Spring-damping characteristic simulation method and system

Abstract

This application relates to a simulation method and system for spring damping characteristics. The simulation method includes: establishing a damper simulation model, the simulation model containing variable preliminary performance parameters of internal components; monitoring and accumulating aging physical quantities of the internal components of the damper during the simulation model's operation; periodically correcting the preliminary performance parameters of the corresponding internal components in the simulation model based on the aging physical quantities, and recording these as corrected performance parameters; predicting the performance of the damper at different aging stages based on the corrected performance parameters, and outputting the state information of the internal components of the damper. This application, by incorporating physical quantities from the actual aging process into the simulation loop and dynamically adjusting the model parameters, provides a more realistic prediction of damper performance, enabling accurate assessment of the damper's performance consistency throughout its entire lifespan from the initial design stage.

Description

Technical Field

[0001] This application relates to the field of spring simulation design, and in particular to a method and system for simulating spring damping characteristics. Background Technology

[0002] In vehicle suspension design, traditional spring damping characteristic simulation can predict initial performance, but it is difficult to capture the performance degradation caused by material aging, wear and fatigue during long-term use of the damper, thus making it impossible to predict its durability and affecting the performance consistency of the vehicle throughout its entire life cycle.

[0003] In actual research and development, engineers determine the spring stiffness and damping coefficient according to vehicle parameters, and build models using material data under standard conditions to simulate the dynamic response of standard road surfaces, assisting in the selection of solutions. However, after the physical prototype undergoes durability tests such as rugged road conditions and extreme temperature zones, its driving quality will decline. The measured damping force after high mileage deviates significantly from the simulation and the condition of a new vehicle, and the attenuation is irreversible.

[0004] Disassembly and analysis revealed that this degradation stemmed from internal changes, including: molecular chain breakage in the damping fluid due to long-term shearing, resulting in decreased viscosity and turbidity; increased clearance and reduced elasticity due to valve plate wear; and leakage or air ingress caused by aging of seals. All of these altered the damping force generation mechanism, leading to a decline in damping performance. Existing simulations, based solely on initial parameters, lack models describing time-dependent processes such as material fatigue, wear, and fluid degradation, failing to reflect performance degradation and consistently predicting an "ideal state" of damping force. This forces R&D to heavily rely on expensive and time-consuming physical testing, with problems often emerging late in the design phase. It is difficult to optimize damper materials, structures, or damping fluid formulations in the early stages to improve performance, resulting in significant time and effort commitments and high modification costs. Summary of the Invention

[0005] This application provides a spring damping characteristic simulation method and system to at least solve the limitations of existing spring damping characteristic simulation methods in predicting the long-term performance degradation of components, and the difficulty in accurately predicting and optimizing the durability performance of dampers in the early stages of design.

[0006] Firstly, this application provides a method for simulating the damping characteristics of a spring, comprising the following steps:

[0007] Establish a simulation model of the damper, which includes preliminary performance parameters of variable internal components;

[0008] During the simulation model's operation, the aging physical quantities of the internal components of the damper are monitored and accumulated;

[0009] Based on the aging physical quantities, the preliminary performance parameters of the corresponding internal components in the simulation model are periodically corrected and recorded as corrected performance parameters;

[0010] Based on the corrected performance parameters, the performance of the damper at different aging stages is predicted, and the status information of the internal components of the damper is output.

[0011] Optionally, during the simulation model's operation, monitoring and accumulating the aging physical quantities of the damper's internal components includes:

[0012] Monitor the local pressure of the fluid inside the damper and the piston speed;

[0013] When the local pressure is lower than the saturated vapor pressure of the internal fluid and the piston speed exceeds a preset speed threshold, the cavitation region inside the damper is identified.

[0014] Calculate the local impact energy generated by the collapse of cavitation bubbles in the cavitation generation area, and based on the local impact energy, accumulate the amount of cavitation erosion damage on the surface of the damper piston.

[0015] The cavitation erosion damage is converted into a correction of the geometric parameters of the damper piston surface, and the corrected geometric parameters are accumulated as the aging physical quantity of the internal components of the damper.

[0016] Optionally, the step of periodically correcting the preliminary performance parameters of the corresponding internal components in the simulation model based on the aging physical quantities and recording them as corrected performance parameters includes:

[0017] Read the aging physical quantities and identify the interaction relationships between the various aging mechanisms inside the damper based on the aging physical quantities;

[0018] Based on the aforementioned interaction relationships, the first weight of each aging mechanism for correcting the preliminary performance parameters is adjusted;

[0019] Based on the aging physical quantities and the adjusted first weight, the correction values ​​of the preliminary performance parameters are calculated and recorded as the corrected performance parameters.

[0020] Optionally, identifying the interaction relationships between various aging mechanisms within the damper based on the aging physical quantities includes:

[0021] Monitor the instantaneous changes and rates of change of the external operating parameters of the damper, and dynamically adjust the transient coupling strength coefficient between the various aging mechanisms inside the damper based on the instantaneous changes and rates of change.

[0022] Based on the instantaneous change and the rate of change, the dynamic response lag time of each aging mechanism inside the damper is predicted, and based on the dynamic response lag time, the aging physical quantity at the current moment is time-calibrated to obtain the transient effective cumulative quantity.

[0023] Based on the transient coupling strength coefficient and the transient effective cumulative amount, calculate the transient correction weights of each aging mechanism inside the damper on the preliminary performance parameters;

[0024] Based on the transient correction weights, the interaction relationships between various aging mechanisms within the damper are identified.

[0025] Optionally, adjusting the first weight of each aging mechanism on the correction of the preliminary performance parameters according to the interaction relationship includes:

[0026] Monitor the cumulative rate of various aging physical quantities inside the damper;

[0027] When any of the aforementioned cumulative rates changes significantly within multiple consecutive simulation time steps, a change in operating condition is triggered, and the current operating condition is determined to have entered a new operating phase based on the magnitude of the change in the current simulation operating condition parameters.

[0028] When it is determined to be the new operating phase, according to the current simulation operating condition parameters, the pre-configured correlation mapping table between operating conditions and aging mechanisms is queried to obtain the correlation coefficient between each aging mechanism inside the damper.

[0029] Based on the correlation coefficient, the first weight of each aging mechanism for correcting the preliminary performance parameters is adjusted.

[0030] Optionally, predicting the dynamic response hysteresis time of each aging mechanism within the damper based on the instantaneous change and the rate of change includes:

[0031] Monitor the cumulative damage state of the internal material of the damper;

[0032] Based on the cumulative damage state, dynamically adjust the material property parameters related to the aging mechanism in the material property database;

[0033] Based on the adjusted material property parameters, the instantaneous change, and the rate of change, the dynamic response hysteresis time of each aging mechanism inside the damper is predicted.

[0034] Optionally, monitoring the cumulative damage state of the material inside the damper includes:

[0035] Multiple sensors are deployed in key areas inside the damper to collect physical quantities in each area in real time.

[0036] Based on the physical quantities of each region, calculate the local damage index of each region, and evaluate the local cumulative damage status of each region based on the local damage index of each region.

[0037] Based on the local cumulative damage state of each region, the overall cumulative damage state of the internal material of the damper is comprehensively evaluated.

