Turbine blade profile optimization method, blade, computer device and storage medium

Abstract

The present invention relates to a method for optimizing the blade profile of a turbomachine, a blade, a computer device, and a storage medium. The method includes: determining the mid-arc coordinates of the initial blade profile; determining the tangent angles and corresponding coordinates of each point of the mid-arc coordinates of the initial blade profile and dimensionlessizing them; taking the derivative of the dimensionless coordinates and angles to obtain the dimensionless angle slope of the mid-arc; applying the calculated dimensionless angle slope of the mid-arc to the perturbation function to obtain the perturbed dimensionless angle slope; integrating the calculated perturbed dimensionless angle slope to obtain the perturbed dimensionless angle distribution and normalizing it using the trailing-edge dimensionless angle so that the trailing-edge dimensionless angle Θ ’= 1; performing inverse operations on the perturbed dimensionless angles and coordinates to obtain the actual mid-arc angle distribution, and integrating it to obtain the perturbed mid-arc coordinates; determining the newly constructed mid-arc, and generating a reconstructed blade profile according to the original blade parameters and modeling method.

Description

Technical Field

[0001] The present invention relates to the technical field of turbomachine blade design, and particularly to a turbomachine airfoil optimization method, a blade, a computer device, and a storage medium, which can be applied to turbomachine structures in aeroengines such as fans and compressors. Background Art

[0002] Turbomachines such as fans and compressors are important components of aeroengines. Aerodynamic optimization design of blades is of great significance for improving the aerodynamic performance of aeroengines. However, the internal flow conditions in aeroengines are very complex, with complex flow characteristics such as multi-stage and unsteady flows. At the same time, aerodynamic design of blades is restricted by design characteristics such as noise and structure, which makes the design process of turbomachine components such as fans and compressors in aeroengines particularly complex, facing problems such as multi-stage matching and multi-disciplinary coupling.

[0003] Traditional aerodynamic design of fans and compressors is carried out based on experimental or numerical simulation technologies, and needs to be iterated repeatedly through design-evaluation-redesign. When facing multi-objective and multi-disciplinary optimization problems, repeated iteration is extremely costly and cannot meet the high-performance requirements of the aerodynamic design of new-generation aeroengines. As the basic unit of turbomachine blade modeling, two-dimensional airfoil design has a crucial impact on the performance of turbomachines. Existing two-dimensional airfoil designs of turbomachines mostly adopt the idea of superimposing the mean camber line with the thickness, which has a strong dependence on experience. In engineering applications, it is necessary to combine experimental or numerical results and adjust the airfoil design parameters to iterate and optimize repeatedly, which requires a large amount of experimental and computational consumption and has a large time cost.

[0004] To meet the high aerodynamic performance and multi-disciplinary joint design requirements of the aerodynamic design of new-generation aeroengines, using fast and accurate optimization algorithms can achieve efficient design of high-performance two-dimensional airfoils of turbomachines. Whether using traditional optimization algorithms or artificial-intelligence-based optimization algorithms, it is necessary to construct a sample database of the characteristics of airfoils and aerodynamics, noise, structure, etc., and it is necessary to perturb the parameters of blade modeling and reconstruct the airfoil to form a sample space. Modern fan and compressor airfoils all adopt controlled diffusion airfoils. Different from traditional airfoils, the bending law of the mean camber line of controlled diffusion airfoils cannot be described by several parameters, but is a free curve optimized according to the flow field characteristics, so that the fluid flows and diffuses according to the intention of the designer to achieve the best performance of fluid machinery. The mean camber line of the controlled diffusion airfoil needs to be described by coordinates from the leading edge to the trailing edge or metal angles. However, in the optimization design, direct perturbation of the mean camber line coordinates or angles will bring problems of too many parameters and too large a demand for the sample size. At the same time, direct perturbation has the problem of too high a degree of freedom, which is difficult to ensure the rationality of the perturbed mean camber line and easily causes sample failure and waste of optimization resources.

