A method, system, device and storage medium for designing a fan blade of an axial flow fan

Abstract

The present disclosure provides a method, system, device and storage medium for designing a fan blade of an axial flow fan. Based on target parameters for defining the overall performance and structure of the axial flow fan, a corresponding target performance relationship is calculated for at least one radial position of the fan blade to represent the mutual relationship between at least two aerodynamic parameters. The target performance relationship and a preset geometric constraint condition are used as filtering conditions to screen a wing profile from a wing profile performance database, which meets the design requirements in terms of performance and geometric shape. By connecting the wing profiles at different radial positions, a complete fan blade surface is generated. In this method, the performance indicators used for verification in the traditional process are converted into constraint conditions used for screening, which avoids repeated performance simulation or physical testing for each candidate geometric scheme, thereby significantly shortening the development cycle of the fan blade and reducing the research and development cost.

Description

Technical Field

[0001] This disclosure relates to the field of ventilation and refrigeration technology, and in particular to a design method, system, device and storage medium for axial flow fan blades. Background Technology

[0002] The core component of an axial flow fan is the geometric design of its blades, which directly determines the overall performance of the fan, including key indicators such as airflow, pressure, efficiency, and noise. In the traditional axial flow fan blade design process, engineers typically first build an initial three-dimensional blade geometry model based on experience. Then, they use computational fluid dynamics (CFD) simulations or physical testing to verify the model's aerodynamic performance. Regardless of whether CFD simulation or physical testing is used, if the verification results fail to meet the preset design goals, engineers must return to the initial design steps, analyze the verification results to revise the geometric model, and then perform performance verification again. This design-revision-verification process usually needs to be repeated multiple times to converge to an acceptable design solution, consuming huge computational resources, resulting in a lengthy development cycle, and uncertainty in the design results. Summary of the Invention

[0003] This disclosure provides a method, system, device, and storage medium for designing axial flow fan blades, which can solve the problems in the prior art where the iterative design, correction, and verification process based on engineers' experience leads to huge consumption of computing resources, lengthy development cycles, and uncertainty in design results.

[0004] The technical solution disclosed herein is implemented as follows:

[0005] In a first aspect, this disclosure provides a method for designing axial flow fan blades, the method comprising:

[0006] Obtain the target parameters for the overall performance and structure of the axial flow fan;

[0007] Based on the target parameters, for at least one radial position of the fan blade, the corresponding target performance relationship is calculated and generated;

[0008] Based on the target performance relationship and the preset geometric constraints, the airfoil performance database containing multiple airfoil profiles is filtered to identify one or more airfoil profiles that simultaneously satisfy the target performance relationship and the geometric constraints.

[0009] Among them, the target performance relationship characterizes the relationship between at least two aerodynamic parameters.

[0010] Secondly, this disclosure provides an axial flow fan blade design system, the system comprising:

[0011] The parameter acquisition module is used to acquire the target parameters that limit the overall performance and structure of the axial flow fan.

[0012] The data processing module is used to calculate and generate the corresponding target performance relationship based on the target parameters for at least one radial position of the fan blade;

[0013] The result generation module is used to filter an airfoil performance database containing multiple airfoil profiles based on the target performance relationship and geometric constraints, in order to identify one or more airfoil profiles that simultaneously satisfy the target performance relationship and geometric constraints.

[0014] Among them, the target performance relationship characterizes the relationship between at least two aerodynamic parameters.

[0015] Thirdly, this disclosure provides a computer device, including a processor and a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the axial flow fan blade design method described in the first aspect.

[0016] Fourthly, this disclosure provides a computer-readable storage medium storing at least one instruction, which is executed by a processor to implement the axial fan blade design method described in the first aspect.

[0017] This disclosure provides a method, system, device, and storage medium for designing axial flow fan blades. First, target parameters defining the overall performance and structure of the axial flow fan are obtained. Based on these target parameters, corresponding target performance relationships are calculated for at least one radial position of the fan blade, characterizing the relationship between at least two aerodynamic parameters. Using these target performance relationships as performance constraints, along with preset geometric constraints, an airfoil performance database is filtered to select airfoil profiles that meet both performance and geometric design requirements. By connecting airfoil profiles at different radial positions, a complete fan blade profile is generated. This method transforms the performance indicators used for verification in traditional processes into pre-emptive constraints for selection, avoiding repeated performance simulations or physical tests for each candidate geometry. This significantly shortens the development cycle of fan blades and reduces R&D costs. Attached Figure Description

[0018] Figure 1 A flowchart of the axial flow fan blade design method provided in this disclosure.

