Lift type fan blade airfoil optimization method

Abstract

The invention relates to the technical field of fan blade design, in particular to a lift type fan blade airfoil optimization method. According to the method, the historical operation data of the symmetric airfoil blade are systematically collected and analyzed, so that the basic characteristics and the operation environment of the blade can be accurately mastered. Furthermore, by constructing and optimizing an airfoil profile upper bone line model and solving model parameters, the method can efficiently find out an optimal airfoil profile upper bone line equation under the conditions of different wind speeds and attack angles in combination with the strong search capability of a genetic algorithm. And finally, coordinate points in the optimization equation are selected, model parameters are determined, and symmetric folding is performed, so that the fan blade airfoil with excellent performance can be obtained. In the process, the lift coefficient of the fan is remarkably improved, the application range of the fan is greatly expanded, and the fan can utilize wind energy more efficiently and adapt to more diversified operating environments.

Description

Technical Field

[0001] The invention relates to the technical field of fan blade design, and in particular to a lift-type fan blade airfoil optimization method. Background Art

[0002] The working principle of a vertical axis wind turbine is to use the principle of aerodynamics to drive the generator to generate electricity through the impeller. Specifically, the vertical axis wind turbine is composed of a wind wheel, a generator, a controller and a battery. The impeller is composed of multiple blades, which are generally in the shape of an aircraft wing. The blades are fixed to the hub, and the hub is connected together by a connecting rod. When the wind blows through the impeller, the blades rotate under the action of the wind, thereby driving the generator to generate electricity.

[0003] At present, the most common vertical axis wind turbine type on the market is the H-type vertical axis wind turbine, whose blade airfoil is usually a symmetrical airfoil, which has the characteristics of a larger lift coefficient and a smaller drag coefficient. It is precisely because of these characteristics that this type of symmetrical airfoil is widely used in various wind power plants.

[0004] Since existing wind turbines are all mass-produced, their blade airfoils are of the same specifications and can achieve the effect of wind power generation after assembly. However, due to the different regions where the generators are located, the power generation efficiency of the same type of blade airfoils is also different. This is mainly because each blade airfoil corresponds to a wind speed range, under which the blade airfoil can maximize its lift coefficient, thereby improving power generation efficiency. However, the wind speed ranges in different regions are often different, so it is easy for the blade airfoils produced in mass production to be incompatible with the wind speed range in the current region, so that the maximum power generation efficiency of the generator cannot be achieved.

[0005] It can be seen that at the current stage, the blade airfoil of wind turbines still needs to be optimized to improve power generation efficiency. Summary of the invention

[0006] In order to avoid and overcome the technical problems existing in the prior art, the present invention provides a method for optimizing the airfoil of a lift-type wind turbine blade. The present invention can optimize the airfoil of the blade according to the local wind speed to effectively improve the power generation efficiency of the wind turbine.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A lift-type fan blade airfoil optimization method comprises the following optimization steps:

[0009] S1. Acquire historical operation data of a symmetrical airfoil blade, where the historical operation data includes basic parameters of the blade, original airfoil data of the blade, and wind speed in an environment where the blade is located;

[0010] S2, constructing an optimized airfoil upper bone line model, and inputting the original airfoil data of the blade into the optimized airfoil upper bone line model, solving the value range of each model parameter in the optimized airfoil upper bone line model, and storing it in the initial population;

[0011] S3. Calculate the value range of the blade attack angle based on the basic parameters of the blade and the wind speed in the environment where the blade is located;

[0012] S4, combining the value range of the blade angle of attack and the original airfoil data of the blade, and calculating the specific optimized airfoil upper bone line equation corresponding to each wind speed under each blade angle of attack from the initial population through a genetic algorithm;

[0013] S5. Select corresponding coordinate points from the bone line equations on each optimized airfoil, and input each coordinate point into the bone line model on the optimized airfoil to determine the specific values ​​of each model parameter, and perform symmetrical folding to obtain the optimized fan blade airfoil.

[0014] As a further solution of the present invention, the optimized airfoil upper bone line model is specifically expressed as follows:

[0015]

[0016] Where y represents the ordinate of the optimized airfoil upper bone line model, in m; x represents the abscissa of the optimized airfoil upper bone line model, in m; a 1 、a 2 、b 1 、b 2 and b 3 Both represent the model parameters in the optimized airfoil upper bone line model.

