JP2008195341A - Designing method of tire and designing device of tire - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To effectively design in a short period of time a tire in which a transient response characteristic when a relative angle with a road surface changes in time series becomes optimum in the act of rolling on the road surface. <P>SOLUTION: A tire model with a designing parameter as a variable and a road surface model to recreate a road surface are at least formed, simulation computation to change the relative angle of the tire model against the road surface model in time series is carried out while rolling the tire model by making it contact with the road surface model, a value of a target function to express the transient response characteristic of the tire is found in accordance with a result of the simulation computation, a value of a designing parameter to give to the tire model is repeatedly changed, the simulation computation and derivation of the target value are practiced concerning the tire model formed by the change in every change, and an optimizing process to find the value of the designing parameter when the value of the target function is optimized is carried out in accordance with the plurality of values of the target functions found by repetition of the above. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to a tire design method and apparatus, and more particularly, a transient when a relative angle with a road surface changes in time series while rolling on a road surface with reference to a predetermined tire. The present invention relates to a method and an apparatus for designing a tire having an optimum response characteristic.

  Conventionally, the structure, shape, etc. of the structure are designed by evaluating the performance by making a prototype and conducting an experiment, creating a structure analysis model of the structure, and various other methods including the finite element method There were many design searches by so-called trial and error, in which performance evaluation was performed by performing numerical experiments using the structural analysis method of, and based on the performance evaluation results, the prototype and re-creation of the structure and structural analysis model were re-created. Therefore, to design an optimum structure desired by the designer, it has been necessary to spend a great deal of labor, a lot of time, and a lot of trial production costs.

  This also applies to tire manufacturers, and the tire design required a great deal of labor, time and cost through trial and error trial manufacture and numerical experiments. For example, a cross-sectional shape cut along a plane including the rotation axis of the tire, that is, a tire cross-sectional shape has a great influence on the tire performance, and therefore, it has been necessary to design it with particular care in order to obtain a desired tire performance. By the way, various optimum design methods based on numerical calculation for obtaining optimum product performance by improving high-speed numerical calculation by a supercomputer or the like have been proposed today.

  For example, Patent Document 1 below proposes a multi-component material optimization analysis apparatus that can be used for rubber compounding design for tires and the like. Further, Patent Document 2 below proposes a tire design method and an optimization analysis device that can obtain an optimization design plan such as a tire shape, structure, and pattern. In the following Patent Document 1, for example, the blending parameters of the constituent components of the multicomponent material are used as design parameters, and hardness, tensile strength, elongation, compression set, tensile strength change rate, elongation change rate, hardness A steady characteristic of the tire such as change and volume change rate is used as an objective function, and design parameters when the objective function satisfies a predetermined condition are output as a material optimization design plan. Further, in Patent Document 2 below, for example, in order to improve steering stability, the tire circumferential direction belt tension at the time of air filling, a lateral spring constant, and the like, which are steady characteristics of the tire, are used as an objective function.

Japanese Patent Laid-Open No. 10-55348 WO99 / 07543

  By the way, when designing a racing tire, for example, it is necessary to develop a tire having excellent steering stability during steering at high speeds. A tire's transient response characteristic, which is a characteristic related to the response characteristic of an automobile when fast steering is performed, greatly contributes to such tire steering stability performance during high-speed driving. Here, the transient response characteristic of a tire means the tire lateral force, cornering force, self-aligning torque, etc., when the tire is steered quickly, in response to input to the tire such as load, slip angle, camber angle, and slip ratio. This is a characteristic represented by a response characteristic. Such a transient response characteristic of the tire cannot be quantitatively expressed by one of the numerical values indicating the steady characteristic of the tire. That is, even if a tire having an optimal steady-state characteristic can be designed using the methods disclosed in Patent Document 1 and Patent Document 2, a tire having an optimal transient response characteristic should be designed. I couldn't.

  In addition, as a conventional test method for evaluating tire steering stability during high-speed driving, the evaluator steers the vehicle with the test tire mounted on the vehicle, and the steering stability felt by the evaluator. There is an actual vehicle feeling test to get information on. In this actual vehicle feeling test, there is an advantage that information on steering stability when the steering is actually turned can be obtained. However, such information obtained based on the senses of the evaluator is not an objective evaluation result, and takes a long time for the evaluation. Thus, conventionally, even an evaluation method has not been established for the steering stability performance of a tire during high-speed driving. For this reason, in the past, a great deal of effort was required to design a tire that optimizes the steering stability performance of the tire during high-speed driving, that is, the response characteristic (transient response characteristic) when the tire is steered quickly. And a great deal of time and a lot of prototype cost.

  Therefore, the present invention can effectively design a tire with an optimum transient response characteristic when the relative angle with the road surface changes in time series while rolling on the road surface in a short time. It is an object of the present invention to provide a tire design method and apparatus.

  In order to solve the above-mentioned problems, the present invention has an optimum transient response characteristic when a relative angle with the road surface changes in time series while rolling on the road surface with reference to a predetermined tire. A design parameter of a tire to be changed for optimization, time series information of the relative angle, at least one objective function representing the transient response characteristic, and for the optimization A condition setting step for setting at least an optimum condition, a tire generation model using the design parameter as a variable, a model generation step for generating at least a road surface model for reproducing the road surface, and a value for the design parameter While rolling the tire model in contact with the road surface model, a phase of the tire model with respect to the road surface model is determined according to the time series information of the relative angle. An operation step for performing a simulation operation for changing the angle in time series, an objective value derivation step for obtaining the value of the objective function based on the result of the simulation operation, and the design parameter value to be given to the tire model are repeatedly changed. Then, for each tire model generated by the change, the calculation step and the objective value derivation step are executed for the tire model generated by the change, and based on the values of the plurality of objective functions obtained by the repetition step. And an optimization step of performing an optimization process for obtaining the value of the design parameter when the value of the objective function satisfies the optimal condition.

  The time series information of the relative angle includes a plurality of continuous time intervals in which the amount of change in the relative angle per unit time is different, and the amount of change in the relative angle per unit time is the same for each time interval. Each of them is preferably substantially constant.

  In the time series information of the relative angle, it is preferable that time intervals in which the change amount of the relative angle per unit time is zero are intermittently arranged.

  Further, the objective function uses the time information of each end point of the plurality of time sections and the information on the axial force when the time series axial force of the rotating shaft is divided into a plurality of time sections, and the tire function A function for obtaining a value representing a response characteristic of the axial force applied to the rotating shaft is preferable.