[0019] Figure 2 This is a schematic diagram of the airfoil performance database provided in this disclosure.

[0020] Figure 3A flowchart for generating target performance relationships provided in this disclosure.

[0021] Figure 4 This is a schematic diagram of the final generated fan blade shape provided in this disclosure.

[0022] Figure 5 This is a structural schematic diagram of the axial flow fan blade design system provided in this disclosure.

[0023] Figure 6 This is a schematic diagram of the structure of the computing device provided in this disclosure. Detailed Implementation

[0024] The technical solutions in this disclosure will now be clearly and completely described with reference to the accompanying drawings.

[0025] Figure 1 A flowchart illustrating the axial flow fan blade design method provided in this disclosure. Figure 1 As shown, the method includes:

[0026] Step S101: Obtain the target parameters for the overall performance and structure of the axial flow fan.

[0027] In this embodiment, the target parameters include performance parameters and structural parameters. Among them, the performance parameters include at least air volume, operating point static pressure, rotational speed, and total pressure efficiency.

[0028] Structural parameters include at least the rotor diameter, hub diameter, number of blades, variable circulation index (an empirical coefficient used to control the radial aerodynamic load distribution of the blades), and aspect ratio (a dimensionless parameter used to describe the blade geometry).

[0029] Step S102: Based on the target parameters, which are at least one radial position of the fan blade, calculate and generate the corresponding target performance relationship.

[0030] Among them, the target performance relationship characterizes the relationship between at least two aerodynamic parameters.

[0031] In this embodiment, the entire three-dimensional fan blade is conceptually decomposed radially into multiple two-dimensional "stacked layers." For each stacked layer (i.e., each radial position), its local, specific aerodynamic parameters are calculated. Then, a mapping relationship between these aerodynamic parameters can be established to obtain the target performance relationship corresponding to each radial position.

[0032] Among them, aerodynamic parameters include lift coefficient and Reynolds number, which are the core parameters that determine the aerodynamic performance of airfoils.

[0033] Step S103: Based on the target performance relationship and preset geometric constraints, filter the airfoil performance database containing multiple airfoil profiles to identify one or more airfoil profiles that simultaneously satisfy the target performance relationship and geometric constraints.

[0034] Figure 2 This is a schematic diagram of the airfoil performance database provided in this disclosure. In this embodiment, a database as described above is pre-constructed. Figure 2 The airfoil performance database shown contains detailed information on hundreds or thousands of known airfoils (such as the NACA series, Althaus AH series, Eppler series, etc.). For each airfoil, the airfoil performance database records its geometric parameters (such as thickness, camber, location of maximum thickness, etc.), operating parameters (such as Reynolds number, angle of attack, etc.), and performance parameters (such as lift coefficient, drag coefficient, etc.) at different angles of attack and Reynolds numbers.

[0035] In this embodiment, after calculating the target performance relationship, a non-iterative filtering process is performed in the airfoil performance database. During the filtering process, the conditions for the required airfoil profile are determined, and airfoil profiles that meet the requirements are selected based on these conditions. The conditions for the required airfoil profile include performance constraints and geometric constraints. Performance constraints mean that at least one or more of the actual performance data points recorded for the airfoil profile in the airfoil performance database must satisfy the target performance relationship generated in step S102. For example, the coordinates of the data points (lift coefficient, Reynolds number) must fall within the specified tolerance range of the target performance relationship curve. Geometric constraints mean that the geometric parameters of the airfoil profile must satisfy the geometric constraints corresponding to its radial position.

[0036] In this embodiment, target parameters defining the overall performance and structure of the axial flow fan are first obtained. Based on these target parameters, corresponding target performance relationships are calculated for at least one radial position of the fan blade, characterizing the interrelationship between at least two aerodynamic parameters. Using these target performance relationships as performance constraints, along with preset geometric constraints, the airfoil performance database is filtered to select airfoil profiles that meet both performance and geometric design requirements. By connecting airfoil profiles at different radial positions, a complete fan blade profile is generated. This method transforms the performance indicators used for verification in traditional processes into pre-emptive constraints for selection, avoiding repeated performance simulations or physical tests for each candidate geometric scheme. This significantly shortens the development cycle of the fan blade and reduces R&D costs.

