True module matching and database optimization method for design of high-strength silicone oil shock absorber

Abstract

The invention discloses a real module matching and database optimization method for designing a high-strength silicone oil shock absorber, and particularly relates to the technical field of data processing. By combining structural parameters and application conditions of the shock absorber, a preliminary mechanical simulation system integrated with the coupling model is established, a preliminary true module is formed, actual measurement damping data under multiple temperatures and multiple rates are obtained through a dynamic loading test, iterative correction is carried out through an advanced parameter optimization algorithm, high-precision true module matching is completed, and the actual measurement damping data are obtained. Real module parameters obtained through optimization under different design schemes and a corresponding damping performance index system are filed into a database, and data reliability and retrieval efficiency are continuously improved through database internal data quality evaluation and intelligent optimization; accurate prediction and efficient design of the damping performance of the silicone oil shock absorber under the extreme temperature and high-speed working conditions are achieved, and the design consistency, adaptability and engineering application reliability of the shock absorber are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a true module matching and database optimization method for the design of high-strength silicone oil shock absorbers. Background Art

[0002] In the design of high-strength silicone oil shock absorbers, true module matching refers to ensuring that the model parameters (such as rheological properties, damping properties, etc.) are highly consistent with the actual silicone oil behavior through precise measurement and calculation; while database optimization refers to constructing and continuously improving the material and structural performance database based on a large amount of experimental and simulation data, so as to quickly and accurately support the selection and adjustment of shock absorber design schemes, and improve the design efficiency and performance reliability.

[0003] The existing technologies have the following deficiencies: In the existing design methods of high-strength silicone oil shock absorbers, the temperature influence (such as the Arrhenius viscosity model) and the shear rate influence (such as the power-law fluid model) are mostly processed separately. In practical applications, such as railway trains in cold regions and plateau rescue equipment, when moving at low temperatures and high speeds, the two produce complex non-linear couplings, resulting in a significant lag in damping response or mechanical instability. The existing technologies fail to accurately capture the temperature-shear rate bivariate coupling characteristics of silicone oil, resulting in a significant deviation in design performance under actual working conditions. Summary of the Invention

[0004] The purpose of the present invention is to provide a true module matching and database optimization method for the design of high-strength silicone oil shock absorbers to solve the deficiencies in the background art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions: A true module matching and database optimization method for the design of high-strength silicone oil shock absorbers, including: Collecting rheological performance test data of silicone oil materials under multiple temperature points and multiple shear rate conditions; Based on the rheological performance test data, respectively constructing an Arrhenius-type temperature influence model and a power-law-type shear rate influence model of silicone oil, and introducing a cross-coupling correction term to form a temperature-shear rate bivariate coupling viscosity model; According to the structural parameters and application working conditions of the shock absorber, establishing a preliminary mechanical simulation model, and integrating the bivariate coupling viscosity model into the simulation model to obtain a preliminary true module; Conducting a dynamic loading test to obtain the measured damping response data of the shock absorber at different temperatures and different movement speeds; Comparing the simulation output with the measured output, and using a parameter optimization algorithm to adjust the parameters in the preliminary true module until the error between the simulation output and the measured data under each test condition meets the preset accuracy requirements, and completing the true module matching; The optimized true module parameters and corresponding damping performance indicators under different design schemes are classified and stored in a database, and the database is optimized according to the internal data quality of the database.

[0006] Preferably, the collected rheological property test data of silicone oil materials include: at a set of multiple temperature points, including -50°C to 100°C and multiple shear rate ranges including to under which, a rotational rheometer or a capillary rheometer is used to conduct constant shear rate scanning and constant shear stress scanning tests, and the shear stress and apparent viscosity values are recorded.

[0007] Preferably, constructing a temperature-shear rate bivariate coupled viscosity model includes: fitting the influence relationship of temperature on viscosity based on the Arrhenius equation under the condition of a fixed shear rate, fitting the influence relationship of shear rate on viscosity based on the power-law fluid model under the condition of a fixed temperature, and introducing a cross-coupling correction term by analyzing the modulation law of temperature on the power-law model parameters K and n to form a joint function model of viscosity with respect to temperature and shear rate.