  Further, the objective function is a graph showing a time change of the axial force in which one axis of the orthogonal coordinate system is expressed as time and the other axis is expressed as the axial force. The function is preferably a function for obtaining a value representing a response characteristic of the axial force applied to the rotation axis of the tire, using information on each end point in a plurality of time intervals when the time interval is divided.

  The objective function is the shortest distance between the end points, the distance in the direction along the one axis between the end points, and the other axis between the end points for at least two of the end points in the graph. It is preferable that the function represents at least one of the direction distance and the slope of the straight line connecting the end points.

  The present invention also provides an apparatus for designing a tire that has an optimum transient response characteristic when a relative angle with the road surface changes in time series while rolling on the road surface with a predetermined tire as a reference. Tire design parameters to be changed for optimization, time series information of the relative angles, at least one objective function representing the transient response characteristics, and optimum conditions for this optimization. At least a condition setting unit to set, a tire model having the design parameter as a variable, a model generation unit that generates at least a road surface model that reproduces the road surface, and the tire model generated by giving a value of the design parameter, A simulation that changes the relative angle of the tire model with respect to the road surface model in time series according to the time series information of the relative angle while rolling in contact with the road surface model. A calculation unit for performing a calculation operation, an objective value deriving unit for obtaining a value of the objective function based on the result of the simulation calculation, and repeatedly changing the value of the design parameter given to the tire model, A repetition control unit that causes the model generation unit to generate a tire model, causes the calculation unit to perform a simulation calculation, and causes the target value derivation unit to derive the target value; An optimization control unit that performs an optimization process for obtaining a value of the design parameter when the value of the objective function satisfies the optimal condition based on the value of the function, To provide.

  According to the present invention, it is possible to effectively design a tire that has an optimum transient response characteristic when the relative angle with the road surface changes in time series while rolling on the road surface in a short time. . The present invention can be suitably used particularly in the development of tires that improve steering stability during high-speed running, such as racing tires.

  Hereinafter, a tire designing method of the present invention will be described in detail based on a preferred embodiment shown in the accompanying drawings.

  FIG. 1 is a schematic configuration diagram of a tire design device 10 (hereinafter, referred to as a device 10), which is an example of a tire design device according to the present invention. The device 10 is a device for designing a tire having an optimum transient response characteristic when a relative angle with the road surface changes in time series while rolling on the road surface with a predetermined tire as a reference. is there. The apparatus 10 includes an input unit 12, a computer 14, and an output unit 16. The input unit 12 is a mouse or a keyboard, and is a device that inputs various types of information according to instructions from an operator. The output unit 16 is a device that displays and outputs various types of information on an output medium, such as a display and a printer.

  The computer 14 includes a memory 13 and a CPU 15, and further includes a ROM (not shown). The computer 14 functionally forms each part of the optimization control unit 18 and the analysis unit 20 when the CPU 15 executes computer software stored in a ROM or the like. The analysis unit 20 includes a model generation unit 22, a simulation calculation unit 24, and a target value derivation unit 26. The device 10 may be configured by a computer in which each part functions by executing a program, or may be a dedicated device in which each part is configured by a dedicated circuit.

  The optimization control unit 18 is based on the conditions input using the input unit 12 such as a keyboard or a mouse, the tire standard plan, the design parameter to be changed for optimization, and the value of the design parameter to be changed. And at least one objective function representing the transient response characteristics of the tire, and the like are set. The optimization control unit 18 receives these pieces of information from the input unit 12 and stores them in the memory 13. The optimization control unit 18 appropriately reads necessary information from the memory 13 and performs various processes related to optimization design. Here, the simulation condition is a condition when a tire rolling simulation is performed by the simulation calculation unit 24 described later. For example, as one of the simulation conditions, time series information of the relative angle between the tire and the road surface while the tire is rolling on the road surface is set. In addition, as one of the simulation conditions, a condition of the relative speed between the tire and the road surface while the tire is rolling on the road surface is set. In addition, as one of the simulation conditions, a boundary condition such as an internal pressure of the tire, a condition of an additional load on the road surface, a condition of a coefficient of friction between the road surface model and the tire model may be set. As design parameters, parameters that define the shape and material characteristics of the tire may be set and are not particularly limited. In addition, as the objective function representing the transient response characteristic of the tire, for example, a function representing the response characteristic of the tire axial force described later applied to the rotation axis of the tire may be set. The present invention is particularly characterized by the simulation conditions and the objective function representing the transient response characteristics of the tire. The simulation conditions and the objective function will be described in detail later.

  The optimization control unit 18 also assigns design parameter values to be changed using an experimental design method, and stores the assigned design parameter values in the memory 13. The values of the design parameters stored in the memory 13 are read out and acquired by the model generation unit 22 of the analysis unit 20. As the experimental design method, for example, various known experimental design methods such as an orthogonal experimental method, a method based on the D optimality criterion, a Latin hypercube method, and a piecewise conte-carlo method may be used, and there is no particular limitation.

  Further, the optimization control unit 18 acquires target values for various tire models generated based on the assigned values of the design parameters, which are derived by the target value deriving unit 26 of the analysis unit 10. As will be described later, this objective value is a value of an objective function representing a transient response characteristic of the tire obtained by the objective value deriving unit 26 based on a simulation calculation result by the simulation calculation unit 24 of the analyzing means 10. The simulation calculation unit 24 temporarily stores the derived target value in the memory 13, and the optimization control unit 18 reads out and acquires the stored target value from the memory 13.

  The optimization control unit 18 further performs a set optimization process for a plurality of target values derived for various tire models, and extracts an optimization design plan that satisfies the set optimization conditions as necessary. To do. For example, as an optimization process, a search for an optimal solution (optimal design parameter value) using the response surface method may be performed, or a Pareto optimal solution is obtained and self-organized with information on the Pareto optimal solution You may generate a conversion map. The objective function has a preferable direction in terms of performance, and the value increases, decreases, or approaches a predetermined value. As one of the conditions for the optimization process, such information on the preferable direction of the objective function is also set together with the setting of the objective function. When it is preferable that the objective function approaches a predetermined value, a design plan in which the absolute value of the difference between the value of the objective function and the predetermined value is reduced as an optimal design plan as one of the conditions for the optimization process. Set to select. The functions of the optimization control unit 18 and each unit will be described in detail later.

