A method and system for airfoil grating design based on particle swarm optimization algorithm

Abstract

This invention discloses a design method and system for airfoil grilles based on particle swarm optimization (PSO) algorithm, belonging to the field of wind turbine equipment design technology. This invention simulates biological swarm intelligence, dynamically adjusting inertia weights and acceleration constants to achieve a balance between global search and local convergence, optimizing grille geometric parameters, and verifying the design effect through CFD simulation. Compared with traditional methods, this scheme significantly shortens the development cycle and reduces experimental costs. Simultaneously, by optimizing airflow distribution and boundary layer control, it effectively reduces flow separation vortices and pressure difference losses, lowers noise, and increases airflow. Experiments show that the radial grille designed using this scheme can increase airflow by 10% at the same rotational speed, while reducing the shape drag coefficient, meeting high-strength safety regulations, and possessing significant technical advantages and engineering application value.

Description

Technical Field

[0001] This invention belongs to the field of wind turbine equipment design technology, specifically relating to a method and system for airfoil grid design based on particle swarm optimization algorithm. Background Technology

[0002] In air conditioning and ventilation equipment, the design of air outlet grilles directly affects airflow organization, air volume output, and noise levels. Traditional airfoil grille design methods mainly rely on prototype experiments combined with simulation optimization, which suffers from long development cycles and significant waste of experimental resources. Deterministic optimization algorithms, such as the steepest descent method and the conjugate gradient method, can solve local optimization problems, but they are inefficient when dealing with large-scale nonlinear global optimization and struggle to balance the complex relationship between airflow uniformity, noise control, and structural manufacturability. Furthermore, while existing technologies employ parametric modeling methods (such as B-spline curves) or dynamically adjust the number of transverse grilles to optimize the structure, their algorithms lack flexibility and struggle to balance global optimization with engineering constraints. This results in limited improvements in grille performance (such as air volume, drag coefficient, and noise) and difficulty in balancing structural strength and aerodynamic efficiency. Existing air outlet grille designs often suffer from airflow attenuation and increased noise due to airflow separation and adverse pressure gradients, and the air delivery range is limited, impacting user experience. Summary of the Invention

[0003] To address the shortcomings of the existing technology, this application provides a method and system for airfoil grid design based on particle swarm optimization algorithm.

[0004] In its first aspect, this application proposes an airfoil grating design method based on particle swarm optimization, comprising the following steps:

[0005] Determine the target design parameters for the airfoil grid, including grid feature length, thickness, and airflow angle of attack, and set the parameters for the particle swarm algorithm;

[0006] Based on the set parameters of the particle swarm optimization algorithm, the airfoil control point optimization calculation is performed to obtain the airfoil control point parameters corresponding to the optimal solution;

[0007] Airfoil grid ribs are designed based on the airfoil control point parameters. Multiple grids are simulated and preset based on the airfoil grid ribs. CFD simulation is performed on the multiple preset grids to select the grid with the optimal airflow.

[0008] In some embodiments, the step of performing airfoil control point optimization calculations based on set particle swarm optimization parameters to obtain the airfoil control point parameters corresponding to the optimal solution includes:

[0009] Step A1: Set the parameters of the particle swarm optimization algorithm, including particle dimension, population size, number of iterations, and inertia weight range. and acceleration constant and ;

[0010] Step A2: Initialize the particle swarm, randomly generate the initial position and velocity of the particles. The initial position represents the coordinate set of the airfoil control points, and the velocity represents the adjustment direction and magnitude of the control points.

[0011] Step A3: Calculate the fitness value for each particle, where the fitness function is the mean square error between the airfoil profile model function value and the measured data;

[0012] Step A4: Update the individual optimal solution of the particles. and the global optimal solution And dynamically adjust the inertia weight w based on the current fitness value;

[0013] Step A5: Update the particle's velocity and position, and apply boundary constraints;

[0014] Step A6: Repeat steps A3 to A5 until the maximum number of iterations is reached or the global fitness value is less than the set precision, and output the airfoil control point parameters corresponding to the optimal solution.

[0015] In some embodiments, the specific formula for the fitness function in step A3 is as follows:

[0016]

[0017] in, , This indicates that the airfoil curve is generated using cubic spline interpolation. and Let represent the coordinates of the i-th measurement point, and n represent the number of measurement points.

[0018] In some embodiments, the dynamic adjustment formula for the inertia weight w in step A4 is:

[0019]

[0020] in, and They are respectively The maximum and minimum values; The current objective function of the particle; and These are the average fitness of all current particles and the minimum fitness of the population, respectively.

