A spoke structure design method for reducing the transmission rate of non-pneumatic tires

Abstract

The application discloses a spoke structure design method for reducing force transmission rate of a non-pneumatic tire, and belongs to the field of structure optimization. Main technical points are as follows: on the basis of meeting certain radial stiffness, the spoke is subjected to structure optimization design, aiming at reducing force transmission rate of the non-pneumatic tire and tire weight. In the optimization process, an interface of simulation software and an optimization algorithm is established, the simulation software is used for analyzing mechanical performance of a finite element model of the non-pneumatic tire, simulation data is extracted and transmitted to a multi-island genetic algorithm, and geometric parameters are optimized. Through the simulation software combined with the optimization algorithm, on the basis of meeting certain radial stiffness, multi-performance target optimization of the non-pneumatic tire is realized, the problems of severe vibration of the non-pneumatic tire at high speed and large tire weight are effectively solved, the NVH level of the automobile is improved, the driving comfort of the automobile is improved, and the energy consumption of the automobile is reduced.

Description

Technical Field

[0001] This invention relates to the field of vehicle tire design technology, and specifically to a spoke structure design method for reducing the transmission rate of non-pneumatic tires. Background Technology

[0002] Tires are the only medium through which a car interacts with the road surface, directly affecting its handling stability, driving safety, and ride comfort. However, traditional pneumatic tires often pose safety hazards such as tire blowouts, deflation, and unstable tire pressure, seriously impacting driving safety. For modern new energy vehicles without engines, tire noise is a key factor affecting ride comfort. Tire cavity resonance is one of the main causes of tire noise. It occurs because the gas in the closed cavity inside the tire resonates at a specific frequency when excited by the road surface. This resonance is transmitted to the vehicle body through the axle and suspension system, creating low-frequency structural noise of 180-250 Hz inside the vehicle, severely affecting NVH performance. Therefore, non-pneumatic tires (NPTs) have gradually come into focus. Because they lack an inflatable structure, spokes replace air as the key support for the car, directly eliminating the tire's dependence on gas and fundamentally solving safety problems caused by gas-related factors such as tire blowouts, thus improving tire safety performance. Furthermore, the absence of a cavity prevents cavity resonance noise, further improving NVH performance.

[0003] In recent years, experts and scholars at home and abroad have conducted numerous studies on non-pneumatic tires, such as vibration characteristics, ground impression, radial stiffness, and fatigue life. Honeycomb structures are considered to have great application potential in non-pneumatic tires. However, although honeycomb non-pneumatic tires do not have the risk of tire blowout, the high-speed vibration problem caused by discontinuous supports has limited the commercial development of non-pneumatic tires. Therefore, vibration reduction of non-pneumatic tires is an urgent problem to be solved. In addition, non-pneumatic tires of the same specifications are much heavier than pneumatic tires, which will significantly increase vehicle energy consumption.

[0004] Force transmission rate refers to the ratio of the force (or acceleration) that the tire transmits from the road surface unevenness excitation to the suspension or body to the input excitation. It is a key indicator for evaluating the tire's vibration isolation performance and directly reflects the tire's transmission characteristics of road surface excitation. Summary of the Invention

[0005] The technical problem to be solved by this invention is to address the issues of severe vibration and heavy weight of non-pneumatic tires at high speeds. To solve these problems, a spoke structure design method is provided to reduce the force transmission rate of non-pneumatic tires. While meeting certain radial stiffness requirements, the structure of the spoke honeycomb cells is optimized to reduce the force transmission rate and tire weight of non-pneumatic tires.

