A method for reducing the wind resistance of a wheel by shaping the spoke opening profile

Abstract

The present invention discloses a method for reducing the wheel wind resistance by determining the spoke opening contour shape, comprising: step one, establishing an initial wheel spoke model; step two, constructing an initial wheel calculation model; step three, designing an experimental scheme; step four, constructing a functional relationship between the wheel spoke opening contour shape and its aerodynamic drag coefficient according to the test data in step three, and verifying its accuracy; step five, using an optimization algorithm to obtain the wheel spoke opening contour shape when the aerodynamic drag coefficient value is the smallest. Beneficial effects: The present invention proposes a new method for reducing wheel wind resistance by taking the spoke opening contour shape as the starting point, effectively avoiding the blindness problem existing in the wheel spoke opening contour shape design, thereby shortening the design cycle, improving efficiency, and at the same time improving the fuel economy of the vehicle, and improving the pressure pulsation near the wheel, which is of great significance for guiding the wheel spoke opening contour shape design and thus reducing the vehicle wind resistance and improving the fuel economy.

Description

Technical Field

[0001] The invention relates to an optimization method for reducing the spoke opening contour shape, in particular to a method for reducing the spoke opening contour shape of a wheel wind resistance, and belongs to the technical field of vehicle engineering. Background Art

[0002] In recent years, the global energy crisis has intensified, while the number of cars is still increasing. The energy consumption of cars is increasing day by day. The government and consumers are paying more and more attention to the fuel consumption of cars, and the requirements for car fuel economy are more stringent than before. It is reported that a 10% reduction in aerodynamic drag of a car can reduce fuel consumption by 2%-3%. Since aerodynamic drag is proportional to the square of the speed, improving the aerodynamic characteristics of the car can significantly reduce fuel consumption, which is especially important when driving at high speeds. When the car speed reaches 60km / h, the power required to overcome aerodynamic drag accounts for half of the power required for the car to overcome driving resistance.

[0003] During the driving process of a car, the wheel area contributes up to 25-30% to the aerodynamic drag of the car. The flow field in the wheel area is affected by the front flow from the side and bottom of the car and the flow field in the engine compartment, and has a relatively strong mutual interference with the body, bottom structure and ground. In addition, the rotation of the wheel will also generate energy input to the flow field in this area, making the flow field structure in the wheel area more complex. Existing studies have also shown that the different opening shapes of the rims of automobile wheels will have a direct impact on the aerodynamic drag of the wheel and the aerodynamic drag of the car. However, the current understanding of the flow phenomena and flow laws in the wheel area is relatively limited, so that the reduction of the aerodynamic drag in the wheel area has a strong potential for optimization to improve the aerodynamic drag of the whole vehicle. Therefore, it is of great significance to master the flow field characteristics in the wheel area and propose targeted drag reduction methods in the wheel area to achieve the purpose of reducing the aerodynamic drag of the car, which is of great significance to reducing fuel consumption.

[0004] The arrangement of the openings on the wheel spoke has a great influence on the aerodynamic characteristics: when the total opening area is the same, increasing the number of openings appropriately is conducive to improving the aerodynamic characteristics; with the increase of the total opening area, the drag coefficient of the wheel itself shows irregular changes to a certain extent. Therefore, the wheel spoke is taken as the optimization object, and the aerodynamic drag of a single wheel is reduced as the optimization goal without changing the wheel opening area. The small arc radius R1, large arc radius R2, small arc tangent L1 and L2 and the corresponding circular angle α of a single spoke hole are selected as design variables. The optimal super Latin cube design, RBF approximate model and adaptive simulated annealing algorithm (ASA) are comprehensively adopted to optimize the spoke holes, and the optimal spoke shape is obtained. Summary of the invention

[0005] Purpose of the invention: In view of the deficiencies in the prior art, the present invention provides a method for reducing the spoke opening contour shape of a wheel wind resistance. The method takes reducing the aerodynamic drag of a single wheel as an optimization goal, can effectively avoid the blindness problem existing in the design process of the wheel spoke opening contour, can effectively reduce the wheel spoke opening contour design cycle, and thus improve the improvement efficiency. At the same time, it can also improve the aerodynamic characteristics of the wheel area, thereby achieving the purpose of reducing vehicle resistance.

