JP5157983B2 - Vehicle aerodynamic force calculation device, vehicle motion analysis device, and vehicle suspension control device - Google Patents

Description

  The present invention relates to a vehicle aerodynamic force calculation device, a vehicle motion analysis device, and a vehicle suspension control device, and in particular, a vehicle aerodynamic force calculation device that calculates aerodynamic force acting on a running vehicle, and a vehicle motion analysis device using the same. The present invention relates to a vehicle suspension control device.

  In order to improve the ride comfort or steering stability of the vehicle, a motion analysis while the vehicle is running is performed, and the suspension mechanism is controlled based on this analysis. Since the vehicle is subjected to the action of aerodynamic force while traveling, the influence is also taken into consideration.

  For example, Patent Document 1 discloses an active suspension that estimates a vertical force generated in a vehicle body by air resistance during traveling and corrects a command value for a pressure control valve according to the estimated value. Yes. As a result, even if the air resistance applied to the car body changes and the vertical force applied to each wheel fluctuates, fluctuations in the vehicle height can be prevented and a good car body posture can be maintained. ing.

  Further, in Patent Document 2, as a vehicle suspension control device, an equation of motion of a vehicle that takes into account the aerodynamic coefficient in the heaving direction and the pitching direction with respect to the lift coefficient and the pitching moment coefficient of the vehicle is established, thereby increasing the vehicle speed. It is stated that the amount of change in the apparent spring constant and damping constant of the vehicle suspension due to the reduction is calculated.

  Specifically, it is described that the external force in the direction of the heaving motion and the external force in the direction of the pitching motion are obtained as being proportional to the amount of displacement of the heaving, the amount of pitching angular displacement, the heaving displacement speed, and the pitching angle displacement speed. The obtained external force in the direction of heaving motion is incorporated into the external force in the translational motion equation, the external force in the pitching motion direction is incorporated into the external force in the rotational motion equation, and the calculated result is zero, that is, the air Calculate the apparent spring constant of the suspension of the vehicle so that it is equivalent to the calculation result when no force is applied, and the difference from the actual spring constant is the amount of change in the apparent spring constant due to the action of the aerodynamic force. It is stated that

  Non-Patent Document 1 discloses that vehicle motion analysis is performed by incorporating a quasi-stationary aerodynamic model for a gentle attitude change and an aeroelastic coefficient model for an abrupt attitude change into general-purpose mechanism analysis software ADAMS. Yes.

Japanese Patent No. 2503244 Japanese Patent No. 3863197

F. Cheri et al., Effects of Unsteady Aerodynamics on Vehicle Operation, 2000 International ADAMS User Conference Orlando, June 2000, p1-4

  As described above, in the prior art, the influence of the aerodynamic force received during traveling is taken into consideration when analyzing the motion of a vehicle. In Patent Document 1, a change in the posture of the vehicle is not taken into account when estimating the lift generated in the vehicle body as the vertical force acting on the vehicle body. In Patent Document 2, the posture and the posture change speed are considered as the posture change of the vehicle, but the posture change acceleration is not taken into consideration. The quasi-stationary aerodynamic model and the aeroelastic coefficient model in Non-Patent Document 1 also remain as functions of displacement and displacement velocity, displacement angle and displacement angular velocity.

  As described above, the conventional technology is based on a stationary aerodynamic model in which an external force due to aerodynamic force is in a proportional relationship with displacement, a quasi-stationary aerodynamic model in which a proportional relationship between displacement and displacement speed is used, and the like.

  In the field of handling fluid-related vibrations, the influence of the added mass when considering that a virtual mass is added to the object by the fluid force as a term proportional to the displacement acceleration has been considered. From the unsteady blade theory, a model has been derived in which the fluid force acting on the flat blade is proportional to the displacement acceleration. Even in this case, when the density of the fluid is about 1000 times larger than the air, such as when the structure vibrates in water, it is added unless the mass of the air is not negligible compared to the airframe mass, such as an airship. The mass term is not taken up much. In addition, for vehicles traveling in the atmosphere, the effect of additional mass, that is, displacement acceleration has been ignored.

  From the comparison of the results of wind tunnel experiments and numerical simulations with the calculation results of the conventional model, it is well suited to consider the additional mass as an influence of acceleration when the vehicle is accelerating, especially vertical acceleration. It was found to be. This is considered to be because the influence of the added mass becomes larger than that of an aircraft cruising in the atmosphere due to the influence of the ground as a solid wall surface. However, as described above, in the prior art, a dynamic aerodynamic model that takes into account the effect of added mass has not been studied.

  An object of the present invention is to provide a vehicle aerodynamic force calculation device that can more appropriately calculate the aerodynamic force acting on a running vehicle. Another object is to provide a vehicle motion analysis device and a vehicle suspension control device using the vehicle aerodynamic force calculation device.

The vehicle aerodynamic force calculation apparatus according to the present invention includes a condition acquisition means for acquiring an air density ρ, a vehicle speed U, a front projection area A of the vehicle, a representative length L of the vehicle, and a characteristic radius R, The lift coefficient fluctuation component, which is a fluctuation component from the reference lift coefficient in the reference posture of the vehicle, is ΔC _Z, and the pitching moment coefficient fluctuation component, which is the fluctuation component from the reference pitching moment coefficient in the vehicle reference posture, is ΔC _MY. Modeling means for modeling each of the component ΔC _Z and the pitching moment coefficient variation component ΔC _MY as a linear model with terms up to the second derivative of each input variable, with the vertical displacement z and the pitch displacement angle θ as input variables; and conditions acquired by the condition acquisition unit, based on the modeled linear model, the lift coefficient variation component [Delta] C _Z and pitching moment coefficient variation formed A fluctuation component calculation means for calculating [Delta] C _MY, respectively, based on the calculated lift coefficient and variation component [Delta] C _Z and pitching moment coefficient variation component [Delta] C _MY, effect level calculation for calculating respectively a vertical force and pitching moment acting on the vehicle And the modeling means is modeled as a linear model having an acceleration term related to a lift coefficient fluctuation component ΔC _Z when at least vertical displacement z is an input variable as a second-order differential term. .

Further, the vehicle aerodynamic force calculation apparatus according to the present invention includes a condition acquisition unit that acquires an air density ρ, a vehicle speed U, a front projection area A of the vehicle, a representative length L of the vehicle, and a characteristic radius R. the lateral force coefficient variation component which is the fluctuation component from the reference lateral force coefficient in the reference posture of the vehicle and [Delta] C _Y, the yawing moment coefficient variation component which is the fluctuation component from the reference yawing moment coefficient in the reference posture of the vehicle as a [Delta] C _MZ Each of the lateral force coefficient fluctuation component ΔC _Y and the yawing moment coefficient fluctuation component ΔC _MZ is modeled as a linear model with terms up to the second derivative of each input variable with the lateral displacement y and the yaw displacement angle β as input variables. and modeling means, and conditions acquired by the condition acquisition unit, based on the modeled linear model, the lateral force coefficient variation component [Delta] C _Y and yawing moment coefficient variation Based minute [Delta] C _MZ on the fluctuation component calculating means for calculating each of the calculated lateral force coefficient and variation component [Delta] C _Y and yawing moment coefficient variation component [Delta] C _MZ, act of calculating respectively the lateral force and yawing moment acting on the vehicle comprising a quantity calculating means, the modeling means, as second-order differential term, to model a linear model having an acceleration section in the lateral force coefficient variation component [Delta] C _Y when an input variable at least lateral displacement y Features.

  The vehicle aerodynamic force calculation means according to the present invention further includes acceleration term coefficient acquisition means for acquiring a coefficient of an acceleration term in a linear model based on a wind tunnel experiment or a numerical simulation using a real vehicle or a model. The converting means preferably performs linear modeling using the acquired acceleration term coefficient.

Further, in the vehicle aerodynamic force calculation apparatus according to the present invention, the condition acquisition means further acquires the representative volume V of the vehicle, and the effect of the acceleration term of the lift coefficient fluctuation component ΔC _Z when the vertical displacement z is an input variable. It is assumed that this is the additional mass force received from the surrounding air during acceleration motion, and the actual mass or model is used for the additional mass coefficient, which is the value obtained by making the additional mass dimensionless by the mass of air excluded by the vehicle. It is preferable that an additional mass coefficient acquisition unit that acquires a wind tunnel experiment, a numerical simulation, or a literature value is used, and the modeling unit treats the acceleration term coefficient as a product of the additional mass coefficient and the representative volume V of the vehicle.

In the vehicle air force calculating device according to the present invention, to get a representative volume V of condition acquiring means further vehicle, the vehicle effects acceleration term of the lateral force coefficient variation component [Delta] C _Y when the lateral displacement y and the input variables Considering that it is an additional mass force received from the surrounding air during acceleration motion, the actual mass or model was used for the additional mass coefficient, which is a value obtained by making the additional mass dimensionless by the mass of air excluded by the vehicle. It is preferable that an additional mass coefficient acquisition unit that is acquired based on a wind tunnel experiment, a numerical simulation, or a literature value is provided, and the modeling unit treats the acceleration term coefficient as a product of the additional mass coefficient and the representative volume V of the vehicle.

