JP6634840B2 - Tire simulation method - Google Patents

Description

  The present invention relates to a simulation method for evaluating traction performance of a tire.

  In recent years, a simulation method for evaluating the traction performance of a tire using a computer has been proposed. In this simulation method, a computer performs a simulation of rolling a tire model on a road surface model.

  In the above simulation method, for example, traction performance is compared for each tire model having a different tread pattern and tire structure. In the rolling simulation, for example, similarly to an actual vehicle, tire models rolling on a road surface model are calculated based on a predetermined rotational force. Then, physical quantities relating to traction performance are acquired from the rolling tire models.

Japanese Patent No. 4659070 Japanese Patent No. 4394866 Japanese Patent No. 4777040

  Each tire model has a different slip ratio depending on the tread pattern and the tire structure. For this reason, each tire model has a different running speed when the same rotational force is defined. That is, the running speed becomes faster as the tire model has a lower slip ratio. Therefore, in the simulation method as described above, the physical quantity cannot be obtained under the same traveling condition, and there is a problem that it is difficult to accurately evaluate the traction performance.

  Further, in Patent Literatures 1 to 3, predetermined translation speeds and rotation speeds are defined for each tire model. However, in Patent Documents 1 to 3, since the translation speed and the rotation speed are defined without considering the slip ratio of each tire model, there is a problem that a physical quantity cannot be obtained under the same traveling condition.

  The present invention has been devised in view of the above situation, and has as its main object to provide a tire simulation method capable of accurately evaluating the traction performance of a tire.

The present invention is a simulation method for evaluating the traction performance of a tire using a computer, wherein the computer inputs a tire model obtained by modeling the tire with a finite number of elements, the computer Inputting a road surface model in which the road surface is modeled by a finite number of elements, setting boundary conditions to the computer including boundary conditions including a peripheral speed and a translation speed for rolling the tire model on the road surface model; Step, the computer, based on the boundary conditions, a simulation step of rolling the tire model on the road surface model, and, including the step of obtaining a physical quantity related to traction performance from the rolling tire model, The boundary condition setting step is performed in advance in the simulation step. As the tire model in the slip ratio of the obtained range rolls, said saw including a step of peripheral speed and said translation speed is input independently, soft the road model obtained by modeling the soft road surface A road surface model, wherein the simulation step calculates the tire model that rolls while gradually sinking into the soft road surface model for each unit time of the simulation .

  In the tire simulation method according to the present invention, it is preferable that the translation speed is smaller than the peripheral speed.

  In the tire simulation method according to the present invention, it is preferable that the physical quantity includes a longitudinal force of the tire model.

  In the tire simulation method according to the present invention, it is preferable that the soft road surface is a snowy road surface or a muddy road surface.

In the simulation method of the tire according to the present invention, the boundary condition setting step further comprises desirably a step of inputting a displacement speed which is gradually sinking the tire model.

  The tire simulation method of the present invention, a computer, a boundary condition setting step of inputting boundary conditions including a peripheral speed and a translation speed for rolling a tire model on a road surface model, the computer, based on the boundary conditions, The method includes a simulation step of rolling the tire model on the road surface model and a step of acquiring a physical quantity related to traction performance from the rolling tire model.

  The boundary condition setting step includes, in the simulation step, a step of independently inputting a peripheral speed and a translation speed so that the tire model rolls at a slip ratio in a predetermined range.

  As described above, in the simulation method according to the present embodiment, the physical quantity of the tire model is acquired in the slip ratio in the predetermined range. Therefore, physical quantities can be obtained under the same running conditions for tire models having different tread patterns and tire structures, so that the traction performance of the tire can be accurately evaluated.

FIG. 2 is a perspective view of a computer for executing a simulation method according to the embodiment. FIG. 2 is a cross-sectional view of a tire evaluated by a simulation method according to the embodiment. 4 is a flowchart illustrating an example of a simulation method according to the embodiment. It is a sectional view of a tire model of this embodiment. It is a side view which visualizes and shows a soft road surface model. (A), (b) is a diagram which illustrates deformation of a soft road surface model. It is a flowchart which shows an example of the processing procedure of the boundary condition setting process of this embodiment. (A), (b) is a schematic diagram which shows the balance of the force at the time of contact of a tire model and a muddy-road surface model, (a) is a case where normal force is compression, (b) is a case of tension force Is shown. It is a side view of a tire model and a road surface model of this embodiment. It is a flowchart which shows an example of the processing procedure of the simulation process of this embodiment. It is a flowchart which shows an example of the processing procedure of deformation | transformation calculation of the tire model of this embodiment. It is a flowchart which shows an example of the processing procedure of deformation | transformation calculation of a soft road surface model of this embodiment. It is a flow chart which shows an example of a processing procedure of a boundary condition setting process of other embodiments of the present invention. It is a side view of a tire model and a road surface model of another embodiment of the present invention. It is a side view of a tire model and a road surface model of yet another embodiment of the present invention. 9 is a graph showing a relationship between a peripheral speed and a translation speed, and a simulation time. It is a graph which shows the relationship between the rolling time of a 1st tire model and a 2nd tire tire model of an Example, and longitudinal force. It is a graph which shows the relationship between the rotation angle of a 1st tire and a 2nd tire of an experimental example, and longitudinal force. It is a graph which shows the rotation angle of the 1st tire model and the 2nd tire model of an Example, and the relationship between longitudinal force.

Hereinafter, an embodiment of the present invention will be described with reference to the drawings.
The tire simulation method of the present embodiment (hereinafter, sometimes simply referred to as “simulation method”) is a method for evaluating traction performance of a tire.

  FIG. 1 is a perspective view of a computer 1 for executing the simulation method according to the present embodiment. The computer 1 includes a main body 1a, a keyboard 1b, a mouse 1c, and a display device 1d. The main body 1a is provided with an arithmetic processing unit (CPU), a ROM, a working memory, a storage device such as a magnetic disk, and disk drive devices 1a1 and 1a2. Note that a processing procedure (program) for executing the simulation method of the present embodiment is stored in the storage device in advance.