[0008] Preferably, establishing a preliminary mechanical simulation model includes: Based on the piston diameter, cavity size, damping hole diameter, and silicone oil filling amount parameters of the shock absorber, a multi-physical field simulation model including piston movement, throttle channel flow, and temperature field change is constructed using finite element analysis software, and the bivariate coupled viscosity model is integrated in the fluid region.

[0009] Preferably, a dynamic loading test is carried out: at different set temperature points and different movement rate conditions, a servo-hydraulic fatigue testing machine is used to apply sine wave, triangular wave, or step wave loading, and displacement, velocity, damping force, and temperature data are collected in real-time and synchronized. Force-displacement curves and force-velocity curves are plotted, and equivalent damping coefficient and hysteretic energy loss parameters are extracted.

[0010] Preferably, using a parameter optimization algorithm to adjust the parameters in the preliminary true module, specifically including: Determine the optimization objective, which is to minimize the error between the simulation output and the measured data; Define the objective function, ; where: is the damping force at the i-th moment of the simulation, is the damping force at the i-th moment of the measurement, and W is the number of data points; Set the true module parameters to be optimized and define the number of particle swarms; each particle represents a set of true module parameter combinations to be optimized; the initial positions of the particles are randomly generated within the allowable change ranges of the parameters; the initial velocities of the particles are also randomly generated; Initial set the inertial weight winit of the particle swarm; After each iteration, dynamically adjust the inertia weight; For each iteration: Each particle adjusts its velocity and position according to its own historical best position and the current optimal position in the population; For each particle, run the simulation model according to its corresponding parameter group; calculate its objective function value as the fitness; update the historical best position pbesti of each particle; update the global best position gbest of the population; Judge that the stop condition is reaching the maximum number of iterations or the error index of the optimal particle is lower than the preset threshold; Input the optimized true module parameters into the simulation system, complete the matching of the final simulation and the measured data, and obtain the final true module.

[0011] Preferably, optimize the database according to the internal data quality of the database, including obtaining the temperature-shear response uniformity value: at each test temperature and each shear rate combination, collect the corresponding shock absorber output damping force , sort all the test data according to different temperature-shear points, and form a matrix M; for each temperature , calculate its average damping force at all corresponding shear rates, denoted as: ; where m is the number of shear rates; For each temperature , calculate the standard deviation of the damping force at all its shear rates , the expression is: ; define the damping force uniformity ratio at each temperature as: ; average the uniformity ratios at all temperature points to obtain the overall temperature-shear response uniformity value UK, the expression is: ; where q is the number of temperature points.

[0012] Preferably, obtain the energy dissipation stability index: at each different temperature and each shear rate working condition, collect the energy dissipation value of a single-cycle hysteresis loop, and obtain a two-dimensional energy matrix R; arrange all energy values in a one-dimensional sequence according to the test order, denoted as E(k), where k = 1, 2,..., N, and N is the total number of samples; apply the discrete Fourier transform to the energy sequence E(k) to obtain its frequency-domain expression: ; where: X(f) is the complex amplitude at the frequency component f; E(k) is the kth energy sampling value; is the Fourier transform kernel function; calculate the energy density of each frequency component: ; where, The energy intensity at the representative frequency f; set a frequency threshold ; calculate the sum of low-frequency energy respectively: ; the sum of high-frequency energy: ; define the energy dissipation stability index as the ratio of the low-frequency energy proportion to the total energy, and the expression is: .

[0013] Preferably, normalize the temperature shear response uniformity value and the energy dissipation stability index so that they are both within [0, 1], and perform weighted average summation calculation on the normalized temperature shear response uniformity value and the energy dissipation stability index to obtain the internal data quality score value of the database.

[0014] Preferably, compare the obtained internal data quality score value of the database with a preset score threshold. If the internal data quality score value of the database is greater than or equal to the preset score threshold, it indicates that the overall data quality among the internal samples of the database is high. At this time, the database can be directly used for the true module deduction and rapid retrieval of the shock absorber without additional optimization; if the internal data quality score value of the database is less than the preset score threshold, it indicates that there are problems with uneven samples in the database, and at this time, the database needs to be further optimized.