[0005] The present invention is proposed based on the above research background, aiming to provide a method for optimizing the blade profile of a turbomachine, which can ensure the rationality of blade geometry while reducing perturbation variables and improve the efficiency of blade profile optimization. Summary of the Invention

[0006] In order to solve the following technical problems existing in the shaping optimization of the controllable diffusion blade profile of blades in turbomachines such as fans and compressors: (1) There are too many variables in the reconstruction of the mean camber line, which will lead to a too large demand for samples and reduce the optimization efficiency; (2) There is a problem of too high freedom in the direct perturbation of the mean camber line shaping parameters, which will lead to many geometric irrationalities in the reconstructed blade profile, resulting in sample failure and wasting optimization resources; (3) The variable ranges of the mean camber line parameters of different blades, different blade heights, and different axial positions are all different, and it is difficult to determine a reasonable perturbation range. The purpose of the present invention is to provide a method for optimizing the blade profile of a turbomachine, which has the advantages of reasonable design, reducing the number of perturbation parameters and the demand for samples, improving the sample utilization efficiency, and improving the blade performance.

[0007] To achieve the above object, on the one hand, the present invention proposes a method for optimizing the blade profile of a turbomachine, which includes the following steps:

[0008] Step 1: Obtain the initial blade profile of the turbomachine and determine the coordinates (x i , y i ) of the mean camber line of the initial blade profile, and the above initial blade profile includes a controllable diffusion blade profile;

[0009] Step 2: Determine the tangent angle θ i of each point on the mean camber line coordinate of the initial blade profile and the corresponding coordinates (x i , y i ), and make them dimensionless to obtain dimensionless coordinates X and dimensionless angle Θ, where x i、 y i are the axial coordinate of the mean camber line on the X-axis and the vertical coordinate on the Y-axis respectively, i is the serial number of the point on the mean camber line of the initial blade profile. When i = 1, it represents the serial number of the leading edge position point of the initial blade profile. When i = 2, it represents the serial number of the trailing edge position point of the initial blade profile. When i is any other value, it represents the serial number of any position point on the mean camber line except the leading edge and trailing edge positions;

[0010] Step 3: Take the derivative of the dimensionless coordinates X and dimensionless angle Θ obtained in Step 2 to obtain the dimensionless angle slope of the mean camber line. The dimensionless angle slope k characterizes the second derivative of the mean camber line coordinates;

[0011] Step 4: Construct a perturbation function according to the requirements of the turbomachine flow field , apply the dimensionless angular slope of the mean camber line calculated in step 3 to the above perturbation function to obtain the perturbed dimensionless angular slope ;

[0012] Step 5: Integrate the perturbed dimensionless angular slope calculated in step 4. The integration formula is , to obtain the perturbed dimensionless angular distribution (X ’ , Θ ’ ), where X ’ is the perturbed dimensionless coordinate, and Θ ’ is the perturbed dimensionless angle, and normalize it using the trailing-edge dimensionless angle so that the trailing-edge dimensionless angle Θ ’= = 1;

[0013] Step 6: Inverse the operation of the perturbed dimensionless angle and coordinate obtained in step 5 according to the method in step 2 to obtain the actual mean camber line angle distribution ( x’, θ’ ), and perform integration to obtain the perturbed mean camber line coordinates ( x i ’ , y i ’ ). The above integration formula is ;

[0014] Step 7: Determine the newly constructed mean camber line according to the perturbed mean camber line coordinates calculated in step 6, and generate the reconstructed blade profile according to the original blade parameters and modeling method.

[0015] As a further optimization of the above solution, the calculation formula of the tangent angle θ in step 2 is as follows:

[0016] ;

[0017] The dimensionless angle Θ represents the first derivative of the mean camber line coordinates; the dimensionless angle Θ i and the dimensionless coordinate X i The calculation formulas are as follows:

[0018] X i = ([[]] x i - x 1 ) / ([[]] x 2 - x 1 ), Θ i = ([[]] θ i - θ 1 ) / ([[]] θ 2 -θ 1 ), where x 1 、x 2 、x i have the same meanings as before; θ 1 is the tangent angle of the leading edge of the initial blade profile, θ 2 is the tangent angle of the trailing edge of the initial blade profile, θ i is the tangent angle of any position point on the mean camber line except the leading edge and trailing edge positions.

[0019] As a further optimization of the above solution, the shape of the reconstructed blade profile is smooth without inflection points.

[0020] On the other hand, the present invention proposes a turbine blade, which is obtained by the aforementioned turbine blade profile optimization method, and the surface of the blade is smooth.

[0021] As a further optimization of the above solution, the turbine blade is a fan blade or a compressor blade of an aeroengine.

[0022] On yet another aspect, the present invention also proposes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the turbine blade profile optimization method are implemented.