[0017] As a further solution of the present invention: the specific content of step S5 is as follows:

[0018] S51, setting a plurality of horizontal coordinate value points in sequence and at equal intervals along the positive direction of the X-axis, and each bone line equation on the optimized airfoil is located between the starting point and the end point of the horizontal coordinate value point;

[0019] S52, sequentially obtaining the curve coordinates corresponding to the bone line equations on each optimized airfoil at each abscissa value point;

[0020] S53. Based on all the acquired curve coordinates, the weighted ordinates corresponding to all the ordinates at the same abscissa value point are calculated using the ordinate weighting formula; the ordinate weighting formula is expressed as follows:

[0021]

[0022] In the formula, represents the weighted ordinate corresponding to the i-th horizontal coordinate value point; C k represents the angle of attack weighting coefficient of the kth blade angle of attack, k∈[1,K], K represents the total number of blade angles of attack; D n represents the wind speed weighting coefficient of the nth wind speed, n∈[1,N], N represents the total number of wind speeds; y k,n,i It represents the ordinate of the bone line equation on the optimized airfoil corresponding to the nth wind speed at the kth blade angle of attack;

[0023] S54, inputting each abscissa value point and its corresponding weighted ordinate into the optimized airfoil upper bone line model, establishing an equation group, and solving the equation group to obtain specific values ​​of each model parameter;

[0024] S55. Bring the specific values ​​of each model parameter into the optimized airfoil upper bone line model to obtain the optimized airfoil upper bone line equation; symmetric the optimized airfoil upper bone line equation along the chord line of the blade and connect the trailing edge to obtain the optimized fan blade airfoil.

[0025] As a further solution of the present invention: the calculation formula of the angle of attack weighting coefficient is expressed as follows:

[0026]

[0027] In the formula, r 1 represents the lift-to-drag ratio corresponding to the first blade attack angle; r 2 represents the lift-to-drag ratio corresponding to the second blade attack angle; r k represents the lift-to-drag ratio corresponding to the kth blade attack angle; r K represents the lift-to-drag ratio corresponding to the Kth blade attack angle.

[0028] As a further solution of the present invention: the calculation formula of the wind speed weighting coefficient is as follows:

[0029]

[0030] Where, t n Indicates the time that the nth wind speed exists within the set time T.

[0031] As a further solution of the present invention: in the genetic algorithm of step S4, the fitness of each model parameter is obtained by the crossover operator verification method combined with the fitness function calculation, and the calculated fitness is compared with a pre-set fitness threshold; if the fitness is not less than the fitness threshold, the current value of the model parameter is the target, otherwise, continue to iterate to solve the target value.

[0032] As a further solution of the present invention: the fitness function is expressed as follows:

[0033]

[0034] In the formula, R 2 represents fitness; ∑ represents the summation symbol; y j It represents the jth horizontal coordinate calculated by optimizing the bone line model on the airfoil in the genetic algorithm; Represents y j The original coordinate values ​​of the corresponding original airfoil data; Represents the average value of all original coordinate values ​​in the original airfoil data.

[0035] As a further solution of the present invention: the specific content of step S3 is as follows:

[0036] S31, obtaining the tip speed ratio λ of the blade from the basic parameters of the blade;

[0037] S32. Establish the following blade angle of attack calculation formula:

[0038]

[0039] In the formula, It represents the blade attack angle of the blade when the leading edge angle of the blade is θ; tan represents the tangent function; sin represents the sine function; cos represents the cosine function;

[0040] S33, through Taking the derivative of θ and setting the derivative to zero, we can obtain

[0041] S34, will Substitute it into the blade angle calculation formula to calculate the blade angle of attack of the blade, and set the blade angle of attack to the maximum value of the blade angle of attack The range of blade attack angle is

[0042] As a further solution of the present invention: in the process of solving the value range of the model parameter, all the original airfoil data of the blade are input into the optimized airfoil upper bone line model, multiple equations are constructed, and solved; then the maximum and minimum values ​​of the same model parameter are obtained, and the maximum and minimum values ​​constitute the value range of the model parameter.