In the vehicle aerodynamic force calculation apparatus according to the present invention, the condition acquisition means includes a characteristic radius R _P12 included in the speed term of the lift coefficient fluctuation component ΔC _Z or a characteristic included in the speed term of the pitching moment coefficient fluctuation component ΔC _MY. It includes characteristic radius acquisition means for acquiring at least one of the radius _RP22 based on a wind tunnel experiment or numerical simulation using an actual vehicle or model, and the fluctuation component calculation means calculates the value of the characteristic radius acquired by the characteristic radius acquisition means. It is preferable to calculate the lift coefficient variation component ΔC _Z and the pitching moment coefficient variation component ΔC _MY when the pitch displacement angle θ is used as an input variable.

In the vehicle aerodynamic force calculation apparatus according to the present invention, the condition acquisition means is included in the characteristic radius R _Y12 included in the speed term of the lateral force coefficient fluctuation component ΔC _{Y or in} the speed term of the yawing moment coefficient fluctuation component ΔC _MZ. Characteristic radius acquisition means for acquiring at least one of the characteristic radius _RY22 based on a wind tunnel experiment or numerical simulation using an actual vehicle or a model, and the fluctuation component calculation means is a value of the characteristic radius acquired by the characteristic radius acquisition means Is preferably used to calculate the lateral force coefficient fluctuation component ΔC _Y and the yawing moment coefficient fluctuation component ΔC _MZ when the yaw displacement angle β is an input variable.

A vehicle motion analysis apparatus according to the present invention includes an air density ρ, a vehicle speed U, a front projection area A of a vehicle, a representative length L of the vehicle, and a condition radius R for acquiring a characteristic radius R, The lift coefficient fluctuation component, which is the fluctuation component from the reference lift coefficient in the reference posture, is ΔC _Z, and the pitching moment coefficient fluctuation component, which is the fluctuation component from the reference pitching moment coefficient, in the vehicle reference attitude is ΔC _MY , the lift coefficient fluctuation component Modeling means for modeling each of ΔC _Z and pitching moment coefficient variation component ΔC _MY as a linear model with terms up to second derivative of each input variable, with vertical displacement z and pitch displacement angle θ as input variables, and conditions Based on the condition acquired by the acquisition means and the modeled linear model, the lift coefficient variation component ΔC _Z and the pitching moment coefficient variation component Δ Based C _MY to a fluctuation component calculation means for calculating each of the calculated lift coefficient and variation component [Delta] C _Z and pitching moment coefficient variation component [Delta] C _MY, effect level calculation for calculating respectively a vertical force and pitching moment acting on the vehicle And a motion equation analysis means for executing the motion analysis of the vehicle by incorporating the calculated vertical force and the pitching moment into the vehicle motion equation as external forces acting on the vehicle. The term is modeled as a linear model having an acceleration term related to the lift coefficient variation component ΔC _Z when at least the vertical displacement z is an input variable.

A vehicle motion analysis apparatus according to the present invention includes an air density ρ, a vehicle speed U, a front projection area A of a vehicle, a representative length L of the vehicle, and a condition radius R for acquiring a characteristic radius R, The lateral force coefficient fluctuation component that is a fluctuation component from the reference lateral force coefficient in the reference posture is ΔC _Y, and the yawing moment coefficient fluctuation component that is the fluctuation component from the reference yawing moment coefficient in the vehicle reference posture is ΔC _MZ , and the lateral force Modeling means for modeling the coefficient fluctuation component ΔC _Y and the yawing moment coefficient fluctuation component ΔC _MZ as linear models with terms up to the second derivative of each input variable, with the lateral displacement y and the yaw displacement angle β as input variables. When the condition acquired by the condition acquisition unit, based on the modeled linear model, the lateral force coefficient variation component [Delta] C _Y and yawing moment coefficient variation component [Delta] C _MZ Component calculation means for calculating the lateral force and yawing moment acting on the vehicle based on the calculated lateral force coefficient fluctuation component ΔC _Y and yawing moment coefficient fluctuation component ΔC _MZ , respectively. And the calculated lateral force and yawing moment as external forces acting on the vehicle, which are incorporated in the motion equation of the vehicle and the motion equation analyzing means for executing the motion analysis of the vehicle, and the modeling means is a second-order differential term. as, characterized by modeled as a linear model having an acceleration section in the lateral force coefficient variation component [Delta] C _Y when an input variable at least lateral displacement y.

  The vehicle motion analysis apparatus according to the present invention further includes acceleration term coefficient acquisition means for acquiring a coefficient of an acceleration term in a linear model based on a wind tunnel experiment or numerical simulation using a real vehicle or a model, and modeling The means preferably performs linear modeling using the acquired acceleration term coefficient.

In the vehicle motion analysis apparatus according to the present invention, the condition acquisition means further acquires the representative volume V of the vehicle, and the vehicle has the effect of the acceleration term of the lift coefficient fluctuation component ΔC _Z when the vertical displacement z is an input variable. Considering that it is an additional mass force received from the surrounding air during acceleration motion, the actual mass or model was used for the additional mass coefficient, which is a value obtained by making the additional mass dimensionless by the mass of air excluded by the vehicle. It is preferable that an additional mass coefficient acquisition unit that is acquired based on a wind tunnel experiment, a numerical simulation, or a literature value is provided, and the modeling unit treats the acceleration term coefficient as a product of the additional mass coefficient and the representative volume V of the vehicle.

In the vehicle motion analysis device according to the present invention, to get a representative volume V of condition acquiring means further vehicle, the effect of the acceleration term of the lateral force coefficient variation component [Delta] C _Y when the lateral displacement y and an input variable vehicle It is assumed that this is the additional mass force received from the surrounding air during the acceleration motion of the vehicle, and the actual vehicle or model is used for the additional mass coefficient, which is the value obtained by making the additional mass dimensionless by the mass of air excluded by the vehicle. It is preferable that an additional mass coefficient acquisition unit that acquires a wind tunnel experiment, a numerical simulation, or a literature value is used, and the modeling unit treats the acceleration term coefficient as a product of the additional mass coefficient and the representative volume V of the vehicle.

In the vehicle motion analysis apparatus according to the present invention, the condition acquisition means includes a characteristic radius R _P12 included in the velocity term of the lift coefficient variation component ΔC _Z or a characteristic radius included in the velocity term of the pitching moment coefficient variation component ΔC _MY. It includes characteristic radius acquisition means for acquiring at least one of R _P22 based on a wind tunnel experiment or numerical simulation using an actual vehicle or model, and the fluctuation component calculation means uses the value of the characteristic radius acquired by the characteristic radius acquisition means Thus, it is preferable to calculate the lift coefficient variation component ΔC _Z and the pitching moment coefficient variation component ΔC _MY when the pitch displacement angle θ is an input variable.

In the vehicle motion analysis apparatus according to the present invention, the condition acquisition means includes a characteristic radius R _Y12 included in the speed term of the lateral force coefficient fluctuation component ΔC _Y or a characteristic included in the speed term of the yawing moment coefficient fluctuation component ΔC _MZ. Characteristic radius acquisition means for acquiring at least one of the radius R _Y22 based on a wind tunnel experiment or numerical simulation using an actual vehicle or a model, and the fluctuation component calculation means calculates the value of the characteristic radius acquired by the characteristic radius acquisition means. used, it is preferable to calculate the lateral force coefficient variation component [Delta] C _Y and yawing moment coefficient variation component [Delta] C _MZ upon an input variable β yaw displacement angle.

The vehicle suspension control device according to the present invention includes an air density ρ, a vehicle speed U, a front projection area A of the vehicle, a representative length L of the vehicle, and a condition radius R for acquiring a characteristic radius R, The lift coefficient fluctuation component, which is the fluctuation component from the reference lift coefficient in the reference posture, is ΔC _Z, and the pitching moment coefficient fluctuation component, which is the fluctuation component from the reference pitching moment coefficient, in the vehicle reference attitude is ΔC _MY , the lift coefficient fluctuation component Modeling means for modeling each of ΔC _Z and pitching moment coefficient variation component ΔC _MY as a linear model with terms up to second derivative of each input variable, with vertical displacement z and pitch displacement angle θ as input variables, and conditions and conditions acquired by the acquisition means, based on the modeled linear model, the lift coefficient variation component [Delta] C _Z and pitching moment coefficient Based fluctuation component [Delta] C _MY on the fluctuation component calculating means for calculating each of the calculated lift coefficient variation component [Delta] C _Z and pitching moment coefficient variation component [Delta] C _MY, respectively calculates the vertical force and pitching moment acting on the vehicle acts The amount calculation means and the calculated vertical force and pitching moment are incorporated into the vehicle's equation of motion as external forces acting on the vehicle, and the suspension elements of the vehicle are controlled based on a comparison with the equation of motion when no aerodynamic force acts. Suspension control means for performing modeling, and the modeling means performs modeling as a linear model having an acceleration term related to the lift coefficient fluctuation component ΔC _Z when at least vertical displacement z is an input variable as a second-order differential term. Features.

  The vehicle suspension control apparatus according to the present invention further includes an acceleration term coefficient acquisition means for acquiring the coefficient of the acceleration term in the linear model based on a wind tunnel experiment or a numerical simulation using a real vehicle or a model. The means preferably performs linear modeling using the acquired acceleration term coefficient.

In the vehicle suspension control apparatus according to the present invention, the condition acquisition means further acquires the representative volume V of the vehicle, and the vehicle exhibits the effect of the acceleration term of the lift coefficient fluctuation component ΔC _Z when the vertical displacement z is an input variable. Considering that it is an additional mass force received from the surrounding air during acceleration motion, the actual mass or model was used for the additional mass coefficient, which is a value obtained by making the additional mass dimensionless by the mass of air excluded by the vehicle. It is preferable that an additional mass coefficient acquisition unit that is acquired based on a wind tunnel experiment, a numerical simulation, or a literature value is provided, and the modeling unit treats the acceleration term coefficient as a product of the additional mass coefficient and the representative volume V of the vehicle.