[0023] Finally, the present invention also proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the turbine blade profile optimization method are implemented.

[0024] The turbine blade profile optimization method of the present invention has the following beneficial effects:

[0025] 1. By performing high-order perturbation on the mean camber line of the initial blade profile, the continuity of the mean camber line can be ensured, and the appearance of unreasonable geometries such as blade reverse bending can be avoided. This can not only unify the mean camber line parameters of different blades, different blade heights, and different axial positions, facilitate the determination of a unified perturbation range, but also reduce the number of perturbation parameters and the sample number requirements, improve the sample utilization efficiency, and ultimately achieve the goal of improving the optimization efficiency;

[0026] 2. Since the airflow realizes compression and work through airflow turning in the blade passage, the slope of the mean camber line angle of the blade represents the turning angle of the blade at that position and also represents the load intensity at that position. The perturbation amount in the present invention is directly reflected in the load position of the controlled diffusion blade profile, and has a clear correlation with optimization methods such as adjusting the shock wave position and reducing the flow separation on the suction surface;

[0027] 3. The controllable diffusion airfoil obtained by the high-order optimization of the blade mean camber line can control the surface velocity distribution of the blade, suppress the shock wave intensity, and improve the blade performance. Description of the Drawings

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those skilled in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0029] Figure 1 It is a schematic diagram of the initial airfoil of the turbomachine, where the dashed line is the mean camber line of the airfoil.

[0030] Figure 2 For the angle and coordinate definition, the coordinates of the leading edge point of the mean camber line are (x 1 , y 1 ), the included angle between the tangent line and the axis is θ 1 , the coordinates of the trailing edge are (x 2 , y 2 ), the included angle between the tangent line and the axis is θ 2 , and the coordinates of any intermediate position are (x, y) and the tangent angle is θ.

[0031] Figure 3 It is the distribution of the dimensionless angle with respect to the dimensionless coordinate. The solid line is the initial distribution, and the dashed line is the distribution after perturbation.

[0032] Figure 4 It is the distribution of the slope of the dimensionless angle with respect to the dimensionless coordinate. The solid line is the initial distribution, and the dashed line is the distribution after perturbation.

[0033] Figure 5 It is the distribution of the perturbation amount applied to the slope of the dimensionless angle.

[0034] Figure 6 It is a schematic diagram of the reconstructed airfoil after applying the perturbation, where the dashed line is the mean camber line of the airfoil. Among them, the camber of the blade changes significantly in the places with strong perturbation, but the blade itself still maintains a reasonable airfoil shape.

[0035] Figure 7 It is a comparison diagram of the initial airfoil and the reconstructed airfoil. The solid line is the initial airfoil, and the dashed line is the reconstructed airfoil.

[0036] Figure 8 It is the flowchart corresponding to the method of the present invention.

[0037] Figure 9 It is the Mach number contour map of the flow field of the initial airfoil.

[0038] Figure 10 It is the Mach number contour map of the flow field of the optimized airfoil.

[0039] Figure 11 It is a comparison diagram of the isentropic Mach number distribution on the front and rear blade profiles before and after optimization. Specific implementation manners

[0040] To make the above objects, features, and advantages of the present application more obvious and understandable, the following will describe the specific implementation manners of the present application in detail with reference to the accompanying drawings. Many specific details are set forth in the following description in order to fully understand the present application. However, the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without departing from the connotation of the present application. Therefore, the present application is not limited by the specific embodiments disclosed below.

[0041] The following will describe Figures 1 - 11 in detail the method for optimizing the blade profile of a turbomachine according to the present invention.

[0042] The present invention provides a method for optimizing the blade profile of a turbomachine, which includes the following steps:

[0043] Step 1: Obtain the initial blade profile of the turbomachine, and determine the coordinates (x i , y i ) of the mean camber line of the initial blade profile. The above initial blade profile includes a controlled diffusion blade profile.

[0044] Step 2: Determine the tangent angle θ i of each point on the mean camber line coordinate of the initial blade profile and the corresponding coordinates (x i , y i ), and make them dimensionless to obtain dimensionless coordinates X and dimensionless angle Θ. Among them, x i、 y i are respectively the axial coordinate of the mean camber line on the X-axis and the ordinate on the Y-axis. i is the serial number of the point on the mean camber line of the initial blade profile. When i = 1, it represents the serial number of the leading edge position point of the initial blade profile. When i = 2, it represents the serial number of the trailing edge position point of the initial blade profile. When i is any other value, it represents the serial number of any position point on the mean camber line except the leading edge and trailing edge positions.

