Method and device for determining fan blade parameters of a fan, storage medium and electronic device

Abstract

This application provides a method, apparatus, storage medium, and electronic device for determining the blade parameters of a wind turbine. The method includes: determining the current simulation parameters of the wind turbine based on a simulation model using fluid dynamics; determining the performance evaluation function of the wind turbine based on the wind turbine's airflow and blade torque, and determining the noise evaluation function of the wind turbine based on vorticity and velocity vector; automatically optimizing the wind turbine's blade parameters using a multi-objective optimization algorithm based on the performance evaluation function and the noise evaluation function to obtain the target blade parameters; and manufacturing the wind turbine blades using the target blade parameters to achieve a noise level lower than a preset noise level, an airflow greater than a preset airflow, and a power level lower than a preset power. This method optimizes the wind turbine blades by simultaneously considering both airflow and power through a single performance objective function, and uses both the noise function and the performance function as objective functions. Through multi-objective optimization, the wind turbine blade parameters are optimized, simultaneously improving both the noise and performance of the wind turbine blades.

Description

Technical Field

[0001] This application relates to the field of wind turbine technology, and more specifically, to a method for determining wind turbine blade parameters, a device for determining wind turbine blade parameters, a computer-readable storage medium, and an electronic device. Background Technology

[0002] Air conditioners are common household appliances, and axial fans are the main components of outdoor air conditioner units for heat dissipation and air delivery. Currently, mainstream fan blade design methods are mainly based on traditional mechanical engineering, relying on the experience of designers. This makes it difficult to simultaneously achieve optimal performance in terms of air volume, power, and noise, and to achieve the best balance between performance and noise. Summary of the Invention

[0003] The main objective of this application is to provide a method for determining the blade parameters of a wind turbine, a device for determining the blade parameters of a wind turbine, a computer-readable storage medium, and an electronic device, so as to at least solve the problem in the prior art that wind turbine blades rely on manual design and it is difficult to achieve the best balance between performance and noise.

[0004] To achieve the above objectives, according to one aspect of this application, a method for determining the blade parameters of a fan is provided, comprising: constructing a fan simulation model; determining current simulation parameters of the fan based on the fan simulation model using fluid dynamics, wherein the current simulation parameters of the fan include fan airflow, blade torque, fan vorticity, and fan velocity vector, and the fan airflow is the airflow at the fan outlet; determining a performance evaluation function of the fan based on the fan airflow and blade torque, and determining a noise evaluation function of the fan based on the vorticity and velocity vector, wherein the performance evaluation function characterizes the fan airflow and power, and the noise evaluation function characterizes the fan noise; employing a multi-objective optimization algorithm to automatically optimize the fan blade parameters based on the performance evaluation function and the noise evaluation function to obtain target fan blade parameters, wherein the fan blades manufactured using the target fan blade parameters result in the fan noise being less than a preset noise level, the airflow being greater than a preset airflow, and the power being less than a preset power.

[0005] Optionally, determining the performance evaluation function of the fan based on the fan air volume and the blade torque includes: obtaining the fan speed; calculating the shaft power of the fan based on the fan speed and the blade torque to obtain the fan power; and determining the ratio of the fan air volume to the fan power as the performance evaluation function.

[0006] Optionally, calculating the shaft power of the fan based on the fan speed and the blade torque to obtain the fan power includes: determining the work done by the blades based on pi, the fan speed, and the blade torque, wherein the work done by the blades is the work done by the fan blades rotating once per unit time, and the work done by the blades is the product of a first coefficient, pi, the fan speed, and the blade torque; determining the ratio of the work done by the blades to the unit time as the calculated power, and determining the ratio of the calculated power to a second coefficient as the fan power, wherein the second coefficient is a unit conversion factor for converting the power unit from watts to kilowatts.

[0007] Optionally, determining the noise evaluation function of the fan based on the vorticity and the velocity vector includes: determining the rotation domain of the fan simulation model, wherein the rotation domain is a region including the fan blades and the fluid rotating together with the blades; determining the vortex source term of the fan's vortex sound function based on the vorticity and the velocity vector; and determining the noise evaluation function by integrating the absolute value of the vortex source term over the rotation domain.

[0008] Optionally, determining the vortex source term of the vortex acoustic function of the wind turbine based on the vorticity and the velocity vector includes: extracting the components of the vorticity in a first direction, a second direction, and a third direction based on the wind turbine simulation model to obtain a first vorticity component, a second vorticity component, and a third vorticity component, where the first direction, the second direction, and the third direction are the positive directions of the three coordinate systems in a spatial rectangular coordinate system; extracting the components of the velocity vector in the first direction, the second direction, and the third direction based on the wind turbine simulation model to obtain a first velocity component, a second velocity component, and a third velocity component; calculating the cross product of the vorticity and the velocity vector based on the first vorticity component, the second vorticity component, the third vorticity component, the first velocity component, the second velocity component, and the third velocity component, and calculating the divergence of the cross product to obtain the vortex source term.

[0009] Optionally, a multi-objective optimization algorithm is used to automatically optimize the blade parameters of the wind turbine based on the performance evaluation function and the noise evaluation function to obtain the target blade parameters of the wind turbine. This includes: determining the performance evaluation function and the noise evaluation function as the first objective function and the second objective function of the multi-objective optimization algorithm, respectively; taking the maximization of the first objective function and the minimization of the second objective function as the optimization objective, and using the multi-objective optimization algorithm to automatically optimize the blade parameters of the wind turbine to obtain the target blade parameters. The target blade parameters include at least the chord length, installation angle, bend angle, and sweep angle of the blade.

[0010] Optionally, constructing a wind turbine simulation model includes: constructing a simplified geometric model containing a wind turbine guide ring and axial flow blades, wherein the cross-section of the simplified geometric model is circular, and there are three axial flow blades in the simplified geometric model; cutting off one-third of the simplified geometric model along the circumferential direction of the cross-section of the simplified geometric model to obtain a computational domain, and defining the computational domain as the wind turbine simulation model, wherein the interface of the computational domain is a sector with a preset angle, the computational domain includes one of the axial flow blades, and the computational domain includes a rotational domain and a stationary domain, wherein the rotational domain is the region including the axial flow blades and in which the fluid rotates together with the axial flow blades, and the stationary domain is all regions in the computational domain except for the rotational domain.

[0011] According to another aspect of this application, a device for determining the blade parameters of a fan is provided, comprising: a construction unit for constructing a fan simulation model; a first determination unit for determining current simulation parameters of the fan based on fluid dynamics according to the fan simulation model, the current simulation parameters of the fan including fan airflow, blade torque, fan vorticity, and fan velocity vector, wherein the fan airflow is the airflow at the fan outlet; a second determination unit for determining a performance evaluation function of the fan based on the fan airflow and blade torque, and determining a noise evaluation function of the fan based on the vorticity and velocity vector, wherein the performance evaluation function characterizes the fan airflow and power, and the noise evaluation function characterizes the fan noise; and an optimization unit for automatically optimizing the fan blade parameters of the fan using a multi-objective optimization algorithm based on the performance evaluation function and the noise evaluation function to obtain target fan blade parameters, wherein the fan blades manufactured using the target fan blade parameters result in the fan noise being less than a preset noise, the airflow being greater than a preset airflow, and the power being less than a preset power.

[0012] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform any of the methods for determining the blade parameters of the wind turbine.

[0013] According to another aspect of this application, an air conditioner is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including a method for performing a method for determining the blade parameters of any of the described fans.