[0038] Optionally, assessing the local cumulative damage status of each region based on the local damage indices of each region includes:

[0039] Monitor the changing trends of local damage indicators in each region, and based on the changing trends, identify deviations from the changing trends, and identify the regions with deviations as abnormal regions;

[0040] Based on the deviation, the physical characteristics of the abnormal region, and the current simulation parameters, the second weight of the local damage index of the abnormal region in the assessment of the local cumulative damage state is adjusted.

[0041] The local cumulative damage state of the abnormal region is calculated based on the adjusted second weight and the contributions of other local damage indicators.

[0042] Based on the dynamic correlation between local damage indicators in different regions, the third weight of the local damage indicators in assessing the local cumulative damage is adjusted, and the local cumulative damage status of each region is calculated based on the adjusted third weight.

[0043] Optionally, adjusting the second weight of the local damage index of the abnormal region for the assessment of the local cumulative damage state based on the deviation, the physical characteristics of the abnormal region, and the current simulation condition parameters includes:

[0044] Monitor the microstructure changes of the material inside the abnormal region, and correct the material anisotropy parameters and defect distribution parameters of the abnormal region based on the microstructure changes;

[0045] Based on the corrected material anisotropy parameters, the corrected defect distribution parameters, and the current simulation condition parameters, the second weight of the local damage index of the abnormal region in the assessment of the local cumulative damage state is adjusted.

[0046] Secondly, this application provides a spring damping characteristic simulation system, the system comprising:

[0047] The model building module is used to build a damper simulation model, which includes preliminary performance parameters of variable internal components.

[0048] An aging physical quantity accumulation module is used to monitor and accumulate the aging physical quantities of the internal components of the damper during the operation of the simulation model.

[0049] The performance parameter correction module is used to periodically correct the preliminary performance parameters of the corresponding internal components in the simulation model based on the aging physical quantities, and record them as corrected performance parameters.

[0050] The performance prediction output module is used to predict the performance of the damper at different aging stages based on the corrected performance parameters, and output the status information of the internal components of the damper.

[0051] Compared with related technologies, the spring damping characteristic simulation method and system provided in this application have at least the following technical advantages:

[0052] This application establishes a damper simulation model incorporating preliminary performance parameters of variable internal components. During simulation, it monitors and accumulates the aging physical quantities of these components. Based on these aging physical quantities, the preliminary performance parameters in the simulation model are periodically corrected. Finally, the performance of the damper at different aging stages is predicted based on the corrected performance parameters. By incorporating physical quantities from the actual aging process into the simulation loop and dynamically adjusting model parameters, this application provides a more realistic prediction of damper performance. This allows engineers to accurately assess the performance consistency of the damper throughout its entire lifecycle from the initial design stage, thereby optimizing material selection, structural design, and damping fluid formulation. This significantly reduces R&D costs and time, and improves the long-term driving quality and handling stability of vehicles.

[0053] Details of one or more embodiments of this application are set forth in the following drawings and description to make other features, objects and advantages of this application more readily apparent. Attached Figure Description

[0054] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0055] Figure 1 This is a flowchart illustrating a spring damping characteristic simulation method according to an exemplary embodiment.

[0056] Figure 2 This is a flowchart illustrating step S2 according to an exemplary embodiment.

[0057] Figure 3 This is a flowchart illustrating step S3 according to an exemplary embodiment.

[0058] Figure 4 This is a flowchart illustrating step S31 according to an exemplary embodiment.

[0059] Figure 5 This is a flowchart illustrating step S32 according to an exemplary embodiment.

[0060] Figure 6 This is a partial flowchart illustrating step S312 according to an exemplary embodiment.

[0061] Figure 7 This is a block diagram illustrating a simulation system for spring damping characteristics according to an exemplary embodiment. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of this application clearer, the application is described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0063] Obviously, the accompanying drawings described below are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar scenarios based on these drawings without any creative effort. Furthermore, it is understood that although the efforts made in this development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, any changes to design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as insufficient disclosure of the content of this application.

[0064] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0065] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," and "third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0066] In related technologies, existing simulations are based solely on initial parameter calculations and lack models that describe time-dependent processes such as material fatigue, wear, and fluid degradation. They cannot reflect performance degradation and always predict the "ideal state" of damping force. This forces research and development to rely heavily on expensive and time-consuming physical testing, and problems often emerge in the later stages of design. It is difficult to optimize damper materials, structures, or damping fluid formulations in the early stages to improve them, which is time-consuming, labor-intensive, and costly to modify.

[0067] Based on the above, embodiments of the present invention provide a method and system for simulating spring damping characteristics, which will be described in detail below with reference to specific embodiments and accompanying drawings.

[0068] Example 1

[0069] This invention provides a method for simulating the damping characteristics of a spring. Figure 1 This is a flowchart illustrating a simulation method for spring damping characteristics according to an exemplary embodiment. Figure 1 As shown, the method includes the following steps:

[0070] S1. Establish a damper simulation model, which includes preliminary performance parameters of variable internal components;

[0071] In this embodiment, the damper simulation model refers to a mathematical or physical model capable of simulating the working principle and performance of a damper. Its core function is to reflect the dynamic behavior of the internal components of the damper. This model includes variable preliminary performance parameters of the internal components. These parameters are the design values ​​of the damper in a brand-new or initial state, such as the initial viscosity of the damping fluid, the initial stiffness of the valve plate, and the initial friction coefficient of the seal. Moreover, these parameters are not fixed during the simulation process but can be adjusted according to the aging state of the damper.

[0072] The damper simulation model established in this embodiment is based on multibody dynamics software (such as Adams and RecurDyn) to construct a macroscopic motion model of the damper. Simultaneously, computational fluid dynamics (CFD) software (such as Fluent and Star-CCM+) is used to refine the internal fluid flow model of the damper, capturing the complex flow behavior of the damping fluid within the valve system. Furthermore, finite element analysis (FEA) software (such as Abaqus and ANSYS) can be comprehensively used to perform structural mechanical analysis on key internal components of the damper (such as valve plates, piston rods, and seals) to evaluate their stress, strain, and fatigue life. In these models, the initial performance parameters of internal components can be set as variables. For example, the initial viscosity of the damping fluid can be set to 0.05 Pa·s, the initial elastic modulus of the valve plate can be set to 200 GPa, and the initial friction coefficient of the piston ring can be set to 0.1. These parameters are defined as modifiable variables in the model for subsequent adjustment based on aging conditions.

[0073] S2. During the simulation model operation, monitor and accumulate the aging physical quantities of the internal components of the damper;

[0074] In this embodiment, aging physical quantities refer to physical indicators that reflect the degree of aging of the internal components of the damper during operation. Examples include changes in the viscosity of the damping fluid, wear of the valve plates, changes in the hardness of the seals, and the surface roughness of the piston rod. Aging physical quantities are direct or indirect causes of damper performance degradation. By monitoring and accumulating these quantities, the aging process inside the damper can be quantified.