[0021] In some embodiments, the update formulas for velocity and position in step A5 are as follows:

[0022]

[0023] in, , Represents a random number between 0 and 1. , The position and velocity of the i-th particle at the k-th iteration; , These are the individual optimal solution for the i-th particle and the global optimal solution for the population at the k-th iteration, respectively.

[0024] In some embodiments, in step A6, airfoil grid ribs are generated according to the optimized control point parameters. The thickness H of the airfoil grid ribs ranges from H=(0.22~0.45)*L, where L represents the characteristic length of the grid. The airflow angle of attack α ranges from 20° to 50° and adopts an arc curve design to ensure that the thickness gradually increases and then decreases.

[0025] In some embodiments, the step of designing airfoil grille ribs based on the airfoil control point parameters, simulating and pre-setting multiple grilles based on the airfoil grille ribs, performing CFD simulation on the multiple pre-set grilles, and selecting the grille with the optimal airflow includes:

[0026] The preset grids include radial, ring and square grids. CFD simulation is performed on the three preset grids, and the radial grid with the optimal air volume is selected.

[0027] The airflow guiding device of the radial grille has a centrally symmetrical structure, and the guiding angle is consistent with the airflow angle of attack α.

[0028] The CFD simulation used the k-ε turbulence model, with an inlet wind speed of 10 m / s and a mesh size of 2 mm, and mesh independence was verified.

[0029] The thickness of the ribs varies as follows: it gradually increases from the starting point P to the middle point, and then gradually decreases from the middle point to the ending point Q.

[0030] Secondly, this application proposes an airfoil grid design system based on particle swarm optimization algorithm, including a parameter setting module, an optimization calculation module, and a simulation screening module;

[0031] The parameter setting module is used to determine the target design parameters of the airfoil grille, including the grille feature length, thickness and airflow angle of attack, and to set the parameters of the particle swarm algorithm.

[0032] The optimization calculation module is used to perform airfoil control point optimization calculations based on the set parameters of the particle swarm algorithm, and obtain the airfoil control point parameters corresponding to the optimal solution.

[0033] The simulation screening module is used to design airfoil grid ribs according to the airfoil control point parameters, simulate and preset multiple grids according to the airfoil grid ribs, perform CFD simulation on the preset multiple grids, and screen out the grid with the optimal airflow.

[0034] Thirdly, this application proposes an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.

[0035] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described above.

[0036] The beneficial effects of this invention are:

[0037] This scheme simulates biological swarm intelligence, dynamically adjusting inertia weights and acceleration constants to achieve a balance between global search and local convergence. It optimizes grid geometry parameters (such as thickness H, airflow angle of attack α, and rib distribution), and verifies the design effectiveness using CFD simulation. Compared to traditional methods, this scheme significantly shortens the development cycle and reduces experimental costs. Simultaneously, by optimizing airflow distribution and boundary layer control, it effectively reduces flow separation vortices and pressure difference losses, lowers noise, and increases airflow. Experiments show that the radial grid designed using this scheme can increase airflow by 10% at the same rotational speed while reducing the shape drag coefficient, meeting stringent safety regulations and demonstrating significant technical advantages and engineering application value. Attached Figure Description

[0038] Figure 1 This is the overall flowchart of the present invention.

[0039] Figure 2 This is a schematic diagram of the arc-shaped curve design of the grating ribs of the present invention.

[0040] Figure 3 This is a schematic diagram of the airflow distribution on the surface of the mesh grille.

[0041] Figure 4 This is a schematic diagram showing the design of the thickness of the grating ribs in this invention.

[0042] Figure 5 This is a schematic diagram of the annular grille of the present invention.

[0043] Figure 6 This is a schematic diagram of the square grille of the present invention.

[0044] Figure 7 This is a schematic diagram of the radial grid of the present invention.

[0045] Figure 8 This is a schematic diagram of the overall grid division of the present invention.

[0046] Figure 9 This is a streamline diagram of the radial grid of the present invention.

[0047] Figure 10 This is a streamline diagram of the annular grille of the present invention.

[0048] Figure 11 This is a streamline diagram of the square grille of the present invention.

[0049] Figure 12 This is a velocity cloud diagram of the radial grid of the present invention.

[0050] Figure 13 This is a velocity cloud diagram of the annular grid of the present invention.

[0051] Figure 14 This is a velocity cloud diagram of the square grid of the present invention.

