CN115062415A - Method for reducing spoke opening profile shape of wheel wind resistance - Google Patents
Method for reducing spoke opening profile shape of wheel wind resistance Download PDFInfo
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Abstract
The invention discloses a method for reducing the spoke opening profile shape of wheel wind resistance, which comprises the following steps: step one, establishing an initial wheel spoke model; step two, constructing an initial wheel calculation model; step three, designing an experimental scheme; step four, establishing a functional relation between the opening profile shape of the wheel spoke and the aerodynamic resistance coefficient of the wheel spoke according to the test data in the step three, and verifying the precision of the function; and step five, obtaining the wheel spoke opening contour shape with the minimum pneumatic resistance coefficient value by adopting an optimization algorithm. Has the advantages that: the invention provides a brand new method for reducing the wind resistance of the wheel by taking the opening profile shape of the spoke as an entry point, effectively avoids the blindness problem in the design of the opening profile shape of the spoke of the wheel, further shortens the design period, improves the efficiency, simultaneously improves the fuel economy of the automobile, improves the pressure pulsation near the wheel, and has important significance for guiding the design of the opening profile shape of the spoke of the wheel so as to reduce the wind resistance of the automobile and improve the fuel economy.
Description
Technical Field
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 wheel wind resistance, and belongs to the technical field of vehicle engineering.
Background
In recent years, global energy crisis is increased, the quantity of automobiles is still continuously increased, the energy consumption of the automobiles is increased day by day, the governments and consumers pay more attention to the oil consumption of the automobiles, and the requirement on the fuel economy of the automobiles is more strict than before. It is reported that the aerodynamic drag of the car is reduced by 10%, which can reduce the fuel consumption by 2% -3%. Since the aerodynamic resistance is proportional to the square of the vehicle speed, improving the aerodynamic characteristics of an automobile significantly reduces fuel consumption, which is particularly important when driving at high speeds. When the speed of the automobile reaches 60km/h, the power required for overcoming the pneumatic resistance is half of the power required for overcoming the driving resistance by the automobile.
During the running of the automobile, the contribution of the wheel area to the aerodynamic resistance of the automobile is up to 25-30%. The flow field of the wheel area is affected by the coming flow from the side surface of the automobile and the front of the bottom of the automobile and the flow field in the engine compartment, so that the flow field of the wheel area is strongly interfered with the automobile body, the structure of the bottom of the automobile and the ground, and the rotation of the wheels can also generate energy input to the flow field of the area, so that the flow field structure of the wheel area is more complicated. The prior research also shows that the different opening shapes of the rims of the automobile wheels can directly influence the aerodynamic resistance of the wheels and the aerodynamic resistance of the automobiles. However, at present, the cognition on the flow phenomenon and the flow rule of the wheel area is limited, so that the pneumatic resistance reduction of the wheel area has a strong potential optimization space for improving the pneumatic resistance of the whole vehicle. Therefore, the flow field characteristics of the wheel area are mastered, and a targeted resistance reduction method of the wheel area is provided, so that the aim of reducing the aerodynamic resistance of the automobile is fulfilled, and the method has important significance for reducing the fuel consumption.
The arrangement of the openings in the wheel web has a great influence on the aerodynamic properties: under the condition that the total opening area is the same, the number of the openings is increased by a proper amount, which is beneficial to improving the aerodynamic characteristics; the resistance coefficient of the wheel per se shows irregular variation to some extent as the total opening area increases. Therefore, the wheel spoke is taken as an optimization objectOn the premise of changing the opening area of the wheel, the small arc radius R of a single spoke hole is selected by taking the reduction of the aerodynamic resistance of a single wheel as an optimization target 1 Large arc radius R 2 Small arc tangent line L 1 And L 2 And the corresponding circumferential angle alpha is a design variable. And optimizing the spoke holes by comprehensively adopting an optimal hyper-latin cube design, an RBF (radial basis function) approximation model and an adaptive simulated annealing algorithm (ASA), so as to obtain an optimal spoke shape.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the defects in the prior art, the invention provides a method for reducing the spoke opening profile shape of the wheel wind resistance, which takes the pneumatic resistance of a single wheel as an optimization target, can effectively avoid the blindness problem in the design process of the spoke opening profile of the wheel, can effectively reduce the design period of the spoke opening profile of the wheel, further improve the improvement efficiency, and simultaneously improve the pneumatic characteristic of a wheel area, thereby achieving the purpose of reducing the vehicle resistance.