[0075] The monitoring and accumulation in this embodiment are achieved by integrating virtual sensors or physical models into the simulation model. For example, simulating the molecular chain breakage process of damping fluid under high-speed shear, the viscosity decay of the damping fluid is accumulated by calculating shear stress, shear rate, and contact time. Similarly, simulating the wear of valve plates during repeated opening and closing, the wear depth of the valve plate is accumulated by calculating the contact pressure, relative sliding speed, and contact time between the valve plate and valve seat. Furthermore, material fatigue of seals under long-term compression and friction can be simulated, and the hardness change of the seal is accumulated by calculating the number of stress cycles and stress amplitude. Finally, the accumulated aging physical quantities, such as viscosity decay percentage, wear depth in micrometers, and hardness change in Shore degrees, are recorded as a basis for subsequent correction of performance parameters.

[0076] S3. Based on aging physical quantities, periodically correct the preliminary performance parameters of the corresponding internal components in the simulation model and record them as corrected performance parameters.

[0077] In this embodiment, the corrected performance parameters refer to the values ​​adjusted from the initial performance parameters based on the accumulated aging physical quantities during the simulation process. The corrected parameters can more realistically reflect the actual performance of the damper at a specific aging stage. When the simulation runs for a certain time step or the accumulated aging physical quantities reach a preset threshold, the system will trigger the correction mechanism. If the accumulated damping fluid viscosity decreases by 10%, the viscosity parameter of the damping fluid in the simulation model will be corrected from the initial 0.05 Pa·s to 0.045 Pa·s. If the wear depth of the valve plate reaches 5 micrometers, the geometric dimensions of the valve plate in the simulation model will be adjusted accordingly, or its elastic modulus will be corrected to reflect material fatigue. Through periodic correction, it can be ensured that the simulation model always keeps pace with the actual aging state of the damper. Moreover, the above correction can be linear or nonlinear, depending on the mapping relationship between the aging physical quantities and the performance parameters. For example, a lookup table or empirical formula between the aging physical quantities and the corrected performance parameter values ​​can be established in advance, or a machine learning model can be used for dynamic correction.

[0078] S4. Based on the corrected performance parameters, predict the performance of the damper at different aging stages and output the status information of the internal components of the damper.

[0079] In this embodiment, the modified performance parameters are used to simulate the damping force characteristics, energy dissipation capacity and other key performance indicators of the damper at different service life stages (e.g., after driving 10,000 km, 50,000 km and 100,000 km), and output status information including the current viscosity of the damping fluid, the wear degree of the valve plate, and the fatigue condition of the seals, providing engineers with detailed diagnostic information.

[0080] Meanwhile, after the performance parameters are corrected, the simulation model continues to run and outputs the damper's performance curves under its current aging condition, such as damping force-velocity curves and damping force-displacement curves. These curves reflect the actual performance of the damper after aging. Simultaneously, the system also outputs the status information of the damper's internal components, such as the current viscosity of the damping fluid, the current wear of the valve plates, and the current hardness of the seals. This information can be presented in the form of reports, charts, or 3D visualizations, providing engineers with intuitive diagnostic and evaluation data. For example, a report could be generated showing that after 100,000 kilometers of driving, the damper's maximum damping force decreased by 15%, the damping fluid viscosity decreased by 20%, and the valve plates wore down by 10 micrometers.

[0081] The technical solution of the above embodiments establishes a damper simulation model containing preliminary performance parameters of variable internal components, and monitors and accumulates the aging physical quantities of the internal components during simulation, thereby periodically correcting the preliminary performance parameters of the corresponding internal components in the simulation model. For example, when the simulation reaches the point where the viscosity of the damping fluid decreases due to long-term shearing, the method of this application will adjust the viscosity parameter of the damping fluid in the simulation model accordingly to make it closer to the actual aging state. This dynamic correction mechanism enables the simulation model to realistically reflect the performance evolution of the damper throughout its entire life cycle, thereby accurately predicting its performance at different aging stages. Finally, based on the corrected performance parameters, the performance of the damper at different aging stages is accurately predicted, and the state information of the internal components of the damper is output, providing engineers with more reliable simulation results. This allows them to predict and optimize the durability performance of the damper in the early stages of design, evaluate the impact of different material or structural improvement schemes on long-term performance stability, and reduce R&D costs and time.

[0082] In one possible design, Figure 2 This is a flowchart illustrating step S2 according to an exemplary embodiment. (Refer to the attached document.) Figure 2 Step S2 includes:

[0083] S21. Monitor the local pressure of the fluid inside the damper and the piston speed.

[0084] In this embodiment, pressure sensors and velocity sensors are deployed at key locations inside the damper to acquire real-time instantaneous pressure values ​​of the fluid in different regions and piston velocity. Local pressure refers to the pressure of the fluid within a specific small region, and piston velocity refers to the rate of change of instantaneous displacement of the piston relative to the damper housing.

[0085] S22. When the local pressure is lower than the saturated vapor pressure of the internal fluid and the piston speed exceeds the preset speed threshold, the cavitation region inside the damper is identified.

[0086] In this embodiment, saturated vapor pressure is the critical pressure at which a fluid undergoes a phase change (vaporization) at a given temperature. The preset speed threshold is used to distinguish between the low-pressure area under normal operating conditions and the violent motion state that may trigger cavitation. The cavitation occurrence region refers to the area in the fluid where a large number of bubbles are formed due to a sudden drop in pressure. When the local pressure is lower than the saturated vapor pressure of the internal fluid and the piston movement speed exceeds the preset speed threshold, the cavitation phenomenon is identified.

[0087] S23. Calculate the local impact energy generated by the collapse of cavitation bubbles in the cavitation generation area, and based on the local impact energy, accumulate the amount of cavitation erosion damage on the surface of the damper piston.

[0088] In this embodiment, the localized impact energy generated by cavitation bubble collapse refers to the energy carried by the high-intensity shock wave released when these bubbles rapidly rupture upon entering the high-pressure zone. Cavitation erosion damage is the total amount of material loss or surface deformation on the piston surface caused by long-term impact from cavitation bubble collapse.

[0089] S24. The cavitation erosion damage is converted into a correction of the geometric parameters of the damper piston surface, and the corrected geometric parameters are accumulated as the aging physical quantity of the internal components of the damper.

[0090] In this embodiment, the cavitation erosion damage is transformed into a correction of the geometric parameters of the damper piston surface. Material loss or deformation can be quantified as changes in geometric features such as piston diameter and surface roughness. Finally, the corrected geometric parameters are accumulated as aging physical quantities of the internal components of the damper. The purpose is to incorporate the specific aging mechanism of cavitation erosion into the overall aging physical quantity accumulation system to more comprehensively reflect the actual aging state of the damper.

[0091] The technical solution of the above embodiment firstly captures the conditions for cavitation by monitoring the local pressure of the fluid inside the damper and the piston's movement speed. Then, after identifying the cavitation region, the local impact energy generated by the collapse of cavitation bubbles is further calculated, thereby quantifying the actual damage caused by cavitation to the damper piston surface. Based on this local impact energy, the cavitation erosion damage is accumulated and converted into a correction to the piston surface geometric parameters. This directly incorporates cavitation erosion, an important aging mechanism, and its impact on component geometry into the accumulation of aging physical quantities of the internal components of the damper. Through this quantification and conversion process, the simulation model can more accurately reflect the physical changes of the damper caused by cavitation during actual operation.

[0092] In one example, assume a hydraulic damper is operating under high-speed reciprocating motion conditions.