[0006] Technical solution: A method for reducing the spoke opening profile shape of a wheel wind resistance, comprising the following steps:

[0007] Step 1, establishing an initial wheel model, and drawing a three-dimensional model of the wheel according to the selected number of wheel spokes and the initial values ​​of the wheel spoke opening contour shape; the spoke opening contour shape parameters include the small arc radius R1, the large arc radius R2, the small arc tangents L1 and L2, and the circumferential angle α corresponding to the large arc;

[0008] Step 2: constructing an initial wheel wind resistance calculation model, constructing a wheel wind resistance calculation model based on the above three-dimensional wheel model, and performing simulation analysis to obtain the aerodynamic drag coefficient value of the initial wheel;

[0009] Step 3: Design a test plan. On the premise that the wheel spoke opening area remains unchanged, select the wheel opening contour shape as a variable, set the value range of the variable, and take the aerodynamic drag coefficient of the wheel as the target response value; design a plan for the wheel spoke opening contour shape through a statistical sampling test design method, perform wheel wind resistance simulation analysis on models under different spoke opening contour shapes, and obtain the aerodynamic drag coefficient value under each spoke opening contour shape;

[0010] Step 4: Construct a functional relationship between the wheel spoke opening profile shape and its aerodynamic drag coefficient value based on the test data in step 3, and verify its accuracy.

[0011] Step 5: Use an optimization algorithm to obtain the wheel spoke opening contour shape when the aerodynamic drag coefficient value is the smallest.

[0012] Furthermore, in the step 1, a three-dimensional model of an initial wheel is constructed, and an opening ratio of the initial wheel is selected to be between 20% and 80%.

[0013] Furthermore, the method for constructing the wheel wind resistance calculation model in step 2 is a fluid dynamics calculation method, which constructs a three-dimensional virtual wind tunnel model of the wheel and the road surface, imports the three-dimensional model into the meshing software, generates the fluid domain mesh and boundary layer mesh around the wheel, and uses computational fluid dynamics analysis software to set the velocity inlet, pressure outlet and wheel wall motion mode to realize the simulation analysis of the wheel aerodynamic wind resistance.

[0014] Furthermore, in the step three, the value range of the wheel spoke opening contour shape is that the small arc radius R1 is 5.41mm~10.46mm, the large arc radius R2 is 41.58mm~58.68mm, the circular angle α corresponding to the large arc is 15°~30°, and the lengths of the small arc tangents L1 and L2 are equal, both of which are 27.12mm~46.83mm.

[0015] Furthermore, the statistical sampling test design method is an optimal Latin hypercube test design method; through statistical design and data analysis, the key parameters that have a significant impact on the aerodynamic drag coefficient value are screened out. The optimal hyper Latin cube design, Latin hypercube is a stratified sampling method. For multiple random variable inputs, general stratified sampling requires the input sample space to be converted into N regions with equal probability, which is very difficult to operate. Latin hypercube uses a multidimensional stratified sampling method, and its working principle is as follows:

[0016] (1) Define the number of samples N involved in the computer operation;

[0017] (2) Divide each input into N with equal probability, and we have:

[0018] P(x in <x<x i(n+1) )=1 / N

[0019] (3) Only one sample is drawn from each column, and the positions of the samples drawn in each column are random.

[0020] Compared with simple stratified sampling, the biggest advantage of Latin hypercube sampling is that any size of sampling can be easily generated. However, from the perspective of spatial distribution, as the number of points decreases, the chance of missing certain areas of the design space will also increase. The optimal hyper Latin cube design makes all test points as evenly distributed as possible in the design space, with very good space filling and balance.

[0021] Furthermore, the functional relationship in step 4 is to construct a regression function according to the wheel spoke opening contour shape and the aerodynamic drag coefficient value, and the regression function used is the RBF approximation model.

[0022] The advantages of using the RBF model include:

[0023] 1. Strong ability to approximate complex nonlinear functions.

[0024] 2. No mathematical assumptions are required and it has black box characteristics.

[0025] 3. Fast learning speed and excellent generalization ability.

[0026] 4. Strong fault tolerance function. Even if the sample contains "noise" input, it will not affect the overall performance of the network.

[0027] Radial Basis Functions (RBF) network: The Euclidean distance between the test point and the sample point is used as the independent variable, that is, assuming represents a set of input vectors,

[0028] is the basis function. j ‖, is the Euclidean distance:

[0029] (xx j ) T (xx j ), and 0.2≤c≤3

[0030] Where x1,...,x N It represents the wheel aerodynamic drag coefficient values ​​under different schemes obtained by the optimal super Latin cube test design. The subscript represents the 1st to Nth samples in the optimal super Latin cube test design. Ω represents the space of the wheel aerodynamic drag coefficient values ​​under different schemes.

