Semi-steel tire cavity noise prediction method and system
By constructing a finite element model and simulation of transient rolling of the tire, and combining it with the dynamic signal of vertical force for spectral correction, the problems of low prediction accuracy and poor adaptability of cavity noise in semi-steel tires are solved, and more accurate noise prediction and optimization are achieved.
Patent Information
- Application Number
- CN202511276896.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-08
- Publication Date
- 2026-01-20
AI Technical Summary
Existing technologies for predicting cavity noise in semi-steel tires suffer from low prediction accuracy and poor adaptability. They cannot accurately obtain noise amplitude, and commercial tire models are costly, with parameter calibration relying on real vehicle testing, making it difficult to adapt to different specifications and operating conditions.
A finite element model of tire transient rolling was constructed, and transient rolling simulation was performed. The frequency and amplitude of cavity noise were obtained by solving multiple parameters. The spectrum was corrected by combining the dynamic signal of vertical force, and the results were compared and verified to extract the prediction results.
It achieves simultaneous acquisition of cavity noise frequency and amplitude, improves prediction accuracy and reliability, provides quantitative noise optimization basis, reduces model application cost, and is more adaptable.
Smart Images
Figure CN121365538A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of data processing, and in particular to a semi-steel tire cavity noise prediction method and system. BACKGROUND
[0002] During the driving of the automobile, the semi-steel tire cavity noise is one of the key factors affecting the acoustic comfort in the car, which is derived from the coupled vibration of the air in the tire cavity and the tire structure when the tire rolls, and needs to be accurately predicted to achieve the optimal design of the tire noise. Therefore, the semi-steel tire cavity noise prediction method has become the research focus in the field of tire engineering. At present, the simulation and prediction technology for semi-steel tire cavity noise in the industry mainly focuses on the frequency characteristics, forming three types of mainstream technical solutions, which are the simulation method based on the finite element model, the theoretical method based on the analysis and the prediction method based on the commercial tire model. These methods provide a basis for preliminarily understanding the frequency characteristics of the tire cavity noise, but there are still significant limitations in practical application.
[0003] The semi-steel tire cavity noise prediction method based on the finite element model needs to establish a fine finite element model containing the cavity according to the tire structure design, and then obtain the tire cavity modal and cavity frequency through steady-state rolling simulation. The theoretical method based on the analysis directly calculates the tire cavity frequency by deducing the acoustic theory formula. However, these two methods can only realize the prediction of the cavity noise frequency, and cannot obtain the amplitude of the cavity noise under the actual use condition of the tire. Since the influence of noise on the comfort in the car is not only related to the frequency, but also directly related to the amplitude (i.e. noise intensity), the above methods cannot meet the quantitative evaluation demand of the noise level in engineering, and cannot provide complete noise characteristic data for the optimization of the tire structure.
[0004] Although the prediction method based on the commercial tire model can involve both the cavity noise frequency and the amplitude, the commercial tire model is a black box model, and its internal theoretical derivation logic and calculation method are not publicly disclosed. Moreover, the calibration of the model parameters needs to rely on a large number of indoor tests and real vehicle tests. This not only leads to high application cost and long cycle of the model, but also makes the parameter calibration results easily affected by the test conditions, and difficult to adapt to the noise prediction demand of different specifications of semi-steel tires or complex use conditions.
[0005] Therefore, the traditional semi-steel tire noise prediction scheme has the technical problems of low prediction accuracy and poor adaptability. SUMMARY
[0006] The present application provides a semi-steel tire cavity noise prediction method and system to solve the defects of low prediction accuracy and poor adaptability of the traditional semi-steel tire noise prediction scheme.
[0007] In one aspect, the present application provides a semi-steel tire cavity noise prediction method, comprising: A tire transient rolling finite element model is constructed, and transient rolling simulation is performed on the semi-steel tire to be tested to obtain rolling simulation results. According to the rolling simulation results, the multi-parameter of the semi-steel tire to be tested is solved to obtain cavity key parameters representing cavity noise frequency and amplitude; The vertical force dynamic signal of the semi-steel tire to be tested is obtained, and the original frequency spectrum data is determined according to the vertical force dynamic signal; The cavity key parameters are added to the original frequency spectrum data as cavity noise components to obtain frequency spectrum correction data; The frequency spectrum correction data is compared and verified, and after the comparison and verification is passed, the cavity noise prediction result in the frequency spectrum correction data is extracted.
[0008] According to the semi-steel tire cavity noise prediction method provided by the application, a tire transient rolling finite element model is constructed, which specifically comprises: Determine the related basic structure of the semi-steel tire to be tested and the target structure parameters of each basic structure; According to the target structure parameters, each basic structure is modeled differently to obtain a tire initial model; Determine the material properties of each basic structure in the tire initial model, and perform mesh partitioning on the tire initial model by differentiating the mesh element type and mesh size to obtain an initial finite element model; Set the boundary conditions and load conditions, and set the coupling interaction conditions of the mechanical field and the temperature field in the initial finite element model to obtain a tire transient rolling finite element model.