  FIG. 2 is a cross-sectional view of a tire evaluated by the simulation method of the present embodiment. The tire 2 is configured as a passenger car tire, for example. The tire 2 of the present embodiment is, for example, a carcass 6 extending from the tread portion 2a to the bead core 5 of the bead portion 2c via the sidewall portion 2b, a tire radially outside the carcass 6 and inside the tread portion 2a, and A belt layer 7 including two inner and outer belt plies 7A and 7B is provided. Further, the tire 2 is provided with a rubber portion 8 including a tread rubber 8a and a sidewall rubber 8b.

  The tread portion 2a has a tread pattern formed by a tread 9 that contacts the road surface and a groove 10 that is recessed from the tread 9. The groove 10 is provided with, for example, a vertical groove 10a extending continuously in the tire circumferential direction and a plurality of horizontal grooves (not shown) extending in a direction intersecting with the vertical groove 10a. Such a vertical groove 10a and a horizontal groove can take in a water film between the tread portion 2a and the road surface and smoothly discharge the water film. In addition, the vertical groove 10a and the horizontal groove can dig into a soft road surface and exhibit traction performance. The vertical groove 10a may be, for example, linearly extending in the tire circumferential direction or zigzag.

  The carcass 6 is composed of at least one carcass ply 6A in this embodiment. The carcass ply 6A has a main body 6a extending from the tread portion 2a to the bead core 5 of the bead portion 2c via the side wall portion 2b, and a folded back in which the bead core 5 is connected to the main body portion 6a and turned around from the inside in the tire axial direction to the outside. 6b. A bead apex rubber 8d extending from the bead core 5 to the outside in the tire radial direction is disposed between the main body 6a and the folded portion 6b. The carcass ply 6A is provided with, for example, carcass cords arranged at an angle of 80 to 90 degrees with respect to the tire equator C.

  The belt layer 7 is disposed outside the carcass 6 in the tire radial direction and inside the tread portion 2a. The belt layer 7 of the present embodiment includes an inner belt ply 7A and an outer belt ply 7B arranged outside the inner belt ply 7A in the tire radial direction. The belt cords of these belt plies 7A and 7B are arranged at an angle of, for example, 10 to 35 degrees with respect to the tire circumferential direction. The belt cords of the belt plies 7A and 7B are overlapped in a direction crossing each other.

  FIG. 3 is a flowchart illustrating an example of the simulation method according to the present embodiment. In the simulation method of the present embodiment, first, a tire model obtained by modeling the tire 2 shown in FIG. 2 is input to the computer 1 (step S1). FIG. 4 is a sectional view of the tire model 11 of the present embodiment.

  In step S1, the carcass ply 6A, the belt plies 7A and 7B, and the rubber portion 8 shown in FIG. 2 are modeled by a finite number of elements F (i) that can be numerically analyzed. Thus, the tire model 11 having the carcass ply model 12A, the belt ply models 13A and 13B, and the rubber model 14 is set. Further, the tire model 11 is provided with a tread 15 and a groove 16 in which the tread 9 and the groove 10 shown in FIG. 2 are reproduced. The groove 16 of the present embodiment includes a vertical groove 16a and a horizontal groove (not shown) in which the vertical groove 10a and the horizontal groove (not shown) shown in FIG. 2 are reproduced.

  For setting (modeling) of such a model, for example, design data (for example, CAD data) of a vulcanizing mold and meshing software are used in the same manner as in the conventional method. Further, it is desirable that the finished value of the actual tire 2 (shown in FIG. 2) is reflected in the tire model 11. Thereby, a highly accurate tire model 11 can be set.

  “Numerical analysis is possible” means that it can be handled by a numerical analysis method such as a finite element method, a finite volume method, a difference method, or a boundary element method. In the present embodiment, a finite element method and a finite volume method are employed. A Lagrange element is used as the element F (i).

  Each element F (i) is provided with a plurality of nodes 19. Each element F (i) has an element number, a number of the node 19, a coordinate value of the node 19, and numerical values such as material properties (for example, density, Young's modulus, damping coefficient, and loss tangent tan δ). Data is defined.

  Next, in the simulation method of the present embodiment, a road surface model 21 obtained by modeling the road surface with a finite number of elements is input to the computer 1 (step S2). The road surface model 21 of the present embodiment is a soft road surface model 21A that models a soft road surface. The soft road surface is exemplified by a mud road surface having a sufficient mud thickness. In the present embodiment, a soft road surface model 21A is defined by a method similar to that of Patent Document 1. FIG. 5 is a side view visualizing and showing the soft road surface model 21A.

  In the present specification, mud means clay (particle size: less than 0.005 mm), silt (particle size: 0.005 mm to 0.075 mm), fine sand (particle size: 0.075 mm to 0.250 mm), medium Sand (particle size: 0.250 mm to 0.850 mm), coarse sand (particle size: 0.850 mm to 2.00 mm), fine gravel (2.00 mm to 4.75 mm) and medium gravel (4.75 mm to 19.50 mm). 00 mm) and water (10% to 50%). Although not particularly limited, the particle diameter is desirably 9.50 mm or less in order to approximate a general mud running road surface. Similarly, the water content (% by weight of water in the entire mud) is preferably 10% to 50%, more preferably 10% to 40%.

  Euler elements that can be handled by the finite volume method are used as the elements of the soft road surface model 21A of the present embodiment. The soft road surface model 21A includes a grid-like element (mesh) G (i) (i = 1, 2,...) Defined in the space above the plane rigid element 22 and a mud defined by the element G (i). And a filler 21c. In each element G (i), the presence or absence of the mud filler 21c is calculated based on the VOF (Volume of Fluid) method used in the calculation of the flow at the free interface. In the VOF method, the movement of the interface of the two fluids is not directly calculated, but the filling rate (volume fraction) of the fluid in the volume of each element (sometimes referred to as a “cell”) is defined, The interface is represented. When VOF is 1, it indicates that the element G (i) is clogged with mud. When VOF is greater than 0 and less than 1, it indicates that a part of the element G (i) is clogged with mud. When VOF is 0, it indicates that the element G (i) is not clogged with mud.

  The mud filling 21c is equivalent to actual road surface mud. In FIG. 5, the thickness H of the mud filling 21c corresponds to the actual mud thickness on the mud road surface. Therefore, the thickness H of the present embodiment is given a sufficiently large value such that the effect of the plane rigid element 22 can be ignored. Further, the soft road surface model 21A is provided with a necessary and sufficient width and length for contacting the tire model 11 and rolling the tire model 11. Here, for the rolling of the tire model 11, a rotation amount necessary to obtain a necessary analysis is set. Note that the rotation amount of the tire model 11 may be smaller than one rotation.