[0037] In an optional embodiment, after obtaining the target parameters defining the overall performance and structure of the axial flow fan, the method further includes:

[0038] The target parameters are preprocessed to transform them into specific global parameters that can be directly used in engineering design.

[0039] In this embodiment, the parameter conversion process is achieved using fluid dynamics formulas (such as velocity and pressure calculations) and classical axial flow fan design theories (such as specific speed and circulation law). The global parameters and their calculation process are detailed in Table 1.

[0040] Table 1 Global parameters and their calculation formulas

[0041]

[0042] The global parameters obtained through the above calculations provide basic data for subsequent airfoil selection and detailed blade design. The calculation logic combines fluid dynamics formulas and classical design theories of axial flow fans (such as specific speed and variable circulation law) to ensure that the obtained global parameters conform to engineering practice.

[0043] Figure 3 A flowchart for generating target performance relationships provided in this disclosure. For example... Figure 3 As shown, based on the target parameters, for at least one radial position of the fan blade, the corresponding target performance relationship is calculated and generated, including:

[0044] Step S301: Based on the target parameters, calculate multiple discrete data points; each data point corresponds to a set of aerodynamic parameter values;

[0045] Step S302: Use a preset fitting method to fit multiple discrete data points to generate a continuous function that characterizes the target performance relationship.

[0046] In an optional embodiment, multiple discrete data points are calculated based on the target parameters, including:

[0047] Calculate the initial operating parameters corresponding to each radial position based on the target parameters;

[0048] The fluctuation range is determined based on the initial operating parameters, and multiple discrete data points are obtained by sampling within this fluctuation range.

[0049] In this embodiment, for each stacked layer (i.e., radial position), its initial operating parameters can be calculated, such as cross-sectional radius, diameter ratio, circumferential velocity, airflow angle and angle of attack, and blade geometry parameters. These initial operating parameters can be calculated based on global parameters.

[0050] Optionally, for multiple stacked layers, a step-by-step calculation method is used. First, the multiple stacked layers are numbered radially. For example, if a total of 5 stacked layers are obtained, then stacked layers 1 to 5 are obtained along the direction from the hub to the blade tip. Second, the intermediate stacked layers are determined according to their numbers, and their initial operating parameters are calculated as the reference for the calculation of other stacked layers.

[0051] The specific method for iteratively calculating the initial operating parameters of each stack layer is shown in Table 2:

[0052] Table 2 Initial operating parameters and their calculation formulas

[0053]

[0054] After the above steps, corresponding initial operating parameters can be selected for subsequent discretization and data point sampling. In this embodiment, angle of attack, lift coefficient, and Reynolds number are selected. Among them, the lift coefficient and Reynolds number determine the blade performance; the same airfoil has different lift-to-drag ratios (efficiencies) at different angles of attack and Reynolds numbers; the angle of attack and airfoil of each stacked layer determine the blade profile; therefore, these three parameters are the core operating parameters for airfoil selection.

[0055] For example, the initial lift coefficients of stacked layers 1 to 5 (from hub to blade tip) were calculated to be 1.2, 1.0, 1.0, 1.2 and 1.1, respectively.

[0056] This embodiment does not stop at calculating an isolated operating point (e.g., using only a lift coefficient of 1.2 for matching). Instead, it generates a continuous "target performance relationship" based on this operating point. The process of generating the target performance relationship is to expand an isolated operating point into a continuous "performance envelope" or "performance corridor." This process greatly enhances the flexibility of the selection and the possibility of finding the global optimum.

[0057] For example, for the first stacked layer, the calculated target lift coefficient is 1.2. In this embodiment, a fluctuation range can be set, such as [1.1, 1.3]. Then, a series of discrete lift coefficient values ​​(such as 10) are uniformly generated within this fluctuation range, forming a sampling sequence Cy=[1.1,…,1.3].

[0058] Next, for each lift coefficient value Cy[i] in this sampling sequence, the Reynolds number Re[i] required to achieve that lift coefficient value is calculated in reverse using the calculation process shown in Table 2. Here, i represents the i-th sampling point.

[0059] Through the above process, a series of discrete data points, namely P, can be obtained. i=(Cy[i],Re[i]). Each data point represents an ideal combination of (lift coefficient, Reynolds number) that can meet the initial overall machine performance requirements.