[0043] As a further solution of the present invention: the basic parameters of the blades are input into finite element analysis software, and the lift-to-drag ratio corresponding to each blade attack angle can be obtained through simulation.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The present invention systematically collects and analyzes the historical operating data of symmetrical airfoil blades, and the method can accurately grasp the basic characteristics and operating environment of the blades. Furthermore, by constructing an optimized airfoil upper bone line model and solving the model parameters, combined with the powerful search capability of the genetic algorithm, the method can efficiently find the optimal airfoil upper bone line equation under different wind speeds and angles of attack. Finally, by selecting the coordinate points in the optimization equation and determining the model parameters, and then folding them symmetrically, a wind turbine blade airfoil with excellent performance can be obtained. This process not only significantly improves the lift coefficient of the fan, but also greatly expands the scope of application of the fan, enabling it to more efficiently utilize wind energy and adapt to more diverse operating environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flow chart of the overall optimization steps of the present invention.

[0047] Figure 2 It is a curve diagram of the numerical simulation results of the blade attack angle in the present invention.

[0048] Figure 3 It is a wind speed variation curve diagram in the present invention.

[0049] Figure 4 This is a comparison diagram of the fan blade airfoil before and after optimization in the present invention.

[0050] Figure 5 This is the pressure cloud diagram when the blade attack angle is 3° and the wind speed is 5m / s.

[0051] Figure 6 This is the pressure cloud diagram when the blade attack angle is 6° and the wind speed is 5m / s.

[0052] Figure 7 This is the velocity cloud diagram when the blade attack angle is 3° and the wind speed is 5m / s.

[0053] Figure 8 This is the velocity cloud diagram when the blade attack angle is 6° and the wind speed is 5m / s.

[0054] Fig. 9 This is a comparison diagram of the lift-to-drag ratio before and after optimization when the blade attack angle is 3° in the present invention.

[0055] Fig.10 This is a comparison diagram of the lift-to-drag ratio before and after optimization when the blade attack angle is 6° in the present invention. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] See also Figures 1 to 10 , this implementation specifically includes the following contents.

[0058] 1. Obtain historical operation data.

[0059] The historical operation data of the wind blade includes basic parameters of the blade, original airfoil data of the blade, and wind speed in the environment where the blade is located. This embodiment is aimed at a small and medium-sized wind turbine with a rated power of 1 kW. The basic parameters of the blade of the wind turbine are shown in Table 1.

[0060] Table 1 Basic parameters

[0061]

[0062] The original airfoil data of the blade refers to the curvilinear coordinates formed by the original airfoil of the blade in the coordinate system O-XY.

[0063] 2. Build the model and determine the model parameters.

[0064] Construct an optimized airfoil upper bone line model, and the optimized airfoil upper bone line model is shown in formula (1):

[0065]

[0066] The optimized airfoil upper bone line model is a polynomial combination equation, with its starting point being the origin and its end point being (0,1). 1 、a 2 、b 1 、b 2 and b 3 , the geometric structure of the bone line can be adjusted. In this way, a standard symmetrical airfoil can be obtained. When using this standard symmetrical airfoil, it is only necessary to determine the airfoil chord length according to the design parameters of the wind turbine, such as power, speed, current, voltage, etc. After the chord length is determined, the geometric structure of the wind turbine airfoil that adapts to the design parameters can be obtained.

[0067] The curve coordinates in the original airfoil data of the blade are input into the bone line model of the optimized airfoil in turn, and a set of equations is established and solved; then the maximum and minimum values ​​of the same model parameter are obtained, and the value range of the model parameter is formed by the maximum and minimum values, and stored in the initial population for subsequent use of the genetic algorithm.

[0068] The value ranges of the model parameters of this embodiment are shown in Table 2.

[0069] Table 2 Model parameters

[0070]

[0071] 3. Determine the range of angle of attack.

[0072] Through the above basic parameters, we can get the tip speed ratio λ = 3. The angle of attack of the blade is the sum of the relative speed and the blade chord line.

[0073]

[0074] Through the equation Taking the derivative of θ, we get:

[0075]

[0076] Let it be 0, we get:

[0077]

[0078] Substituting the tip speed ratio λ=3 into formula (5), we get θ≈109.47°. Substituting into formula (3), we get: Therefore, the blade attack angle should not exceed 19.47°.