  FIGS. 6A and 6B are diagrams illustrating deformation of the soft road surface model 21A. The soft road surface model 21A considers contact with the surface of the tire model 11. In the deformation calculation of the tire model 11 and the soft road surface model 21A (a simulation step S5 described later), an intersection J between the two is calculated from mutual positional information. The tire model 11 and the soft road surface model 21A are handled as contacting via the boundary JL of the intersection J. That is, the soft road surface model 21A applies pressure or the like to the tire model 11 via the boundary JL. Conversely, the tire model 11 gives its surface as a “wall” (coupling surface) to the soft road surface model 21A. Accordingly, the mud filling 21c (shown in FIG. 5) existing at the intersection J is moved to the element G (i) in the other area except the intersection J by the pressure of the tire model 11.

  The physical property of the muddy road surface is defined in the soft road surface model 21A of the present embodiment. The physical properties of the mud surface include elasto-plastic properties and collapse properties. It is desirable that the elasto-plastic property and the collapse property are preferably set according to the physical properties of the actual mud to be analyzed. In the present embodiment, based on the same method as in Patent Document 1, the physical properties of the mud to be analyzed (running target) are examined by a triaxial compression test and a hollow cylindrical tension test. Then, the elasto-plastic characteristics and the collapse characteristics of the soft road surface model 21A are set based on the physical characteristics of the mud. Thereby, the soft road surface model 21A is useful for incorporating the actual condition of the soft road surface (mud road surface) when the tire 2 (shown in FIG. 2) travels into the simulation result.

  Next, in the simulation method of the present embodiment, a boundary condition for rolling the tire model 11 on the road surface model 21 is input to the computer 1 (boundary condition setting step S3). FIG. 7 is a flowchart illustrating an example of a processing procedure of the boundary condition setting step S3 of the present embodiment.

  In the boundary condition setting step S3 of the present embodiment, first, as in Patent Document 1, a friction coefficient, an adhesive force, and an adhesive friction force are input between the tire model 11 and the road surface model 21 (step S31). . For example, as schematically shown in FIG. 8A, in a simulation step S5 described later, when the tire model 11 and the soft road surface model 21A are in a compressive stress state, the normal reaction force N and the frictional force μN due to the movement are obtained. (Μ: dynamic friction coefficient), and these forces are given to the tire model 11.

  On the other hand, as schematically shown in FIG. 8B, in a simulation step S5 described later, when the tire model 11 and the soft road surface model 21A are in a tensile stress state, the tire model 11 has an adhesive force and An accompanying frictional force (hereinafter, such a frictional force is referred to as “adhesive frictional force”) (βF) is given. Here, β is a variable (≦ 1) that depends on the sliding speed. F is a predetermined maximum adhesive friction force.

  Adhesion is a normal force. The adhesive force continuously increases until the mud filling 21c (shown in FIG. 5) of the soft road surface model 21A breaks. The adhesive friction force is a force in a direction perpendicular to the adhesive force. The adhesive friction force is generated until the maximum friction force is reached. The adhesive strength is set to a value that is significantly larger than the breaking strength of the soft road surface model 21A. The friction coefficient, the adhesive force, and the adhesive friction force are stored in the computer 1.

  Next, in the boundary condition setting step S3, for example, the rim on which the tire model 11 is mounted, the internal pressure, the axial load applied to the tire model 11, the slip angle during rolling, the camber angle, the tire model 11 and the road surface model 21 The initial time increment at the time of the deformation calculation and the conditions such as the initial positions of the tire model 11 and the road surface model 21 are input (step S32). These conditions are appropriately set in accordance with the rolling conditions of the tire 2 that rolls on a road surface (not shown), and are stored in the computer 1.

  Next, in the boundary condition setting step S3 of the present embodiment, the peripheral speed Va and the translation speed Vb for rolling the tire model 11 on the road surface model 21 are input (step S33). In the present embodiment, the peripheral speed Va is specified on the ground surface of the tire model 11. FIG. 9 is a side view of the tire model 11 and the road surface model 21 of the present embodiment.

  In the present embodiment, the peripheral speed Va and the translation speed Vb are input independently. Thereby, for example, the tire model 11 that travels at the predetermined translation speed Vb can be calculated without considering the traveling speed determined by the rotational force, the slip ratio, and the like of the tire model 11.

  Further, the translation speed Vb of the present embodiment is set to be smaller than the peripheral speed Va. Thus, in a simulation step S5 described later, the tire model 11 that travels while slipping on the soft road surface model 21A can be calculated.

Further, in the present embodiment, the peripheral speed Va and the translation speed Vb are input independently so that the tire model 11 rolls at a slip ratio Sa in a predetermined range in a simulation step S5 described later. Note that the peripheral speed Va and the translation speed Vb may change in the simulation step S5 or may be constant. The slip ratio Sa during braking is defined by the following equation (1). The slip ratio Sa at the time of driving is defined by the following equation (2). Thus, in the simulation step S5, the tire models 11 having different tread patterns and tire structures can be rolled at the same range of the slip ratio Sa.
Sa = (Vb−Va) / Vb (1)
Sa = (Va−Vb) / Va (2)

  In the above equation (1), the slip ratio Sa during braking becomes 100% when the peripheral speed Va is zero. Note that, in the above equation (1), when the translation speed Vb is zero, it indicates a stopped state, and the slip ratio Sa during braking is not calculated.

  In the above equation (2), the slip ratio Sa at the time of driving becomes 100% when the translation speed Vb is zero. In the above equation (1), when the peripheral speed Va is zero, it indicates a stopped state, and the slip ratio Sa during driving is not calculated.

  The slip ratio Sa can be appropriately set according to, for example, the characteristics of the soft road surface model 21A. If the slip ratio Sa is small, the peripheral speed Va and the translation speed Vb are close to each other, so that the tire model 11 that runs while slipping on the soft road surface model 21A may not be sufficiently reproduced. Conversely, when the slip ratio Sa is large, the friction between the tire model 11 and the soft road surface model 21A increases, and the element F (i) of the tire model 11 may be crushed. Therefore, calculation stability may be reduced. From such a viewpoint, when the soft road surface model 21A is set based on a muddy road surface, the slip ratio Sa is preferably 50% or less, and more preferably 10% or more.