The technical scheme is as follows: a method of reducing the wind resistance of a wheel comprising the steps of:
establishing an initial wheel model, and drawing a three-dimensional model of the wheel according to the number of the selected wheel spokes and the initial value of the opening profile shape of the wheel spokes; the spoke opening profile shape parameters comprise small arc radius R 1 Radius of large circular arc R 2 Small arc tangent line L 1 And L 2 And a circumferential angle alpha corresponding to the great circle arc;
step two, constructing an initial wheel wind resistance calculation model, constructing a wheel wind resistance calculation model according to the three-dimensional model of the wheel, and carrying out simulation analysis to obtain a pneumatic resistance coefficient value of the initial wheel;
designing a test scheme, namely selecting the outline shape of the opening of the wheel as a variable on the premise that the opening area of the spoke of the wheel is not changed, setting the value range of the variable, and taking the aerodynamic resistance coefficient of the wheel as a target response value; carrying out scheme design on the opening profile shape of the wheel spoke by a statistical sampling test design method, carrying out wheel wind resistance simulation analysis on models in different spoke opening profile shapes, and obtaining the pneumatic resistance coefficient value in each spoke opening profile shape;
and step four, constructing a functional relation between the opening profile shape of the wheel spoke and the pneumatic resistance coefficient value of the wheel spoke according to the test data in the step three, and verifying the precision of the functional relation.
And step five, obtaining the wheel spoke opening contour shape with the minimum pneumatic resistance coefficient value by adopting an optimization algorithm.
Further, a three-dimensional model of the initial wheel is constructed in the first step, and the hole opening ratio of the selected initial wheel is between 20% and 80%.
Further, the wheel wind resistance calculation model construction method in the second step is a fluid dynamics calculation method, a wheel and road surface virtual wind tunnel three-dimensional model is constructed, the three-dimensional model is guided into mesh division software, fluid domain meshes and boundary layer meshes around the wheels are generated, and a velocity inlet, a pressure outlet and a wheel wall surface movement mode are set by utilizing computational fluid dynamics analysis software, so that simulation analysis of the wheel pneumatic wind resistance is realized.
Furthermore, the value range of the opening outline shape of the wheel spoke in the step three is the radius R of the small arc 1 5.41 mm-10.46 mm, large arc radius R 2 41.58 mm-58.68 mm, a circumference angle alpha corresponding to the great arc of 15-30 degrees, and a tangent L of the small arc 1 And L 2 The lengths are equal and are all between 27.12mm and 46.83 mm.
Further, the statistical sampling test design method is an optimal Latin hypercube test design method; and screening out key parameters of which the opening profile shape of the wheel spoke has obvious influence on the pneumatic resistance coefficient value through statistical design and data analysis. The optimal hyper-Latin cube design is that Latin hyper-cube is a layered sampling method, and for a plurality of random variable inputs, the input sample space needs to be converted into N regions with equal probability in general layered sampling, which is difficult to operate. The Latin hypercube uses a multidimensional layered sampling method, and the working principle is as follows:
(1) defining the sampling number N participating in the operation of the computer;
(2) each input equiprobability is divided into N, and there are:
P(x in <x<x i(n+1) )＝1/N
(3) only one sample is taken for each column, the position of the sample taken in each column being random.
The biggest advantage of latin hypercube sampling over pure hierarchical sampling is that the number of samples of any size can be easily generated. But from a spatial distribution perspective, as the number of points decreases, the chances of missing certain regions of the design space also increase. The optimal hyper-latin cube design enables all test points to be uniformly distributed in the design space as much as possible, and has very good space filling property and balance.
Further, the function relationship in the fourth step is to construct a regression function according to the wheel spoke opening profile shape and the aerodynamic resistance coefficient value, wherein the used regression function is an RBF approximate model.
The RBF model has the advantages that:
1. and a strong ability to approximate complex nonlinear functions.
2. The method has the characteristics of a black box without mathematical hypothesis.
3. The learning speed is high, and the generalization capability is excellent.
4. The strong fault-tolerant function does not affect the overall performance of the network even if the samples contain 'noise' input.