In the vehicle suspension control apparatus according to the present invention, the condition acquisition means includes a characteristic radius R _P12 included in the speed term of the lift coefficient fluctuation component ΔC _Z or a characteristic radius included in the speed term of the pitching moment coefficient fluctuation component ΔC _MY. It includes characteristic radius acquisition means for acquiring at least one of R _P22 based on a wind tunnel experiment or numerical simulation using an actual vehicle or model, and the fluctuation component calculation means uses the value of the characteristic radius acquired by the characteristic radius acquisition means Thus, it is preferable to calculate the lift coefficient variation component ΔC _Z and the pitching moment coefficient variation component ΔC _MY when the pitch displacement angle θ is an input variable.

With at least one of the above-described configurations, the vehicle aerodynamic force calculation apparatus sets ΔC _Z as a lift coefficient fluctuation component that is a fluctuation component from the reference lift coefficient in the vehicle reference attitude, and changes from the reference pitching moment coefficient in the vehicle reference attitude. The pitching moment coefficient fluctuation component which is a component is ΔC _MY , the lift coefficient fluctuation component ΔC _Z and the pitching moment coefficient fluctuation component ΔC _MY are respectively input to the second floor of each input variable using the vertical displacement z and the pitch displacement angle θ as input variables. Dynamic aerodynamic modeling as a linear model with terms up to differentiation. At this time, the modeling is performed as having at least an acceleration term related to the lift coefficient fluctuation component ΔC _Z when the vertical displacement z is an input variable. Based on the modeling, a lift coefficient fluctuation component ΔC _Z and a pitching moment coefficient fluctuation component ΔC _MY are calculated.

と基準ピッチングモーメント係数

とが必要である。これらを考慮して、各入力変数の２階微分までの項による線形モデルとして動的空力モデル化が行われる。 Here, the calculation of the lift coefficient variation component [Delta] C _Z and pitching moment coefficient variation component [Delta] C _MY is, C _Z, C _MY partial differential of z, C _Z, C _MY partial differential for theta, i.e. aerodynamic derivative Is required. In order to calculate the vertical force and the pitching moment acting on the vehicle based on the calculation of the lift coefficient fluctuation component ΔC _Z and the pitching moment coefficient fluctuation component ΔC _MY , respectively,

And reference pitching moment coefficient

Is necessary. Considering these, dynamic aerodynamic modeling is performed as a linear model with terms up to the second derivative of each input variable.

As described above, at least the acceleration term of the lift coefficient fluctuation component ΔC _Z with respect to the vertical displacement z can be taken into consideration with respect to the vertical force and pitching moment as external forces that the vehicle receives due to the action of aerodynamic forces. As described above, since the dynamic aerodynamic model can be applied to the calculation of the vertical force and the pitching moment as the action of the aerodynamic force, it is possible to perform more appropriate analysis as compared with the conventional quasi-stationary aerodynamic model.

Also, Similarly, the vehicle is about the lateral force and yawing moment as an external force experienced by the action of aerodynamic forces, it can be considered an acceleration term of the lateral force coefficient fluctuation component [Delta] C _Y to at least lateral displacement y. As described above, since the dynamic aerodynamic model can be applied to the calculation of the lateral force and yawing moment as the action of the aerodynamic force, it is possible to perform more appropriate analysis as compared with the conventional quasi-stationary aerodynamic model.

と基準姿勢における基準ヨーイングモーメント係数

、すなわち空力係数が必要である。これらを考慮して、各入力変数の２階微分までの項による線形モデルとして動的空力モデル化が行われる。 Also in this case, in order to calculate the lateral force coefficient fluctuation component ΔC _Y and the yawing moment coefficient fluctuation component ΔC _MZ , the partial differentiation of C _Y and C _MZ with respect to β, that is, the aerodynamic differential coefficient, is required. Further, in order to calculate each of the lateral force and yawing moment acting on the vehicle based on the calculation of the lateral force coefficient variation component [Delta] C _Y and yawing moment coefficient variation component [Delta] C _MZ, the reference lateral force coefficient in the reference posture

And reference yawing moment coefficient in reference posture

That is, an aerodynamic coefficient is required. Considering these, dynamic aerodynamic modeling is performed as a linear model with terms up to the second derivative of each input variable.

  In addition, since the coefficient of the acceleration term in the linear model is acquired based on a wind tunnel experiment or numerical simulation using an actual vehicle or a model, it is possible to calculate aerodynamic force close to the actual vehicle state.

The acceleration term coefficient of the lift coefficient fluctuation component ΔC _Z when the vertical displacement z is an input variable or the acceleration term coefficient of the lateral force coefficient fluctuation component ΔC _Y when the lateral displacement y is an input variable is an additional mass coefficient. And the product of the representative volume of the vehicle. The additional mass coefficient is a dimensionless value, but depends on the vehicle shape, position, vehicle speed, vibration amplitude, and frequency. In particular, the movement perpendicular to the wall surface depends on the position, that is, the distance from the ground in the case of a vehicle. Even in this case, when the dimensionless frequency range described later is the same, and the model shape, the distance between the model and the ground, the excitation amplitude, etc. are almost similar to the actual vehicle scale, the wind tunnel experiment using the model Alternatively, an additional mass coefficient obtained from a numerical simulation can be used. It is also possible to use an additional mass coefficient that is already known in the literature. Therefore, by using the additional mass coefficient obtained in this way and the representative volume of the vehicle, it is easy to calculate the aerodynamic force considering the acceleration term coefficient.

Further, at least one of the characteristic radii included in the velocity term of the lift coefficient variation component ΔC _{Z and} the velocity term of the pitching moment coefficient variation component ΔC _MY when the pitch displacement angle θ is used as an input variable is determined by a wind tunnel experiment or numerical value. Obtain based on simulation. In the prior art, the characteristic radius is the half vehicle length of the vehicle and the length from the center of gravity position of the vehicle to the tip of the vehicle body, but with the above configuration, a value closer to the state of the actual vehicle can be used, which is much more appropriate. Analysis is possible.

Similarly, speed terms of lateral force coefficient variation component [Delta] C _Y when an input variable β yaw displacement angle, at least one of the characteristic radius respectively included in speed terms yawing moment coefficient variation component [Delta] C _MZ, wind tunnel test Or since it acquires based on numerical simulation, a much more appropriate analysis is attained.

  In addition, the aerodynamic force calculated in this way is incorporated into the translational motion equation and the rotational motion equation of the vehicle as an external force acting on the vehicle, and the vehicle motion analysis is performed by executing the vehicle motion analysis device. It can be.

  In addition, the aerodynamic force calculated in this way is incorporated in the translational motion equation and the rotational motion equation of the vehicle as an external force acting on the vehicle, and compared with the motion equation when no aerodynamic force is applied. It can be set as the apparatus which performs suspension control of this. For example, the apparent spring constant and damping constant of a vehicle are calculated so that the equation of motion incorporating aerodynamic force and the equation of motion when air does not act are equivalent, and the difference between the actual spring constant and damping constant is calculated. Can be defined as the change amount of the apparent spring constant and the change amount of the damping constant due to the action of the aerodynamic force.

It is a figure which compares the predicted value by the quasi-stationary aerodynamic model of a prior art with the data obtained by the wind tunnel experiment etc. about the gain of the lift coefficient at the time of pitch sine vibration. Corresponding to FIG. 1, the predicted value based on the quasi-stationary aerodynamic model of the prior art is compared with the data obtained by the wind tunnel experiment etc. with respect to the phase of the lift coefficient at the time of pitch sine excitation. It is a figure compared with the data obtained by the wind tunnel experiment etc. about the gain of the lift coefficient at the time of an up-and-down sine vibration as a predicted value by the quasi-stationary aerodynamic model of a prior art. Corresponding to FIG. 3, the predicted value based on the quasi-stationary aerodynamic model of the prior art is compared with the data obtained in the wind tunnel experiment etc. with respect to the phase of the lift coefficient at the time of vertical sine excitation. It is a figure explaining the structure of the vehicle aerodynamics calculation apparatus in embodiment which concerns on this invention. It is a figure which shows the mode of the Ahmed model which is the simplified vehicle model used in embodiment which concerns on this invention. It is a figure which compares the calculated value by the dynamic aerodynamic model of embodiment which concerns on this invention with the data obtained by the wind tunnel experiment etc. about the gain of the lift coefficient at the time of pitch sine vibration. Corresponding to FIG. 7, the predicted value by the dynamic aerodynamic model of the embodiment according to the present invention is compared with the data obtained by the wind tunnel experiment etc. with respect to the phase of the lift coefficient at the time of pitch sine excitation. . It is a figure which compares the prediction value by the dynamic aerodynamic model of embodiment which concerns on this invention with the data obtained by the wind tunnel experiment etc. about the gain of the lift coefficient at the time of a vertical sine vibration. Corresponding to FIG. 9, the predicted value by the dynamic aerodynamic model of the embodiment according to the present invention is compared with the data obtained in the wind tunnel experiment etc. with respect to the phase of the lift coefficient at the time of vertical sine excitation. . It is a figure which compares the Lissajous curve of the lift coefficient with respect to the vertical displacement of the quasi-stationary aerodynamic model of a prior art with the data obtained by experiment. It is a figure which compares the Lissajous curve of the lift coefficient with respect to the vertical displacement of the dynamic aerodynamic model of embodiment which concerns on this invention with the data obtained by experiment.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. In the description in the text, the symbols described before are used as necessary.