[0093] During the simulation, virtual sensors monitor the local pressure of the oil inside the damper and the instantaneous velocity of the piston in real time. When the simulation detects that the local pressure of the piston during a certain stroke is consistently lower than the saturated vapor pressure of the oil (e.g., 0.05 MPa) and the piston velocity exceeds a preset threshold (e.g., 5 m / s), the system identifies cavitation in the vicinity of the piston. Subsequently, the simulation model calculates the local impact energy generated by bubble collapse within this cavitation region based on fluid dynamics principles. For example, by calculating the energy of each bubble collapse event and summing them up, the total impact energy within a specific simulation time step is obtained. Based on this total impact energy, the cavitation erosion damage on the piston surface is accumulated. For example, using a preset material erosion model, the impact energy is converted into microscopic material loss on the piston surface (e.g., micrometer-level depth loss).

[0094] Ultimately, this accumulated erosion damage is translated into corrections to geometric parameters such as piston diameter or surface roughness. For example, the piston diameter may decrease slightly due to erosion, or the surface roughness may increase. These corrected geometric parameters are then used as aging physical quantities of the damper's internal components, continuously accumulated, and input into the simulation model for subsequent performance parameter correction and performance prediction.

[0095] In one possible design, Figure 3 This is a flowchart illustrating step S3 according to an exemplary embodiment. (Refer to the attached diagram.) Figure 3 Step S3 includes:

[0096] S31. Read the aging physical quantities and identify the interaction between various aging mechanisms inside the damper based on the aging physical quantities.

[0097] In this embodiment, the aging physical quantities include, but are not limited to, the amount of cavitation erosion damage on the piston surface, the degree of internal fluid contamination, the amount of seal wear, and the amount of fatigue crack propagation in the material. By analyzing the correlation, variation trends, and coupling effects between different aging physical quantities under specific operating conditions, it is possible to determine whether the various aging mechanisms within the damper (such as cavitation, fatigue, and wear) reinforce, weaken, or act independently. For example, cavitation erosion may accelerate fatigue damage on the piston surface, while fluid contamination may alter fluid viscosity, thereby affecting cavitation behavior. Identifying these interactions allows for a more comprehensive understanding of the damper's aging process.

[0098] S32. Adjust the first weight of each aging mechanism for the correction of the initial performance parameters according to the interaction relationship;

[0099] In this embodiment, after identifying the interactions between aging mechanisms, the weight of different aging mechanisms in correcting preliminary performance parameters is dynamically adjusted based on these interactions. For example, if a significant synergistic effect between cavitation erosion and material fatigue is identified, the first weights of cavitation erosion and material fatigue may be increased or adjusted accordingly when correcting performance parameters related to piston material strength, in order to more accurately reflect their combined impact. This ensures that parameter corrections can accurately reflect the actual contribution of each aging mechanism and their mutual influence.

[0100] S33. Based on the aging physical quantities and the adjusted first weight, calculate the correction value of the preliminary performance parameters and record it as the corrected performance parameters.

[0101] In this embodiment, the currently read aging physical quantities and the first weights of each aging mechanism adjusted according to the interaction relationship are substituted into a preset correction algorithm or model to calculate the final corrected values ​​of the preliminary performance parameters of the internal components of the damper. The corrected performance parameters are recorded and used to update the corresponding parameters in the simulation model to ensure that the simulation model always remains consistent with the actual aging state of the damper, thereby providing a quantitative and dynamic parameter correction mechanism.

[0102] The technical solution described above solves the problem of insufficient accuracy that may exist in traditional simple periodic corrections by introducing the identification of the interaction relationships between various aging mechanisms inside the damper and dynamically adjusting the first weight of each aging mechanism for the correction of preliminary performance parameters. Specifically, after the aging physical quantities inside the damper are read, these quantities are no longer simply accumulated or averaged. Instead, the system first analyzes whether there are synergistic, competitive, or independent interactions between the aging mechanisms such as cavitation, fatigue, and wear reflected by these aging physical quantities. For example, if it is found that cavitation erosion and material fatigue promote each other under specific working conditions, then the combined effect of these two mechanisms will be more fully considered when correcting performance parameters related to material strength. By adjusting their respective correction weights, the corrected parameters can more accurately reflect this composite damage. Thus, the correction of preliminary performance parameters in the simulation model is no longer static or preset, but can be adaptively adjusted according to the actual aging state of the damper and the dynamic coupling relationship of its internal mechanisms, ensuring that the simulation model can more accurately track the real performance changes of the damper at different aging stages.

[0103] In one example, suppose a damper is in operation, and the local pressure of the internal fluid and the piston velocity are monitored to calculate the amount of cavitation erosion damage on the piston surface. Simultaneously, sensors deployed in critical areas also monitor changes in the material's microstructure, such as the initiation and propagation of fatigue cracks, thus accumulating the amount of fatigue damage.

[0104] At this point, the system reads these cavitation erosion damage and material fatigue damage as aging physical quantities. Next, the system analyzes these aging physical quantities to identify the interaction between the cavitation erosion mechanism and the material fatigue mechanism. For example, through historical data analysis or physical models, the system may identify that cavitation erosion significantly accelerates fatigue damage to piston materials under high-speed motion and low-pressure environments.

[0105] Based on this interaction, the system adjusts the initial weights of cavitation erosion and material fatigue—two aging mechanisms—in the correction of the damper piston material strength parameters. For example, if a strong synergistic effect is identified, the weights of cavitation erosion and material fatigue will be dynamically increased when correcting preliminary performance parameters such as the piston material's elastic modulus or yield strength, to reflect the greater damage they jointly cause.

[0106] Finally, the system inputs the cavitation erosion damage, material fatigue damage, and adjusted first weights into a preset correction algorithm to calculate the corrected values ​​for the piston material strength parameters. For example, the corrected value might represent a percentage reduction in the effective strength of the piston material. These corrected values ​​are then recorded as corrected performance parameters and used to update the simulation model, enabling the model to more accurately predict the damper's performance at the current aging stage, such as its damping force characteristics and energy dissipation capacity. In this way, the simulation model can dynamically adapt to the actual aging condition of the damper, providing more reliable performance predictions.

[0107] In one possible design, Figure 4 This is a flowchart illustrating step S31 according to an exemplary embodiment. (Refer to the attached diagram.) Figure 4 Step S31 includes:

[0108] S311. Monitor the instantaneous changes and rates of change of the external operating parameters of the damper, and dynamically adjust the transient coupling strength coefficient between various aging mechanisms inside the damper based on the instantaneous changes and rates of change.

[0109] In this embodiment, the external operating parameters of the damper include, but are not limited to, ambient temperature, external load, vibration frequency, and stroke speed. Their instantaneous changes and rates of change can be collected in real time by various sensors installed outside the damper. For example, an accelerometer can monitor changes in vibration frequency and stroke speed, while a temperature sensor can monitor changes in ambient temperature. The transient coupling strength coefficient refers to the degree of mutual influence between different aging mechanisms (such as cavitation erosion, wear, fatigue, etc.) under specific instantaneous operating conditions. The dynamic adjustment of the transient coupling strength coefficient is based on a preset empirical model, machine learning algorithm, or real-time data analysis to reflect the impact of operating condition changes on the correlation between aging mechanisms.

[0110] S312. Based on the instantaneous change and the rate of change, predict the dynamic response lag time of each aging mechanism inside the damper, and based on the dynamic response lag time, calibrate the aging physical quantity at the current moment to obtain the transient effective cumulative quantity.