Radial Basis functions (rbf) network: using the Euclidean distance between the point to be measured and the sample point as an argument, i.e. assuming
Represents a set of input vectors that are to be processed,
(x-x j ) T (x-x j ) And c is not less than 0.2 and not more than 3
In the formula, x 1 ,...,x N And the aerodynamic drag coefficient values of the wheels under different schemes obtained by the optimal hyper-latin cube test design are expressed, subscripts represent 1 st to N th samples in the optimal hyper-latin cube test design, and omega represents the space where the aerodynamic drag coefficient values of the wheels under different schemes are located.
g i ≡g(‖x-x j ‖ c ) Is a base function, | x-x j |, is the euclidean distance, also known as the euclidean norm, with the subscript j denoting the jth sample, j ═ 1.. N.
Further, the precision verification method in the fourth step is to adopt a decision coefficient R after model linear regression 2 And checking the precision of the RBF approximate model, wherein the closer the decision coefficient is to 1, the higher the precision of the model is, comparing the predicted value with the actual value, and verifying the validity of the predicted value.
Further, it is characterized in that:
the determination coefficient R 2 The expression of (a) is:
in the formula, n is the number of data points for checking the model precision; approximate model prediction for the ith response; y is i Is a simulated value of the ith response;
are averages.
Further, it is characterized in that: and the optimization algorithm in the fifth step is a self-adaptive simulation annealing optimization method, and the wheel spoke opening contour shape with the minimum pneumatic resistance coefficient value is obtained by carrying out nonlinear optimization design on the wheel spoke opening contour shape.
Further, it is characterized in that: the algorithm principle is that the similarity between the cooling process of solid matters in physics and a general combinatorial optimization problem is taken as a starting point, and the combinatorial optimization problem is solved by solid annealing simulation:
Step1：initializing an optional initial solution, setting an initial temperature T 0 Termination temperature T f Calculating the initial energy value E by setting the iteration index k to 0 0 The energy function is defined as:
in the formula, x i Is the gray value of the original image; x' i Is the predicted output gray value; n is the number of output image imaging points.
Step 2: a new solution x' is randomly generated and the energy increment deltae is calculated.
ΔE＝E(x')－E(x)
Step 3: new solutions were accepted according to Metropolis criteria:
wherein T is the current temperature, the value of which is equal to the initial temperature T 0 And the cooling rate alpha.
Step 4: reducing the temperature according to a temperature attenuation function, judging whether an iteration termination condition is reached, if so, stopping the iteration; otherwise, go to Step3 for a temperature decay function:
T k+1 ＝αT k
in the formula, T k The temperature before cooling; t is a unit of k+1 Alpha is a positive number less than 1 for the temperature after cooling
The simulated annealing algorithm simulates the combination optimization problem and the thermal balance problem in statistical mechanics through a simulated annealing process, the target function is evaluated once every step from an initial point, as long as the function value is reduced, a new design point is accepted and repeated until an optimal point is found. After adding the adaptation, the algorithm consists of an inner and an outer loop, which for the inner loop ensures that sufficient search and iteration are performed on the samples in the solution space at each temperature; and the external circulation can ensure the trend that the temperature of the algorithm is continuously reduced in the process, and finally the equilibrium stable state is reached.
Has the advantages that: the invention can effectively avoid the blindness problem in the design of the opening profile of the wheel spoke, thereby shortening the design period, improving the efficiency, improving the pneumatic characteristic of the wheel area and achieving the aim of reducing the vehicle resistance; in the invention, an RBF network approximation model is selected to verify the model precision, and the RBF network approximation model has strong capability of approximating a complex nonlinear function; the method has the characteristics of a black box without mathematical hypothesis; the learning speed is high, and the generalization capability is extremely good; the method has the advantages of strong fault-tolerant function, no influence on the overall performance of the network even if the sample contains 'noise' input, great improvement on the efficiency of model precision verification, further reduction of blindness in the research and development process, and shortening of the design period.
Drawings
FIG. 1 is a design flow chart of the present invention.
Fig. 2 is a schematic diagram of (a) wheel spoke opening variation and (b) single spoke opening of the present invention.
FIG. 3 is a schematic view of a virtual wind tunnel model of a wheel according to the present invention.
FIG. 4 is a schematic diagram of the structure of trial (a), scheme 7(b), scheme 11(c), scheme 16 of the Latin hypercube design of the present invention.
FIG. 5 is a schematic diagram of prediction accuracy of the RBF approximation model of the present invention.