  Further, the peripheral speed Va and the translation speed Vb can also be set as appropriate. The peripheral speed Va is desirably about 0 km / h to 200 km / h. Further, the translation speed Vb is desirably 0 km / h to 100 km / h. The peripheral speed Va and the translation speed Vb are stored in the computer 1.

  Next, in the simulation method of the present embodiment, the computer 1 calculates the tire model 11 after the internal pressure filling (step S4). In step S4, the tire model 11 after filling with the internal pressure is calculated based on the rim and the internal pressure input in the boundary condition setting step S3. In step S4, first, as shown in FIG. 4, fitting of the rim model 18 to the tire model 11 is calculated so as to restrain the bead portions 11c, 11c of the tire model 11. The rim model 18 is a model of the rim 17 shown in FIG. Further, the deformation of the tire model 11 is calculated based on the uniformly distributed load w corresponding to the internal pressure condition. Thereby, in step S4, the tire model 11 after the internal pressure filling is calculated. The tire model 11 after such internal pressure filling is stored in the computer 1.

  In step S4, in the deformation calculation of the tire model 11, a mass matrix, a rigidity matrix, and a damping matrix of each element F (i) are respectively created based on the shape and material characteristics of each element. Further, each of these matrices is combined to create a matrix of the entire system. The computer 1 creates equations of motion by applying the above various conditions, and transforms the equations of motion into unit time T (x) (x = 0, 1,...) (For example, every 1 μsec) of the tire model 11. Perform calculations. For such deformation calculation, for example, commercially available finite element analysis application software such as LS-DYNA, Abaqus, ANSYS or Dytran manufactured by LSTC is used.

  Next, in the simulation method of the present embodiment, the computer 1 rolls the tire model 11 on the road surface model 21 based on the boundary condition (simulation step S5). FIG. 10 is a flowchart illustrating an example of a processing procedure of the simulation step S5 of the present embodiment.

  In the simulation step S5 of the present embodiment, as shown in FIG. 9, the state in which the tire model 11 after the internal pressure filling comes into contact with the soft road surface model 21A and rolls is a unit time (small time increment) of the simulation. It is calculated every time (steps S51 to S54). In the simulation step S5 of the present embodiment, the deformation calculation of the tire model 11 (step S51) and the deformation calculation of the soft road surface model 21A (step S52) are individually performed.

  In steps S51 to S54 of the present embodiment, the deformation calculation of the tire model 11 and the soft road surface model 21A is calculated by an explicit method, similarly to the above-mentioned Patent Document 1. In the explicit solution, the time is set at time 0 at the moment when a load acts on the tire model 11 and the soft road surface model 21A, and time is divided at each set time increment. Then, displacements of the tire model 11 and the soft road surface model 21A at each time are obtained. In order to perform such calculations stably, the time increment is set so as to satisfy the Courant condition.

  In the simulation step S5 of the present embodiment, the stress wave transmission times of all elements are calculated. Further, the minimum value of the stress wave transmission time is multiplied by the safety factor, and an initial time increment is set. Therefore, optimal deformation calculation is guaranteed for all elements. The safety factor is preferably, for example, 0.66 or more and less than 1.0. The initial time increment of the tire model 11 and the soft road surface model 21A is desirably about 0.1 μsec to 5 μsec.

  In the simulation step S5 of the present embodiment, first, a deformation calculation of the tire model 11 is performed (tire model deformation step S51). FIG. 11 is a flowchart illustrating an example of a processing procedure of a deformation calculation (step S51) of the tire model 11 of the present embodiment. In the tire model deformation step S51, the deformation of the tire model 11 is calculated by the same method as in the above-mentioned Patent Document 1. In the tire model deformation step S51, first, a deformation calculation of the tire model 11 after a time increment Δt is performed (step S511). The deformation calculation is performed by the computer 1 solving a necessary equation of motion.

  In step S511, a state when the tire model 11 rolls the soft road surface model 21A for a short time Δt is calculated based on the peripheral speed Va, the translation speed Vb, and the like. Accordingly, in step S511, the tire model 11 that rolls while slipping on the soft road surface model 21A is calculated. Since the peripheral speed Va and the translation speed Vb are input so that a predetermined slip ratio Sa is satisfied, for example, the tire models 11 having different tread patterns and tire structures are rolled under the same running conditions. Can be.

  The translation speed Vb of the tire model may be set to zero during driving. As a result, a tire model that rolls on a soft road surface model with a slip ratio Sa of 100% is calculated, so that a tire model 11 that rolls on a soft road surface model 21A under the same running conditions can be calculated. Such a translation speed Vb is desirably set in a soft road surface model 21A defined based on a muddy road surface.

  Since the soft road surface model 21A of this embodiment is set based on the muddy road surface, the friction with the tire model 11 is calculated to be relatively small. Therefore, in a plurality of tire models 11 having different tread patterns and tire structures, the peripheral speed Va and the translation speed Vb, which tend to change on a hard road surface, can be kept constant, so that the tires are easily rolled at the same slip ratio Sa. be able to. Thereby, the traction under the same traveling condition can be compared.

  Next, in the tire model deformation step S51 of the present embodiment, the stress wave transmission time is calculated again using the size, density and / or hardness of each element of the deformed tire model 11 ( Step S512). Next, in the tire model deformation step S51 of this embodiment, a time increment appropriate for the next deformation calculation is set based on the stress wave transmission time so as to satisfy the Courant condition (step S513). The stress wave transmission time changes with each deformation of the element. Therefore, by calculating the optimal time increment according to the deformation situation, more accurate deformation calculation of the tire model 11 can be performed.

  Next, in the tire model deformation step S51 of the present embodiment, it is determined whether or not a time specified (defined) has elapsed (step S514). If it is determined in step S514 that the time has elapsed (“Y” in step S514), a series of processing in the tire model deformation step S51 is completed, and step S54 is performed. On the other hand, if it is determined in step S514 that the time has not elapsed, steps S511 to S514 are performed again. Thus, in the tire model deformation step S51, the deformation calculation of the tire model 11 can be repeatedly performed at the newly calculated time increment. In the tire model deformation step S51, the rolling calculation may be performed by the tire model 11 defined as a rigid body that cannot be deformed. Accordingly, it is not necessary to calculate the deformation state of the tire model 11, so that the calculation time can be reduced.