[0009] According to the semi-steel tire cavity noise prediction method provided by the application, according to the target structure parameters, each basic structure is modeled differently to obtain a tire initial model, which comprises: Determine the three-dimensional form of the tread pattern in the basic structure; According to the three-dimensional form, the tread pattern is modeled in full size; According to the target structure parameters, the other basic structures except the tread pattern are modeled in a conventional three-dimensional manner to obtain a tire initial model.
[0010] According to the semi-steel tire cavity noise prediction method provided by the application, the tire initial model is meshed by differentiating the mesh element type and mesh size, which comprises: For the tread pattern area in the tire initial model, set the mesh element type to tetrahedral mesh and the mesh size to less than 2mm; For the flexible structure area in the tire initial model, set the mesh element type to hexahedral mesh and the mesh size to 5mm-8mm; For the rigid structure area in the initial model of the tire, the grid cell type is set to a hexahedral grid, and the grid size is set to 10mm-15mm.
[0011] According to the cavity noise prediction method of the semi-steel tire provided by the application, the vertical force dynamic signal of the semi-steel tire to be measured is obtained, including: The vertical force time domain signal of the axle center position of the semi-steel tire to be measured is obtained. The static load component in the vertical force time domain signal is removed to obtain a vertical force dynamic signal containing only transient fluctuations.
[0012] According to the cavity noise prediction method of the semi-steel tire provided by the application, according to the rolling simulation result, the multi-parameter solution of the semi-steel tire to be measured is obtained to obtain the cavity key parameters representing the cavity noise frequency and amplitude, including: According to the rolling simulation result, the average temperature of the air in the tire cavity is determined. According to the average temperature of the air, the actual sound speed in the tire cavity is calculated. According to the pre-set tire cavity structure parameters and the actual sound speed, the cavity low-frequency component and the cavity high-frequency component are calculated. According to the simulation load in the rolling simulation result, the pressure fluctuation amplitude in the tire cavity is calculated. The cavity low-frequency component and the cavity high-frequency component and the pressure fluctuation amplitude are taken as the cavity key parameters.
[0013] According to the cavity noise prediction method of the semi-steel tire provided by the application, according to the simulation load in the rolling simulation result, the pressure fluctuation amplitude in the tire cavity is calculated, including: The vertical load under the standard working condition is taken as the calibration load, and the quotient of the simulation load and the calibration load is obtained to obtain the load coefficient. The driving speed of the semi-steel tire to be measured is determined in advance, and the quotient of the standard speed is obtained to obtain the speed coefficient. The pressure fluctuation reference value under the standard working condition is determined, and the pressure fluctuation amplitude in the tire cavity is obtained based on the speed coefficient, the load coefficient and the pressure fluctuation reference value.
[0014] According to the cavity noise prediction method of the semi-steel tire provided by the application, the cavity key parameters are taken as the cavity noise components and added to the original frequency spectrum data to obtain frequency spectrum correction data, including: According to the pressure fluctuation amplitude, the low-frequency peak value corresponding to the cavity low-frequency component and the high-frequency peak value corresponding to the cavity high-frequency component are determined. The low-frequency peak is added to the position corresponding to the low-frequency component of the cavity in the original spectrum data, and the high-frequency peak is added to the position corresponding to the high-frequency component of the cavity in the original spectrum data, to obtain spectrum correction data.
[0015] According to the cavity noise prediction method of the semi-steel tire provided by the application, the spectrum correction data is compared and verified, comprising: Obtaining the tire cavity noise signal collected by the indoor drum test bench under the same working condition, and converting the tire cavity noise signal into test spectrum data; Determining the peak frequency deviation value and the amplitude deviation value between the spectrum correction data and the test spectrum data; Judging whether the peak frequency deviation value is within the preset frequency deviation range to obtain a first comparison result; Judging whether the amplitude deviation value is within the preset amplitude deviation range to obtain a second comparison result; If the first comparison result and the second comparison result are both yes, it is determined that the comparison and verification are passed, and if the first comparison result and / or the second comparison result is no, it is determined that the comparison and verification are not passed.
[0016] In another aspect, the application also provides a semi-steel tire cavity noise prediction system, comprising: A simulation module is configured to construct a tire transient rolling finite element model and perform transient rolling simulation on the semi-steel tire to be tested to obtain a rolling simulation result. A solving module is configured to solve multiple parameters of the semi-steel tire to be tested according to the rolling simulation result to obtain cavity key parameters representing cavity noise frequency and amplitude. An acquisition module is configured to acquire a vertical force dynamic signal of the semi-steel tire to be tested and determine original spectrum data according to the vertical force dynamic signal. A noise adding module is configured to add the cavity key parameters as cavity noise components to the original spectrum data to obtain spectrum correction data. A verification module is configured to compare and verify the spectrum correction data, and extract a cavity noise prediction result in the spectrum correction data after the comparison and verification are passed.