[0006] The object of this invention is achieved in the following manner: A wheel spoke structure design method for reducing the force transmission rate of non-pneumatic tires includes the following steps: S1. Based on the design dimensions, the tread, outer cover, outer reinforcement, shear band, inner reinforcement, inner cover, spokes, and rim components are modeled and assembled into a non-pneumatic tire model in 3D software. S2. The 3D model from S1 is imported into finite element analysis software. The spoke structure parameters are parameterized, including the spoke projection length and spoke thickness. Then, material properties are assigned to the model, analysis steps, interactions, and boundary conditions are set, and a mesh is generated to obtain a complete finite element analysis model. S3. Using the optimized Latin hypercube method, the parameterized spoke projection length and spoke thickness from S2 are used as design variables. Uniform sampling is performed within the design space formed by the design variables to generate multiple sample points. A corresponding finite element model is established based on the spoke structure parameters corresponding to each sample point, and the calculation is submitted to obtain the structural response results. S4. Using the spoke structure parameters corresponding to each sample point in S3 as input variables, extract the structural response results of the corresponding model in S3 as the output response. The structural response results include at least the support reaction force N at the centroid reference point, the weight M of the entire tire, and the tire's sinking S under radial load. Construct a radial basis function neural network approximation model using the input variables and output response. S5. Based on the radial basis function neural network approximation model constructed in S4, use a multi-objective optimization algorithm to optimize and solve for the optimal spoke structure parameters. Specifically, this includes: S51. Using the spoke projection length and spoke thickness as design variables and setting their value ranges; S52. Using the sinking S as a constraint condition and setting its value range; S53. Constructing an objective function with the objectives of minimizing the support reaction force N and minimizing the tire weight M; S54. Optimizing the approximation model using a multi-island genetic algorithm to obtain the optimal combination of spoke structure parameters. S6. Based on the optimal spoke structure parameters obtained in S54, establish the corresponding finite element model and perform numerical simulation verification. Compare the force transmission rate performance of the optimized structure with the original structure to verify the effectiveness of the optimization.

[0007] In step S1, the rim is simplified to an aluminum alloy ring during modeling, and the tread pattern and chamfer are ignored.

[0008] In step S2, the parameterization settings specifically include: parameterizing the wheel spoke projection lengths as L1, L2, and L3, and parameterizing the wheel spoke thicknesses as T1, T2, T3, T4, and T5; when setting material properties, the tread rubber material is simultaneously set with hyperelastic and viscoelastic parameters; the analysis steps include at least: modal analysis, harmonic response analysis, tire-road contact analysis, and radial load application analysis; when setting interactions, the contact properties between the non-pneumatic tire tread and the analytical rigid road surface are defined; the inner surface of the rim is kinematically coupled to a centroid reference point; all components are connected using a common node method; when setting boundary conditions, the six degrees of freedom of the centroid reference point and the analytical rigid road surface reference point are fixed; in the harmonic response analysis step, an excitation force is applied to the center point of the tread; in the radial load application analysis step, the radial load is applied to the analytical rigid road surface reference point.

[0009] In step S2, when dividing the mesh, for rubber and polyurethane material components, the element type used must support hybridization formulas and reduced integrals, and hourglass control must be enabled.

[0010] In step S3, the number of sample points generated using the optimized Latin hypercube method is 100.

[0011] In step S4, the accuracy of the constructed radial basis neural network approximation model is checked using the cross-validation method. Specifically, a portion of the sample points used to construct the model are randomly selected as validation points, and the coefficient of determination R² and root mean square error RMSE of the model prediction are calculated. When R² > 0.9 and RMSE < 0.2, the model accuracy is deemed to meet the requirements. Otherwise, the sample size is increased and the model is reconstructed and validated.

[0012] In step S51, the design variables L1, L2, and L3 take values ​​ranging from ±20% of their initial values, and the design variables T1, T2, T3, T4, and T5 take values ​​ranging from ±50% of their initial values.

[0013] In step S52, the constraint condition for the sinking amount S is that its maximum value does not exceed 16mm.

[0014] In step S53, the objective function is in a weighted form, where the first objective weight for minimizing the support reaction force N is 0.8, and the second objective weight for minimizing the total tire weight M is 0.2.