[0111] In this embodiment, the transient effective cumulative value is a cumulative value that, after time calibration, accurately reflects the impact of aging physical quantities on damper performance under current operating conditions. Dynamic response lag time refers to the time required for various aging mechanisms within the damper to respond to changes in external operating parameters. Different aging mechanisms may have different response rates; for example, cavitation erosion may occur rapidly when local pressure drops sharply, while material fatigue may require a longer accumulation period. The dynamic response lag time prediction in this embodiment is based on a material property database, finite element analysis, or a historical data regression model. By calibrating the aging physical quantities at the current moment, the actual response time of the aging mechanisms can be considered during calculation, resulting in a more timely transient effective cumulative value.

[0112] S313. Based on the transient coupling strength coefficient and the transient effective cumulative amount, calculate the transient correction weights of each aging mechanism inside the damper on the initial performance parameters.

[0113] In this embodiment, the transient correction weight is an indicator that measures the contribution of each aging mechanism to the correction of the initial performance parameters of the damper under the current transient operating condition. It is calculated by comprehensively considering the transient coupling strength coefficient and the transient effective accumulation. For example, when the transient coupling strength of cavitation erosion is high and the transient effective accumulation is large, the corresponding transient correction weight will increase accordingly.

[0114] S314. Identify the interaction between various aging mechanisms within the damper based on the transient correction weight;

[0115] In this embodiment, by comprehensively analyzing the transient correction weights of each aging mechanism, it is possible to identify which aging mechanisms are dominant under the current dynamic operating conditions and how they interact with each other, thereby providing a more accurate basis for subsequent performance parameter correction.

[0116] The technical solution of the above embodiments, by introducing the monitoring of the instantaneous changes and rates of change of the external operating parameters of the damper, can perceive the dynamic operating environment of the damper in real time, improving the accuracy of the damper simulation model in correcting the performance parameters of internal components under dynamic conditions. Specifically, by dynamically adjusting the coupling strength coefficient between aging mechanisms, the simulation model can better adapt to rapid changes in external operating conditions, avoiding correction deviations caused by static assumptions. In addition, the introduction of prediction of dynamic response lag time and time calibration of aging physical quantities makes the assessment of aging accumulation more timely and realistic, thereby ensuring that the corrected performance parameters can more accurately reflect the actual aging state of the damper. Thus, the solution of this application makes the performance prediction of the damper at different aging stages more reliable, especially in the face of complex and ever-changing operating environments, providing more accurate performance assessments and status information outputs, effectively extending the service life of the damper and improving its operational safety.

[0117] In one example, suppose a vehicle damper encounters a bumpy road surface while traveling at high speed, causing a sudden and drastic change in external load and vibration frequency. Traditional simulation methods may identify the interaction of aging mechanisms based solely on accumulated cavitation erosion and wear, but they cannot capture in time the drastic changes in the coupling relationships between these mechanisms under such transient conditions.

[0118] The proposed solution first monitors the instantaneous changes and rates of change of external load and vibration frequency. Based on this dynamic data, the system dynamically adjusts the transient coupling strength coefficient between cavitation erosion and wear mechanisms. For example, during severe vibrations, the coupling strength of cavitation erosion may significantly increase. Simultaneously, the system predicts the dynamic response lag time of cavitation erosion and wear mechanisms to these transient changes. For instance, cavitation erosion may respond faster, while wear may respond slightly slower.

[0119] Based on this, the aging physical quantities such as local pressure and piston speed at the current moment are time-calibrated to obtain the transient effective cumulative amount. Subsequently, combined with the dynamically adjusted transient coupling strength coefficient and the transient effective cumulative amount, the transient correction weights of cavitation erosion and wear mechanisms on the initial performance parameters of the damper are calculated. For example, at the moment of severe turbulence, the transient correction weight of cavitation erosion may be much higher than that of wear. Finally, based on these transient correction weights, the system can accurately identify that cavitation erosion is the dominant aging mechanism under the current severe turbulence condition, and that its interaction with the wear mechanism has also undergone transient changes. In this way, the simulation model can correct the performance parameters of the damper more timely and accurately, thereby more realistically predicting its performance under extreme conditions.

[0120] In one possible design, Figure 5This is a flowchart illustrating step S32 according to an exemplary embodiment. (Refer to the attached document.) Figure 5 Step S32 includes:

[0121] S321. Monitor the cumulative rate of various aging physical quantities inside the damper;

[0122] In this embodiment, by monitoring the accumulation rate, dynamic changes in the aging process inside the damper can be obtained in real time. For example, the accumulation rate of aging physical quantities such as cavitation erosion damage on the damper piston surface, wear of seals, and change rate of internal fluid viscosity can be continuously monitored. The dynamic change in the accumulation rate can serve as an early warning signal that the damper's operating condition may be changing.

[0123] S322. When any cumulative rate changes significantly within multiple consecutive simulation time steps, trigger condition change detection and determine whether the current condition has entered a new operating phase based on the magnitude of the change in the current simulation condition parameters.

[0124] In this embodiment, when any cumulative rate changes significantly over multiple consecutive simulation time steps, it indicates that the aging mode or strength within the damper may have changed, at which point the system will trigger a condition change detection. This condition change detection process determines whether the damper has entered a new operating phase that is significantly different from the previous one, based on the magnitude of changes in current simulation operating parameters, such as external load, ambient temperature, and operating frequency. For example, if the external load or ambient temperature continuously exceeds a preset threshold, it can be determined that the damper has entered a new operating phase.

[0125] S323. When it is determined to be a new operating phase, according to the current simulation operating condition parameters, query the pre-configured correlation mapping table between operating conditions and aging mechanisms to obtain the correlation coefficient between various aging mechanisms inside the damper.

[0126] In this embodiment, a pre-configured correlation mapping table between operating conditions and aging mechanisms stores empirical data or pre-calculated results on the degree of correlation or dominant role of various aging mechanisms (such as cavitation erosion, fatigue, thermal degradation, and wear) within the damper under different typical operating conditions. By querying this table, the correlation coefficients between each aging mechanism can be obtained under the current new operating condition. For example, under high-temperature and high-frequency operating conditions, the correlation coefficients between thermal degradation and fluid shear wear may be set to a higher value.

[0127] S324. Adjust the first weight of each aging mechanism for the correction of the initial performance parameters according to the correlation coefficient;

[0128] In this embodiment, the dynamic adjustment of the first weight enables the influence weight of different aging mechanisms on the overall performance parameters of the damper to no longer be fixed at different operating stages, but to be optimized and allocated according to the characteristics of actual working conditions, so as to more accurately reflect the actual aging process of the damper.

[0129] The technical solution of the above embodiment, by introducing real-time monitoring of the accumulation rate of aging physical quantities, can sensitively capture the dynamic changes in the aging process inside the damper. When a significant change in the accumulation rate is detected, the system further combines the change magnitude of the current simulation operating condition parameters to determine whether the damper has entered a new operating phase. This mechanism effectively solves the limitation in the basic scheme where weight adjustment may lag behind actual operating condition changes. Once a new operating phase is identified, a more accurate correlation coefficient between each aging mechanism under that specific operating condition can be obtained by querying a pre-configured correlation mapping table between operating conditions and aging mechanisms. These correlation coefficients are directly used to dynamically adjust the first weight of each aging mechanism for the initial performance parameter correction, thereby ensuring the real-time nature of weight allocation and the adaptability of operating conditions.