[0017] The application provides a semi-steel tire cavity noise prediction method and system. BRIEF DESCRIPTION OF DRAWINGS
[0018] In order to more clearly illustrate the technical solutions in the application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.
[0019] Figure 1 Fig. 1 is a flowchart of the semi-steel tire cavity noise prediction method provided by the embodiment of the application; Figure 2 Fig. 2 is a structural diagram of the tire transient rolling finite element model; Figure 3 Fig. 3 is a schematic diagram of the vertical force dynamic signal of the axle extracted when the tire transient rolling simulation is carried out on the drum with the protrusion; Figure 4 Fig. 4 is a schematic diagram of the spectrum result comparison of the tire rolling over the protrusion in the test and the traditional simulation; Figure 5 Fig. 5 is a schematic diagram of the comparison of the improved spectrum obtained by using the prediction scheme provided by the embodiment of the application and the test result; Figure 6 Fig. 6 is a schematic diagram of the comparison of the improved spectrum obtained by using the prediction scheme provided by the embodiment of the application and the test result; Figure 5 Fig. 7 is a partial enlarged view of the cavity noise spectrum in the 200-250Hz frequency band in Fig. 6; Figure 7 Fig. 8 is a structural diagram of the semi-steel tire cavity noise prediction system provided by the embodiment of the application. DETAILED DESCRIPTION
[0020] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below with reference to the drawings in the present application. Obviously, the described embodiments are only part of, rather than all of, the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.
[0021] The details of the semi-steel tire cavity noise prediction method and system provided by the embodiments of the present application will be described below. Figures 1-7 The details of the semi-steel tire cavity noise prediction method and system provided by the embodiments of the present application will be described below.
[0022] As shown in the semi-steel tire cavity noise prediction method provided by the embodiments of the present application, the method mainly includes the following steps: Figure 1 Step 110: constructing a tire transient rolling finite element model, and performing transient rolling simulation on the semi-steel tire to be measured to obtain rolling simulation results.
[0023] Step 120: solving multiple parameters according to the rolling simulation results to obtain cavity key parameters representing cavity noise frequency and amplitude.
[0024] Step 130: obtaining a vertical force dynamic signal of the semi-steel tire to be measured, and determining original frequency spectrum data according to the vertical force dynamic signal.
[0025] Step 140: adding the cavity key parameters as cavity noise components to the original frequency spectrum data to obtain frequency spectrum correction data.
[0026] Step 150: comparing and verifying the frequency spectrum correction data, and extracting cavity noise prediction results in the frequency spectrum correction data after the comparison and verification is passed.
[0027] In an embodiment, the tire transient rolling finite element model is constructed, specifically including: First, the related basic structures of the semi-steel tire to be measured and the target structure parameters of each basic structure are determined.
[0028] In this embodiment, the semi-steel tire to be measured needs to be structurally split, and specifically can be disassembled into five basic structures of the tread containing three-dimensional patterns, the sidewall, the carcass, the belt and the bead according to the tire design drawing, and the target structure parameters of each basic structure are determined, such as the tread width, the sidewall height, the belt thickness, etc.
[0029] In addition, for the two structures related to the semi-steel tire to be tested, i.e., the rim and the drum, the rim is determined according to the corresponding specifications to obtain target structure parameters such as the inner hole diameter and the rim width, and the drum is required to have a diameter matched with the tire rolling to ensure the rationality of the rolling condition simulation, and a rectangular protrusion is processed on the surface of the drum to simulate uneven road excitation, and parameters such as the height, width and spacing of the protrusion need to be determined.
[0030] In a specific implementation, the tire initial model is obtained by differentiating modeling of the target structure parameters of each basic structure.
[0031] In a specific implementation, the tire initial model is obtained by differentiating modeling of the target structure parameters of each basic structure, and the specific process includes the following steps. First, the three-dimensional form of the tread pattern in the basic structure is determined.
[0032] Then, the full-size modeling of the tread pattern is performed according to the three-dimensional form.
[0033] In this embodiment, the three-dimensional form of the pattern block and the pattern groove can be restored according to the actual pattern design drawing of the tire, including detailed information such as the height, width of the pattern block, the depth and angle of the groove, and the full-size modeling is performed according to the above information, so that the discontinuous structure of the pattern can truly transmit the road excitation, such as the local impact when rolling over the drum protrusion, thereby laying a geometric foundation for accurately extracting the vertical force fluctuation signal in the subsequent process.
[0034] Finally, the other basic structures except the tread pattern are regularly three-dimensionally modeled according to the target structure parameters to obtain the tire initial model.