[0015] In the above technical solution, the technical effects and advantages provided by the present invention are as follows: 1. By introducing multi-temperature and multi-shear rate conditions in the silicone oil rheological property test stage and constructing a temperature-shear rate bivariate viscosity model containing cross-coupling correction terms, the present invention significantly improves the ability to accurately describe the rheological behavior of silicone oil materials in extreme environments. Combining the construction of a preliminary mechanical simulation model, dynamic loading test calibration, and the application of an advanced parameter optimization algorithm, a high degree of matching between the simulation model and the actual damping response data is achieved, solving the problem of poor working condition adaptability caused by separately processing temperature and shear rate in the prior art, thereby ensuring the reliability and stability of high-strength silicone oil shock absorbers in extreme working conditions such as cold regions and plateaus.

[0016] 2. By systematically classifying the optimized true module parameters and corresponding damping performance indicators into the database and introducing the temperature shear response uniformity value and the energy dissipation stability index as data quality evaluation indicators, combined with the normalization process and weighted scoring method, the intelligent self-optimization of the database is realized. Through the dynamic evaluation and update of the internal data quality of the database, not only the consistency and representativeness of the samples in the database are improved, but also the reliability of rapid retrieval, accurate deduction, and performance prediction in the subsequent shock absorber design process is ensured, significantly improving the overall design efficiency and the accuracy level of engineering applications. Description of the Drawings

[0017] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is the method mind map of the present invention. Specific embodiments

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0020] For the embodiments, please refer to Figure 1 As shown, the true module matching and database optimization method for the design of high-strength silicone oil shock absorbers in this embodiment includes: Collect rheological property test data of silicone oil materials under multiple temperature points and multiple shear rate conditions; Based on the rheological property test data, respectively construct the Arrhenius-type temperature influence model and the power-law type shear rate influence model of silicone oil, and introduce a cross-coupling correction term to form a temperature-shear rate bivariate coupling viscosity model; According to the structural parameters and application working conditions of the shock absorber, establish a preliminary mechanical simulation model, and integrate the bivariate coupling viscosity model into the simulation model to obtain a preliminary true module; Conduct dynamic loading tests to obtain the measured damping response data of the shock absorber at different temperatures and different movement speeds; Compare the simulation output with the measured output, and use a parameter optimization algorithm to adjust the parameters in the preliminary true module until the error between the simulation output and the measured data meets the preset accuracy requirements to complete the true module matching; Classify and store the true module parameters and corresponding damping performance indicators optimized under different design schemes into the database, and optimize the database according to the internal data quality of the database.

[0021] Collecting rheological property test data of silicone oil materials under multiple temperature points and multiple shear rate conditions includes: Select representative models of high-strength silicone oil to ensure batch consistency; The samples need to be stored in a sealed manner to avoid water absorption or contamination. Especially before low-temperature tests, degassing treatment (such as vacuum defoaming) is required.

[0022] The temperature points cover the possible extreme range of the silicone oil's use, usually set at 8 points: -50°C, -30°C, -10°C, 0°C, 25°C, 50°C, 75°C, and 100°C. If the silicone oil is applicable to a wider environment (such as aviation, deep sea), the temperature points should be extended to -70°C or above 120°C.

[0023] The shear rate covers the strain rate from extremely low to extremely high, generally set at 、 ; If it is necessary to simulate impact loads, data at higher shear rates (such as ) also need to be collected.

[0024] Use a high-precision rotational rheometer or capillary rheometer; the test modes include constant shear rate scanning and constant shear stress scanning; the temperature control system needs to ensure that the temperature fluctuation during the sample test is less than ±0.1°C; select a rotor system suitable for the viscosity range of the silicone oil, such as a cone plate, flat plate, or double cylinder.

[0025] Place the sample in the temperature control system of the rheometer for preheating or precooling for at least 20 minutes to ensure uniform temperature. Monitor the core temperature of the sample in real time and start the test after confirming that the set temperature is reached.

[0026] At each temperature point, gradually apply the preset shear rate, from low to high (or high to low to verify hysteresis); at each shear rate, record the shear stress and the corresponding viscosity at the equilibrium state; ensure that the stable time at each shear rate point is not less than 10 seconds and collect the average value; for high shear rates, the instantaneous scanning technique can be used to reduce the heat accumulation effect.

[0027] Each temperature-shear combination should be tested at least 3 times and the average value should be taken to reduce accidental errors; check whether obvious physical changes (such as foaming, delamination, color change, etc.) occur in the sample after the test and exclude abnormal data.