[0014] Applying the technical solution of this application, the method for determining the blade parameters of the aforementioned wind turbine first constructs a wind turbine simulation model; based on fluid dynamics, the current simulation parameters of the wind turbine are determined according to the simulation model; then, the performance evaluation function of the wind turbine is determined based on the wind turbine airflow and blade torque, and the noise evaluation function of the wind turbine is determined based on vorticity and velocity vector; finally, a multi-objective optimization algorithm is used to automatically optimize the blade parameters of the wind turbine based on the performance evaluation function and the noise evaluation function to obtain the target blade parameters of the wind turbine. The wind turbine blades manufactured using the target blade parameters result in wind turbine noise being lower than the preset noise, airflow being higher than the preset airflow, and power being lower than the preset power. This method optimizes the wind turbine blades by simultaneously considering the airflow and power of the blades through a single performance objective function, and the noise function and performance function can be used as objective functions. By optimizing the blade parameters through multi-objective optimization, the method simultaneously optimizes the wind turbine blade noise and performance, solving the problem in the prior art where wind turbine blades rely on manual design, making it difficult to achieve the optimal balance between performance and noise. Attached Figure Description

[0015] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0016] Figure 1 A hardware structure block diagram of a mobile terminal for performing a method for determining the blade parameters of a wind turbine, according to an embodiment of this application, is shown.

[0017] Figure 2 A flowchart illustrating a method for determining the blade parameters of a fan according to an embodiment of this application is shown.

[0018] Figure 3 A schematic diagram of a wind turbine simulation model provided according to an embodiment of this application is shown;

[0019] Figure 4 A schematic diagram of the rotation domain of a wind turbine simulation model provided according to an embodiment of this application is shown;

[0020] Figure 5 The diagram shows the absolute value cloud map of the vortex sound source of a fan blade according to an embodiment of this application;

[0021] Figure 6 A flowchart illustrating another method for determining the blade parameters of a fan according to an embodiment of this application is shown.

[0022] Figure 7 A structural block diagram of a device for determining the blade parameters of a fan according to an embodiment of this application is shown.

[0023] The above figures include the following reference numerals:

[0024] 102. Processor; 104. Memory; 106. Transmission device; 108. Input / output device. Detailed Implementation

[0025] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0026] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] As described in the background section, in existing wind turbine systems, the motor drives the blades to rotate at high speed, converting the mechanical energy of the rotating shaft into the pressure and kinetic energy of the air, thereby accelerating heat dissipation. Currently, mainstream wind turbine blade design methods rely primarily on traditional mechanical engineering, using computational fluid dynamics (CFD) and 3D prototyping assistance, which heavily depends on the designer's experience, resulting in low optimization efficiency. To address the problem of existing wind turbine blades relying on manual design and struggling to achieve the optimal balance between performance and noise, embodiments of this application provide a method, apparatus, storage medium, and electronic device for determining wind turbine blade parameters.

[0029] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0030] The methods and embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a method of determining the blade parameters of a wind turbine according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0031] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the method for determining the wind turbine blade parameters in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0032] This embodiment provides a method for determining the blade parameters of a fan that runs on a mobile terminal, computer terminal, or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than that shown here.

[0033] Figure 2 This is a flowchart illustrating a method for determining the blade parameters of a fan according to an embodiment of this application. Figure 2 As shown, the method includes the following steps:

[0034] Step S201: Construct a wind turbine simulation model;

[0035] Specifically, a simplified geometric model is established based on the outdoor unit model of the air conditioner, and a simulation model is built using the simulation software Fluent / CFX. Constructing a fan simulation model is a complex process involving multiple steps, primarily applied in the field of computational fluid dynamics (CFD). Its purpose is to predict and optimize the aerodynamic characteristics of the fan in a virtual environment, including airflow, air pressure, efficiency, and noise.

[0036] First, an accurate geometric model of the wind turbine needs to be established. This typically involves using CAD software (such as SolidWorks, Pro / E, AutoCAD, etc.) to create 3D models of the main components of the wind turbine, including the blades, shroud, motor, and support frame. For rotating machinery like wind turbines, the geometric accuracy of the model directly affects the accuracy of fluid dynamics analysis, especially the shape and size of the blades. A balance needs to be struck between model complexity and simulation speed during modeling. For complex wind turbine structures, appropriate simplification can be adopted, such as retaining only the key geometric features of the blades and shroud while ensuring the accuracy of details in critical areas. Building an accurate wind turbine simulation model can be used to predict and optimize wind turbine performance.

[0037] Step S202: Based on fluid dynamics, determine the current simulation parameters of the fan according to the above-mentioned fan simulation model. The current simulation parameters of the fan include the fan air volume, blade torque, fan vorticity and fan velocity vector. The fan air volume is the air volume at the fan outlet.

[0038] Specifically, fluid dynamics is the study of the behavior and dynamic properties of fluids (liquids and gases). It is based on a set of fundamental equations, such as the continuity equation, momentum equation, and energy equation, which describe the flow state and dynamic behavior of fluids. In CFD simulations, these equations are solved numerically to predict the behavior of fluids under specific boundary conditions.

[0039] A wind turbine simulation model is a digital model built using CFD software. It includes the wind turbine's geometry, material properties, operating conditions (such as inlet velocity and outlet pressure), and other physical phenomena (such as heat exchange and frictional losses). This model aims to simulate the actual operation of a wind turbine and predict its performance through calculations. Fluid dynamics-based wind turbine simulation models provide in-depth insights into wind turbine performance. By quantifying parameters such as airflow, blade torque, vorticity, and velocity vectors, wind turbine design can be evaluated and optimized.

[0040] Step S203: Determine the performance evaluation function of the fan based on the fan air volume and the blade torque, and determine the noise evaluation function of the fan based on the vorticity and the velocity vector. The performance evaluation function characterizes the air volume and power of the fan, and the noise evaluation function characterizes the noise level of the fan.

[0041] Specifically, the performance evaluation function for wind turbines is typically used to comprehensively consider both airflow capacity and power efficiency. In CFD simulations, a higher function value means the turbine can generate a larger airflow while consuming the same amount of power, or save more electricity while generating the same airflow, thus demonstrating higher performance. The noise evaluation function is used to quantify and predict the noise level generated by the operation of the wind turbine. In CFD simulations, noise generation is often related to turbulence, vortices, and fluid dynamic fluctuations in the fluid.

[0042] By using performance evaluation functions and noise evaluation functions, quantitative information about wind turbine performance and noise can be obtained, which can then be used in the optimization process of wind turbine design to achieve higher efficiency and lower noise.

[0043] Step S204: Using a multi-objective optimization algorithm, the fan blade parameters of the fan are automatically optimized based on the above performance evaluation function and the above noise evaluation function to obtain the target fan blade parameters. The fan blades made using the above target fan blade parameters make the noise of the fan less than the preset noise, the air volume greater than the preset air volume, and the power less than the preset power.

[0044] Specifically, multi-objective optimization algorithms are mathematical methods that are highly effective in handling optimization problems with multiple conflicting objectives. In the context of wind turbine design, objectives typically include increasing airflow, reducing power consumption, and reducing noise. These objectives are often contradictory—increasing airflow may increase power consumption, while reducing noise may require changing the shape of the fan blades, thus affecting both airflow and efficiency.

[0045] In multi-objective optimization, performance evaluation functions and noise evaluation functions are used as objective functions to quantify the design performance of wind turbines. Multi-objective optimization algorithms can automatically explore the optimal combination of wind turbine blade parameters, which may include chord length, installation angle, bend angle, sweep angle, etc. The algorithm's goal is to find a set of blade parameters that maximizes the performance evaluation function (meaning high airflow and low power) while minimizing the noise evaluation function (meaning low noise).