[0035] In this embodiment, except for the tread pattern, the rim, the drum, and the sidewall, the carcass, the belt and the bead of the tire can be modeled by using a regular three-dimensional modeling method, such as drawing by using a CAD software, to ensure that the geometric shape is consistent with the actual component, wherein the rim and the drum can be modeled according to the geometric characteristics of a rigid body, and the non-pattern part of the tire can be modeled according to the structural characteristics of a flexible body.
[0036] In a specific implementation, the tire initial model is obtained by differentiating modeling of the target structure parameters of each basic structure, and the specific process includes the following steps.
[0037] In this embodiment, the traditional single material assumption is broken, and different material properties can be defined for different components, and the viscoelasticity of rubber and the characteristics of composite materials are highlighted. Specifically, for the rubber components of the tire, such as the tread and the sidewall, a viscoelastic constitutive model can be used, and the static parameters to be input include the elastic modulus and the Poisson's ratio; the dynamic parameters include the dynamic shear modulus and the loss factor, and the parameters need to be associated with temperature changes, so as to reflect the influence of temperature on the mechanical properties of rubber.
[0038] For the tire reinforcement components, such as the carcass and the belt, the fiber-matrix composite can be defined by the input parameters of the cord direction, including the elastic modulus and the Poisson's ratio; the transverse parameters include the elastic modulus and the Poisson's ratio perpendicular to the cord direction, which reflect the anisotropy of the composite material and the mechanical response of the actual cord reinforcement structure.
[0039] For the rim and the drum, the rigid material can be defined by the input parameters of the elastic modulus and the Poisson's ratio, which ensure that no deformation occurs in the simulation and only provide constraints and rolling excitation.
[0040] In one specific implementation, the initial tire model is meshed by differentiating the mesh element type and the mesh size, specifically including: On the one hand, for the tread pattern area in the initial tire model, the mesh element type is set to a tetrahedral mesh, and the mesh size is set to less than 2 mm.
[0041] In this embodiment, according to the geometric characteristics and mechanical response requirements of each component, a differentiated mesh element type and size setting scheme can be used. For the three-dimensional pattern area of the tire, a refined tetrahedral mesh can be used, with a mesh element size of less than 2 mm, to ensure the grid discretization accuracy of the pattern detail structure, and to avoid the problem of distorted transmission of discontinuous excitation caused by too coarse mesh.
[0042] On the other hand, for the flexible structure area in the initial tire model, the mesh element type is set to a hexahedral mesh, and the mesh size is set to 5-8 mm.
[0043] It can be understood that for the flexible structure area of the tire carcass, belt, etc., a hexahedral mesh can be used, with a mesh element size of 5-8 mm, which can balance the simulation efficiency while ensuring the calculation accuracy.
[0044] On the other hand, for the rigid structure area in the initial tire model, the mesh element type is set to a hexahedral mesh, and the mesh size is set to 10-15 mm.
[0045] For the rigid structure area of the rim and the drum, a relatively coarse hexahedral mesh can be used, with a mesh element size of 10-15 mm, without the need for high mesh accuracy to ensure simulation efficiency.
[0046] In practical applications, the grid quality can be checked by using the grid twist degree, aspect ratio, Jacobian determinant, etc. through the self-tool of the finite element software, and unqualified grids such as mesh elements with excessive twist degree can be removed, to ensure that the grid can accurately transmit mechanical load and avoid the problem of divergence or distortion of the simulation results.
[0047] Fourthly, the boundary conditions and load conditions are set, and the coupling interaction conditions of the mechanical field and the temperature field are set in the initial finite element model to obtain the tire transient rolling finite element model.
[0048] In this embodiment, for the boundary conditions, the constraints of the inner hole of the rim and the drum can be set, and the contact conditions of the tire and the contact surface of the drum are set.
[0049] Specifically, for the constraint setting of the inner hole of the rim, a fixed constraint can be applied, such as limiting the translational and rotational degrees of freedom in X, Y and Z directions, to simulate the fixed connection state of the rim and the axle during actual loading. For the constraint setting of the drum, a rotational constraint around its own axis can be applied, only the rotational degree of freedom around the Z axis is reserved, and the translation and rotation in other directions are limited, to ensure that the drum only rolls at the set angular velocity.
[0050] For the contact condition setting of the contact surface between the tire and the drum, a penalty function contact algorithm can be set, the friction coefficient is defined, the dry friction characteristics of rubber and metal are referred to, and the value is about 0.7, to ensure that the rolling friction and normal impact force can be transmitted between the tire and the drum, and the contact surface is prevented from penetrating.
[0051] In the load condition setting link, a constant vertical load perpendicular to the surface of the drum can be applied at the center of the rim to simulate the actual load state of the tire.
[0052] At the same time, the angular velocity of the drum is set, which can be calculated from the target speed, such as 100 km / h, through the circumference of the tire and the diameter of the drum, such as 8.8 rad / s, and applied to the drum to simulate the tire rolling driving condition.
