Spoke structure optimization method for improving lateral rigidity of honeycomb type non-pneumatic tire

Abstract

The invention discloses a spoke structure optimization method for improving the lateral rigidity of a honeycomb type non-pneumatic tire, relates to the technical field of wheel design, and solves the problem of control stability of the non-pneumatic tire in the driving process. According to the technical scheme, the method is characterized in that an NPT model is divided at equal intervals in the tire width direction to set different spoke structure thickness gradients, and multi-objective optimization design is carried out. The method aims to adjust the thickness of each segmented spoke structure of the NPT to maintain the original radial rigidity, and meanwhile, aims to reduce the lateral rigidity of the NPT to achieve collaborative optimization of the rigidity performance of the two. Sample points are generated by adopting Latin hypercube sampling, a radial basis function (RBF) proxy model is constructed, and a multi-objective optimization problem is solved by utilizing a non-dominated sorting genetic algorithm NSGA-II. The optimized NPT achieves better balance between lateral rigidity and radial rigidity, meanwhile achieves better performance in the aspects of light weight and maximum stress of spokes, and provides precious reference and reference for design of non-pneumatic tires.

Description

Technical Field

[0001] The invention relates to a non-pneumatic tire, in particular to a spoke structure optimization method for improving the lateral stiffness of a honeycomb non-pneumatic tire, belonging to the technical field of vehicle tires. Background Art

[0002] Tires are the only part of a vehicle that comes into contact with the road, and they play a vital role in the driving process. Traditional pneumatic tires have advantages in low lateral stiffness, low energy loss, low mass, and low radial stiffness, but they also have potential dangers such as air leakage, unstable tire pressure, and tire blowouts. According to statistics, about 46% of traffic accidents are caused by tire failures, and tire blowouts account for as much as 70%. Compared with traditional pneumatic tires, non-pneumatic tires (NPT) rely on the spoke structure instead of the air pressure of pneumatic tires to play the role of bearing, cushioning and reducing vibration, and providing force, avoiding the dangers of tire blowouts and air leakage.

[0003] Although there are many studies on the response characteristics of non-pneumatic tires in terms of load-deflection relationship, wheel-ground contact pressure distribution, vibration mode, rolling resistance, temperature distribution, and spoke and tread stress, there is limited work on the out-of-plane characteristics of non-pneumatic tires. The honeycomb spoke structure NPT is composed of polygonal units, and the overall performance characteristics of the NPT can be affected by changing the arrangement, wall thickness or length of the units. Therefore, the honeycomb structure is considered to have great application potential in non-pneumatic tires. However, the honeycomb NPT has very significant in-plane and out-of-plane mechanical properties, especially lateral stiffness, which makes the multi-stiffness performance and ground contact state of the honeycomb NPT significantly different from those of pneumatic tires, which seriously restricts the development of non-pneumatic tires to replace traditional pneumatic tires. However, the change of honeycomb spoke structure parameters has a domino effect on the mechanical properties of NPT, and will directly lead to changes in the multi-stiffness performance of the tire. Therefore, while adjusting the NPT spoke structure parameters so that the radial stiffness is not significantly affected, it is the key scientific problem to reduce the lateral stiffness of the NPT and achieve the coordinated optimization of stiffness performance. Summary of the invention

[0004] Purpose of the invention: In view of the shortcomings of the prior art, the present invention provides a spoke structure optimization method for improving the lateral stiffness of a honeycomb non-pneumatic tire, by equally dividing the honeycomb spoke structure in the tire width direction and setting different thickness gradients for the divided parts. Subsequently, while adjusting the honeycomb NPT spoke structure parameters to ensure that the radial stiffness of the tire is not affected, the invention is committed to reducing the excessive lateral stiffness of the NPT, achieving a coordinated optimization of the stiffness performance of the two, and greatly improving the performance of the non-pneumatic tire.

[0005] Technical solution: A spoke structure optimization method for improving the lateral stiffness of a honeycomb non-pneumatic tire, comprising:

[0006] S1, establishing a honeycomb non-pneumatic tire model according to tire width, tire sidewall height, and rim diameter;

[0007] S2. Apply a radial load to the honeycomb non-pneumatic tire model through finite element simulation software. The tire remains stationary in the lateral direction and applies a lateral displacement to the road surface to obtain simulation results, and extract the radial displacement U2 and the lateral force RF3 of the honeycomb non-pneumatic tire;

[0008] S3. Obtain the stress distribution cloud map of the bottom of the spoke of the honeycomb non-pneumatic tire through the simulation results, select the path where the Mises stress changes significantly on the cross section of the bottom of the spoke, and divide it into even-numbered equidistant segments along the tire width direction;