[0031] g i ≡g(‖xx j ‖ c ) is the basis function, ‖xx j ‖ is the Euclidean distance, also known as the Euclidean norm. The subscript j represents the jth sample, j=1...N.

[0032] Furthermore, the accuracy in step 4 is verified by using the determination coefficient R2 to test the accuracy of the RBF approximation model after linear regression of the model. The closer the determination coefficient is to 1, the higher the accuracy of the model is. The predicted value is compared with the actual value to verify its effectiveness.

[0033] Further, it is characterized in that:

[0034] The coefficient of determination R 2 The expression is:

[0035]

[0036] Where n is the number of data points for testing the accuracy of the model; is the approximate model prediction value of the i-th response; y i is the simulation value of the ith response; is the average value.

[0037] Furthermore, it is characterized in that: the optimization algorithm in step five is an adaptive simulated annealing optimization method, and the wheel spoke opening contour shape with the minimum aerodynamic drag coefficient value is obtained by performing nonlinear optimization design on the wheel spoke opening contour shape.

[0038] Furthermore, the algorithm is characterized in that: the principle is to take the similarity between the cooling process of solid matter in physics and the general combinatorial optimization problem as a starting point, and use solid annealing simulation to solve the combinatorial optimization problem:

[0039] Step 1: Initialize the optional initial solution, set the initial temperature T0, the end temperature T f , let the iteration index k = 0, calculate the initial energy value E0, and the energy function is defined as:

[0040]

[0041] Where: x i is the gray value of the original image; x' i is the predicted output grayscale value; N is the number of output image imaging points.

[0042] Step 2: Randomly generate a new solution x' and calculate the energy increment ΔE.

[0043] ΔE=E(x')-E(x)

[0044] Step 3: Accept the new solution according to the Metropolis criterion:

[0045]

[0046] Where: T is the current temperature, and its value is related to the initial temperature T0 and the cooling rate α.

[0047] Step 4: Reduce the temperature according to the temperature attenuation function to determine whether the iteration termination condition is reached. If so, stop the iteration; otherwise, turn to Step 3, the temperature attenuation function is:

[0048] T k+1 =αT k

[0049] Where: T k is the temperature before cooling; T k+1 is the temperature after cooling, α is a positive number less than 1

[0050] The simulated annealing algorithm compares the combinatorial optimization problem with the thermal equilibrium problem in statistical mechanics through the simulated annealing process. Starting from the initial point, the objective function is evaluated once for each step forward. As long as the function value decreases, the new design point is accepted, and the process is repeated until the optimal point is found. After adding adaptation, the algorithm includes two loops, the inner loop and the outer loop. For the inner loop, it can ensure that the samples in the solution space are fully searched and iterated at each temperature; while the outer loop can ensure that the algorithm has a trend of continuously decreasing temperature during the process, and finally reaches a balanced stable state.

[0051] Beneficial effects: The present invention can effectively avoid the blindness problem existing in the design of the wheel spoke opening profile, thereby shortening the design cycle, improving efficiency, and also plays an improving role in improving the aerodynamic characteristics of the wheel area, thereby achieving the purpose of reducing vehicle resistance; the present invention selects the RBF network approximation model to verify the model accuracy. The RBF network approximation model has a strong ability to approximate complex nonlinear functions; no mathematical assumptions are required, and it has black box characteristics; fast learning speed, and excellent generalization ability; strong fault tolerance function, even if the sample contains "noise" input, it does not affect the overall performance of the network, etc., which greatly improves the efficiency of model accuracy verification, further reduces the blindness in the research and development process, and shortens the design cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a design flow chart of the present invention.

[0053] Figure 2 (a) Schematic diagram of wheel spoke opening variables and (b) Schematic diagram of a single spoke opening according to the present invention.

[0054] Figure 3 It is a schematic diagram of the virtual wind tunnel model of the wheel of the present invention.

[0055] Figure 4 Schematic diagram of the structures of the experiments (a) Scheme 7 (b) Scheme 11 (c) Scheme 16 designed for the Latin hypercube of the present invention.

[0056] Figure 5 It is a schematic diagram of the prediction accuracy of the RBF approximation model of the present invention.