[0053] In the process of setting the coupling interaction conditions of the mechanical field and the temperature field in the initial finite element model, the influence of the heat generated by the viscoelasticity of the rubber material on the air characteristics in the tire cavity during tire rolling can be considered, the thermal-structure coupling function is opened in the model, and specifically, the heat generation parameter, the thermal boundary condition and the coupling solution rule can be set.
[0054] In the setting of the heat generation parameter, the internal friction heat calculation logic can be defined based on the viscoelastic constitutive model of the rubber part, such as the heat generation rate which can be calculated by the product of the loss factor and the strain energy density, to ensure that the heat generation during the rolling of the rubber can be calculated in real time in the simulation.
[0055] In the setting of the thermal boundary condition, the convection heat transfer coefficient between the air in the tire cavity and the inner surface of the tire can be set, such as , the heat dissipation condition of the outer surface of the tire and the environment is defined, to ensure that the temperature field in the tire cavity can truly reflect the actual heat generation and heat dissipation balance, and to provide accurate temperature data for the subsequent calculation of the sound speed and cavity frequency in the tire cavity.
[0056] In the setting of coupling solution rule link, the thermal-structure coupling analysis module can be opened in the finite element solver, and the coupling interaction logic of mechanical field and temperature field is set, such as the influence of temperature change on rubber material properties, the influence of material deformation on heat generation distribution, to realize the bidirectional coupling simulation of the two.
[0057] Figure 2 An exemplary tire transient rolling finite element model is shown, which includes a tire 210, a rim 220 and a drum 230.
[0058] In an embodiment, the vertical force dynamic signal of the semi-steel tire to be tested is obtained, specifically including: First, the vertical force time domain signal of the axle center position of the semi-steel tire to be tested is obtained.
[0059] In actual application, a force sensor can be arranged at the axle center to collect the vertical force time domain signal in real time at a fixed sampling frequency, so as to avoid aliasing of high frequency signals. At the same time, the displacement and stress signals of each node on the inner surface of the tire are also collected, and the purpose of collecting these signals is to verify the rationality of the model subsequently.
[0060] Then, the static load component in the vertical force time domain signal is removed to obtain a vertical force dynamic signal containing only transient fluctuations.
[0061] In this embodiment, the collected vertical force signal can be de-trended, that is, the static load component is removed, and finally a vertical force dynamic signal containing only transient fluctuations is obtained. The vertical force dynamic signal can reflect the original mechanical response of the tire and the road impact, and is the basis for subsequent frequency spectrum analysis. For example, in actual situation, the tire carrying the weight of the vehicle body will produce static load when the vehicle is running. The dynamic signal after removing this part of the static load can more accurately reflect the transient mechanical changes caused by the tire rolling over the road bumps and the like.
[0062] Figure 3 An exemplary vertical force dynamic signal of the axle extracted when the tire transient rolling simulation is performed on the drum with bumps is shown. Figure 3 The results shown are similar to the indoor drum test results, and the curve waveforms are basically consistent.
[0063] In an embodiment, according to the rolling simulation results, the semi-steel tire to be tested is solved for multiple parameters to obtain cavity key parameters representing the cavity noise frequency and amplitude, specifically including: First, according to the rolling simulation results, the average temperature of the air in the tire cavity is determined.
[0064] In this embodiment, the temperature data of the air in the tire cavity during the simulation process can be extracted from the rolling simulation results, and then the average temperature of the air in the tire cavity is determined, such as the temperature stabilizing at 45℃ after 1s of simulation, so as to avoid the contingency of single-point temperature.
[0065] Then, the actual sound speed in the tire cavity is calculated according to the average temperature of the air.
[0066] In this embodiment, the actual sound speed in the tire cavity can be calculated by the following formula: c = 331 + 0.6T (1) Wherein, c represents the actual sound speed, and T represents the average temperature of the air.
[0067] The above formula is based on the ideal gas sound speed theory, and the corrected actual sound speed reflects the influence of heat generation on the acoustic characteristics of the air.
[0068] Then, the cavity low-frequency component and the cavity high-frequency component are calculated according to the pre-set tire cavity structure parameters and the actual sound speed.
[0069] In this embodiment, the cavity low-frequency component and the cavity high-frequency component can be solved by combining the tire cavity structure parameters and the actual sound speed. Specifically, the cavity low-frequency component can be represented as: (2) The cavity high-frequency component can be represented as: (3) Wherein, f l The cavity low-frequency component is represented by, f h The cavity high-frequency component is represented by c, and V represents the driving speed.
[0070] The embodiment associates the heat generation effect with the cavity frequency, solving the problem of large deviation between the frequency prediction of the traditional method and the actual value.
[0071] At the same time, the pressure fluctuation amplitude in the tire cavity is calculated according to the simulation load in the rolling simulation results.
[0072] In one specific implementation, the pressure fluctuation amplitude in the tire cavity is calculated according to the simulation load in the rolling simulation results, specifically including: On the one hand, the vertical load under the standard working condition is taken as the calibration load, and the quotient of the simulation load and the calibration load is taken as the load coefficient.
