Hydraulic shock absorber fatigue life prediction method considering multi-dimensional effect coupling

Abstract

The invention discloses a hydraulic shock absorber fatigue life prediction method considering multi-dimensional effect coupling, which constructs a dynamic coupling model of a hydraulic shock absorber by introducing a lateral force effect, transient oil pressure fluctuation, a non-proportional hardening effect and a temperature effect, and realizes high-precision prediction of the fatigue life of the shock absorber. The method comprises the following working steps that 1, axial force, lateral force and bending stress borne by the shock absorber in the working process are measured, oil pressure fluctuation in an oil storage barrel of the shock absorber is monitored in real time, and the temperature of the surface of a piston rod and oil liquid is recorded synchronously; by means of the method, the fatigue life prediction problem of the hydraulic shock absorber under the complex working condition is solved, the precision of traditional stress component proportional loading and uniaxial fatigue life prediction is improved, and the reliability design of an automobile suspension system is remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of automotive suspension reliability design technology, specifically involving a method for predicting the fatigue life of a hydraulic shock absorber that considers the coupling of multiple factors such as lateral force, transient oil pressure fluctuation, non-proportional hardening effect, and temperature effect. Background Technology

[0002] In the design and reliability assessment of automotive hydraulic shock absorbers, multiaxial fatigue failure is one of the main causes of piston rod fracture, weld cracking, and seal failure. Traditional fatigue analysis methods are usually based on the uniaxial load assumption, considering only the axial tension-compression cycle of the shock absorber, while ignoring lateral bending stress, transient oil pressure fluctuations, non-proportional hardening effects, and material property degradation caused by temperature rise in actual operating conditions. This simplification leads to a fatigue life prediction error of up to ±50%, severely restricting the reliability design and durability optimization of shock absorbers. To overcome the above problems, it is urgent to develop a high-precision multiaxial fatigue life analysis method that integrates multi-effect coupling to simultaneously characterize the interaction of mechanical load, oil pressure fluctuations, non-proportional hardening, and temperature effects, improve the prediction accuracy of hydraulic shock absorber fatigue life, and provide theoretical and methodological support for the reliability design of next-generation new energy vehicle intelligent suspension systems. Summary of the Invention

[0003] This invention comprehensively considers mechanical loads (axial force, lateral force, and bending stress), transient oil pressure fluctuations, non-proportional hardening effects, and temperature effects, and proposes a method for predicting the fatigue life of hydraulic shock absorbers that takes into account the coupling of multidimensional effects, so as to improve the prediction accuracy of shock absorber fatigue life and enhance the reliability design of automotive suspension.

[0004] This invention proposes a method for predicting the fatigue life of hydraulic vibration dampers that considers the coupling of multidimensional effects. The invention includes the following:

[0005] A multi-sensor fusion measurement system was constructed to collect load, oil pressure, and temperature data. A GPS time synchronization module was used to coordinate the sampling of each sensor to ensure that the time alignment error of each sensor data was lower than a set threshold. Real-time fusion of multi-channel sensor data was achieved through the LabVIEW platform, while controlling the sampling clock jitter to remain within a set range.

[0006] After completing the installation and setup of the sensors and the data acquisition platform, test conditions covering typical road conditions and extreme conditions were developed.

[0007] Preliminary experimental data calibration was performed to verify the linearity between the strain gauge output value and the theoretical value. At the same time, the dynamic pressure response of the piezoelectric sensor was calibrated using a hydraulic pulsation generator.

[0008] The strain-stress conversion is performed, the hydraulic pressure-structural force decoupling is performed, and outlier data is removed to generate a load spectrum that includes the synchronous time histories of axial force, lateral force, bending stress, hydraulic pressure, and temperature.

[0009] A fluid-structure-thermal coupled model of the hydraulic vibration damper was established using the finite element method. Mechanical loads, hydraulic loads, and temperature loads were mapped onto the finite element mesh, and fluid-structure-thermal coupled dynamic analysis was performed to solve for stress and strain. The main components include:

[0010] In terms of macroscopic model processing, firstly, the geometric design model of the hydraulic damper is preprocessed to retain essential components that significantly affect fatigue life, such as helical springs, piston rod systems, oil reservoirs, and valve systems. Then, the geometric model is assembled, preserving the initial assembly relationships between components, and the assembly is meshed, with necessary mesh refinement in critical areas.