[0060] In an optional embodiment, a preset fitting method is used to fit multiple discrete data points to generate a continuous function characterizing the target performance relationship, as follows:

[0061] The fitting method used in this embodiment is polynomial fitting. A cubic polynomial method can be used to fit these data points, resulting in a continuous function curve Re=f(Cy) that best approximates these data points. This fitted function curve is the "target performance relationship" defined in this embodiment. The lift coefficient and Reynolds number represented by each point on this function curve meet its performance requirements. In other words, any airfoil profile is considered a candidate airfoil profile that satisfies both the target performance relationship and geometric constraints, provided that its actual performance curve (i.e., the relationship between Cy and Re at different angles of attack) intersects this function curve or passes through its very nearest neighborhood.

[0062] In subsequent filtering steps, the performance data points of a specific airfoil profile in the airfoil performance database are examined to see if its Reynolds number falls within a certain tolerance range (e.g., ±3%) of the predicted value of the target performance relationship. This tolerance judgment based on continuous functional relationships greatly improves the probability and quality of finding a suitable airfoil profile compared to finding a single exact matching point.

[0063] In an optional embodiment, the geometric constraints are differential constraints applied based on the position type of the radial position;

[0064] Specifically, when the location type is a leaf tip region, the first geometric constraint is applied; when the location type is a non-leaf tip region, the second geometric constraint is applied.

[0065] Optionally, the first geometric constraint includes a limitation on the airfoil profile thickness; the second geometric constraint includes a limitation on the continuity of the location of maximum airfoil profile thickness or maximum camber relative to adjacent radial locations.

[0066] In the actual engineering design of wind turbine blades, the physical environment and stress conditions at different radial positions of the blades vary greatly, thus requiring different geometric shapes. A complete three-dimensional fan blade can be considered as multiple two-dimensional airfoil profiles (stacked layers) stacked radially. In this embodiment, based on the location type of the stacked layers, it is divided into at least two types of regions: the tip region and the non-tip region.

[0067] The blade tip region refers to the outermost edge of the blade. This region is characterized by the highest linear velocity, the most complex aerodynamic loads, and is the main source of tip vortices and aerodynamic noise. Therefore, the airfoil profile geometry in this region has special requirements.

[0068] In this embodiment, a first geometric constraint is applied when processing the stacked layers located in the blade tip region. This constraint primarily focuses on strictly limiting the thickness of the airfoil profile. For example, a constraint can be set requiring that the maximum thickness of the candidate airfoil profile must be within a specific percentage range [t1%, t2%] of the airfoil length. In a specific, non-limiting example, this range can be set to [8%, 12%]. Constraining the thickness can prevent shape drag and potential airflow separation and noise caused by excessive thickness. Constraining the thickness can also prevent blade deformation or even damage due to excessive thinness. Therefore, by applying the first geometric constraint, it can be ensured that the selected airfoil profile meets aerodynamic performance requirements while also taking into account the structural strength and noise control requirements of the blade tip region.

[0069] The non-tip region includes the root area near the hub and the middle section of the blade. For these areas, in addition to basic strength requirements, a more important design consideration is to ensure the smoothness and continuity of the entire blade profile. Abrupt changes in the three-dimensional profile will lead to uneven airflow transitions on the blade surface, potentially causing localized flow separation, reducing efficiency, and also creating difficulties in mold manufacturing and processing.

[0070] Therefore, when dealing with stacked layers located in non-tip regions, a second geometric constraint, different from the first geometric constraint, is applied. This constraint primarily focuses on ensuring the continuity of airfoil geometry between adjacent radial locations. Specifically, the second geometric constraint may include restrictions on the location of maximum thickness or maximum camber of the airfoil profile.

[0071] For example, when selecting an airfoil profile for the k-th stack (located in the non-tip region), the following constraint can be applied: the coordinate difference between the maximum thickness location of the candidate airfoil profile and the maximum thickness location of the airfoil profile of the (k-1)-th stack that has already been designed must be within a preset tolerance range (e.g., less than 2% of the airfoil length). Similarly, a similar continuity constraint can be applied to the location of maximum camber.

[0072] By applying this second geometric constraint, it can be ensured that the key geometric feature points of the airfoil (such as the point of maximum thickness) can form a smooth spatial curve from the blade root to the blade tip, thereby ensuring the surface continuity of the final generated fan blade shape.