[0009] Along the tire width direction, the segmented bodies that present symmetrical distribution characteristics are set as a set, and a total of i sets are set. The sets are named L from the outside to the inside. i (i=1, 2, ..., i), and set the corresponding spoke thickness parameter for each set, and the corresponding spoke thickness parameter range is 2 mm to 4 mm;

[0010] The criteria for significant stress changes are: the area along the path length where the Mises stress first increases and then decreases to the initial value;

[0011] S4. Import the obtained simulation results into a multidisciplinary multi-objective optimization tool, take the spoke thickness parameters of i sets in the width direction of the honeycomb non-pneumatic tire as independent variables, the lateral stiffness of the honeycomb non-pneumatic tire as the target response, and the radial stiffness of the honeycomb non-pneumatic tire as a constraint condition, and perform the following steps:

[0012] S4.1. Perform Latin super test to generate sample points;

[0013] S4.2. Establish a surrogate model, import sample point data, use the sample point data to simulate the relationship between the design variables and the target response, and test the prediction accuracy of the surrogate model by cross-validation;

[0014] S4.3. Through the multi-objective optimization algorithm, the minimization of lateral stiffness is set as the target response, the radial stiffness is used as the constraint condition, and the weights of the optimization objectives and constraints are set, namely, the lateral force RF3 and the radial displacement U2, to generate a Pareto optimal solution set, and select the optimal solution that satisfies the radial displacement constraint and minimizes the lateral force from the solution set.

[0015] The S1 is specifically:

[0016] The tire width is 215 mm, the sidewall height of the tire is 129 mm, which is 60% of the width, and the rim diameter is 16 inches, thereby constructing a three-dimensional numerical simulation model of the honeycomb non-pneumatic tire.

[0017] The S2 is specifically:

[0018] The radial load applied to the tire is fixed at 4000N; the tire remains stationary in the lateral direction, and the lateral displacement applied to the road surface is fixed at 7mm.

[0019] The S3 is specifically:

[0020] Ten symmetrically distributed equidistant segments are made along the tire width direction. Two symmetrically distributed segments along the longitudinal centerline of the tire are grouped together. The group is named L from outside to inside. i (i=1, 2, 3, 4, 5), the initial thickness of L1 to L5 of the honeycomb non-pneumatic tire is set to 3 mm.

[0021] The S4.1 is specifically:

[0022] Through the Latin hypertest, 100 sample points are generated.

[0023] The S4.2 is specifically:

[0024] The radial basis function (RBF) is used to construct the proxy models of spoke thickness parameters and lateral stiffness corresponding to the five sets, and the prediction accuracy of the RBF proxy model is tested by cross-validation. When the accuracy does not meet the requirements, the sample size is increased and the RBF model is rebuilt to verify the accuracy until the prediction accuracy of the RBF proxy model meets the requirements.

[0025] The cross-validation method is as follows: 90 sample points in the sample are used to build the model, and the remaining 10 sample points are used to verify the accuracy of the model;

[0026] Select RMSE, R 2 As an evaluation index for the prediction accuracy of the validation model, if R 2 >0.9 and RMSE <0.2, the accuracy meets the requirements.

[0027] The S4.3 is specifically:

[0028] Multi-objective optimization is performed using the non-dominated sorting genetic algorithm NSGA-II. The optimization objective is the minimum lateral force RF3, with a weight of 0.6; the tire radial displacement U2 is constrained, with a weight of 0.4. Through multi-objective optimization, a Pareto optimal solution set is obtained.

[0029] Beneficial effects: The present invention optimizes the structural design of the spokes by performing equal-interval divisions in the spoke width direction and setting a thickness gradient according to the stress distribution when subjected to lateral force; the thickness of each segment is kept consistent in the spoke height direction, thereby ensuring the consistency of the tire structure and the uniformity of strength; this design method not only improves the overall stability of the tire, but also optimizes the mechanical properties of the spokes, so that the tire exhibits better performance under the same working conditions.

[0030] A multi-objective optimization design method based on RBF surrogate model and non-dominated sorting genetic algorithm (NSGA-II) is proposed by equidistantly dividing the honeycomb spoke structure in the width direction of the tire and setting different thickness gradients. This method can significantly reduce the lateral stiffness of the tire without significantly affecting the radial stiffness, thereby improving the handling and comfort of the tire and achieving coordinated optimization of the two stiffness properties.

