JP6349723B2 - Simulation method, apparatus thereof, and program - Google Patents

Description

  The present invention relates to a tire simulation method using a computer, an apparatus thereof, and a program, and more particularly, to a tire simulation method, an apparatus, and a program thereof that suppress the failure of simulation calculation and improve calculation efficiency.

  Currently, a method of creating a tire model that can be analyzed by a computer and simulating the performance of the tire has been proposed. In the performance simulation, a tire model obtained by dividing a tire into finite elements is created, and the tire model configured by the finite elements is rolled on a virtual road surface simulating a road surface. When designing a tire using a tire performance simulation, a plurality of tire shapes are simulated based on an optimization design method (see Patent Document 1).

  Patent Document 1 describes a method for designing a tire cross-sectional shape in which a tire cross-sectional shape is defined with a small design variable and a wide design range is defined, and an optimum design according to tire performance is efficiently performed. Patent Document 1 describes that a plurality of eigenmodes in the tire cross-sectional direction are used as the base shape to optimize the tire shape.

Japanese Patent No. 4723057

  In the optimum design of the tire of Patent Document 1 described above, simulation is performed for a plurality of tires. When performing a plurality of simulations, as shown in FIG. 7A, a tire model 100 that can be analyzed by a computer is created, and a road surface model 110 that can be analyzed by a computer is created facing the tire model 100. At this time, when the relative position between the tire model 100 and the road surface model 110 is not appropriate, the tire is inflated by applying an internal pressure, or the tire shape parameters including changes in the outer diameter and the structure are changed and simulated. Like the tire model 102 shown in FIG. 7B, for example, a part of the tread 104 penetrates the road surface model 110. In such a case, the calculation of the tire simulation is broken. This is a big problem when repeatedly calculating characteristic values for various shapes such as optimization calculation or experimental design method.

  Therefore, in consideration of changes in the shape of the tire due to changes in inflation and shape parameters, the distance between the tire model 100 and the road surface model 110 is increased so that the tire model 102 penetrates into the road surface model 110. Can be avoided, and the failure of the above calculation can be avoided. However, in this case, there is a problem that the calculation time until the tire model 102 and the road surface model 110 come into contact increases and the calculation time required for the simulation increases.

  In addition to the above-described method, the operator confirms the interval between the tire model 100 and the road surface model 110 every time the tire shape is changed by changing the inflation or the shape parameter, and the tire model 100 and the road surface model 110 according to the interval. Can be prevented from penetrating into the road surface model 110 such as the tire model 102 described above (see FIG. 7B). Even in this case, it is sufficient to calculate only one tire model. However, when handling a large number of analysis models for multiple tire models such as changing the shape, the input work increases and man-hours are required. Inefficient. In addition, there is a problem that the time required until the simulation is finally finished increases.

  An object of the present invention is to solve the above-described problems based on the prior art, suppress the calculation failure of the simulation, stabilize the calculation, and improve the calculation efficiency. To provide an apparatus and a program.

  In order to achieve the above object, the present invention provides a creation step of creating a tire model that can be analyzed by a computer for a tire, and a position where the tire model is subjected to an internal pressure filling process and is maximum in the radial direction of the tire model. A position information acquisition step of acquiring the position information of the tire and a road surface model that can be analyzed by a computer for the road surface in contact with the tire, and predetermined in the radial direction from the maximum position of the tire model based on the position information The setting position information of the setting position that gives the distance of is obtained, and based on the setting position information, the setting step of setting the road surface model at the setting position, and either the rotation axis of the tire model or the road surface model The present invention provides a tire simulation method characterized by having an analysis step of performing contact analysis with restraint

In the creation step, at least two of the tire models that have changed the shape or structure of the tire are created, and in the position information acquisition step, setting position information is acquired for each tire model, and in the setting step, The road surface model is set based on each set position information, and the analysis step can perform contact analysis for each tire model.
In the creation step, a two-dimensional axisymmetric tire cross-section model is created as the tire model, and in the setting step, the road surface model is set for the two-dimensional axisymmetric tire cross-section model, and the analysis In the process, it is preferable to perform contact analysis using a three-dimensional tire model obtained by developing the two-dimensional axisymmetric tire cross-sectional model in the circumferential direction.

  The design variable includes at least a factor that changes the shape of the tire, and includes at least the physical characteristics of the tire as an objective function. After the contact analysis in the analysis step, the physical characteristics of the tire are determined using the design variables. It is also possible to calculate the physical quantity of the tire by performing optimization calculation as a function.

  The present invention provides a tire model that can be analyzed by a computer for a tire, a model creation unit that creates a road surface model that can be analyzed by a computer for a road surface in contact with the tire, and a predetermined internal pressure applied to the tire model. A position information acquisition unit that acquires position information of a position that is maximum in the radial direction of the tire model, and a set position that gives a predetermined distance in the radial direction from the maximum position of the tire model based on the position information Is set, and based on the set position information, the setting unit that sets the road surface model at the set position, and the rotation axis of the tire model and the road surface model are constrained to perform contact analysis. The present invention provides a tire simulation apparatus including an analysis unit.