  Next, in the simulation step S5 of the present embodiment, a deformation calculation of the soft road surface model 21A is performed (a soft road surface model deformation step S52). FIG. 12 is a flowchart illustrating an example of a processing procedure of a deformation calculation (soft road model deformation step S52) of the soft road surface model 21A according to the present embodiment. In step S52, the deformation calculation of the soft road surface model 21A is performed by the same method as in the above-described Patent Document 1.

  In the soft road surface model deformation step S52, first, the deformation calculation of the soft road surface model 21A after the time increment is performed (step S521). In the deformation calculation, the current density ρ is calculated for each element of the soft road surface model 21A. Next, the volumetric strain εv due to the pressure after the time increment is calculated based on the previously determined density. Then, the pressure P of each element (average value of the triaxial stress components acting on each element) P after the time increment is calculated.

  Next, in the soft road surface model deformation step S52, stress calculation after time increment is performed for each element of the soft road surface model 21A (step S522). Here, the secondary invariant J2 of the deviation stress is calculated for each element. Next, in the soft road surface model deformation step S52, a stress-plastic strain curve (not shown) after time increment is calculated for each element of the soft road surface model 21A (step S523). The pressure P for each element of the soft road surface model 21A has been calculated in the above step S521. Therefore, in step S523, a stress-plastic strain curve corresponding to the current pressure P and strain rate is obtained from the elasto-plastic characteristics defined in step S2.

  Next, in the soft road surface model deformation step S52, it is determined whether or not the current stress state of each element of the soft road surface model 21A exceeds the previously obtained stress-plastic strain curve (not shown) ( Step S524). In the deformation calculation of the soft road surface model 21A, in step S522, the calculation is performed on the assumption that all the strain increments of each element of the soft road surface model 21A are elastic deformation. If the stress state of the element is above the stress-plastic strain curve, the stress of the element must be reduced to an accurate value (the value above the stress-plastic strain curve). Therefore, in step S524, it can be checked whether the above assumption was correct.

  In step S524, when it is determined that the current stress state exceeds the previously obtained stress-plastic strain curve (not shown), step S525 for performing stress relaxation processing is performed, and then step S526 is performed. Is done. In step S525, iteration is performed until the stress state of each element is located on the stress-plastic strain curve. This reduces the stress of the element to an accurate value (the value above the stress-plastic strain curve).

  On the other hand, when it is determined that the current stress state does not exceed the previously obtained stress-plastic strain curve (not shown), the next step S526 is performed without performing the step S525.

  Next, in the soft road surface model deformation step S52, when the average stress of the element of the soft road surface model 21A is tensile, the equivalent plastic strain of the element is compared with the fracture strain of the predefined collapse condition (step S526). ). In step S526, when it is determined that the equivalent plastic strain is greater than the fracture strain ("Y" in step S526), the stress of the element is set to zero (step S527), and the next step S528 is performed. You. Thereby, running resistance due to mud adhesion and separation from the ground due to mud destruction can be considered in the simulation. On the other hand, when it is determined that the equivalent plastic strain is not larger than the fracture strain (“N” in step S526), the next step S528 is performed without performing step S527.

  In the next step S528, similarly to the deformation calculation step S51 of the tire model 11, the stress wave transmission time is calculated again for each element of the deformed soft road surface model 21A, and the minimum value of the stress wave transmission time is multiplied by the safety factor. Is set as the next time increment.

  Next, in the soft road surface model deformation step S52, it is checked whether or not a time specified (defined) has elapsed (step S529). In step S529, when it is determined that the predetermined time has elapsed (“Y” in step S529), the deformation calculation of the soft road surface model 21A is completed, and the next step S54 is performed. On the other hand, in step S529, when it is determined that the time has not elapsed (“N” in step S529), steps S521 to S529 are performed again. Thereby, in the soft road surface model deformation step S52, the deformation calculation of the soft road surface model 21A can be repeatedly performed at the newly calculated time increment.

  Next, in the simulation step S5 of the present embodiment, a physical quantity related to traction performance is acquired from the rolling tire model 11 (step S53). In step S53, based on the deformation calculation result of the tire model 11 in the tire model deformation step S51 and the deformation calculation result of the soft road surface model 21A in the soft road model deformation step S52, the tire model 11 Is calculated. The physical quantity can be appropriately selected according to the performance of the tire model 11 to be evaluated. The longitudinal force of the tire model 11 is acquired as the physical quantity in the present embodiment. The physical quantity (fore-and-aft force) of the tire model 11 is stored in the computer 1.

  As described above, in the tire model deformation step S51, the state in which the tire model 11 rolls on the soft road surface model 21A is calculated based on the slip ratio Sa. For this reason, in step S53, for example, the longitudinal force of the tire model 11 can be acquired under the same running conditions for the tire models 11 having different tread patterns and tire structures.

  Next, as shown in FIG. 10, in the simulation step S5, it is determined whether or not a previously specified calculation end time has elapsed (step S54). In step S54, when it is determined that the calculation end time has elapsed (“Y” in step S54), the next step S6 is performed. On the other hand, if it is determined that the calculation end time has not elapsed, steps S51 to S54 are performed again. Thereby, as shown in FIG. 9, the tire model 11 that rolls while sinking to the soft road surface model 21A is calculated every minute time Δt from the start of calculation to the end of calculation. The calculation end time is determined according to the simulation to be performed so that a stable calculation result is obtained. For the series of processes in the simulation step S5, for example, the above-mentioned commercially available finite element analysis application software is used.

  Next, in the simulation method of the present embodiment, it is determined whether or not the physical quantity related to the traction performance is good (Step S6). In step S6 of the present embodiment, for example, if the physical quantity of the tire model 11 (in this embodiment, the longitudinal force) is larger than a predetermined target value, it is determined that the traction performance is good. This means that the larger the front-rear force of the tire model 11, the higher the traction due to the effective penetration of the tire model 11 into the soft road surface model 21A and the friction and adhesive friction between the tire surface and the mud. Because it is possible.