FIG. 6 shows a view of the present invention C d With one of the design variables R 1 Schematic diagram of RBF model (b).
FIG. 7 shows a view of the present invention C d With one of the design variables R 1 Schematic diagram of the RBF model of (1).
FIG. 8 shows the present invention C d With one of the design variables R 2 Schematic diagram of the RBF model of (1).
FIG. 9 shows the present invention C d With one of the design variables R 2 Schematic diagram of the RBF model of (1).
FIG. 10 is a schematic diagram of the iterative steps of the optimization process of the adaptive simulation optimization algorithm of the present invention.
FIG. 11 is a pair of different design variables C for a spoke according to the present invention d Schematic diagram of the contribution of (1).
FIG. 12 is a schematic view comparing the shapes of the optimized front and rear spoke openings of the present invention.
Figure 13 is a schematic representation of the invention optimizing front and rear wheel longitudinal plane pressure.
The invention is further described with reference to the following figures and specific examples.
As shown in fig. 1, a method of reducing the spoke opening profile shape of the windage of a vehicle wheel includes the steps of:
establishing an initial wheel model, and drawing a three-dimensional model of the wheel according to the number of the selected wheel spokes and the initial value of the opening profile shape of the wheel spokes; the spoke opening ratio of the common vehicle on the market is concentrated in the range of 20% -80%, the opening area ratio of the five-spoke wheel in the wheel is 54%, and the five-spoke vehicle shown in figure 2(a) is selected. As shown in fig. 2(b), the initial values of the individual spoke holes of the five-spoke wheel are respectively: the circumferential angle alpha is 22.5 degrees, and the radius R of the small arc 1 Is 8mm, and the radius of the large arc R 2 Is 55mm, and the small arc tangent line L 1 And L 2 The length is 35.69mm, and the design variables of the spoke openings are parameterized by three-dimensional modeling software Solidworks.
Step two, constructing an initial wheel wind resistance calculation model, constructing a wheel wind resistance calculation model according to the three-dimensional model of the wheel, and carrying out simulation analysis to obtain a pneumatic resistance coefficient value of the initial wheel;
on the basis of the wheel model, a wheel-road surface virtual wind tunnel model as shown in fig. 3 is established. Setting boundary conditions: calculating a domain entrance-speed entrance, V ═ 40 m/s; calculating a domain outlet-pressure outlet, 0 Pa; calculating a domain side surface and a top surface-symmetry plane; the vehicle body and the upright post-fixed wall surface have no slippage; computing domain floor-moving floor, V ═ 40 m/s. As the wheel is positioned in an external flow field which is regarded as three-dimensional, steady, isothermal and constant pressure, in an incompressible atmospheric environment, the physical field parameters are as follows: the temperature is-20 ℃; pressure is-101325 Pa; viscosity-1.7894 e-6Pa · s; the density is-1.225 kg.m-3. Boundary layer mesh settings refer to table 1 below;
TABLE 1 computational Domain grid size
| Different regions | Wheel of vehicle | Ground surface | Computing domain |
| Mesh size (mm) | 0.8-2 | 0.8-15 | 1-50 |
The calculation process is divided into two processes of constant calculation and unsteady calculation. Carrying out steady-state solution on the model by adopting an SST k-omega model in steady-state calculation, and satisfying convergence residual error 0.0001 after iteration for 4000 steps; then taking the result of the constant calculation as an initial flow field of the unsteady calculation; unsteady calculation adopts SIMLEC algorithm to couple pressure and velocity field, turbulence model is adjusted to DES model, discrete format is second-order windward format, and calculation time step is 1 × 10 -3 And s, iterating for 5 times in unit time step, wherein the unsteady calculation time length is 2s, and the total calculation time step is 10000 steps. The simulation calculations were based on commercial fluid calculation software STAR-CCM +.