  The model generation unit 22 generates a tire model using the design parameters to be changed set in the optimization control unit 18 as variables. In other words, the tire model uses the set design parameter as a variable, and assigns the assigned value of the design parameter assigned by the optimization control unit 18 to the above-described variable, so that the tire model can be analyzed according to the assigned value. Is generated. In addition, the model generation unit 22 also generates at least a road surface model that is a target for rolling the tire model. In addition to the tire model, a model that reproduces the rim, wheel, and tire rotation axis on which the tire is mounted may be generated. At this time, the tire model, the rim model, the wheel model, and the tire rotation axis model may be integrated based on a preset boundary condition and used for the simulation calculation in the simulation calculation unit 24. In this document, a tire model, a rim model, a wheel model, and a model that integrates a tire rotation axis model are also simply referred to as a 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 road surface models and tire models, models that reproduce inclusions existing on the road surface, such as when seeking tire design plans that optimize tire performance, including tire wet performance, using tire models Should be generated. For example, various models that reproduce water, snow, mud, sand, gravel, ice, and the like on the road surface may be generated as discretization models 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.

  FIG. 2 is a schematic diagram for explaining processing in each of the simulation calculation unit 24 and the target value deriving unit 26. The simulation calculation unit 24 calculates the physical quantity such as the behavior of the tire model and the force acting on the tire model when the simulation condition for reproducing the rolling of the tire rolling on the road surface is given to the tire model or the road surface model. Find in time series. The simulation calculation unit 24 functions by executing a subroutine using a known finite element solver, for example. As described above, the optimization control unit 18 stores (sets) the time series information of the relative angle between the tire and the road surface, the condition of the relative speed between the tire and the road surface, and the like in the memory 13 based on the input information. ) The simulation calculation unit 24 receives these simulation conditions from the memory 13 and obtains a physical quantity such as a behavior of the tire model and a force acting on the tire model in time series according to the set objective function. FIG. 2 shows a case where, for example, a function representing the response characteristic of the axial force applied to the tire rotation axis is set as the objective function representing the transient response characteristic of the tire. In this case, the simulation calculation unit 24 may output time-series information of force (axial force response) applied to the tire rotation axis. The axial force applied to the rotation axis of the tire is a force applied to the tire rotation axis of the tire assembly in which the tire is mounted on the rim and the wheel, and particularly refers to a force in a direction along the tire rotation axis. It can be said that the magnitude of the tire axial force corresponds to the magnitude of the cornering force generated in the tire. In the present invention, the type of objective function representing the transient response characteristic of the tire is not particularly limited, and a function for determining the transient response characteristic of the tire, such as the size of the cornering force or the time delay of the cornering force, may be set.

  FIGS. 3A and 3B respectively show an example of simulation conditions in the simulation calculation unit 24 and an example of a result of tire rolling simulation in the simulation calculation unit 24 performed based on the simulation conditions. ing. FIG. 3A is an example of time-series information of the relative angle between the tire and the road surface while the tire is rolling on the road surface, which is a simulation condition in the simulation calculation unit 24. Shows the time series information of the slip angle. In the present invention, the relative angle between the tire being rolled on the road surface and the road surface, which is set as the simulation condition, is not limited to the slip angle, and may be a camber angle, for example. May be.

  In the present invention, it is preferable that the time-series slip angle of the tire with respect to the road surface in the simulation calculation is changed to a so-called ramp shape or step shape as shown in FIG. By setting the slip angle that changes in a ramp shape or step shape, relatively high frequency components can be included in the time series data of the slip angle input to the tire model.

Relative angle time-series information changes in a ramp shape or step shape. Roughly, the relative angle time-series information consists of multiple time intervals with different relative angle changes per unit time. The amount of change in the relative angle per unit time is substantially constant in each time interval. For example, end point A ₀ to end point A ₁ , end point A ₁ to end point A ₂ , end point A ₂ to end point A ₃ , end point A ₃ to end point A ₄ , end point A ₄ to end point A ₅ , respectively. It can be said that the amount of change in relative angle (slip angle) per unit time is almost constant. That is, the graph shown in FIG. 3A can be approximated with high accuracy by a graph (linear function with time as a variable) that is different for each time segment. More specifically, the time series information of the relative angle changes in a ramp shape or a step shape means that the change amount of the relative angle per unit time is zero (in FIG. 3A). In this case, the end point A ₀ to the end point A ₁ , the end point A ₂ to the end point A ₃ , and the end point A ₄ to the end point A ₅ ) are intermittently arranged. Here, as shown in FIGS. 3A and 3B, the end points are graphs of different straight lines (linear functions using time as a variable) in a graph representing a change in time of a predetermined physical quantity. Each of a plurality of time intervals that can be approximated with high accuracy refers to a start point and an end point with respect to a time axis.

In the present invention, in the portion where the slip angle (relative angle) changes in time series (in the case of FIG. 3A, the end point A ₁ to the end point A ₂ , the end point A ₃ to the end point A ₄ ), the slip angle changes. The rate is preferably 0.1 to 0.5 (sec / deg) (in the example of FIG. 3A, about 0.2 (sec / deg)). The reason why the slip angle input is set in such a range is that the rate of change of the slip angle input that accompanies actual steering of the vehicle during high speed traveling is approximately in this range. As described above, the change rate of the slip angle input to the tire is set to the change rate of the slip angle input accompanying the actual steering of the vehicle, so that the transient response characteristic that is closer to the state in which the vehicle is actually steered by the simulation calculation. (The target value representing) can be obtained.

  In the simulation calculation unit 24, for example, while the tire model indicated by the tire rotation axis model is brought into contact with the road surface model and rolling, the tire model or the road surface model is moved relatively to set the slip angle in time series. Perform a fluctuating simulation. FIG. 3B is an example of a tire rolling simulation result in the simulation calculation unit 24. FIG. 3B shows the tire axial force obtained by inputting the time-series relative slip angle shown in FIG. 3A to the tire model and the road surface model and performing the tire rolling simulation calculation. It is an example of the time series information of a response. As shown in FIG. 3B, the tire axial force during the change of the tire slip angle changes in time series with the change of the tire slip angle shown in FIG. respond). However, this time-series change (response) of the axial force does not follow the relative slip angle shown in FIG. 3 (a) in a complete form, but a tire-specific transient response characteristic such as occurrence of a certain time delay. Appears. The magnitude of the generated axial force is also unique to the tire.

