （21） in, For the first i The speed of each particle For location, w For inertial weights, c 1. c 2 is the learning factor. r 1. r 2 is a random number. p best,i , g best These are the individual and global optimal solutions, respectively. Simulated annealing acceptance probability P for: P = exp (-Δ E / T )(22) Where Δ E The increment of the objective function, T This is the current temperature.

6. The structural design method for an ultra-high temperature heavy-duty cylindrical roller bearing according to claim 1, characterized in that, In step 5, the verification indicators include: optimization magnitude, which calculates the percentage reduction in maximum contact force and contact force non-uniformity; and fatigue life prediction, which estimates the bearing life based on rated dynamic load and equivalent dynamic load.

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