  In step S6, when it is determined that the physical quantity is good (“Y” in step S6), the tire 2 is manufactured based on the tire model 11 (step S7). On the other hand, when it is determined that the physical quantity is not good (“N” in step S6), the design factors including the tread pattern and the tire structure of the tire model 11 are changed (step S8), and steps S1 to S6 are performed again. Is done.

  In the present embodiment, since the rolling state of the tire model 11 on the soft road surface model 21A is calculated based on the slip ratio Sa, the tire model 11 in which the design factors including the tread pattern and the tire structure are changed. The physical quantity (force in the longitudinal direction) can be acquired under the same running conditions. Therefore, in the step S6 of the present embodiment, the traction performance of the tire model 11 having different design factors can be accurately evaluated, so that the tire 2 that can exhibit high traction performance can be reliably manufactured.

  By the way, in the tire model 11, the settlement amount D to the soft road surface model 21A differs depending on the tread pattern, the tire structure, and the load condition. That is, the more the tire model 11 digs into the soft road surface model 21A, the larger the amount of settlement D (shown in FIG. 9) to the soft road surface model 21A. When the sinking amount D increases, the wall of the soft road surface model 21A increases in front of the tire model 11, so that the longitudinal force of the tire model 11 decreases. Therefore, there is a possibility that it is difficult to acquire the physical quantity of the tire model 11 under the same condition due to the difference in the settlement amount D that differs for each tire model 11.

  From such a viewpoint, the boundary condition setting step S3 further includes a step S34 of inputting the sink amount Da of the tire model 11 so that the tire model 11 sinks to the soft road surface model 21A in the simulation step S5. Is desirable. FIG. 13 is a flowchart illustrating an example of a processing procedure of the boundary condition setting step S3 according to another embodiment of the present invention. FIG. 14 is a side view of a tire model 11 and a road surface model 21 according to another embodiment of the present invention. In this embodiment, the steps in which the same processes as those in the previous embodiment are performed, and the same models and the like are denoted by the same reference numerals and description thereof may be omitted.

  The settlement amount Da is a parameter for causing the tire model 11 to sink by a fixed amount on the soft road surface model 21A from the start of the calculation in the simulation step S5 to the end of the calculation. The settlement amount Da is defined by a simulation performed in advance for each tire model 11 having a different tread pattern and tire structure.

  In the simulation for obtaining the settlement amount Da, the peripheral speed Va and the translation speed Vb are set to 0, and each tire model 11 is caused to sink only under the load condition. Then, the squat amount at which the reaction force and the load of the soft road surface model 21A are balanced and stopped is defined as the squat amount Da of each tire model 11. Thereby, in the simulation step S5 of this embodiment, the physical quantity of the tire model 11 is acquired in consideration of not only the slip rate Sa but also the sinking amount Da due to the load condition, so that the tire 2 (shown in FIG. 2) is obtained. Traction performance can be accurately evaluated.

  The settlement amount Da can be appropriately set according to, for example, the characteristics of the soft road surface model 21A. If the settlement amount Da is small, the settlement amount may be smaller than the actual settlement amount of the tire 2 traveling on a soft road surface (not shown), and the calculation accuracy of the physical quantity may be reduced. Conversely, if the sinking amount Da is large, in an actual vehicle equipped with the tire 2 (shown in FIG. 2), the lower surface of the vehicle comes into contact with a soft road surface, and the load applied to the tire 2 decreases. As a result, the load of the tire model 11 becomes larger than the actual load of the tire 2, and the calculation accuracy may be reduced. From such a viewpoint, when the soft road surface model 21A is set based on a muddy road surface, the settlement amount Da is preferably 5 cm or more, and more preferably 20 cm or less.

  In the boundary condition setting step S3 of the previous embodiment, the mode including the step S34 of inputting the constant settlement amount Da is exemplified, but the embodiment is not limited to this. For example, a subsidence amount D that changes based on the translation speed, the peripheral speed, and the load condition (that is, the tire model 11 gradually subsides on the soft road surface model 21A every minute time Δt) may be defined. In particular, in the case of a soft road surface model 21A defined as a sandy road surface or a snowy road surface, it is desirable that the changing settlement amount D be defined. Note that the settlement amount D is changed based on the displacement speed Vc defined in the tire model 11. FIG. 15 is a side view of a tire model 11 and a road surface model 21 according to still another embodiment of the present invention.

  The displacement speed Vc is a parameter for increasing the settlement amount D of the tire model 11 to the soft road surface model 21A at a constant rate every minute time Δt from the start of the calculation of the simulation step S5 to the end of the calculation. . The same value is set as the displacement speed Vc in each tire model 11 having a different tread pattern and tire structure. Therefore, each tire model 11 can be sunk with the same squat amount D every minute time Δt. Thereby, in the simulation step S5 of this embodiment, the physical quantity of the tire model 11 can be acquired under the same conditions including not only the slip ratio Sa but also the displacement speed Vc. Therefore, in the simulation method of this embodiment, the traction performance of the tire 2 (shown in FIG. 2) can be accurately evaluated. Moreover, in this embodiment, unlike the previous embodiment in which a certain amount of settlement Da is defined, the actual tire 2 that gradually sinks on a soft road surface can be reproduced, so that the traction performance of the tire 2 is more accurate. Can be evaluated.

  It is desirable that the displacement speed Vc is defined by a preliminary simulation performed based on predetermined conditions (for example, a translation speed, a peripheral speed, a load condition, and the like). Thereby, in the simulation step S5, the tire model 11 can be stably settled on the soft road surface model 21A. Further, as the displacement speed Vc, a sink speed on a soft road surface measured using at least one tire may be used prior to the simulation.

  If the displacement speed Vc is low, the time required for the tire model 11 to sink down by a certain amount increases, and the calculation cost may increase. Conversely, if the displacement speed Vc is high, the amount of deformation of the tire model 11 increases, and the stress wave generated in the soft road surface model 21A increases, so that the calculation stability may decrease. From such a viewpoint, when the soft road surface model 21A is set based on a muddy road surface, the displacement speed Vc is preferably 5 cm / S or more, and more preferably 20 cm / S or less.

  In the embodiments described above, the mode in which the soft road surface model 21A in which the soft road surface is modeled as a muddy road surface is used, but the embodiment is not limited thereto. For example, the soft road surface may be a sandy road surface or a snowy road surface.

