How to calculate the physical quantities of tires

Abstract

To provide a method capable of calculating, in a short time, physical quantities including stress on or distortion of a portion of a tire whose durability is to be predicted.SOLUTION: A method of predicting tire durability includes: a step S1 of inputting first time-series data on tire rolling conditions; a step S2 of inputting a representative rolling condition specified from the first time-series data; a step S3 of inputting a tire model; and a step S4 of inputting a first element specified from the tire model. Further, a computer performs: a step S5 of calculating the temperature distribution of the tire model under the representative rolling condition; a step S6 of calculating physical quantities including stress or distortion that act on the first element; and a step S7 of acquiring a first function indicating the relationship between rolling conditions and physical quantities.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a tire. How to calculate physical quantities Regarding. [Background technology]

[0002] The following Patent Document 1 describes a method for predicting tire durability performance. In this method, first, data on the running speed measured when a vehicle runs on a predetermined running course and data on the condition of the vehicle's tires are obtained. Then, based on these data, the degree of damage to the tires on the running course is calculated, and based on this damage, the predicted running distance of the tires is calculated. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2008-256442 A Summary of the Invention [Problem to be solved by the invention]

[0004] For such predictions, it is important to identify the part of the tire whose durability performance is to be predicted and obtain physical quantities such as stress and strain acting on that part. These physical quantities are calculated artificially, for example, by simulating a tire model, in which the tire is discretized into a finite number of elements, rolling on a road model.

[0005] In such a simulation, not only the above-mentioned parts but also the physical quantities acting on all the elements constituting the tire model are calculated at predetermined intervals of very short time, which causes a problem that the calculation of the physical quantities in those parts takes a long time.

[0006] The present invention has been devised in view of the above circumstances, and has as its main object to provide a method capable of calculating, in a short period of time, physical quantities including stress or strain of a portion of a tire whose durability performance is to be predicted. [Means for solving the problem]

[0007] The present invention is a method for predicting durability performance of a tire, comprising the steps of: inputting first time series data of the rolling conditions of a tire rolling on a road surface into a computer; inputting representative rolling conditions identified from the first time series data based on a predetermined criterion into the computer; inputting a tire model in which the tire is discretized into a finite number of elements into the computer; and inputting a first element, identified from the tire model, for which the durability performance is to be predicted, into the computer, wherein the computer executes the steps of: calculating a temperature distribution of the tire model rolling under the representative rolling conditions; calculating a physical quantity, including a stress or strain acting on the first element, based on the tire model having the temperature distribution and the rolling condition at least at one time point identified from the first time series data; and acquiring a first function indicating a relationship between the rolling condition and the physical quantity, using the rolling condition at the at least one time point and the physical quantity.

[0008] In the tire durability prediction method according to the present invention, the rolling conditions may include a force Fx in an x-axis direction, a force Fy in a y-axis direction, a force Fz in a z-axis direction, a camber angle CA, an internal pressure P, and a running speed V acting on the tire, and the first function may be defined by the following formula (1): Physical quantity=a×Fz+b×Fy+c×Fx+d×CA+e×P+f×V+g (1) Where: Fz: Force in the z-axis direction Fy: Force in the y-axis direction Fx: Force in the x-axis direction CA: Camber angle P: Internal pressure V: Traveling speed a~g: coefficients

[0009] In the tire durability performance prediction method according to the present invention, the method may further include a step in which the computer substitutes the first time series data into the first function to obtain second time series data which is time series data of the physical quantity.

[0010] In the tire durability performance prediction method according to the present invention, the computer may further include a step of acquiring third time series data, which is time series data of a fatigue degree of the first element, based on the first time series data and the second time series data.

[0011] In the tire durability performance prediction method according to the present invention, the degree of fatigue may be a ratio x / Y of a fatigue life Y of the first element identified from the physical quantity to a number x of times the physical quantity is loaded onto the first element.

[0012] In the tire durability performance prediction method according to the present invention, the first time series data to the third time series data may be acquired in a predetermined rolling section, and the computer may further include a step of calculating, based on a total value of the third time series data, the number of times the first element can roll in the rolling section before it is destroyed.

[0013] In the tire durability performance prediction method of the present invention, the step of inputting the first time series data may include a step of inputting a vehicle model, which models a vehicle on which the tire is mounted, into the computer, a step of inputting a road surface model, which models a road surface on which the vehicle runs, into the computer, and a step of the computer running the vehicle model on the road surface model to obtain the first time series data.