The model creating unit creates at least two tire models in which the shape or structure of the tire is changed, and the position information obtaining unit obtains set position information for each tire model. In addition, the setting unit sets the road surface model based on the set position information, and the analysis unit can perform contact analysis on each tire model.
The model creation unit creates a two-dimensional axisymmetric tire section model as the tire model, and further creates a three-dimensional tire model obtained by developing the two-dimensional axisymmetric tire section model in the circumferential direction. The setting unit sets the road surface model with respect to the two-dimensional axisymmetric tire section model, and the analysis unit performs contact analysis using the three-dimensional tire model. It is preferable.

  The design variable includes at least a factor that changes the shape of the tire, and includes at least the physical characteristics of the tire as an objective function. The analysis unit uses the design variables to determine the physical characteristics of the tire after the contact analysis. It is also possible to calculate the physical quantity of the tire by performing optimization calculation as a function.

  A program for causing a computer to execute each step of the tire simulation method of the present invention as a procedure is provided.

According to the present invention, it is possible to provide a tire simulation method and a simulation apparatus, and a tire simulation program capable of stabilizing calculation by suppressing failure of simulation calculation and improving calculation efficiency. Can do.
In the present invention, it becomes possible to prevent the road surface model from penetrating into the tire model due to the change in the tire size, and is very suitable for iterative calculation such as shape optimization calculation including contact analysis with the road surface model etc. It is. In addition, regardless of changes in tire size, the computer can automatically set the position of the road surface model, and efficiency can be expected by shortening the calculation time.

It is a schematic diagram which shows the simulation apparatus used for the simulation method of the 1st Embodiment of this invention. It is a schematic diagram which shows the tire model used for the simulation method of the 1st Embodiment of this invention. It is a flowchart which shows the simulation method of the 1st Embodiment of this invention. (A)-(c) is a schematic diagram which shows the analysis process of the simulation method of the 1st Embodiment of this invention. It is a flowchart which shows the simulation method of the 2nd Embodiment of this invention. (A) is a schematic diagram which shows the example of the shape change of a tire model, (b) is a schematic diagram which shows the other example of the shape change of a tire model. (A) is a schematic diagram which shows the conventional tire tire model, (b) is a schematic diagram for demonstrating the problem of the conventional tire tire model.

The simulation method, apparatus and program of the present invention will be described in detail below based on preferred embodiments shown in the accompanying drawings.
FIG. 1 is a schematic diagram showing a simulation apparatus used in the simulation method according to the first embodiment of the present invention.

  A simulation apparatus 10 (hereinafter simply referred to as a processing apparatus 10) shown in FIG. 1 is an example of an apparatus that implements the tire simulation method of the present invention. The processing device 10 is configured using hardware such as a computer. 1 is used in the tire simulation method of the present invention, but the tire simulation method is limited to the processing device 10 as long as the tire simulation method can be executed using hardware such as a computer and software. is not.

The processing device 10 includes a processing unit 12, an input unit 14, and a display unit 16. The processing unit 12 includes a condition setting unit 20, a model creation unit 22, an analysis unit 24, a search unit 26, a memory 28, a display control unit 30, and a control unit 32. In addition, although not shown, it has a ROM and the like.
The processing unit 12 is controlled by the control unit 32. In the processing unit 12, the condition setting unit 20, the model creation unit 22, the analysis unit 24, the search unit 26, and the display control unit 30 are connected to the memory 28, and the condition setting unit 20, model creation unit 22, analysis unit 24 is connected. , And data of the search unit 26 are stored in the memory 28.

  The input unit 14 is various input devices for inputting various information such as a mouse and a keyboard in accordance with an operator instruction. The display unit 16 displays, for example, results obtained by the tire simulation method of the present invention, and various known displays are used. The display unit 16 also includes a device such as a printer for displaying various types of information on an output medium.

  The processing device 10 functions each part of the condition setting unit 20, the model creation unit 22, the analysis unit 24, and the search unit 26 by executing a program (computer software) stored in a ROM or the like using the control unit 32. Form. As described above, the processing apparatus 10 may be configured by a computer in which each part functions by executing a program, or may be a dedicated apparatus in which each part is configured by a dedicated circuit.

  The condition setting unit 20 is input with various conditions and information such as the tire size necessary for the tire simulation method of the present embodiment, the size of each member constituting the tire, the physical position such as the arrangement position and the elastic coefficient. , To set. Various conditions and information are input via the input unit 14. Various conditions and information set by the condition setting unit 20 are stored in the memory 28.