[0057] Figure 6 For the present invention C d Schematic diagram of the RBF model with one of the design variables R1.

[0058] Figure 7 For the present invention C d Schematic diagram of the RBF model with one of the design variables R1.

[0059] Figure 8 For the present invention C dSchematic diagram of the RBF model with one of the design variables R2.

[0060] Fig. 9 For the present invention C d Schematic diagram of the RBF model with one of the design variables R2.

[0061] Fig.10 It is a schematic diagram of iterative steps in the optimization process of the adaptive simulation optimization algorithm of the present invention.

[0062] Fig.11 The different design variables of the spokes of the present invention have an effect on C d Schematic diagram of the contribution of .

[0063] Fig.12 This is a schematic diagram comparing the front and rear spoke opening shapes optimized by the present invention.

[0064] Fig.13 Schematic diagram of optimizing the longitudinal plane pressure of the front and rear wheels according to the present invention.

[0065] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0066] like Figure 1 As shown, a method for reducing the wind resistance of a wheel by shaping the spoke opening profile includes the following steps:

[0067] Step 1: Establish an initial wheel model. Draw a three-dimensional model of the wheel according to the initial values ​​of the number of wheel spokes and the shape of the wheel spoke opening profile. The spoke opening ratio of common wheels on the market is concentrated in the range of 20%-80%. The opening area ratio of the five-spoke wheel in this wheel is 54%. Figure 2 (a) shows a five-spoke vehicle. Figure 2 The initial values ​​of a single spoke hole of the five-spoke wheel shown in (b) are: the circumferential angle α is 22.5°, the small arc radius R1 is 8 mm, the large arc radius R2 is 55 mm, the lengths of the small arc tangents L1 and L2 are 35.69 mm, and the design variables of the spoke opening are parameterized using the 3D modeling software Solidworks.

[0068] Step 2: constructing an initial wheel wind resistance calculation model, constructing a wheel wind resistance calculation model based on the above three-dimensional wheel model, and performing simulation analysis to obtain the aerodynamic drag coefficient value of the initial wheel;

[0069] Based on the wheel model, Figure 3The wheel-road virtual wind tunnel model shown. Boundary condition settings: calculation domain inlet-velocity inlet, V=40m / s; calculation domain outlet-pressure outlet, 0Pa; calculation domain side and top surface-symmetry plane; body and pillar-fixed wall, no slip; calculation domain ground-moving ground, V=40m / s. Since the external flow field where the wheel is located is regarded as a three-dimensional, steady, isothermal, constant pressure, incompressible atmospheric environment, the physical field parameters are: temperature-20℃; pressure-101325Pa; viscosity-1.7894e-6Pa·s; density-1.225kg·m-3. The boundary layer grid settings refer to Table 1 below;

[0070] Table 1 Grid size of computational domain

[0071] Different regions wheel ground Computed fields Grid size (mm) 0.8-2 0.8-15 1-50

[0072] The calculation process is divided into two processes: steady calculation and unsteady calculation. The steady calculation uses the SST k-ω model to solve the model steadily, and after 4000 iterations, the convergence residual meets 0.0001; then the steady calculation results are used as the initial flow field for the unsteady calculation; the unsteady calculation uses the SIMLEC algorithm to couple the pressure and velocity fields, the turbulence model is adjusted to the DES model, the discretization format is the second-order upwind format, and the calculation time step is 1×10 -3 s, 5 iterations per unit time step, unsteady calculation duration is 2s, and the total calculation time step is 10000 steps. The simulation calculation is based on the commercial fluid calculation software STAR-CCM+.

[0073] Step 3: Design a test plan, take the wheel spoke opening profile shape as a variable, set the value range of the variable, and take the aerodynamic drag coefficient of the wheel as the target response value; design a plan for the wheel spoke opening profile shape through a statistical sampling test design method, perform wheel wind resistance simulation analysis on models under different spoke opening profile shapes, and obtain the aerodynamic drag coefficient value under each spoke opening profile shape;

[0074] Under the condition that the single opening area is ensured to be unchanged, the wheel radius R is fixed to 75mm, and the large arc radius R2 is used as a variable for design. The remaining parameters are solved in Matlab. The solution formula is shown in the formula:

[0075]

[0076] R1=20.91·sin(α)

[0077] L1=L2=R2-20.91·cos(α)

[0078] The range of the circumferential angle α corresponding to the large arc of each spoke opening is: [15°, 30°], so the range of the radius R2 of the large arc is: R2∈[41.5824, 58.6799] mm. According to the range of variables, the optimal super Latin cube test method is used for design, and the corresponding values ​​of L1, L2 and R1 are solved using the above formula. A total of 20 test schemes are designed, as shown in Table 2.