[0015] Step S6 specifically includes: performing finite element simulation verification based on the optimal parameters. If the error between the verified response value and the predicted value of the approximate model in S4 exceeds 5%, the verification data is added to the sample set of S3 as a new sample point. Steps S4 and S5 are repeated until the prediction accuracy meets the requirements, and finally the optimal spoke structure parameters are output.

[0016] The beneficial effects of this invention are as follows: By establishing a parameterized model, constructing a high-precision proxy model, and combining it with a global optimization algorithm, this invention significantly reduces the force transmission rate (vibration) and weight of the tire while ensuring that the radial stiffness of the tire is within a suitable range. This solves the core problems of severe high-speed vibration and heavy weight of non-pneumatic tires. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the overall technical approach of the present invention.

[0018] Figure 2 This is a schematic diagram of a honeycomb non-pneumatic tire.

[0019] Figure 3 This is a parameterized schematic diagram of the spoke structure of a honeycomb non-pneumatic tire (labeled L1, L2, L3, T1-T5).

[0020] Figure 4 This is a schematic diagram of a tire analysis model created in finite element software (indicating excitation force, radial load, constraints, etc.).

[0021] Figure 5 This is a schematic diagram of the prediction error of the RBF approximation model obtained by cross-validation.

[0022] Figure 6 A schematic diagram of the Pareto front solution set obtained after optimization by the Multi-Island Genetic Algorithm (MIGA).

[0023] Figure 7 A comparison of simulation results for optimizing the front and rear tire support reaction force N (indirect force transmission rate). Detailed Implementation

[0024] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0025] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same technical meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0026] This invention provides a wheel spoke structure design method for reducing the force transmission rate of non-pneumatic tires, comprising the following steps: S1. Based on the design dimensions, the tread, outer cover, outer reinforcement, shear band, inner reinforcement, inner cover, spokes, and rim components are modeled and assembled into a non-pneumatic tire model in 3D software. S2. The 3D model from S1 is imported into finite element analysis software. The spoke structure parameters are parameterized, including the spoke projection length and spoke thickness. Then, material properties are assigned to the model, analysis steps, interactions, and boundary conditions are set, and a mesh is generated to obtain a complete finite element analysis model. S3. Using the optimized Latin hypercube method, the parameterized spoke projection length and spoke thickness from S2 are used as design variables. Uniform sampling is performed within the design space formed by the design variables to generate multiple sample points. A corresponding finite element model is established based on the spoke structure parameters corresponding to each sample point, and the calculation is submitted to obtain the structural response results. S4. Using the spoke structure parameters corresponding to each sample point in S3 as input variables, extract the structural response results of the corresponding model in S3 as the output response. The structural response results include at least the support reaction force N at the centroid reference point, the weight M of the entire tire, and the tire's sinking S under radial load. Construct a radial basis function neural network approximation model using the input variables and output response. S5. Based on the radial basis function neural network approximation model constructed in S4, use a multi-objective optimization algorithm to optimize and solve for the optimal spoke structure parameters. Specifically, this includes: S51. Using the spoke projection length and spoke thickness as design variables and setting their value ranges; S52. Using the sinking S as a constraint condition and setting its value range; S53. Constructing an objective function with the objectives of minimizing the support reaction force N and minimizing the tire weight M; S54. Optimizing the approximation model using a multi-island genetic algorithm to obtain the optimal combination of spoke structure parameters. S6. Based on the optimal spoke structure parameters obtained in S54, establish the corresponding finite element model and perform numerical simulation verification. Compare the force transmission rate performance of the optimized structure with the original structure to verify the effectiveness of the optimization.

[0027] In step S1, the rim is simplified to an aluminum alloy ring during modeling, and the tread pattern and chamfer are ignored.