As a model when aerodynamic force acts on a vehicle during traveling, the contents of Patent Document 1 as a prior art will be briefly described first. In Patent Document 2, modeling is performed as a quasi-stationary aerodynamic model using Equation (5), which is well known as an aerodynamic force acting on a vehicle during traveling.

Here, U is the vehicle speed, z is the vertical displacement, and θ is the pitch displacement angle. Further, ΔC _Z represents a lift coefficient fluctuation component that is a fluctuation component from the reference lift coefficient represented by Expression (1). Further, ΔC _MY is a pitching moment coefficient fluctuation component that is a fluctuation component from the reference pitching moment coefficient in the reference posture of the vehicle expressed by the equation (2). S is a Laplace operator.

Thus, in equation (5), each of the lift coefficient variation component ΔC _Z and the pitching moment coefficient variation component ΔC _MY uses the vertical displacement z and the pitch displacement angle θ as input variables, and the velocity term and displacement term of each input variable. So-called quasi-stationary aerodynamic modeling is performed assuming that there is a linear proportional relationship. Therefore, does not include the section on s ² is the acceleration term in this model.

  In Patent Document 2, the characteristic radius R of Expression (5) is the length from the position of the center of gravity of the vehicle to the tip of the body of the vehicle.

  Next, the result of comparing the value obtained by the wind tunnel experiment and the numerical simulation with the appropriate vehicle model and the value predicted by the aerodynamic model of Expression (5) will be described with reference to FIGS.

  FIG. 1 compares the gain of the lift coefficient during pitch sine excitation obtained by wind tunnel experiments and numerical simulation with the value predicted by the aerodynamic model of Equation (5). 2 corresponds to FIG. 1 and compares the phase of the lift coefficient during pitch sine excitation obtained by the wind tunnel experiment and numerical simulation with the value predicted by the aerodynamic model of Equation (5).

The gain and phase of the lift coefficient during pitch sine excitation are the frequency transfer function for the upper right expression in the matrix of 2 rows and 2 columns of equation (5), where the input variable is the pitch displacement angle θ and the output is ΔC _Z. It can be obtained by calculating the frequency response of the gain and phase shift. Here, the dimensionless frequency f ^* = fL / U can be used as the frequency. Here, f is the frequency of pitch sine excitation.

  According to the above method, the frequency characteristics for the gain and the phase can be obtained based on Equation (5) of the conventional aerodynamic model. On the other hand, according to wind tunnel experiments or numerical simulations, a sinusoidal wave of the excitation frequency is fitted to each of the time waveform of displacement and aerodynamic coefficient, or each of the time waveform of displacement angle and aerodynamic coefficient, and gain is obtained from the amplitude ratio of both. And the phase difference can be obtained from the time difference between the two. Thus, the accuracy of the model of the prior art can be evaluated by comparing the predicted value from the model with the value obtained from the wind tunnel experiment or numerical simulation.

をとり、図２は横軸が図１と同じで、縦軸にピッチ正弦加振時の揚力係数の位相∠Ｇ₁₂がとられている。ここで、風洞実験によって得られた値は、白抜マーク、つまり中空シンボルで示され、数値シミュレーションによって得られた値は、黒塗マーク、つまり中実シンボルで示され、式（５）による予測値は実線で示されている。 Fig. 1 shows the dimensionless frequency f ^* on the horizontal axis and the gain of the lift coefficient during pitch sine excitation on the vertical axis.

2, the horizontal axis is the same as FIG. 1, and the vertical axis represents the lift coefficient phase ∠G ₁₂ at the time of pitch sine excitation. Here, the value obtained by the wind tunnel experiment is indicated by a white mark, that is, a hollow symbol, and the value obtained by numerical simulation is indicated by a black mark, that is, a solid symbol, and is predicted by the equation (5). Values are shown as solid lines.

  In particular, as shown in FIG. 2, in the wind tunnel experiment and the numerical simulation, the lift coefficient with respect to the pitch displacement angle shows a tendency of phase advance, whereas the predicted value by the model of Equation (5) shows a tendency of phase delay. Show. Thus, it can be seen that in the model of equation (5), the phase characteristic of the lift coefficient with respect to the pitch displacement angle cannot be expressed correctly. This cause is considered to be caused by setting the characteristic radius R to the half vehicle length in the model of Expression (5), as will be described later.

  FIG. 3 compares the gain of the lift coefficient at the time of vertical sine excitation obtained by the wind tunnel experiment and numerical simulation with the value predicted by the aerodynamic model of Equation (5). FIG. 4 corresponds to FIG. 3 and compares the phase of the lift coefficient during vertical sine excitation obtained by wind tunnel experiments and numerical simulation with the value predicted by the aerodynamic model of equation (5).

Here, the gain and phase of the lift coefficient at the time of vertical sine vibration are the same as those explained at the time of pitch sine vibration, the input variable is vertical displacement z, the output is ΔC _Z , and the equation (5) The frequency transfer function is obtained for the upper left expression of the 2-by-2 matrix, and the frequency response of the gain and phase shift can be calculated. Further, as described above, the accuracy of the model of the prior art can be evaluated by comparing the predicted value obtained in this way with the value obtained from the wind tunnel experiment or numerical simulation.

をとり、図４は横軸が図３と同じで、縦軸に上下正弦加振時の揚力係数の位相∠Ｇ₁₁がとられている。ここで、図１，２と同様に、風洞実験によって得られた値は、白抜マークで示され、数値シミュレーションによって得られた値は、黒塗マークで示され、式（５）による予測値は実線で示されている。 3 shows the dimensionless frequency f ^* on the horizontal axis and the gain of the lift coefficient during vertical sine excitation on the vertical axis.

4, the horizontal axis is the same as FIG. 3, and the vertical axis represents the phase ∠G ₁₁ of the lift coefficient during vertical sine excitation. Here, as in FIGS. 1 and 2, the value obtained by the wind tunnel experiment is indicated by a white mark, the value obtained by the numerical simulation is indicated by a black mark, and is a predicted value according to the equation (5). Is shown as a solid line.

  In particular, as shown in FIG. 4, in the wind tunnel experiment and the numerical simulation, the phase lag in the lift coefficient with respect to the vertical displacement shows a behavior that saturates around 45 degrees, whereas the predicted value based on the model of Equation (5) The phase lag reaches 70 degrees or more. Thus, it can be seen that in the model of equation (5), the phase characteristic of the lift coefficient with respect to the vertical displacement is not correctly grasped. Here, since the characteristic radius R is not included in the upper left expression of the matrix of 2 rows and 2 columns of Expression (5), the cause is that there is no acceleration term in the model of Expression (5), as will be described later. This is thought to be caused by this.

  The expression (5) in Patent Document 2 has been described above. Next, the contents of the vehicle aerodynamic force calculation apparatus to which the dynamic aerodynamic model can be applied will be described. FIG. 5 is a diagram illustrating the configuration of the vehicle aerodynamic force calculation apparatus 10 to which the dynamic aerodynamic model can be applied.

  The vehicle aerodynamic force calculation device 10 includes a CPU 12 that executes arithmetic processing, an input unit 14 such as a keyboard, an output unit 16 such as a display, and a storage unit 18 such as a hard disk. Each of these elements is connected to each other by an internal bus. The vehicle aerodynamic force calculation device 10 can be configured with a computer suitable for arithmetic processing.

  The storage unit 18 is a storage device that stores programs and the like used for vehicle aerodynamic force calculation, and particularly stores various data obtained by wind tunnel experiments and numerical simulations in association with the vehicle. Examples of various data to be stored include aerodynamic coefficients in the reference posture represented by equations (1) to (4), aerodynamic coefficient gradients that are aerodynamic derivatives, acceleration term coefficient data 20 relating to acceleration terms, addition This includes mass coefficient data, characteristic radius data 22, etc. Details of the procedure for obtaining acceleration term coefficient data 20 and characteristic radius data 22 will be described later.

The CPU 12 performs modeling of a condition acquisition processing unit 24 that acquires necessary conditions such as ρ, U, A, L, and R as analysis conditions and a dynamic aerodynamic model of aerodynamic force acting on the vehicle. For the aerodynamic force acting on the processing unit 26 and the vehicle, if the vertical force and the pitching moment are external forces, the lift coefficient variation component ΔC _Z and the pitching moment coefficient variation component ΔC _MY are calculated respectively, and the lateral force and yawing moment are calculated. when the external force, the lateral force coefficient variation component [Delta] C _Y and yawing moment coefficient variation fluctuation component calculation processing unit 28 to the component [Delta] C _MZ respectively calculates, based on the calculated fluctuation component, the external force acting on the vehicle, respectively An action amount calculation processing unit 30 to be calculated is included.

  In FIG. 5, when the vehicle aerodynamic force calculation device 10 is expanded to be a vehicle motion analysis device, the motion equation analysis processing unit 32 is indicated by a broken line frame as a function of the CPU 12. The motion equation analysis processing unit 32 has a processing function of incorporating an external force generated by the aerodynamic force calculated by the vehicle aerodynamic force calculation device 10 into the motion equation of the vehicle and executing a vehicle motion analysis. In this way, the vehicle aerodynamic force calculation device 10 can be expanded as a vehicle motion analysis device by adding an appropriate arithmetic processing function or the like.