[0046] Once the target fan blade parameters are identified—that is, the design parameters that ensure the fan noise is lower than the preset noise level, airflow is higher than the preset airflow, and power is lower than the preset power—the next step is to apply these parameters to the actual manufacturing of the fan blades. These optimized parameters can be used to guide the geometric design of the fan blades. The optimized fan blades can then be manufactured using 3D printing or traditional machining techniques, installed, and their performance tested in an actual fan system to verify whether the optimization goals have been achieved.

[0047] Through multi-objective optimization, wind turbine design can be more scientific and efficient, ensuring that specific performance requirements are met while reducing noise pollution to the environment, improving energy efficiency, and ultimately achieving harmonious coexistence between technology and the environment.

[0048] The method for determining the blade parameters of the aforementioned wind turbine in this application first constructs a wind turbine simulation model; based on fluid dynamics, the current simulation parameters of the wind turbine are determined according to the simulation model; then, the performance evaluation function of the wind turbine is determined based on the wind turbine airflow and blade torque, and the noise evaluation function of the wind turbine is determined based on vorticity and velocity vector; finally, a multi-objective optimization algorithm is used to automatically optimize the wind turbine blade parameters based on the performance evaluation function and the noise evaluation function to obtain the target wind turbine blade parameters. The wind turbine blades manufactured using the target wind turbine blade parameters achieve a noise level lower than the preset noise level, an airflow greater than the preset airflow, and a power level lower than the preset power. This method optimizes the wind turbine blades by simultaneously considering the airflow and power of the blades through a single performance objective function, and the noise function and performance function can be used as objective functions. By optimizing the wind turbine blade parameters through multi-objective optimization, it simultaneously optimizes the wind turbine blade noise and performance, solving the problem in the prior art where wind turbine blades rely on manual design, making it difficult to achieve the optimal balance between performance and noise.

[0049] The above embodiments, combining multi-objective optimization with CFD algorithm-based optimization, can compensate for the lack of experience among designers. To optimize and obtain wind turbine blades with superior performance and low noise, a suitable comprehensive performance and noise evaluation method needs to be proposed. For a wind turbine blade, designers aim to achieve high airflow while consuming low power; therefore, the ratio of airflow to power is proposed as the performance objective function. Currently, when analyzing wind turbine blade noise through simulation, qualitative analysis is generally performed using vorticity contour maps or turbulent kinetic energy contour maps. Solving for noise through simulation requires solving the transient flow field with a fine mesh, which is time-consuming. Given that multi-objective optimization often involves thousands of simulations, directly calculating noise is clearly impractical. Therefore, based on the vortex acoustics equation, the absolute value integral of the vortex source is proposed as the noise objective function.

[0050] The construction of the wind turbine simulation model includes the following steps:

[0051] Step S2011: Construct a simplified geometric model that includes the fan's guide ring and axial flow blades. The cross-section of the simplified geometric model is circular, and there are three axial flow blades in the simplified geometric model.

[0052] The simplified geometric model has a circular cross-section to better simulate the aerodynamic characteristics of actual wind turbines while reducing model complexity. Three axial fan blades are designed, reflecting common axial fan configurations in practice and allowing the model to maintain a certain level of accuracy while reducing computational load.

[0053] The simplified geometric model is constructed based on the axisymmetric nature of axial flow fans. Most axial flow fans are designed to be axisymmetric, meaning their geometric features and hydrodynamic behavior are identical in any direction about the axis of rotation. Therefore, by simulating only a portion of the fan, computational resources can be significantly saved, and the solution speed is also accelerated due to the model simplification.

[0054] Step S2012: Along the circumferential direction of the cross section of the simplified geometric model, one-third of the simplified geometric model is cut off to obtain the computational domain, and the computational domain is determined as the fan simulation model. The interface of the computational domain is a fan shape with a preset angle. The computational domain includes one axial flow fan blade. The computational domain includes a rotational domain and a stationary domain. The rotational domain is the region including the axial flow fan blade and the fluid rotates together with the axial flow fan blade. The stationary domain is all regions in the computational domain except for the rotational domain.

[0055] By truncating one-third of the simplified geometric model along its cross-section (i.e., the circumferential direction) as the computational domain, this sector-truncation strategy leverages the axisymmetric nature of axial fans. Only one-third of the model needs to be simulated to represent the complete behavior of the fan, further reducing computational resource requirements.

[0056] The computational domain is clearly divided into the rotating domain and the stationary domain. The rotating domain refers to the region containing the axial flow fan blades. In CFD simulations, the fluid in this region rotates along with the fan blades, and it is usually simulated using a rotating coordinate system or a multiple reference frame (MRF) method. The stationary domain is all regions in the computational domain other than the rotating domain. Here, the fluid is relatively stationary or moves at different speeds, and it is usually simulated using a stationary coordinate system.

[0057] Specifically, by simplifying the model and truncating the computational domain, the computational load is significantly reduced, allowing CFD simulations to run faster while lowering the demand for computational resources (such as memory and CPU time). This reduced demand for computational resources translates to lower costs, both in terms of computational time and hardware resource costs. Despite the model simplification, the axisymmetric nature of the axial fan does not affect the accuracy of the simulation results. By simulating a fan-shaped region, similar hydrodynamic behavior to a complete circular model can be obtained. The simplified model is also applicable to the calculation of noise and performance evaluation functions, meaning designers can efficiently perform comprehensive noise and performance analysis without sacrificing computational comprehensiveness. Constructing a simplified model and using it for CFD simulations provides a foundation for multi-objective optimization algorithms, making it possible to find the optimal trade-off between airflow, power consumption, and noise with limited computational resources.

[0058] In summary, by constructing a simplified geometric model that includes a guide ring and axial flow blades, and taking one-third of it as the computational domain, we can not only greatly improve computational efficiency and reduce costs, but also maintain the accuracy of simulation results, providing strong support for the optimization of wind turbine design.

[0059] In some embodiments, taking a 3-bladed axial flow fan with a diameter of 550mm as an example and a rotational speed of 800rpm as an example, CFX is used as the simulation software, and the specific scheme is as follows:

[0060] The outdoor unit model of the air conditioner is simplified, and a simplified geometric model including a guide ring and axial fan blades is established. Considering that the fan blades are a 3-blade axisymmetric model, to improve computational efficiency, 1 / 3 of the geometric model (i.e., 1 / 3 of the cylinder) is selected as the computational domain. Figure 3 As shown, the computational domain is divided into a rotational domain and a stationary domain. The rotational domain is as follows: Figure 4 As shown, the static region is Figure 3 The region outside the rotation domain. After meshing, boundary conditions are set in CFX, with the inlet and outlet set as pressure inlet and pressure outlet, and the blade surface set as a no-slip boundary. The rotation domain rotates at a speed of 800 rpm. Finally, the MRF (Multiple Reference Frames) solution method is used to establish a simulation model.

[0061] In some embodiments, the performance evaluation function of the fan is determined based on the fan air volume and the blade torque, including the following steps:

[0062] Step S301: Obtain the fan speed;

[0063] Obtaining the fan speed is a prerequisite for calculating shaft power. Fan speed, measured in revolutions per minute (rpm), is a key parameter of fan performance. Understanding the speed is crucial for accurately estimating the fan's performance under specific operating conditions, as it directly affects the fan's output energy and hydrodynamic performance.

[0064] Step S302: Calculate the shaft power of the fan based on the fan speed and blade torque to obtain the fan power.

[0065] Step S303: The ratio of the above-mentioned fan air volume to the above-mentioned fan power is determined as the above-mentioned performance evaluation function.

[0066] The effect of step S303 is to provide a unified index to measure the efficiency of the fan in meeting air supply requirements. A higher airflow-to-power ratio indicates that the fan can generate more airflow with less energy consumption, thus indicating better fan performance. This evaluation method can seek ways to reduce power consumption while ensuring sufficient airflow, thereby achieving the goal of energy conservation and environmental protection.