[0011] In terms of local model processing, the chromium plating layer of the piston rod is independently divided into layered meshes and assigned anisotropic material properties; for the valve plate structure, a parametric method is used to establish a micro-surface model with surface roughness; for the welded area between the helical spring and the oil reservoir, a non-uniform material gradient model is established based on the results of material metallographic analysis.

[0012] By coupling the material constitutive model with temperature, a temperature-corrected Chaboche hybrid hardening model is established:

[0013]

[0014] in, Let C(T) be the stress rate tensor and C(T) be the temperature-dependent elastic stiffness tensor. For the total strain rate, ε is the plastic strain rate. e For elastic strain, γ k (T) represents the temperature-dependent back stress hardening modulus of the k-th term, X k Let be the back stress tensor of the k-th term, and n be the tensor of the plastic flow direction. The formula for calculating the elastic stiffness C(T) is:

[0015] C(T)=C 0 exp[-1.5×10 -4 (T-25)] (2)

[0016] Back stress hardening modulus γ k The formula for calculating (T) is:

[0017] γ k (T)=γ k0 [1+0.004(T-25)] (3)

[0018] Among them, C 0 and γ k0 These are the elastic stiffness and back stress hardening modulus at the reference temperature, respectively.

[0019] A two-way coupling algorithm is used to set the dynamic interaction between the fluid (oil) and the structure (piston / oil reservoir). Simultaneously, key contact pairs in the damper structural model are configured, including those between the piston and oil reservoir, the valve plate and valve seat, and the oil seal and piston rod. Furthermore, necessary thermal conductivity settings are added to all the aforementioned contact areas.

[0020] Furthermore, the various types of loads are mapped onto the established finite element model, specifically as follows:

[0021] For mechanical loads including axial force, lateral force and bending stress, the six components of force and moment are converted into finite element nodal forces and bending moments through the dynamic substructure method.

[0022] For transient hydraulic loads, the spatial distribution of pressure is first obtained through dynamic simulation, and then the hydraulic load is mapped to the finite element mesh through radial basis function interpolation.

[0023] For temperature loads, considering thermal radiation and convective heat transfer coefficients, the temperature data measured by infrared thermal imagers and thermocouples are converted into nodal temperature boundary conditions.

[0024] After setting the interactions and boundary conditions, transient explicit dynamic analysis was used to solve the model, calculating the stress and strain at key locations. The Richardson extrapolation method was used to evaluate stress convergence.

[0025]

[0026] Where, φ ext Let φ1 be the theoretical stress value under an infinitely fine mesh, and φ2 be the stress solutions under fine and coarse meshes, respectively. r is the mesh refinement ratio, p is the convergence order, GCI is the mesh convergence exponent, and F is the theoretical stress value under an infinitely fine mesh. s For safety factors.

[0027] Based on the above load acquisition and model establishment, multiaxial fatigue damage calculation of the hydraulic damper is performed, and fatigue life is predicted.

[0028] First, based on the transient dynamic analysis results of the fluid-structure-thermal coupling model of the vibration damper, stress and strain time history data at critical points are extracted. Simultaneously, rigid body rotation non-deformation processing is performed to ensure consistency between the material coordinate system and the global coordinate system.

[0029] A temperature-corrected Fatemi-Socie damage model was established, and damage parameters were calculated based on the temperature-compensated multiaxial fatigue criterion.

[0030]

[0031] Where, Δγ max / 2 represents the maximum shear strain amplitude on the critical plane, and k(T) is a temperature-dependent non-proportional sensitivity coefficient. σ is the maximum normal stress on the critical plane. y (T) represents the temperature-dependent yield strength, β represents the material temperature sensitivity index, and T0 represents the reference temperature.

[0032] Then, the temperature-compensated SN curve is constructed, and the prediction model for the fatigue life of the damper under a single load cycle is as follows:

[0033] logN f (T,FP mod )=A(T)-B(T)log(FP mod (7)

[0034] Where, N f A(T) represents the predicted number of fatigue failure cycles under the current cycle parameters and temperature; A(T) and B(T) are temperature-dependent material parameters obtained through step temperature fatigue testing.

[0035] Cyclic damage of different amplitudes and directions is accumulated over time, and the overall fatigue failure of the vibration damper is determined by the Miner linear accumulation method.

