Determination method and device for fan blade parameters of fan, storage medium and electronic equipment

Abstract

The invention provides a method and device for determining fan blade parameters of a fan, a storage medium and electronic equipment. The method comprises the steps that current simulation parameters of the fan are determined based on fluid dynamics according to a fan simulation model; determining a performance evaluation function of the fan according to the fan air volume and the blade torque, and determining a noise evaluation function of the fan according to the vorticity and the velocity vector; and adopting a multi-objective optimization algorithm to automatically optimize the fan blade parameters of the fan based on the performance evaluation function and the noise evaluation function to obtain target fan blade parameters of the fan, and adopting the fan blades manufactured by the target fan blade parameters to enable the noise of the fan to be smaller than the preset noise, the air volume to be larger than the preset air volume and the power to be smaller than the preset power. According to the method, the fan blades are optimized by considering the air volume and the power of the fan blades through a performance objective function, a noise function and the performance function can serve as objective functions, fan blade parameters are optimized through multi-objective optimization, and the noise and the performance of the fan blades are optimized at the same time.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of a fan, in particular to a fan blade parameter determination method, a fan blade parameter determination device, a computer readable storage medium and an electronic device. BACKGROUND

[0002] An air conditioner is a commonly used household appliance, and an axial flow fan is a main component for heat dissipation and air supply of an outdoor unit of the air conditioner. At present, the mainstream fan blade design method mainly relies on traditional mechanical engineering and the experience of designers, and cannot simultaneously optimize the performance of air volume, power and noise, and it is difficult to achieve the best balance between performance and noise. SUMMARY

[0003] The main purpose of the present application is to provide a fan blade parameter determination method, a fan blade parameter determination device, a computer readable storage medium and an electronic device to at least solve the problem that the fan blade relies on manual design and it is difficult to achieve the best balance between performance and noise in the prior art.

[0004] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a fan blade parameter determination method is provided, comprising: constructing a fan simulation model; determining a current simulation parameter of a fan based on fluid dynamics according to the fan simulation model, the current simulation parameter of the fan including fan air volume, blade torque, vorticity of the fan and velocity vector of the fan, the fan air volume being the air volume at the outlet of the fan; determining a performance evaluation function of the fan according to the fan air volume and the blade torque, and determining a noise evaluation function of the fan according to the vorticity and the velocity vector, the performance evaluation function representing the air volume and power of the fan, and the noise evaluation function representing the noise of the fan; automatically optimizing the fan blade parameter based on the performance evaluation function and the noise evaluation function by using a multi-objective optimization algorithm to obtain a target fan blade parameter, and the fan blade made of the target fan blade parameter makes the noise of the fan less than a preset noise, the air volume greater than a preset air volume and the power less than a preset power.

[0005] Optionally, determining the performance evaluation function of the fan according to the fan air volume and the blade torque comprises: obtaining a fan speed; calculating the shaft power of the fan according to 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, the shaft power of the fan is calculated according to the fan rotating speed and the blade moment, to obtain a fan power, including: determining a blade work according to a constant pi, the fan rotating speed and the blade moment, the blade work being work done by a blade of the fan in a unit time for one rotation, the blade work being a product of a first coefficient, the constant pi, the fan rotating speed and the blade moment; determining a calculation power by a ratio of the blade work to the unit time, and determining the fan power by a ratio of the calculation power to a second coefficient, the second coefficient being a unit conversion coefficient for converting power units from watts to kilowatts.

[0007] Optionally, the noise evaluation function of the fan is determined according to the vorticity and the velocity vector, including: determining a rotating domain of the fan simulation model, the rotating domain being an area including a blade of the fan and a fluid rotating together with the blade; determining a vortex sound source term of a vortex sound function of the fan according to the vorticity and the velocity vector; determining the noise evaluation function by integrating an absolute value of the vortex sound source term in the rotating domain.

[0008] Optionally, the vortex sound source term of the vortex sound function of the fan is determined according to the vorticity and the velocity vector, including: extracting components of the vorticity in a first direction, a second direction and a third direction based on the fan simulation model, to obtain a first vorticity component, a second vorticity component and a third vorticity component, the first direction, the second direction and the third direction being positive directions of three coordinate systems of a space rectangular coordinate system respectively; extracting components of the velocity vector in the first direction, the second direction and the third direction based on the fan simulation model, to obtain a first velocity component, a second velocity component and a third velocity component; calculating a cross product of the vorticity and the velocity vector according to 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 a divergence of the cross product to obtain the vortex sound source term.

[0009] Optionally, a multi-objective optimization algorithm is used to automatically optimize the fan blade parameters of the fan based on the performance evaluation function and the noise evaluation function, to obtain target fan blade parameters of the fan, including: determining the performance evaluation function and the noise evaluation function as a first objective function and a second objective function of the multi-objective optimization algorithm respectively; using the multi-objective optimization algorithm to automatically optimize the fan blade parameters of the fan with the first objective function being maximum and the second objective function being minimum as an optimization target, to obtain the target fan blade parameters, the target fan blade parameters including at least a chord length, an installation angle, a bending angle and a sweep angle of the fan blade.

[0010] Optionally, the fan simulation model is constructed, including: constructing a simplified geometric model comprising a guide ring and an axial fan blade of the fan, a cross section of the simplified geometric model is circular, and the axial fan blade in the simplified geometric model is three; a third of the simplified geometric model is cut along a circumferential direction of the cross section of the simplified geometric model to obtain a calculation domain, and the calculation domain is determined as the fan simulation model, an interface of the calculation domain is a sector with a preset angle, the calculation domain comprises one axial fan blade, and the calculation domain comprises a rotating domain and a static domain, the rotating domain is a region comprising the axial fan blade and in which fluid rotates together with the axial fan blade, and the static domain is all regions in the calculation domain except the rotating domain.

[0011] According to another aspect of the present application, a fan blade parameter determination device is provided, including: a construction unit configured to construct a fan simulation model; a first determination unit configured to determine a current simulation parameter of a fan based on fluid dynamics according to the fan simulation model, the current simulation parameter of the fan comprising a fan air volume, a blade torque, a vorticity of the fan, and a velocity vector of the fan, the fan air volume being an air volume at an outlet of the fan; a second determination unit configured to determine a performance evaluation function of the fan according to the fan air volume and the blade torque, and determine a noise evaluation function of the fan according to the vorticity and the velocity vector, the performance evaluation function representing a size of the fan air volume and a size of power, and the noise evaluation function representing a size of noise of the fan; and an optimization unit configured to automatically optimize a fan blade parameter of the fan based on the performance evaluation function and the noise evaluation function by using a multi-objective optimization algorithm to obtain a target fan blade parameter of the fan, and a fan blade made of the target fan blade parameter makes the noise of the fan less than a preset noise, the fan air volume greater than a preset fan air volume, and the power less than a preset power.