  The time series information of the axial force response, which is the simulation result output from the simulation calculation unit 24, is sent to the target value deriving unit 26. The objective value deriving unit 26 obtains a predetermined objective value (objective function value) from the time series information of the axial force response. In the present invention, a function for obtaining a value representing the tire-specific transient response characteristic described above is used as an objective function, and an optimum tire design plan is searched based on the value (objective value) of the objective function. The objective function is, for example, a value represented by using information on each end point in a plurality of time intervals when the time series information of the axial force response is divided into a plurality of time intervals. For example, in the time series graph of the axial force response shown in FIG. 3B, for at least two end points, the shortest distance between the end points and the time axis between the end points (the horizontal axis in FIG. 3B). A value representing at least one of the distance in the direction, the distance in the direction along the axial force axis between the end points (the vertical axis in FIG. 3A), and the slope of the straight line connecting the end points is set. As the objective function, a function for obtaining these values may be set in advance.

Each of the end points B ₀ , B ₁ , B ₂ , B ₃ , B ₄ , and B ₅ shown in FIG. 3B is a line segment when the tire axial force time series information is approximated by a piecewise line segment. It corresponds to the joint part between each other. The time-series information of the axial force response shown in FIG. 3B that changes in accordance with the change in the time-series relative angle shown in FIG. 3A corresponds to A _{0 to} A ₅ shown in FIG. It can be divided in time by the end points B _{0 to} B ₅ . The time series information of the axial force response shown in FIG. 3 (b) can be approximated with a certain degree of correlation by a linear graph (linear function) for each time segment, as in FIG. 3 (a). Here, the fact that each end point of A _{0 to} A ₅ corresponds to each end point of B _{0 to} B ₅ does not necessarily mean that the time information of each end point is coincident. The end points of the simulation calculation results roughly correspond to the inflection points (A ₁ , A ₂ , A ₃ , A ₄ ) of the step input or ramp input as shown in FIG. A point in time where the amount of change in the simulation result in a predetermined time unit is the largest in a nearby time region. If the axial force of the tire changes following a change in the relative angle without a time delay, the end points A _{0 to} A ₅ shown in FIG. 3A and the end points B ₀ shown in FIG. completely coincides with the ~B _5. In this case, of course, the shortest distance between the endpoints, the distance in the direction along the time axis between the endpoints, the distance in the direction along the axial force axis between the endpoints, the slope of the straight line connecting the endpoints, etc. The case shown in FIG. 3A is exactly the same as the case shown in FIG. In reality, however, the profiles of the graph shown in FIG. 3A and the graph shown in FIG. 3B do not completely match. This is because the cornering force generated in the tire responds with a delay to the change in the relative angle between the tire and the road surface, and the tire axial force that can be regarded as corresponding to the cornering force also responds with a delay.

The coordinates of the end points shown in FIG. 3B are B ₁ (x ₁ , y ₁ ), B ₂ (x ₂ , y ₂ ), and B ₃ (x ₃ , y ₃ ), respectively. For example, the distance F ₁ (= | y ₂ −y ₁ |) in the vertical axis direction between B ₁ and B ₂ , which is obtained by the objective function f ₁ , is set to the end points A _{1 to} A ₂ in FIG. It can be said that it represents the size of the cornering force generated in the tire in the time range corresponding to various time segments. The time range corresponding to the time segment from the end points A _{1 to} A ₂ increases the relative angle in a short time during the straight rolling (for example, in an actual vehicle, during high speed traveling). Corresponds to the time range during the minute steering of the steering wheel. For example, when the handle was very small steering during high-speed running, if you want to design a tire characteristics such as cornering force as large as possible to occur in the tire, it may be designed tires target value F ₁ is as large as possible.

Further, for example, the slope F ₂ (= | y ₃ −y ₂ | / | x ₃ −x ₂ |) of the straight line connecting B ₂ and B ₃ obtained by the objective function f ₂ is shown in FIG. ) Of the cornering force generated in the tire in the time range corresponding to the time interval from the end points A _{2 to} A ₃ . This time range corresponding to the leading time segment end points A ₂ to A _3, after the relative angle was increased in a short time while you are straight rolls, corresponding to the time range which maintains the relative angle is doing. Furthermore, this rate of change represents the degree of delay in the response of the tire axial force (corresponding to the magnitude of the cornering force) while the tire relative angle such as the slip angle is raised from zero. It can be said. For example, when the small steering wheel during high speed running, cornering force generated in the tire is, if you want to design a tire characteristic such respond without delay as much as possible time, by designing tires which is the target value F ₃ becomes as small as possible Good.

Further, for example, the slope F ₃ (= | y ₄ −y ₃ | / | x ₄ −x ₃ |) of the straight line connecting B ₃ and B ₄ obtained by the objective function f ₃ is shown in FIG. ) Of the cornering force generated in the tire in the time range corresponding to the time interval from the end points A _{3 to} A ₄ . A time range corresponding to the time division leading to the end point A ₃ to A _4, after maintained the relative angle corresponds to a time range while returning the slip angle to the straight tumbling condition. Furthermore, it can be said that this rate of change represents the degree of delay in the response of the tire axial force while the tire relative angle such as the slip angle is being returned to zero. For example, when the steering of the so-called crosscut returning the handle to the straight traveling during high-speed running, if you want to design a tire characteristic such as cornering force generated in the tire to respond without delay as much as possible time, is as small as possible target value F ₃ Design tires.

  In the present invention, as described above, in the graph representing the change in tire axial force in time series, the shortest distance between the end points, the distance in the direction along the time axis between the end points, the axial force axis between the end points A function for obtaining the distance in the direction along the line, the slope of the straight line connecting the end points, and the like is preferably used as the objective function. In the time series information of the simulation calculation result, the method for setting the end points is not particularly limited. For example, a point having the same timing as the inflection point of the input relative angle (a point having the same time information) may be set as an end point. In addition, the start point and the end point of the partial region where the correlation is best when approximated by a linear function may be set as the end points in the time region sandwiched between the points where the shift rate is maximum.

  In the present embodiment, as described above, for each of various tire models generated by changing the design parameters, the simulation calculation unit 24 uses, for example, ramps or steps as time series data of slip angles input to the tire models. A slip angle that changes in shape is set. By setting a slip angle that changes in a ramp shape or a step shape, relatively high frequency components can be included in the time series data of the slip angle, and the steering state of the tire while the vehicle is traveling at high speed It is possible to carry out a simulation that reproduces the above with high accuracy. The objective value deriving unit 26 obtains an objective value (objective function value) that accurately represents the responsiveness (transient response characteristic) of the tire to this input. The obtained objective values (objective function values) for the respective models are stored in the memory 13 in association with the values of the respective design parameters. When performing the optimization process, the optimization control unit 18 reads out the target value for each model (that is, each combination of design parameters) from the memory 13 and performs the optimization process.