[0036]

[0037] Where D is the cumulative total damage, n i N represents the actual number of occurrences in the i-th cycle. f,i This represents the fatigue life prediction result under the i-th load cycle. The damper is considered to have failed due to fatigue when D≥1.

[0038] Finally, a multi-axis fatigue bench test was designed to verify the simulation model and adjust the parameters. The test bench includes an axial force loading module, a lateral force strengthening module, an oil temperature circulation control system, and an oil pressure pulsation adjustment system.

[0039] A multi-channel synchronous system was developed based on the LabVIEW platform to apply loads and acquire experimental data, obtaining the measured fatigue life of the vibration damper. The life error is defined as:

[0040]

[0041] Where, N predict To predict fatigue life, N test The fatigue life is measured on the test bench.

[0042] Meanwhile, the shear crack direction of fatigue failure obtained from simulation and the shear crack direction obtained from experiment are compared, and the consistency error between simulation and experiment failure modes is calculated.

[0043] Based on the calculation results of life error and failure mode consistency, when the error value is higher than the set threshold, further sensitivity analysis of model parameters is performed, and the material constitutive model or boundary conditions are adjusted to improve the accuracy of vibration damper fatigue life prediction and realize the widespread application of simulation model.

[0044] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0045] This invention proposes a method for predicting the fatigue life of hydraulic shock absorbers that considers multidimensional effects. By taking into account multiaxial mechanical loads, transient oil pressure fluctuations, non-proportional hardening effects, and temperature effects, a fluid-structure-thermal coupling model of the shock absorber is established. This method overcomes the limitations of traditional methods that only consider uniaxial mechanical loads and proportional stress component loading, and can capture multiaxial failure modes that traditional models cannot identify. Furthermore, the temperature-corrected Fatemi-Socie criterion proposed in this invention can effectively improve the prediction accuracy of shock absorber fatigue life under complex operating conditions, significantly enhancing the reliability design of automotive suspension systems. Attached Figure Description

[0046] Figure 1 This is the overall implementation process of the vibration damper fatigue life prediction method proposed in this invention.

[0047] Figure 2 This describes the process for multi-sensor data acquisition and load spectrum generation.

[0048] Figure 3 This is the geometric and assembly model of a hydraulic vibration damper.

[0049] Figure 4 This is a finite element mesh model of a hydraulic vibration damper.

[0050] Figure 5 This is for the iterative optimization process of bench testing and simulation models. Figure 6 The image shows the displacement results obtained from the simulation. Figure 7 This is a schematic diagram of the simulation model for multi-axis fatigue bench testing of vibration dampers. Detailed Implementation

[0051] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. The models and processes shown in the drawings are only used to explain the present invention and should not be construed as limiting the present invention. All other embodiments obtained based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0052] The overall implementation process of the fatigue life prediction method for hydraulic vibration dampers considering multidimensional effect coupling proposed in this invention is as follows: Figure 1 As shown, the implementation method will be described in detail below.

[0053] First step, such as Figure 2 As shown, a multi-sensor fusion measurement system is constructed to collect load, oil pressure, and temperature data, achieving a comprehensive load characterization of the hydraulic damper under real-world operating conditions. Specifically:

[0054] Step (1): Install six-component force sensors at the upper and lower connection points of the shock absorber to measure the triaxial force and torque experienced by the shock absorber during actual operation.

[0055] Step (2): Attach a triaxial micro strain rosette at the transition fillet position at the root of the piston rod, measure the lateral bending strain in the contact area between the root of the piston rod and the guide, and calculate the bending stress.

[0056] Step (3): A set of embedded piezoelectric sensors are evenly distributed along the circumference at the three axial division points on the inner wall of the damper oil reservoir to cover the radial oil pressure pulsation signal and measure the transient oil pressure fluctuation in the cylinder.

[0057] Step (4): Install an infrared thermal imager on the exposed part of the piston rod to measure the temperature field distribution on the piston rod surface; arrange oil thermocouples inside the oil reservoir to measure the oil temperature inside the cylinder.

[0058] Step (5): Use a GPS time synchronization module to coordinate the sampling of each sensor to ensure that the time alignment error of each sensor data is lower than the set threshold, which is 1ms in this embodiment.