[0073] In an optional embodiment, the method further includes:

[0074] When multiple airfoil profiles that simultaneously satisfy the target performance relationship and geometric constraints are identified, the multiple airfoil profiles are sorted based on the third performance index.

[0075] The airfoil profile with the best ranking result is selected as the final design.

[0076] In some cases, filtering from an airfoil performance database may identify multiple candidate airfoil profiles that meet the criteria. In such cases, an additional decision-making mechanism is needed to select the optimal airfoil profile from these candidates for the final design. This mechanism introduces a third performance index, which most directly reflects the aerodynamic efficiency of the airfoil. Optionally, this third performance index is the lift-to-drag ratio, the ratio of lift coefficient to drag coefficient, which is the most core and universally applicable indicator for measuring airfoil aerodynamic efficiency. Under the premise of generating the same lift, an airfoil profile with a higher lift-to-drag ratio has lower drag and less energy loss, thus resulting in higher overall wind turbine efficiency.

[0077] The process of ranking multiple candidate airfoil profiles by lift-to-drag ratio is as follows:

[0078] For each candidate airfoil profile, determine all its performance data points in the airfoil performance database, and identify the data points that satisfy the target performance relationship as applicable points. For each applicable point, extract its corresponding lift-to-drag ratio value, and record the maximum lift-to-drag ratio value that appears among all applicable points of the candidate airfoil profile.

[0079] Finally, based on the maximum lift-to-drag ratio recorded for all candidate airfoil profiles, they are sorted in descending order, and the airfoil profile with the best sorting result is selected as the final design.

[0080] By introducing a sorting and optimization selection mechanism based on lift-to-drag ratio, it is ensured that the design result with the best performance can be selected from all candidate airfoil profiles that meet the basic design requirements.

[0081] In an optional embodiment, the method further includes:

[0082] Connect the optimal designs at each radial position to generate the fan blade shape.

[0083] In this embodiment, an optimal two-dimensional airfoil profile is selected for all radial positions (e.g., stacked layers from 1 to 5). The geometric data of these airfoil profiles (typically a series of coordinate points) are then passed to graphic design software, such as a CAD application. In the CAD environment, these airfoil profiles are precisely arranged in three-dimensional space according to their respective radial positions and mounting angles (the mounting angles are also parameters determined during aerodynamic calculations).

[0084] Finally, by calling standard and mature surface modeling functions in CAD software, such as "Loft" or "Smooth Blend," this series of orderly arranged airfoil sections are smoothly connected, automatically generating a complete, smooth, and complex 3D fan blade solid model. This digital model can be directly used for subsequent more detailed structural strength analysis (such as finite element analysis, FEA), manufacturability analysis, or directly output as manufacturing instructions (such as STL files for 3D printing or toolpaths for CNC machining).

[0085] This disclosure provides an example of applying the above-described axial flow fan blade design method. In this example, the target air volume of the fan is 2000 m³ / h, the static pressure at the operating point is 60 Pa, the rotational speed is 1400 RPM, and the total pressure efficiency is approximately 35%. The impeller diameter is 350 μm; the hub ratio is 0.35; the axial velocity of the airflow at the average radius is 6.6 m / s; the number of custom blades is 5; and the number of stacked layers is 5.

[0086] After calculation, the initial working parameters of the stacked layers 1-5 were obtained, with angles of attack of 4°, 0.5°, 2°, 2°, and 2°, respectively; and lift coefficients of 0.9, 0.7, 0.7, 0.9, and 0.8, respectively.

[0087] Then, from layer 1 to layer 5, the airfoil profiles with the highest efficiency (lift-to-drag ratio) under the corresponding initial operating parameters (angle of attack, lift coefficient, Reynolds number) and their corresponding actual operating parameters are sequentially selected from the airfoil performance database. In this example, the airfoil names are Althaus AH7.476, E62 (5.62%), BE50 (onginal)-1, HE82R1-6, and BE50 (smoothed)-1. Simultaneously, the actual operating parameters for layers 1-5 are obtained: angles of attack of 4°, 0.5°, 2°, 2°, and 2°; and lift coefficients of 0.9, 0.7, 0.7, 0.9, and 0.8, respectively. Finally, the curves of the corresponding airfoils in the airfoil performance database are smoothly connected to generate fan blade profile 401, where... Figure 4 This is a schematic diagram of the final generated fan blade shape provided in this disclosure.