[0073] In the load coefficient determination link, the vertical load under the standard working condition can be selected as the calibration load, and the ratio of the simulation load of the current simulated overload working condition to the calibration load can be taken as the load coefficient.
[0074] On the other hand, the speed coefficient is obtained by multiplying the predetermined running speed of the semi-steel tire to be tested by the standard speed.
[0075] In this embodiment, the ratio of the running speed to the standard speed is taken as the speed coefficient, such as the ratio of 120 km / h to 100 km / h is 1.2, and the speed coefficient is 1.2.
[0076] Finally, the pressure fluctuation reference value under the standard working condition is determined, and the pressure fluctuation amplitude in the tire cavity is obtained based on the speed coefficient, the load coefficient, and the pressure fluctuation reference value.
[0077] In this embodiment, the pressure fluctuation amplitude in the tire cavity can be expressed as: (4) Wherein, p represents the pressure fluctuation amplitude in the tire cavity, p0 is the pressure fluctuation reference value under the standard working condition, which is calibrated by a small amount of test, p0 = 50 Pa; v represents the speed coefficient, and k represents the load coefficient.
[0078] The traditional method ignores the influence of load and speed on the pressure fluctuation amplitude, and the double coefficient correction in this embodiment makes the prediction of the pressure fluctuation amplitude more in line with the actual situation.
[0079] Finally, the cavity low-frequency component and the cavity high-frequency component and the pressure fluctuation amplitude are taken as the cavity key parameters.
[0080] In some embodiments, the pressure fluctuation amplitude calculated can be combined with the cavity low-frequency component and the cavity high-frequency component to construct a pressure fluctuation time domain signal, which can simulate the acoustic vibration response of the air in the tire cavity.
[0081] In an embodiment, the cavity key parameters are taken as the cavity noise components and added to the original frequency spectrum data to obtain frequency spectrum correction data, which specifically includes: First, according to the pressure fluctuation amplitude, the low-frequency peak corresponding to the cavity low-frequency component and the high-frequency peak corresponding to the cavity high-frequency component are determined respectively.
[0082] Then, the low-frequency peak is added to the position corresponding to the cavity low-frequency component in the original frequency spectrum data, and the high-frequency peak is added to the position corresponding to the cavity high-frequency component in the original frequency spectrum data, to obtain the frequency spectrum correction data.
[0083] In practical applications, the vertical force dynamic signal can be first subjected to fast Fourier transform to obtain the original frequency spectrum data, which contains the mechanical noise components of the road impact, but lacks the cavity noise peak of 200-250 Hz, which is a typical defect of traditional transient simulation and is also a key that needs to be made up in this embodiment.
[0084] After that, at the position t1 corresponding to the cavity low-frequency component of the original spectrum data l and the position t2 corresponding to the cavity high-frequency component, a low-frequency peak and a high-frequency peak with corresponding amplitudes are added respectively, the peak bandwidth is set to 5 Hz, and the spectrum correction data is obtained by fusion.
[0085] In some embodiments, the spectrum correction data obtained after fusion can be smoothed, such as by using 5-point moving average smoothing, so as to avoid the problem of signal distortion caused by peak mutation.
[0086] In actual application, the spectrum correction data also needs to be subjected to inverse Fourier transform to obtain a complete time-domain signal containing the cavity noise component, so that the mechanical vibration signal and the acoustic vibration signal can be fused to realize the noise prediction of mechanical-acoustic coupling.
[0087] In an embodiment, the spectrum correction data is compared and verified, specifically including: First, the tire cavity noise signal collected by the indoor drum test bench under the same working condition is obtained, and the tire cavity noise signal is converted into test spectrum data.
[0088] In actual application, the same working condition test environment can be built on the indoor drum test bench, such as a tire specification of 205 / 55R16, a load of 5500N, a speed of 120km / h, a bump parameter consistent with simulation, and a microphone used to collect the tire cavity noise signal. The signal is subjected to Fourier transform to obtain test spectrum data.
[0089] Then, the peak frequency deviation value and the amplitude deviation value between the spectrum correction data and the test spectrum data are determined.
[0090] Next, it is judged whether the peak frequency deviation value is within the preset frequency deviation range to obtain a first comparison result.
[0091] At the same time, it is judged whether the amplitude deviation value is within the preset amplitude deviation range to obtain a second comparison result.
[0092] Finally, if the first comparison result and the second comparison result are both yes, it is determined that the comparison and verification are passed, and if the first comparison result and / or the second comparison result is no, it is determined that the comparison and verification are not passed.
[0093] In this embodiment, by comparing the peak frequency and amplitude of the simulation spectrum correction data and the test spectrum data, the peak frequency deviation value and the amplitude deviation value are obtained, wherein the frequency deviation range can be set to -3Hz to +3Hz, and the amplitude deviation range can be set to -10% to +10%. If the error exceeds the above range, the model parameters are adjusted, such as optimizing the pattern grid size, correcting the rubber loss factor, etc., until the accuracy requirement is met.