[0059] Step (6): Real-time fusion of multi-channel sensor data is achieved through the LabVIEW platform, while the sampling clock jitter is controlled to remain within the set range, which is ±1μs in this embodiment.

[0060] Step (7): After completing the installation and setup of the sensors and the data acquisition platform, formulate test conditions that cover typical road conditions and extreme conditions.

[0061] Step (8): Perform experimental data calibration by applying axial and lateral forces of known magnitude and direction on the calibration platform to verify the linearity between the strain gauge output value and the theoretical value. In this embodiment, the linear fitting degree R is controlled. 2 Greater than 0.99; at the same time, the dynamic pressure response of the piezoelectric sensor is calibrated by a hydraulic pulsation generator, and in this embodiment, the rise time is controlled to be less than 0.1ms.

[0062] Step (9): Strain-stress conversion is performed based on the generalized Hooke's law, and the hydraulic-structural force decoupling is performed using the blind source separation algorithm. Abnormal data is then removed based on the 3σ criterion. Finally, a load spectrum containing the synchronous time histories of axial force, lateral force, bending stress, hydraulic pressure, and temperature is generated.

[0063] The technical parameters of the sensors mentioned above are shown in the table below.

[0064]

[0065] The second step involves establishing a fluid-structure-thermal coupled model of the hydraulic damper using the finite element method. Mechanical loads, hydraulic loads, and temperature loads are mapped onto the finite element mesh for dynamic analysis, resulting in the stress and strain determination. Specifically:

[0066] by Figure 3 The hydraulic vibration damper model shown is an example. In terms of macroscopic model processing, firstly, the geometric design model of the vibration damper is preprocessed, retaining necessary components that mainly affect the fatigue life of the vibration damper, such as helical springs, piston rod systems, oil reservoirs, and valve systems, while removing minor features such as fillets and small holes. Then, the geometric model is assembled, retaining the initial assembly relationships between the components.

[0067] Perform assembly mesh generation, such as Figure 4 As shown, non-critical areas are divided into 5mm meshes; critical areas undergo necessary mesh refinement, with mesh sizes no larger than 2mm. Furthermore, regular areas are divided into hexahedral meshes and solved using linear integration elements, while irregular areas are divided into tetrahedral meshes and solved using quadratic integration elements.

[0068] In terms of local model processing, the chromium plating layer of the piston rod is independently divided into layered meshes with a circumferential thickness of 0.05 mm, and the mesh size in the axial direction can be appropriately enlarged and anisotropic material properties are assigned. For the valve plate structure, a parametric method is used to establish a micro-surface model with surface roughness. For the welding area between the helical spring and the oil reservoir, a non-uniform material gradient model is established based on the results of material metallographic analysis.

[0069] By coupling the material constitutive model with temperature, a temperature-corrected Chaboche hybrid hardening model is established:

[0070]

[0071] in, Let C(T) be the stress rate tensor and C(T) be the temperature-dependent elastic stiffness tensor. For the total strain rate, ε is the plastic strain rate. e For elastic strain, γk (T) represents the temperature-dependent back stress hardening modulus of the k-th term, X k Let be the back stress tensor of the k-th term, and n be the tensor of the plastic flow direction.

[0072] In the above formula, For the elastic part of the hybrid hardening model, This is a nonlinear hardening term. This is a thermoelastic coupling term. The formula for calculating the elastic stiffness C(T) is:

[0073] C(T)=C 0 exp[-1.5×10 -4 (T-25)] (2)

[0074] Back stress hardening modulus γ k The formula for calculating (T) is:

[0075] γ k (T)=γ k0 [1+0.004(T-25)] (3)

[0076] Among them, C 0 and γ k0 These are the elastic stiffness and back stress hardening modulus at the reference temperature, respectively.

[0077] A two-way coupling algorithm is used to set the dynamic interaction between fluid (oil) and structure (piston / oil reservoir). In the fluid domain, a Realizable k-ε turbulence model and a Schnerr-Sauer cavitation model are used to capture oil pressure fluctuations. In the structural domain, pressure loads are mapped to the finite element mesh through conservative interpolation, and an implicit partitioning coupling method is used to ensure force / energy conservation. Dynamic meshing technology is used to reduce the maximum element distortion rate and handle deformation coordination problems in small gaps.