[0067] Specifically, steps S302 and S303 quantify the efficiency of the fan, i.e., the air volume achievable with a given amount of electricity. This is crucial for fan design and selection, as it directly relates to the fan's energy efficiency ratio. The above steps provide a method for evaluating fan performance during the design phase, avoiding unnecessary testing and modifications later, thus saving time and resources. Prioritizing the air volume to power ratio effectively encourages designers to develop more energy-efficient fans, which is beneficial for promoting green buildings, reducing energy consumption, and lowering carbon emissions. The determined performance evaluation function, together with the subsequent noise evaluation function, constructs the framework of a multi-objective optimization algorithm, allowing designers to consider both fan noise and air delivery performance and energy efficiency.

[0068] In some embodiments, the shaft power of the fan is calculated based on the fan speed and blade torque to obtain the fan power, including the following steps:

[0069] Step S3021: Determine the work done by the blades based on pi, the aforementioned fan speed and the aforementioned blade torque. The work done by the blades is the work done by the blades of the aforementioned fan rotating once per unit time. The work done by the blades is the product of the first coefficient, the aforementioned pi, the aforementioned fan speed and the aforementioned blade torque.

[0070] Step S3022: The ratio of the work done by the blades to the unit time is determined as the calculated power, and the ratio of the calculated power to the second coefficient is determined as the power of the wind turbine. The second coefficient is a unit conversion factor for converting the power unit from watts to kilowatts.

[0071] Specifically, in step S3021, the energy conversion efficiency of the wind turbine is initially quantified by calculating the work done by the blades. Blade work refers to the amount of work completed by the wind turbine blades in one rotation per unit time; it is considered the fundamental form of energy transfer between fluid and mechanical systems. The "first coefficient" mentioned here typically refers to the conversion factor required to convert torque units (N*m) multiplied by speed units (rpm) to power units (kW).

[0072] In step S3022, the ratio of the work done by the blades to the unit time (usually seconds) is determined as the calculated power. Then, the calculated power is divided by the conversion factor (called the "second factor") to obtain the final wind turbine power. Here, the "second factor" is actually a unit conversion factor used to convert the calculation result from watts (W) to kilowatts (KW).

[0073] By combining blade torque, rotational speed, and necessary constants and unit conversion factors, the calculations are directly correlated with the actual working principle of the wind turbine, ensuring accuracy and practicality. The calculated shaft power and turbine power provide designers with tools to evaluate the efficiency and energy consumption of the wind turbine under different load conditions. This is crucial for optimizing wind turbine design, improving its energy efficiency, and reducing costs. In multi-objective optimization design, turbine power, as a key performance indicator, along with airflow and noise objective functions, helps designers find the optimal balance point among a range of design options—that is, ensuring the lowest energy consumption while meeting airflow requirements and noise control.

[0074] In this process, after solving the flow field, the outlet air volume Q of the fan and the torque τ of the blades around the rotation axis are extracted. The shaft power is calculated based on the blade torque, and the ratio of air volume to power is used as the performance evaluation function. The formula for calculating the shaft power is shown in Formula 1, and the formula for calculating the performance evaluation function is shown in Formula 2.

[0075]

[0076] Where τ is the torque of the blade around the rotation axis (N*m), i.e., the blade torque, n is the fan speed (rpm), P is the fan power (kW), and Q is the fan air volume (m³ / h). 3 / s), f1 is the performance evaluation function. 60 represents 60s, and 1000 represents the unit conversion from watts to kilowatts.

[0077] In some embodiments, the noise evaluation function of the wind turbine is determined based on the vorticity and velocity vector described above, including the following steps:

[0078] Step S401: Determine the rotation domain of the above-mentioned wind turbine simulation model. The rotation domain is the region that includes the blades of the wind turbine and in which the fluid rotates together with the blades.

[0079] Step S402: Determine the vortex source term of the vortex sound function of the above-mentioned fan based on the above-mentioned vorticity and velocity vector.

[0080] Step S403: The integral of the absolute value of the above vortex source term in the rotating domain is determined as the above noise evaluation function.

[0081] Vortex acoustics theory is one of the cornerstones of the intersection of fluid dynamics and acoustics. It shows that the interaction between vorticity and velocity vector in rotating flow is one of the main causes of noise generation. When fluid flows over a wind turbine blade, the shape and rotation of the blade create complex vortex structures around it. The motion and interaction of these vortices excite sound waves, known as vortex acoustics.

[0082] In step S401, the rotating domain in the simulation model is first determined, which is the region including the blades and where the fluid rotates together with the blades. This selection is based on the working principle of the fan, as the rotating domain is the primary location where the fan generates hydrodynamics and vortex noise. By focusing on the rotating domain, the noise generation mechanism can be analyzed more directly, while reducing the computational load.

[0083] Step S402 involves determining the vortex source term of the vortex acoustic function based on vorticity and velocity vector. The vortex source term is essentially an operation of taking the divergence of the cross product of vorticity and fluid velocity vector. This operation captures the sound source information induced by the fluid vortex structure and is a core step in quantifying the noise generation mechanism.

[0084] In step S403, the integral of the absolute value of the vortex source term in the rotating domain is used as the noise evaluation function. This is because the positive and negative values ​​of the vortex source term reflect the sound source contributions in different directions, while the absolute value integral comprehensively considers the contributions of all sound sources, regardless of their direction. This method provides a quantitative noise index, facilitating optimization during the design process.

[0085] Specifically, compared to traditional methods that directly solve transient flow and sound fields, this embodiment provides a rapid quantitative noise assessment method through the integration of vortex source terms. This significantly shortens simulation time, especially for multi-iteration or multi-objective optimization design scenarios, improving the efficiency and cost-effectiveness of wind turbine design. By calculating the absolute value of the vortex source terms in the rotating domain, the main noise source regions on the blade surface can be accurately identified. This helps designers pinpoint the problem and optimize the shape, size, or material of the wind turbine blades in a targeted manner to reduce noise. The introduction of a noise evaluation function enhances the ability of multi-objective optimization, enabling designers to consider wind turbine noise control while pursuing high performance. Through comprehensive optimization of multiple objective functions, an ideal balance can be found between performance, energy consumption, and noise. With a noise evaluation function, designers can more systematically explore the impact of different design parameters on noise, thereby guiding the direction and extent of design modifications and avoiding blind experimentation and over-optimization.

[0086] By calculating the vortex source term resulting from the interaction of vorticity and velocity vectors within the rotating domain, and then integrating its absolute value as the noise evaluation function, this embodiment provides an efficient and accurate method for predicting and controlling wind turbine noise. This has significant theoretical and practical value for improving the overall performance of wind turbines, especially reducing noise pollution. Simultaneously, it provides a solid foundation for multi-objective optimization design, promoting the development of wind turbine design towards a more intelligent and environmentally friendly direction.

[0087] In some embodiments, the vortex source term of the vortex sound function of the wind turbine is determined based on the vorticity and the velocity vector, including the following:

[0088] Step S4021: Based on the above wind turbine simulation model, extract the components of the vorticity in the first direction, the second direction and the third direction to obtain the first vorticity component, the second vorticity component and the third vorticity component. The first direction, the second direction and the third direction are the positive directions of the three coordinate systems of the spatial rectangular coordinate system.

[0089] Step S4022: Based on the above wind turbine simulation model, extract the components of the above velocity vector in the first direction, the second direction and the third direction to obtain the first velocity component, the second velocity component and the third velocity component.

[0090] Step S4023: Based on the first vortex component, the second vortex component, the third vortex component, the first velocity component, the second velocity component, and the third velocity component, calculate the cross product of the vortex and the velocity vector, and calculate the divergence of the cross product to obtain the vortex source term.