[0130] In one example, suppose a damper initially experiences low-frequency, small-amplitude vibrations, where internal aging primarily manifests as slight wear on the seals. The simulation model monitors the cumulative rate of seal wear. When the damper is suddenly applied to high-frequency, large-amplitude vibration conditions, such as when a vehicle travels at high speed over a bumpy road, the piston speed and local pressure increase dramatically, potentially exacerbating internal fluid cavitation and significantly increasing the cumulative rate of cavitation erosion damage on the piston surface.

[0131] At this point, a significant change in the cumulative rate of cavitation erosion damage was detected over multiple consecutive simulation time steps, triggering a change in operating conditions detection. Based on the amplitude of the changes in the current high-frequency, high-amplitude vibration parameters, it was determined that the damper had entered a new "high-frequency, high-load" operating phase. Subsequently, a pre-configured correlation table between operating conditions and aging mechanisms was queried. This table may pre-determine that under "high-frequency, high-load" conditions, the correlation coefficient between cavitation erosion mechanisms and fatigue mechanisms is relatively high, while the correlation coefficient between seal wear mechanisms is relatively low.

[0132] Based on these obtained correlation coefficients, the first weight of each aging mechanism in correcting the initial performance parameters is dynamically adjusted. Specifically, the weights of cavitation erosion and fatigue mechanisms are increased accordingly, while the weight of seal wear mechanisms may be decreased or remain unchanged. Through this dynamic adjustment, the simulation model can more accurately reflect the dominant role of cavitation erosion and fatigue in damper performance degradation under high-frequency, high-load conditions, thus making the corrected performance parameters closer to the actual aging state of the damper and improving the accuracy of simulation predictions.

[0133] In one possible design, Figure 6 This is a partial flowchart illustrating step S312 according to an exemplary embodiment. (Refer to the attached diagram.) Figure 6 In step S312, based on the instantaneous change and the rate of change, the dynamic response hysteresis time of each aging mechanism inside the damper is predicted, including:

[0134] S3121. Monitor the cumulative damage status of the internal materials of the damper;

[0135] In this embodiment, the physical damage level of key materials inside the damper (such as piston rods, seals, valve plates, etc.) is acquired in real time or near real time through various detection methods. The damage may include fatigue cracks, wear, corrosion, material performance degradation, etc. In practical applications, the cumulative damage state can be monitored by sensors deployed in key areas inside the damper, such as strain sensors, ultrasonic sensors, or acoustic emission sensors, to collect changes in the material's microstructure, stress-strain distribution, or damage signals.

[0136] S3122. Based on the cumulative damage state, dynamically adjust the material property parameters related to the aging mechanism in the material property database;

[0137] In this embodiment, the material property data used in the simulation model is updated in real time based on the monitored cumulative material damage. These material property parameters may include elastic modulus, yield strength, fatigue life curve, coefficient of thermal expansion, and thermal conductivity, and these parameters change as damage accumulates. This ensures that the material parameters used in the simulation model accurately reflect the actual physical performance of the damper at different aging stages, improving the accuracy of simulation predictions. For example, when fatigue damage is detected, the elastic modulus or fatigue strength parameters can be reduced accordingly.

[0138] S3123. Based on the adjusted material property parameters, instantaneous changes and rates of change, predict the dynamic response hysteresis time of each aging mechanism inside the damper.

[0139] In this embodiment, after considering the dynamic material properties under actual damage conditions, and combining the instantaneous changes and rates of change of external operating conditions, the time required for different aging mechanisms (such as cavitation, wear, thermal decay, etc.) within the damper to respond to these changes is calculated using an established physical model or machine learning model. This yields a more accurate response time for the aging mechanisms, providing a more reliable basis for subsequent time calibration of aging physical quantities. For example, when material damage accumulates to a certain extent, its response speed to temperature changes may slow down, or its hysteresis time to pressure shocks may increase; these changes will be incorporated into the prediction model.

[0140] The technical solution described above improves the accuracy of predicting the dynamic response lag time of various aging mechanisms within the damper by introducing monitoring of the cumulative damage state of the internal materials. Specifically, when cumulative damage occurs in the internal materials of the damper, their physical properties change, thereby affecting their response speed and manner to changes in external operating conditions. By monitoring these cumulative damage states in real time and dynamically adjusting the material property parameters related to aging mechanisms in the material property database accordingly, the simulation model can more accurately reflect the real physical behavior of the internal materials of the damper at different aging stages. Finally, the dynamic updating of material parameters allows for more precise capture of the dynamic response lag time of each aging mechanism when combining instantaneous changes and rates of change for prediction, thus avoiding prediction bias caused by the failure to consider material performance degradation.

[0141] In one possible design, step S3121 includes:

[0142] S31211. Multiple sensors are deployed in key areas inside the damper to collect physical quantities in each area in real time.

[0143] In this embodiment, the critical area inside the damper refers to specific parts of the damper that are susceptible to aging mechanisms such as wear, fatigue, corrosion, and cavitation during operation, such as the piston rod surface, seal contact surface, valve plate area, or oil chamber wall. Material damage in the critical area has a significant impact on the overall performance of the damper. The aforementioned sensors may include, but are not limited to, strain sensors, temperature sensors, pressure sensors, vibration sensors, ultrasonic sensors, or optical sensors, which are deployed in the critical area to acquire physical quantities in that area in real time, such as strain, temperature, pressure, vibration frequency, acoustic emission signals, or optical images.

[0144] S31212. Calculate the local damage index of each region based on the physical quantities of each region, and evaluate the local cumulative damage status of each region based on the local damage index of each region.

[0145] In this embodiment, local damage indices are parameters that quantify the degree of material damage in a specific area, such as fatigue damage factor, wear amount, corrosion depth, cavitation erosion depth, or crack propagation length. The calculation of local damage indices is based on material mechanics models, damage mechanics theories, or empirical formulas. Based on local damage indices, the local cumulative damage state of each area can be assessed, for example, by comparing the local damage indices with a preset damage threshold, or by judging the trend of cumulative damage indices over time.

[0146] S31213. Based on the local cumulative damage status of each region, comprehensively evaluate the overall cumulative damage status of the internal material of the damper.

[0147] In this embodiment, the comprehensive evaluation involves weighted averaging, maximum value selection, or integration based on a specific damage propagation model of the local cumulative damage status of different key areas to obtain a macroscopic indicator that can represent the overall health status of the materials inside the damper.

[0148] The technical solution described above, by deploying sensors in key areas within the damper, enables real-time acquisition of local physical quantities, thereby allowing for precise capture of the damage evolution process of the material at different locations. By calculating local damage indices and assessing the local cumulative damage state, errors that may exist in traditional overall assessments can be avoided, as the stress, heat, and corrosion conditions vary significantly in different regions within the damper, and damage accumulation does not occur uniformly. Ultimately, by comprehensively assessing the local cumulative damage state of each region, a more accurate overall cumulative damage state of the material within the damper is obtained, thereby improving the accuracy of dynamic response lag time prediction.