[0014] In the tire durability performance prediction method of the present invention, the tire model may include a belt ply model that models a belt ply arranged inside a tread portion of the tire, and the first element may be identified as an element that constitutes an axial outer end portion of the belt ply model. Effect of the Invention

[0015] By employing the above steps, the tire durability prediction method of the present invention makes it possible to calculate physical quantities including stress or strain of a portion of a tire whose durability performance is to be predicted in a short period of time. [Brief description of the drawings]

[0016] [Figure 1] FIG. 1 is a perspective view showing an example of a computer for executing a tire durability performance prediction method. [Diagram 2] 1 is a flowchart showing an example of a processing procedure of a tire durability performance prediction method. [Diagram 3] 13 is a flowchart showing an example of a processing procedure of a first time-series data input step. [Figure 4] 3 is a conceptual diagram showing an example of a vehicle model and a road surface model. FIG. [Diagram 5] 3A to 3C are diagrams illustrating an example of first time series data, second time series data, and third time series data. [Figure 6] FIG. 2 is a perspective view showing an example of a tire model and a road surface model. [Figure 7] FIG. 2 is a cross-sectional view showing an example of a tire model. [Figure 8] 1 is a graph showing an example of an SN curve. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0017] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. Note that the drawings are intended to enhance understanding of the contents of the invention, and therefore some of the drawings may be exaggerated, and the scales of the drawings may not be strictly consistent.

[0018] [Method of predicting tire durability] A computer is used in the tire durability performance prediction method of the present embodiment (hereinafter, may be simply referred to as the "prediction method.") Fig. 1 is a perspective view showing an example of a computer 1 for executing the tire durability performance prediction method.

[0019] [computer] The computer 1 of this embodiment includes, for example, a main body 1a, a keyboard 1b, a mouse 1c, and a display device 1d. The main body 1a includes, for example, a central processing unit (CPU), a ROM, a working memory, a storage device such as a magnetic disk, and disk drive devices 1a1 and 1a2. The storage device stores software for executing the prediction method of this embodiment in advance. Thus, the computer 1 is configured as a tire durability performance predictor.

[0020] [tire] In the prediction method of this embodiment, the tire whose durability performance is predicted may or may not actually exist. The tire may be a tire of another category, such as a racing tire, a passenger tire, a heavy-duty tire, or an airless tire. The tire of this embodiment has a known structure, but may also have a new structure.

[0021] [First time series data input process] 2 is a flowchart showing an example of a processing procedure of a tire durability performance prediction method. In the prediction method of this embodiment, first, first time series data of the rolling conditions of a tire rolling on a road surface is input to a computer 1 (first time series data input step S1). The first time series data of this embodiment includes a plurality of rolling conditions acquired at predetermined intervals (time intervals) for the tire rolling on the road surface.

[0022] The rolling conditions are for specifying the state of the tire rolling on the road surface. The rolling conditions can be set as appropriate. The rolling conditions in this embodiment include the force Fx in the x-axis direction acting on the tire, the force Fy in the y-axis direction, the force Fz in the z-axis direction, the camber angle CA, the internal pressure P, and the running speed V. Note that only a part of these rolling conditions may be acquired, or other rolling conditions may be acquired as well. The interval at which the rolling conditions are acquired can be set as appropriate based on the prediction accuracy of the required durability performance, etc. The interval in this embodiment is set to 0.01 to 0.1 seconds.

[0023] In this embodiment, the forces Fx, Fy, and Fz (shown in FIG. 4) acting on the tire are specified based on an x-axis parallel to the tire axial direction, a y-axis parallel to the tire traveling direction, and a z-axis parallel to the tire radial direction. Note that the x-axis, y-axis, and z-axis are not limited to these directions.

[0024] The first time series data of this embodiment is acquired in a predetermined rolling section. The rolling section can be appropriately set as, for example, one lap of a circuit course or a forward route on a general road (including an expressway) between two predetermined points. The rolling section of this embodiment is set as one lap of a circuit course.

[0025] In the first time-series data input step S1, the first time-series data may be acquired by running a vehicle equipped with the tire in a rolling section and measuring the rolling conditions in the rolling section, as in Patent Document 1. Note that the rolling conditions may be measured using, for example, a sensor or the like described in Patent Document 1.

[0026] In the first time series data input step S1 of the present embodiment, unlike Patent Document 1, the first time series data is calculated by a running simulation of a vehicle model using a computer 1. Fig. 3 is a flowchart showing an example of a processing procedure of the first time series data input step S1.

[0027] [Vehicle model input process] In the first time-series data input step S1 of this embodiment, first, a vehicle model that models a vehicle on which the tire is mounted is input to the computer 1 (step S11). Fig. 4 is a conceptual diagram showing an example of the vehicle model 2 and the road surface model 3.

[0028] The vehicle model 2 can be set appropriately based on a conventional procedure such as that described in a patent document (JP Patent Publication No. 2003-330997). The vehicle model 2 of this embodiment includes a tire model 4 that models a tire, a wheel rim model 5 that models a wheel rim, and a suspension model 6 that models a suspension. These models are modeled, for example, with a finite number of elements (not shown). Such a vehicle model 2 can simulate the forces (rolling conditions) acting on an actual tire by running on a road surface model 3.