[0091] Specifically, in CFD simulation, the first step is to extract the components of vorticity and velocity vectors in the three directions (X, Y, Z) of the spatial rectangular coordinate system from the model. This involves a precise description of fluid motion, including the rotational direction of vorticity and the flow direction of velocity vectors. The three components of vorticity correspond to the three degrees of freedom of fluid rotation, while the components of the velocity vector reflect the fluid's flow characteristics in three dimensions. Next, based on the extracted vorticity and velocity vector components, their cross product is calculated. The cross product result is a new vector whose direction is perpendicular to the plane of vorticity and velocity vectors, and its magnitude is related to the intensity of fluid motion in the mutually perpendicular directions. Then, the divergence of the cross product result is calculated. This step essentially calculates the degree of "diffusion" of the cross product result at various points in space. The result reflects the spatiotemporal rate of change of pulsating pressure in the fluid, i.e., the vortex source term, which is proportional to the noise intensity.

[0092] This method enables accurate prediction of noise sources in rotating machinery, such as wind turbines. Compared to traditional methods, this approach eliminates the need for direct solution of the transient sound field, reducing computational costs and time while improving the accuracy and reliability of noise prediction. The calculation of vortex source terms provides designers with direct insights into noise sources during the optimization process. Designers can observe and compare vortex source terms in different designs by adjusting parameters such as blade shape, size, and installation angle, thereby guiding the design to reduce noise. This method is particularly important for wind turbine designs that pursue low noise and high efficiency. As part of the noise evaluation function, the vortex source term, along with the performance evaluation function, provides the necessary mathematical framework for multi-objective optimization. By setting the objective function, designers can use optimization algorithms to automatically find design parameters that strike the optimal balance between performance and noise. Because the calculation of the vortex function is based on CFD simulation results, it is much faster than directly solving for the sound field. In rapid iterative design processes, this method can significantly improve design efficiency, enabling designers to evaluate the noise impact of numerous design schemes in a short time.

[0093] In some embodiments, the components ω1 (first vortex component), w2 (second vortex component), and w3 (third vortex component) of the vortex vector ω in the x, y, and z directions, and the components v1 (first velocity component), v2 (second velocity component), and v3 (third velocity component) of the velocity vector v in the x, y, and z directions are extracted in the simulation software CFX. The divergence of the cross product of the two is used to obtain the vortex source term of the vortex sound function. The absolute value is then integrated in the rotating domain to obtain the noise evaluation function f2, as shown in Equations 3, 4, and 5.

[0094]

[0095] w×v=(w2*v3-w3*v2,w3*v1-w1*v3,w1*v2-w2*v1) (Formula 4)

[0096]

[0097] Where v is the velocity vector, ω is the vortex vector, div(w×v) is the vortex source term, and f2 is the noise evaluation function.

[0098] To identify the main sound-generating areas on the blade surface, |div(w×v)| can be defined as a variable, and the pressure and suction surface vortex source cloud maps can be displayed in the post-processing software, as shown below. Figure 5 As shown, the red area represents the main sound source. Identifying the sound-generating area allows for targeted optimization of the local features of the wind turbine blades. Specifically, the absolute value cloud of the vortex sound source... Figure 5 The "variable" mentioned refers to the physical quantity or value represented by the cloud map. Specifically, in this type of cloud map, the "variable" is the absolute value of the vortex source intensity. (Vortex source absolute value cloud map) Figure 5 In this context, "contour" usually refers to a "contour map" or "contour plot." Against the background of the absolute value cloud map of the vortex source, the contour plot shows the distribution of the vortex source intensity on the blade surface or in the rotation domain. By drawing contour lines, the intensity changes of the vortex source can be clearly observed.

[0099] The core of the strategy of "targeted optimization of local features of wind turbine blades after identifying the noise-generating areas" lies in the fact that by analyzing and locating the specific noise-generating parts of the wind turbine blades, engineers can more precisely adjust the geometric features or other relevant parameters of these parts to achieve the goal of reducing the overall noise level. This process first relies on advanced simulation technology and data analysis, such as the absolute value integration method of vortex source terms mentioned above, to identify noise hotspots on the blade surface. Then, based on the characteristics of these hotspots, engineers take corresponding optimization measures, which may include changing the blade geometry, adjusting material properties, coatings, or introducing sound-absorbing structures.

[0100] Suppose that simulation analysis reveals the blade edges (i.e., blade tips) of a certain wind turbine to be the primary source of noise. Here are some specific optimization directions:

[0101] 1. Consider reshaping the blade tip, such as using a twisted or serrated edge design. Twisted edges can better adapt to the airflow direction at different flow rates, reducing fluid separation and vortex formation, while serrated edges can reduce the radiation intensity of high-frequency noise by dispersing sound waves.

[0102] 2. While maintaining the overall lightweight design of the blade, increasing the thickness of the blade tip region can enhance structural rigidity and reduce noise caused by vibration. This optimization typically requires the use of finite element analysis (FEA) to verify its impact on the blade's dynamic response.

[0103] 3. In identified high-noise areas, applying sound-absorbing materials or special sound-absorbing coatings can absorb or weaken sound waves and reduce noise propagation. For example, using porous materials or coatings with microporous structures can convert sound energy into heat energy, thereby reducing noise.

[0104] 4. By adjusting the blade tilt angle and angle of attack, the interaction between the blades and the incoming flow can be improved, reducing vortex generation and the intensity of vortex noise sources. This involves a deep understanding of fluid dynamics and a trade-off between fan performance and noise.

[0105] 5. Adding acoustic grilles or deflectors to specific areas of the blade (such as near the blade root or at the deflector ring) can guide airflow, reduce turbulence, and block or scatter noise from these areas.

[0106] 6. Using materials with better sound damping characteristics, such as certain polymer composites, can reduce noise radiation during blade vibration, especially for high-frequency noise.

[0107] In some embodiments, a multi-objective optimization algorithm is used to automatically optimize the blade parameters of the wind turbine based on the aforementioned performance evaluation function and noise evaluation function to obtain the target blade parameters of the wind turbine, including the following steps:

[0108] Step S2041: The above performance evaluation function and the above noise evaluation function are respectively determined as the first objective function and the second objective function of the multi-objective optimization algorithm;

[0109] Step S2042: Taking the maximization of the first objective function and the minimization of the second objective function as the optimization objectives, the multi-objective optimization algorithm is used to automatically optimize the blade parameters of the wind turbine to obtain the target blade parameters. The target blade parameters include at least the chord length, installation angle, bending angle, and sweep angle of the blade.

[0110] Specifically, a multi-objective optimization algorithm is employed to automatically optimize the fan blade parameters based on performance and noise evaluation functions, simultaneously considering the two mutually constraining objectives of energy efficiency and noise control. This is more complex but also more comprehensive than traditional single-objective optimization, as single-objective optimization may overlook potential improvements in the other aspect. By setting the objective function to be "maximized in the first objective function and minimized in the second objective function"—that is, maximizing the performance evaluation function and minimizing the noise evaluation function—the algorithm can search for a Pareto optimal set of solutions within the fan blade parameter space. These solutions represent optimization schemes where there is no significant improvement in one aspect without worsening the other in performance and noise control. This optimization strategy ensures that the fan blade design meets the requirements of efficient air delivery while minimizing operating noise, thereby improving user experience and product competitiveness.

[0111] The automatic optimization in step S2042 means that the algorithm independently traverses, evaluates, and optimizes the blade parameters without the need for repeated trial and error or subjective judgment by humans. This greatly reduces the design cycle and lowers labor costs. The flexibility and powerful functionality of programming languages ​​such as Python make them ideal tools for implementing this type of multi-objective optimization. They can quickly call simulation software such as CFX for fluid dynamics and acoustic analysis, accelerating the iteration speed of the overall design process.