[0012] According to another aspect of the present application, a computer readable storage medium is provided, the computer readable storage medium comprising a stored program, wherein the program, when executed, controls a device in which the computer readable storage medium is located to perform any one of the fan blade parameter determination methods.

[0013] According to another aspect of the present application, an air conditioner is provided, including: 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, and the one or more programs comprise a program for performing any one of the fan blade parameter determination methods.

[0014] With the technical solution of the present application, the method for determining the fan blade parameters of the fan first constructs a fan simulation model; determines the current simulation parameters of the fan based on fluid dynamics according to the fan simulation model, then determines the performance evaluation function of the fan according to the fan air volume and blade torque, and determines the noise evaluation function of the fan according to the vorticity and velocity vector, and finally automatically optimizes the fan blade parameters based on the performance evaluation function and the noise evaluation function by using a multi-objective optimization algorithm to obtain the target fan blade parameters, and the fan blade made by using the target fan blade parameters makes 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 method simultaneously considers the air volume and power of the fan blade by using a performance objective function to optimize the fan blade, and the noise function and the performance function can be used as the objective function, the fan blade parameters are optimized by using the multi-objective optimization, the noise and performance of the fan blade are simultaneously optimized, and the problem that the fan blade in the prior art depends on manual design and it is difficult to achieve the best balance between performance and noise is solved. BRIEF DESCRIPTION OF DRAWINGS

[0015] The drawings accompanying the specification of the present application form a part thereof, serve to provide further understanding of the present application, and together with the specification explain the present application. The use of these drawings in the description of the present application is only to explain specific embodiments of the present application and is not intended to limit the present application. In the drawings:

[0016] Figure 1 A hardware structure block diagram of a mobile terminal for executing a method for determining fan blade parameters of a fan is shown according to an embodiment of the present application;

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

[0018] Figure 3 A schematic diagram of a fan simulation model is shown according to an embodiment of the present application;

[0019] Figure 4 A schematic diagram of a rotating domain of a fan simulation model is shown according to an embodiment of the present application;

[0020] Figure 5 A vortex sound source absolute value cloud chart of a fan blade of a fan is shown according to an embodiment of the present application;

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

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

[0023] In the above drawings, the following reference signs are used:

[0024] 102, processor; 104, memory; 106, transmission device; 108, input and output device. DETAILED DESCRIPTION

[0025] It should be noted that the embodiments and features in the present application can be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0026] In order for those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0027] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0028] As introduced in the background, in the prior art, when the fan system is running, the motor drives the fan blade to rotate at high speed, and the mechanical energy of the rotating shaft is converted into the pressure energy and kinetic energy of the air, thereby accelerating heat dissipation. And the current mainstream fan blade design method is mainly based on traditional mechanical engineering, assisted by computational fluid dynamics (CFD) and 3D proofing, and relies more on the experience of designers, and the optimization efficiency is low. In order to solve the problem that the fan blade in the prior art relies on manual design and it is difficult to achieve the best balance between performance and noise, the embodiments of the present application provide a method and device for determining the parameters of the fan blade of a fan, a storage medium and an electronic device.

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

[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] A method for determining a fan blade parameter of a fan running on a mobile terminal, a computer terminal or the like computing device is provided in the present embodiment. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown.

[0033] Figure 2 is a flowchart of a method for determining a fan blade parameter of a fan according to an embodiment of the present application. As shown in Figure 2 , the method comprises the following steps:

[0034] Step S201, constructing a fan simulation model;

[0035] Specifically, a simplified geometric model is established according to an air conditioner outdoor unit model, and a simulation model is established by using a simulation software Fluent / CFX. The construction of the fan simulation model is a complex process involving multiple steps, mainly applied in the field of computational fluid dynamics (CFD), and the purpose is to predict and optimize the aerodynamic characteristics of the fan in a virtual environment, including air volume, air pressure, efficiency and noise, etc.

[0036] Firstly, an accurate geometric model of the fan needs to be established. This usually involves using CAD software (such as SolidWorks, Pro / E, AutoCAD, etc.) to create a three-dimensional model of the main components of the fan, such as the fan blade, the fairing, the motor, the support frame, etc. For rotating machinery such as fans, the geometric accuracy of the model directly affects the accuracy of the fluid dynamics analysis, especially the shape and size of the blades. When modeling, the complexity of the model and the simulation speed need to be balanced. For complex fan structures, appropriate simplification can be used, such as only keeping the key geometric features of the fan blade and the fairing, while ensuring the accuracy of the details in the key areas. An accurate fan simulation model can be used to predict and optimize the performance of the fan.

[0037] Step S202, determining the current simulation parameters of the fan based on fluid dynamics according to the fan simulation model, the current simulation parameters of the fan including the fan air volume, the blade torque, the vorticity of the fan and the velocity vector of the fan, the fan air volume being the air volume at the outlet of the fan;

[0038] Specifically, fluid dynamics is a discipline that studies the behavior and dynamic characteristics of fluids (liquids and gases), which is based on a set of basic equations such as the continuity equation, the momentum equation and the energy equation, which describe the flow state and dynamic behavior of the fluid. In CFD simulation, these equations are solved by numerical methods to predict the behavior of the fluid under certain boundary conditions.

[0039] The fan simulation model is a digital model established through CFD software, which includes the geometric structure, material properties, operating conditions (such as inlet velocity, outlet pressure), and other physical phenomena (such as heat exchange, friction loss) of the fan. This model aims to simulate the operating conditions of the actual fan and predict its performance through calculations. The fan simulation model based on fluid dynamics provides the ability to gain deep insights into fan performance by quantifying parameters such as air volume, blade torque, vorticity, and velocity vectors, allowing for the evaluation and optimization of fan design.

[0040] In step S203, 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 represents the air volume and power size of the fan, and the noise evaluation function represents the noise size of the fan.

[0041] Specifically, the performance evaluation function of the fan is typically used to comprehensively consider the air volume capacity and power efficiency of the fan. In CFD simulation, the higher the function value, the more air volume the fan can produce under the same power consumption, or the more power it can save under the same air volume, thus reflecting higher performance. The noise evaluation function is used to quantify and estimate the noise level generated by the operation of the fan. In CFD simulation, the generation of noise is often related to turbulence, vortex, and fluid dynamics fluctuations in the fluid.

[0042] Through the performance evaluation function and the noise evaluation function, quantitative information about the performance and noise of the fan can be obtained, which can be used in the optimization process of fan design to achieve higher efficiency and lower noise.

[0043] In step S204, a multi-objective optimization algorithm is used to automatically optimize the fan blade parameters based on the performance evaluation function and the noise evaluation function, obtaining target fan blade parameters. The fan blade made with the target fan blade parameters has noise less than the preset noise, air volume greater than the preset air volume, and power less than the preset power.