[0029] The tire model 4 set in the vehicle model 2 is not intended to calculate physical quantities acting on the inside of the tire, and therefore, compared to a tire model 11 (shown in FIG. 7) described below, it is desirable to omit some of its structure and elements F(i). This simplifies the running calculations of the vehicle model 2 (tire model 4) in the first time-series data input step S1, making it possible to determine the rolling conditions in a short time. The vehicle model 2 is stored in the computer 1.

[0030] [Road surface model input process] Next, in the first time-series data input step S1 of this embodiment, a road surface model 3 that models the road surface on which the vehicle runs is input to the computer 1 (step S12). The road surface model 3 can be set appropriately based on a conventional procedure such as that described in a patent document (JP Patent Publication No. 2003-330997). The road surface model 3 of this embodiment is set continuously from the start point 3a to the end point 3b of the rolling section (in this example, one lap of the circuit course). This allows the vehicle model 2 to run between the start point 3a and the end point 3b of the road surface model 3. The road surface model 3 is stored in the computer 1.

[0031] [First time series data acquisition process] Next, in the first time-series data input step S1 of this embodiment, the computer 1 makes the vehicle model 2 run on the road surface model 3 to acquire the first time-series data (step S13). For the procedure of making the vehicle model 2 run on the road surface model 3, a conventional procedure such as that described in a patent document (JP Patent Publication No. 2003-330997) may be adopted. Such a running simulation can be easily performed by using, for example, commercially available finite element analysis application software such as LS-DYNA manufactured by LSTC.

[0032] In step S13, the running of the vehicle model 2 is calculated between the start point 3a and the end point 3b of the road surface model 3 (rolling section). Then, between the start point 3a and the end point 3b, the rolling conditions of the tire model 4 (in this example, the forces Fx, Fy, Fz, the camber angle CA (not shown), the internal pressure P (not shown), and the running speed V) are calculated for each of the above intervals. These rolling conditions are calculated, for example, appropriately for the vehicle model 2 (including the tire model 4) using elements (not shown) of the area in which the sensors and the like described in Patent Document 1 are provided.

[0033] In this manner, in this embodiment, the first time series data can be acquired without actually running the vehicle on the road surface by running the vehicle model 2 on the road surface model 3. As a result, in this embodiment, since it is not necessary to produce a prototype of a tire to be evaluated, it is possible to acquire the first time series data in a short time and at low cost.

[0034] Fig. 5 is a diagram showing an example of the first time series data D1, the second time series data D2, and the third time series data D3, in which specific numerical values ​​are omitted.

[0035] 5, in the first time series data D1, the rolling conditions B (in this example, the force Fx in the x-axis direction, the force Fy in the y-axis direction, the force Fz in the z-axis direction, the camber angle CA, the internal pressure P, and the running speed V) are acquired for each of a plurality of times. The first time series data D1 is stored in the computer 1.

[0036] [Representative rolling condition input process] Next, in the prediction method of this embodiment, a representative rolling condition E identified based on a predetermined criterion from the first time series data D1 is input to the computer 1 (step S2). The representative rolling condition E can be appropriately identified based on a plurality of rolling conditions B included in the first time series data D1. In this embodiment, the average value of the rolling conditions B at all times included in the first time series data D1 is identified as the representative rolling condition E.

[0037] The representative rolling condition E in this embodiment includes an average value of the force Fx in the x-axis direction, an average value of the force Fy in the y-axis direction, an average value of the force Fz in the z-axis direction, an average value of the camber angle CA, an average value of the internal pressure P, and an average value of the running speed V. Note that the present invention is not limited to this embodiment, and for example, the median value of the rolling condition B may be specified as the representative rolling condition E. The representative rolling condition E is stored in the computer 1.

[0038] [Tire model input process] Next, in the prediction method of this embodiment, a tire model in which the tire is discretized into a finite number of elements is input to the computer 1 (step S3). Fig. 6 is a perspective view showing an example of the tire model 11 and the road surface model 12. Fig. 7 is a cross-sectional view showing an example of the tire model 11. Note that Fig. 6 shows the tire model 11 in a simplified manner, and the tread pattern of the tread portion 11a and the element F(i) shown in Fig. 7 are omitted. In Fig. 7, the belt ply models 16A and 16B are colored.

[0039] The tire model 11 is a model of a tire (not shown) to be analyzed. As shown in Fig. 7, the tire model 11 can be defined, for example, by modeling (discretizing) the tire (not shown) to be analyzed with a finite number of elements F(i) (i = 1, 2, ...) that can be handled by a numerical analysis method.