  Further, the vehicle aerodynamic force calculation device 10 can be expanded to be a vehicle suspension control device. In this case, although not shown in FIG. 5, a vehicle suspension control processing unit is provided as a function of the CPU 12. Here, the vehicle suspension control processing unit incorporates the external force calculated by the vehicle aerodynamic force calculation device 10 as an external force acting on the vehicle in the equation of motion of the vehicle and based on a comparison with the equation of motion when no aerodynamic force acts. It has a processing function for controlling the suspension elements of the vehicle. Thus, the vehicle aerodynamic force calculation device 10 can be expanded as a vehicle suspension control device by adding an appropriate arithmetic processing function or the like.

  Such a function is realized by software, and specifically, can be realized by executing a corresponding vehicle aerodynamic force calculation program. Note that part of the above functions may be realized by hardware. The function of each processing unit of the CPU 12 is realized by the CPU 12 as hardware executing the vehicle aerodynamic force calculation program. In that sense, the function of each processing unit of the CPU 12 indicates each processing procedure of the vehicle aerodynamic force calculation program.

  The operation of the above configuration will be described in detail below. First, the dynamic aerodynamic model for the aerodynamic force acting on the vehicle used in the CPU 12 will be described, and then the procedure for obtaining the acceleration term coefficient and the characteristic radius stored in the storage unit 18 from a wind tunnel experiment or the like will be described. To do. Then, the procedure of the vehicle motion analysis will be described, and as a result, the effect of using the dynamic aerodynamic model will be described.

The well-known equations (8) and (9) are used as the aerodynamic force acting on the vehicle as an external force incorporated in the equation of motion of the vehicle.

Here, among the aerodynamic forces acting on the vehicle, the vertical force F _Z is expressed by the equation (8), and the pitching moment M _Y is expressed by the equation (9).

ΔC _Z in the equation (8) is a lift coefficient fluctuation component that is a fluctuation component from the reference lift coefficient in the vehicle reference posture described in the expression (1). ΔC _MY in the equation (9) is a pitching moment coefficient variation component that is a variation component from the reference pitching moment coefficient in the vehicle reference posture described in the equation (2).

In the vehicle aerodynamic force calculation device 10, the relationship between the vertical displacement z and pitch displacement angle θ as input variables and ΔC _Z and ΔC _{MY as} outputs is added to the quasi-stationary aerodynamic model described in Equation (5). As a linear system to which the resulting acceleration term is assigned, modeling represented by Expression (10) is performed.

を用いた式（１２）で示されるモデル化を行うものとしてもよい。基準姿勢における抗力係数も、準定常空力モデルとしてよく知られている定式化の１つである。

なお、式（１０），（１２）における特性半径Ｒ_P12，Ｒ_P22は後述のようにフィッティングで同定すべきであるが、場合によって、特許文献２で用いられる「車両の重心位置から車両のボデー先端までの長さ」、あるいは非特許文献１で用いられる「半車長」であってもよい。 In place of equation (10), the expression of the velocity term is also a drag coefficient.

Modeling represented by the equation (12) using may be performed. The drag coefficient in the reference posture is also one of the formulations well known as a quasi-stationary aerodynamic model.

Note that the characteristic radii R _P12 and R _P22 in the equations (10) and (12) should be identified by fitting as will be described later. It may be the “length to the tip” or “half car length” used in Non-Patent Document 1.

Here, the term of s ² is an acceleration term due to the added mass. The added mass is a concept used in hydrodynamics, and in this case, an added mass is an apparent increase in mass due to the movement of surrounding fluid with the acceleration motion of the vehicle. The surrounding fluid here is air.

  Next, the calculation procedure of each coefficient in Expression (10) will be described. Each coefficient is calculated by a wind tunnel experiment or a numerical simulation using an actual vehicle or a vehicle model. The actual vehicle or vehicle model needs to be used for the actual calculation of each coefficient. In the following, a three-dimensional model shown in FIG. 6 known as an Ahmed model is used as an example of a simplified vehicle model. The analysis was advanced. The Ahmed model is a rectangular parallelepiped shape having an inclined back surface, and is provided with appropriate roundness on four sides on the front surface side. Here, FIG. 6 shows the X, Y, and Z directions used in each equation. Thus, the X direction is the vehicle traveling direction, the Y direction is the vehicle width direction, and the Z direction is the vertical direction of the vehicle.

Of the coefficients in equation (10), the gradient of change C _Z for z, gradient of change C _Z relative theta, the gradient of change C _MY for z, the slope of change C _MY for theta is called static aerodynamic coefficient gradient . In these calculations, first, a plurality of static lift coefficients and static pitching moment coefficients are acquired in a predetermined range around the reference posture with respect to the vertical displacement and the pitch displacement angle by a wind tunnel experiment or numerical simulation. As the predetermined range, when the above-mentioned Ahmed model is used, for example, the vertical displacement can be ± 8 mm, the pitch displacement angle can be ± 0.878 °, and the like.

Then, the gradient of the vertical displacement of the plurality of static lift coefficients obtained in the predetermined range of the vertical displacement is calculated to obtain the change gradient of C _Z with respect to z, and the plurality of the gradient angles obtained in the predetermined range of the pitch angle are obtained. The gradient of the static lift coefficient with respect to the pitch angle is calculated to determine the change gradient of C _Z with respect to θ. Similarly, determine the change in slope of the C _MY for z by calculating the gradient of the vertical displacement of the plurality of static pitching moment obtained in a predetermined range of vertical displacement, obtained within a predetermined range of the pitch angle static Request C _MY the change gradient for θ to calculate the gradient of the pitch angle of the pitching moment.

An example of the static aerodynamic coefficient gradient calculated in this way is shown in Expression (13).

Among the coefficients in the equation (10), the characteristic radii R _P12 and R _P22 and the acceleration term coefficients α _P11 , α _P12 , α _P21 and α _P22 are obtained as follows. That is, the vehicle model is vibrated in a predetermined range around the reference posture with respect to the vertical displacement and the pitch displacement angle by a wind tunnel experiment or numerical simulation. As the predetermined range of vibration, for example, when the above-mentioned Ahmed model is used, the vertical displacement may be ± 8 mm sine vibration, the pitch displacement angle may be ± 0.878 ° sine vibration, and the like. . Then, the lift coefficient and the pitching moment coefficient with respect to this excitation are obtained while simultaneously associating time-series signals.

For example, in equation (10), if a model of the lift coefficient fluctuation component for vertical vibration is extracted, equation (14) is obtained.

Therefore, when the vertical displacement z can be obtained from the vehicle model, time series signals of the posture change speed dz / dt and the posture change acceleration d ² z / dt ² are obtained by numerical differentiation from the vertical displacement z. Alternatively, when the acceleration of the vehicle model can be acquired, the time series signals of the attitude change speed dz / dt and the vertical displacement z may be obtained from this acceleration by numerical integration.

Here, A and U in Equation (14) are known, and the static aerodynamic coefficient gradient can be separately calculated as shown in Equation (13) above, so that z, dz / dt, d are used as input data. ² z / dt ² and ΔC _Z as output data are given, and a curve approximation method or a data approximation method as an optimum approximation method that is generally used is used to approximate the equation (14) most closely. The coefficient α _P11 is identified. In this way, the acceleration term coefficient α _P11 can be obtained.

Similarly, the acceleration term coefficient α _P12 and the characteristic radius R _P12 can be obtained from the lift coefficient for pitch excitation. Further, the acceleration term coefficient α _P21 can be obtained from the pitching moment with respect to the vertical vibration. Further, the acceleration term coefficient α _P22 and the characteristic radius R _P22 can be obtained from the pitching moment with respect to the pitch excitation.

  In this way, all unknowns in Equation (10) can be obtained by performing at least one wind tunnel experiment or numerical simulation for vertical and pitch excitations at a certain excitation frequency.

As another embodiment, the characteristic radii R _P12 and R _P22 are identified for pitch excitation at a certain excitation frequency, and the acceleration term coefficient α _P12 for pitch excitation at another excitation frequency. , Α _P22 may be identified. As yet another embodiment, an acceleration term coefficient and a characteristic radius are obtained by fitting the gain or phase obtained from Equation (10) to the gain or phase obtained at a plurality of excitation frequencies. Also good.

Each characteristic radius and each acceleration obtained by identifying using the results of these two cases, assuming that the amplitude of vertical vibration is 8 mm, the amplitude angle of pitch vibration is 0.878 °, and the vibration frequency at that time is 8 Hz. An example of the term coefficient is shown in Expression (15).

  Each acceleration term coefficient thus obtained is stored in the storage unit 18 as acceleration term coefficient data 20 and each characteristic radius is stored as characteristic radius data 22.

  The procedure for obtaining the acceleration term coefficient and the characteristic radius stored in the storage unit 18 from the wind tunnel experiment has been described above. Next, the procedure for calculating the aerodynamic force of the vehicle will be described.

  When the vehicle aerodynamic force calculation device 10 is activated, a vehicle aerodynamic force calculation program stored in the storage unit 18 is launched. Therefore, the user inputs the air density ρ, the vehicle speed U, the front projection area A of the vehicle, and the representative length L of the vehicle as analysis conditions from the input unit 14. Each input condition is acquired as an analysis condition by the function of the condition acquisition processing unit 24 of the CPU 12 (analysis condition acquisition step).