[0044] Specifically, the multi-objective optimization algorithm is a mathematical method that is very effective in dealing with optimization problems with multiple conflicting objectives. In the context of fan design, the objectives usually include increasing air volume, reducing power consumption, and reducing noise, which often conflict with each other - increasing air volume may increase power consumption, and reducing noise may require changing the shape of the fan blade, thereby affecting air volume and efficiency.

[0045] In multi-objective optimization, the performance evaluation function and the noise evaluation function are used as objective functions to quantify the fan design performance. Multi-objective optimization algorithms can automatically explore the optimal combination of fan blade parameters, which may include chord length, installation angle, bend angle, sweep angle, etc. The goal of the algorithm is to find a set of fan blade parameters that make the performance evaluation function as large as possible (meaning high air volume, low power), while the noise evaluation function is as small as possible (meaning low noise).

[0046] Once the target fan blade parameters are found, those that can make the noise of the above fan less than the preset noise, the air volume greater than the preset air volume, and the power less than the preset power, the next step is to apply these parameters to the actual manufacturing of the fan blade. These optimized parameters can be used to guide the geometric shape design of the fan blade, and the optimized fan blade can be made through 3D printing or traditional processing technology, installed and tested in the actual fan system to verify whether the optimization goal is achieved.

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

[0048] The above method for determining the fan blade parameters of the fan of the present application first constructs a fan simulation model; determines the current simulation parameters of the fan based on fluid dynamics according to the fan simulation model, then determines the performance evaluation function of the fan according to the fan air volume and blade torque, and determines the noise evaluation function of the fan according to the vorticity and velocity vector, and finally uses 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 the target fan blade parameters of the fan. The fan blade made using the target fan blade parameters makes 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. This method optimizes the fan blade by considering the air volume and power of the fan blade through a performance objective function, and the noise function and the performance function can be used as the objective function. Through multi-objective optimization, the fan blade parameters are optimized, the fan blade noise and performance are optimized, and the problem of relying on manual design of fan blades in the prior art, which makes it difficult to achieve the best balance between performance and noise, is solved.

[0049] The multi-objective optimization of the above embodiment combined with CFD relies on algorithm optimization, which can make up for the lack of experience of designers. In order to optimize the wind blade with good performance and low noise, a suitable performance and noise comprehensive evaluation method needs to be proposed. For a wind blade, the designer hopes to achieve large air volume while consuming less power, so the ratio of air volume to power is proposed as the performance objective function. At present, when simulating the noise of the wind blade, the vortex cloud map or the turbulent kinetic energy cloud map is generally used to qualitatively analyze the wind blade. When the noise is simulated, a fine grid is needed to solve the transient flow field, which takes a long time. In the face of thousands of times of simulation calculation in multi-objective optimization, it is obviously unrealistic to directly calculate the noise. Therefore, according to the vortex sound equation, the absolute value integral of the vortex sound source is proposed as the noise objective function.

[0050] The method comprises the following steps:

[0051] In step S2011, a simplified geometric model of the guide circle and the axial flow blade of the fan is constructed. The cross section of the simplified geometric model is circular. The axial flow blade in the simplified geometric model is three.

[0052] The cross section of the simplified geometric model is circular, which is to better simulate the aerodynamic characteristics of the actual fan and reduce the complexity of the model. The axial flow blade is designed to be three, which not only reflects the common configuration of the axial flow fan in practice, but also enables the model to reduce the calculation amount while maintaining a certain accuracy.

[0053] The construction of the simplified geometric model is based on the axial symmetry of the axial flow fan. Most axial flow fans have axial symmetry in design, which means that their geometric characteristics and fluid dynamics behavior are the same in any direction around the rotation axis. Therefore, by simulating only a part of the fan, the calculation resources can be greatly saved, and the solving speed is also accelerated due to the simplification of the model.

[0054] In step S2012, one third of the simplified geometric model is cut along the circumferential direction of the cross section of the simplified geometric model to obtain a calculation domain. The calculation domain is determined as the fan simulation model. The interface of the calculation domain is a sector with a preset angle. The calculation domain includes one axial flow blade. The calculation domain includes a rotating domain and a stationary domain. The rotating domain is the area including the axial flow blade and the fluid rotating with the axial flow blade. The stationary domain is all areas in the calculation domain except the rotating domain.

[0055] By cutting one third of the simplified geometric model along the cross section (i.e. the circumferential direction) as the calculation domain. This sector cutting strategy takes advantage of the axial symmetry of the axial flow fan, and only 1 / 3 of the model needs to be simulated to represent the complete behavior of the fan, which further reduces the demand for calculation resources.

[0056] The computational domain is explicitly divided into a rotating domain and a stationary domain. The rotating domain refers to the region containing the axial flow fan blades, and in the CFD simulation, the fluid in this region rotates with the fan blades, which usually needs to be simulated using a rotating coordinate system or a multi-reference frame (MRF) method. The stationary domain is all regions in the computational domain except the rotating domain, where the fluid is relatively stationary or moves at different speeds, and is usually simulated using a stationary coordinate system.

[0057] Specifically, through model simplification and computational domain interception, the computational load is significantly reduced, which makes the CFD simulation run faster and reduces the demand for computing resources such as memory and CPU time. The reduction in demand for computing resources means a reduction in cost, whether from the perspective of computing time cost or from the perspective of hardware resource cost. Although the model is simplified, due to the axial symmetry of the axial flow fan, this simplification does not affect the accuracy of the simulation results. By simulating the sector region, similar fluid dynamics behavior to the complete circular model can be obtained. The simplified model is also applicable to the calculation of noise evaluation functions and performance evaluation functions, which means that designers can efficiently perform comprehensive analysis of noise and performance without sacrificing the comprehensiveness of the calculation. Building a simplified model and using it for CFD simulation provides a basis for multi-objective optimization algorithms, making it possible to find the best compromise between air volume, power consumption, and noise under limited computing resources.

[0058] In summary, by constructing a simplified geometric model containing a guide vane and an axial flow fan blade and intercepting 1 / 3 of it as a computational domain, not only is the computational efficiency greatly improved and the cost reduced, but also the accuracy of the simulation results is maintained, providing strong support for the optimization of fan design.

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

[0060] The model of the air conditioner outdoor unit is simplified, and a simplified geometric model containing a guide vane and an axial flow fan blade is established. Considering that the fan blade is an axial symmetric model, to improve the computational efficiency, 1 / 3 of the geometric model (i.e., 1 / 3 of the cylinder) is selected as the computational domain as shown in Figure 3 The computational domain is divided into a rotating domain and a stationary domain, the rotating domain is as shown in Figure 4 and the stationary domain is the region outside the rotating domain in Figure 3 . After dividing the grid, the boundary conditions are set in CFX, the inlet and outlet are set as pressure inlet and pressure outlet, the fan blade surface is set as no-slip boundary, and the rotating domain rotates at a speed of 800 rpm; finally, the MRF (multi-reference frame) solving method is used to establish the simulation model.