[0040] As the numerical analysis method, for example, the finite element method, the finite volume method, the difference method, or the boundary element method (in this embodiment, the finite element method) can be appropriately adopted. For example, a three-dimensional tetrahedral solid element, a pentahedral solid element, or a hexahedral solid element is used as the element F(i). Each element F(i) is configured to include a plurality of nodes 13. For each element F(i), numerical data such as an element number, a node 13 number, coordinate values ​​of the node 13, and material properties (for example, density, Young's modulus, damping coefficient, thermal conductivity, and heat transfer coefficient) are defined.

[0041] In step S3 of this embodiment, for example, a rubber portion including tread rubber of a tire to be analyzed (not shown), a carcass ply, and a belt ply disposed inside the tread portion are each discretized (modeled) by elements F(i). As a result, a rubber member model (e.g., a tread rubber model, etc.) 14, a carcass ply model 15, and belt ply models 16A and 16B are set in the tire model 11.

[0042] The road surface model 3 shown in Fig. 4 may be used for the road surface model 12. In the road surface model 12 of this embodiment, the road surface is discretized using a finite number of elements G(i) (i = 1, 2, ...) that can be handled by a numerical analysis method (in this embodiment, the finite element method) based on information about the road surface.

[0043] The element G(i) is defined as a rigid plane element that is defined to be undeformable. The element G(i) has a plurality of nodes 18. Furthermore, the element G(i) is defined with numerical data such as an element number and the coordinate values ​​of the nodes 18. The road surface model 12 is input to the computer 1.

[0044] [First element input process] Next, in the prediction method of this embodiment, a first element 21 for which durability performance is to be predicted, which is identified from the tire model 11, is input to the computer 1 (step S4). As shown in Fig. 7, at least one first element 21 is identified from elements F(i) constituting the tire model 11. As the first element 21, it is desirable to identify, for example, an element F(i) that is arranged in a portion of the tire that is prone to damage during rolling (i.e., a portion for which durability performance is to be predicted).

[0045] The first element 21 in this embodiment is identified as an element F(i) constituting the axially outer end portion of the belt ply models 16A, 16B. Identifying such a first element 21 makes it possible to predict the BEL durability performance (i.e., damage resistance of the outer end portion of the belt ply). In this embodiment, one first element 21 is identified, but multiple first elements 21 may be identified. The first element 21 is stored in the computer 1.

[0046] [Temperature distribution calculation process] Next, in the prediction method of this embodiment, the computer 1 calculates the temperature distribution of the tire model 11 rolling under the representative rolling condition E (shown in FIG. 5) (step S5). The temperature distribution of the tire model 11 can be calculated appropriately based on the procedure described in, for example, a patent document (JP Patent Publication No. 2020-131758 A) or the like.

[0047] In step S5 of this embodiment, first, as shown in Fig. 7, the bead portions 11c, 11c of the tire model 11 are constrained by a rim model 17 that models a tire rim (not shown). Then, the deformation of the tire model 11 is calculated based on a uniformly distributed load w that corresponds to the average value of the internal pressure P included in the representative rolling conditions E shown in Fig. 5.

[0048] For deformation calculations of the tire model 11 (including temperature distribution calculations described later), a mass matrix, a stiffness matrix, and a damping matrix are created for each element F(i) based on the shape and material properties of each element F(i). These matrices are then combined to create a matrix for the entire system. Then, equations of motion are created by applying the various conditions described above, and these are calculated for each infinitesimal time (unit time) T(x) (x=0, 1, ...). In this way, deformation calculations of the tire model 11 are performed. For example, the above-mentioned finite element analysis application software is used for such deformation calculations. The infinitesimal time T(x) is appropriately set (for example, 1 μsec) based on the required simulation accuracy.

[0049] Next, in step S5 of this embodiment, the tire model 11 after internal pressure filling is brought into contact with the road surface model 12, as shown in Fig. 6. Next, in step S5 of this embodiment, the deformation of the tire model 11 is calculated based on the average value of the force Fx in the x-axis direction, the average value of the force Fy in the y-axis direction, the average value of the force Fz in the z-axis direction, and the average value of the camber angle CA, which are included in the representative rolling condition E shown in Fig. 5. In this embodiment, the average value of the force Fx, the average value of the force Fy, and the average value of the force Fz are set to the rotation axis 19 of the tire model 11.

[0050] In step S5 of this embodiment, the energy loss is calculated based on the strain history of each element F(i) for one rotation obtained by a static ground contact simulation of the tire model 11. Such a calculation can be performed based on the procedure described in, for example, a patent document (JP 2005-186900 A).

[0051] In step S5 of this embodiment, the calorific value of each element F(i) is calculated assuming that a predetermined ratio of the calculated energy loss of each element F(i) is converted into heat energy. The ratio of the converted energy loss is appropriately set based on, for example, the average value of the running speed V included in the representative rolling condition E shown in FIG. 5. The calorific value can be easily calculated by using the above-mentioned finite element analysis application.