  When the soft road surface is a sand road surface, for example, it is desirable to set a soft road surface model in which the physical properties of sand that does not generate mud-like adhesive friction are defined based on Patent Document 3 described above. By using such a soft road surface model 21A, in the simulation step S5, the tire 2 rolling on a sandy road surface can be reproduced.

  When the soft road surface is a snowy road surface, it is desirable that the soft road surface model is set by a snow model that models snow, for example, based on Patent Document 2 described above. This snow model does not have mud-like adhesive friction, can express a volume change due to compression, and is composed of elements in which the volume change is substantially permanent. Further, the snow model is defined such that a break occurs when the shear strain exceeds a predetermined value. By using such a soft road surface model 21A, in the simulation step S5, it is possible to reproduce the destruction of snow when the tire 2 travels on a snowy road surface.

  Also in this embodiment, the peripheral speed Va and the translation speed Vb are input independently so that the tire model 11 rolls at the slip ratio Sa. Therefore, traction performance on a snowy road surface can be accurately evaluated. When the soft road surface model 21A is set based on a snowy road surface, the slip ratio Sa is preferably 20% or less, and more preferably 5% or more. The settlement amount Da is preferably 0.1 cm or more, and more preferably 5 cm or less. In this embodiment, the tire model 11 is not gradually lowered to the soft road surface model 21A that models a snowy road surface. Therefore, the displacement speed Vc is not set.

  Since the tread surface rubber of the snow tire is generally soft, when the rolling of the tire model 11 is calculated based on the slip ratio Sa from the start of the calculation in the simulation step S5, the element F ( i) may be crushed, resulting in a loss of calculation. Therefore, it is desirable that the same peripheral speed Va and the same translation speed Vb are set so that the slip ratio Sa becomes zero from the start of the calculation in the simulation step S5 until the rolling of the tire model 11 is stabilized. The translation speed Vb of the tire model 11 may be set to zero during driving, as described in the previous embodiment.

  FIG. 16 is a graph showing an example of the relationship between the peripheral speed Va and the translation speed Vb, and the simulation time. In this graph, for example, the same peripheral speed Va and translation speed Vb are set so that the slip ratio Sa becomes zero from 0 (s) to 0.18 (s) at the start of calculation. Further, the peripheral speed Va and the translation speed Vb are gradually increasing. Thereby, the rolling of the tire model 11 can be stabilized while suppressing the friction between the tire model 11 and the soft road surface model 21A.

  Next, in this graph, for example, in the range of 0.18 (s) to 0.28 (s), only the peripheral speed Va is gradually increased. Further, in this graph, the peripheral speed Va and the translation speed Vb are set so that, for example, after 0.28 (s), the tire model 11 rolls at a constant slip ratio Sa. As described above, since the constant slip ratio Sa is set after the rolling of the tire model 11 is stabilized, calculation omission can be effectively prevented, and the calculation time can be reduced. In step S6, the traction performance is evaluated based on the longitudinal force of the tire model 11 while traveling at the slip ratio Sa (0.28 (s) or later in the graph).

  Although the case where the road surface model 21 of the embodiments described above is configured by the soft road surface model 21A has been exemplified, the present invention is not limited to this. For example, the road surface model 21 may be a hard road surface model (not shown) that models a hard road surface such as asphalt or on ice. By using such a hard road surface model, the tire 2 rolling on the hard road surface can be reproduced in the simulation step S5. Further, since the peripheral speed Va and the translation speed Vb are input independently so that the tire model 11 rolls at the slip ratio Sa, the traction performance on a hard road surface can be accurately evaluated.

  When the road surface model 21 is set based on a hard road surface (asphalt), the slip ratio Sa is preferably 10% or less, and more preferably 0.01% or more. When the road surface model 21 is set based on a hard road surface (on ice), the slip ratio Sa is preferably 80% or less, and more preferably 1% or more. In this embodiment, as in the case of the road surface model 21 set based on the snowy road surface, the slip ratio Sa is zero from the start of the calculation in the simulation step S5 until the rolling of the tire model 11 is stabilized. Thus, it is desirable that the same peripheral speed Va and the same translation speed Vb are set. The peripheral speed Va and the translation speed Vb are set in a range where the element F (i) of the tire model 11 does not collapse.

  Although the embodiment in which the translation speed Vb is smaller than the peripheral speed Va has been exemplified in the embodiments described above, the embodiment is not limited to this. For example, the translation speed Vb may be higher than the peripheral speed Va. Thus, in the simulation step S5, the tire model 11 to be braked on the road surface model 21 can be calculated. In this case, the slip ratio Sa is obtained based on the above equation (2). Further, the mode in which the translation speed Vb is set in the tire model 11 has been illustrated, but the present invention is not limited to this. For example, a translation speed Vb in the opposite direction to the tire model 11 may be set in the road surface model 21.

  As described above, particularly preferred embodiments of the present invention have been described in detail. However, the present invention is not limited to the illustrated embodiments, and can be implemented in various forms.

[Example A]
A first tire and a second tire having the same structure and different tread patterns were produced. Each tire was mounted on all the wheels of a domestic FR car with a displacement of 1800 cc, traveled on snowy roads, and the traction performance of the tires was evaluated by the feeling of a professional test driver. The evaluation was indicated by an index with the first tire being 100. The higher the value, the better.

  According to the processing procedure shown in FIG. 3, the tire model and the soft road surface model set based on the snowy road surface were input to the computer. The tire model includes a first tire model set based on the first tire and a second tire model set based on the second tire (Example).

  Further, the peripheral speed Va and the translation speed Vb are independently input according to the processing procedure shown in FIGS. 10 to 12 so that each tire model rolls at a slip ratio Sa in a predetermined range. . The peripheral speed Va and the translation speed Vb were set based on the graph shown in FIG. Next, a simulation step of rolling each tire model on a soft road surface model was performed, and the longitudinal force of each tire model was determined. The longitudinal force was calculated at the axis of rotation of the tire model. Then, the traction performance of each tire model was evaluated based on the average longitudinal force at the slip ratio Sa. The evaluation was indicated by an index with the average front-rear force of the first tire model being 100. The higher the value, the better. FIG. 17 is a graph showing the relationship between the rolling time of the first tire model and the second tire model and the longitudinal force.