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

[0062] Step S301, obtaining the fan speed;

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

[0064] Step S302, calculating the shaft power of the fan according to the fan speed and the blade torque, obtaining the fan power;

[0065] Step S303, determining the ratio of the fan air volume and the fan power as the performance evaluation function.

[0066] The effect of step S303 is to provide a unified index to measure the efficiency of the fan in meeting the air supply demand. The larger the ratio of air volume to power, the more air volume the fan can produce while consuming less energy, so the performance of the fan is better. This evaluation method can seek to reduce power consumption while ensuring sufficient air volume, thereby achieving the purpose of energy saving and environmental protection.

[0067] Specifically, through steps S302 and S303, the efficiency of the fan can be quantified, i.e., the air volume that can be achieved under a certain power consumption. This is crucial for the design and selection of the fan, as it is directly related to the energy efficiency ratio of the fan. The above steps provide a method to evaluate the performance of the fan at the design stage, avoiding unnecessary testing and modification later, thereby saving time and resources. Prioritizing the ratio of air volume to power actually encourages designers to develop more energy-efficient fans, which has a positive impact on promoting green buildings, reducing energy consumption, and reducing carbon emissions. The determined performance evaluation function, together with the subsequent noise evaluation function, forms the framework of a multi-objective optimization algorithm, allowing designers to consider the noise of the fan while also considering its air supply performance and energy consumption efficiency.

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

[0069] Step S3021, determining the blade work according to the circumference, the fan speed and the blade torque, the blade work being the work done by the blades of the fan in one revolution per unit time, the blade work being the product of the first coefficient, the circumference, the fan speed and the blade torque;

[0070] Step S3022, the ratio of the above blade work to the above unit time is determined as the calculated power, and the ratio of the above calculated power to a second coefficient is determined as the above fan power, the above second coefficient being a unit conversion coefficient for converting power units from watts to kilowatts.

[0071] Specifically, in step S3021, the energy conversion efficiency of the fan is quantified by calculating the blade work. The blade work refers to the amount of work done by the fan blades in one revolution per unit time, which is considered as the basic form of energy transfer between fluid and mechanical systems. The "first coefficient" mentioned here generally refers to the conversion coefficient required to convert torque units (N*m) and rotational speed units (rpm) into power units (kW).

[0072] In step S3022, the ratio of the blade work to the unit time (usually seconds) is determined as the calculated power, and the calculated power is divided by the conversion coefficient (referred to as "second coefficient") to obtain the final fan power. The "second coefficient" here is actually a unit conversion coefficient, which is used to convert the calculation result from watts (W) to kilowatts (KW).

[0073] By combining the blade torque, rotational speed, and necessary constants and unit conversion coefficients, the calculation is directly related to the actual working principle of the fan, ensuring the accuracy and practicality of the calculation. The calculated shaft power and fan power provide designers with a tool to evaluate the efficiency and energy consumption of the fan under different load conditions. This is crucial for optimizing fan design, improving energy efficiency, and reducing costs. In multi-objective optimization design, fan power as a key performance indicator, together with wind volume and noise objective functions, helps designers find the best balance point among a series of design schemes, i.e., meeting the wind volume demand and noise control while ensuring the lowest energy consumption.

[0074] wherein, after solving and calculating the flow field, the outlet air volume Q of the fan and the torque τ of the blade around the rotating shaft are extracted, the shaft power is calculated according to the blade torque, and the ratio of the air volume to the power is taken as the performance evaluation function, the calculation formula of the shaft power is shown as formula 1, and the calculation formula of the performance evaluation function is shown as formula 2:

[0075]

[0076] wherein, τ is the torque of the blade around the rotating shaft (N*m), i.e., the blade torque, n is the rotational speed of the fan (rpm), P is the fan power (kW), Q is the fan air volume (m 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, determining the noise evaluation function of the fan according to the vorticity and the velocity vector comprises the following steps:

[0078] Step S401, determining a rotational domain of the fan simulation model, the rotational domain being a region including blades of the fan and fluid rotating with the blades;

[0079] Step S402, determining a vortical sound source term of the vortical sound function of the fan according to the vorticity and the velocity vector;

[0080] Step S403, determining the noise evaluation function as an integral of an absolute value of the vortical sound source term in the rotational domain.

[0081] Vortical acoustic theory is one of the cornerstones in the intersection of fluid dynamics and acoustics, which indicates that the interaction between vorticity and velocity vector in rotational flow is one of the main causes of noise. When fluid flows through the fan blades, due to the shape and rotational motion of the blades, complex vortex structures will be generated around the blades, and the movement and interaction of these vortices will excite sound waves, i.e. so-called vortical sound.

[0082] In step S401, the rotational domain in the simulation model is first determined, i.e. the region including the blades and the fluid rotating with the blades. This selection is based on the working principle of the fan, because the rotational domain is the main place where the fan generates fluid dynamics and vortical sound. By focusing on the rotational domain, the mechanism of noise generation can be more directly analyzed, while reducing the amount of calculation.

[0083] Step S402 involves determining the vortical sound source term of the vortical sound function according to the vorticity and the velocity vector. The vortical sound source term is essentially the divergence of the cross product of the vorticity and the fluid velocity vector. This operation can capture the sound source information induced by the fluid vortex structure, and is the core step of quantifying the noise generation mechanism.

[0084] In step S403, the integral of the absolute value of the vortical sound source term in the rotational domain is taken as the noise evaluation function. This is because the positive and negative values of the vortical sound source term reflect the sound source contributions in different directions, and the absolute value integral takes into account the contributions of all sound sources regardless of their direction. This method provides a quantitative noise indicator, which facilitates optimization during the design process.

[0085] Specifically, compared with the traditional method of directly solving the transient flow field and sound field, this embodiment provides a fast noise quantitative evaluation method through the integration of the vortex sound source term. This greatly shortens the simulation time, especially for multiple iterations or multi-objective optimization design scenarios, improving the efficiency and cost-effectiveness of fan design. By calculating the absolute value of the vortex sound source term in the rotating domain, the main noise source area on the blade surface can be accurately identified. This helps designers to locate the problem and optimize the shape, size or material of the fan blade in a targeted manner to reduce noise. The introduction of the noise evaluation function enhances the ability of multi-objective optimization, enabling designers to consider noise control while pursuing high performance. Through the comprehensive optimization of multiple objective functions, an ideal balance point between performance, energy consumption and noise can be found. With the noise evaluation function, designers can more systematically explore the impact of different design parameters on noise, guiding the direction and amplitude of design changes and avoiding blind attempts and over-optimization.