In the present invention, contact analysis is performed, but the definition of contact is also set. The contact can be defined by, for example, a skim touch (the last load that a tire contacts). Here, skim touch refers to the state of the last load applied when the tire is in contact with the road surface (see Japanese Patent Nos. 4160709 and 4119585). In the contact analysis, for example, a skim touch state is obtained.
In addition to this, the contact can be defined by a load state, and can be regarded as being in contact if the contact load is, for example, 5N or 10N. The contact load is not particularly limited.
When calculating the balance of static force such as static analysis, the tire model or road surface model is displaced for contact in calculation. For this reason, when calculating the deflection amount or the spring constant, it is possible to easily calculate the skim touch state as a reference.

The condition setting unit 20 is set with a plurality of parameters determined as design variables among parameters defining the tire and the material constituting the tire. The design variable parameters may be set with variation factors such as load and boundary conditions, and in the case of products, constraints such as size and mass.
In addition, a plurality of parameters determined as characteristic values (objective functions) among the parameters defining the tire and the material constituting the tire are set. As the characteristic value, an index for evaluating the tire and the material constituting the tire other than the physical and chemical characteristic values such as cost may be used. The tire and the material constituting the tire may be a whole system including the tire, such as a part constituting the tire, a tire assembly form, or a part thereof, instead of the tire alone.

The characteristic value set in the condition setting unit 20 is a physical quantity to be evaluated. The objective function is a function for obtaining a physical quantity to be evaluated.
In the case of a tire, the characteristic value is a characteristic value of the tire. The characteristic value is, for example, a physical quantity to be evaluated as tire performance, CP (cornering power) which is a lateral force in the vicinity of zero slip angle as an index of steering stability, and 1 of a tire as an index of riding comfort. Wear of tire tread members as indicators of secondary natural frequency, rolling resistance as an indicator of fuel efficiency, lateral stiffness as an indicator of steering stability, longitudinal stiffness, deflection, contact pressure distribution, cornering force, and wear resistance Energy and the like. Other than this, examples of the physical quantity of the tire include a shape and a dimensional value. The objective function is a function for obtaining them. The objective function has a preferable direction in terms of performance, and the value increases, decreases, or approaches a predetermined value.

The design variables are a plurality of parameters among the tire material behavior, the tire shape, the tire cross-sectional shape, the tire natural vibration mode, and the tire structure. Examples of the design variable include a radius of curvature that defines a crown shape in a tread portion of the tire, a belt width dimension of the tire that defines a tire internal structure, and the like. In addition to this, for example, a filler dispersion shape that defines material characteristics in the tread portion, a filler volume ratio, and the like can be given.
The constraint condition is a condition for constraining the value of the objective function to a predetermined range or constraining the value of the design variable to a predetermined range.
In addition, information on vehicle specifications to be used for vehicle running simulation, such as tire load load, tire rolling speed and other running conditions, road surface conditions on which the tire runs, for example, uneven shape, friction coefficient, etc. Is set.

In addition, information for determining a nonlinear response relationship between the parameter of the tire design variable and the parameter of the tire characteristic value is set in the condition setting unit 20. This nonlinear response relationship includes, for example, a numerical simulation such as FEM, a theoretical expression, an approximate expression, and the like.
The condition setting unit 20 sets a simulation condition and a constraint condition in the simulation when performing a numerical simulation such as a tire model generated by a non-linear response relationship, a boundary condition of the tire model, and FEM.

Conditions for optimal solution search used for optimization are a method for searching for an optimal solution and various conditions in the optimal solution search. In the present embodiment, for example, a genetic algorithm (GA) can be used as a method for searching for an optimal solution. In general, it is known that the search ability of a genetic algorithm decreases as the characteristic value (objective function) increases. One way to solve this is to increase the number of individuals. On the other hand, when the number of individuals is increased and the optimum solution is searched, many optimum solutions are calculated.
In addition to this, a design variable definition area is set in the condition setting unit 20. The domain of design variables may be continuous or discrete level values, the domain may be set only for specific design variables, and the remaining design variables may be constants.

  Since there are a plurality of design variables, a discrete level value may be set for each of all the design variables, and the optimal solution may be calculated using the domain as a constant for the remaining design variables. For optimization calculation, either a search method using a nonlinear relationship (response surface) between input variables and output variables or a search method by calculating output values while sequentially changing input variables according to an optimization algorithm It may be used.

The model creation unit 22 creates a tire model that can be analyzed by a computer with respect to a tire and a road surface model that can be analyzed by a computer with respect to a road surface in contact with the tire, and the road surface is a target for rolling the tire.
As the tire model, a tire model that reproduces a rim on which a tire is mounted, a wheel, and a tire rotation axis may be used. Further, if necessary, a model that reproduces a vehicle to which a tire is attached may be incorporated into the tire model. At this time, a model in which the tire model, the rim model, the wheel model, and the tire rotation axis model are integrated based on a preset boundary condition can be created.
Moreover, the form of the tire model used for the analysis is not particularly limited, and may be a smooth tire without a groove, only a main groove, or with a pattern.