  The analysis conditions can be acquired by other methods. For example, the vehicle speed U may be sequentially updated as an output from the vehicle motion analysis program. Moreover, it is good also as what acquires the data obtained via an external connection interface as analysis conditions. For example, it is possible to acquire online measurement data during actual vehicle travel and acquire this as analysis conditions. In addition, various analysis conditions are stored in the storage unit 18 and can be read out and acquired. Among various analysis conditions, a plurality of acquisition means may be used such that a part is acquired from the input unit 14, a part is acquired from the storage unit 18, and a part is acquired from the external interface.

  Then, the acceleration term coefficient data 20 and the characteristic radius data 22 stored in the storage unit 18 are read out and acquired, and the linear model of Expression (10) is modeled using the data (modeling). Process). This process is executed by the function of the modeling processing unit 26 of the CPU 12.

Next, using the obtained analysis condition and the modeled linear model, the lift coefficient variation component ΔC _Z and the pitching moment coefficient variation component ΔC _MY are calculated based on the equation (10) (variation component). Calculation step). This process is executed by the function of the fluctuation component calculation processing unit 28 of the CPU 12.

Then, using the calculated lift coefficient variation component ΔC _Z and the pitching moment coefficient variation component ΔC _MY , the vertical force F _Z is calculated based on the equation (8) as an external force acting on the vehicle by the aerodynamic force. The pitching moments M _Y are respectively calculated based on 9) (action amount calculating step). This step is executed by the function of the action amount calculation processing unit 30 of the CPU 12.

In this way, aerodynamic force as an external force acting on the vehicle is calculated based on a dynamic aerodynamic model using an acceleration term coefficient or the like obtained in a wind tunnel experiment or the like. Incidentally, built in advance the motion analysis of the vehicle based on the calculation of the calculated air force, then, the vertical force F _Z and pitching moment M _Y calculated as an external force acting on the vehicle, the equation of motion of the vehicle The vehicle motion analysis is executed (motion equation analysis step). This step is executed by the function of the motion equation analysis processing unit 32 of the CPU 12 indicated by a broken line frame in FIG. As described above, the vehicle aerodynamic force calculation device 10 can be developed to appropriately perform the vehicle motion analysis in consideration of the aerodynamic force acting on the vehicle. Similarly, the vehicle aerodynamic force calculation device 10 can be developed to appropriately perform vehicle suspension control.

  Next, the effect of the vehicle aerodynamic force calculation device 10 will be described with reference to FIGS. FIGS. 7 and 8 correspond to FIGS. 1 and 2, and the gain and phase of the lift coefficient at the time of pitch sine excitation obtained by the wind tunnel experiment and numerical simulation are calculated for each acceleration term coefficient by the above method. It compares with the result of the dynamic aerodynamic model of Formula (10) calculated using the above. 7 are the same as those in FIG. 1, and the horizontal and vertical axes in FIG. 8 are the same as those in FIG. As a result of the wind tunnel experiment of the white mark, the result of the numerical simulation of the black mark is the same as FIGS.

  The broken lines in FIGS. 7 and 8 are the results of the dynamic aerodynamic model of Expression (10) calculated using each acceleration term coefficient calculated by the above method. The solid line is the result of calculation in Equation (10) assuming that the acceleration term is not included. The solid line in FIGS. 7 and 8 is different from FIGS. 1 and 2 in that the characteristic radius is the half vehicle length of the vehicle in FIGS. 1 and 2, whereas the solid line in FIGS. It is identified by experiment or numerical simulation.

  7 and 8, since the solid line and the broken line are not so different, it can be seen that the influence of the acceleration term is small on the lift coefficient during pitch sine excitation. On the other hand, the solid line and the broken line both agree well with the results of the wind tunnel experiment and the numerical simulation, and are greatly different from the results of FIGS. 1 and 2, so it can be seen that the characteristic radius should be identified by the wind tunnel experiment or the numerical simulation. .

  Therefore, by using the characteristic radius identified by the wind tunnel experiment or numerical simulation, the lift coefficient at the time of pitch excitation can be expressed more accurately even in the existing quasi-stationary aerodynamic model.

  FIGS. 9 and 10 correspond to FIGS. 3 and 4, and the gain and phase of the lift coefficient at the time of upper and lower sine excitation obtained by the wind tunnel experiment and numerical simulation are obtained by calculating each acceleration term coefficient by the above method. It compares with the result of the dynamic aerodynamic model of Formula (10) calculated using the above. The contents of the horizontal and vertical axes in FIG. 9 are the same as in FIG. 3, and the contents of the horizontal and vertical axes in FIG. 10 are the same as in FIG. As a result of the wind tunnel experiment of the white mark, the result of the numerical simulation of the black mark is the same as in FIGS.

  The broken lines in FIGS. 9 and 10 are the results of the dynamic aerodynamic model of Expression (10) calculated using each acceleration term coefficient calculated by the above method. The solid line is the result of calculation in Equation (10) assuming that the acceleration term is not included. That is, the solid line is the same as the result by the quasi-stationary aerodynamic model described with reference to FIGS.

As shown in FIGS. 9 and 10, the results of the wind tunnel experiment and the like match well with the broken line and are greatly different from the solid line. From this, it can be seen that in the range where the dimensionless frequency f ^* is larger than 0.05, the acceleration term due to the added mass cannot be ignored. For example, the dimensionless frequency f ^* when a vehicle with a total length of 4.5 m traveling at 100 km / h vibrates at 2 Hz is 0.324. As the vehicle speed decreases, the dimensionless frequency f ^* further increases. From this, it can be understood that in a general vehicle traveling environment, the influence of the added mass cannot be ignored for the movement of the vehicle, and it is necessary to apply a dynamic aerodynamic model.

11 and 12 compare the Lissajous curves of the lift coefficient C _Z with respect to the vertical displacement z between the results of the wind tunnel experiment and the calculation results of each aerodynamic model. FIG. 11 is a comparison with the calculation result by the quasi-stationary aerodynamic model of the prior art, and FIG. 12 is a comparison with the calculation result by the dynamic aerodynamic model. In the experimental results, the circle mark is the excitation frequency of 2 Hz, the X mark is the excitation frequency of 4 Hz, and the Δ mark is the excitation frequency of 8 Hz.

  Comparing FIGS. 11 and 12, the calculation result of the quasi-stationary aerodynamic model cannot appropriately express the increase in the slope of the Lissajous curve accompanying the increase in the excitation frequency, that is, the increase in the air spring constant, but the acceleration caused by the added mass It can be seen that the dynamic aerodynamic model considering the terms can express its characteristics well.

Here, the acceleration term coefficient α _P11 can be expressed by Expression (16) by using the additional mass coefficient c _P11 which is a value obtained by making the additional mass dimensionless with the mass of the fluid excluded by the object.

  Here, V is a vehicle volume. When a vehicle model is used, it is a vehicle model volume.

From this analogy, the other acceleration term coefficients α _P12 , α _P21 , α _P22 can be similarly described using dimensionless coefficients c _P12 , c _P21 , c _P22 . For example, it can be expressed as equation (17).

Alternatively, the acceleration term coefficients α _P12 and α _P21 can be expressed by, for example, Expression (18) using the coefficients a _P12 and a _P21 having the dimension of length.

In the numerical simulation, the acceleration term coefficients α _P11 , α _P12 , α _P21 , α _P22 can be identified by the procedure as described above using the calculation results under the actual vehicle driving conditions. In a wind tunnel experiment that may be difficult, the dimensionless coefficients c _P11 , c _P12 , c _P21 , and c _P22 may be identified from the experimental results using a scale model. That is, the dimensionless coefficients c _P11 , c _P12 , c _P21 , and c _P22 depend on the vehicle shape, the distance from the ground, the vehicle speed, the vibration amplitude, the frequency, and the like, but have the same dimensionless frequency range, This is because when the model shape, the distance between the model and the ground, and the excitation amplitude are almost similar to the actual vehicle scale, the values obtained by the scale model can be applied to the actual vehicle scale problem. .

Note that the acceleration term of the non-diagonal component in the matrix of equation (10), that is, the acceleration term of the lift coefficient for pitch excitation and the acceleration term of the pitching moment coefficient for vertical excitation can be ignored. That is, α _P12 = α _P21 = 0.

On the other hand, V is included in the expressions of α _P11 and α _P22 which are diagonal components multiplied by the air density ρ, and the integral term for dV of (x ² + z ² ) shown in Expression (17) is air. Multiplying the density ρ corresponds to the mass of air, which is the fluid excluded from the model, and its moment of inertia, which can be easily calculated by general-purpose CAD software or the like. The added mass coefficient c _P11 related to the excluded air mass calculated in this way and the added inertia moment coefficient c _P22 which is a dimensionless coefficient related to the inertia moment of the excluded air are shown in Expression (19).

Note that α _P22 also has a small influence as a whole, and can be omitted and ignored in some cases.

For example, the additional mass coefficient is 1 when a two-dimensional cylinder in an infinite static fluid moves in translation. However, when a fixed wall exists close to an object, the additional mass coefficient for movement in a direction perpendicular to the wall surface increases. It is known to do. When the vehicle travels on the road, it is similar to the case where the fixed wall exists, and from these, it can be seen that the value of _{cP11 in} the equation (19) is appropriate.