[0028] Record the shear stress (Pa)-shear rate curve at each temperature point; calculate the corresponding apparent viscosity (Pa·s) curve; indicate whether there are rheological phenomena such as yield stress, shear thinning, and shear thickening; if significant viscoelasticity is found (for example, filler particles are added to the silicone oil), additional data on storage modulus (G') and loss modulus (G'') need to be recorded.

[0029] Check the consistency of shear scan data at different temperatures and the rationality of the trend; if there is a sudden change or abnormal fluctuation, retest and record the cause of the abnormality; perform preliminary fitting on the final data, such as Arrhenius fitting (ln viscosity vs 1 / temperature) and power law fitting (logarithmic relationship between stress vs shear rate), to verify the rationality of the data.

[0030] Based on the rheological properties test data, the Arrhenius temperature influence model and power-law shear rate influence model of silicone oil were constructed respectively, and the cross-coupling correction term was introduced to form a temperature-shear rate dual variable coupled viscosity model.

[0031] Collect silicone oil at different temperatures (T) and different shear rates Rheological properties data under shear stress and the value of the apparent viscosity η; Under the condition of fixed shear rate, the viscosity at each temperature point is extracted , and fitting the Arrhenius equation to establish the model of the effect of temperature on viscosity: ;in, is the theoretical reference viscosity, is the rheological activation energy, R is the universal gas constant, and T is the absolute temperature (unit K).

[0032] Extract the viscosity at each shear rate point under fixed temperature conditions , and fit the power law fluid model to establish the effect of shear rate on viscosity: ; Where K is the consistency coefficient of the fluid, and n is the flow behavior index (n<1 indicates shear thinning, and n>1 indicates shear thickening).

[0033] The influence trend of temperature change on power law parameters K and n was analyzed, the modulation relationship of temperature on power law model parameters was extracted, and the preliminary cross-influence law was obtained; Based on the above preliminary model, a two-variable modified viscosity model with temperature-shear rate cross-coupling is constructed in the form of: ; Wherein, both K and n can be expressed as a function of temperature T, reflecting the dynamic modulation effect of temperature on shear behavior; Train the model on all experimental data sets through global fitting methods (such as nonlinear least squares fitting) to determine the parameters in the final bivariate model and ensure that the overall fitting error is below the set threshold; Verify the fitting accuracy and extrapolation ability, predict the test data that are not involved in the modeling, evaluate the applicability of the model under actual engineering conditions, and adjust the coupling correction term if the error exceeds the allowable range.

[0034] Based on the structural parameters and application conditions of the shock absorber, a preliminary mechanical simulation model is established, and a dual-variable coupled viscosity model is integrated into the simulation model to obtain a preliminary simulation module.

[0035] Collect the basic design parameters of the shock absorber, including: piston diameter, stroke, cavity size; damping hole diameter, number of holes, channel geometry; silicone oil filling amount, initial pressure (if any).

[0036] Define the application condition parameters, including: expected operating temperature range (e.g., -40°C to 80°C); piston movement speed range (e.g., 0.01 m / s to 10 m / s); loading form (unidirectional loading, bidirectional loading, impact, vibration).

[0037] Use finite element analysis (FEA) or multi-physics simulation tools (such as ANSYS Fluent, COMSOL Multiphysics); The internal modeling details of the shock absorber include: relative movement between the piston and the cavity; flow path and boundary conditions in the damping hole; the silicone oil material model is defined as a rheological response material rather than a simple Newtonian fluid.

[0038] Set dynamic boundary conditions: apply periodic displacement or velocity input to the piston; use no-slip boundary conditions for the wall surface; set the temperature field to multi-stage change or steady state.

[0039] Define the silicone oil viscosity η in the fluid region as a function of temperature T and shear rate That is: ; Import the custom dual-variable function as a user-defined material subroutine (such as a UDF script) into the simulation software; ensure that the fluid elements dynamically adjust the viscosity in real time according to the local temperature and shear rate at each moment; if the simulation platform does not directly support dual-variable viscosity, the multi-table interpolation method or the weak-coupling sub-step algorithm can be used to achieve it.

[0040] Run the simulation and output: damping force-displacement curve (F-d curve); damping force-velocity curve (F-v curve); internal shear rate and temperature distribution field of the silicone oil; determine the integrity and continuity of the simulation output characteristic curve within the set temperature and speed range; use the overall dynamic response characteristics of the shock absorber obtained from the simulation as a preliminary simulation module for subsequent actual measurement matching and optimization steps.