[0028] In step S2, the parameterization settings specifically include: parameterizing the wheel spoke projection lengths as L1, L2, and L3, and parameterizing the wheel spoke thicknesses as T1, T2, T3, T4, and T5; when setting material properties, the tread rubber material is simultaneously set with hyperelastic and viscoelastic parameters; the analysis steps include at least: modal analysis, harmonic response analysis, tire-road contact analysis, and radial load application analysis; when setting interactions, the contact properties between the non-pneumatic tire tread and the analytical rigid road surface are defined; the inner surface of the rim is kinematically coupled to a centroid reference point; all components are connected using a common node method; when setting boundary conditions, the six degrees of freedom of the centroid reference point and the analytical rigid road surface reference point are fixed; in the harmonic response analysis step, an excitation force is applied to the center point of the tread; in the radial load application analysis step, the radial load is applied to the analytical rigid road surface reference point.

[0029] In step S2, when dividing the mesh, for rubber and polyurethane material components, the element type used must support hybridization formulas and reduced integrals, and hourglass control must be enabled.

[0030] In step S3, the number of sample points generated using the optimized Latin hypercube method is 100.

[0031] In step S4, the accuracy of the constructed radial basis neural network approximation model is checked using the cross-validation method. Specifically, a portion of the sample points used to construct the model are randomly selected as validation points, and the coefficient of determination R² and root mean square error RMSE of the model prediction are calculated. When R² > 0.9 and RMSE < 0.2, the model accuracy is deemed to meet the requirements. Otherwise, the sample size is increased and the model is reconstructed and validated.

[0032] In step S51, the design variables L1, L2, and L3 take values ​​ranging from ±20% of their initial values, and the design variables T1, T2, T3, T4, and T5 take values ​​ranging from ±50% of their initial values.

[0033] In step S52, the constraint condition for the sinking amount S is that its maximum value does not exceed 16mm.

[0034] In step S53, the objective function is in a weighted form, where the first objective weight for minimizing the support reaction force N is 0.8, and the second objective weight for minimizing the total tire weight M is 0.2.

[0035] Step S6 specifically includes: performing finite element simulation verification based on the optimal parameters. If the error between the verified response value and the predicted value of the approximate model in S4 exceeds 5%, the verification data is added to the sample set of S3 as a new sample point. Steps S4 and S5 are repeated until the prediction accuracy meets the requirements, and finally the optimal spoke structure parameters are output.

[0036] like Figures 1 to 7 As shown, a spoke structure design method for reducing the force transmission rate of non-pneumatic tires includes: S1. Based on the design dimensions, the tread, outer cover, outer reinforcement, shear band, inner reinforcement, inner cover, spokes, and rim components are modeled and assembled into a non-pneumatic tire model using the 3D software CREO. Specifically: In the modeling, the rim is simplified to an aluminum alloy ring; the tread pattern and chamfer are ignored. The specific structural dimensional parameters of the NPT finite element model are shown in Table 1 below; Table 1. Structural Design Parameters for Non-Pneumatic Tires S2 involves importing the 3D model from S1 into the finite element analysis software ABAQUS, parameterizing the spoke projection length and spoke thickness, assigning material properties to the model, setting the analysis step, setting interactions, adding boundary conditions, meshing, setting field outputs, and finally obtaining the complete finite element analysis model. Specifically: When setting parameters, set the wheel spoke projection length to L. i (i=1,2,3), the spoke thickness is set to T respectively. i (i=1,2,3,4,5); When setting material properties, the tread material is rubber, and both hyperelastic and viscoelastic parameters need to be set simultaneously; the material parameters of the NPT finite element model are shown in Table 2 below. Table 2 Specific parameters of some materials The tire tread is made of viscoelastic rubber. The generalized Prony series is used to characterize the viscoelasticity of the rubber, the Neo-Hooke series is used to characterize the hyperelasticity of the rubber, and Rayleigh damping is used to define the damping parameters of the rubber. The specific parameters are shown in Tables 3 and 4. Table 3 Hyperelasticity and Damping Parameters of Rubber Materials Table 4 Viscoelastic parameters of rubber materials For polyurethane materials, only uniaxial tensile data are available. The hyperelasticity of the material is characterized using Marlow's constitutive model. The damping parameters are defined using Rayleigh damping, and their specific values ​​are the same as those for the rubber materials mentioned above.