[0088] Figure 5 This is a structural schematic diagram of the axial flow fan blade design system provided in this disclosure. Figure 5 As shown, the system includes:

[0089] The parameter acquisition module 501 is used to acquire the target parameters that limit the overall performance and structure of the axial flow fan.

[0090] The data processing module 502 is used to calculate and generate the corresponding target performance relationship based on the target parameters for at least one radial position of the fan blade;

[0091] The result generation module 503 is used to filter the airfoil performance database containing multiple airfoil profiles based on the target performance relationship and geometric constraints, so as to identify one or more airfoil profiles that simultaneously satisfy the target performance relationship and geometric constraints.

[0092] Among them, the target performance relationship characterizes the relationship between at least two aerodynamic parameters.

[0093] In this embodiment, target parameters defining the overall performance and structure of the axial flow fan are first obtained. Based on these target parameters, corresponding target performance relationships are calculated for at least one radial position of the fan blade, characterizing the interrelationship between at least two aerodynamic parameters. Using these target performance relationships as performance constraints, along with preset geometric constraints, the airfoil performance database is filtered to select airfoil profiles that meet both performance and geometric design requirements. By connecting airfoil profiles at different radial positions, a complete fan blade profile is generated. This method transforms the performance indicators used for verification in traditional processes into pre-emptive constraints for selection, avoiding repeated performance simulations or physical tests for each candidate geometric scheme. This significantly shortens the development cycle of the fan blade and reduces R&D costs.

[0094] The axial flow fan blade design system provided in this application embodiment can achieve... Figures 1 to 4 The various processes implemented in the method embodiments shown will not be described again here to avoid repetition.

[0095] The axial flow fan blade design system of this application embodiment can execute the axial flow fan blade design method provided in this application embodiment. The implementation principle is similar. The actions performed by each module and unit in the axial flow fan blade design system in each embodiment of this application correspond to the steps in the axial flow fan blade design method in each embodiment of this application. For detailed functional descriptions of each module of the axial flow fan blade design system, please refer to the descriptions in the corresponding axial flow fan blade design methods mentioned above. They will not be repeated here.

[0096] Figure 6This is a schematic diagram of the structure of the computing device provided in this disclosure. In some examples, the computing device 60 can be at least one of devices such as a smartphone, smartwatch, desktop computer, laptop, virtual reality terminal, augmented reality terminal, wireless terminal, and laptop computer. The computing device 60 has communication functions and can access a wired or wireless network. The computing device 60 can refer to one of multiple terminals, and those skilled in the art will understand that the number of such terminals can be more or less. In some examples, the computing device 60 can receive target parameters that define the overall performance and structure of the axial flow fan based on the accessed wired or wireless network. It is understood that the computing device 60 undertakes the calculation and processing work of the technical solution of this disclosure, and this disclosure does not limit it in this regard.

[0097] like Figure 6 As shown, the computing device in this disclosure may include one or more components such as a processor 610 and a memory 620.

[0098] Optionally, the processor 610 connects various parts within the computing device using various interfaces and lines, and performs various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 620, and by calling data stored in the memory 620. Optionally, the processor 610 can be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 610 can integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), Neural-network Processing Unit (NPU), and baseband chip. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required to be displayed on the touch screen; the NPU is used to implement Artificial Intelligence (AI) functions; and the baseband chip is used to handle wireless communication. It is understandable that the aforementioned baseband chip may not be integrated into the processor 610, but may be implemented using a separate chip.

[0099] The memory 620 may include random access memory (RAM) or read-only memory (ROM). Optionally, the memory 620 may include a non-transitory computer-readable storage medium. The memory 620 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 620 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the various method embodiments described above, etc.; the data storage area may store data created according to the use of the computing device, etc.

[0100] In addition, those skilled in the art will understand that the structure of the computing device shown in the above figures does not constitute a limitation on the computing device. The computing device may include more or fewer components than shown, or combine certain components, or have different component arrangements. For example, the computing device may also include a display screen, camera assembly, microphone, speaker, radio frequency circuit, input unit, sensors (such as accelerometer, angular velocity sensor, fiber optic sensor, etc.), audio circuit, WiFi module, power supply, Bluetooth module, etc., which will not be described in detail here.

[0101] This disclosure also provides a computer-readable storage medium storing at least one instruction that is executed by a processor to implement the axial fan blade design method as described in the above embodiments.