[0041] Conduct a dynamic loading test to obtain the measured damping response data of the shock absorber at different temperatures and different movement rates, specifically including: Test equipment selection: servo-hydraulic fatigue testing machine or electro-dynamic shaker system, with the ability to precisely control piston displacement / speed; equipped with high-sensitivity displacement sensors (such as LVDT) and damping force sensors (such as load cell); equipped with high and low temperature environmental chambers, with a temperature control range generally from -60°C to 120°C; equipped with a data acquisition system, with a sampling frequency of at least 1000 Hz or more to ensure complete recording of dynamic responses.

[0042] Install the silicone oil shock absorber to be tested onto the loading platform through a rigid fixture; ensure coaxial installation to eliminate eccentricity and initial stress; check the internal oil filling status and seal integrity of the shock absorber to avoid abnormal testing.

[0043] Set several temperature points according to actual application requirements, for example: -40°C, 0°C, 25°C, 60°C, 90°C; cover the service temperature range of the shock absorber, and try to cover the extreme low and high temperature environments.

[0044] Before the test, place the entire shock absorber in the temperature control chamber for at least 30 minutes; use the built-in temperature sensor to monitor the actual temperature of the sample in real time to ensure that the sample reaches and stabilizes at the target temperature (fluctuation < ±1°C).

[0045] Set the motion mode, mainly using sine wave loading, triangular wave loading, or step loading; the motion amplitude range is, for example, ±20 mm, ±50 mm, which can be selected according to actual applications.

[0046] Set the rate (frequency): low speed: 0.01 m / s to 0.1 m / s; medium speed: 0.5 m / s to 2 m / s; high speed: 5 m / s to 10 m / s; the rate setting can be adjusted according to actual usage, especially for high strain rate tests, pay attention to the acceleration ability of the equipment.

[0047] At each temperature point, apply dynamic loading at different motion rates in sequence; under each loading condition, cycle at least 3 - 5 times to observe the stability and repeatability of the damping characteristics.

[0048] Real-time synchronous acquisition: displacement data (x, unit: mm); force data (F, unit: N); time data (t, unit: ms); at the same time, record the temperature change data to ensure that the temperature deviation is within the allowable range during the loading process.

[0049] Plot the force-displacement curve (F-x) to observe the energy dissipation situation (the area of the hysteresis loop represents the energy consumption); plot the force-velocity curve (F-v) to analyze the relationship change between the damping force and the velocity; calculate key parameters such as the equivalent viscous damping coefficient and hysteresis energy loss.

[0050] Check for the existence of force signal drift, sudden jumps, and abnormal noises; eliminate the test data with abnormal vibrations, leaks, structural failures, etc.

[0051] Classify and file each group of test data according to the temperature–motion rate combination; form a standardized damping performance data packet for subsequent calibration of the true module parameters.

[0052] Compare the simulation output with the measured output, and use the parameter optimization algorithm to adjust the parameters in the preliminary true module until the error between the simulation output and the measured data under each test condition meets the preset accuracy requirements, thus completing the matching of the true module, specifically including: Determine the optimization objective: minimize the error between the simulation output and the measured data; Define the error evaluation index (objective function), such as the root mean square error (RMSE) or the sum of relative errors (SRE); ; where: is the damping force at the i-th moment of the simulation, is the damping force at the i-th moment of the measurement, W is the number of data points, and set the true module parameters to be optimized, such as: 、 ; K and n in the power-law model; the coupling term correction coefficient; Define the number of particle swarms (usually select 20 - 50 particles); Each particle represents a set of true module parameter combinations to be optimized; The initial position of the particle is randomly generated within the allowable variation range of each parameter; The initial velocity of the particle is also randomly generated, and the maximum velocity is appropriately restricted to avoid excessive jumps.

[0053] Initially set the inertia weight winit of the particle swarm (for example, 0.9); After each iteration, dynamically adjust the inertia weight; For each round of iteration: Each particle adjusts its velocity and position according to its own historical best position and the current optimal position in the group, thus continuously approaching the region with the minimum error: ; where: is the velocity of the i-th particle, is the position (parameter group) of the i-th particle, is the historical best position of the particle itself, is the global best position, is a random number between [0, 1], is the learning factor (usually set to 1.5 - 2.0). w is the inertia weight, which is commonly used to control the influence degree of the current velocity of the particle on its next velocity.