[0037] The analysis process consists of four steps: first, modal analysis; second, harmonic response analysis, which uses the steady-state subspace method; third, tire-road contact; and fourth, application of radial load.

[0038] When setting the interaction, the non-pneumatic tire contacts the analytical rigid road surface, with the tangential behavior set to frictionless and the normal behavior set to hard contact, and separation is allowed after contact; the inner surface of the aluminum alloy rim is kinematically coupled to the center of mass reference point; all components are connected by a common node method; When setting boundary conditions, the six degrees of freedom of the centroid reference point and the analytical rigid body road surface reference point are fixedly constrained; the excitation force applied to the center point of the tread is 1000 N in the harmonic response analysis step; the radial load applied in the fourth step is 3665 N. When meshing, the element types for rubber and polyurethane material components should be selected using hybrid formulas and reduced integrals, and hourglass reinforcement should be enabled to avoid shear self-locking and volume compression. The element types for rubber and polyurethane should be set to C3D6H and C3D8RH, while the element types for other material components should be C3D6 and C3D8.

[0039] When setting the field output, output the displacement changes of all nodes in the model and the stress changes of all elements. The harmonic response analysis step requires additional setting of the radial support reaction force N at the centroid reference point.

[0040] S3 uses an optimized Latin hypercube to uniformly sample the design space in S2, which uses the spoke projection length and spoke thickness as design variables, generating sample points. Then, based on the spoke structural parameters corresponding to different sample points, a corresponding finite element model is established. Finally, the finite element model is submitted for calculation to obtain the structural response results. Specifically: Using an optimized Latin hypercube experiment, 100 sample points were generated.

[0041] S4 uses the simulated wheel spoke structure parameters at different sample points in S3 as input variables, extracts the support reaction force N at the centroid reference point, the weight M of the entire tire, and the tire's sinking S under radial load from the structural response results of S3 as output responses, and generates a radial basis function (RBF) approximation model. Specifically: Using the wheel spoke projection length and wheel spoke thickness as input variables, and the support reaction force N at the centroid reference point, the weight M of the entire tire, and the tire sinking S under radial load as output responses, a radial basis function neural network is used to construct an approximate model. The prediction accuracy of the RBF approximate model is tested by cross-validation. If the accuracy does not meet the requirements, the sample size is increased and the RBF approximate model is reconstructed to re-verify the accuracy until the prediction accuracy of the RBF approximate model meets the requirements. The cross-validation method is as follows: 100 sample points are used to build the model, and 10 sample points are randomly selected from them to validate the model's accuracy; the root mean square error (RMSE) and coefficient of determination (R²) are selected. 2 As an evaluation metric for verifying the reliability of the model's predictions, if R...2 If the accuracy is greater than 0.9 and RMSE is less than 0.2, then the accuracy requirement is met. The accuracy of the RBF approximation model in this invention is shown in Table 5.

[0042] Table 5. Accuracy values ​​of the RBF approximation model S5, obtain the optimal solution of the objective function from the RBF approximation model of S4 through optimization methods to obtain the optimal spoke structure parameters; step S5 specifically includes: S51, in ISIGHT, the Multi-Island Genetic Algorithm (MIGA) is selected as the optimization method; S52, uses the wheel spoke projection length and wheel spoke thickness as design variables and sets the range of values ​​for these design variables; specifically: The projected length L of the spokes i and spoke thickness T i As a design variable; Design variable L i (i=1,2,3) The value range is ±20%, T i (i=1,2,3,4,5) The value range is ±50%; 16≤L1≤24, 24≤L2≤36, 16≤L3≤24, 4≤T1≤12, 2≤T2≤6, 2≤T3≤6, 2≤T4≤6, 2≤T5≤6, Where L1, L2, and L3 are the lengths of the spoke honeycomb cell edges projected onto the Y-axis; T1, T2, T3, T4, and T5 are the thicknesses of each segment of the spoke honeycomb cell.