[0031] The optimized honeycomb non-pneumatic tire has shown better handling, comfort and durability in practical applications, providing a reliable technical solution and valuable reference, and offering new ideas and effective solutions for the design and development of non-pneumatic tires. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0033] Figure 1 Technology roadmap for multi-objective optimization design.

[0034] Figure 2 It is a honeycomb NPT model.

[0035] Figure 3 Diagram of the settings for the spoke structure gradient optimization design.

[0036] Figure 4 Diagram of the settings for the spoke structure gradient optimization design.

[0037] Figure 5 Select a plot for the stress distribution and path of the NPT bottom spoke.

[0038] Figure 6 Mises stress plot for the selected path at the base of an NPT spoke.

[0039] Figure 7 A two-dimensional graph made to export data from the Pareto optimal solution set generated by Isight. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] In the description of the present invention, it is necessary to understand that the terms "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship are based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0042] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may include that the first and second features are in direct contact, or may include that the first and second features are not in direct contact but are in contact through another feature between them. Moreover, a first feature being "above", "above" and "above" a second feature includes that the first feature is directly above and obliquely above the second feature, or simply indicates that the first feature is higher in level than the second feature. A first feature being "below", "below" and "below" a second feature includes that the first feature is directly below and obliquely below the second feature, or simply indicates that the first feature is lower in level than the second feature.

[0043] like Figures 1 to 7 As shown, a spoke structure optimization method for improving the lateral stiffness of a honeycomb non-pneumatic tire comprises:

[0044] S1. Establish a honeycomb non-pneumatic tire model according to tire width, tire sidewall height, and rim diameter; specifically:

[0045] The tire width is 215 mm, the tire sidewall height is 129 mm, which is 60% of the width, and the rim diameter is 16 inches, and then a three-dimensional numerical simulation model of a honeycomb non-pneumatic tire is constructed. The specific result parameters and material parameters are shown in Tables 1 and 2;

[0046] Table 1 Non-pneumatic tire structural parameters

[0047]

[0048] Table 2 Material parameters of aluminum alloy and high-strength steel and polyurethane and synthetic rubber

[0049]

[0050] S2. Using finite element simulation software, a radial load is applied to the honeycomb non-pneumatic tire model. The tire remains stationary in the lateral direction and a lateral displacement is applied to the road surface. The simulation results are obtained to extract the radial displacement U2 and the lateral force RF3 of the honeycomb non-pneumatic tire. Specifically:

[0051] The radial load applied to the tire is fixed at 4000N; the tire remains stationary in the lateral direction, and the lateral displacement applied to the road surface is fixed at 7mm.

[0052] S3. Obtain the stress distribution cloud map of the bottom of the spoke of the honeycomb non-pneumatic tire through the simulation results, select the path where the Mises stress changes significantly on the cross section of the bottom of the spoke, and divide it into even-numbered equidistant segments along the tire width direction;

[0053] Along the tire width direction, the segmented bodies that present symmetrical distribution characteristics are set as a set, and a total of i sets are set. The sets are named L from the outside to the inside. i (i=1, 2, ..., i), and set the corresponding spoke thickness parameter for each set, and the corresponding spoke thickness parameter range is 2 mm to 4 mm;

[0054] The criterion for significant stress change is: the area along the path length where the Mises stress first increases and then decreases to the initial value; specifically:

[0055] The areas where stress distribution is concentrated are represented by darker colors, and the stress values ​​in these areas are significantly higher than those in the surrounding areas. Based on the cloud map, the path where stress is more concentrated on the spoke bottom section is selected for specific analysis. The uneven distribution of Mises stress intuitively reflects the stress differences in various areas along the spoke width direction. Therefore, a section with significant initial stress changes is selected as the benchmark, and 10 equidistant sections with symmetrical distribution are made along the tire width direction.

[0056] Two segments symmetrically distributed along the longitudinal center line of the tire are grouped together, and the group is named L from the outside to the inside. i (i=1, 2, 3, 4, 5), the initial thickness of L1 to L5 of the honeycomb non-pneumatic tire is set to 3 mm; the radial displacement U2 when the tire is subjected to a radial load of 4000 N is 21.3 mm; the lateral force exerted on the tire is 3144 N.

[0057] S4. Import the obtained simulation results into a multidisciplinary multi-objective optimization tool, take the spoke thickness parameters of i sets in the width direction of the honeycomb non-pneumatic tire as independent variables, the lateral stiffness of the honeycomb non-pneumatic tire as the target response, and the radial stiffness of the honeycomb non-pneumatic tire as a constraint condition, and perform the following steps:

[0058] S4.1. Perform a Latin hypertest to generate 100 sample points.