[0079] Table 2 Experimental scheme and calculation results

[0080] Trial plan <![CDATA[L1、L2 / mm]]> <![CDATA[R1 / mm]]> <![CDATA[R2 / mm]]> <![CDATA[C d ]]> 1 30.6066 9.6906 42.1299 0.957 2 29.4051 10.2053 47.6498 0.951 3 29.9652 7.5985 58.0359 0.960 4 31.7047 7.0503 60.1428 0.959 5 27.1245 8.6576 52.3857 0.891 6 28.434 8.1408 54.1919 0.919 7 46.8267 5.4106 67.0194 0.940 8 45.1861 5.6968 65.2999 0.943 9 42.4004 6.2377 62.3531 0.956 10 31.8865 9.187 50.6646 0.951 11 34.2891 7.8704 55.1935 0.950 12 41.1032 6.5165 60.9666 0.954 13 34.1555 8.3904 53.3028 0.885 14 32.6025 8.9232 51.5074 0.946 15 37.7722 7.3251 57.3518 0.951 16 28.863 10.4525 46.9672 0.968 17 39.8928 6.7939 59.663 0.950 18 43.7944 5.9578 63.8325 0.938 19 29.9908 9.9489 48.3766 0.950 20 31.2544 9.4303 49.9115 0.957

[0081] Aerodynamic drag is used as the target response value. In order to explore the optimal spoke opening profile shape to improve the wind resistance performance of the wheel, it is particularly important to choose a reasonable test design method. The purpose of the test design is to select a limited number of sample points in the entire design space so that they can reflect the information of the entire space to the greatest extent. A three-dimensional model is established based on the parameters obtained by the optimal super Latin cube test method design in Table 2. Typical schemes are as follows Figure 4 As shown in the figure, it can be seen that the spoke opening shapes of different schemes are quite different, which leads to certain differences in the aerodynamic drag coefficients between the wheels.

[0082] Step 4: Construct a functional relationship between the wheel spoke opening profile shape and its aerodynamic drag coefficient value based on the test data in step 3, and verify its accuracy.

[0083] The RBF approximate model is a spatial prediction method based on minimizing the average error of the weighted sum of sampling values. Error analysis is usually used to evaluate the fitting accuracy of the approximate model. The fitting coefficient R2 is a commonly used error evaluation index in statistics. The closer its value is to 1, the better the fitting effect. Figure 5 The figure shows the prediction accuracy of the constructed RBF model. From the figure, we can see that C d The simulation value and the predicted value fit very well, which means that the prediction accuracy of the selected approximate model is very high and meets the requirements.

[0084] Figure 6-9 The following are C d With two of the design variables in the RBF model; Figure 6 The α and R1 shown in Figure 2 are related to C d The value has the greatest impact. When the two variables increase to their maximum values, C d reached their maximum values; Figure 7 When α is changed to L1 to study the relationship between different variables, the topological relationship between the three variables is explored. It is found that when L1 changes, the effect of C d The influence of is very small, indicating that L1 has less influence on the results than R1 variable; Figure 8In the above example, α is changed to R2. As R2 increases, the change in the response is still small. Taking all factors into consideration, the large arc angle α and the small arc radius R1 of each spoke opening are both the same as C. d Positively correlated, C d As α and R1 increase, the radius of the large arc R2 and the tangent lines of the small arc L1 and L2 increase the radius of the C d The impact is small.

[0085] Step 5: Use an optimization algorithm to obtain the wheel spoke opening contour shape when the aerodynamic drag coefficient value is the smallest.

[0086] The algorithm is characterized by: the principle is to take the similarity between the cooling process of solid materials in physics and the general combinatorial optimization problem as the starting point, and use solid annealing simulation to solve the combinatorial optimization problem:

[0087] Step 1: Initialization, select the initial solution, set the initial temperature T0, the end temperature T f , let the iteration index k = 0, calculate the initial energy value E0, and the energy function is defined as:

[0088]

[0089] Where: x i is the gray value of the original image; x' i is the predicted output grayscale value; N is the number of output image imaging points.