[0043] S53 sets the subsidence S as a constraint and defines its range. Specifically: Since the NPT used in this invention is designed based on the size of a 215 / 60 R70 pneumatic tire, the range of the sinking value is also consistent with that of a 215 / 60 R70 pneumatic tire. The subsidence S is used as a constraint condition, and its value range is 12≤Max(S)≤16 mm; S54 uses the support reaction force N at the center of mass reference point and the total tire weight M as the objective functions. Specifically: F = Min(N), M = Min(M) The objective function sets the minimum support reaction force N as the first objective with a weight of 0.8, and the minimum tire mass M as the second objective with a weight of 0.2.

[0044] S55, the optimal solution of the RBF approximate model is obtained through the multi-island genetic algorithm to obtain the optimal spoke structure parameters; S6 establishes a corresponding finite element model based on the optimal spoke structure parameters obtained in S55, and conducts numerical simulation calculations based on the analysis settings in S2 and the extracted objects in S3. The effectiveness of the optimization method is verified by comparing the tire force transmission rates of the optimal spoke structure and the original structure. Specifically: Based on the optimal structural parameters of the spokes obtained in S55, a corresponding finite element model is established. Numerical simulation calculations are carried out based on the analysis settings in S2 and the extracted objects in S3. By comparing the tire force transmission rate of the optimal spoke structure with that of the original structure, if the error between the verification result response value and the approximate model prediction value is greater than 5%, the predicted data is imported into the sample point data. Steps S4 and S5 are repeated until the prediction accuracy of MIGA reaches the required level. Finally, the optimal structural parameters are output.

[0045] Based on the final optimal honeycomb non-pneumatic tire spoke design scheme that meets the requirements, its response value is extracted and compared with the initial honeycomb non-pneumatic tire. The comparison results are shown in Table 6.

[0046] Table 6 Comparison of results before and after optimization (Initial tire: NPT-1, Optimized tire: NPT-2) The comparison results in Table 6 show that the final MIGA predicted values ​​are basically consistent with the ABAQUS simulation analysis results, with a maximum error of less than 1%. This error may stem from the rounding of dimensions. This demonstrates the effectiveness of the combined RBF approximation model and MIGA approach in replacing the traditional single finite element model simulation analysis method for structural optimization, improving the optimization efficiency for multi-performance objectives of NPT while meeting certain radial stiffness requirements. The settlement increased from 9.84 mm to 15.57 mm, according to the radial stiffness formula... Calculations show that the radial stiffness decreased from 372.46 N / mm before optimization to 235.09 N / mm after optimization, a reduction of 36.7%. The excessive radial stiffness before optimization, while improving vehicle handling, reduced tire grip and made the vehicle prone to slippage. After optimization, the radial stiffness is within a suitable range, providing sufficient support for the vehicle while allowing the NPT to achieve greater grip. This indicates that the radial stiffness was customized during the multi-performance optimization process. The weight decreased from 30.07 kg to 28.94 kg, a reduction of 3.76%. Without affecting other performance characteristics, the reduction in NPT weight effectively reduces vehicle energy consumption. This is based on the force transmission rate calculation formula. It can be seen that the peak force transmission rate after optimization decreased from 13.9 times to 8.3 times, a reduction of 40.5%. The reduction in force transmission rate can effectively solve the problem of severe vibration at high speeds in NPT, which helps to improve the driving comfort and handling stability of the car, and helps to improve the NVH level of the car.