[0112] This method effectively explores the multidimensional space of wind turbine blade parameters. Parameters such as chord length, installation angle, bend angle, and sweep angle, as mentioned above, each have a different impact on turbine performance and noise levels. Through multi-objective optimization, designers can simultaneously consider the influence of these parameters, avoiding the local optimum trap that can occur with single-parameter optimization. This means that even with numerous parameters and complex interactions, designers can find a set of parameter combinations that balance various design objectives, including but not limited to performance and noise.

[0113] Multi-objective optimization not only helps find robust solutions to current designs but may also reveal unexpected spaces for innovation. Sometimes, through clever parameter adjustments, designers may discover entirely new wind turbine geometries that exceed expectations in both performance and noise. Such design innovations often require pushing the boundaries of traditional design thinking, and multi-objective optimization algorithms are precisely capable of exploring these opportunities.

[0114] For example, suppose a fan's original design exhibits high noise levels under high airflow conditions. Through a multi-objective optimization algorithm, the designer can set up an optimization process aimed at improving the airflow-to-power ratio while reducing noise levels. The algorithm might suggest increasing the chord length and sweep angle, while moderately adjusting the mounting angle and bend angle to achieve the optimal balance between these two objectives. The resulting target blade parameters ensure that the fan operates efficiently while meeting stringent standards or user comfort requirements in terms of noise levels.

[0115] This involves using Python to call CFX for automated simulation, writing a multi-objective optimization algorithm with the performance evaluation function and noise evaluation function as objective functions, and optimizing blade parameters such as chord length, installation angle, bending angle, and sweep angle with the optimization objectives of maximizing the performance evaluation function and minimizing the noise evaluation function.

[0116] The comprehensive evaluation method and system for the performance and noise of the semi-open axial flow fan proposed in the above embodiments are not only applicable to axial flow fan blades of different diameters and numbers of blades, but can also be extended to centrifugal fan blades, cross-flow fan blades and other rotating machinery.

[0117] To enable those skilled in the art to better understand the technical solution of this application, the implementation process of the method for determining the blade parameters of the wind turbine of this application will be described in detail below with reference to specific embodiments.

[0118] This embodiment relates to a specific method for determining the blade parameters of a wind turbine, such as... Figure 6 As shown, the process includes: establishing a simplified geometric model based on the outdoor unit model of the air conditioner, and establishing a simulation model using simulation software such as Fluent / CFX; extracting the air volume and the torque of the blades around the rotation axis after solving the flow field, calculating the shaft power based on the blade torque, and using the ratio of air volume to power as the performance evaluation function; extracting vorticity and velocity vector in the simulation software, calculating the vortex source term of the vortex sound function, taking the absolute value, and integrating it in the rotating domain as the noise evaluation function; using the performance evaluation function and the noise evaluation function as multi-objective optimization objective functions to achieve automatic optimization of the blade parameters.

[0119] The above embodiments use the ratio of airflow to power as the performance objective function, allowing for simultaneous consideration of both airflow and power through a single function. By integrating the absolute values ​​of vortex sources in the vortex sound function as the noise objective function, quantitative noise analysis can be performed. Furthermore, the absolute values ​​of the vortex source terms can be taken and displayed in a cloud map format to identify the main sound-generating areas on the blade surface. Moreover, the noise function and performance function can be used as objective functions to optimize blade parameters through multi-objective optimization, simultaneously improving both blade noise and performance.

[0120] This application also provides a device for determining the blade parameters of a fan. It should be noted that this device can be used to execute the method for determining the blade parameters of a fan provided in this application. This device is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0121] The following describes the device for determining the blade parameters of a fan provided in the embodiments of this application.

[0122] Figure 7 This is a schematic diagram of a device for determining the blade parameters of a fan according to an embodiment of this application. Figure 7As shown, the device includes a construction unit 10, a first determination unit 20, a second determination unit 30, and an optimization unit 40. The construction unit 10 is used to construct a fan simulation model. The first determination unit 20 is used to determine the current simulation parameters of the fan based on fluid dynamics according to the aforementioned fan simulation model. The current simulation parameters of the fan include the fan airflow, blade torque, fan vorticity, and fan velocity vector. The fan airflow is the airflow at the fan outlet. The second determination unit 30 is used to determine the performance evaluation function of the fan based on the fan airflow and the blade torque. The optimization unit 40 is used to automatically optimize the fan blade parameters based on the aforementioned vorticity and velocity vector, using a multi-objective optimization algorithm to obtain the target fan blade parameters. The fan blades manufactured using the target fan blade parameters ensure that the fan noise is less than the preset noise, the air volume is greater than the preset air volume, and the power is less than the preset power.

[0123] The device for determining the blade parameters of a wind turbine as described in this application includes a construction unit, a first determination unit, a second determination unit, and an optimization unit. The construction unit is used to construct a wind turbine simulation model. The first determination unit is used to determine the current simulation parameters of the wind turbine based on fluid dynamics and the simulation model. The second determination unit is used to determine the performance evaluation function of the wind turbine based on the wind turbine airflow and blade torque, and to determine the noise evaluation function of the wind turbine based on vorticity and velocity vector. The optimization unit is used to automatically optimize the blade parameters of the wind turbine using a multi-objective optimization algorithm based on the performance evaluation function and the noise evaluation function to obtain the target blade parameters. The wind turbine blades manufactured using the target blade parameters result in a wind turbine with noise lower than a preset noise level, airflow greater than a preset airflow, and power lower than a preset power. This device optimizes the wind turbine blades by simultaneously considering the airflow and power of the blades through a single performance objective function. Furthermore, the noise function and performance function can be used as objective functions. By optimizing the blade parameters through multi-objective optimization, the device simultaneously optimizes the wind turbine blade noise and performance, solving the problem in the prior art where wind turbine blades rely on manual design, making it difficult to achieve the optimal balance between performance and noise.

[0124] In some embodiments, the second determining unit includes a first acquisition module, a first calculation module, and a first determining module. The first acquisition module is used to acquire the fan speed; the first calculation module is used to calculate the fan shaft power based on the fan speed and the blade torque to obtain the fan power; and the first determining module is used to determine the ratio of the fan air volume to the fan power as the performance evaluation function. This evaluation method can seek ways to reduce power consumption while ensuring sufficient air volume, thereby achieving the goal of energy saving and environmental protection.

[0125] In some embodiments, the first calculation module includes a first determining submodule and a second determining submodule. The first determining submodule is used to determine the work done by the blades based on pi, the aforementioned fan speed, and the aforementioned blade torque. The work done by the blades is the work done by the fan blades in one revolution per unit time, and the work done by the blades is the product of a first coefficient, the aforementioned pi, the aforementioned fan speed, and the aforementioned blade torque. The second determining submodule is used to determine the calculated power by the ratio of the work done by the blades to the unit time, and to determine the fan power by the ratio of the calculated power to a second coefficient, where the second coefficient is a unit conversion factor for converting power units from watts to kilowatts. By combining the blade torque, speed, and necessary constants and unit conversion factors, the calculation is directly related to the actual working principle of the fan, ensuring the accuracy and practicality of the calculation.

[0126] In some embodiments, the second determining unit includes a second determining module, a third determining module, and a fourth determining module. The second determining module is used to determine the rotation domain of the aforementioned wind turbine simulation model, wherein the rotation domain is a region including the blades of the aforementioned wind turbine and in which the fluid rotates together with the blades. The third determining module is used to determine the vortex source term of the vortex sound function of the aforementioned wind turbine based on the aforementioned vorticity and the aforementioned velocity vector. The fourth determining module is used to determine the noise evaluation function by integrating the absolute value of the aforementioned vortex source term over the rotation domain. This significantly shortens the simulation time, especially for scenarios involving multiple iterations or multi-objective optimization design, improving the efficiency and cost-effectiveness of wind turbine design.