For comparison, the same rotation speed was defined for the first tire model and the second tire model based on a conventional simulation method, and the state of rolling on the road surface model was calculated. Then, the longitudinal force was obtained from the rolling first and second tire models, and the traction performance was evaluated based on the average longitudinal force of each tire model. The evaluation was indicated by an index with the average front-rear force of the first tire model being 100. The higher the numerical value, the better (Comparative Example). Table 1 shows the test results. The common specifications are as follows.
Tire size: 195 / 65R15
Load: 469.07kgf
Internal pressure: 1.795 kgf / cm ²
Rim size: 8.0J × 15.0
Road surface, road surface model:
Temperature: -5 ° C
Density: 480 kg / m ³
Fresh snow thickness: 5cm
Experimental example:
Travel speed: 5km / h-30km / h
Example:
Slip ratio Ra: 12.5% (peripheral speed Va: 5.625 km, translation speed Vb: 5 km / h)
Comparative example:
Rotation speed: 42 rpm (5 km / h), 47 rpm (5.625 km / h)

  As a result of the test, in the simulation method of the embodiment, the state in which the first tire model and the second tire model roll at the same slip ratio Sa is calculated, so that the physical quantity of the tire model 11 can be acquired under the same conditions. Was. On the other hand, in the simulation method of the comparative example, since the same rotation speed was defined for the first tire model and the second tire model, the states of rolling at different traveling speeds were calculated. Therefore, in the simulation method of the comparative example, the physical quantity of the tire model 11 could not be obtained under the same conditions.

  As shown in Table 1, the longitudinal force of the second tire model of the example was closer to the longitudinal force of the second tire model of the experimental example than the longitudinal force of the second tire model of the comparative example. Therefore, the simulation method of the example was able to accurately evaluate the traction performance of the tire as compared with the simulation method of the comparative example.

  Further, in the simulation method of the embodiment, the peripheral speed Va and the translation speed Vb are set based on the graph shown in FIG. On the other hand, in the simulation method of the comparative example, a torque smaller than the actual tire was set in order to prevent a drop in calculation in advance, so that the translation speed of each tire model was calculated to be smaller than the translation speed of the tire model of the example. Was. Therefore, in the simulation method of the example, the calculation time was significantly reduced as compared with the simulation method of the comparative example.

[Example B]
First and second tires having the same structure and different tread patterns were prepared, and the longitudinal force (traction force) of each tire was measured on a muddy road surface. For the measurement of the longitudinal force, a bench test apparatus for a tire described in JP-A-2012-215550 was used (an experimental example). Further, in the experimental example, the average front-rear force during mud scratching (tire rotation angle: 40 ° to 80 °) and the average back and forth force during mud friction (tire rotation angle: 80 ° to 100 °) for the first tire and the second tire. The longitudinal force was calculated as an index with the second tire as 100. The test results are shown in Table 2. FIG. 18 is a graph showing the relationship between the rotation angles of the first tire and the second tire and the longitudinal force.

  According to the processing procedure shown in FIG. 3, the tire model and the soft road surface model set based on the muddy road surface were input to the computer. The tire model includes a first tire model set based on the first tire and a second tire model set based on the second tire.

Further, the peripheral speed Va and the translation speed Vb are independently input according to the processing procedure shown in FIGS. 10 to 12 so that the tire model rolls at a slip ratio Sa in a predetermined range. Then, a simulation step of rolling each tire model on the road surface model was performed, and the longitudinal force of each tire model was determined (Example). The longitudinal force was calculated at the axis of rotation of the tire model. Further, in the example, the average longitudinal force at the time of scratching the mud and the average longitudinal force at the time of mud friction were calculated by an index with the second tire model being 100 for the first tire model and the second tire model. The test results are shown in Table 2. FIG. 19 is a graph showing the relationship between the rotational angles of the first tire model and the second tire model, and the longitudinal force. The common specifications are as follows.
Tire size: 285 / 60R18
Experimental example:
Depth of mud stored in mud section: 15cm
Tire sinking amount: 8cm (fixed)
Tire rotation speed: 0.3km (fixed)
Mud: sandy clay (collection place: Nara)
Example:
Peripheral speed Va: 30km (fixed)
Translation speed Vb: 0 km (fixed)
Slip ratio Sa: 100%
Depth of soft road surface model: 15cm
Settlement amount of tire model Da: 8cm (fixed)
Particle size distribution of soft road surface model:
Sand content: 45%
Silt: 28%
Clay content: 27%
Water content: 38%
Calculation days: 3 days

  As a result of the test, as shown in the graph of FIG. 18 and Table 2, the longitudinal force of the first tire was larger than the longitudinal force of the second tire during mud scraping and mud friction. On the other hand, as shown in the graph of FIG. 19 and Table 2, during mud scraping and mud friction, the longitudinal force of the first tire model was larger than the longitudinal force of the second tire model. Therefore, the simulation method of the present embodiment was able to accurately measure the traction performance.

11 tire model 21 road surface model Sa slip ratio Va peripheral speed Vb translation speed

Claims (5)

A simulation method for evaluating traction performance of a tire using a computer,
Inputting a tire model in which the tire is modeled with a finite number of elements to the computer;
A step of inputting a road surface model obtained by modeling the road surface with a finite number of elements to the computer,
A boundary condition setting step of inputting boundary conditions including a peripheral speed and a translation speed for rolling the tire model on the road surface model to the computer;
The computer, based on the boundary condition, a simulation step of rolling the tire model on the road model, and a step of obtaining a physical quantity related to traction performance from the rolling tire model,
The boundary condition setting step includes a step in which, in the simulation step, the peripheral speed and the translation speed are input independently so that the tire model rolls at a slip ratio in a predetermined range. See
The road surface model is a soft road surface model that models a soft road surface,
The simulation method of a tire, wherein the simulation step calculates the tire model that rolls while gradually sinking on the soft road surface model for each unit time of the simulation.   The tire simulation method according to claim 1, wherein the translation speed is lower than the peripheral speed.   The tire simulation method according to claim 1, wherein the physical quantity includes a longitudinal force of the tire model. The tire simulation method according to claim 1 , wherein the soft road surface is a snowy road surface or a muddy road surface . The tire simulation method according to any one of claims 1 to 4, wherein the boundary condition setting step further includes a step of inputting a displacement speed at which the tire model is gradually lowered .