[0102] This disclosure also provides a computer program product including computer instructions stored in a computer-readable storage medium; a processor of a computing device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computing device to perform the axial flow fan blade design method described in the above embodiments.

[0103] Those skilled in the art will recognize that the functions described in this disclosure in one or more of the examples above can be implemented using hardware, software, firmware, or any combination thereof. When implemented in software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any medium that facilitates the transfer of a computer program from one place to another. Storage media can be any available medium accessible to a general-purpose or special-purpose computer.

[0104] It should be noted that the technical solutions described in this disclosure can be combined arbitrarily as long as they do not conflict.

[0105] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for designing axial flow fan blades, characterized in that, The method includes: Obtain the target parameters for the overall performance and structure of the axial flow fan; Based on the target parameters, a corresponding target performance relationship is calculated and generated for at least one radial position of the fan blade; Based on the target performance relationship and the preset geometric constraints, the airfoil performance database containing multiple airfoil profiles is filtered to identify one or more airfoil profiles that simultaneously satisfy the target performance relationship and the geometric constraints. The target performance relationship characterizes the relationship between at least two aerodynamic parameters; The step of calculating and generating the corresponding target performance relationship based on the target parameters for at least one radial position of the fan blade includes: Based on the target parameters, multiple discrete data points are calculated; each data point corresponds to a set of values ​​for the aerodynamic parameters. A preset fitting method is used to fit the multiple discrete data points to generate a continuous function characterizing the target performance relationship; The airfoil profile that simultaneously satisfies the target performance relationship and the geometric constraints means that if the actual performance curve of the airfoil profile used to characterize the relationship between the lift coefficient and the Reynolds number at different angles of attack intersects with the function curve corresponding to the continuous function or passes through a very close neighborhood, then the airfoil profile is considered to satisfy the target performance relationship and the geometric constraints.

2. The axial flow fan blade design method according to claim 1, characterized in that, The geometric constraints are differential constraints applied based on the position type of the radial position; Specifically, when the location type is a leaf tip region, a first geometric constraint is applied; when the location type is a non-leaf tip region, a second geometric constraint is applied.

3. The axial flow fan blade design method according to claim 2, characterized in that, The first geometric constraint includes a limitation on the airfoil profile thickness; The second geometric constraint includes restrictions on the continuity of the location of maximum thickness or maximum camber of the airfoil profile relative to adjacent radial locations.

4. The axial flow fan blade design method according to claim 1, characterized in that, The aerodynamic parameters include lift coefficient and Reynolds number.

5. The axial flow fan blade design method according to claim 1, characterized in that, The method further includes: In the case of identifying multiple airfoil profiles that simultaneously satisfy the target performance relationship and the geometric constraints, the multiple airfoil profiles are sorted based on the third performance index. The airfoil profile with the best ranking result is selected as the final design.

6. The axial flow fan blade design method according to claim 5, characterized in that, The third performance indicator is the rise-to-drag ratio.

7. An axial flow fan blade design system, characterized in that, The system includes: The parameter acquisition module is used to acquire the target parameters that limit the overall performance and structure of the axial flow fan. The data processing module is used to calculate and generate the corresponding target performance relationship for at least one radial position of the fan blade based on the target parameters. The result generation module is used to filter the airfoil performance database containing multiple airfoil profiles based on the target performance relationship and geometric constraints, so as to identify one or more airfoil profiles that simultaneously satisfy the target performance relationship and the geometric constraints. The target performance relationship characterizes the relationship between at least two aerodynamic parameters; The step of calculating and generating the corresponding target performance relationship based on the target parameters for at least one radial position of the fan blade includes: Based on the target parameters, multiple discrete data points are calculated; each data point corresponds to a set of values ​​for the aerodynamic parameters. A preset fitting method is used to fit the multiple discrete data points to generate a continuous function characterizing the target performance relationship; The airfoil profile that simultaneously satisfies the target performance relationship and the geometric constraints means that if the actual performance curve of the airfoil profile used to characterize the relationship between the lift coefficient and the Reynolds number at different angles of attack intersects with the function curve corresponding to the continuous function or passes through a very close neighborhood, then the airfoil profile is considered to satisfy the target performance relationship and the geometric constraints.

8. A computer device, characterized in that, It includes a processor and a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the axial flow fan blade design method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, which is executed by a processor to implement the axial fan blade design method as described in any one of claims 1-6.

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