[0086] By calculating the vortex sound source term of the interaction between the vorticity and the velocity vector in the rotating domain, and then integrating its absolute value as the noise evaluation function, this embodiment provides an efficient and accurate method for fan noise prediction and control. This has important theoretical and practical value for improving the overall performance of the fan, especially reducing noise pollution. At the same time, it also provides a solid foundation for multi-objective optimization design, promoting the development of fan design towards a more intelligent and environmentally friendly direction.

[0087] In some embodiments, the vortex sound source term of the vortex sound function of the fan is determined according to the vorticity and the velocity vector, comprising the following:

[0088] Step S4021, based on the fan simulation model, extracting 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 respectively;

[0089] Step S4022, based on the fan simulation model, extracting the components of the 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, according to 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, calculating the cross product of the vorticity and the velocity vector, and taking the divergence of the cross product to obtain the vortex sound source term.

[0091] Specifically, during the CFD simulation process, the first step is to extract the components of the vorticity and velocity vector in the three directions (X, Y, Z) of the spatial rectangular coordinate system. This involves an accurate description of fluid motion, including the rotation direction of vorticity and the flow direction of velocity vector. The three components of vorticity correspond to the three degrees of freedom of fluid rotation, while the components of velocity vector reflect the flow characteristics of fluid in three dimensions. Next, the cross product of the extracted vorticity components and velocity vector components is calculated. The cross product result is a new vector whose direction is perpendicular to the plane of vorticity and velocity vector, and its size is related to the intensity of fluid motion in the direction perpendicular to each other. Then, the divergence of the cross product result is calculated, which essentially calculates the "diffusion" degree of the cross product result at each point in space. The result reflects the rate of change of the fluctuating pressure in the fluid over time and space, i.e., the vortex sound source term, which is directly proportional to the intensity of noise.

[0092] Through this method, the noise source of rotating machinery (such as fans) can be accurately predicted. Compared with traditional methods, this method does not need to directly solve the transient acoustic field, reducing the computational cost and time, while also improving the accuracy and reliability of noise prediction. The calculation of the vortex sound source term provides designers with direct insights into noise sources during the optimization process. Designers can observe and compare the vortex sound source terms of different designs by adjusting parameters such as blade shape, size, installation angle, etc., thereby guiding the design to reduce noise. This method is particularly important for fan design that pursues low noise and high efficiency. The vortex sound source term, as part of the noise evaluation function, together 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 the design parameters that strike the best 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 the acoustic field. In the fast iterative design process, this method can significantly improve design efficiency, allowing designers to evaluate the impact of a large number of design schemes on noise in a short time.

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

[0094]

[0095] w x v = (w2 * v3 - w3 * v2, w3 * v1 - v1 * v3, v1 * v2 - w2 * v1) (Equation 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 the identified high-noise areas, apply sound-absorbing materials or special sound-absorbing coatings to absorb or attenuate sound waves and reduce noise propagation. For example, use porous materials or coatings with micro-porous structures that can convert sound energy into heat energy, thereby reducing noise.

[0104] 4. Adjust the pitch and angle of attack of the blades to improve their interaction with the incoming flow, reduce the generation of vorticity and the strength of the vortex sound source. This involves a deep understanding of fluid dynamics and a trade-off between fan performance and noise.

[0105] 5. Add acoustic gratings or vanes in specific areas of the blades, such as near the blade root or at the guide vanes, to guide the airflow, reduce turbulence, and block or scatter noise from these areas.

[0106] 6. Use materials with better acoustic damping properties, such as certain polymer composites, to reduce noise radiation when the blades vibrate, especially for high-frequency noise.

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

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

[0109] Step S2042, with the first objective function maximum and the second objective function minimum as the optimization target, the multi-objective optimization algorithm is used to automatically optimize the fan blade parameters, obtaining the target fan blade parameters, which at least include the chord length, installation angle, bend angle, and sweep angle of the fan blade.

[0110] Specifically, the multi-objective optimization algorithm is used to automatically optimize the fan blade parameters based on the performance evaluation function and the noise evaluation function, which can consider both the energy efficiency and noise control of the fan, which are two mutually restrictive targets. This is more complex than traditional single-objective optimization, but also more comprehensive, because single-objective optimization may overlook potential improvements in the other aspect. By setting the first objective function maximum and the second objective function minimum, i.e., maximizing the performance evaluation function and minimizing the noise evaluation function, the algorithm can search for the "Pareto optimal" solution set in the fan blade parameter space, which represents the optimization scheme that does not significantly improve one side without deteriorating the other side between performance and noise control. This optimization strategy ensures that the fan blade design meets the high-efficiency air supply while minimizing the operating noise, improving user experience and product competitiveness.

[0111] The automatic optimization in step S2042 means that the algorithm independently traverses, evaluates, and optimizes the fan blade parameters without the need for repeated trial and error or subjective judgment by humans, greatly reducing the design cycle and lowering labor costs. The flexibility and power of programming languages such as Python make them ideal tools for implementing such multi-objective optimization, enabling rapid invocation of simulation software such as CFX for fluid dynamics and acoustic analysis, thereby accelerating the iteration speed of the overall design process.

[0112] This method can effectively explore the multi-dimensional space of fan blade parameters. As mentioned, parameters such as "chord length, installation angle, bend angle, and sweep angle" each have different effects on fan performance and noise levels. Through multi-objective optimization, designers can consider the effects of these parameters simultaneously, avoiding the local optimal trap that may result from single-parameter optimization. This means that even in the case of numerous parameters and complex interactions, designers can find a set of parameter combinations that can balance various design objectives, including but not limited to performance and noise.

[0113] Multi-objective optimization not only helps to find robust solutions for the current design, but also may reveal unexpected innovation space. Sometimes, through clever adjustment of parameters, designers may discover entirely new fan blade geometries that exceed expectations in both performance and noise. This design innovation often requires crossing the boundaries of traditional design thinking, and multi-objective optimization algorithms are exactly capable of exploring these innovative opportunities.

[0114] For example, suppose an original design of a fan performs with higher noise under high airflow conditions. Through a multi-objective optimization algorithm, designers can set an optimization process aimed at improving the airflow-to-power ratio while reducing noise levels. The algorithm may suggest increasing the chord length and sweep angle, while making moderate adjustments to the installation angle and bend angle, in order to achieve the best balance between the two objectives. The resulting target fan blade parameters can ensure that the fan operates efficiently while its noise level meets strict standards or user comfort requirements.

[0115] That is, by using Python to call CFX for automated simulation, a multi-objective optimization algorithm is written with the performance evaluation function and the noise evaluation function as the objective functions, with the maximum performance evaluation function and the minimum noise evaluation function as the optimization objectives, to optimize the fan blade parameters such as chord length, installation angle, bend angle, and sweep angle.

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

[0117] In order for those skilled in the art to more clearly understand the technical solutions of the present application, the implementation process of the fan blade parameter determination method of the fan will be described in detail below in conjunction with specific embodiments.