The tire model created by the model creation unit 22 is created using each type of design parameters set by the condition setting unit 20, and a known creation method can be used for creating the tire model. .
For example, the tire model 40 is created by dividing the tire into a finite number of elements including a plurality of nodes. The road surface model 42 may be created in the same manner as the tire model 40, or may be an analytical model as an elastic body, or may be an analytical model as a rigid body. Further, the road surface model 42 may be a three-dimensional discretization model or an analysis model as a surface.
The elements constituting the tire model 40 or the road surface model 42 are, for example, a quadrilateral element in a two-dimensional plane, a solid element such as a tetrahedral solid element, a pentahedral solid element, a hexahedral solid element in a three-dimensional plane, a triangular shell element, a quadrilateral An element that can be analyzed by a computer, such as a shell element such as a shell element or a surface element. In the process of analysis, the elements divided in this way are identified one by one using three-dimensional coordinates in the three-dimensional model and using two-dimensional coordinates in the two-dimensional model.

  The model creation unit 22 creates various calculation models based on the set nonlinear response relationship. As described above, the nonlinear response relationship includes a numerical simulation such as FEM. In this case, the model creation unit 22 generates a mesh model corresponding to a design parameter representing a design variable and a characteristic value parameter representing a characteristic value. Is done. Also in the case of theoretical formulas and approximate formulas, theoretical formulas and approximate formulas corresponding to design parameters and characteristic value parameters are created. When the structure is a tire, a tire model is created. The analysis unit 24 performs a simulation calculation using the tire model.

Each of these models may be a discretized model capable of numerical calculation, such as a finite element model for use in a known finite element method (FEM). In addition to the road surface model and the tire model, the inclusions existing on the road surface are reproduced, for example, when a tire design plan that optimizes tire performance such as tire wet performance is obtained using the tire model. Generate a model. For example, as an inclusion model, various models that reproduce water, snow, mud, sand, gravel, ice, and the like on the road surface may be generated as a discretized model that can be numerically calculated. The road surface model is not limited to a model that reproduces a road surface with a flat surface, and may be a model that reproduces a road surface shape having irregularities on the surface as necessary.
In the present embodiment, the numerical calculation method used for the simulation is not limited to the finite element method (FEM), and the boundary element method (BEM), the finite difference method (FDM), and the like can also be used. Also, the most appropriate numerical calculation method can be selected according to the boundary condition or the like, or a plurality of numerical calculation methods can be combined.

The analysis unit 24 applies a predetermined internal pressure to the tire model to perform an internal pressure filling process (also referred to as an inflation process), and obtains position information of the maximum position in the radial direction of the tire model after the internal pressure filling process. To do. Based on this position information, a position given a predetermined distance in the radial direction from the maximum position in the radial direction of the tire model is set as a set position, and set position information of this set position is acquired. A road surface model is set at the set position based on the set position information. The analysis unit 24 also serves as a position information acquisition unit that acquires position information of a maximum position and a setting unit that acquires setting position information and sets a road surface model at the setting position.
Specifically, as shown in FIG. 2, the maximum position A in the radial direction D of the tire model 40 subjected to the internal pressure filling process by applying a predetermined internal pressure, that is, the maximum diameter of the tire model 40 in the radial direction D. The position information of the position that becomes X (mm) is acquired. The road surface model 42 is arranged at a predetermined distance from the position A, which is a distance Y (mm) in FIG. Note that the tire model 40 subjected to the internal pressure filling process shown in FIG. 2 is an axisymmetric two-dimensional tire cross-sectional model with the rotational axis C as the symmetry axis, although a part of the illustration is omitted. The rotation axis C is also called an axle. Reference numeral 44 denotes a rim model. FIG. 2 shows a tire model in which a tire fitted on a rim can be analyzed by a computer. The rim may not be provided.

In the analysis unit 24, contact analysis is performed by restraining either the rotation axis C of the tire model 40 or the road surface model 42 in the state shown in FIG. The analysis unit 24 calculates the characteristic value using the various models created by the model creation unit 22. Thereby, a characteristic value for the design variable is obtained. The obtained characteristic value is stored in the memory 28.
In the analysis unit 24, for example, the behavior of the tire model when the simulation conditions for reproducing the rolling of the tire rolling on the road surface are given to the tire model generated by the model creation unit 22, the road surface model, or the like, or A physical quantity such as force acting on the tire model is obtained in time series. The analysis unit 24 functions, for example, by executing a subroutine using a known finite element solver.
In the analysis unit 24, when the model creation unit 22 creates a theoretical formula, an approximate formula, and the like, the theoretical formula, the approximate formula, and the like are solved, and a characteristic value is calculated.
The analysis unit 24 can also analyze physical quantities such as contact pressure, tire stiffness, and rolling characteristics after the contact analysis.