[0149] In one possible design, step S31212 involves assessing the local cumulative damage status of each region based on local damage indices, including:

[0150] S312121. Monitor the changing trends of local damage indicators in each region, and based on the changing trends, identify deviations in the changing trends and identify the regions with deviations as abnormal regions.

[0151] In this embodiment, monitoring the trend of change refers to continuously tracking and analyzing the local damage index data of each region over a period of time to obtain the pattern of its evolution over time. Subsequently, by comparing it with preset normal change patterns or historical data, abnormalities in the rate, magnitude, or direction of change of local damage indicators are detected. When a significant deviation is identified, the region will be identified as an abnormal region, indicating that its damage accumulation may have a special or accelerated pattern.

[0152] S312122. Based on the physical characteristics of the deviation and abnormal regions and the current simulation conditions, adjust the second weight of the local damage index of the abnormal region for the assessment of the local cumulative damage state.

[0153] In this embodiment, after identifying the abnormal region, the importance or influence of the local damage index of the abnormal region in calculating the local cumulative damage state is dynamically adjusted by comprehensively considering the degree of damage deviation, its material properties, geometric structure and other physical characteristics, as well as the current operating conditions (such as temperature, pressure, vibration frequency, etc.). For example, for critical regions that show significant deviations under harsh operating conditions, their second weight can be appropriately increased to more accurately reflect the severity of their damage.

[0154] S312123. Calculate the local cumulative damage status of the abnormal area based on the adjusted second weight and the contributions of other local damage indicators.

[0155] In this embodiment, after determining the second weight of the abnormal region, the local cumulative damage state of the abnormal region is accurately calculated by combining the contributions of other local damage indicators in the region that are not identified as abnormal, through weighted averaging or other aggregation algorithms.

[0156] S312124. Based on the dynamic correlation between local damage indicators in different regions, adjust the third weight of local damage indicators for assessing local cumulative damage, and calculate the local cumulative damage status of each region based on the adjusted third weight.

[0157] In this embodiment, considering that damage in different regions within the damper does not occur in isolation but may interact or synergistically, it is necessary to analyze the statistical correlation or physical coupling relationship between local damage indicators in different regions. Based on this, the third weight of each region's local damage indicator in the overall local cumulative damage assessment is adjusted to more comprehensively reflect the propagation and cumulative effects of damage. Finally, based on the adjusted third weight, the local cumulative damage state of each region is calculated, resulting in a more accurate and comprehensive local cumulative damage assessment.

[0158] The technical solution of the above embodiments, by introducing monitoring and deviation identification of the changing trends of local damage indicators, can promptly detect abnormal damage areas that may exist inside the damper. Especially in complex working conditions or the early stages of damage, the method of this embodiment can more sensitively identify potential damage risks and perform refined quantification of damage in key areas.

[0159] In one possible design, step S31212 includes:

[0160] S312121. Monitor the microstructure changes of the material inside the abnormal area, and correct the material anisotropy parameters and defect distribution parameters of the abnormal area based on the microstructure changes.

[0161] In this embodiment, a material evolution sub-model within the simulation model tracks real-time changes at the microscale, such as grain structure, phase transformation, dislocation density, and the initiation and propagation of voids or cracks. Microstructural changes are the fundamental cause of macroscopic performance degradation. Material anisotropy parameters refer to the characteristics of a material exhibiting different mechanical or physical properties in different directions, such as crystal orientation and fiber reinforcement direction. Defect distribution parameters describe the quantity, size, shape, and spatial arrangement of internal defects (such as pores, inclusions, and microcracks). Based on the monitored microstructural changes, preset material constitutive relations or machine learning models can be used to correct the material anisotropy parameters and defect distribution parameters in abnormal regions, making them more accurately reflect the current actual state of the material. For example, when grain coarsening or a specific phase transformation is detected, the material's elastic modulus, yield strength, and other anisotropy parameters can be adjusted accordingly; when the formation and aggregation of micropores are detected, the defect distribution parameters can be corrected to reflect the intensification of internal damage.

[0162] S312122. Based on the corrected material anisotropy parameters, the corrected defect distribution parameters, and the current simulation conditions, adjust the second weight of the local damage index in the abnormal region for the assessment of the local cumulative damage state.

[0163] In this embodiment, the corrected material anisotropy parameters and defect distribution parameters, together with the current simulation condition parameters, are used to more finely adjust the second weight of the local damage index in the abnormal region for the assessment of the local cumulative damage state.

[0164] The technical solution of the above embodiments, by introducing monitoring of changes in the microstructure of the material within the anomalous region and correcting the anisotropy parameters and defect distribution parameters of the material accordingly, enables the adjustment of the second weight to more accurately reflect the true damage sensitivity of the material in the anomalous region. Specifically, when the microstructure of the material changes, its mechanical response and damage evolution path also change. By correcting the anisotropy parameters and defect distribution parameters, the simulation model can more accurately simulate the impact of these microscopic changes on macroscopic damage behavior. Thus, the second weight no longer relies solely on macroscopic physical properties but incorporates the essential information of damage within the material, making the assessment of the local cumulative damage state more realistic.

[0165] In summary, the spring damping characteristic simulation method provided by this invention establishes a damper simulation model containing preliminary performance parameters of variable internal components. During simulation, it monitors and accumulates the aging physical quantities of the internal components, then periodically corrects the preliminary performance parameters in the simulation model based on these aging physical quantities. Finally, it predicts the damper's performance at different aging stages based on the corrected performance parameters. This application incorporates physical quantities from the actual aging process into the simulation loop and dynamically adjusts the model parameters, thereby providing a more realistic damper performance prediction. This allows engineers to accurately assess the damper's performance consistency throughout its entire lifespan from the initial design stage, thus optimizing material selection, structural design, and damping fluid formulation. This significantly reduces R&D costs and time, and improves the long-term driving quality and handling stability of vehicles.

[0166] Example 2

[0167] Embodiment 2 of this application provides a spring damping characteristic simulation system. Figure 7 This is a block diagram illustrating a simulation system for spring damping characteristics according to an exemplary embodiment. (Refer to the attached diagram.) Figure 7 The system includes:

[0168] Model building module 01 is used to build a damper simulation model, which includes preliminary performance parameters of variable internal components.

[0169] The aging physical quantity accumulation module 02 is used to monitor and accumulate the aging physical quantities of the internal components of the damper during the simulation model operation.

[0170] The performance parameter correction module 03 is used to periodically correct the preliminary performance parameters of the corresponding internal components in the simulation model based on aging physical quantities, and record them as corrected performance parameters.

[0171] The performance prediction output module 04 is used to predict the performance of the damper at different aging stages based on the corrected performance parameters, and output the status information of the internal components of the damper.

[0172] In summary, the spring damping characteristic simulation method and system provided by this invention establishes a damper simulation model containing preliminary performance parameters of variable internal components. During simulation, it monitors and accumulates the aging physical quantities of the internal components, then periodically corrects the preliminary performance parameters in the simulation model based on these aging physical quantities. Finally, it predicts the damper's performance at different aging stages based on the corrected performance parameters. This application incorporates physical quantities from the actual aging process into the simulation loop and dynamically adjusts the model parameters, thereby providing a more realistic damper performance prediction. This allows engineers to accurately assess the damper's performance consistency throughout its entire lifespan during the initial design phase, thus optimizing material selection, structural design, and damping fluid formulation. This significantly reduces R&D costs and time, and improves the long-term driving quality and handling stability of vehicles.