[0118] The present embodiment relates to a specific fan blade parameter determination method, as shown in the following formula (1), comprising: establishing a simplified geometric model according to an air conditioner outdoor unit model, and establishing a simulation model through simulation software Fluent / CFX, etc.; extracting the air volume and the torque of the blade around the rotating shaft after solving the flow field, calculating the shaft power according to the blade torque, and taking the ratio of the air volume to the power as a performance evaluation function; extracting the vorticity and the velocity vector in the simulation software, calculating the vortex sound function vortex source term, taking the absolute value and integrating in the rotating domain as a noise evaluation function; taking the performance evaluation function and the noise evaluation function as the multi-objective optimization objective function, and realizing the automatic optimization of the fan blade parameters. Figure 6

[0119] The above embodiment takes the ratio of the air volume to the power as the performance objective function, which can simultaneously consider the air volume and the power through a function; the absolute value of the vortex sound source in the vortex sound function is integrated as the noise objective function, which can quantitatively analyze the noise; at the same time, the absolute value of the vortex source term can be taken and displayed in the form of a cloud chart to identify the main sound emitting area of the fan blade. The noise function and the performance function can be taken as the objective function, and the fan blade parameters can be optimized through multi-objective optimization, while optimizing the fan blade noise and performance.

[0120] The present application also provides a fan blade parameter determination device, and it should be noted that the fan blade parameter determination device of the present application can be used to execute the fan blade parameter determination method provided by the present application. The device is used to realize the above-mentioned embodiments and preferred embodiments, and will not be described again. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware, or a combination of software and hardware can also be implemented and conceived.

[0121] The fan blade parameter determination device provided by the present application will be described below.

[0122] Figure 7 is a schematic diagram of the fan blade parameter determination device according to the present application. As shown in the figure, Figure 7 ​As shown, the device comprises a construction unit 10, a first determination unit 20, a second determination unit 30 and an optimization unit 40, the construction unit 10 is configured to construct a fan simulation model; the first determination unit 20 is configured to determine the current simulation parameters of the fan based on fluid dynamics according to the fan simulation model, the current simulation parameters of the fan include the fan air volume, the blade torque, the vorticity of the fan and the velocity vector of the fan, the fan air volume is the air volume at the outlet of the fan; the second determination unit 30 is configured to determine the performance evaluation function of the fan according to the fan air volume and the blade torque, and determine the noise evaluation function of the fan according to the vorticity and the velocity vector, the performance evaluation function represents the air volume and the power of the fan, and the noise evaluation function represents the noise of the fan; the optimization unit 40 is configured to automatically optimize the fan blade parameters based on the performance evaluation function and the noise evaluation function by using a multi-objective optimization algorithm, and obtain the target fan blade parameters of the fan, and the fan blade made by using the target fan blade parameters makes 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.

[0123] The determination device of the fan blade parameters of the fan in the application comprises a construction unit, a first determination unit, a second determination unit and an optimization unit, the construction unit is configured to construct a fan simulation model; the first determination unit is configured to determine the current simulation parameters of the fan based on fluid dynamics according to the fan simulation model, the second determination unit is configured to determine the performance evaluation function of the fan according to the fan air volume and the blade torque, and determine the noise evaluation function of the fan according to the vorticity and the velocity vector, and the optimization unit is configured to automatically optimize the fan blade parameters based on the performance evaluation function and the noise evaluation function by using a multi-objective optimization algorithm, and obtain the target fan blade parameters of the fan, and the fan blade made by using the target fan blade parameters makes 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 device considers the air volume and the power of the fan blade at the same time by using a performance objective function, and the noise function and the performance function can be used as the objective function, the fan blade parameters are optimized by using the multi-objective optimization, the noise and the performance of the fan blade are optimized at the same time, and the problem that the fan blade in the prior art depends on artificial design and it is difficult to realize the best balance between the performance and the noise is solved.

[0124] In some embodiments, the second determination unit comprises a first acquisition module, a first calculation module and a first determination module, the first acquisition module is configured to acquire the fan rotating speed; the first calculation module is configured to calculate the shaft power of the fan according to the fan rotating speed and the blade torque, and obtain the fan power; and the first determination module is configured to determine the ratio of the fan air volume and the fan power as the performance evaluation function. This evaluation method can seek a method to reduce power consumption on the premise of ensuring sufficient air volume, so as to achieve the purpose of energy saving and environmental protection.

[0125] In some embodiments, the first calculation module comprises a first determination submodule and a second determination submodule, the first determination submodule is configured to determine blade work according to the constant pi, the fan speed and the blade moment, the blade work is the work done by the blade of the fan in one revolution per unit time, and the blade work is the product of a first coefficient, the constant pi, the fan speed and the blade moment; the second determination submodule is configured to determine the ratio of the blade work to the unit time as a calculation power, and determine the ratio of the calculation power to a second coefficient as the fan power, and the second coefficient is a unit conversion coefficient for converting the power unit from watts to kilowatts. By combining the blade moment, the speed and the necessary constants and unit conversion coefficients, the actual working principle of the fan is directly associated, ensuring the accuracy and practicality of the calculation.

[0126] In some embodiments, the second determination unit comprises a second determination module, a third determination module and a fourth determination module, the second determination module is configured to determine a rotation domain of the fan simulation model, the rotation domain is an area including the blade of the fan and the fluid rotating together with the blade; the third determination module is configured to determine a vortex sound source term of a vortex sound function of the fan according to the vorticity and the velocity vector; and the fourth determination module is configured to determine the integral of the absolute value of the vortex sound source term in the rotation domain as the noise evaluation function. This greatly shortens the simulation time, especially for multiple iterations or multi-objective optimization design scenarios, improving the efficiency and cost-effectiveness of fan design.

[0127] In some embodiments, the third determination module comprises a first extraction submodule, a second extraction submodule and a calculation submodule, the first extraction submodule is configured to extract components of the vorticity in a first direction, a second direction and a third direction based on the fan simulation model to obtain a first vorticity component, a second vorticity component and a third vorticity component, the first direction, the second direction and the third direction are the positive directions of the three coordinate systems of a space rectangular coordinate system; the second extraction submodule is configured to extract components of the velocity vector in the first direction, the second direction and the third direction based on the fan simulation model to obtain a first velocity component, a second velocity component and a third velocity component; and the calculation submodule is configured to calculate the cross product of the vorticity and the velocity vector according to 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 calculate the divergence of the cross product to obtain the vortex sound source term. Through this method, the noise source of a rotating machine (such as a fan) can be accurately predicted. Compared with traditional methods, this method does not need to directly solve the transient sound field, reducing the calculation cost and time, while also improving the accuracy and reliability of noise prediction.