  The distance Y is preferably about 0 <Y ≦ 10 (mm). The distance Y is preferably set to a value close to zero in consideration of calculation errors in the computer or software, for example, rounding error, truncation error, digit loss and information loss. Due to the calculation error, for example, the tire model 40 may be in contact with the road surface model 42 even if it is less than 1 mm. On the other hand, if the distance Y exceeds 10 mm, the calculation time may be long.

The search unit 26 searches for an optimal solution based on the characteristic value obtained by the analysis unit 24 according to the optimal solution search conditions set by the condition setting unit 20 and calculates the optimal solution. The obtained optimal solution is stored in the memory 28.
The optimum solution can be searched for by the following method. For example, the optimum solution is searched from the characteristic values obtained by the analysis unit 24. A response surface is created based on the obtained characteristic values and an optimal solution is searched. Based on the obtained characteristic values, an optimal solution is searched using an optimization algorithm.
Here, the optimal solution cannot be said to be superior to any other solution in a plurality of characteristic values (objective functions) in a trade-off relationship, but a solution that has no other superior solution exists. Say. In general, there are a plurality of optimal solutions as a set.

The search unit 26 searches for an optimal solution using, for example, a genetic algorithm (GA).
Genetic algorithms include, for example, DRMOGA (Divided Range Multi-Objective GA) or NCGA (Neighborhood Cultivation GA), which divides a solution set into a plurality of regions along an objective function and performs multi-objective GA for each divided solution set. , Using known methods such as DCMOGA (Distributed Cooperation model of MOGA and SOGA), NSGA (Non-dominated Sorting GA), NSGA2 (Non-dominated Sorting GA-II), and SPEAII (Strength Pareto Evolutionary Algorithm-II) Can do. At that time, it is necessary to obtain a set of optimal solutions with high accuracy, with the solution set widely distributed in the solution space. For this reason, in the search part 26, selection using the vector evaluation genetic algorithm (Vector Evaluated Generic Algorithms: VEGA), the Pareto ranking method, or the tournament method is performed, for example. In addition to the genetic algorithm (GA), for example, an evolutionary calculation method such as annealing (SA) or particle swarm optimization (PSO) can be used to efficiently search for a solution.

In the present invention, the non-linear response relationship defined between the design variable and the characteristic value, that is, the one used when the characteristic value is obtained using the design variable is not limited to the simulation such as FEM. It is also possible to use theoretical formulas, approximate formulas, and the like. For example, the value of the objective function may be calculated using a simulation approximation formula instead of the calculation using the simulation model. In this case, an optimal solution can be obtained from an experimental result obtained based on the experimental design using an approximate expression between the design variable and the objective function, for example, a simulation approximate expression. As this simulation approximate expression, a known nonlinear function obtained by a polynomial or a neural network can be used.
Here, for example, when creating a non-linear relationship (response surface) using a quadratic polynomial, the number of necessary samples is at least N (N + 3) / 2 + 1 cases if the number of input variables is N. If the number of input variables is 10, the number of samples is 66. If the number of input variables is 30, the number of samples is 496. Thus, many tire models are required for optimization.

The display control unit 30 displays the tire model, the road surface model, the result of numerical calculation, and the optimum solution on the display unit 16. For example, the display control unit 30 reads the tire model and the road surface model from the memory 28 and displays the tire model and road surface model on the display unit 16. .
In the processing device 10, in the input file when changing the shape, the common part such as the boundary condition and the analysis step and the different part depending on the individual shape such as the nodal coordinate value, the arrangement angle of the reinforcing material and the initial tension are divided, By automating using a file format that can be imported into a common part, that is, by converting individual information into an include file, it is possible to easily examine tire shapes even when examining many tire shapes. is there.

Next, a tire simulation method according to this embodiment will be described.
FIG. 3 is a flowchart showing the simulation method according to the first embodiment of the present invention. In the present embodiment, a tire having a size of 215 / 55R17 is a simulation target.
Before the simulation, the tire shape, the tire size, and the tire pattern are set in the condition setting unit 20. As the design variable, for example, a tire shape parameter is set. As the characteristic value (objective function), longitudinal rigidity, for example, rolling resistance and lateral rigidity are set. In the present embodiment, the input is a tire shape parameter, and the output is longitudinal rigidity, rolling resistance, lateral rigidity, and the like. It simulates how the longitudinal stiffness, rolling resistance and lateral stiffness change depending on the value of the tire shape parameter. The tire shape parameters, longitudinal rigidity, rolling resistance and lateral rigidity are set in the condition setting unit 20. Define the nonlinear response to be used when obtaining the characteristic value (objective function) from the design variables.

In the simulation method, as shown in FIG. 3, a tire model 40 (see FIG. 2) such as a mesh model is created by the model creation unit 22 using information set in the condition setting unit 20 (step S10). Next, the analysis unit 24 performs an internal pressure filling process on the tire model 40 (see FIG. 2), and acquires information on the maximum diameter (step S12). Thereby, the position information of the position A shown in FIG. 2 can be acquired.
Next, the road surface model 42 (see FIG. 2) is arranged at a set position where a predetermined distance Y (mm) is given in the radial direction D with respect to the position A, that is, a position separated by a predetermined distance (step S14). Thereby, as shown in FIG. 2, the tire model 40 and the road surface model 42 are arranged at a predetermined distance. The road surface model 42 may be created at the same time as the tire model 40 by the model creation unit 22 or may be created when the position information of the position A is acquired.