[0047] Table 6 shows the changes in the length and spoke thickness of each segment of the honeycomb spoke cell before and after optimization. It can be seen that the lengths of L1 and L2 and the spoke thickness of T3 have a negative correlation with the NPT force transmission rate; the length of L3 and the spoke thicknesses of T1, T2, T4 and T5 have a positive correlation with the NPT force transmission rate.

[0048] In summary, the effectiveness of the spoke structure design method for reducing the force transmission rate of non-pneumatic tires proposed in this invention has been verified, and it can provide a reference for the structural vibration reduction design and improvement of other non-pneumatic tire structures.

[0049] This invention discloses a spoke structure design method for reducing the force transmission rate of non-pneumatic tires. By optimizing the length of each segment of the honeycomb cell and the thickness of the spoke, the force transmission rate of the non-pneumatic tire (NPT) and the tire weight are reduced while meeting certain radial stiffness requirements. This solves the problems of severe vibration and heavy tire weight at high speeds in non-pneumatic tires, improves the driving comfort of the vehicle, enhances the NVH level of the vehicle, and achieves lightweight design of the NPT.

[0050] This invention discloses a design method for wheel spoke structures to reduce the force transmission rate of non-pneumatic tires. It proposes a multi-objective optimization design method based on a radial basis function neural network approximation model combined with a multi-island genetic algorithm, achieving collaborative optimization of multiple performance objectives. This avoids high-intensity simulation calculations, reduces iteration time, and improves optimization efficiency; it also effectively avoids being limited to local optima, making it possible for numerical optimization algorithms to find global solutions.

[0051] This invention discloses a design method for spoke structures to reduce the force transmission rate of non-pneumatic tires. During the optimization process, an interface is established between finite element software and the optimization method. The mechanical performance of the NPT model is analyzed using ABAQUS, and the extracted simulation data is passed to a multi-island genetic algorithm in ISIGHT for optimizing the spoke geometric parameters. By combining simulation software with the genetic algorithm, multi-performance objective synergistic optimization is achieved, efficiently handling multiple objective functions and solving the problems of long optimization times and frequent operations required in traditional optimization processes, thus improving the efficiency of structural optimization.

[0052] The above description is only a preferred embodiment of the present invention. It should be noted that those skilled in the art can make several changes and improvements without departing from the overall concept of the present invention, and these should also be considered within the scope of protection of the present invention.

Claims

1. A method for designing a spoke structure to reduce the force transmission rate of a non-pneumatic tire, characterized in that: Includes the following steps: S1. Based on the design dimensions, the tread, outer cover, outer reinforcement, shear band, inner reinforcement, inner cover, spokes, and rim components are modeled and assembled into a non-pneumatic tire model in 3D software. S2. The 3D model from S1 is imported into finite element analysis software. The spoke structure parameters are parameterized, including the spoke projection length and spoke thickness. Then, material properties are assigned to the model, analysis steps, interactions, and boundary conditions are set, and a mesh is generated to obtain a complete finite element analysis model. S3. Using the optimized Latin hypercube method, the parameterized spoke projection length and spoke thickness from S2 are used as design variables. Uniform sampling is performed within the design space defined by these design variables to generate multiple sample points. Based on the spoke structure parameters corresponding to each sample point, a corresponding finite element model is established and submitted for calculation to obtain the structural response results; S4, the spoke structure parameters corresponding to each sample point in S3 are used as input variables, and the structural response results of the corresponding model in S3 are extracted as output responses. The structural response results include at least the support reaction force N at the centroid reference point, the weight M of the entire tire, and the tire's sinking S when subjected to radial load; a radial basis function neural network approximation model is constructed using the input variables and output responses; S5, based on the radial basis function neural network approximation model constructed in S4, a multi-objective optimization algorithm is used to optimize and solve the model to obtain the optimal spoke structure parameters; specifically, this includes: S51, the spoke projection length and spoke thickness are used as design variables, and their value ranges are set; S52, Set the sinking amount S as a constraint and define its range; S53, Construct an objective function with the goals of minimizing the support reaction force N and minimizing the total tire weight M; S54, Optimize the approximate model using a multi-island genetic algorithm to obtain the optimal combination of spoke structure parameters; S6, Establish the corresponding finite element model based on the optimal spoke structure parameters obtained in S54 and perform numerical simulation verification. Compare the force transmission rate performance of the optimized structure with the original structure to verify the effectiveness of the optimization.