[0173] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0174] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for simulating the damping characteristics of a spring, characterized in that, Includes the following steps: Establish a damper simulation model, which includes preliminary performance parameters of variable internal components; During the simulation model's operation, the aging physical quantities of the internal components of the damper are monitored and accumulated; Based on the aging physical quantities, the preliminary performance parameters of the corresponding internal components in the simulation model are periodically corrected and recorded as corrected performance parameters; Based on the corrected performance parameters, the performance of the damper at different aging stages is predicted, and the status information of the internal components of the damper is output. The monitoring and accumulation of aging physical quantities of the internal components of the damper during the simulation model operation includes: Monitor the local pressure of the fluid inside the damper and the piston speed; When the local pressure is lower than the saturated vapor pressure of the internal fluid and the piston speed exceeds a preset speed threshold, the cavitation region inside the damper is identified. Calculate the local impact energy generated by the collapse of cavitation bubbles in the cavitation generation area, and based on the local impact energy, accumulate the amount of cavitation erosion damage on the surface of the damper piston. The cavitation erosion damage is converted into a correction of the geometric parameters of the damper piston surface, and the corrected geometric parameters are accumulated as the aging physical quantity of the internal components of the damper.

2. The spring damping characteristic simulation method according to claim 1, characterized in that, The preliminary performance parameters of the corresponding internal components in the simulation model are periodically corrected based on the aging physical quantities and recorded as corrected performance parameters, including: Read the aging physical quantities and identify the interaction relationships between the various aging mechanisms inside the damper based on the aging physical quantities; Based on the aforementioned interaction relationships, the first weight of each aging mechanism for correcting the preliminary performance parameters is adjusted; Based on the aging physical quantities and the adjusted first weight, the correction values ​​of the preliminary performance parameters are calculated and recorded as the corrected performance parameters.

3. The spring damping characteristic simulation method according to claim 2, characterized in that, The step of identifying the interaction relationships between various aging mechanisms within the damper based on the aging physical quantities includes: The instantaneous changes and rates of change of the external operating parameters of the damper are monitored, and the transient coupling strength coefficient between the various aging mechanisms inside the damper is dynamically adjusted based on the instantaneous changes and rates of change. Based on the instantaneous change and the rate of change, the dynamic response lag time of each aging mechanism inside the damper is predicted, and based on the dynamic response lag time, the aging physical quantity at the current moment is time-calibrated to obtain the transient effective cumulative quantity. Based on the transient coupling strength coefficient and the transient effective cumulative amount, calculate the transient correction weights of each aging mechanism inside the damper on the preliminary performance parameters; Based on the transient correction weights, the interaction relationships between various aging mechanisms within the damper are identified.

4. The spring damping characteristic simulation method according to claim 3, characterized in that, The step of adjusting the first weight of each aging mechanism for correcting the preliminary performance parameters based on the interaction relationship includes: Monitor the cumulative rate of various aging physical quantities inside the damper; When any of the aforementioned cumulative rates changes significantly within multiple consecutive simulation time steps, a change in operating condition is triggered, and the current operating condition is determined to have entered a new operating phase based on the magnitude of the change in the current simulation operating condition parameters. When it is determined to be the new operating phase, according to the current simulation operating condition parameters, the pre-configured correlation mapping table between operating conditions and aging mechanisms is queried to obtain the correlation coefficient between each aging mechanism inside the damper. Based on the correlation coefficient, the first weight of each aging mechanism for correcting the preliminary performance parameters is adjusted.

5. The spring damping characteristic simulation method according to claim 4, characterized in that, The step of predicting the dynamic response hysteresis time of each aging mechanism within the damper based on the instantaneous change and the rate of change includes: Monitor the cumulative damage state of the internal material of the damper; Based on the cumulative damage state, dynamically adjust the material property parameters related to the aging mechanism in the material property database; Based on the adjusted material property parameters, the instantaneous change, and the rate of change, the dynamic response hysteresis time of each aging mechanism inside the damper is predicted.

6. The spring damping characteristic simulation method according to claim 5, characterized in that, The monitoring of the cumulative damage state of the internal material of the damper includes: Multiple sensors are deployed in key areas inside the damper to collect physical quantities in each area in real time. Based on the physical quantities of each region, calculate the local damage index of each region, and evaluate the local cumulative damage status of each region based on the local damage index of each region. Based on the local cumulative damage state of each region, the overall cumulative damage state of the internal material of the damper is comprehensively evaluated.

7. The spring damping characteristic simulation method according to claim 6, characterized in that, The assessment of the local cumulative damage status of each region based on the local damage indices of each region includes: Monitor the changing trends of local damage indicators in each region, and based on the changing trends, identify deviations from the changing trends, and identify the regions with deviations as abnormal regions; Based on the deviation, the physical characteristics of the abnormal region, and the current simulation parameters, the second weight of the local damage index of the abnormal region in the assessment of the local cumulative damage state is adjusted. The local cumulative damage state of the abnormal region is calculated based on the adjusted second weight and the contributions of other local damage indicators. Based on the dynamic correlation between local damage indicators in different regions, the third weight of the local damage indicators in assessing the local cumulative damage is adjusted, and the local cumulative damage status of each region is calculated based on the adjusted third weight.

8. The spring damping characteristic simulation method according to claim 7, characterized in that, The step of adjusting the second weight of the local damage index of the abnormal region in the assessment of the local cumulative damage state based on the deviation, the physical characteristics of the abnormal region, and the current simulation operating condition parameters includes: Monitor the microstructure changes of the material inside the abnormal region, and correct the material anisotropy parameters and defect distribution parameters of the abnormal region based on the microstructure changes; Based on the corrected material anisotropy parameters, the corrected defect distribution parameters, and the current simulation condition parameters, the second weight of the local damage index of the abnormal region in the assessment of the local cumulative damage state is adjusted.

9. A simulation system for spring damping characteristics, characterized in that, The system includes: The model building module is used to build a damper simulation model, which includes preliminary performance parameters of variable internal components. An aging physical quantity accumulation module is used to monitor and accumulate the aging physical quantities of the internal components of the damper during the operation of the simulation model. The performance parameter correction module is used to periodically correct the preliminary performance parameters of the corresponding internal components in the simulation model based on the aging physical quantities, and record them as corrected performance parameters. The performance prediction output module is used to predict the performance of the damper at different aging stages based on the corrected performance parameters, and output the status information of the internal components of the damper. The monitoring and accumulation of aging physical quantities of the internal components of the damper during the simulation model operation includes: Monitor the local pressure of the fluid inside the damper and the piston speed; When the local pressure is lower than the saturated vapor pressure of the internal fluid and the piston speed exceeds a preset speed threshold, the cavitation region inside the damper is identified. Calculate the local impact energy generated by the collapse of cavitation bubbles in the cavitation generation area, and based on the local impact energy, accumulate the amount of cavitation erosion damage on the surface of the damper piston. The cavitation erosion damage is converted into a correction of the geometric parameters of the damper piston surface, and the corrected geometric parameters are accumulated as the aging physical quantity of the internal components of the damper.

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