Next, the analysis unit 24 restrains either the tire model 40 or the road surface model 42, and performs contact analysis in this state (step S16). In the contact analysis, for example, a skim touch state is obtained.
After the contact analysis, the analysis unit 24 calculates a characteristic value based on the above-described nonlinear response relationship. Specifically, the rolling resistance and the lateral stiffness with respect to the tire shape parameter are calculated and stored in the memory 28, for example.
Thereafter, the search unit 26 performs optimization using the characteristic value as an objective function for the calculation result of the characteristic value to obtain a Pareto solution. For the calculation of the Pareto solution, for example, an evolutionary calculation method such as a genetic algorithm, an annealing method (SA), or a particle swarm optimization (PSO) is used. The obtained Pareto solution is stored in the memory 28.

  In the present embodiment, the road surface is positioned at a predetermined distance from the position A at the maximum outer diameter, for example, greater than 0 mm and less than 10 mm, with respect to the tire model 40 subjected to the internal pressure filling process by applying a predetermined internal pressure. A model 42 is set. As a result, the tire model 40 can be prevented from penetrating into the road surface model 42, and calculation failure does not occur. For this reason, it is particularly effective when many iterative calculations such as calculation of characteristic values for various shapes such as optimization calculation are required. Moreover, since the road surface model 42 is automatically set for the tire model 40 subjected to the internal pressure filling process, it is not necessary for the operator to set the position of the road surface model from the inflation shape of the tire model subjected to the internal pressure filling process. The efficiency of tire simulation can be improved.

In the present embodiment, the tire model 40 of the two-dimensional tire cross-sectional model shown in FIG. 2 has been described as an example, but the present invention is not limited to this, and a three-dimensional tire model can also be used.
FIGS. 4A to 4C are schematic views showing analysis steps of the simulation method according to the first embodiment of the present invention.
FIG. 4A corresponds to FIG. 2, and the rim is modeled so that it can be analyzed by a computer. A rim model 44 is fitted to the tire model 40. In the state shown in FIG. 4A, the position of the road surface model 42 is set with respect to the tire model 40, and the road surface model 42 is installed at that position.

  The tire model 40 is developed in the circumferential direction with the rotational axis C as the axis of symmetry, and a tire model 46 shown in FIG. 4B is obtained. When the tire model 46 is displayed three-dimensionally, a three-dimensional tire model 48 shown in FIG. 4C is obtained. At this time, the road surface model 43 is also made three-dimensional. Accordingly, as shown in FIG. 4C, the three-dimensional road surface model 43 is arranged at a set position where a predetermined distance is given to the three-dimensional tire model 48, that is, at a position away from the predetermined distance. Either the tire model 48 or the road surface model 43 is constrained, and contact processing is performed in that state.

  By using the two-dimensional tire model, the setting position of the road surface model is determined, and then the three-dimensional tire model is used, so that the calculation amount is larger than the calculation of the setting position of the road surface model using the three-dimensional tire model from the beginning. The position of the road surface model can be determined easily with less. Further, since the axially symmetric two-dimensional tire model is developed in the circumferential direction to form a three-dimensional tire model, the calculation load can be reduced and the calculation process can be made more efficient.

Next, a second embodiment of the present invention will be described.
FIG. 5 is a flowchart showing a simulation method according to the second embodiment of the present invention.
Compared with the simulation method of the first embodiment shown in FIG. 3, the simulation method of the present embodiment has a plurality of tire models to be subjected to contact analysis, and the contact processing is performed for all tire models (step S18). The other steps (steps S10 to S16) are the same as the simulation method of the first embodiment shown in FIG. Various conditions and information such as the tire size of a plurality of tire models, the size of each member constituting the tire, the physical position such as the arrangement position and the elastic coefficient, and the like are input to the condition setting unit 20 in advance and are input to the memory 28. Remembered.

  Specifically, in the simulation method of the present embodiment, tire models that can be analyzed by a computer are created (step S10), and after the internal pressure filling process is performed on each tire model, the radial direction D of each tire model is set. The position information of the maximum position A (see FIG. 2) in (see FIG. 2) is acquired (step S12). Based on the position information of the maximum position, a road surface model that can be analyzed by a computer is created at a set position given a predetermined distance from the maximum position, that is, a position separated by a predetermined distance (step S14), and contact analysis is performed. Execute (step S16). Steps S10 to S16 are repeated until all tire models to be subjected to contact analysis are executed (step S18).