[0127] In some embodiments, the third determining module includes a first extraction submodule, a second extraction submodule, and a calculation submodule. The first extraction submodule is used to extract the components of the vorticity in a first direction, a second direction, and a third direction based on the aforementioned wind turbine simulation model, obtaining a first vorticity component, a second vorticity component, and a third vorticity component, where the first direction, the second direction, and the third direction are the positive directions of the three coordinate systems in a spatial rectangular coordinate system, respectively. The second extraction submodule is used to extract the components of the velocity vector in the first direction, the second direction, and the third direction based on the aforementioned wind turbine simulation model, obtaining a first velocity component, a second velocity component, and a third velocity component. The calculation submodule is used to calculate the cross product of the vorticity and the velocity vector based on the first vorticity component, the second vorticity component, the third vorticity component, the first velocity component, the second velocity component, and the third velocity component, and to calculate the divergence of the cross product, obtaining the vortex source term. This method allows for accurate prediction of noise sources in rotating machinery (such as wind turbines). Compared to traditional methods, this method does not require direct solution of the transient sound field, reducing computational costs and time, while also improving the accuracy and reliability of noise prediction.

[0128] In some embodiments, the optimization unit includes a fifth determining module and an optimization module. The fifth determining module is used to determine the performance evaluation function and the noise evaluation function as the first objective function and the second objective function of the multi-objective optimization algorithm, respectively. The optimization module is used to automatically optimize the blade parameters of the wind turbine using the multi-objective optimization algorithm, with the first objective function being maximized and the second objective function being minimized as the optimization objective, to obtain the target blade parameters. The target blade parameters include at least the chord length, installation angle, bend angle, and sweep angle of the blades. Using a multi-objective optimization algorithm to automatically optimize the wind turbine blade parameters based on the performance evaluation function and the noise evaluation function can simultaneously consider the two mutually constraining objectives of wind turbine energy efficiency and noise control.

[0129] In some embodiments, the construction unit includes a construction module and a sixth determining module. The construction module is used to construct a simplified geometric model containing the fan's guide ring and axial flow blades. The simplified geometric model has a circular cross-section and contains three axial flow blades. The sixth determining module is used to cut off one-third of the simplified geometric model along the circumferential direction of its cross-section to obtain a computational domain, and to determine the computational domain as the fan simulation model. The interface of the computational domain is a sector with a preset angle. The computational domain includes one of the axial flow blades and includes a rotational domain and a stationary domain. The rotational domain is the region containing the axial flow blades and in which the fluid rotates together with the axial flow blades. The stationary domain is all regions in the computational domain except for the rotational domain. By cutting off one-third of the simplified geometric model along its cross-section (i.e., the circumferential direction) as the computational domain, this sector-shaped cutting strategy utilizes the axisymmetric characteristics of the axial flow fan. Only 1 / 3 of the model needs to be simulated to represent the complete behavior of the fan, which further reduces the demand for computational resources.

[0130] The device for determining the blade parameters of the aforementioned wind turbine includes a processor and a memory. The aforementioned building units are all stored as program units in the memory, and the processor executes these program units to achieve the corresponding functions. All of the aforementioned modules reside in the same processor; alternatively, the modules may be located in different processors in any combination.

[0131] The processor contains a kernel, which retrieves the corresponding program unit from memory. One or more kernels can be configured, and adjusting kernel parameters can address the problem in existing technologies where wind turbine blades rely on manual design, making it difficult to achieve the optimal balance between performance and noise.

[0132] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0133] This invention provides a computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform a method for determining the blade parameters of a fan.

[0134] This invention provides a processor for running a program, wherein the program executes a method for determining the blade parameters of a wind turbine.

[0135] This invention provides a device including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs at least the following steps:

[0136] Step S201: Construct a wind turbine simulation model;

[0137] Step S202: Based on fluid dynamics, determine the current simulation parameters of the fan according to the above-mentioned fan simulation model. The current simulation parameters of the fan include the fan air volume, blade torque, fan vorticity and fan velocity vector. The fan air volume is the air volume at the fan outlet.

[0138] Step S203: Determine the performance evaluation function of the fan based on the fan air volume and the blade torque, and determine the noise evaluation function of the fan based on the vorticity and the velocity vector. The performance evaluation function characterizes the air volume and power of the fan, and the noise evaluation function characterizes the noise level of the fan.

[0139] Step S204: Using a multi-objective optimization algorithm, the fan blade parameters of the fan are automatically optimized based on the above performance evaluation function and the above noise evaluation function to obtain the target fan blade parameters. The fan blades made using the above target fan blade parameters make the noise of the fan less than the preset noise, the air volume greater than the preset air volume, and the power less than the preset power.

[0140] The devices mentioned in this article can be servers, PCs, tablets, mobile phones, etc.

[0141] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having at least the following method steps:

[0142] Step S201: Construct a wind turbine simulation model;

[0143] Step S202: Based on fluid dynamics, determine the current simulation parameters of the fan according to the above-mentioned fan simulation model. The current simulation parameters of the fan include the fan air volume, blade torque, fan vorticity and fan velocity vector. The fan air volume is the air volume at the fan outlet.

[0144] Step S203: Determine the performance evaluation function of the fan based on the fan air volume and the blade torque, and determine the noise evaluation function of the fan based on the vorticity and the velocity vector. The performance evaluation function characterizes the air volume and power of the fan, and the noise evaluation function characterizes the noise level of the fan.

[0145] Step S204: Using a multi-objective optimization algorithm, the fan blade parameters of the fan are automatically optimized based on the above performance evaluation function and the above noise evaluation function to obtain the target fan blade parameters. The fan blades made using the above target fan blade parameters make the noise of the fan less than the preset noise, the air volume greater than the preset air volume, and the power less than the preset power.

[0146] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0147] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0148] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0149] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0150] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0151] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0152] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0153] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0154] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0155] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0156] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0157] 1) The method for determining the blade parameters of the aforementioned wind turbine in this application first constructs a wind turbine simulation model; based on fluid dynamics, the current simulation parameters of the wind turbine are determined according to the simulation model; then, the performance evaluation function of the wind turbine is determined based on the wind turbine airflow and blade torque, and the noise evaluation function of the wind turbine is determined based on vorticity and velocity vector; finally, a multi-objective optimization algorithm is used to automatically optimize the blade parameters of the wind turbine based on the performance evaluation function and the noise evaluation function to obtain the target blade parameters of the wind turbine. The wind turbine blades manufactured using the target blade parameters ensure that the wind turbine noise is less than the preset noise, the airflow is greater than the preset airflow, and the power is less than the preset power. This method optimizes the wind turbine blades by simultaneously considering the airflow and power of the blades through a single performance objective function, and the noise function and performance function can be used as objective functions. By optimizing the blade parameters through multi-objective optimization, the noise and performance of the wind turbine blades are optimized simultaneously, solving the problem in the prior art where wind turbine blades rely on manual design and it is difficult to achieve the best balance between performance and noise.