[0094] Considering that the traditional method does not have a closed-loop verification link, the embodiment can ensure the reliability of the prediction result through simulation-experiment-optimization closed-loop iterative optimization.
[0095] In actual application, Fourier transform is performed on the test result and the traditional simulation result to obtain a frequency spectrum result of tire rolling over the convex block in the test and the traditional simulation, as shown in the following Figure 4 .
[0096] As can be seen from Figure 4 , in the curve corresponding to the test result, there is an obvious peak value in the range of 200-250 Hz, which is a cavity noise peak. In the curve corresponding to the traditional simulation result, there is no peak value in the frequency range. It is indicated that the traditional tire transient rolling simulation link cannot predict the frequency and amplitude of the tire cavity noise.
[0097] In addition, the improved frequency spectrum obtained by processing the prediction scheme provided in the embodiment is compared with the test result, as shown in the following Figure 5 . As can be seen from Figure 5 , the improved simulation result after processing the embodiment is in good agreement with the test result.
[0098] Figure 5 The local enlarged view of the cavity noise frequency spectrum in the 200-250 Hz frequency band in Figure 6 is shown in the following Figure 6 . As can be seen from Figure 6 , the improved simulation result has two peak values in the frequency band, which respectively correspond to the low-frequency part and the high-frequency part of the tire cavity noise, but the test result has only one obvious peak value in the frequency band. The reason is that the actual tire has non-uniformity, which masks the low-frequency peak value coupled with the tire cavity, resulting in that the low-frequency peak value is not obvious.
[0099] In summary, the semi-steel tire cavity noise prediction method provided in the embodiment is more in line with the actual use conditions of the tire, and can consider the excitation of the uneven road, and has higher simulation result precision than the steady-state rolling method; at the same time, the method can not only predict the cavity noise frequency under the use conditions of the tire, but also predict the cavity noise amplitude, and the cavity noise prediction can be performed without multiple experiments for calibrating the tire model parameters, and the prediction precision and feasibility are effectively improved.
[0100] Based on the same overall inventive concept, the present application also protects a semi-steel tire cavity noise prediction system. The semi-steel tire cavity noise prediction system provided in the present application is described below, and the semi-steel tire cavity noise prediction system described below can be mutually corresponding and referred to the semi-steel tire cavity noise prediction method described above.
[0101] As shown in the following Figure 7 , the semi-steel tire cavity noise prediction system provided in the embodiment of the present application specifically comprises: The simulation module 310 is configured to construct a tire transient rolling finite element model, and perform transient rolling simulation on the semi-steel tire to be tested to obtain a rolling simulation result. The solving module 320 is configured to perform multi-parameter solving on the semi-steel tire to be tested according to the rolling simulation result to obtain cavity key parameters representing cavity noise frequency and amplitude. The acquisition module 330 is configured to acquire a vertical force dynamic signal of the semi-steel tire to be tested, and determine original frequency spectrum data according to the vertical force dynamic signal. The noise adding module 340 is configured to add the cavity key parameters as cavity noise components into the original frequency spectrum data to obtain frequency spectrum correction data. The verification module 350 is configured to perform comparative verification on the frequency spectrum correction data, and extract cavity noise prediction results in the frequency spectrum correction data after the comparative verification is passed.
[0102] As to the system in the above-described embodiments, the specific manner in which each module performs operations has been described in detail in the embodiments of the method, and will not be described in detail here.
[0103] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims
1. A method of predicting the cavity noise of a semi-steel tire, characterized by, The method comprises the following steps: a tire transient rolling finite element model is constructed, and transient rolling simulation is performed on a semi-steel tire to be tested to obtain rolling simulation results; based on the rolling simulation results, multi-parameter solving is performed on the semi-steel tire to be tested to obtain cavity key parameters representing cavity noise frequency and amplitude; a vertical force dynamic signal of the semi-steel tire to be tested is obtained, and original frequency spectrum data is determined based on the vertical force dynamic signal; the cavity key parameters are taken as cavity noise components and added to the original frequency spectrum data to obtain frequency spectrum correction data; the frequency spectrum correction data is compared and verified, and after the comparison and verification are passed, cavity noise prediction results in the frequency spectrum correction data are extracted.
2. The semi-steel tire cavity noise prediction method of claim 1, wherein, The method for constructing a tire transient rolling finite element model comprises the following steps: determining the related basic structures of the semi-steel tire to be tested and target structure parameters of each basic structure; differential modeling is performed on each basic structure based on the target structure parameters to obtain a tire initial model; material properties of each basic structure in the tire initial model are determined, and the tire initial model is meshed by differential mesh element types and mesh sizes to obtain an initial finite element model; boundary conditions and load conditions are set, and a coupling interaction condition of a mechanical field and a temperature field is set in the initial finite element model to obtain a tire transient rolling finite element model.