  In the present embodiment, at least two tire models having different shapes and structures, that is, a plurality of tire models are created, and calculation of each tire model is performed. Since the automatic setting is performed without adjusting the position, the efficiency of the simulation can be improved while preventing the calculation from being broken. Thus, the present invention is particularly effective when performing calculations for a plurality of tire models.

  The present invention is basically configured as described above. Although the simulation method, apparatus, and program of the present invention have been described in detail above, the present invention is not limited to the above embodiment, and various improvements or modifications may be made without departing from the spirit of the present invention. Of course.

Hereinafter, the effect of the simulation method of the present invention will be specifically described.
In this example, the longitudinal rigidity of the tire is calculated by the simulation methods of Examples 1 to 4 and Comparative Example 1 and the reference example, and the failure of the calculation and the time of calculation are examined to determine the effect of the simulation method of the present invention. It was confirmed.

In this example, the longitudinal stiffness of all the tire models to be analyzed created according to the experiment plan with the change amount of the outer diameter and the change amount of the width, which are the shape parameters, as design variables based on the tire model of the tire size 215 / 55R17. An analysis was performed using the finite element method (FEM), and the number of calculations converged and the time required for the calculations were compared.
As an experimental design method, a design matrix, that is, a combination of design variables, was created using the Latin hypersquare method. And according to the combination of the design variables in each matrix, 50 cases of random tire shapes were expressed from the linear sum of those shapes, and analyzed as tire models to be analyzed.
As for the change of the outer diameter, as shown in FIG. 6A, a tire model 52 in which the outer diameter is changed parametrically using the tire model 50 of the reference tire size 215 / 55R17 as a design variable in the radial direction D is used. . As for the change in width, as shown in FIG. 6B, a tire model 54 in which the width is changed parametrically using the tire model 50 of the reference tire size 215 / 55R17 as a design variable in the width direction W is used. .

Hereinafter, Examples 1 to 4, Comparative Example 1, and a reference example will be described.
As a reference example, a three-dimensional tire model and a three-dimensional road surface model were used from the beginning. As a simulation procedure of the reference example, first, a three-dimensional tire model and a three-dimensional road surface model are created, and then the position of the three-dimensional road surface model is set, and the three-dimensional road surface model is arranged at the set position. Grounding calculation was performed. Thereafter, the longitudinal rigidity was calculated by FEM.
The above procedure was performed for the above-mentioned 50 cases. In addition, the position of the road surface model was set to the outer diameter of the reference model + 1 mm, and was uniform in 50 cases without changing from case to case.

In Comparative Example 1, a three-dimensional tire model and a three-dimensional road surface model were used from the beginning. As a simulation procedure of Comparative Example 1, first, a three-dimensional tire model and a three-dimensional road surface model are created, and then the position of the three-dimensional road surface model is set to a position that is largely separated in advance. Was placed at the set position and the ground contact calculation was performed. Thereafter, the longitudinal rigidity was calculated by FEM.
The above procedure was performed for the above-mentioned 50 cases. In addition, the position of the road surface model was set to the outer diameter of the reference model +50 mm, and was uniform in 50 cases without changing from case to case.

  In Example 1, a three-dimensional tire model and a three-dimensional road surface model were used from the beginning. As a simulation procedure of the first embodiment, a three-dimensional tire model and a three-dimensional road surface model are created, an internal pressure filling process is performed on the three-dimensional tire model, and an inflated state is set. The diameter was set to +1 mm. Then, a three-dimensional road surface model was placed at the set position, the ground contact calculation was performed, and then the longitudinal rigidity was calculated by FEM.

Since the second embodiment was simulated in the same manner as the first embodiment except that the position of the three-dimensional road surface model was set to the inflation outer diameter +8 mm as compared with the first embodiment, the detailed description thereof is as follows. Omitted.
Since the third embodiment was simulated in the same manner as in the first embodiment except that the position of the three-dimensional road surface model was set to the outer diameter +15 mm at the time of inflation as compared with the first embodiment, the detailed description thereof is as follows. Omitted.
In Example 4, a two-dimensional axisymmetric tire model was used. As a simulation procedure of the fourth embodiment, a two-dimensional axisymmetric model is created, and an internal pressure filling process is performed on the two-dimensional axisymmetric tire model so as to be in an inflated state. Set to +1 mm. Then, a two-dimensional axisymmetric tire model was developed in the circumferential direction in an inflated state to create a three-dimensional tire model. Then, the ground contact calculation was performed, and then the longitudinal rigidity was calculated by FEM.

Table 1 below shows the number of convergence cases and the calculation time for calculating the longitudinal stiffness of the tires in Examples 1 to 4 and Comparative Example 1 and the reference example.
The number of convergence cases indicates how many calculations have failed among the 50 cases. If there is no calculation out of 50 cases, it is expressed as 50/50. The calculation failed when the maximum radial position of the tire model penetrated the road surface model. Specifically, in this embodiment, the contact analysis program has an error trap that does not execute the calculation when the maximum position penetrates the road surface model, and the calculation fails when this error trap works. And not counted in the number of convergence cases.
The calculation time was an average time obtained by dividing all the calculation times by the number of convergence cases. It was shown as an index with the calculation time of the reference example as 100. In the reference example, since 43 cases are calculated, the average time is 43 cases.