[0052] Figure 15 This is a vector diagram of the radial grid outlet of the present invention.

[0053] Figure 16 This is a vector diagram of the annular grille outlet of the present invention.

[0054] Figure 17 This is a vector diagram of the square grille outlet of the present invention.

[0055] Figure 18 This is a system principle block diagram of the present invention. Detailed Implementation

[0056] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein; rather, these embodiments are provided so that a more thorough understanding of the invention can be achieved and that the full scope of the invention can be conveyed to those skilled in the art.

[0057] In its first aspect, this application proposes an airfoil grating design method based on particle swarm optimization, such as... Figure 1 As shown, it includes the following steps:

[0058] S100: Determine the target design parameters of the airfoil grid, including the grid feature length, thickness and airflow angle of attack, and set the parameters of the particle swarm algorithm;

[0059] S200: Calculate the airfoil control points based on the set parameters of the particle swarm optimization algorithm to obtain the airfoil control point parameters corresponding to the optimal solution;

[0060] In some embodiments, the step of performing airfoil control point optimization calculations based on set particle swarm optimization parameters to obtain the airfoil control point parameters corresponding to the optimal solution includes:

[0061] Step A1: Set the parameters of the particle swarm optimization algorithm, including particle dimension, population size, number of iterations, and inertia weight range. and acceleration constant and ;

[0062] Step A2: Initialize the particle swarm, randomly generate the initial position and velocity of the particles. The initial position represents the coordinate set of the airfoil control points, and the velocity represents the adjustment direction and magnitude of the control points.

[0063] Step A3: Calculate the fitness value for each particle, where the fitness function is the mean square error between the airfoil profile model function value and the measured data;

[0064] Step A4: Update the individual optimal solution of the particles. and the global optimal solution And dynamically adjust the inertia weight w based on the current fitness value;

[0065] Step A5: Update the particle's velocity and position, and apply boundary constraints;

[0066] Step A6: Repeat steps A3 to A5 until the maximum number of iterations is reached or the global fitness value is less than the set precision, and output the airfoil control point parameters corresponding to the optimal solution.

[0067] In some embodiments, the specific formula for the fitness function in step A3 is as follows:

[0068]

[0069] in, , This indicates that the airfoil curve is generated using cubic spline interpolation. and Let represent the coordinates of the i-th measurement point, and n represent the number of measurement points.

[0070] In some embodiments, the dynamic adjustment formula for the inertia weight w in step A4 is:

[0071]

[0072] in, and They are respectively The maximum and minimum values; The current objective function of the particle; and These are the average fitness of all current particles and the minimum fitness of the population, respectively.

[0073] In some embodiments, the update formulas for velocity and position in step A5 are as follows:

[0074]

[0075] in, , Represents a random number between 0 and 1. , The position and velocity of the i-th particle at the k-th iteration; , These are the individual optimal solution for the i-th particle and the global optimal solution for the population at the k-th iteration, respectively.

[0076] Among them, the air outlet grille has an airflow scattering effect. An unreasonable airfoil grille design can affect the airflow volume and organization. Traditional grille design methods combine hand-made experiments with simulations for optimization, resulting in low R&D efficiency and wasted experimental resources. Optimization methods are generally divided into two categories: deterministic algorithms and stochastic algorithms. Deterministic algorithms mainly include steepest descent, conjugate gradient, branch and bound, and quasi-Newton methods, but these algorithms cannot effectively solve large-scale nonlinear global optimization problems. Therefore, stochastic optimization methods are considered. Particle swarm optimization (PSO) is a typical stochastic optimization algorithm, inspired by the natural habitat of birds. Furthermore, the algorithm is easy to implement, converges quickly, and easily finds the global optimum.

[0077] Example:

[0078] Parameter settings:

[0079] The characteristic length of the grille is L = 0.5m, and the inertial weight range is... =0.4, =0.9, acceleration constant = =2, population size 50, number of iterations 80;

[0080] Initialize particles:

[0081] Fifty particles are randomly generated, each containing 10 control point coordinates (XY plane), with position ranging from 0 to L and velocity ranging from −0.1L to 0.1L;

[0082] Fitness calculation:

[0083] The measured data points n=20 are used to generate the airfoil curves through cubic spline interpolation. Calculate the mean square error;

[0084] Dynamically adjust weights:

[0085] When a particle's fitness (below the population average) Its inertia weight is adjusted as follows: ;

[0086] Boundary constraints:

[0087] If a particle's position Exceeding the limit Then it will be forcibly modified to ;

[0088] Output: After 80 iterations, the coordinates of the control points corresponding to the global optimal solution generate an arc-shaped rib with a thickness of H=0.3L and α=35°.