[0128] In some embodiments, the optimization unit comprises a fifth determination module and an optimization module. The fifth determination module is configured to determine the performance evaluation function and the noise evaluation function as a first objective function and a second objective function of a multi-objective optimization algorithm, respectively. The optimization module is configured to use the multi-objective optimization algorithm to automatically optimize the fan blade parameters of the fan, with the first objective function being maximized and the second objective function being minimized, to obtain the target fan blade parameters, wherein the target fan blade parameters at least include the chord length, the installation angle, the bending angle, and the sweep angle of the fan blade. The multi-objective optimization algorithm can be used to automatically optimize the fan blade parameters based on the performance evaluation function and the noise evaluation function, so that the energy efficiency and noise control of the fan can be considered simultaneously.

[0129] In some embodiments, the construction unit comprises a construction module and a sixth determination module. The construction module is configured to construct a simplified geometric model of the fan, the guide circle, and the axial flow fan blade. The cross section of the simplified geometric model is circular, and the axial flow fan blade in the simplified geometric model is three. The sixth determination module is configured to obtain a calculation domain by intercepting one third of the simplified geometric model along the circumferential direction of the cross section of the simplified geometric model, and determine the calculation domain as the fan simulation model. The interface of the calculation domain is a sector with a preset angle. The calculation domain includes one axial flow fan blade. The calculation domain includes a rotating domain and a stationary domain. The rotating domain is a region including the axial flow fan blade and the fluid rotating together with the axial flow fan blade. The stationary domain is all regions in the calculation domain except the rotating domain. By intercepting one third of the simplified geometric model along the circumferential direction of the cross section of the simplified geometric model as the calculation domain, the sector interception strategy takes advantage of the axial symmetry of the axial flow fan, and only 1 / 3 of the model needs to be simulated to represent the complete behavior of the fan, which further reduces the demand for computing resources.

[0130] The determination device of the fan blade parameters comprises a processor and a memory. The construction unit and the like are stored in the memory as program units. The corresponding functions are realized by the processor executing the program units stored in the memory. The modules are located in the same processor, or the modules are located in different processors in any combination.

[0131] The processor comprises a core, and the core retrieves the corresponding program unit from the memory. The core can be set to one or more, and the problem that the fan blade in the prior art depends on manual design and it is difficult to achieve the best balance between performance and noise can be solved by adjusting the core parameters.

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

[0133] The embodiment of the present application provides a computer readable storage medium, which comprises a stored program, wherein the program controls a device where the computer readable storage medium is located to perform the determination method of the fan blade parameter of the fan when the program is run.

[0134] The embodiment of the present application provides a processor, which is used for running a program, wherein the processor performs the determination method of the fan blade parameter of the fan when the program is run.

[0135] The embodiment of the present application provides a device, which comprises a processor, a memory and a program stored in the memory and capable of running on the processor, and the processor performs at least the following steps when the program is run:

[0136] Step S201, a fan simulation model is constructed;

[0137] Step S202, current simulation parameters of the fan are determined according to the fan simulation model based on fluid dynamics, wherein the current simulation parameters of the fan comprise a fan air volume, a blade torque, a vorticity of the fan and a velocity vector of the fan, the fan air volume is an air volume at an outlet of the fan;

[0138] Step S203, a performance evaluation function of the fan is determined according to the fan air volume and the blade torque, and a noise evaluation function of the fan is determined according to the vorticity and the velocity vector, wherein the performance evaluation function represents the air volume and the power of the fan, and the noise evaluation function represents the noise of the fan;

[0139] Step S204, a multi-objective optimization algorithm is used to automatically optimize the fan blade parameter based on the performance evaluation function and the noise evaluation function, and target fan blade parameters of the fan are obtained, and a fan blade made of the target fan blade parameters makes the noise of the fan less than a preset noise, the air volume greater than a preset air volume and the power less than a preset power.

[0140] The device herein can be a server, a PC, a PAD, a mobile phone or the like.

[0141] The present application further provides a computer program product, which is adapted to perform the program initialized with at least the following method steps when executed on a data processing device:

[0142] Step S201, a fan simulation model is constructed;

[0143] In step S202, the current simulation parameters of the fan are determined based on the fluid dynamics according to the fan simulation model, the current simulation parameters of the fan including the fan air volume, the blade torque, the vorticity of the fan, and the velocity vector of the fan, the fan air volume being the air volume at the outlet of the fan.

[0144] In step S203, the performance evaluation function of the fan is determined according to the fan air volume and the blade torque, and the noise evaluation function of the fan is determined according to the vorticity and the velocity vector, the performance evaluation function representing the air volume and the power of the fan, and the noise evaluation function representing the noise of the fan.

[0145] In step S204, the multi-objective optimization algorithm is used to automatically optimize the fan blade parameters based on the performance evaluation function and the noise evaluation function, and the target fan blade parameters of the fan are obtained, the fan blade made by using the target fan blade parameters making 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] Obviously, those skilled in the art should understand that the modules or steps of the present application can be realized by general computing devices, which can be concentrated on a single computing device or distributed on a network composed of multiple computing devices, and they can be realized by program codes executable by the computing devices, so that they can be stored in storage devices and executed by the computing devices, and in some cases, the steps shown or described can be executed in different order, or they can be made into individual integrated circuit modules, or multiple modules or steps can be made into a single integrated circuit module. Therefore, the present application is not limited to any specific hardware and software combination.

[0147] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can be in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can be in the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes.

[0148] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks.

[0149] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks.

[0150] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 Figure 1 The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks.

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

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

[0153] Computer-readable media includes permanent and non-permanent, movable and non-movable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules 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, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device. According to the definition herein, computer-readable media does not include transitory media such as modulated data signals and carriers.

[0154] The technical features of the above-described embodiments can be combined in any manner. In order to make the description concise, not all possible combinations of the technical features in the above-described embodiments are described, but as long as the combinations of the technical features do not exist contradictions, it should be considered as the scope of the present disclosure.

[0155] It should also be noted that the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusion, so that processes, methods, articles or devices including a series of elements not only include those elements, but also include other elements not explicitly listed, or inherent to such processes, methods, articles or devices. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0156] From the above description, it can be seen that the above-described embodiments of the present application achieve the following technical effects:

[0157] 1) The fan blade parameter determination method of the application, first constructs a fan simulation model; determines the current simulation parameters of the fan based on fluid dynamics according to the fan simulation model, then determines the performance evaluation function of the fan according to the fan air volume and blade torque, and determines the noise evaluation function of the fan according to the vorticity and velocity vector, and finally uses 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 the target fan blade parameters. The fan blade made of the target fan blade parameters makes 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. This method optimizes the fan blade by considering the air volume and power of the fan blade through a performance objective function, and the noise function and performance function can be used as the objective function. Through multi-objective optimization, the fan blade noise and performance are optimized simultaneously, solving the problem that the fan blade in the prior art relies on manual design and it is difficult to achieve the best balance between performance and noise.