[0052] Next, in step S5 of this embodiment, the amount of heat dissipation of each element F(i) is calculated based on the thermal conductivity of the element F(i), the heat transfer coefficients set on the outer surface and inner cavity surface of the tire model 11, and the temperatures of the outside air and the road surface. These heat transfer coefficients are appropriately set based on, for example, the average value of the running speed V included in the representative rolling conditions.

[0053] Next, in step S5 of this embodiment, the heat balance between the heat generation amount of each element F(i) and the heat dissipation amount of each element F(i) is calculated. As a result, in step S5, the temperature distribution (saturation temperature distribution) of the tire model 11 rolling under the representative rolling conditions can be calculated. The temperature distribution is stored in the computer 1.

[0054] [Physical quantity calculation process for the first element] Next, in the prediction method of this embodiment, the computer 1 calculates physical quantities (hereinafter, sometimes simply referred to as "physical quantities") including the stress σ or strain ε acting on the first element 21 (shown in FIG. 7) (step S6). The physical quantities of this embodiment are calculated based on the tire model 11 having a temperature distribution and the rolling condition B at least at one time point specified from the first time-series data D1 shown in FIG.

[0055] In this embodiment, the stress σ is calculated as the physical quantity, but the strain ε may be calculated, or both the stress σ and the strain ε may be calculated. Since the stress σ and the strain ε are in a proportional relationship in the elastic region with the Young's modulus as a proportionality constant, one of the stress σ and the strain ε can be converted into the other of the stress σ and the strain ε by adjusting the other based on the Young's modulus. The rolling condition B used in the calculation of the physical quantity may be selected randomly from the first time-series data D1 shown in FIG. 5, or may be selected based on a predetermined condition (for example, a combination of three levels of the forces Fx, Fy, and Fz acting on the tire, the camber angle CA, the internal pressure P, and the running speed V).

[0056] In step S6 of this embodiment, the deformation of the tire model 11 (shown in FIG. 6) having a temperature distribution is calculated based on the rolling condition B at at least one time point identified from the first time-series data D1 shown in FIG. 5. The deformation calculation in this embodiment is performed as a static ground contact simulation of the tire model 11 having a temperature distribution. As a result, in step S6, the physical quantity acting on the first element 21 for the tire model 11 rolling under the rolling condition B at one time point can be calculated in a pseudo manner. For the deformation calculation, for example, the above-mentioned finite element analysis application software is used.

[0057] In step S6 of this embodiment, in order to accurately obtain a first function described later, it is desirable to calculate the physical quantities acting on the first element 21 based on the rolling conditions B at a plurality of (for example, 18 to 30) times. In this embodiment, since the temperature distribution of the tire model 11 rolling under the representative rolling conditions E is used, it is not necessary to obtain the temperature distribution for each rolling condition B. Therefore, the physical quantities acting on the first element 21 can be calculated in a short time. These physical quantities are stored in the computer 1.

[0058] [First function acquisition process] Next, in the prediction method of this embodiment, the computer 1 acquires a first function indicating the relationship between the rolling condition and the physical quantity acting on the first element 21 (step S7). In this embodiment, the first function is acquired using the rolling condition B at least at one time and the physical quantity of the first element 21 under the rolling condition B.

[0059] The first function can be set appropriately as long as it indicates the relationship between the rolling condition B and the physical quantity. The first function in this embodiment is defined by the following formula (1). Physical quantity = a×Fz+b×Fy+c×Fx+d×CA+e×P+f×V+g …(1) Where: Fz: Force in the z-axis direction Fy: Force in the y-axis direction Fx: Force in the x-axis direction CA: Camber angle P: Internal pressure V: Traveling speed a~g: coefficients

[0060] In the first function, coefficients (fitting parameters) a to g must be specified. In step S7 of this embodiment, the coefficients a to g are fitted using rolling conditions B (in this example, the force Fx in the x-axis direction, the force Fy in the y-axis direction, the force Fz in the z-axis direction, the camber angle CA, the internal pressure P, and the running speed V) at least at one time and physical quantities. In this embodiment, the rolling conditions at multiple times and the physical quantities under those rolling conditions are used, so that the coefficients a to g can be fitted with high accuracy.

[0061] The first function of this embodiment can calculate the physical quantities of the first element 21 acting during rolling under any rolling condition B (x-axis force Fx, y-axis force Fy, z-axis force Fz, camber angle CA, internal pressure P, and running speed V) by substituting the first function. Therefore, the prediction method of this embodiment makes it possible to calculate the physical quantities of the portion (first element 21) for which the durability performance of the tire is to be predicted in a short time, without performing a simulation (including a rolling simulation) using the tire model 11.