[0079] Initially, the original airfoil was selected for numerical simulation at a wind speed of 5m / s and different blade angles of attack. The numerical simulation results are as follows: Figure 2 Observation Figure 2 When the blade angle of attack is 0° to 6°, the lift-to-drag ratio is constantly increasing, and this range has the greatest impact on the efficiency of the wind turbine. When the blade angle of attack is 6° to 10°, the lift-to-drag ratio shows a clear downward trend, and this range has no significant impact on the efficiency of the wind turbine. Therefore, we select As the reference angle of attack, the original blade airfoil is numerically simulated under this angle of attack and combined with different wind speeds. When the lift-to-drag ratio and efficiency of the generator blades are greater, the weighted coefficients should not be evenly divided. The weighted coefficients of the angle of attack are C 1 and C 2 . C 1 and C 2 It can be determined according to formulas (6) and (7):

[0080]

[0081]

[0082] Among them, r 1 for The corresponding lift-to-drag ratio. 2 for The corresponding lift-to-drag ratio.

[0083] 4. Calculate the bone line equation of the optimized airfoil under various conditions.

[0084] According to the area where the wind turbine is located, determine the annual wind speed change curve of the area and the wind speed change range, such as Figure 3 As shown in the figure, different wind speed weighting coefficients are set according to the actual local wind speed.

[0085] The ratio of the time occupied by different wind speeds to the time of one year is used as the wind speed weighting coefficient and is calculated using formula (7).

[0086]

[0087] The calculation results are shown in Table 3.

[0088] Table 3 Wind speed weighting coefficient

[0089]

[0090] The above article has conducted a preliminary investigation on the wind speed in this area. If the average wind speed is divided by day or hour, there may be some differences. Therefore, the wind speed range is conservatively selected to be 2 to 11 m / s. According to Table 3, the wind speed is divided into 2 m / s, 5 m / s, 8 m / s and 11 m / s, and the corresponding wind speed weighting coefficients are: D 1 , D 2 , D 3 and D 4 The value range of each wind speed weighting coefficient is as follows: 0.4≤D 1 ≤0.5; 0.2≤D 2 ≤0.5; 0.015≤D 3 ≤0.02; 0.005≤D 4 ≤0.01.

[0091] After determining the range of blade angle of attack and wind speed, a genetic algorithm is used to use the model parameters stored in the initial population, and then numerical simulation is used to simulate the lift coefficient and drag coefficient of each individual under different working conditions to obtain its lift-to-drag ratio. The fitness of each individual is obtained by crossover operator verification, and the multivariate regression coefficient R 2 As the fitness function, the credibility of the obtained model is verified. Its definition is shown in formula (8):

[0092]

[0093] When R 2>0.90, the model can be considered to have high credibility and can replace the simulation program for further multi-objective optimization. 2 When >0.90, the optimal coefficient combination under this working condition is obtained, that is, the upper bone line equation of the optimized airfoil under this working condition is obtained. The specific calculation results are as follows:

[0094] At blade angle of attack hour:

[0095] Wind speed v = 2m / s, the optimal airfoil upper bone line equation is obtained as:

[0096]

[0097] Wind speed v = 5m / s, the optimal airfoil upper bone line equation is obtained:

[0098]

[0099] Wind speed v = 8m / s, the optimal airfoil upper bone line equation is obtained:

[0100]

[0101] Wind speed v = 11m / s, the optimal airfoil upper bone line equation is obtained:

[0102]

[0103] At Angle of Attack hour:

[0104] Wind speed v = 2m / s, the optimal airfoil upper bone line equation is obtained:

[0105]

[0106] Wind speed v = 5m / s, optimize the airfoil upper bone line equation:

[0107]

[0108] Wind speed v = 8m / s, optimize the airfoil upper bone line equation:

[0109]

[0110] Wind speed v = 11m / s, optimize the airfoil upper bone line equation:

[0111]

[0112] 5. Obtain the fan blade airfoil.

[0113] Each optimized airfoil bone line equation is segmented non-uniformly according to the standard airfoil, and several curve coordinate points are selected from the beginning to the end. For example, the first curve segmentation method is: (0, y 1,1 )、(0.05,y 1,2 )、(0.10,y 1,3 )、(0.15,y 1,4 )、(0.20,y 1,5 )、(0.25,y 1,6 )、(0.30,y 1,7 )、(0.40,y 1,8 )、(0.50,y 1,9 )、(0.60,y 1,10 )、(0.70,y 1,11 )、(0.75,y 1,12 )、(0.80,y 1,13 )、(0.85,y 1,14 )、(0.90,y 1,15 )、(0.95,y 1,16 ) and (1.00,y 1,17 ), a total of 17 curve coordinate points. The same goes for other curves.