  In the present invention, the function for obtaining the frequency response characteristics of the tire axial force response obtained by frequency analysis of the time series slip angle information of the tire with respect to the road surface and the time series tire axial force response information, respectively. May be the objective function. For example, the objective function may be a function for obtaining information on the amplitude ratio (gain) between the slip ratio information subjected to frequency analysis and the tire axial force response information and information on the phase angle (phase delay). In the present invention, as described above, the relative angle between the tire and the road surface is input by so-called ramp input or step input. For example, if the rate of change of the relative angle at the ramp input is 0.1 to 0.5 (sec / deg), the rate of change of the slip angle input due to actual steering of the vehicle during high speed traveling can be roughly reproduced. In this way, the rate of change of the slip angle input to the tire by ramp input or step input is in a range close to the rate of change of slip angle input due to actual vehicle steering, so the tire axial force obtained by simulation calculation The frequency component of the response is very close to the frequency component in actual vehicle steering. The information on the amplitude ratio (gain) and the phase angle (phase delay) described above can also be said to represent the transient response characteristics of the tire during actual vehicle steering during high-speed driving. As shown in FIG. 3A, if the absolute value of the relative angle between the tire and the road surface is, for example, 2.0 (°) or less (in the case of FIG. 3A, 1. 2 (°)), it is generally known that the magnitude of the slip angle to be inputted and the response of the tire lateral force to the slip angle input (tire axial force response, etc.) have a substantially linear relationship. In such a linear region, the relationship of the lateral force response to the slip angle input is represented by a first-order lag system. For example, in the present invention, a dynamic parameter of a tire in such a first-order lag model may be set as a design parameter.

  In addition to the road surface model, for example, when a tire design plan that optimizes tire performance such as tire wet performance is obtained using the tire model described above, snow, mud, sand, gravel, ice, etc. A finite element model that reproduces the above and the like is generated, and a simulation that takes into account the influence of the flow of each model may be performed. An example of a simulation that takes into account the influence of the flow using such a finite element model is described in Japanese Patent Application Laid-Open No. 2004-338660, which is a publication of an application by the present applicant. In addition to the road surface model, a particle method model that reproduces snow, mud, sand, gravel, ice, and the like may be generated, and a simulation that takes into account the influence of the flow of each model may be performed. Here, the particle method model is a fluid model such as water or snow (inclusions) that is reproduced with multiple particle models, and this particle model is arranged in an isotropic shape at regular intervals. It is a model that reproduces this fluid (inclusions) by moving under the conditions and using the target model of the velocity and total energy of the particle model. An example of a simulation that takes into account the influence of a flow performed using such a particle model is described in Japanese Patent Application Laid-Open No. 2003-220808, which is a publication of an application by the applicant of the present application. In addition, when a model that reproduces the road surface shape with unevenness on the surface is used as the road surface model, the size of the tire model element is the size of the unevenness of the road surface model for the contact portion between the tire model and the road surface model. It is preferable to set a smaller value than. In addition, Japanese Patent Application Laid-Open No. 2006-21551, which is a publication of an application by the applicant of the present application, describes an example of a tire rolling simulation using a model that reproduces a road surface shape having an uneven surface as a road surface model. Yes. The simulation calculation performed in the present invention is not limited to the above examples, and may be performed using any conventionally known method.

  Hereinafter, an embodiment of a tire design method performed using the apparatus 10 will be described in detail. FIG. 4 is a flowchart for explaining an example of the tire designing method of the present invention. In the embodiment described below, a tire cross section in which the transient response characteristic when the relative angle with the road surface changes in time series while rolling on the road surface with respect to a predetermined tire is optimal. Derive a design plan for the shape.

  First, the optimization control unit 18 sets various conditions (optimization conditions) relating to the derivation of the optimization design plan based on the conditions input using the input unit 12 such as a keyboard or a mouse (step S1). S102). For example, the proposed tire standard, the design parameter to be changed for optimization, the allowable range of the value of the design parameter to be changed, the boundary condition of the model to be generated, the simulation condition, the constraint condition, the condition of the optimization process, and Set various conditions related to the optimization design plan, such as at least one objective function that represents the transient response characteristics of the tire, conditions for assigning design parameter values to be changed (conditions for experimental design), and conditions for optimization processing. To do.

In the present embodiment, three types of tire reference cross-sectional shapes of M ₀ , M ₁ , and M ₂ shown in FIG. 5 are set as tire reference proposals. In the present embodiment, the model generation unit 22 creates various tire design plans by linearly combining each tire reference shape using a weighting coefficient. In this embodiment, this weighting coefficient is set as a design parameter.

For example, in the present embodiment, time series information of the relative angle between the tire and the road surface while the tire is rolling on the road surface is set as the simulation condition. In addition, as one of the simulation conditions, a condition of the relative speed between the tire and the road surface while the tire is rolling on the road surface is set. In addition, as one of the simulation conditions, the conditions of the internal pressure of the tire and the additional load on the road surface are set. In addition, road surface model conditions, wheel models, rim model conditions, and various boundary conditions in simulation are also set as appropriate. Further, as the objective function representing the transient response characteristic of the tire, for example, from the time series information of the tire axial force as shown in FIG. A function for deriving the value to be expressed is set. In the present embodiment, a plurality of functions for deriving values representing the response characteristics of tire axial force, that is, objective functions, are set. For example, functions f ₁ , f ₂ , and f ₃ for obtaining F ₁ , F ₂ , and F ₃ shown in FIG. 3B are set as the objective functions, respectively. In addition, conditions for optimization processing are set. For example, as an optimization process, a condition for searching for an optimal solution (optimal design parameter value) using the response surface method may be set. Alternatively, a condition for obtaining a Pareto optimal solution and generating a self-organizing map to which information of the Pareto optimal solution is added may be set. Such as when deriving a design plan to optimize the value (target value) of a plurality of objective functions, e.g., the target value F ₁ is as large as possible, reducing the target value F ₂ as much as possible, as large as possible the desired value F _3, such Set multipurpose optimization conditions. For example, when deriving a design plan that optimizes the values (objective values) of multiple objective functions that are in a trade-off relationship, finding conditions for generating a Pareto optimal solution and setting conditions for generating a self-organizing map Is preferred.