In the above, an experiment or numerical simulation limited to the vertical motion of the vehicle is performed, but the same idea can be extended to the lateral motion of the vehicle. That is, as an external force to be incorporated into the equation of motion of the vehicle, the lateral force F _Y caused by aerodynamic force can be expressed as in equation (20), and the yawing moment M _Z can be expressed as in equation (21).

Here, ΔC _Y is a lateral force coefficient fluctuation component that is a fluctuation component from the reference lateral force coefficient in the reference posture of the vehicle represented by Expression (3). ΔC _MZ is a yawing moment coefficient fluctuation component that is a fluctuation component from the reference yawing moment coefficient in the reference posture of the vehicle represented by the equation (4).

Here, the linear system in which the acceleration term due to the added mass is given to the relationship between the lateral displacement y and the yaw displacement angle β as the input variables and the outputs ΔC _Y and ΔC _MZ as in Expression (10). As a result, the modeling represented by the equation (22) can be performed.

Here, the term of s ² is an acceleration term due to the added mass. As described in relation to Expression (12), modeling represented by Expression (23) using a drag coefficient in the reference posture may be performed instead of Expression (22).

Note that α _Y12 , α _Y21 , and α _Y22 can be ignored here as well, as described in relation to the acceleration term of the off-diagonal component in the matrix of equation (10). In addition, the influence of α _Y11 is considered to be small as compared with the case of vertical vibration.

  As described above, the dynamic aerodynamic force acting on the vehicle with the attitude change is obtained by using the dynamic aerodynamic model of the equations (10) and (22) constructed using the acceleration term coefficient identified by the wind tunnel experiment or the like. Can be appropriately calculated. Further, by developing this, it is possible to perform vehicle motion analysis based on the calculated aerodynamic force, and to perform vehicle suspension control. The motion analysis may be performed using all of the vertical force, pitching moment, lateral force, and yawing moment.

  Application of vehicle aerodynamic force calculation to vehicle suspension control can be performed as follows. In other words, the aerodynamic force based on the dynamic aerodynamic model is incorporated into the translational motion equation and the rotational motion equation as an external force acting on the vehicle, and is equivalent to the motion equation when the aerodynamic force does not act. Calculate the apparent spring constant and damping constant of the suspension. Then, the difference between the calculated apparent spring constant and damping constant and the actual spring constant and damping constant can be used as the amount of change in the apparent spring constant and the amount of change in the apparent damping constant due to the action of aerodynamic force. it can. Note that by assuming sinusoidal vibration, the effect of additional mass can be incorporated into the apparent spring constant. Thereby, the control apparatus which performs suspension control appropriately about the vehicle on which an aerodynamic force acts can be comprised.

  The vehicle aerodynamic force calculation apparatus according to the present invention can be used for an apparatus that analyzes a motion state when an aerodynamic force acts on a traveling vehicle, and can be used for a vehicle suspension control apparatus that takes aerodynamic forces into consideration. The vehicle suspension control device can be used for setting a spring constant and a damping constant of a suspension in consideration of the influence of aerodynamic forces.

  DESCRIPTION OF SYMBOLS 10 Vehicle aerodynamics calculation apparatus, 12 CPU, 14 input part, 16 output part, 18 memory | storage part, 20 acceleration term coefficient data, 22 characteristic radius data, 24 condition acquisition process part, 26 modeling process part, 28 fluctuation component calculation process Part, 30 action amount calculation processing part, 32 equation of motion analysis processing part.

Claims (18)