[0090] Step 2: Randomly generate a new solution x' and calculate the energy increment ΔE;

[0091] ΔE=E(x')-E(x)

[0092] Step 3: Accept the new solution according to the Metropolis criterion:

[0093]

[0094] Where: T is the current temperature, and its value is related to the initial temperature T0 and the cooling rate α.

[0095] Step 4: Reduce the temperature according to the temperature attenuation function to determine whether the iteration termination condition is reached. If so, stop the iteration; otherwise, turn to Step 3, the temperature attenuation function is:

[0096] T k+1 =αT k

[0097] Where: T k is the temperature before cooling; T k+1 is the temperature after cooling, α is a positive number less than 1

[0098] The accuracy of the approximate model predicted in step 5 meets our requirements. The adaptive simulated annealing optimization algorithm is used, which has a wide range of applications, does not require high initial conditions, and has a fast convergence speed, and can obtain all optimal solutions. The aerodynamic drag coefficient is optimized as the optimization target, and a set of optimal solutions is obtained after 382 iterations. The iterative process is as follows: Fig.10 shown. Fig.11 The effects of different design variables on C d The contribution of the circular angle α and the small arc radius R1 to C d The value has a significant impact, and the effects of other design variables on the aerodynamic characteristics can be ignored.

[0099] The optimization target is optimized by using the adaptive simulated annealing optimization method. The wheel spoke opening contour shape with the minimum aerodynamic drag is taken as the optimal wheel spoke opening contour shape arrangement form. The optimized wheel spoke opening contour structural design parameters are L1, L2 = 33.9051 mm, R1 = 8.4729 mm, R2 = 53.0161 mm, and the optimized wheel spoke opening contour shape arrangement form is as follows: Fig.12 shown.

[0100] Next, the aerodynamic characteristics of the front and rear wheels are analyzed. Fig.13 It is the pressure distribution in the longitudinal plane of the front and rear wheels that is optimized. Most of the aerodynamic drag of the wheel comes from the pressure difference drag before and after the wheel. As can be seen in the figure, the pressure on the front of the wheel before and after optimization has hardly changed, while the pressure at the rear of the wheel has increased to a certain extent after optimization, which leads to a decrease in the pressure difference of the wheel, thereby reducing the aerodynamic drag of the wheel to a certain extent.

[0101] The wind resistance simulation analysis of the optimized wheel is carried out using step 1, and the analysis results before and after optimization are shown in Table 2. As shown in Table 3, the optimized spoke shape significantly improves the aerodynamic performance of the wheel, and its aerodynamic drag coefficient is reduced by 5.7% compared with that before optimization, thereby reducing the wind resistance of the wheel.

[0102] Table 3 Comparison of aerodynamic drag coefficient before and after optimization

[0103] <![CDATA[L1、L2 / mm]]> <![CDATA[R1 / mm]]> <![CDATA[R2 / mm]]> <![CDATA[C d ]]> Before optimization 35.6863 8 47.7952 0.934 After optimization 33.9051 8.4729 53.0161 0.883

[0104] The design parameters of the wheel spoke opening profile are as follows: After the original structure is optimized, the small arc tangent L1, L2 = 33.9051 mm, the small arc radius R1 = 8 mm, the large arc radius R2 = 55 mm, the circumferential angle α = 22.5°, and the aerodynamic drag coefficient C d=0.87. The above embodiments are preferred implementations of the present invention, but the present invention is not limited to the above embodiments. Any obvious improvements, substitutions or modifications that can be made by those skilled in the art without departing from the essence of the present invention belong to the protection scope of the present invention.