[0045] Step 3: Take the derivatives of the dimensionless coordinates X and dimensionless angle Θ obtained in Step 2 to obtain the dimensionless angle slope of the mean camber line. The dimensionless angle slope k characterizes the second derivative of the mean camber line coordinates.

[0046] Step 4: Construct a perturbation function according to the requirements of the turbomachine flow field, and apply the dimensionless angle slope of the mean camber line calculated in Step 3 to the above perturbation function to obtain the perturbed dimensionless angle slope ;

[0047] Step 5: Integrate the dimensionless angular slope after perturbation calculated in Step 4. The integration formula is , to obtain the dimensionless angular distribution after perturbation (X ’ , Θ ’ ), where X ’ is the dimensionless coordinate after perturbation, and Θ ’ is the dimensionless angle after perturbation, and it is normalized using the dimensionless trailing-edge angle to make the dimensionless trailing-edge angle Θ ’= 1;

[0048] Step 6: Inverse-operate the dimensionless angle and coordinate after perturbation obtained in Step 5 according to the method in Step 2 to obtain the actual mean-curve angle distribution ( x’, θ’ ), and integrate to obtain the mean-curve coordinate after perturbation ( x i ’,y i ’ ). The above integration formula is ;

[0049] Step 7: Determine the newly constructed mean curve based on the mean-curve coordinate after perturbation calculated in Step 6, and generate a reconstructed blade profile according to the original blade parameters and profiling method.

[0050] The calculation formula for the tangent angle θ in Step 2 is as follows:

[0051] ;

[0052] The dimensionless angle Θ represents the first derivative of the mean-curve coordinate; the calculation formulas for the dimensionless angle Θ i and the dimensionless coordinate X i are as follows:

[0053] X i = ([[]] x i - x 1 <000053> / ([[]] x 2 - x 1 <000059>), Θ i = ([[]] θ i - θ 1 <000067> / ([[]] θ 2 - θ 1 <000073>), where the meanings of x 1 , x 2 , x i are the same as before; θ 1is the tangent angle of the leading edge of the initial airfoil, θ 2 is the tangent angle of the trailing edge of the initial airfoil, θ i is the tangent angle of any point on the mean camber line except the leading edge and trailing edge positions.

[0054] Furthermore, the shape of the reconstructed airfoil is smooth without inflection points.

[0055] In another embodiment of the present invention, a turbine blade is provided. The turbine blade is obtained by the aforementioned turbine airfoil optimization method, and the surface of the blade is smooth.

[0056] Exemplarily, the turbine blade can be a fan blade or a compressor blade of an aeroengine.

[0057] In another embodiment of the present invention, a computer device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the turbine airfoil optimization method are implemented.

[0058] The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function;

[0059] In another embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the turbine airfoil optimization method are implemented.

[0060] It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and, of course, the extended storage medium supported by the computer device. The computer-readable storage medium provides storage space, and this storage space stores the operating system of the terminal. Moreover, in this storage space, there is also stored one or more instructions suitable for being loaded and executed by the processor, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory.

[0061] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes. The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices, and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks. These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in this computer-readable memory generate a manufactured article including instruction means, and this instruction means implements the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks. These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0062] It should be noted that the reason for only using the x coordinate is as follows: In the blades of a turbomachine, the axial position or the position in the chord length direction is generally used for representation. The axial direction is the x direction, and the axial coordinate is the x coordinate of a point on the mean camber line. The chord length direction is the direction of the line connecting the leading and trailing edge points (chord line), and the chordwise coordinate is the coordinate of the projection point of this point on the chord line from the leading edge point. Since the axial coordinate and the longitudinal coordinate are in a proportional relationship, the dimensionless coordinates obtained during dimensionlessization are equal. Therefore, a relatively simple expression method is used here.

[0063] Next, a pair of comparative examples will be compared with the airfoil optimization method of the present invention.

[0064] Select the unoptimized airfoil of the fan blade root of a certain aeroengine and operate it in a high subsonic incoming flow environment, such as Figure 9 shown. It shows that high Mach number regions appear at the position near the leading edge of the pressure surface and at the mid-span position of the suction surface, and there are relatively large shock losses.