[0062] In the present embodiment, when the physical quantity is stress σ, the first function is defined by the following formula (2). σ=a1×Fz+b1×Fy+c1×Fx+d1×CA+e1×P+f1×V+g1…(2) Here, the variables and the parameters are the same as in formula (1) above, except as follows: σ: Stress a1~g1: Coefficients

[0063] On the other hand, when the physical quantity is strain ε, the first function is defined by the following formula (3). ε=a2×Fz+b2×Fy+c2×Fx+d2×CA+e2×P+f2×V+g2…(3) Here, the variables and the parameters are the same as in formula (1) above, except as follows: ε: Distortion a2~g2: Coefficients

[0064] When both the stress σ and the strain ε are calculated as the first physical quantity, it is preferable to obtain the first functions based on the above formulas (2) and (3), respectively. The first functions are stored in the computer 1.

[0065] [Second time series data acquisition process] Next, in the prediction method of this embodiment, the computer 1 substitutes the first time series data D1 (shown in FIG. 5) into a first function to obtain second time series data D2, which is time series data of the physical quantity of the first element 21 (step S8). In this embodiment, the rolling conditions B (in this example, the force Fx in the x-axis direction, the force Fy in the y-axis direction, the force Fz in the z-axis direction, the camber angle CA, the internal pressure P, and the running speed V) at each time of the first time series data D1 are substituted into the first function. As a result, in step S8, the physical quantities of the first element 21 are obtained at each time, as shown in FIG. 5.

[0066] In this manner, in the prediction method of this embodiment, the second time series data D2, which is a series (time series data) of the physical quantity of the first element 21, can be acquired from the first time series data D1 based on the first function. As a result, in the prediction method of this embodiment, the physical quantity of the first element 21 at each time acting during the rolling of the road surface model 3 from the starting end 3a to the ending end 3b shown in FIG. 3 can be calculated in a short time without performing a rolling simulation of the tire model 11. The second time series data D2 is stored in the computer 1.

[0067] [Third time series data acquisition process] Next, in the prediction method of the present embodiment, the computer 1 acquires third time series data based on the first time series data D1 and the second time series data D2 (step S9). As shown in Fig. 5, the third time series data D3 is time series data of the fatigue degree of the first element 21.

[0068] The degree of fatigue indicates the rate of fatigue until the portion corresponding to the first element 21 shown in Fig. 7 (in this example, the outer end portion of the belt ply) breaks. The degree of fatigue in this embodiment is indicated by a ratio x / Y of the fatigue life (number of loads until breakage) Y of the first element 21 identified from the physical quantity to the number x of times the physical quantity is loaded on the first element 21 at one time in the second time series data D2 shown in Fig. 5. When the degree of fatigue x / Y is "1", the first element 21 (the portion corresponding to the first element 21) is treated as having broken.

[0069] The fatigue life Y can be determined by an SN curve that shows the relationship between the stress σ applied to the first element 21 and the number of repetitions N that require a load of a physical quantity until the first element 21 breaks. When the physical quantity is a strain ε, the fatigue life can be determined by an ε-N curve that shows the relationship between the strain ε and the number of repetitions N. Fig. 8 is a graph showing an example of an SN curve.

[0070] The fatigue life Y is the number of repetitions N that the first element 21 (shown in FIG. 7) can bear before it breaks, with respect to a physical quantity (stress σ in this example) that is applied to the first element 21 (shown in FIG. 7). That is, this means that when stress σ is repeatedly applied N times to a portion corresponding to the first element 21 (the outer end portion of the belt ply in this example), breakage occurs in that portion.

[0071] The SN curve (ε-N curve) can be obtained as appropriate, for example, based on a conventional method, using a test piece (not shown) having the same composition as the portion corresponding to the first element 21. It is preferable that the temperature of the first element 21 in the temperature distribution of the tire model 11 described above is set in the test piece.

[0072] In step S9 of this embodiment, first, the fatigue life Y (number of repetitions N) corresponding to the stress σ applied to the first element 21 is obtained for each time based on the second time-series data D2 shown in Fig. 5 and the S-N curve shown in Fig. 8. Since the stress σ differs depending on the rolling condition B, the fatigue life Y also differs for each time.

[0073] Next, in step S9 of the present embodiment, the number x of times that the stress σ is applied to the first element 21 is calculated based on the first time-series data D1 (shown in FIG. 5) and the tire model 11 having a temperature distribution (shown in FIG. 7). Such number x is calculated by respectively calculating the deformation of the tire model 11 having a temperature distribution based on the rolling condition B at each time of the first time-series data D1. The deformation calculation of the present embodiment is performed as a static ground contact simulation of the tire model 11. As a result, in step S9, the number x of times that the stress σ is applied to the first element 21 at each time is calculated. For such deformation calculation, for example, the above-mentioned finite element analysis application software is used.

[0074] A second function having at least one coefficient (constant) and using the fatigue life Y and temperature T as variables may be used to calculate the number of times x. Such a second function is based on the fact that the inventors found that there is a correlation between the fatigue life Y and the number of times x when the temperature T is constant.