[0089] S300: Design airfoil grid ribs according to the airfoil control point parameters, simulate and preset multiple grids according to the airfoil grid ribs, perform CFD simulation on the multiple preset grids, and select the grid with the optimal airflow.

[0090] In some embodiments, in step A6, airfoil grid ribs are generated according to the optimized control point parameters. The thickness H of the airfoil grid ribs ranges from H=(0.22~0.45)*L, where L represents the characteristic length of the grid. The airflow angle of attack α ranges from 20° to 50° and adopts an arc curve design to ensure that the thickness gradually increases and then decreases.

[0091] Among them, the particle swarm optimization algorithm results in an irregular region in the first iteration, with a large solution range. After 80 iterations, a relatively optimal solution is obtained. However, considering practical engineering applications, it is necessary to comprehensively evaluate the product's airflow attenuation, structural installation space, appearance matching, and the impact of the air outlet range. Among these, the air outlet range and airflow attenuation have the greatest impact. Therefore, CFD simulation analysis is performed, with a design speed of 1400 rpm, to calculate the airflow.

[0092] Further considering the structural design, the air outlet grille is located on the outermost side of the fuselage when the air is venting. The airflow flows into the indoor space through the outermost grille. The air outlet grille is improved by using airfoil disturbance technology to further enhance the temperature uniformity.

[0093] The grating ribs, while meeting the demolding requirements, adopt an arc-shaped curve design, resulting in a smooth overall transition. Figure 2 As shown, L0 represents the characteristic length of the grid, points Ai (i=1~n) represent several points on the lower surface of the ribs (low speed, high pressure), points Bi (i=1~n) represent several points on the upper surface of the ribs (high speed, low pressure), H represents the maximum thickness of the grid cross-section, H=AmaxBmax. In the figure, P represents the starting point of the entire grid rib (i.e., the point where the airflow first contacts the wall), and Q represents the ending point of the entire grid rib (the point where the airflow wake converges). α represents the angle between the airflow and the chord length of the grid.

[0094] H = (0.22~0.45)*L, airflow angle of attack α = 20°~50°. When the ratio of grid thickness to length and the airflow angle of attack are within this range, the airflow distribution on the upper and lower surfaces can be effectively controlled, ensuring the shape drag coefficient Cd remains at a low value, while simultaneously delaying boundary layer flow separation. Problems arising from a ratio that is too small include poor grid strength, making it difficult to meet safety regulations in various regions; a ratio exceeding 0.45 will increase shape drag, expand the adverse pressure gradient region, and facilitate boundary layer separation, generating separation vortices and wake vortices on the windward side, similar to... Figure 3 In such cases, flow separation leads to a larger pressure difference, which can easily generate longitudinal or transverse vortex-induced oscillations, resulting in louder noise and the "popping" sound from the outdoor unit.

[0095] like Figure 4 As shown, the variation law of line segments A1B1, A2B2, A3B3, …AiBi…AnBn must satisfy the condition of increasing from small to large and then increasing again, and the position of point N at the projection of PQ must satisfy NP = (0.1-0.4)PQ. First, only when the thickness increases from small to large and then decreases again can a pressure gradient be generated between the upper and lower surfaces (Bernoulli's theorem). Then, under the pressure gradient, the airflow near the wall accelerates locally to overcome viscous resistance and improve the grid efficiency. Second, it is necessary to control point N to be in a reasonable position. If it is not within the set range, the airflow separation will occur prematurely, affecting the airflow efficiency. The direct loss is increased power consumption and noise.

[0096] In some embodiments, the step of designing airfoil grille ribs based on the airfoil control point parameters, simulating and pre-setting multiple grilles based on the airfoil grille ribs, performing CFD simulation on the multiple pre-set grilles, and selecting the grille with the optimal airflow includes:

[0097] The preset grids include radial, ring and square grids. CFD simulation is performed on the three preset grids, and the radial grid with the optimal air volume is selected.

[0098] The airflow guiding device of the radial grille has a centrally symmetrical structure, and the guiding angle is consistent with the airflow angle of attack α.

[0099] The CFD simulation used a k-ε turbulence model with an inlet wind speed of 10 m / s and a mesh size of 2 mm.

[0100] The thickness of the ribs varies as follows: it gradually increases from the starting point P to the middle point, and then gradually decreases from the middle point to the ending point Q.