Next, the optimization control unit 18 sets design parameter values using an experimental design method (step S104). That is, a plurality of combinations of weighting coefficients for linearly combining the tire reference shapes are set, and various design proposals based on the combinations of the coefficients are set. Information on the set weighting coefficient combinations is sent to the model generation unit 22. For example, assuming that predetermined design factor vectors of three types of tire reference shapes M ₀ , M ₁ , and M ₂ are X ₁ , X ₂ , and X ₃ , a design factor vector represented by X in the following formula (1) The shape with is the tire design plan. In Equation (1), α ₁ and α ₂ are weighting coefficients, that is, design parameters. The optimization control unit 18 sets various combinations (α ₁ , α ₂ ) of the design parameters using an experimental design method. Hereinafter, each combination of design parameters is referred to as a case. In step S102, as an allowable range of the value of the design parameter to be changed, for example, the shape of the design plan represented by the formula (1) is within a range that can be manufactured without any problems even after the vulcanization molding process. In addition, the conditions for defining the ranges of the design parameters α ₁ and α ₂ may be set.
X = X ₀ + α ₁ (X ₁ −X ₀ ) + α ₂ (X ₂ −X ₀ ) (1)

  Next, the model generation unit 22 generates a tire model with the design parameters to be changed set in the optimization control unit 18 as variables (step S106). FIG. 6 is a schematic perspective view of a model showing an example of a model generated in the present embodiment. In the present embodiment, the model generation unit 22 generates a finite element model for use in a known finite element method (FEM). In addition to the tire model 32, the model generation unit 22 also generates at least a road surface model 42 that is a target for rolling the tire model, according to preset conditions. In addition to the tire model, a wheel model 34 and a tire rotation axis model 36 are also generated. In the model generation unit 22, the tire model 32, the wheel model 34, and the tire rotation shaft model 36 are integrated based on a preset boundary condition, and a tire assembly model 30 (hereinafter simply referred to as a tire model 30). Create it.

  Next, the simulation calculation unit 24 performs a simulation of causing the tire model 30 to roll on the road surface model 42 by, for example, executing a subroutine using a known finite element solver (step S108). The simulation calculation unit 24 receives simulation conditions from the memory 13 and obtains a physical quantity such as a tire model behavior and a force acting on the tire model in a time series according to the set objective function. In the present embodiment, the simulation calculation unit 24 outputs time-series information on the force (axial force response) applied to the rotation axis of the tire.

The objective value deriving unit 26 obtains the values of the objective functions f ₁ , f ₂ , and f ₃ , that is, the values of the objective values F ₁ , F ₂ , and F ₃ from the time series information of the axial force response (steps). S110). Next, it is determined whether or not the target value has been calculated for all cases set based on the above-described allocation value (step S112). If the determination is negative, the case number is changed (step S114), and the target value is executed for each case that has not been executed.

  Next, the optimization control unit 18 reads out the target value for each model (that is, each combination of design parameters) from the memory 13 and performs an optimization process (step S116). At this time, for example, a response surface of each target value with the design parameter to be changed as a variable is obtained, and from this response surface, the value of the design parameter satisfying the constraint condition and the transient response characteristic satisfying the optimum condition is obtained. An optimal solution may be derived. The type of the response surface is not particularly limited, and may be expressed by an orthogonal polynomial, or a response surface expressed by other known methods such as a ringing or radial basis function. For example, when the optimization condition is set so that each target value is closest to a predetermined value, the average of the absolute values of the differences between each target value and the predetermined value is the smallest design parameter. A combination of values may be set as an optimum design plan.

  As an optimization process performed by the optimization control unit 18, a Pareto optimal solution may be obtained and a self-organizing map to which information on the Pareto optimal solution is added may be generated. A Pareto optimal solution is a solution in which a plurality of objective functions in a trade-off relationship do not have an advantage over any other solution, but no better solution exists. In general, there are a plurality of Pareto optimal solutions as a set. The optimization control unit 18 uses a multi-objective GA method for obtaining a set of a plurality of Pareto optimal solutions in one search. A normal GA performs evaluation, selection, crossover, and mutation processing on a sample set of initial solutions, generates a solution set of the next generation, and continues this processing sequentially, after several tens to several hundred generations This is a method for obtaining an optimal solution by obtaining a set of solutions.

  The multi-objective GA is a method for obtaining a boundary of the optimum solution as a Pareto optimum solution by using the GA method because there are a plurality of optimum solutions for a plurality of objective functions. Various methods are currently proposed for the multipurpose GA, but are not particularly limited in the present invention. For example, DRMOGA (Divided Range Multi-Objective GA), NCGA (Neighborhood Cultivation GA), or DCMOGA (Distributed Cooperation model) that divides the solution set into a plurality of regions along the objective function and performs multi-objective GA for each divided solution set. of MOGA and SOGA), NSGA (Non-dominated Sorting GA), NSGA2 (Non-dominated Sorting GA-II), and SPEA II (Strength Pareto Evolutionary Algorithm-II) method can be used. At that time, it is necessary to obtain a set of Pareto optimal solutions with high accuracy, with the solution set widely distributed in the solution space. For this reason, the optimization control unit 18 performs selection using, for example, a vector evaluation genetic algorithm (VEGA), a Pareto ranking method, or a tournament method.

  The optimization control unit 18 controls such an optimal design processing routine by the multi-objective GA, sets a sample set in which each set design parameter is variously changed, performs a simulation operation on the sample set, and sets the value of the objective function To obtain a sample set of initial solutions of the Pareto optimal solution (the processes of steps S102 to S112 are performed), and multipurpose GA may be performed using this set. Evaluation, selection, and crossover operations on the solution set in the multi-objective GA are performed according to the value of the objective function that is a simulation calculation result, and further, a mutation operation is performed to generate a next-generation solution set.