2. The spoke structure design method for reducing the transmission rate of non-pneumatic tires according to claim 1, characterized in that: In step S1, the rim is simplified to an aluminum alloy ring during modeling, and the tread pattern and chamfer are ignored.

3. The spoke structure design method for reducing the transmission rate of non-pneumatic tires according to claim 1, characterized in that: In step S2, the parameterization settings specifically involve: parameterizing the wheel spoke projection length as L1, L2, L3, and parameterizing the wheel spoke thickness as T1, T2, T3, T4, T5; When setting material properties, both hyperelastic and viscoelastic parameters are set for the tread rubber material; the analysis steps include at least: modal analysis step, harmonic response analysis step, tire-road contact analysis step, and analysis step with applied radial load; When setting the interaction, the contact properties between the non-pneumatic tire tread and the analytical rigid road surface are defined; the inner surface of the rim is kinematically coupled to a centroid reference point; the components are connected using a common node method; when setting the boundary conditions, the six degrees of freedom of the centroid reference point and the analytical rigid road surface reference point are fixed; in the harmonic response analysis step, an excitation force is applied to the center point of the tread; in the analysis step of applying radial load, the radial load is applied to the analytical rigid road surface reference point.

4. The spoke structure design method for reducing the transmission rate of non-pneumatic tires according to claim 1 or 3, characterized in that: In step S2, when dividing the mesh, for rubber and polyurethane material components, the element type used must support hybridization formulas and reduced integrals, and hourglass control must be enabled.

5. The spoke structure design method for reducing the transmission rate of non-pneumatic tires according to claim 1, characterized in that: In step S3, the number of sample points generated using the optimized Latin hypercube method is 100.

6. The spoke structure design method for reducing the transmission rate of non-pneumatic tires according to claim 1, characterized in that: In step S4, the accuracy of the constructed radial basis neural network approximation model is checked using the cross-validation method. Specifically, a portion of the sample points used to construct the model are randomly selected as validation points, and the coefficient of determination R² and root mean square error RMSE of the model prediction are calculated. When R² > 0.9 and RMSE < 0.2, the model accuracy is deemed to meet the requirements; otherwise, the sample size is increased and the model is reconstructed and validated.

7. The spoke structure design method for reducing the transmission rate of non-pneumatic tires according to claim 1, characterized in that: In step S51, the design variables L1, L2, and L3 take values ​​ranging from ±20% of their initial values, and the design variables T1, T2, T3, T4, and T5 take values ​​ranging from ±50% of their initial values.

8. The spoke structure design method for reducing the transmission rate of non-pneumatic tires according to claim 1, characterized in that: In step S52, the constraint condition for the sinking amount S is that its maximum value does not exceed 16mm.

9. The spoke structure design method for reducing the transmission rate of non-pneumatic tires according to claim 1, characterized in that: In step S53, the objective function is in a weighted form, where the first objective weight for minimizing the support reaction force N is 0.8, and the second objective weight for minimizing the total tire weight M is 0.

2.

10. The spoke structure design method for reducing the transmission rate of non-pneumatic tires according to claim 1, characterized in that: Step S6 specifically includes: performing finite element simulation verification based on the optimal parameters. If the error between the verified response value and the predicted value of the approximate model in S4 exceeds 5%, the verification data is added to the sample set of S3 as a new sample point. Steps S4 and S5 are repeated until the prediction accuracy meets the requirements, and finally the optimal spoke structure parameters are output.

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