Condition acquisition means for acquiring an air density ρ, a vehicle speed U, a front projection area A of the vehicle, a representative length L of the vehicle, and a characteristic radius R;
The lift coefficient fluctuation component, which is a fluctuation component from the reference lift coefficient in the vehicle's reference attitude, is ΔC _Z, and the pitching moment coefficient fluctuation component, which is the fluctuation component from the reference pitching moment coefficient, in the vehicle's reference attitude is ΔC _MY , the lift coefficient Modeling means for modeling the fluctuation component ΔC _Z and the pitching moment coefficient fluctuation component ΔC _MY as linear models with terms up to the second derivative of each input variable, with the vertical displacement z and the pitch displacement angle θ as input variables; ,
Fluctuation component calculation means for calculating the lift coefficient fluctuation component ΔC _Z and the pitching moment coefficient fluctuation component ΔC _MY based on the condition acquired by the condition acquisition means and the modeled linear model,
Action amount calculating means for calculating the vertical force and the pitching moment acting on the vehicle based on the calculated lift coefficient fluctuation component ΔC _Z and the pitching moment coefficient fluctuation component ΔC _MY ,
With
The vehicle aerodynamic force calculation device characterized in that the modeling means models as a linear model having an acceleration term related to a lift coefficient fluctuation component ΔC _Z when the vertical displacement z is at least an input variable as a second-order differential term. Condition acquisition means for acquiring an air density ρ, a vehicle speed U, a front projection area A of the vehicle, a representative length L of the vehicle, and a characteristic radius R;
A lateral force coefficient fluctuation component that is a fluctuation component from the reference lateral force coefficient in the vehicle's reference attitude is ΔC _Y, and a yawing moment coefficient fluctuation component that is a fluctuation component from the reference yawing moment coefficient in the vehicle's reference attitude is ΔC _MZ , A model that models the lateral force coefficient fluctuation component ΔC _Y and yaw moment coefficient fluctuation component ΔC _MZ as linear models with terms up to the second derivative of each input variable, with the lateral displacement y and yaw displacement angle β as input variables. And
And conditions acquired by the condition acquisition unit, based on the modeled linear model, and the fluctuation component calculating means for calculating respective lateral force coefficient variation component [Delta] C _Y and yawing moment coefficient variation component [Delta] C _MZ,
Action amount calculating means for calculating the lateral force and yawing moment acting on the vehicle based on the calculated lateral force coefficient fluctuation component ΔC _Y and yawing moment coefficient fluctuation component ΔC _MZ ,
With
Modeling means, as second-order differential term, the vehicle air force calculating device, characterized in that the modeled as a linear model having an acceleration section in the lateral force coefficient variation component [Delta] C _Y when an input variable at least lateral displacement y . In the vehicle aerodynamic force calculation device according to claim 1 or 2,
Acceleration term coefficient acquisition means for acquiring a coefficient of acceleration term in a linear model based on a wind tunnel experiment or numerical simulation using an actual vehicle or a model,
The vehicle aerodynamic force calculation apparatus characterized in that the modeling means performs linear modeling using the acquired acceleration term coefficient. In the vehicle aerodynamic force calculation device according to claim 1,
The condition acquisition means further acquires the representative volume V of the vehicle,
The effect of the acceleration term of the lift coefficient fluctuation component ΔC _Z when the vertical displacement z is used as an input variable is considered to be an additional mass force received from the surrounding air when the vehicle performs acceleration motion, and the additional mass is eliminated by the vehicle. Additional mass coefficient acquisition means for acquiring an additional mass coefficient that is a dimensionless value by the mass of air to be acquired based on a wind tunnel experiment or numerical simulation using a real vehicle or a model or literature values,
The vehicle aerodynamic force calculation apparatus characterized in that the modeling means treats the acceleration term coefficient as a product of the additional mass coefficient and the representative volume V of the vehicle. The vehicle aerodynamic force calculation device according to claim 2,
The condition acquisition means further acquires the representative volume V of the vehicle,
Considered to be additional mass force from the ambient air the effect of acceleration term of the lateral force coefficient variation component [Delta] C _Y when the lateral displacement y and the input variables when the accelerated motion of the vehicle, the vehicle and the added mass Additional mass coefficient acquisition means for acquiring an additional mass coefficient, which is a dimensionless value with the mass of air to be excluded, based on a wind tunnel experiment or numerical simulation using a real vehicle or a model or literature values,
The vehicle aerodynamic force calculation apparatus characterized in that the modeling means treats the acceleration term coefficient as a product of the additional mass coefficient and the representative volume V of the vehicle. In the vehicle aerodynamic force calculation device according to claim 1,
Condition acquisition means
Wind tunnel experiment or numerical value using actual vehicle or model for at least one of characteristic radius R _P12 included in speed term of lift coefficient fluctuation component ΔC _Z and characteristic radius R _P22 included in speed term of pitching moment coefficient fluctuation component ΔC _MY Including characteristic radius acquisition means for acquiring based on simulation,
The fluctuation component calculation means calculates the lift coefficient fluctuation component ΔC _Z and the pitching moment coefficient fluctuation component ΔC _MY when the pitch displacement angle θ is used as an input variable, using the value of the characteristic radius acquired by the characteristic radius acquisition means. A vehicle aerodynamic force calculation device. The vehicle aerodynamic force calculation device according to claim 2,
Condition acquisition means
At least one of the characteristic radius R _Y12 included in the velocity term of the lateral force coefficient variation component ΔC _{Y and} the characteristic radius R _Y22 included in the velocity term of the yawing moment coefficient variation component ΔC _MZ is used for a wind tunnel experiment using an actual vehicle or a model or Including characteristic radius acquisition means for acquiring based on numerical simulation,
The fluctuation component calculation means calculates a lateral force coefficient fluctuation component ΔC _Y and a yawing moment coefficient fluctuation component ΔC _MZ when the yaw displacement angle β is used as an input variable, using the value of the characteristic radius acquired by the characteristic radius acquisition means. A vehicle aerodynamic force calculation device. Condition acquisition means for acquiring an air density ρ, a vehicle speed U, a front projection area A of the vehicle, a representative length L of the vehicle, and a characteristic radius R;
The lift coefficient fluctuation component, which is a fluctuation component from the reference lift coefficient in the vehicle's reference attitude, is ΔC _Z, and the pitching moment coefficient fluctuation component, which is the fluctuation component from the reference pitching moment coefficient, in the vehicle's reference attitude is ΔC _MY , the lift coefficient Modeling means for modeling the fluctuation component ΔC _Z and the pitching moment coefficient fluctuation component ΔC _MY as linear models with terms up to the second derivative of each input variable, with the vertical displacement z and the pitch displacement angle θ as input variables; ,
Fluctuation component calculation means for calculating the lift coefficient fluctuation component ΔC _Z and the pitching moment coefficient fluctuation component ΔC _MY based on the condition acquired by the condition acquisition means and the modeled linear model,
Action amount calculating means for calculating the vertical force and the pitching moment acting on the vehicle based on the calculated lift coefficient fluctuation component ΔC _Z and the pitching moment coefficient fluctuation component ΔC _MY ,
An equation of motion analysis means for incorporating the calculated vertical force and pitching moment into the vehicle equation of motion as an external force acting on the vehicle and executing a vehicle motion analysis;
With
A vehicle motion analysis apparatus characterized in that the modeling means models as a linear model having an acceleration term related to a lift coefficient fluctuation component ΔC _Z when at least vertical displacement z is an input variable as a second-order differential term. Condition acquisition means for acquiring an air density ρ, a vehicle speed U, a front projection area A of the vehicle, a representative length L of the vehicle, and a characteristic radius R;
A lateral force coefficient fluctuation component that is a fluctuation component from the reference lateral force coefficient in the vehicle's reference attitude is ΔC _Y, and a yawing moment coefficient fluctuation component that is a fluctuation component from the reference yawing moment coefficient in the vehicle's reference attitude is ΔC _MZ , A model that models the lateral force coefficient fluctuation component ΔC _Y and yaw moment coefficient fluctuation component ΔC _MZ as linear models with terms up to the second derivative of each input variable, with the lateral displacement y and yaw displacement angle β as input variables. And
And conditions acquired by the condition acquisition unit, based on the modeled linear model, and the fluctuation component calculating means for calculating respective lateral force coefficient variation component [Delta] C _Y and yawing moment coefficient variation component [Delta] C _MZ,
Action amount calculating means for calculating the lateral force and yawing moment acting on the vehicle based on the calculated lateral force coefficient fluctuation component ΔC _Y and yawing moment coefficient fluctuation component ΔC _MZ ,
An equation of motion analysis means for incorporating the calculated lateral force and yawing moment into the vehicle equation of motion as an external force acting on the vehicle, and executing the vehicle motion analysis;
With
Modeling means, as second-order differential term, the vehicle motion analysis apparatus characterized by modeled as a linear model having an acceleration section in the lateral force coefficient variation component [Delta] C _Y when an input variable at least lateral displacement y. In the vehicle motion analysis device according to claim 8 or 9,
Acceleration term coefficient acquisition means for acquiring a coefficient of acceleration term in a linear model based on a wind tunnel experiment or numerical simulation using an actual vehicle or a model,
The vehicle motion analysis apparatus characterized in that the modeling means performs linear modeling using the acquired acceleration term coefficient. The vehicle motion analysis apparatus according to claim 8,
The condition acquisition means further acquires the representative volume V of the vehicle,
The effect of the acceleration term of the lift coefficient fluctuation component ΔC _Z when the vertical displacement z is used as an input variable is considered to be an additional mass force received from the surrounding air when the vehicle performs acceleration motion, and the additional mass is eliminated by the vehicle. Additional mass coefficient acquisition means for acquiring an additional mass coefficient that is a dimensionless value by the mass of air to be acquired based on a wind tunnel experiment or numerical simulation using a real vehicle or a model or literature values,
A vehicle motion analysis apparatus characterized in that the modeling means treats an acceleration term coefficient as a product of an additional mass coefficient and a representative volume V of the vehicle. The vehicle motion analysis apparatus according to claim 9,
The condition acquisition means further acquires the representative volume V of the vehicle,
Considered to be additional mass force from the ambient air the effect of acceleration term of the lateral force coefficient variation component [Delta] C _Y when the lateral displacement y and the input variables when the accelerated motion of the vehicle, the vehicle and the added mass Additional mass coefficient acquisition means for acquiring an additional mass coefficient, which is a dimensionless value with the mass of air to be excluded, based on a wind tunnel experiment or numerical simulation using a real vehicle or a model or literature values,
A vehicle motion analysis apparatus characterized in that the modeling means treats an acceleration term coefficient as a product of an additional mass coefficient and a representative volume V of the vehicle. The vehicle motion analysis apparatus according to claim 8,
Condition acquisition means
Wind tunnel experiment or numerical value using actual vehicle or model for at least one of characteristic radius R _P12 included in speed term of lift coefficient fluctuation component ΔC _Z and characteristic radius R _P22 included in speed term of pitching moment coefficient fluctuation component ΔC _MY Including characteristic radius acquisition means for acquiring based on simulation,
The fluctuation component calculation means calculates the lift coefficient fluctuation component ΔC _Z and the pitching moment coefficient fluctuation component ΔC _MY when the pitch displacement angle θ is used as an input variable, using the value of the characteristic radius acquired by the characteristic radius acquisition means. A vehicle motion analysis apparatus characterized by the above. The vehicle motion analysis apparatus according to claim 9,
Condition acquisition means
At least one of the characteristic radius R _Y12 included in the velocity term of the lateral force coefficient variation component ΔC _{Y and} the characteristic radius R _Y22 included in the velocity term of the yawing moment coefficient variation component ΔC _MZ is used for a wind tunnel experiment using an actual vehicle or a model or Including characteristic radius acquisition means for acquiring based on numerical simulation,
The fluctuation component calculation means calculates a lateral force coefficient fluctuation component ΔC _Y and a yawing moment coefficient fluctuation component ΔC _MZ when the yaw displacement angle β is used as an input variable, using the value of the characteristic radius acquired by the characteristic radius acquisition means. A vehicle motion analysis device. Condition acquisition means for acquiring an air density ρ, a vehicle speed U, a front projection area A of the vehicle, a representative length L of the vehicle, and a characteristic radius R;
The lift coefficient fluctuation component, which is a fluctuation component from the reference lift coefficient in the vehicle's reference attitude, is ΔC _Z, and the pitching moment coefficient fluctuation component, which is the fluctuation component from the reference pitching moment coefficient, in the vehicle's reference attitude is ΔC _MY , the lift coefficient Modeling means for modeling the fluctuation component ΔC _Z and the pitching moment coefficient fluctuation component ΔC _MY as linear models with terms up to the second derivative of each input variable, with the vertical displacement z and the pitch displacement angle θ as input variables; ,
Fluctuation component calculation means for calculating the lift coefficient fluctuation component ΔC _Z and the pitching moment coefficient fluctuation component ΔC _MY based on the condition acquired by the condition acquisition means and the modeled linear model,
Action amount calculating means for calculating the vertical force and the pitching moment acting on the vehicle based on the calculated lift coefficient fluctuation component ΔC _Z and the pitching moment coefficient fluctuation component ΔC _MY ,
Suspension control means for incorporating the calculated vertical force and pitching moment into the vehicle equation of motion as external forces acting on the vehicle and controlling suspension elements of the vehicle based on a comparison with the equation of motion when no aerodynamic force acts ,
With
The vehicle suspension control apparatus characterized in that the modeling means models as a linear model having an acceleration term related to a lift coefficient fluctuation component ΔC _Z when the vertical displacement z is at least an input variable as a second-order differential term. The vehicle suspension control device according to claim 15,
Acceleration term coefficient acquisition means for acquiring a coefficient of acceleration term in a linear model based on a wind tunnel experiment or numerical simulation using an actual vehicle or a model,
The vehicle suspension control apparatus, wherein the modeling means performs linear modeling using the acquired acceleration term coefficient. The vehicle suspension control device according to claim 15,
The condition acquisition means further acquires the representative volume V of the vehicle,
The effect of the acceleration term of the lift coefficient fluctuation component ΔC _Z when the vertical displacement z is used as an input variable is considered to be an additional mass force received from the surrounding air when the vehicle performs acceleration motion, and the additional mass is eliminated by the vehicle. Additional mass coefficient acquisition means for acquiring an additional mass coefficient that is a dimensionless value by the mass of air to be acquired based on a wind tunnel experiment or numerical simulation using a real vehicle or a model or literature values,
The vehicle suspension control apparatus characterized in that the modeling means treats the acceleration term coefficient as a product of the additional mass coefficient and the representative volume V of the vehicle. The vehicle suspension control device according to claim 15,
Condition acquisition means
Wind tunnel experiment or numerical value using actual vehicle or model for at least one of characteristic radius R _P12 included in speed term of lift coefficient fluctuation component ΔC _Z and characteristic radius R _P22 included in speed term of pitching moment coefficient fluctuation component ΔC _MY Including characteristic radius acquisition means for acquiring based on simulation,
The fluctuation component calculation means calculates the lift coefficient fluctuation component ΔC _Z and the pitching moment coefficient fluctuation component ΔC _MY when the pitch displacement angle θ is used as an input variable, using the value of the characteristic radius acquired by the characteristic radius acquisition means. A vehicle suspension control device.

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