[0059] S4.2. Establish a surrogate model, import sample point data, use the sample point data to simulate the relationship between the design variables and the target response, and test the prediction accuracy of the surrogate model by cross-validation; specifically:

[0060] The radial basis function (RBF) is used to construct the proxy models of spoke thickness parameters and lateral stiffness corresponding to the five sets, and the prediction accuracy of the RBF proxy model is tested by cross-validation. When the accuracy does not meet the requirements, the sample size is increased and the RBF model is rebuilt to verify the accuracy until the prediction accuracy of the RBF proxy model meets the requirements.

[0061] The cross-validation method is as follows: 90 sample points in the sample are used to build the model, and the remaining 10 sample points are used to verify the accuracy of the model;

[0062] Select RMSE, R 2 As an evaluation index for the prediction accuracy of the validation model, if R 2 >0.9, RMSE <0.2, the accuracy meets the requirements, as shown in Table 3;

[0063] Table 3 Accuracy of RBF model

[0064]

[0065] S4.3, through the multi-objective optimization algorithm, set the lateral stiffness minimization as the target response, the radial stiffness as the constraint condition, and set the weights of the optimization target and the constraint condition, that is, the lateral force RF3 and the radial displacement U2, generate the Pareto optimal solution set, and select the optimal solution that satisfies the radial displacement constraint and minimizes the lateral force in the solution set. Specifically:

[0066] Multi-objective optimization is performed using the non-dominated sorting genetic algorithm NSGA-II. The optimization objective is the minimum lateral force RF3, with a weight of 0.6; the tire radial displacement U2 is constrained, with a weight of 0.4. Through multi-objective optimization, a Pareto optimal solution set is obtained.

[0067] Since the radial displacement U2 of the honeycomb non-pneumatic tire is 21.3mm and the lateral force RF3 is 3144N, in order to keep its radial stiffness within the permitted range and optimize the lateral stiffness to the maximum extent, the radial displacement U2 constraint of the optimization target is controlled within 21mm~22mm, and the solution with the smallest lateral force RF3 is selected in the solution set. The design model of multi-objective optimization is expressed as:

[0068]

[0069] Where L i , U2, and RF3 are the thickness of the spoke in the split part, the displacement of the tire under radial load, and the lateral force exerted on the tire, respectively.

[0070] In this way, it is possible to ensure that the radial stiffness is kept within an appropriate range while reducing the lateral stiffness, thereby improving the overall performance of the non-pneumatic tire.

[0071] According to the use conditions, a design scheme of the honeycomb non-pneumatic tire with the best comprehensive consideration is selected from the Pareto optimal solution set as needed, and the results are compared and analyzed on the initial honeycomb non-pneumatic tire. The optimization results are shown in Table 4.

[0072] Table 4 Comparison of results before and after optimization (initial tire: NPT-1 optimized tire: NPT-OP)

[0073]

[0074] The optimized NPT showed significant changes in multiple parameters. The thickness of the optimized spokes (L1 to L5) changed significantly, with the thickness of L1, L3, and L4 decreasing, while the thickness of L2 and L5 increasing, indicating that increasing the thickness in specific areas can better disperse stress and reduce stress concentration. The radial stiffness decreased from 240.83N / mm before optimization to 232.05N / mm. Although it decreased by about 3.64%, it was still within the allowable range, indicating that other properties were improved without significantly affecting the radial stiffness. The lateral stiffness decreased from 449.17N / mm to 410.95N / mm, a decrease of about 8.51%, which significantly improved the vehicle's handling and steering accuracy and improved the driving experience. The spoke mass decreased from 2.78kg to 2.74kg, a decrease of about 0.04kg. This small mass reduction helps to improve the energy efficiency and lightweight of the tire while ensuring other performance indicators. The maximum stress of the spokes was reduced from 4.92MPa to 4.75MPa, a decrease of about 0.17MPa. The lower maximum stress indicates that the optimized spokes have better load-bearing capacity and durability, which helps to extend the service life of the tire. Through multi-objective optimization design, the overall performance of NPT has been significantly improved. The optimized NPT significantly reduces lateral stiffness while maintaining appropriate radial stiffness, improves handling and comfort, and at the same time, the reduction in spoke mass and maximum stress further improves the energy efficiency and durability of the tire. In summary, the optimized design enables NPT to achieve better balance and improvement in many aspects of performance.