  The self-organizing map (Self-Organizing Map) is a map expressed by dividing multi-dimensional input data without any prior knowledge (unsupervised) and divided into a plurality of regions. The value of the objective function in the Pareto optimal solution is used as this multidimensional data. In the self-organizing map, there are nodes regularly arranged on a two-dimensional plane. Each of these nodes is a vector with the same number of dimensions as the number of objective functions set, and the vector components are initially random numbers. With a generalized reference vector. On the other hand, the Pareto optimal solution has a value for each objective function, and when this value is represented by a vector having a vector component, the node corresponding to the reference vector closest to this vector is selected as the best match node (winner). The Then, the vector of the Pareto optimal solution so that the vector of the best match node and the vector of the nodes existing within a predetermined range from the best match node approach the vector of the Pareto optimal solution according to the distance between the vector of the Pareto optimal solution Updated every time. A map composed of nodes having such reference vectors is a self-organizing map. For details on self-organizing maps, see “Visualization and Data Mining of Pareto Solutions Using Self-Organizing Map” (Shigeru Obayashi and Daisuke Sasaki, 2nd International Conference on Evolutionary Multi-Criterion Optimization, 8-11th April 2003, Portugal). Is stated. A specific example of a self-organizing map is described in Japanese Patent Application Laid-Open No. 2006-285811, which is a publication of an application filed by the present applicant.

7A to 7D show examples of self-organizing maps. FIGS. 7A to 7D show the self-organization of the Pareto solution obtained by multi-objective optimization in which the target value F ₁ is maximized, the target value F ₂ is minimized, and the target value F ₃ is maximized. The objective value (objective function value) in the Pareto optimal solution is clustered and divided into a plurality of areas. Below each map in FIGS. 7A to 7D, a bar indicating the correspondence between each objective function or design parameter value and color display (color, color density) is shown. 7A is F ₁ , FIG. 7B is F ₂ , FIG. 7C is F ₃ , FIG. 7D is X ₁ , FIG. 7E is X ₂ , and FIG. ) Represents X ₃ , respectively. Each node at the same position in FIGS. 7A to 7D represents the same Pareto solution, and the color at each node represents the value of each design parameter and each target value. In FIGS. 7A to 7D, each objective value (objective function value) and design parameters are normalized and represented.

From FIG. 7 (a), the better the target value F ₁ (increase), the self-organizing map on, it can be seen that it is preferable to select a design plan in more clustered on the left node. Similarly, from FIG. 7 (b), the better the target value F ₂ (reduced), self-organizing map on, it can be seen that it is preferable to select a design plan in more clustered on the left node. However, whereas, from the FIG. 7 (c), the To improve the target value F ₃ (increasing), self-organizing map on, it may be preferable to select a design plan in a more clustered nodes on the right Recognize. Further, from FIGS. 7C to 7D, as the optimized Pareto solution, the design parameter X ₁ is all 1 and the design parameter X ₂ is all zero (0) except for some exceptions. It can be seen that it is preferable. For the design parameter X ₃ , the optimized Pareto solution also has various values. For example, when the design parameter X ₃ is small (on the left side of the self-organizing map), the target value F ₁ and the target value F ₂ are You can see it gets better. Further, for example (in a more right SOM) design parameters X ₃ is large, it can be seen that the target value F ₃ becomes better. That is the value of the objective function representing the transient response characteristics of the tire, for the purposes value F ₁ to F _3, it can be said that a large contribution of design parameters X _3. From the self-organizing maps shown in FIGS. 7A to 7C, the target value F ₁ and the target value F ₂ can be relatively improved (the target value) if the design parameter X ₃ is made smaller. F ₁ and the target value F ₂ can be specialized), and if the value of the design parameter X ₃ is made larger, the target value F ₃ can be relatively improved (specific to the target value F ₃ ). You can). As shown in FIGS. 7A to 7D, by displaying a plurality of objective values (objective function values) and design parameter values on the same map (contour display), the correlation of each objective function, etc. Can be understood visually. Similarly, since the relationship between the design parameter and the objective function can be understood visually, information useful for design can be acquired. The optimization control unit 18 may obtain an optimization design plan that satisfies a predetermined condition based on the information of the self-organizing map.

  Finally, the optimum design plan derived using the response surface method or the Pareto optimum solution is displayed and output by the output unit 16 (step S118), and the process ends. When using the Pareto optimal solution method, the generated self-organizing map is displayed and output by the output unit 16, and the operator who views the output self-organizing map selects and extracts the value of the optimum design plan. May be.

  As described above, a case has been described in which a function for obtaining a value representing the transient response characteristic of a tire is set as an objective function, and an optimization design plan for optimizing the value (objective value) of the objective function is derived. In the present invention, not only the value representing the transient response characteristic is optimized, but also a design plan that simultaneously optimizes other physical quantities related to the tire may be obtained as the optimum design plan. For example, the simulation calculation unit 24 of the apparatus 10 uses each of the generated various tire models to obtain a second physical quantity value representing tire performance other than the transient response characteristics, and the second representing the tire performance. A design plan that simultaneously optimizes the physical quantity may be obtained as the optimal design plan. For example, in addition to the tire rolling simulation, the simulation calculation unit 24 obtains other tire performances such as tire wear performance, durability performance, rolling resistance, NV, riding comfort, spring characteristics, high-pre performance, and traction performance. Various simulation calculations are also performed to obtain the value of the second physical quantity. For example, the tire component surplus ratio (the tire component member in the tire model) is a physical quantity representing tire durability, which changes according to the tire cross-sectional shape, the shape of the tire component member, and the physical property value of the tire component member. A simulation calculation for obtaining a ratio of at least one mechanical characteristic value of the maximum principal strain, maximum principal stress, or maximum strain energy density in each element and a mechanical characteristic value at the time of fracture of the material of the tire constituent member), It is described in Japanese Patent Application Laid-Open No. 2005-1649, which is a publication of an application filed by the present applicant.

  In addition to physical quantities related to tires (transient response characteristics and other physical quantities described above), physical properties of tires constituting tires (Young's modulus, shear stiffness, Poisson's ratio, parameters of superelastic potential, external force) A design plan that simultaneously optimizes stress, strain, etc.) may be obtained as an optimal design plan. In addition to the values related to the entire tire, such as the shape of the tire, as design parameters, the material of the microscopic rubber member that constitutes the tire, such as the filler dispersion shape and the filler volume ratio that define the material characteristics of the tread member, etc. Design parameters to be characterized may be set. In such a case, in addition to the tire model that reproduces the tire, the model generation unit 22 reproduces a microscale model that reproduces the microstructure of the tire and a mesoscale that reproduces an area larger than the representative area represented by the microscale model. Models of different scales such as a model and a vehicle model equipped with a tire model are generated. The microscale model is, for example, a model that reproduces a rubber member in which fillers are distributed and a member in which a steel wire is embedded in the rubber member. The mesoscale model is, for example, a model that reproduces a part of a tread member model of a tire that reproduces a state that changes according to the minute unevenness when the tread rubber member comes into contact with the minute uneven surface. And the simulation calculation part 24 should just perform the simulation calculation called what is called a multiscale simulation under predetermined conditions using each simulation model. In the multi-scale simulation, input / output is exchanged between simulations of each scale, and physical quantities represented by models of different scales such as physical property values of rubber members constituting the tire and vehicle behavior may be acquired. For example, the result of micro model simulation is reflected in mesoscale simulation, the result of mesoscale simulation is reflected in simulation using tire model, and the result of simulation using tire model is reflected in simulation using vehicle model. Finally, a simulation result using the vehicle model can be obtained. By using such a multi-scale simulation, it is possible to obtain the physical quantities of the different scales using the structure and physical property values at different scales as design parameters. Further, it is possible to search for a design plan for optimizing the physical quantity of each scale using the physical quantity of each different scale as an objective function. In addition, in Japanese Patent Application Laid-Open No. 2006-285811, which is a publication of the above-mentioned application by the applicant of the present application, a specific example of multiscale simulation is described in addition to a specific example of a self-organizing map.