[0054] For each particle, run the simulation model according to its corresponding parameter set; Calculate its objective function value (error magnitude) as fitness; Update the historical best position pbesti of each particle; Update the global best position gbest of the population.

[0055] Judge the stop condition: reaching the maximum number of iterations or the error index of the optimal particle being lower than the preset threshold (e.g., the overall error is less than 2%). If not satisfied, continue the iteration.

[0056] Output the optimal parameter combination; input the optimized true module parameters into the simulation system; complete the matching of the final simulation and the measured data to obtain the final true module.

[0057] After each shock absorber design and true module parameter optimization is completed, file and store the following information in the database uniformly: The true module parameters include: reference viscosity , rheological activation energy , consistency coefficient (K), flow index (n), temperature-shear coupling correction coefficient, etc.

[0058] The corresponding damping performance indicators include: peak damping force, average damping force, hysteretic energy dissipation (hysteresis loop area), temperature sensitivity coefficient, rate sensitivity coefficient, etc.

[0059] Design scheme characteristics such as piston diameter, channel cross-sectional area, number of throttle holes, operating condition temperature range, shear rate range.

[0060] Each piece of data is indexed by the design number - operating condition number and stored in categories to form a structured database for subsequent rapid retrieval and analysis.

[0061] In order to avoid database expansion redundancy and improve retrieval and deduction efficiency, it is necessary to perform intelligent optimization on the existing database. First, use the feature extraction algorithm to extract the core influencing feature parameters of each sample. Initially screen out the minor parameters with less influence and retain the parameters that have a decisive influence on the change of damping performance. The core influencing feature parameters include the temperature-shear response uniformity value, which characterizes the consistency and smoothness of the damping performance change at different temperatures and different shear rates; and the energy dissipation stability index, which measures the fluctuation degree of the hysteresis loop energy loss area under different operating conditions; The method for obtaining the temperature-shear response uniformity value is: at each test temperature and each shear rate combination, collect the corresponding shock absorber output damping force . Arrange all the test data according to different temperature-shear points (i.e., each group , ) Classify and organize to form matrix M; for each temperature , calculate the average damping force corresponding to all shear rates, denoted as: ; where m is the number of shear rates; For each temperature , calculate the standard deviation of the damping force at all shear rates, and the expression is: ; Define the damping force uniformity ratio at each temperature as: , which reflects the consistency of the damping response at each shear rate.

[0062] Average the uniformity ratios at all temperature points to obtain the overall temperature-shear response uniformity value UK, and the expression is: ; where q is the number of temperature points.

[0063] When the temperature-shear response uniformity value is small (close to zero), it indicates that under different temperature and shear rate conditions, the damping force of the shock absorber changes relatively smoothly, and the performance consistency between different working conditions is good. This shows that the quality of the internal samples in the database is high, the data distribution is uniform, and it can accurately reflect the intrinsic behavior of the silicone oil material, and is suitable as a reliable basis for true module deduction and rapid retrieval.

[0064] On the contrary, when the temperature-shear response uniformity value is large, it means that the damping force fluctuates violently under different temperatures and shear rates, the consistency between samples is poor, and there are obvious discreteness or outliers. This reflects that the quality of the internal data in the database is low, and there may be problems such as test errors, uneven data distribution, or insufficient samples under specific working conditions, and further optimization and correction are needed by means of removing abnormal data and supplementing key missing working conditions.

[0065] The method for obtaining the energy dissipation stability index is as follows: Under different temperatures and shear rates working conditions, collect the energy dissipation value of a single-cycle hysteresis loop to obtain a two-dimensional energy matrix R; Arrange all energy values in a one-dimensional sequence according to the test order, denoted as E(k), where k = 1, 2,..., N, and N is the total number of samples; apply the discrete Fourier transform to the energy sequence E(k) to obtain its frequency-domain expression: ; where: X(f) is the complex amplitude at the frequency component f; E(k) is the kth energy sampling value; is the Fourier transform kernel function; calculate the energy density of each frequency component: ; where P(f) represents the energy intensity at frequency f; a frequency threshold is set (for example, select the first 10% within the entire frequency range as the low-frequency region); calculate the total low-frequency energy respectively: ; the total high-frequency energy: (Note: The actual frequency range only goes up to N / 2 because the data above the Nyquist frequency after DFT is a mirror image.) Define the energy dissipation stability index as the ratio of the low-frequency energy proportion to the total energy, and the expression is: ; where: The closer it is to 1, the more the energy is concentrated in the low frequency and the more stable it is; The smaller it is, the greater the energy fluctuation and the worse the stability.