[0078] The key contact pairs of the vibration damper structural model are set as follows:

[0079] Surface-to-surface contact is established between the piston and the oil reservoir, and between the valve plate and the valve seat. The normal contact behavior adopts hard contact, and the tangential contact behavior adopts the penalty function friction coefficient. An interference contact is established between the oil seal and the piston rod. In addition, necessary thermal conductivity settings are added to the above contact areas.

[0080] Furthermore, the various types of loads are mapped onto the established finite element model, specifically as follows:

[0081] For mechanical loads including axial force, lateral force and bending stress, the six components of force and moment are converted into finite element nodal forces and bending moments through the dynamic substructure method.

[0082] For transient hydraulic loads, the spatial distribution of pressure is first obtained through dynamic simulation. Then, the hydraulic load is mapped to a finite element mesh using radial basis function interpolation. The mathematical model is as follows:

[0083]

[0084] Among them, P FEA (x) represents the mapped pressure value at node x in the finite element mesh, where x is the spatial coordinate (x, y, z) of the finite element mesh node. i Let x be the spatial coordinates of the i-th source node in the fluid mesh. i ,y i ,z i ), ω i Let φ be the weight coefficient of the i-th source node. i Here, n is the radial basis function, n is the number of fluid mesh source nodes for parametric interpolation, and c is the number of source nodes. T p(x) is a polynomial correction term. Based on x and x... i Perform calculations, ||xx i || is the Euclidean distance.

[0085] For temperature loads, considering thermal radiation and convective heat transfer coefficients, the temperature data measured by infrared thermal imagers and thermocouples are converted into nodal temperature boundary conditions.

[0086] For the boundary definition of the heat source term, taking into account the heat generated by oil shear, the calculation model is as follows:

[0087]

[0088] Where, is Q viscous Let μ(T) be the volumetric heat source density generated by oil shear, and μ(T) be the temperature-dependent dynamic viscosity. denoted as shear rate.

[0089] Furthermore, considering frictional heat generation, the frictional heat flux density model of the piston rod and oil seal contact surface is as follows:

[0090] q friction =μ f pv sliding (6)

[0091] Where, q friction Let μ be the heat flux density at the contact surface. f Where is the coefficient of friction, p is the contact pressure, and v is the friction coefficient. sliding This represents the speed of the piston rod.

[0092] For the heat dissipation boundary conditions, the natural convection heat transfer model between the damper oil reservoir and the air is as follows:

[0093] q natural =hnatural (T s -T ∞ (7)

[0094]

[0095] Where, q natural h is the natural convection heat flux density. natural T is the natural convection heat transfer coefficient. s T is the surface temperature of the oil reservoir. ∞ D represents the absolute temperature of the environment far from the surface of the oil reservoir, and D represents the height of the oil reservoir.

[0096] The forced convection heat transfer model under high-speed driving conditions is as follows:

[0097] q forced =h forced (T s -T ∞ (9)

[0098]

[0099] Where, q forced For forced convection heat flux density, h forced The forced convection heat transfer coefficient is given by Re, where Re is the Reynolds number, Pr is the Prandtl number, and k is the coefficient of performance. air ν is the thermal conductivity of air, L is the diameter of the oil reservoir, and v is the vehicle speed.

[0100] The thermal radiation heat transfer model is as follows:

[0101]

[0102] Where, q rad ε is the radiative heat flux density, ε' is the surface emissivity, and σ is the Stefan-Boltzmann constant.

[0103] After setting the interactions and boundary conditions, transient explicit dynamic analysis was used to solve the model, calculating the stress and strain at key locations. The Richardson extrapolation method was used to evaluate stress convergence.

[0104]

[0105]

[0106] Where, φ ext Let φ1 be the theoretical stress value under an infinitely fine mesh, and φ2 be the stress solutions under fine and coarse meshes, respectively. r is the mesh refinement ratio, p is the convergence order, GCI is the mesh convergence exponent, and F is the theoretical stress value under an infinitely fine mesh. s For safety factors. The stress and displacement results of the vibration damper obtained from the simulation are as follows: Figure 5 and Figure 6 As shown.

[0107] The third step, based on the above load acquisition and model establishment, is to perform multiaxial fatigue damage calculation of the hydraulic damper and predict its fatigue life.