Step three, designing a test scheme, setting a value range of a variable by taking the opening profile shape of the wheel spoke as the variable, and taking the aerodynamic resistance coefficient of the wheel as a target response value; carrying out scheme design on the opening profile shape of the wheel spoke by a statistical sampling test design method, carrying out wheel wind resistance simulation analysis on models under different spoke opening profile shapes, and obtaining the pneumatic resistance coefficient value under each spoke opening profile shape;
in the case of ensuring that the area of the individual openings is constant,the radius R of the fixed wheel is 75mm and is large arc radius R 2 Designing as a variable, solving the rest parameters in Matlab, and solving a formula:
R 1 ＝20.91·sin(α)
L 1 ＝L 2 ＝R 2 -20.91·cos(α)
the value range of the circumferential angle alpha corresponding to the large circular arc of each spoke opening is as follows: [15 °,30 ° ]]Thus large arc radius R 2 The value range is as follows: r 2 ∈[41.5824,58.6799]mm. According to the variable range, the optimal hyper-Latin cubic test method is adopted for design, and the formula is adopted for carrying out the design on L 1 、L 2 And R 1 The corresponding values are solved, and 20 groups of test schemes are designed in total, and are specifically shown in table 2.
TABLE 2 test protocol and calculation results
| Test protocol | L 1 、L 2 /mm | R 1 /mm | R 2 /mm | 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 |
And taking the aerodynamic resistance as a target response value. The reasonable test design method is especially important for searching the optimal spoke opening outline shape for improving the wind resistance performance of the wheel. The purpose of the experimental design is to select a limited number of sample points in the entire design space, so that the information of the entire space is reflected to the greatest extent. A three-dimensional model is built according to the parameters designed by the optimal hyper-Latin cubic test method in Table 2, wherein a typical scheme is shown in FIG. 4. As can be seen, the spoke openings have a relatively large difference in shape between the different embodiments, which results in a certain difference in aerodynamic drag coefficient between the wheels.
And step four, constructing a functional relation between the opening profile shape of the wheel spoke and the pneumatic resistance coefficient value of the wheel spoke according to the test data in the step three, and verifying the precision of the functional relation.
The RBF approximation model is a space prediction method based on average error of minimum sampling value weighted sum, and generally adopts error analysis to evaluate the fitting accuracy of the approximation model, and the fitting coefficient R 2 The error evaluation index is a statistically common error evaluation index, and the closer the value is to 1, the better the fitting effect is. The prediction accuracy of the constructed RBF model is shown in FIG. 5, and C can be seen from the figure d The fitting effect of the simulation value and the predicted value is good, which means that the selected approximate model has high prediction precision and meets the requirement.
FIGS. 6 to 9 are each C d An RBF model of two of the design variables; alpha and R are shown in FIG. 6 1 To C d The influence of the value is greatest, when the two variables increase to maximum values, C d All reach the maximum value; while in FIG. 7, when α is changed to L 1 When the relation of different variables is researched, the topological relation among the three is researched, and L in the graph is found 1 Pair when changedC d Has little effect, which is shown in comparison to R 1 Variable, L 1 The effect on the results is less; in FIG. 8, α is changed to R 2 With R 2 The change amplitude of the influence on the response quantity is still not large; considering the combination, the major arc of each spoke opening corresponds to the circumferential angle alpha and the radius R of the minor arc 1 Are all mixed with C d Is in positive correlation with C d With alpha and R 1 Is increased by the increase of the radius R of the large arc 2 And the small arc tangent line L 1 、L 2 To C d Has little effect.
And step five, obtaining the wheel spoke opening contour shape with the minimum pneumatic resistance coefficient value by adopting an optimization algorithm.
The method is characterized in that: the algorithm principle is that the similarity between the cooling process of solid matters in physics and a general combinatorial optimization problem is taken as a starting point, and the combinatorial optimization problem is solved by solid annealing simulation:
step 1: initializing, optionally initializing, setting an initial temperature T 0 End temperature T f Calculating the initial energy value E by setting the iteration index k to 0 0 The energy function is defined as:
in the formula, x i Is the gray value of the original image; x' i Is the predicted output gray value; n is the number of output image imaging points.
Step 2: randomly generating a new solution x', and calculating an energy increment delta E;
ΔE＝E(x')－E(x)
step 3: new solutions were accepted according to Metropolis criteria:
wherein T is the current temperature, the value of which is equal to the initial temperature T 0 And the cooling rate alpha.