[0066] Normalize the temperature shear response uniformity value and the energy dissipation stability index so that they are both between [0, 1]. After weighted average summation calculation of the normalized temperature shear response uniformity value and the energy dissipation stability index, the internal data quality score value of the database is obtained.

[0067] Compare the obtained internal data quality score value of the database with the preset score threshold. If the internal data quality score value of the database is greater than or equal to the preset score threshold, it indicates that the samples inside the database perform well in terms of temperature shear response and energy dissipation stability, the data distribution is reasonable, uniform, and the overall data quality meets the requirements of high-reliability design. At this time, the database can be directly used for the true module deduction and rapid retrieval of the shock absorber without additional optimization processing.

[0068] If the internal data quality score value of the database is less than the preset score threshold, it indicates that there are large performance fluctuations or sample non-uniformity problems inside the database, the consistency of the temperature shear response or the stability of the energy dissipation fails to meet the expected standard, and the overall data quality is insufficient to support the high-precision true module deduction requirements. At this time, the database needs to be further optimized, including operations such as abnormal sample elimination, key working condition data supplementation, and feature space reconstruction, to improve the overall data quality.

[0069] The above formulas are all dimensionless and take their numerical calculations. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters in the formulas are set by technicians in this field according to the actual situation.

[0070] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired or wireless (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that includes one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0071] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood by referring to the context. Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this document can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0072] As described above, the above are only the specific implementation manners of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered by the protection scope of this application.

Claims

1. True module matching and database optimization method for the design of high-strength silicone oil shock absorbers, characterized in that: Including: Collecting rheological property test data of silicone oil materials under multiple temperature points and multiple shear rate conditions; Based on the rheological property test data, respectively constructing an Arrhenius-type temperature influence model and a power-law-type shear rate influence model for silicone oil, and introducing a cross-coupling correction term to form a temperature-shear rate bivariate coupling viscosity model; According to the structural parameters and application working conditions of the shock absorber, establishing a preliminary mechanical simulation model, and integrating the bivariate coupling viscosity model into the simulation model to obtain a preliminary simulation module; Conducting a dynamic loading test to obtain the measured damping response data of the shock absorber at different temperatures and different movement speeds; Comparing the simulation output with the measured output, and using a parameter optimization algorithm to adjust the parameters in the preliminary simulation module until the error between the simulation output and the measured data under each test condition meets the preset accuracy requirements, and completing the simulation module matching; Classifying and storing the optimized simulation module parameters and corresponding damping performance indicators under different design schemes into a database, and optimizing the database according to the internal data quality of the database.

2. The true module matching and database optimization method for the design of a high-strength silicone oil shock absorber according to claim 1, characterized in that: Collecting the rheological property test data of silicone oil materials includes: at a plurality of set temperature points, including -50°C to 100°C and a plurality of shear rate ranges including to under which, a constant shear rate sweep and a constant shear stress sweep test are carried out using a rotational rheometer or a capillary rheometer, and the shear stress and apparent viscosity values are recorded.

3. The true module matching and database optimization method for the design of high-strength silicone oil shock absorbers according to claim 1, characterized in that: Constructing a temperature-shear rate bivariate coupling viscosity model includes: fitting the influence relationship of temperature on viscosity based on the Arrhenius equation under a fixed shear rate condition, fitting the influence relationship of shear rate on viscosity based on the power-law fluid model under a fixed temperature condition, and introducing a cross-coupling correction term by analyzing the modulation law of temperature on the power-law model parameters K and n to form a joint function model of viscosity with respect to temperature and shear rate.

4. The true module matching and database optimization method for the design of a high-strength silicone oil shock absorber according to claim 1, characterized in that: Establishing a preliminary mechanical simulation model includes: Based on the piston diameter, cavity size, damping hole diameter, and silicone oil filling amount parameters of the shock absorber, using finite element analysis software to construct a multi-physical field simulation model including piston movement, throttling channel flow, and temperature field change, and integrating the bivariate coupling viscosity model in the fluid region.