[0108] First, based on the transient dynamic analysis results of the fluid-structure-thermal coupling model of the vibration damper, stress and strain time history data at critical points are extracted. Simultaneously, rigid body rotation non-deformation processing is performed to ensure consistency between the material coordinate system and the global coordinate system.

[0109] A temperature-corrected Fatemi-Socie damage model was established, and damage parameters were calculated based on the temperature-compensated multiaxial fatigue criterion.

[0110]

[0111] Where, Δγ max / 2 represents the maximum shear strain amplitude on the critical plane, and k(T) is a temperature-dependent non-proportional sensitivity coefficient. σ is the maximum normal stress on the critical plane. y (T) represents the temperature-dependent yield strength, β represents the material temperature sensitivity index, and T0 represents the reference temperature.

[0112] The calculation process described above is as follows:

[0113] Step (1): Traverse all possible planes and calculate the shear strain amplitude Δγ / 2 and the maximum normal stress for each plane.

[0114] Step (2): Real-time access to temperature field data T(t) and dynamic update of yield strength σ y (T) and the non-proportional sensitivity coefficient k(T).

[0115] Step (3): Calculate FP for each plane mod The maximum value is selected as the damage index for that point.

[0116] Then, the temperature-compensated SN curve is constructed, and the prediction model for the fatigue life of the damper under a single load cycle is as follows:

[0117] logN f (T,FP mod )=A(T)-B(T)log(FP mod (15)

[0118] Where, N f Here, A(T) represents the predicted number of fatigue failure cycles under the current cycle parameters and temperature; A(T) and B(T) are temperature-dependent material parameters obtained through step-temperature fatigue testing. In this embodiment, an empirical formula is used:

[0119] A(T)=A0-0.015(T-25) (16)

[0120] B(T) = B0 + 1.2 × 10 -4 (T-25) (17)

[0121] Wherein, A0 and B0 are both reference parameters for the fatigue performance of the material under room temperature conditions. In this embodiment, their values ​​are A0 = 12.3 and B0 = -0.05, respectively. These parameters can be reasonably adjusted in the subsequent model verification stage.

[0122] Cyclic damage of different amplitudes and directions is accumulated over time, and the overall fatigue failure of the vibration damper is determined by the Miner linear accumulation method.

[0123]

[0124] Where D is the cumulative total damage, n i N represents the actual number of occurrences in the i-th cycle. f,i This represents the fatigue life prediction result under the i-th load cycle. The damper is considered to have failed due to fatigue when D≥1.

[0125] Step four, as Figure 7 As shown, a multi-axis fatigue bench test of the vibration damper was designed to verify the simulation model and adjust the model parameters.

[0126] The test bench includes an axial force loading module, a lateral force reinforcement module, an oil temperature circulation control system, and an oil pressure pulsation regulation system. The specific implementation methods of each module or system are as follows:

[0127] Axial force loading is achieved through an electro-hydraulic servo actuator, and it satisfies non-proportional loading from 0 to 180°.

[0128] Lateral forces are generated through eccentric mass to simulate the conditions of a car turning or experiencing lateral disturbances.

[0129] The oil temperature circulation control system adjusts the oil temperature in the reservoir using a PID controller to simulate the thermal conditions of a real vehicle.

[0130] The magnitude of oil pressure pulsation is adjusted through the valve system throttling orifice.

[0131] A multi-channel synchronous system was developed based on the LabVIEW platform to apply loads and acquire experimental data, obtaining the measured fatigue life of the vibration damper. The life error is defined as:

[0132]

[0133] Where, N predict To predict fatigue life, N testThe fatigue life is measured on the test bench.

[0134] Meanwhile, the shear crack direction of fatigue failure obtained from simulation and the shear crack direction obtained from experiment are compared, and the consistency error between simulation and experiment failure modes is calculated.

[0135] Based on the calculation results of life error and failure mode consistency error, when the error value is higher than the set threshold of 10%, further sensitivity analysis of model parameters is performed, and the material constitutive model or boundary conditions are adjusted to improve the fatigue life prediction accuracy of the vibration damper and realize the widespread application of the simulation model.

[0136] For the above embodiments, the fatigue life prediction of shock absorbers using the method proposed in this invention improves the prediction accuracy by 20% compared to traditional methods. The fatigue life prediction method for hydraulic shock absorbers that considers multidimensional effect coupling proposed in this invention is of great value for achieving high-reliability design of automotive suspensions.