3. The semi-steel tire cavity noise prediction method of claim 2, wherein, The method for constructing a tire transient rolling finite element model comprises the following steps: determining the three-dimensional form of the tread pattern in the basic structure; full-size modeling is performed on the tread pattern based on the three-dimensional form; regular three-dimensional modeling is performed on other basic structures except the tread pattern based on the target structure parameters to obtain a tire initial model.
4. The method of claim 2, wherein, The method for meshing the tire initial model by differential mesh element types and mesh sizes comprises the following steps: for the tread pattern area in the tire initial model, the mesh element type is set as a tetrahedron mesh, and the mesh size is set as less than 2 mm; for the flexible structure area in the tire initial model, the mesh element type is set as a hexahedron mesh, and the mesh size is set as 5 mm-8 mm; for the rigid structure area in the tire initial model, the mesh element type is set as a hexahedron mesh, and the mesh size is set as 10 mm-15 mm.
5. The method of claim 1, wherein, The method for obtaining a vertical force dynamic signal of a semi-steel tire to be tested comprises the following steps: a vertical force time domain signal of the axle center position of the semi-steel tire to be tested is obtained; the static load component in the vertical force time domain signal is removed to obtain a vertical force dynamic signal containing only transient fluctuations.
6. The semi-steel tire cavity noise prediction method of claim 1, wherein, The method for performing multi-parameter solving on the semi-steel tire to be tested based on the rolling simulation results to obtain cavity key parameters representing cavity noise frequency and amplitude comprises the following steps: the average temperature of air in the tire cavity is determined based on the rolling simulation results; the actual sound speed in the tire cavity is calculated based on the average temperature of air; the cavity low-frequency component and the cavity high-frequency component are calculated based on the pre-set tire cavity structure parameters and the actual sound speed; the pressure fluctuation amplitude in the tire cavity is calculated based on the simulation load in the rolling simulation results; The cavity low-frequency component and the cavity high-frequency component and the pressure fluctuation amplitude are taken as cavity key parameters.
7. The method of claim 6, wherein, According to the simulation load in the rolling simulation result, the pressure fluctuation amplitude in the tire cavity is calculated, including: The vertical load under the standard working condition is taken as a calibration load, and the quotient of the simulation load and the calibration load is obtained to obtain a load coefficient; The speed coefficient is obtained by taking the driving speed of the to-be-tested semi-steel tire and the standard speed as a quotient; The pressure fluctuation reference value under the standard working condition is determined, and the pressure fluctuation amplitude in the tire cavity is obtained based on the speed coefficient, the load coefficient, and the pressure fluctuation reference value.
8. The method of claim 6, wherein, The cavity key parameters are taken as cavity noise components and added to the original frequency spectrum data to obtain frequency spectrum correction data, including: According to the pressure fluctuation amplitude, a low-frequency peak corresponding to the cavity low-frequency component and a high-frequency peak corresponding to the cavity high-frequency component are respectively determined; The low-frequency peak is added to the position corresponding to the cavity low-frequency component in the original frequency spectrum data, and the high-frequency peak is added to the position corresponding to the cavity high-frequency component in the original frequency spectrum data to obtain frequency spectrum correction data.
9. The method of claim 1, wherein, The frequency spectrum correction data is compared and verified, including: The tire cavity noise signal collected by the indoor drum test bench under the same working condition is obtained, and the tire cavity noise signal is converted into test frequency spectrum data; The peak frequency deviation value and the amplitude deviation value between the frequency spectrum correction data and the test frequency spectrum data are determined; It is judged whether the peak frequency deviation value is within the preset frequency deviation range to obtain a first comparison result; It is judged whether the amplitude deviation value is within the preset amplitude deviation range to obtain a second comparison result; If the first comparison result and the second comparison result are both yes, it is determined that the comparison verification is passed, and if the first comparison result and / or the second comparison result is no, it is determined that the comparison verification is not passed.
10. A semi-steel tire cavity noise prediction system characterized by, including: A simulation module is configured to construct a tire transient rolling finite element model and perform transient rolling simulation on the to-be-tested semi-steel tire to obtain a rolling simulation result. A solving module is configured to perform multi-parameter solving on the to-be-tested semi-steel tire according to the rolling simulation result to obtain cavity key parameters representing cavity noise frequency and amplitude. An acquisition module is configured to acquire a vertical force dynamic signal of the to-be-tested semi-steel tire and determine original frequency spectrum data according to the vertical force dynamic signal. A noise adding module is configured to take the cavity key parameters as cavity noise components and add them to the original frequency spectrum data to obtain frequency spectrum correction data. A verification module is configured to compare and verify the frequency spectrum correction data, and extract cavity noise prediction results in the frequency spectrum correction data after the comparison and verification is passed.