5. The true module matching and database optimization method for the design of high-strength silicone oil shock absorbers according to claim 4, characterized in that: Conducting a dynamic loading test: Under different set temperature points and different movement speed conditions, using a servo-hydraulic fatigue testing machine to apply sine wave, triangular wave, or step wave loading, synchronously collecting displacement, speed, damping force, and temperature data in real time, plotting force-displacement curves and force-velocity curves, and extracting equivalent damping coefficient and hysteresis energy loss parameters.

6. The true module matching and database optimization method for the design of high-strength silicone oil shock absorbers according to claim 5, characterized in that: Using a parameter optimization algorithm to adjust the parameters in the preliminary simulation module, specifically including: Determining the optimization objective to minimize the error between the simulation output and the measured data; Define the objective function, ; where: is the damping force at the i-th moment of simulation, is the measured damping force at the i-th moment, and W is the number of data points; Setting the simulation module parameters to be optimized and defining the number of particle swarms; each particle represents a set of simulation module parameter combinations to be optimized; the initial positions of the particles are randomly generated within the allowable change ranges of each parameter; the initial velocities of the particles are also randomly generated; Initializing the inertial weight winit of the particle swarm; Dynamically adjusting the inertial weight after each iteration; For each iteration: Each particle adjusts its velocity and position according to its own historical best position and the current optimal position in the population; For each particle, running the simulation model according to its corresponding parameter group; calculating its objective function value as the fitness; updating the historical best position pbesti of each particle; updating the global best position gbest of the population; Judging that the stop condition is reaching the maximum number of iterations or the error index of the optimal particle is lower than the preset threshold; Input the optimized true module parameters into the simulation system, complete the matching of the final simulation and the measured data, and obtain the final true module.

7. The true module matching and database optimization method for the design of high-strength silicone oil shock absorbers according to claim 6, characterized in that: Optimize the database according to the internal data quality of the database, including obtaining the temperature shear response uniformity value: at each test temperature and each shear rate combination, collect the corresponding damping force output of the shock absorber , classify and organize all test data according to different temperature shear points to form a matrix M; for each temperature , calculate its average damping force corresponding to all shear rates, denoted as: ; where m is the number of shear rates; For each temperature , calculate the standard deviation of the damping force at all shear rates , and the expression is: ; Define the damping force uniformity ratio at each temperature as: ; Average the uniformity ratios at all temperature points to obtain the overall temperature-shear response uniformity value UK, and the expression is: ; where q is the number of temperature points.

8. The true module matching and database optimization method for the design of high-strength silicone oil shock absorbers according to claim 7, characterized in that: Obtain the energy dissipation stability index: At each different temperature and shear rate working conditions, collect the energy dissipation values of a single-cycle hysteresis loop to obtain a two-dimensional energy matrix R; Arrange all energy values in a one-dimensional sequence in the test order, denoted as E(k), where k = 1, 2, …, N, and N is the total number of samples; Apply the discrete Fourier transform to the energy sequence E(k) to obtain its frequency-domain expression: ; where: X(f) is the complex amplitude at frequency component f; E(k) is the k-th energy sampling value; is the Fourier transform kernel function; Calculate the energy density of each frequency component: ; where, P(f) represents the energy intensity at frequency f; Set a frequency threshold ; Calculate the total low-frequency energy respectively: ; The total high-frequency energy: ; Define the energy dissipation stability index as the ratio of the low-frequency energy proportion to the total energy, and the expression is: .

9. The true module matching and database optimization method for the design of high-strength silicone oil shock absorbers according to claim 8, characterized in that: Normalize the temperature shear response uniformity value and the energy dissipation stability index so that they are both within [0, 1]. After calculating the weighted average sum of the normalized temperature shear response uniformity value and the energy dissipation stability index, obtain the internal data quality score value of the database.

10. The true module matching and database optimization method for the design of high-strength silicone oil shock absorbers according to claim 9, characterized in that: Compare the obtained internal data quality score value of the database with the preset score threshold. If the internal data quality score value of the database is greater than or equal to the preset score threshold, it indicates that the overall data quality among the samples inside the database is high. At this time, the database can be directly used for the deduction and rapid retrieval of the shock absorber true module without additional optimization. If the internal data quality score value of the database is less than the preset score threshold, it indicates that there are uneven sample problems inside the database. At this time, the database needs to be further optimized.

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