[0075] In this embodiment, the coefficient (constant) of the second function can be easily specified by fitting the fatigue life Y, the number of times x, and the temperature T at least at one time (in this example, multiple times) obtained by the static ground contact simulation. Such a second function makes it possible to calculate the number of times x in a short time at all times without performing the ground contact simulation.

[0076] In step S9 of this embodiment, the fatigue life Y of the first element 21 and the number of times x that a physical quantity (stress σ in this example) is loaded on the first element 21 are calculated at each time from the start to the end of the rolling section. Thereby, in step S9 of this embodiment, third time series data D3 that is time series data of the fatigue degree x / Y of the first element 21 is acquired. The third time series data D3 is stored in the computer 1.

[0077] [Rolling limit calculation process] Next, in the prediction method of this embodiment, the computer 1 calculates the number of times the first element 21 (shown in FIG. 7) can roll in the rolling section before it breaks, based on the total value of the third time series data D3 (shown in FIG. 5) (step S10). In this embodiment, first, the total value Σ(x / Y) of the fatigue degree x / Y of the first element 21, which changes from moment to moment depending on the rolling conditions, is calculated using the third time series data D3. Such a total value Σ(x / Y) is found as the total value of the fatigue degree of the first element 21 after the tire model 11 rolls in the rolling section (from the start end 3a to the end end 3b of the road surface model 3 shown in FIG. 3).

[0078] When the sum Σ(x / Y) is "1", it can be determined that the first element 21 has been destroyed. Therefore, by finding the reciprocal of the sum Σ(x / Y), the number of times the tire can roll in the rolling section can be calculated. By finding such a number, it becomes possible to easily predict the durability performance of the tire. The number of times the tire can roll is stored in the computer 1.

[0079] [Evaluation process] Next, in the prediction method of this embodiment, the computer 1 judges whether the durability performance of the tire is good or not (step S11). The evaluation of the durability performance of the tire can be calculated as appropriate. The evaluation of the durability performance can be performed as appropriate. In this embodiment, the durability performance can be judged to be good when the possible number of rotations satisfies a predetermined target value. The target value can be set as appropriate according to the durability performance required of the tire, the rolling section, etc.

[0080] If the tire is determined to have good durability performance in step S11 ("Y" in step S11), the tire is designed and manufactured based on the design factors (information about the tire) used to create the tire model 11 (step S12).

[0081] On the other hand, if the durability performance is determined to be unsatisfactory ("N" in step S11), at least one of the tire design factors is changed (step S13), and the first time-series data input steps S1 to S11 are performed again. In this embodiment, the fatigue life Y is determined based on the SN curve (or ε-N curve) of the portion corresponding to the first element 21, so that the influence of material strength on the predicted durability performance, etc. can be easily understood. Therefore, the prediction method of this embodiment makes it possible to make design changes in a short period of time.

[0082] In the prediction method of the present embodiment, the design factors of the tire are changed until the predicted durability performance is determined to be good, so that a tire with good durability performance can be reliably designed and manufactured.

[0083] Although a particularly preferred embodiment of the present invention has been described in detail above, the present invention is not limited to the illustrated embodiment and can be modified in various ways. EXAMPLES

[0084] Based on the processing procedure shown in FIG. 2, durability performance was predicted for four types of tires A to D that differ from each other in at least one of the tire shape (a, b) and the material strength (X, Y) of the constituent members (Examples 1 to 4).

[0085] In Examples 1 to 3, first, a vehicle model was run on a road surface model that modeled the same rolling section (circuit course α) to obtain first time series data. Next, in Examples 1 to 3, the temperature distributions of tire models that modeled tires A to C were calculated based on the representative rolling conditions identified from the first time series data.

[0086] Next, in Examples 1 to 3, physical quantities acting on the first element were calculated based on a tire model having a temperature distribution and a plurality of rolling conditions identified from the first time-series data, and a first function showing the relationship between the rolling conditions and the physical quantities was obtained. Next, in Examples 1 to 3, the first time-series data was substituted into the first function to obtain second time-series data, and further, third time-series data of the fatigue degree of the first element was obtained. Then, in Examples 1 to 3, the number of times (number of laps) that the first element can roll on the rolling section (circuit course α) before it breaks was calculated based on the total value of the third time-series data.

[0087] Next, in Examples 1 to 3, vehicles fitted with tires A to C were actually driven on a rolling section (circuit course α), and the number of laps around circuit course α that the tire component corresponding to the first element rolled until it broke was obtained.

[0088] Meanwhile, in Example 4, a vehicle model was run on a road surface model that modeled a rolling section (circuit course β) different from Examples 1 to 3, and first time-series data was acquired. Next, in Example 4, the temperature distribution of a tire model that modeled tire D was calculated based on the representative rolling conditions identified from the first time-series data. Then, in Example 4, similarly to Examples 1 to 3, the number of times (number of laps) that the first element could roll on the rolling section (circuit course β) before it was destroyed was calculated.