Step 4: reducing the temperature according to a temperature attenuation function, judging whether an iteration termination condition is reached, if so, stopping the iteration; otherwise, turn Step3 temperature decay function as:
T k+1 ＝αT k
in the formula, T k The temperature before cooling; t is k+1 Alpha is a positive number less than 1 for the temperature after cooling
And predicting the precision of the approximate model from the step five, wherein the result meets the requirements of people. By adopting a self-adaptive simulated annealing optimization algorithm, the method has the advantages of wide application range, low requirement on initial conditions, high convergence speed and capability of obtaining all optimal solutions. The aerodynamic drag coefficient is used as an optimization target for optimization, a group of optimal solutions is obtained by iterating 382 times, and the iteration process is shown in FIG. 10. FIG. 11 shows different design variable pairs C d The contribution of (A) is shown in the figure, the circumferential angle alpha and the radius of the small arc R 1 To C d The values have a significant effect, and the effect of the remaining design variables on the aerodynamic properties is negligible.
Carrying out optimization design on an optimization target by using a self-adaptive simulated annealing optimization method, taking the wheel spoke opening profile shape with the minimum pneumatic resistance as an optimal wheel spoke opening profile shape arrangement form, and optimizing each structural design parameter L of the wheel spoke opening profile 1 、L 2 ＝33.9051mm、R 1 ＝8.4729mm、R 2 An optimized wheel spoke opening profile shape arrangement of 53.0161mm is shown in fig. 12.
Next, the aerodynamic characteristics of the wheels before and after the optimization are analyzed. Figure 13 is a graph of the pressure distribution in the longitudinal plane of the front and rear wheels optimized. The pneumatic resistance of the wheels is mainly caused by the pressure difference resistance between the front and the rear of the wheels, and as can be seen in the figure, the pressure on the front sides of the wheels is almost unchanged before and after optimization, while the pressure on the tail parts of the wheels is increased to a certain extent, so that the pressure difference of the wheels is reduced, and the pneumatic resistance of the wheels is reduced to a certain extent.
And (3) performing wind resistance simulation analysis on the optimized wheel by adopting the step1, wherein analysis results before and after optimization are shown in a table 2. As can be seen from Table 3, the optimized spoke shape significantly improves the aerodynamic performance of the wheel, and compared with the aerodynamic resistance coefficient of the wheel before optimization, the aerodynamic resistance coefficient of the wheel is reduced by 5.7%, so that the wind resistance of the wheel is reduced.
TABLE 3 aerodynamic drag coefficient comparison before and after optimization
| L 1 、L 2 /mm | R 1 /mm | R 2 /mm | C d | |
| Before optimization | 35.6863 | 8 | 47.7952 | 0.934 |
| After optimization | 33.9051 | 8.4729 | 53.0161 | 0.883 |
Small arc tangent L after original structure optimization of each structural design parameter of opening profile of wheel spoke 1 、L 2 Radius R of small arc of 33.9051mm 1 Radius of large arc R8 mm 2 55mm, 22.5 deg. of circumference angle alpha, and aerodynamic drag coefficient C d ＝0.87. The present invention is not limited to the above-described embodiments, and any obvious improvements, substitutions or modifications can be made by those skilled in the art without departing from the spirit of the present invention.
Claims (10)
1. A method of reducing the wind resistance of a wheel comprising the steps of:
establishing an initial wheel model, and drawing a three-dimensional model of the wheel according to the number of the selected wheel spokes and the initial value of the opening profile shape of the wheel spokes; the spoke opening profile shape parameters comprise small arc radius R 1 Radius of large circular arc R 2 Small arc tangent line L 1 And L 2 And a circumferential angle alpha corresponding to the great circle arc;
step two, constructing an initial wheel wind resistance calculation model, constructing a wheel wind resistance calculation model according to the three-dimensional model of the wheel, and carrying out simulation analysis to obtain a pneumatic resistance coefficient value of the initial wheel;
designing a test scheme, namely selecting the outline shape of the opening of the wheel as a variable on the premise that the opening area of the spoke of the wheel is not changed, setting the value range of the variable, and taking the aerodynamic resistance coefficient of the wheel as a target response value; carrying out scheme design on the opening profile shape of the wheel spoke by a statistical sampling test design method, carrying out wheel wind resistance simulation analysis on models in different spoke opening profile shapes, and obtaining the pneumatic resistance coefficient value in each spoke opening profile shape;
step four, establishing a functional relation between the opening profile shape of the wheel spoke and the pneumatic resistance coefficient value of the wheel spoke according to the test data in the step three, and verifying the precision of the functional relation;
and step five, obtaining the wheel spoke opening contour shape with the minimum pneumatic resistance coefficient value by adopting an optimization algorithm.