As shown in Table 1 above, in Examples 1 to 4, the longitudinal stiffness was calculated for a 50-case tire model, but none of the calculations failed. Also, the calculation time was shorter or comparable to that of the reference example. As described above, in the simulation method of the present invention, it was possible to both prevent calculation failure and shorten the calculation time. In particular, as in Example 4, the two-dimensional axisymmetric tire model is subjected to internal pressure filling processing to set the position of the road surface model, and then the two-dimensional axisymmetric tire model is developed in the circumferential direction to be three-dimensional. When a tire model is created, the calculation time can be further shortened.
On the other hand, although Comparative Example 1 did not fail in calculation, the calculation time required a great deal. In the reference example, as described above, there are many calculation failures due to the tire model penetrating the road surface model.

10 Simulation device (processing device)
DESCRIPTION OF SYMBOLS 12 Processing part 14 Input part 16 Display part 20 Condition setting part 22 Model preparation part 24 Analysis part 26 Search part 28 Memory 30 Display control part 32 Control part 40,100 Tire model 42,110 Road surface model 44 Rim model 46 Tire model 48 Three-dimensional Tire model

Claims (9)

Computer for the tire, a creation step of creating a parsable tire model in the computer,
A position information acquisition step in which the computer performs an internal pressure filling process on the tire model and acquires position information of a position that is maximum in a radial direction of the tire model;
The computer creates a road surface model that can be analyzed by the computer for the road surface that the tire contacts, and gives a distance of more than 0 mm and less than 10 mm in the radial direction from the maximum position of the tire model based on the position information. A setting step of obtaining setting position information of the set position and setting the road surface model at the setting position based on the setting position information;
A tire simulation method, comprising: an analysis step in which the computer performs contact analysis by constraining one of the rotation axis of the tire model and the road surface model. In the creation step, the computer creates at least two tire models in which the shape or structure of the tire is changed,
In the position information acquisition step, the computer acquires set position information for each tire model,
In the setting step, the computer sets the road surface model based on the set position information,
The tire simulation method according to claim 1, wherein the analysis step by the computer performs contact analysis for each tire model. In the creation step, the computer creates a two-dimensional axisymmetric tire cross-sectional model as the tire model,
In the setting step, the computer sets the road surface model for the two-dimensional axisymmetric tire cross-section model,
The tire simulation method according to claim 1 or 2, wherein in the analysis step by the computer, contact analysis is performed using a three-dimensional tire model obtained by developing the two-dimensional axisymmetric tire cross-sectional model in the circumferential direction. .   The design variable includes at least a factor that changes the shape of the tire, and includes at least the physical characteristics of the tire as an objective function. After the contact analysis in the analysis step, the physical characteristics of the tire are determined using the design variables. The tire simulation method according to claim 1, wherein optimization calculation as a function is performed to calculate a physical quantity of the tire. A tire model that can be analyzed by a computer for a tire, and a model creation unit that creates a road surface model that can be analyzed by a computer for the road surface that the tire contacts,
An internal pressure filling process is performed by applying a predetermined internal pressure to the tire model, and an analysis unit that performs contact analysis by constraining either the rotation axis of the tire model or the road surface model, and the internal pressure filling process A position information acquisition unit that acquires position information of a position that is maximum in the radial direction of the tire model;
Based on the position information, setting position information of a setting position that gives a distance of more than 0 mm and less than 10 mm in the radial direction from the maximum position of the tire model is obtained, and based on the setting position information, the setting position information A tire simulation apparatus comprising: a setting unit that sets a road surface model. The model creation unit creates at least two tire models in which the shape or structure of the tire is changed,
The position information acquisition unit acquires set position information for each tire model,
The setting unit is configured to set the road surface model based on the set position information.
The tire simulation device according to claim 5, wherein the analysis unit performs contact analysis for each tire model. The model creation unit creates a two-dimensional axisymmetric tire section model as the tire model, and further creates a three-dimensional tire model obtained by developing the two-dimensional axisymmetric tire section model in the circumferential direction. Is,
The setting unit sets the road surface model for the two-dimensional axisymmetric tire cross-sectional model,
The tire simulation device according to claim 5 or 6, wherein the analysis unit performs contact analysis using the three-dimensional tire model.   The design variable includes at least a factor that changes the shape of the tire, and includes at least the physical characteristics of the tire as an objective function. The analysis unit uses the design variables to determine the physical characteristics of the tire after the contact analysis. The tire simulation apparatus according to claim 5, wherein optimization calculation as a function is performed to calculate a physical quantity of the tire.   The program for making a computer perform each process of the simulation method of the tire of any one of Claims 1-4 as a procedure.

Images