[0065] However, by using the above optimization method of the present invention to modify and optimize the iteration of the mean camber line of the blade, the airfoil and flow field shown in Figure 10 are finally obtained. Refer to Figure 11 . It is not difficult to find that at the position near the leading edge of the pressure surface and at the mid-span position of the suction surface of the blade of the present invention, the high Mach number is suppressed, thereby improving the flow field and the performance of the blade.

[0066] The above description of the embodiments is to enable those of ordinary skill in the art to understand and apply the present invention. It is obvious that those skilled in the art can easily make various modifications to these embodiments and apply the general principles described herein to other embodiments without creative labor. Therefore, the present invention is not limited to the embodiments herein, and the improvements and modifications made by those skilled in the art without departing from the scope of the present invention should be within the protection scope of the present invention.

Claims

1. A method for optimizing the blade profile of a turbine, characterized in that: The steps include: Step 1: Obtain the initial blade profile of the impeller and determine the mid-arc coordinates of the initial blade profile (x i ,y i ), the above-mentioned initial blade profile includes a controllable diffusion blade profile; Step 2: Determine the tangent angle θ of each point on the initial blade mid-arc coordinate according to the initial blade mid-arc i And the corresponding coordinates (x i ,y i ), and non-dimensionalize it to obtain the dimensionless coordinate X and dimensionless angle Θ, where x i、 y i are the axial coordinates of the mid-camber line on the X-axis and the ordinate on the Y-axis, respectively; i is the serial number of the point on the mid-camber line of the initial blade profile; when i=1, it represents the serial number of the leading edge position point of the initial blade profile; when i=2, it represents the serial number of the trailing edge position point of the initial blade profile; when i is any other value, it represents the serial number of any position point on the mid-camber line except the leading edge and trailing edge positions; the calculation formula of the tangent angle θ is as follows: ； The dimensionless angle θ characterizes the first derivative of the mid-arc coordinate; the dimensionless angle θ i and the dimensionless coordinate X i The calculation formula is as follows: X i =( x i - x 1) / ( x 2- x 1), Θ i =( θ i - θ 1) / ( θ 2- θ 1), where x1, x2, x i The meaning is the same as before; θ 1 is the tangent angle of the leading edge of the initial blade profile, θ 2 is the tangent angle of the trailing edge of the initial blade profile, θ i It is the tangent angle of any point on the mid-camber line except the leading edge and trailing edge; Step 3: Derivative the dimensionless coordinate X and dimensionless angle θ obtained in step 2 to obtain the dimensionless angle slope of the mid-arc , the dimensionless angle slope k characterizes the second derivative of the mid-arc coordinate; Step 4: Construct a disturbance function according to the turbine flow field requirements , apply the dimensionless angle slope of the mid-arc calculated in step 3 to the above perturbation function to obtain the dimensionless angle slope after perturbation ; Step 5: Integrate the dimensionless angle slope after disturbance calculated in step 4. The integral formula is: , and obtain the dimensionless angular distribution after disturbance (X ’ ,Θ ’ ), where X ’ is the dimensionless coordinate after perturbation, Θ ’ is the dimensionless angle after disturbance, and is normalized using the dimensionless angle of the trailing edge to make the dimensionless angle of the trailing edge θ ’= 1; Step 6: The dimensionless angle and coordinates obtained in step 5 are reversed according to the method in step 2 to obtain the actual mid-arc angle distribution ( x ', θ '), and integrate to obtain the coordinates of the mid-arc after disturbance ( x i ', y i '), the above integral formula is: ； Step 7: Determine the newly constructed mid-arc line according to the coordinates of the mid-arc line after disturbance calculated in step 6, and generate the reconstructed blade shape according to the original blade parameters and modeling method.

2. The impeller blade optimization method according to claim 1, characterized in that: The linear shape of the reconstructed blade profile is smooth and has no inflection point.

3. An impeller blade, characterized in that: The impeller blade is obtained by the method according to claim 1 or 2, and the surface of the blade is smooth.

4. The impeller blade according to claim 3, characterized in that: The impeller blades are fan blades or compressor blades of an aircraft engine.

5. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the impeller blade profile optimization method according to claim 1 or 2 are implemented.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the impeller blade profile optimization method according to claim 1 or 2 are implemented.

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