[0075] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0076] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A spoke structure optimization method for improving the lateral stiffness of a honeycomb non-pneumatic tire, characterized in that: include: S1, establishing a honeycomb non-pneumatic tire model according to tire width, tire sidewall height, and rim diameter; S2. Apply a radial load to the honeycomb non-pneumatic tire model through finite element simulation software. The tire remains stationary in the lateral direction and applies a lateral displacement to the road surface to obtain simulation results, and extract the radial displacement U2 and the lateral force RF3 of the honeycomb non-pneumatic tire; S3. Obtain the stress distribution cloud map of the bottom of the spoke of the honeycomb non-pneumatic tire through the simulation results, select the path where the Mises stress changes significantly on the cross section of the bottom of the spoke, and divide it into even-numbered equidistant segments along the tire width direction; Along the tire width direction, the segmented bodies that present symmetrical distribution characteristics are set as a set, and a total of i sets are set. The sets are named L from the outside to the inside. i (i=1, 2, ..., i), and set the corresponding spoke thickness parameter for each set, and the corresponding spoke thickness parameter range is 2 mm to 4 mm; The criteria for significant stress changes are: the area along the path length where the Mises stress first increases and then decreases to the initial value; S4. Import the obtained simulation results into a multidisciplinary multi-objective optimization tool, take the spoke thickness parameters of i sets in the width direction of the honeycomb non-pneumatic tire as independent variables, the lateral stiffness of the honeycomb non-pneumatic tire as the target response, and the radial stiffness of the honeycomb non-pneumatic tire as a constraint condition, and perform the following steps: S4.

1. Perform Latin super test to generate sample points; S4.

2. Establish a surrogate model, import sample point data, use the sample point data to simulate the relationship between the design variables and the target response, and test the prediction accuracy of the surrogate model by cross-validation; S4.

3. Through the multi-objective optimization algorithm, the minimization of lateral stiffness is set as the target response, the radial stiffness is used as the constraint condition, and the weights of the optimization objectives and constraints are set, namely, the lateral force RF3 and the radial displacement U2, to generate a Pareto optimal solution set, and select the optimal solution that satisfies the radial displacement constraint and minimizes the lateral force from the solution set.

2. The spoke structure optimization method for improving the lateral stiffness of a honeycomb non-pneumatic tire according to claim 1, characterized in that: The S1 is specifically: The tire width is 215 mm, the sidewall height of the tire is 129 mm, which is 60% of the width, and the rim diameter is 16 inches, thereby constructing a three-dimensional numerical simulation model of the honeycomb non-pneumatic tire.

3. The spoke structure optimization method for improving the lateral stiffness of a honeycomb non-pneumatic tire according to claim 1, characterized in that: The S2 is specifically: The radial load applied to the tire is fixed at 4000N; the tire remains stationary in the lateral direction, and the lateral displacement applied to the road surface is fixed at 7mm.

4. The spoke structure optimization method for improving the lateral stiffness of a honeycomb non-pneumatic tire according to claim 1, characterized in that: The S3 is specifically: Ten symmetrically distributed equidistant segments are made along the tire width direction. Two symmetrically distributed segments along the longitudinal centerline of the tire are grouped together. The group is named L from outside to inside. i (i=1, 2, 3, 4, 5), the initial thickness of L1 to L5 of the honeycomb non-pneumatic tire is set to 3 mm.

5. The spoke structure optimization method for improving the lateral stiffness of a honeycomb non-pneumatic tire according to claim 1, characterized in that: The S4.1 is specifically: Through the Latin hypertest, 100 sample points are generated.

6. The spoke structure optimization method for improving the lateral stiffness of a honeycomb non-pneumatic tire according to claim 5, characterized in that: The S4.2 is specifically: The radial basis function (RBF) is used to construct the proxy models of spoke thickness parameters and lateral stiffness corresponding to the five sets, and the prediction accuracy of the RBF proxy model is tested by cross-validation. When the accuracy does not meet the requirements, the sample size is increased and the RBF model is rebuilt to verify the accuracy until the prediction accuracy of the RBF proxy model meets the requirements. The cross-validation method is as follows: 90 sample points in the sample are used to build the model, and the remaining 10 sample points are used to verify the accuracy of the model; Select RMSE, R 2 As an evaluation index for the prediction accuracy of the validation model, if R 2 >0.9 and RMSE <0.2, the accuracy meets the requirements.

7. The spoke structure optimization method for improving the lateral stiffness of a honeycomb non-pneumatic tire according to claim 1, characterized in that: The S4.3 is specifically: Multi-objective optimization is performed using the non-dominated sorting genetic algorithm NSGA-II. The optimization objective is the minimum lateral force RF3, with a weight of 0.6; the tire radial displacement U2 is constrained, with a weight of 0.

4. Through multi-objective optimization, a Pareto optimal solution set is obtained.

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