2. The method for designing a wheel spoke opening profile shape for reducing the wind resistance of a wheel according to claim 1, wherein: and constructing a three-dimensional model of the initial wheel in the first step, wherein the aperture ratio of the selected initial wheel is between 20% and 80%.
3. The method for designing a wheel spoke opening profile shape for reducing the wind resistance of a wheel according to claim 1, wherein: and in the second step, the wheel wind resistance calculation model building method is a fluid dynamics calculation method, a wheel and road surface virtual wind tunnel three-dimensional model is built, the three-dimensional model is led into grid division software, fluid domain grids and boundary layer grids around the wheel are generated, and a speed inlet, a pressure outlet and a wheel wall surface movement mode are set by utilizing computational fluid dynamics analysis software, so that the simulation analysis of the wheel pneumatic wind resistance is realized.
4. The method for designing a wheel spoke opening profile shape for reducing the wind resistance of a wheel according to claim 1, wherein: the value range of the opening outline shape of the wheel spoke in the third step is the radius R of the small arc 1 5.41 mm-10.46 mm, large arc radius R 2 41.58 mm-58.68 mm, a circumference angle alpha corresponding to the great arc of 15-30 degrees, and a tangent L of the small arc 1 And L 2 The lengths are equal and are all 27.12 mm-46.83 mm.
5. The method for designing a wheel spoke opening profile shape for reducing the wind resistance of a wheel according to claim 1, wherein: the statistical sampling test design method is a Latin hypercube test design method; and screening out key parameters of which the opening profile shape of the wheel spoke has obvious influence on the pneumatic resistance coefficient value through statistical design and data analysis.
6. The method for designing a wheel spoke opening profile shape for reducing wind resistance of a wheel according to claim 1, wherein: the function relationship in the fourth step is to construct a regression function according to the wheel spoke opening profile shape and the aerodynamic resistance coefficient value, wherein the regression function is a network approximation model of RBF (radial Basis functions).
7. The method for designing a wheel spoke opening profile shape for reducing the wind resistance of a wheel according to claim 6, wherein: and the precision verification method in the fourth step is to verify the precision of the RBF approximate model by adopting a decision coefficient R2 after model linearity regression, wherein the closer the decision coefficient is to 1, the higher the precision of the model is, and the predicted value is compared with the actual value to verify the validity of the model.
8. The method for designing a wheel spoke opening profile shape for reducing the wind resistance of a wheel according to claim 7, wherein:
the determination coefficient R 2 The expression of (a) is:
in the formula, n is the number of data points for checking the model precision; approximate model prediction for the ith response; y is i Is a simulated value of the ith response;
are averages.
9. The method for designing a wheel spoke opening profile shape for reducing the wind resistance of a wheel according to claim 1, wherein: and the optimization algorithm in the fifth step is a self-adaptive simulation annealing optimization method, and the wheel spoke opening contour shape with the minimum pneumatic resistance coefficient value is obtained by carrying out nonlinear optimization design on the wheel spoke opening contour shape.
10. The method of designing a wheel spoke opening profile shape for reducing wheel wind resistance of claim 9, wherein: the algorithm principle is that the similarity between the cooling process of solid matters in physics and a general combinatorial optimization problem is taken as a starting point, and the combinatorial optimization problem is solved by solid annealing simulation:
step 1: initializing, optionally initializing, setting an initial temperature T 0 Termination temperature T f Calculating the initial energy value E by setting the iteration index k to 0 0 The energy function is defined as:
in the formula, x i Is the gray value of the original image; x' i Is the predicted output gray value; n is the number of output image imaging points;
step 2: randomly generating a new solution x', and calculating an energy increment delta E;
ΔE＝E(x')－E(x)
step 3: new solutions were accepted according to Metropolis criteria:
wherein T is the current temperature, the value of which is equal to the initial temperature T 0 And the cooling rate a;
step 4: reducing the temperature according to a temperature attenuation function, judging whether an iteration termination condition is reached, if so, stopping the iteration; otherwise, go to Step3 for a temperature decay function:
T k+1 ＝αT k
in the formula, T k The temperature before cooling; t is k+1 Alpha is a positive number smaller than 1 for the temperature after cooling.
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| PCT/CN2023/087752 WO2023246244A1 (en) | 2022-06-24 | 2023-04-12 | Method for reducing wind resistance of vehicle wheel on basis of contour shape of wheel spoke opening |
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