Claims

1. A method for reducing the wind resistance of a wheel by shaping the spoke opening profile, characterized in that: The following steps are involved: Step 1, establishing an initial wheel model, and drawing a three-dimensional model of the wheel according to the selected number of wheel spokes and the initial values ​​of the wheel spoke opening contour shape; the spoke opening contour shape parameters include the small arc radius R1, the large arc radius R2, the small arc tangents L1 and L2, and the circumferential angle α corresponding to the large arc; Step 2: constructing an initial wheel wind resistance calculation model, constructing a wheel wind resistance calculation model based on the above three-dimensional wheel model, and performing simulation analysis to obtain the aerodynamic drag coefficient value of the initial wheel; Step 3: Design a test plan. On the premise that the wheel spoke opening area remains unchanged, select the wheel opening contour shape as a variable, set the value range of the variable, and take the aerodynamic drag coefficient of the wheel as the target response value; design a plan for the wheel spoke opening contour shape through a statistical sampling test design method, perform wheel wind resistance simulation analysis on models under different spoke opening contour shapes, and obtain the aerodynamic drag coefficient value under each spoke opening contour shape; Step 4: construct a functional relationship between the wheel spoke opening profile shape and its aerodynamic drag coefficient value based on the test data in step 3, and verify its accuracy; Step 5, using an optimization algorithm to obtain the wheel spoke opening profile shape with the minimum aerodynamic drag coefficient value; The optimization algorithm in step 5 is an adaptive simulated annealing optimization method, which performs nonlinear optimization design on the wheel spoke opening contour shape to obtain the wheel spoke opening contour shape with the minimum aerodynamic drag coefficient value; The principle of this algorithm is to take the similarity between the cooling process of solid materials in physics and the general combinatorial optimization problem as the starting point, and use solid annealing simulation to solve the combinatorial optimization problem: Step 1: Initialization, select the initial solution, set the initial temperature T0, the end temperature T f , let the iteration index k = 0, calculate the initial energy value E0, and the energy function is defined as: Where: x i is the gray value of the original image; x' i is the predicted output grayscale value; N is the number of output image imaging points; Step 2: Randomly generate a new solution x' and calculate the energy increment ΔE; ΔE=E(x')-E(x) Step 3: Accept the new solution according to the Metropolis criterion: Where: T is the current temperature, and its value is related to the initial temperature T0 and the cooling rate α; Step 4: Reduce the temperature according to the temperature attenuation function to determine whether the iteration termination condition is reached. If so, stop the iteration; otherwise, turn to Step 3, the temperature attenuation function is: T k+1 =αT k Where: T k is the temperature before cooling; T k+1 is the temperature after cooling, and α is a positive number less than 1.

2. The method for designing the wheel spoke opening profile shape for reducing wheel wind resistance according to claim 1, characterized in that: In the step 1, a three-dimensional model of an initial wheel is constructed, and the opening ratio of the initial wheel is selected to be between 20% and 80%.

3. The method for designing the wheel spoke opening profile shape for reducing wheel wind resistance according to claim 1, characterized in that: The method for constructing the wheel wind resistance calculation model in step 2 is a fluid dynamics calculation method, which constructs a virtual wind tunnel three-dimensional model of the wheel and the road surface, imports the three-dimensional model into the meshing software, generates the fluid domain mesh and boundary layer mesh around the wheel, and uses computational fluid dynamics analysis software to set the velocity inlet, pressure outlet and wheel wall movement mode to realize the simulation analysis of the wheel aerodynamic wind resistance.

4. The method for designing the wheel spoke opening profile shape for reducing wheel wind resistance according to claim 1, characterized in that: The value range of the wheel spoke opening contour shape in step three is that the small arc radius R1 is 5.41mm~10.46mm, the large arc radius R2 is 41.58mm~58.68mm, the circular angle α corresponding to the large arc is 15°~30°, and the lengths of the small arc tangents L1 and L2 are equal, both of which are 27.12mm~46.83mm.

5. The method for designing the wheel spoke opening profile shape for reducing wheel wind resistance according to claim 1, characterized in that: The statistical sampling test design method is a Latin hypercube test design method; through statistical design and data analysis, key parameters that have a significant impact on the aerodynamic drag coefficient value are screened out.

6. The method for designing the wheel spoke opening profile shape for reducing wheel wind resistance according to claim 1, characterized in that: The functional relationship in step 4 is to construct a regression function based on the wheel spoke opening contour shape and the aerodynamic drag coefficient value, and the regression function used is a RBF (Radial Basis Functions) network approximation model.

7. The method for designing the wheel spoke opening profile shape for reducing wheel wind resistance according to claim 6, characterized in that: The accuracy verification method in step 4 is to use the determination coefficient R after linear regression of the model. 2 The accuracy of the RBF approximate model was tested. The closer the determination coefficient was to 1, the higher the accuracy of the model was. The predicted value was compared with the actual value to verify its effectiveness.

8. The method for designing the wheel spoke opening profile shape for reducing wheel wind resistance according to claim 7, characterized in that: The coefficient of determination R 2 The expression is: Where n is the number of data points for testing the accuracy of the model; is the approximate model prediction value of the i-th response; y i is the simulation value of the i-th response; is the average value.

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