[0089] Next, in Example 4, a vehicle equipped with tire A was actually driven on a rolling section (circuit course β), and the number of laps on circuit course β that the tire component corresponding to the first element rolled until it was broken was obtained. The common specifications are as follows, and the test results are shown in Table 1.

[0090] Tire size: 330 / 710R18 (SLIC tire) 1st element: Element corresponding to the outer end of the belt ply Outside temperature: 15℃ Road surface temperature: 30℃

[0091] [Table 1]

[0092] As a result of the test, in Examples 1 and 2, the predicted number of laps could be approximated to the actual number of laps. On the other hand, in Examples 3 and 4, the predicted number of laps was "75 laps" or "90 laps", while the actual number of laps was "no damage". This indicates that the number of laps actually used was about 40 laps, and no damage occurred in the actual vehicle test. In addition, in the SN curve shown in FIG. 8, there is a region where the life is semi-permanently extended when the stress value is below a certain value, and it is considered that Examples 3 and 4 are in that region and did not suffer damage in the actual vehicle test. Therefore, in Examples 3 and 4, the predicted number of laps can be considered to be approximate to the actual number of laps. In this way, Examples 1 to 4 were able to predict the durability performance of the tire with high accuracy.

[0093] In Examples 1 to 4, predictions could be made in 1% to 5% of the time required by the conventional method of rolling the tire model shown in Fig. 7. Therefore, in Examples 1 to 4, the physical quantity of the first element could be calculated in a short time. [Explanation of symbols]

[0094] S1: Step of inputting the first time series data S2: Process for inputting representative rolling conditions S3 Tire model input process S4: Step to input the first element S5 Calculate the temperature distribution in the tire model S6: Calculate the physical quantity of the first element S7: Step to obtain the first function

Claims

1. A method for calculating a physical quantity of a portion of a tire for predicting durability performance, comprising the steps of: inputting first time series data of rolling conditions of a tire rolling on a road surface into a computer; inputting representative rolling conditions identified from the first time-series data based on a predetermined criterion into the computer; inputting a tire model obtained by discretizing the tire into a finite number of elements into the computer; inputting a first element for predicting the durability performance, the first element being identified from the tire model, into the computer; The computer, calculating a temperature distribution of the tire model rolling under the representative rolling conditions; calculating a physical quantity including a stress or a strain acting on the first element based on the tire model having the temperature distribution and a rolling condition at least at one time point identified from the first time-series data; obtaining a first function indicating a relationship between the rolling condition and the physical quantity by using the rolling condition at the at least one time point and the physical quantity; and substituting an arbitrary rolling condition into the first function to calculate a physical quantity of the first element acting during rolling under the arbitrary rolling condition. How to calculate the physical quantities of a tire.

2. The rolling conditions include a force Fx in an x-axis direction, a force Fy in a y-axis direction, a force Fz in a z-axis direction, a camber angle CA, an internal pressure P, and a running speed V acting on the tire, The method for calculating a physical quantity of a tire according to claim 1 , wherein the first function is defined by the following formula (1): Physical quantity = a×Fz+b×Fy+c×Fx+d×CA+e×P+f×V+g…(1) Where: Fz: Force in the z-axis direction Fy: Force in the y-axis direction Fx: Force in the x-axis direction CA: Camber angle P: Internal pressure V: Traveling speed a to g: coefficients

3. 3. The method for calculating a physical quantity of a tire according to claim 1, further comprising the step of: substituting the first time series data into the first function, by the computer, to obtain second time series data which is time series data of the physical quantity.

4. 4. The method for calculating a physical quantity of a tire according to claim 3, further comprising: acquiring, by the computer, third time series data which is time series data of a fatigue degree of the first element, based on the first time series data and the second time series data.

5. 5. The method for calculating a physical quantity of a tire according to claim 4, wherein the degree of fatigue is a ratio x / Y of a fatigue life Y of the first element specified from the physical quantity to a number x of times the physical quantity is applied to the first element.

6. The first time series data to the third time series data are acquired in a predetermined rolling section, 6. The method for calculating a physical quantity of a tire according to claim 4, further comprising the step of calculating, by the computer, the number of times that the first element can roll through the rolling section before being destroyed, based on a total value of the third time-series data.

7. The step of inputting the first time-series data includes a step of inputting a vehicle model, which is a model of a vehicle on which the tire is mounted, into the computer; inputting a road surface model, which is a model of a road surface on which the vehicle runs, into the computer; The method for calculating a physical quantity of a tire according to claim 1 , further comprising: acquiring the first time-series data by causing the vehicle model to run on the road surface model.

8. The tire model includes a belt ply model that models a belt ply disposed inside a tread portion of the tire, The method for calculating a physical quantity of a tire according to claim 1 , wherein the first element is identified as an element that constitutes an outer end portion of the belt ply model in the tire axial direction.

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