[0158] 2) The fan blade parameter determination device of the application, comprising a construction unit, a first determination unit, a second determination unit and an optimization unit, the construction unit is used to construct a fan simulation model; the first determination unit is used to determine the current simulation parameters of the fan based on fluid dynamics according to the fan simulation model, the second determination unit is used to determine the performance evaluation function of the fan according to the fan air volume and blade torque, and determine the noise evaluation function of the fan according to the vorticity and velocity vector, the optimization unit is used to automatically optimize the fan blade parameters based on the performance evaluation function and the noise evaluation function by using a multi-objective optimization algorithm, to obtain the target fan blade parameters. The fan blade made of the target fan blade parameters makes 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. This device optimizes the fan blade by considering the air volume and power of the fan blade through a performance objective function, and the noise function and performance function can be used as the objective function. Through multi-objective optimization, the fan blade noise and performance are optimized simultaneously, solving the problem that the fan blade in the prior art relies on manual design and it is difficult to achieve the best balance between performance and noise.

[0159] The above only describes the preferred embodiments of the application and is not intended to limit the application. Those skilled in the art can make various changes and modifications to the application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the application shall be included in the protection scope of the application.

Claims

1. A method for determining a parameter of a fan blade of a fan, characterized in that, The method comprises the following steps: constructing a fan simulation model; 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 air volume, blade torque, vorticity of the fan and velocity vector of the fan, the fan air volume being air volume at an outlet of the fan; determining a performance evaluation function of the fan according to the fan air volume and the blade torque, and determining a noise evaluation function of the fan according to the vorticity and the velocity vector, the performance evaluation function representing air volume and power of the fan, and the noise evaluation function representing noise of the fan; adopting a multi-objective optimization algorithm to automatically optimize fan blade parameters of the fan based on the performance evaluation function and the noise evaluation function, to obtain target fan blade parameters of the fan, and the fan blade produced by using the target fan blade parameters makes the noise of the fan less than a preset noise, the air volume greater than a preset air volume, and the power less than a preset power.

2. The method of claim 1, wherein, The method of determining the performance evaluation function of the fan according to the fan air volume and the blade torque comprises the following steps: obtaining fan rotating speed; calculating shaft power of the fan according to the fan rotating speed and the blade torque to obtain fan power; determining the performance evaluation function as a ratio of the fan air volume to the fan power.

3. The method of claim 2, wherein, The method of calculating shaft power of the fan according to the fan rotating speed and the blade torque to obtain fan power comprises the following steps: determining blade work according to a circular constant, the fan rotating speed and the blade torque, the blade work being work done by a blade of the fan in one revolution per unit time, the blade work being a product of a first coefficient, the circular constant, the fan rotating speed and the blade torque; determining the fan power as a ratio of the blade work to the unit time, and determining the fan power as a ratio of the calculation power to a second coefficient, the second coefficient being a unit conversion coefficient for converting power unit from watts to kilowatts.

4. The method of claim 1, wherein, The method of determining the noise evaluation function of the fan according to the vorticity and the velocity vector comprises the following steps: determining a rotating domain of the fan simulation model, the rotating domain being a region including blades of the fan and fluid rotating together with the blades; determining a vortex sound source term of a vortex sound function of the fan according to the vorticity and the velocity vector; determining the noise evaluation function as an integral of an absolute value of the vortex sound source term in the rotating domain.

5. The method of claim 4, wherein, The method of determining the vortex sound source term of the vortex sound function of the fan according to the vorticity and the velocity vector comprises the following steps: extracting components of the vorticity in a first direction, a second direction and a third direction to obtain a first vorticity component, a second vorticity component and a third vorticity component based on the fan simulation model, the first direction, the second direction and the third direction being positive directions of three coordinate systems of a space rectangular coordinate system respectively; extracting components of the velocity vector in the first direction, the second direction and the third direction to obtain a first velocity component, a second velocity component and a third velocity component based on the fan simulation model; According to 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, a cross product of the vorticity and the velocity vector is calculated, and divergence of the cross product is calculated to obtain the vortex sound source term.

6. The method of claim 1, wherein, An automatic optimization of the fan blade parameters of the fan is performed based on the performance evaluation function and the noise evaluation function by using a multi-objective optimization algorithm to obtain target fan blade parameters of the fan, including: The performance evaluation function and the noise evaluation function are determined as a first objective function and a second objective function of the multi-objective optimization algorithm, respectively. An automatic optimization of the fan blade parameters of the fan is performed by using the multi-objective optimization algorithm with the first objective function being maximum and the second objective function being minimum as the optimization target to obtain the target fan blade parameters, the target fan blade parameters including at least a chord length, an installation angle, a bending angle and a sweep angle of the fan blade.

7. The method according to any one of claims 1 to 6, characterized in that, A fan simulation model is constructed, including: A simplified geometric model including a guide circle and an axial fan blade of the fan is constructed, a cross section of the simplified geometric model is circular, and the axial fan blade in the simplified geometric model is three; A third of the simplified geometric model is cut along a circumferential direction of the cross section of the simplified geometric model to obtain a calculation domain, and the calculation domain is determined as the fan simulation model, an interface of the calculation domain is a sector with a preset angle, the calculation domain includes one axial fan blade, and the calculation domain includes a rotating domain and a stationary domain, the rotating domain is a region including the axial fan blade and in which fluid rotates together with the axial fan blade, and the stationary domain is all regions in the calculation domain except the rotating domain.

8. A device for determining a parameter of a fan blade of a fan, characterized in that including: A construction unit is configured to construct a fan simulation model; A first determination unit is configured to determine, based on fluid dynamics, current simulation parameters of a fan according to the fan simulation model, the current simulation parameters of the fan including a fan air volume, a blade torque, a vorticity of the fan and a velocity vector of the fan, and the fan air volume being an air volume at an outlet of the fan; A second determination unit is configured to determine a performance evaluation function of the fan according to the fan air volume and the blade torque, and determine a noise evaluation function of the fan according to the vorticity and the velocity vector, the performance evaluation function representing a size of the fan air volume and a size of power, and the noise evaluation function representing a size of noise of the fan; An optimization unit is configured to perform an automatic optimization of fan blade parameters of the fan based on the performance evaluation function and the noise evaluation function by using a multi-objective optimization algorithm to obtain target fan blade parameters of the fan, and a fan blade manufactured by using the target fan blade parameters makes the noise of the fan less than a preset noise, the fan air volume greater than a preset fan air volume and the power less than a preset power.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium includes a stored program, wherein the program controls a device where the computer readable storage medium is located to perform the method for determining fan blade parameters of the fan in any one of claims 1 to 7 when the program is running.

10. An air conditioner characterized by comprising: including: One or more processors, memories, and one or more programs, wherein the one or more programs are stored in the memories and configured to be executed by the one or more processors, the one or more programs including programs for performing the method of determining the fan blade parameter of the fan according to any one of claims 1 to 7.

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