[0114] Based on all the curve coordinates obtained above, the weighted ordinates corresponding to all the ordinates at the same abscissa value point are calculated respectively using the ordinate weighting formula shown in formula (9).

[0115]

[0116] Each horizontal coordinate value point and its corresponding weighted vertical coordinate are input into the optimized airfoil upper bone line model, and a system of equations is established and solved to obtain the specific values ​​of each model parameter. The values ​​are shown in Table 4.

[0117]

[0118] The specific values ​​of each model parameter are brought into the optimized airfoil upper bone line model to obtain the optimized airfoil upper bone line equation as shown in formula (10).

[0119]

[0120] The optimized airfoil upper bone line equation is symmetrical along the chord line of the blade and connected to the trailing edge to obtain the optimized fan blade airfoil, as shown in Figure 4 shown.

[0121] The original airfoil data and the optimized airfoil upper bone line equation are input into the simulation software for finite element simulation, and the following is obtained: Figure 5-Figure 8 Finite element analysis results diagram. Figure 5This is the pressure cloud diagram when the blade attack angle is 3° and the wind speed is 5m / s. Figure 6 This is the pressure cloud diagram when the blade attack angle is 6° and the wind speed is 5m / s. Figure 7 This is the velocity cloud diagram when the blade attack angle is 3° and the wind speed is 5m / s. Figure 8 This is the velocity cloud diagram when the blade attack angle is 6° and the wind speed is 5m / s. Figure 5-Figure 8 The right picture in the figure is the simulation result diagram corresponding to the optimized solution, and the left picture is the simulation structure diagram corresponding to the original solution.

[0122] right Figure 5 and Figure 6 From the pressure cloud diagram, it can be seen that at different angles of attack and wind speeds, the optimized solution has more low-pressure areas on the lower surface than the original solution, and more high-pressure areas on the upper surface than the original solution. Therefore, the pressure difference between the upper and lower surfaces of the optimized solution is higher than that of the original solution, which can provide more lift (taking the negative direction of the y-axis as positive). At the same time, the size of the high-pressure area at the leading edge of the optimized solution is not significantly different from that of the original solution, that is, the pressure difference drag does not change much. Therefore, the lift-to-drag ratio of the optimized solution will be significantly improved compared with the original airfoil.

[0123] right Figure 7 and Figure 8 From the velocity cloud diagram, we can see that the high-speed area on the upper surface of the original solution is larger than that of the optimized solution. According to the Bernoulli principle, the average pressure on the upper surface of the optimized solution is higher than that of the original solution. This is consistent with what is shown in the pressure cloud diagram.

[0124] Integrate the simulation data and draw Fig. 9 and Fig.10 . And by Fig. 9 and Fig.10 It can be seen that under different working conditions of the airfoil of the optimized scheme, the lift-to-drag ratio is significantly improved compared with the original scheme.

[0125] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A lift-type fan blade airfoil optimization method, characterized in that: The optimization steps include: S1. Acquire historical operation data of a symmetrical airfoil blade, where the historical operation data includes basic parameters of the blade, original airfoil data of the blade, and wind speed in an environment where the blade is located; S2, constructing an optimized airfoil upper bone line model, and inputting the original airfoil data of the blade into the optimized airfoil upper bone line model, solving the value range of each model parameter in the optimized airfoil upper bone line model, and storing it in the initial population; S3. Calculate the value range of the blade attack angle based on the basic parameters of the blade and the wind speed in the environment where the blade is located; S4, combining the value range of the blade angle of attack and the original airfoil data of the blade, and calculating the specific optimized airfoil upper bone line equation corresponding to each wind speed under each blade angle of attack from the initial population through a genetic algorithm; S5. Select corresponding coordinate points from the upper bone line equations of each optimized airfoil, and input each coordinate point into the upper bone line model of the optimized airfoil to determine the specific values ​​of each model parameter, and perform symmetrical folding to obtain the optimized fan blade airfoil.

2. A lift-type fan blade airfoil optimization method according to claim 1, characterized in that: The optimized airfoil upper bone line model is specifically expressed as follows: Wherein, y represents the ordinate of the optimized airfoil upper bone line model, and the unit is m; x represents the abscissa of the optimized airfoil upper bone line model, and the unit is m; a1, a2, b1, b2 and b3 all represent the model parameters in the optimized airfoil upper bone line model.