[0101] After designing suitable airfoil grille ribs using the particle swarm optimization algorithm, it is also necessary to design the overall grille shape in conjunction with aesthetic requirements. Three initial grille designs have been selected: circular, square, and radial. The appearance effects of the three designs are as follows: Figures 5-7As shown, a centrally symmetrical flow guiding device was designed based on jet theory, and wind field simulations were performed on three different grid covers. First, each grid was meshed, and an overall mesh calculation model was created, as shown below. Figure 8 As shown, CFD software was used to simulate and calculate three types of grilles, and the results are as follows. Figure 9-11 The results shown Figure 9-11 The streamline diagrams for each grid show that the airflow from the exit of the square grid tends to converge, which is not conducive to the outward radiation of airflow. The calculation results for the radial grid are better, showing an overall radiating trend. The annular grid falls between the two, possessing some radiative diffusion effect, but the effect is not significant.

[0102] To further analyze the airflow diffusion velocity distribution at the grille, cross-sections were set at 0.15m, 0.30m, and 0.45m from the outlet. The velocity distributions were compared and analyzed. The velocity contour maps of the three grilles are shown below. Figure 12-14 As shown, the vector image is as follows Figure 15-17 As shown. Based on the analysis of cloud maps and vector maps, the radial grid has significant advantages. It can take into account both axial and circumferential velocities. The airflow is pressurized once by the fan blades and then pressurized twice by the airfoil grid, achieving air delivery over a wide range of light angles. Simulation results show that the air volume of the radial grid can be increased by 10% at the same rotational speed, which can fully utilize the duct performance of the axial flow fan.

[0103] Secondly, this application proposes an airfoil grating design system based on particle swarm optimization algorithm, such as... Figure 18 As shown, it includes a parameter setting module, an optimization calculation module, and a simulation screening module;

[0104] The parameter setting module is used to determine the target design parameters of the airfoil grille, including the grille feature length, thickness and airflow angle of attack, and to set the parameters of the particle swarm algorithm.

[0105] The optimization calculation module is used to perform airfoil control point optimization calculations based on the set parameters of the particle swarm algorithm, and obtain the airfoil control point parameters corresponding to the optimal solution.

[0106] The simulation screening module is used to design airfoil grid ribs according to the airfoil control point parameters, simulate and preset multiple grids according to the airfoil grid ribs, perform CFD simulation on the preset multiple grids, and screen out the grid with the optimal airflow.

[0107] Thirdly, this application proposes an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.

[0108] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described above.

[0109] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0110] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0111] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0112] In the embodiments provided in this disclosure, it should be understood that the disclosed apparatus / computer devices and methods can be implemented in other ways. For example, the apparatus / computer device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. Multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0113] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0114] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0115] If an integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program may include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. A computer-readable medium may include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in a computer-readable medium may be appropriately added to or subtracted according to the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media may not include electrical carrier signals and telecommunication signals.

[0116] The above are merely preferred embodiments of the present invention. It should be noted that any modifications and improvements made by those skilled in the art without departing from the present technical solution should also be considered to fall within the scope of protection claimed by the present solution.

Claims

1. A method for designing airfoil gratings based on particle swarm optimization, characterized in that: Includes the following steps: Determine the target design parameters for the airfoil grid, including grid feature length, thickness, and airflow angle of attack, and set the parameters for the particle swarm algorithm; Based on the set parameters of the particle swarm optimization algorithm, the airfoil control point optimization calculation is performed to obtain the airfoil control point parameters corresponding to the optimal solution. This includes step A1: setting the parameters of the particle swarm optimization algorithm, including particle dimension, population size, number of iterations, and inertia weight range. and acceleration constant and ; Step A2: Initialize the particle swarm, randomly generate the initial position and velocity of the particles. The initial position represents the coordinate set of the airfoil control points, and the velocity represents the adjustment direction and magnitude of the control points. Step A3: Calculate the fitness value for each particle, where the fitness function is the mean square error between the airfoil profile model function value and the measured data; Step A4: Update the individual optimal solution of the particles. and the global optimal solution The inertia weight w is dynamically adjusted based on the current fitness value. The dynamic adjustment formula for the inertia weight w in step A4 is as follows: in, and They are respectively The maximum and minimum values; The current objective function of the particle; and These are the average fitness of all current particles and the minimum fitness of the population, respectively. Step A5: Update the particle's velocity and position, and apply boundary constraints; Step A6: Repeat steps A3 to A5 until the maximum number of iterations is reached or the global fitness value is less than the set precision. Output the airfoil control point parameters corresponding to the optimal solution. In step A6, airfoil grid ribs are generated based on the optimized control point parameters. The thickness H of the airfoil grid ribs is in the range of H=(0.22~0.45)*L, where L represents the characteristic length of the grid. The airflow angle of attack α is in the range of 20°~50° and an arc curve design is adopted to ensure that the thickness increases from small to large and then decreases again. Airfoil grid ribs are designed based on the airfoil control point parameters. Multiple grids are simulated and preset based on these ribs. CFD simulations are performed on these preset grids to select the grid with the optimal airflow. These preset grids include radial, annular, and square grids. CFD simulations are performed on these three types of grids, and the radial grid with the optimal airflow is selected. The airflow guiding device of the radial grid is a centrally symmetrical structure, and the guiding angle is consistent with the airflow angle of attack α. The CFD simulation uses a k-ε turbulence model with an inlet wind speed of 10 m / s and a grid size of 2 mm. The rib thickness varies as follows: it gradually increases from the starting point P to the middle point, and then gradually decreases from the middle point to the ending point Q.