  If you want to derive a design plan that optimizes the robustness (robustness) of the objective function against variations in driving conditions and tire manufacturing conditions, this robustness is one of the conditions for optimization processing. You can also set the conditions. At this time, the optimization control unit 18 sets predetermined variation factors in addition to the design parameters (load conditions, tire shape and material property value conditions, road surface and tire friction coefficient conditions, and the like). The simulation calculation unit 24 may perform the simulation calculation for each value of each variation factor. The optimization control unit 18 may obtain an optimization design plan that satisfies a predetermined condition in consideration of the set condition for robustness.

  The tire designing method and apparatus of the present invention have been described in detail above. However, the present invention is not limited to the above-described embodiments, and various improvements and modifications may be made without departing from the scope of the present invention. Of course.

It is a schematic block diagram of an example of the tire design apparatus of the present invention. FIG. 2 is a schematic diagram for explaining processing in each of a simulation calculation unit and a target value deriving unit of the tire design device shown in FIG. 1. (A) shows an example of simulation conditions, and (b) shows an example of the result of a tire rolling simulation performed based on the simulation conditions of (a). It is a flowchart explaining an example of the design method of the tire of this invention. FIG. 4 shows a tire reference cross-sectional shape in the tire design method shown in the flowchart. It is a schematic perspective view of an example of the model produced | generated with the design method of the tire of this invention. FIGS. 1A to 1F are examples of self-organizing maps created by the tire designing method of the present invention.

Explanation of symbols

DESCRIPTION OF SYMBOLS 10 Tire design apparatus 12 Input part 13 Memory 14 Computer 15 CPU
DESCRIPTION OF SYMBOLS 16 Output part 18 Optimization control part 20 Analysis means 22 Model production | generation part 24 Simulation operation part 26 Objective value derivation part 32 Tire model 34 Wheel model 36 Tire rotation axis model 42 Road surface model

Claims (7)

A method of designing a tire with an optimum transient response characteristic when a relative angle with the road surface changes in a time series while rolling on a road surface with a predetermined tire as a reference,
At least a design parameter of the tire to be changed for optimization, time series information of the relative angle, at least one objective function representing the transient response characteristic, and an optimum condition for the optimization are set. A condition setting step;
A model generation step of generating at least a tire model having the design parameter as a variable, and a road surface model for reproducing the road surface;
While rolling the tire model generated by giving the value of the design parameter in contact with the road surface model, the relative angle of the tire model with respect to the road surface model is determined according to the time series information of the relative angle. A calculation step for performing a simulation calculation to change to a series;
An objective value deriving step for obtaining a value of the objective function based on a result of the simulation operation;
Repetitively changing the value of the design parameter given to the tire model, and executing the calculation step and the target value derivation step for the tire model generated by the change each time the change is made;
An optimization step for performing an optimization process for obtaining a value of the design parameter when the value of the objective function satisfies the optimum condition based on a plurality of values of the objective function obtained by the repetition step; A method for designing a tire having   The time series information of the relative angle includes a plurality of time intervals in which the amount of change in the relative angle per unit time is different, and the amount of change in the relative angle per unit time is approximately equal in each time interval. The tire design method according to claim 1, wherein the tire design method is constant.   3. The tire design method according to claim 2, wherein the time series information of the relative angle includes intermittently arranged time intervals in which the change amount of the relative angle per unit time is zero. .   The objective function uses the time information of each end point of the plurality of time sections and the information on the axial force when the time series axial force of the rotating shaft is divided into a plurality of time sections, and the rotation axis of the tire The tire design method according to claim 1, wherein the tire design method is a function for obtaining a value representing a response characteristic of the axial force applied to the tire.   The objective function is a graph showing a time change of the axial force in which one axis of the orthogonal coordinate system is expressed as time and the other axis is expressed as the axial force. 5. The function according to claim 4, wherein the function is a function for obtaining a value representing a response characteristic of an axial force applied to the rotation axis of the tire by using information on each end point in a plurality of time sections when divided into sections. Tire design method.   For the at least two end points in the graph, the objective function is the shortest distance between the end points, the distance in the direction along the one axis between the end points, and the direction in the direction along the other axis between the end points. The tire design method according to claim 5, wherein the tire design function is a function representing at least one of a distance and a slope of a straight line connecting the end points. An apparatus for designing a tire that has an optimum transient response characteristic when a relative angle with the road surface changes in time series while rolling on a road surface with a predetermined tire as a reference. A condition setting unit for setting at least the design parameters of the tire to be changed, time series information of the relative angle, at least one objective function representing the transient response characteristics, and an optimum condition for the optimization When,
A tire model with the design parameter as a variable, and a model generation unit that generates at least a road surface model that reproduces the road surface;
While rolling the tire model generated by giving the value of the design parameter in contact with the road surface model, the relative angle of the tire model with respect to the road surface model is determined according to the time series information of the relative angle. A calculation unit for performing a simulation calculation to change to a series;
An objective value deriving unit for obtaining a value of the objective function based on a result of the simulation operation;
The design parameter value given to the tire model is repeatedly changed, and each time the change is made, the model generation unit generates a tire model, the calculation unit causes the calculation unit to perform a simulation calculation, and the target value derivation unit A repetitive control unit for causing the target value to be derived from;
An optimization control unit that performs an optimization process for obtaining a value of the design parameter when the value of the objective function satisfies the optimum condition based on a plurality of values of the objective function obtained. Tire design equipment.