3. A lift-type fan blade airfoil optimization method according to claim 2, characterized in that: The specific content of step S5 is as follows: S51, setting a plurality of horizontal coordinate value points in sequence and at equal intervals along the positive direction of the X-axis, and each bone line equation on the optimized airfoil is located between the starting point and the end point of the horizontal coordinate value point; S52, sequentially obtaining the curve coordinates corresponding to the bone line equations on each optimized airfoil at each abscissa value point; S53. Based on all the acquired curve coordinates, the weighted ordinates corresponding to all the ordinates at the same abscissa value point are calculated using the ordinate weighting formula; the ordinate weighting formula is expressed as follows: In the formula, represents the weighted ordinate corresponding to the i-th horizontal coordinate value point; C k represents the angle of attack weighting coefficient of the kth blade angle of attack, k∈[1,K], K represents the total number of blade angles of attack; D n represents the wind speed weighting coefficient of the nth wind speed, n∈[1,N], N represents the total number of wind speeds; y k,n,i It represents the ordinate of the bone line equation on the optimized airfoil corresponding to the nth wind speed at the kth blade angle of attack; S54, inputting each abscissa value point and its corresponding weighted ordinate into the optimized airfoil upper bone line model, establishing an equation group, and solving the equation group to obtain specific values ​​of each model parameter; S55. Bring the specific values ​​of each model parameter into the optimized airfoil upper bone line model to obtain the optimized airfoil upper bone line equation; fold the optimized airfoil upper bone line equation symmetrically along the chord line of the blade and connect the trailing edge to obtain the optimized fan blade airfoil.

4. A lift-type fan blade airfoil optimization method according to claim 3, characterized in that: The calculation formula of the angle of attack weighting coefficient is as follows: Where r1 represents the lift-to-drag ratio corresponding to the first blade attack angle; r2 represents the lift-to-drag ratio corresponding to the second blade attack angle; r k represents the lift-to-drag ratio corresponding to the kth blade attack angle; r K It represents the lift-to-drag ratio corresponding to the Kth blade attack angle.

5. The method for optimizing the airfoil of a lift-type fan blade according to claim 4, characterized in that: The calculation formula of wind speed weighting coefficient is as follows: Where, t n Indicates the time when the nth wind speed occurs within the set time T.

6. A lift-type wind turbine blade airfoil optimization method according to any one of claims 1 to 5, characterized in that: In the genetic algorithm of step S4, the fitness of each model parameter is obtained by the crossover operator verification method combined with the fitness function calculation, and the calculated fitness is compared with the pre-set fitness threshold; if the fitness is not less than the fitness threshold, the current value of the model parameter is the target, otherwise, continue to iterate to solve the target value.

7. A lift-type fan blade airfoil optimization method according to claim 6, characterized in that: The fitness function is expressed as follows: In the formula, R 2 represents fitness; ∑ represents the summation symbol; y j It represents the jth horizontal coordinate calculated by optimizing the bone line model on the airfoil in the genetic algorithm; Represents y j The original coordinate values ​​of the corresponding original airfoil data; Represents the average value of all original coordinate values ​​in the original airfoil data.

8. A lift-type fan blade airfoil optimization method according to claim 7, characterized in that: The specific contents of step S3 are as follows: S31, obtaining the tip speed ratio λ of the blade from the basic parameters of the blade; S32. Establish the following blade angle of attack calculation formula: In the formula, It represents the blade attack angle of the blade when the leading edge angle of the blade is θ; tan represents the tangent function; sin represents the sine function; cos represents the cosine function; S33, through Taking the derivative of θ and setting the derivative to zero, we can obtain S34, will Substitute it into the blade angle calculation formula to calculate the blade angle of attack of the blade, and set the blade angle of attack to the maximum value of the blade angle of attack The range of blade attack angle is 9. A lift-type fan blade airfoil optimization method according to claim 8, characterized in that: In the process of solving the range of model parameter values, all the original airfoil data of the blade are input into the optimized airfoil upper bone line model, and multiple equations are constructed and solved; Then, the maximum value and the minimum value of the same model parameter are obtained, and the maximum value and the minimum value constitute the value range of the model parameter.

10. A lift-type fan blade airfoil optimization method according to claim 9, characterized in that: The basic parameters of the blades are input into the finite element analysis software, and the lift-to-drag ratio corresponding to each blade attack angle can be obtained through simulation.

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