2. The method according to claim 1, characterized in that: The specific formula for the fitness function in step A3 is as follows: in, , This indicates that the airfoil curve is generated using cubic spline interpolation. and Let represent the coordinates of the i-th measurement point, and n represent the number of measurement points.

3. The method according to claim 2, characterized in that: The update formulas for velocity and position in step A5 are as follows: in, , Represents a random number between 0 and 1. , The position and velocity of the i-th particle at the k-th iteration; , These are the individual optimal solution for the i-th particle and the global optimal solution for the population at the k-th iteration, respectively.

4. An airfoil grating design system based on particle swarm optimization algorithm, characterized in that: It includes a parameter setting module, an optimization calculation module, and a simulation screening module; The parameter setting module is used to determine the target design parameters of the airfoil grille, including the grille feature length, thickness and airflow angle of attack, and to set the parameters of the particle swarm algorithm. The optimization calculation module is used to perform airfoil control point optimization calculations based on the set parameters of the particle swarm optimization algorithm, and obtain the airfoil control point parameters corresponding to the optimal solution. This includes step A1: setting the parameters of the particle swarm optimization algorithm, including particle dimension, population size, number of iterations, and inertia weight range. and acceleration constant and ; Step A2: Initialize the particle swarm, randomly generate the initial position and velocity of the particles. The initial position represents the coordinate set of the airfoil control points, and the velocity represents the adjustment direction and magnitude of the control points. Step A3: Calculate the fitness value for each particle, where the fitness function is the mean square error between the airfoil profile model function value and the measured data; Step A4: Update the individual optimal solution of the particles. and the global optimal solution The inertia weight w is dynamically adjusted based on the current fitness value. The dynamic adjustment formula for the inertia weight w in step A4 is as follows: in, and They are respectively The maximum and minimum values; The current objective function of the particle; and These are the average fitness of all current particles and the minimum fitness of the population, respectively. Step A5: Update the particle's velocity and position, and apply boundary constraints; Step A6: Repeat steps A3 to A5 until the maximum number of iterations is reached or the global fitness value is less than the set precision. Output the airfoil control point parameters corresponding to the optimal solution. In step A6, airfoil grid ribs are generated based on the optimized control point parameters. The thickness H of the airfoil grid ribs is in the range of H=(0.22~0.45)*L, where L represents the characteristic length of the grid. The airflow angle of attack α is in the range of 20°~50° and an arc curve design is adopted to ensure that the thickness increases from small to large and then decreases again. The simulation screening module is used to design airfoil grid ribs based on the airfoil control point parameters, simulate and pre-set multiple grids based on the airfoil grid ribs, perform CFD simulation on the multiple preset grids, and screen out the grid with the optimal airflow. The preset multiple grids include radial, annular, and square grids. CFD simulation is performed on the three preset grids, and the radial grid with the optimal airflow is selected. The airflow guiding device of the radial grid has a centrally symmetrical structure, and the guiding angle is consistent with the airflow angle of attack α. The CFD simulation uses a k-ε turbulence model, with an inlet wind speed of 10 m / s and a grid size of 2 mm. The thickness variation law of the ribs is: gradually increasing from the starting point P to the middle point, and then gradually decreasing from the middle point to the ending point Q.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-3.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method as described in any one of claims 1-3.

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