[0137] The above description is only one embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. All equivalent substitutions or modifications made under the concept of the present invention based on the description and drawings of the present invention, or direct / indirect applications in other related technical fields, should be covered within the scope of protection of the present invention.

Claims

1. A method for predicting the fatigue life of a hydraulic vibration damper considering multidimensional effect coupling, characterized in that, The work includes the following steps: Step (1): Measure the axial force, lateral force and bending stress on the shock absorber, and obtain the shock absorber oil pressure fluctuation and temperature data; Step (2): Establish a fluid-structure-thermal coupled finite element model and solve for stress and strain; Step (3): Establish a temperature-corrected damage model, perform multiaxial fatigue analysis, and predict the fatigue life of the shock absorber; Step (4): Design a fatigue test bench for the vibration damper and dynamically correct the simulation model.

2. The method for predicting the fatigue life of a hydraulic vibration damper considering multidimensional effect coupling as described in claim 1, characterized in that, The axial force and lateral force are measured by installing six-component force sensors at the upper and lower connection points of the vibration damper; the bending stress refers to the lateral bending stress in the contact area between the piston rod root and the guide, which is measured by attaching a triaxial strain rosette to the transition fillet surface at the piston rod root.

3. The method for predicting the fatigue life of a hydraulic vibration damper considering multidimensional effect coupling according to claim 1, characterized in that, The cylinder oil pressure fluctuation is monitored by an embedded piezoelectric sensor array; the temperature data refers to the temperature of the piston rod surface and the oil inside the cylinder, which is collected by a surface infrared thermal imager and thermocouples.

4. The method for predicting the fatigue life of a hydraulic vibration damper considering multidimensional effect coupling according to claim 1, characterized in that, The fluid-structure-thermal coupled finite element model was established using dynamic simulation software, in which the valve plate assembly was subjected to transient hydraulic mapping loading, and the material constitutive model satisfied the modified Chaboche mixed hardening equation: in, Let C(T) be the stress rate tensor and C(T) be the temperature-dependent elastic stiffness tensor. The total strain rate, ε is the plastic strain rate. e For elastic strain, γ k (T) represents the temperature-dependent back stress hardening modulus of the k-th term, X k Let be the back stress tensor of the k-th term, and n be the tensor of the plastic flow direction.

5. The method for predicting the fatigue life of a hydraulic vibration damper considering multidimensional effect coupling according to claim 1, characterized in that, The temperature-corrected damage model is established using the improved Fatemi-Socie criterion, and damage parameters are calculated based on the temperature-compensated multiaxial fatigue criterion. Where, Δγ max / 2 represents the maximum shear strain amplitude on the critical plane, and k(T) is a temperature-dependent non-proportional sensitivity coefficient. σ is the maximum normal stress on the critical plane. y (T) represents the temperature-dependent yield strength, β represents the material temperature sensitivity index, and T0 represents the reference temperature. The calculation model for k(T) is as follows: k(T)=k0[1+0.005(T-T0)] (3).

6. The method for predicting the fatigue life of a hydraulic vibration damper considering multidimensional effect coupling according to claim 1, characterized in that, The fatigue life prediction under a single load cycle is obtained by combining the temperature-corrected SN curve: logN f (T,FP mod )=A(T)-B(T)log(FP mod ) (4) Where, N f Given the current cycle parameters and temperature, the predicted number of fatigue failure cycles is given. A(T) and B(T) are temperature-dependent material parameters.

7. The method for predicting the fatigue life of a hydraulic vibration damper considering multidimensional effect coupling according to claim 1, characterized in that, The overall fatigue failure of the vibration damper is determined by accumulating cyclic damage of different amplitudes and directions over time. Where D is the total damage, n i N represents the actual number of occurrences in the i-th cycle. f,i This represents the fatigue life prediction result under the i-th load cycle. The damper is considered to have failed when D≥1.

8. The method for predicting the fatigue life of a hydraulic vibration damper considering multidimensional effect coupling according to claim 1, characterized in that, The fatigue test bench includes an axial force loading module, a lateral force strengthening module, an oil temperature circulation control system, and an oil pressure pulsation regulation system. Axial force loading is achieved through an actuator, lateral force is generated by an eccentric mass, the temperature control system is regulated by a PID controller, and oil pressure pulsation is regulated through a valve system throttling orifice.

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