[0158] 2) The device for determining the blade parameters of the aforementioned wind turbine according to this application includes a construction unit, a first determination unit, a second determination unit, and an optimization unit. The construction unit is used to construct a wind turbine simulation model; the first determination unit is used to determine the current simulation parameters of the wind turbine based on fluid dynamics and the wind turbine simulation model; the second determination unit is used to determine the performance evaluation function of the wind turbine based on the wind turbine airflow and blade torque, and to determine the noise evaluation function of the wind turbine based on vorticity and velocity vector; the optimization unit is used to automatically optimize the blade parameters of the wind turbine using a multi-objective optimization algorithm based on the performance evaluation function and the noise evaluation function to obtain the target blade parameters of the wind turbine. The wind turbine blades manufactured using the target blade parameters result in wind turbine noise being lower than the preset noise, airflow being higher than the preset airflow, and power being lower than the preset power. This device optimizes the wind turbine blades by simultaneously considering the airflow and power of the blades through a single performance objective function, and the noise function and performance function can be used as objective functions. By optimizing the blade parameters through multi-objective optimization, the device simultaneously optimizes the wind turbine blade noise and performance, solving the problem in the prior art where wind turbine blades rely on manual design, making it difficult to achieve the optimal balance between performance and noise.

[0159] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for determining a parameter of a fan blade of a fan, characterized in that, include: Construct a wind turbine simulation model; Based on fluid dynamics, the current simulation parameters of the fan are determined according to the fan simulation model. The current simulation parameters of the fan include the fan air volume, blade torque, fan vorticity, and fan velocity vector. The fan air volume is the air volume at the fan outlet. The performance evaluation function of the fan is determined based on the fan air volume and the blade torque, and the noise evaluation function of the fan is determined based on the vorticity and the velocity vector. The performance evaluation function characterizes the fan air volume and power, and the noise evaluation function characterizes the fan noise level. A multi-objective optimization algorithm is used to automatically optimize the blade parameters of the fan based on the performance evaluation function and the noise evaluation function to obtain the target blade parameters of the fan. The fan blades made using the target blade parameters make the fan noise less than the preset noise, the air volume greater than the preset air volume, and the power less than the preset power. Determining the performance evaluation function of the fan based on the fan air volume and the blade torque includes: obtaining the fan speed; calculating the shaft power of the fan based on the fan speed and the blade torque to obtain the fan power; and determining the ratio of the fan air volume to the fan power as the performance evaluation function. Determining the noise evaluation function of the fan based on the vorticity and the velocity vector includes: determining the rotation domain of the fan simulation model, wherein the rotation domain is a region including the fan blades and the fluid rotating together with the blades; determining the vortex source term of the fan's vortex acoustic function based on the vorticity and the velocity vector; and determining the noise evaluation function by integrating the absolute value of the vortex source term over the rotation domain. Determining the vortex source term of the vortex acoustic function of the wind turbine based on the vorticity and the velocity vector includes: extracting the components of the vorticity in a first direction, a second direction, and a third direction based on the wind turbine simulation model to obtain a first vorticity component, a second vorticity component, and a third vorticity component, where the first direction, the second direction, and the third direction are the positive directions of the three coordinate systems in a spatial rectangular coordinate system; extracting the components of the velocity vector in the first direction, the second direction, and the third direction based on the wind turbine simulation model to obtain a first velocity component, a second velocity component, and a third velocity component; calculating the cross product of the vorticity and the velocity vector based on the first vorticity component, the second vorticity component, the third vorticity component, the first velocity component, the second velocity component, and the third velocity component, and calculating the divergence of the cross product to obtain the vortex source term.

2. The method of claim 1, wherein, The shaft power of the fan is calculated based on the fan speed and the blade torque to obtain the fan power, including: The work done by the blades is determined based on pi, the fan speed, and the blade torque. The work done by the blades is the work done by the fan blades rotating once per unit time. The work done by the blades is the product of a first coefficient, pi, the fan speed, and the blade torque. The ratio of the work done by the blades to the unit time is determined as the calculated power, and the ratio of the calculated power to the second coefficient is determined as the power of the wind turbine. The second coefficient is a unit conversion factor for converting the power unit from watts to kilowatts.

3. The method of claim 1, wherein, A multi-objective optimization algorithm is used to automatically optimize the blade parameters of the wind turbine based on the performance evaluation function and the noise evaluation function, to obtain the target blade parameters of the wind turbine, including: The performance evaluation function and the noise evaluation function are respectively determined as the first objective function and the second objective function of the multi-objective optimization algorithm; With the first objective function being maximized and the second objective function being minimized as the optimization objectives, the multi-objective optimization algorithm is used to automatically optimize the blade parameters of the wind turbine to obtain the target blade parameters. The target blade parameters include at least the chord length, installation angle, bend angle, and sweep angle of the blade.

4. The method according to any one of claims 1 to 3, characterized in that, Constructing a wind turbine simulation model includes: A simplified geometric model is constructed, which includes the guide ring and axial flow blades of the fan. The cross-section of the simplified geometric model is circular, and there are three axial flow blades in the simplified geometric model. One-third of the simplified geometric model is cut off along the circumferential direction of its cross-section to obtain the computational domain, which is then defined as the wind turbine simulation model. The interface of the computational domain is a sector with a preset angle. The computational domain includes one axial flow fan blade and comprises a rotational domain and a stationary domain. The rotational domain is the region including the axial flow fan blade where the fluid rotates together with the axial flow fan blade. The stationary domain is all regions in the computational domain except for the rotational domain.

5. A device for determining the blade parameters of a fan, characterized in that, include: Building blocks are used to construct wind turbine simulation models; The first determining unit is used to determine the current simulation parameters of the fan based on the fan simulation model according to fluid dynamics. The current simulation parameters of the fan include the fan air volume, blade torque, fan vorticity and fan velocity vector. The fan air volume is the air volume at the fan outlet. The second determining unit is used to determine the performance evaluation function of the fan based on the fan air volume and the blade torque, and to determine the noise evaluation function of the fan based on the vorticity and the velocity vector. The performance evaluation function characterizes the fan air volume and power, and the noise evaluation function characterizes the fan noise. The optimization unit is used to automatically optimize the blade parameters of the fan based on the performance evaluation function and the noise evaluation function using a multi-objective optimization algorithm to obtain the target blade parameters of the fan. The fan blades made using the target blade parameters make the noise of the fan less than the preset noise, the air volume greater than the preset air volume, and the power less than the preset power. The second determining unit includes a first acquisition module, a first calculation module, and a first determining module. The first acquisition module is used to acquire the fan speed. The first calculation module is used to calculate the shaft power of the fan based on the fan speed and the blade torque, and obtain the fan power; the first determination module is used to determine the ratio of the fan air volume to the fan power as the performance evaluation function; The second determining unit includes a second determining module, a third determining module, and a fourth determining module. The second determining module is used to determine the rotation domain of the wind turbine simulation model. The rotation domain is the region that includes the blades of the wind turbine and in which the fluid rotates together with the blades. The third determining module is used to determine the vortex source term of the vortex sound function of the wind turbine based on the vorticity and the velocity vector. The fourth determining module is used to determine the noise evaluation function by integrating the absolute value of the vortex source term in the rotational domain; The third determining module includes a first extraction submodule, a second extraction submodule, and a calculation submodule. The first extraction submodule is used to extract the components of the vorticity in the first direction, the second direction, and the third direction based on the wind turbine simulation model, to obtain the first vorticity component, the second vorticity component, and the third vorticity component. The first direction, the second direction, and the third direction are the positive directions of the three coordinate systems of the spatial rectangular coordinate system. The second extraction submodule is used to extract the components of the velocity vector in the first direction, the second direction and the third direction based on the wind turbine simulation model, to obtain the first velocity component, the second velocity component and the third velocity component; The calculation submodule is used to calculate the cross product of the vorticity and the velocity vector based on the first vorticity component, the second vorticity component, the third vorticity component, the first velocity component, the second velocity component, and the third velocity component, and to calculate the divergence of the cross product to obtain the vortex source term.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the method for determining the blade parameters of the wind turbine as described in any one of claims 1 to 4.

7. An air conditioner characterized by comprising: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including a method for performing a method for determining the blade parameters of a wind turbine as described in any one of claims 1 to 4.

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