A fan blade icing simulation method and device

Abstract

The embodiment of the application provides a wind turbine blade icing simulation method and device, and relates to the technical field of data processing. The method specifically comprises: performing two-dimensional grid simulation and three-dimensional grid simulation on any wind turbine blade of a wind turbine generator set to generate two-dimensional blade grid data and three-dimensional blade grid data; combining the two-dimensional blade grid data and the three-dimensional blade grid data, performing air fluid simulation and gas-liquid coupling simulation on the wind turbine blade using a first meteorological condition to obtain air fluid simulation results and gas-liquid coupling simulation results corresponding to the wind turbine blade; and based on the air fluid simulation results and the gas-liquid coupling simulation results, simulating the icing process of the wind turbine blade in a non-rotating state and a rotating state respectively to obtain a first icing simulation result and a second icing simulation result corresponding to the first meteorological condition, so as to obtain an icing analysis result under the first meteorological condition. The application can simulate the icing process of the wind turbine blade to improve the safety of the wind power plant equipment.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of data processing, in particular to a wind turbine blade icing simulation method and device. BACKGROUND

[0002] The wind turbine blade is one of the key components for the normal operation of the wind turbine, and its performance directly affects the stability of the whole machine. In the winter low temperature environment, ice is often formed on the surface of the blade, which changes the shape and aerodynamic characteristics of the blade, affects the power generation of the wind turbine, and may also cause unplanned shutdown, increase the burden of power grid scheduling, and threaten the safety and stability of the power grid. Therefore, the icing condition of the wind turbine blade is simulated in advance to predict the blade icing and its influence on the unit output, which can help the wind farm to develop an anti-icing strategy in advance, reduce losses and improve economic benefits. The dispatching department can also optimize the operation plan according to the icing condition to ensure the safe and efficient operation of the power grid.

[0003] At present, when simulating the icing of the wind turbine blade, the wind tunnel experiment and numerical simulation method are usually used. The wind tunnel experiment usually controls the meteorological conditions in the wind tunnel to observe the blade icing process to obtain the icing type, strength and its influence on the aerodynamic characteristics. However, the wind tunnel experiment has high cost and long cycle, and is limited by the change of meteorological conditions.

[0004] Therefore, the existing icing research on the wind turbine blade mostly refers to the icing research results of the power transmission line and the aircraft wing, and there is still a lack of research on the icing process of the wind turbine blade, and it is difficult to accurately describe the surface temperature and moisture change of the wind turbine blade after icing.

[0005] Therefore, how to simulate the icing of the wind turbine blade has become a problem to be solved. SUMMARY

[0006] Therefore, the wind turbine blade icing simulation method and device provided by the embodiments of the present disclosure can simulate the icing process of the wind turbine blade to improve the safety of the wind farm equipment.

[0007] In a first aspect, the embodiments of the present disclosure provide a wind turbine blade icing simulation method, comprising:

[0008] performing two-dimensional grid simulation and three-dimensional grid simulation on any wind turbine blade of a wind turbine generator set to generate two-dimensional blade grid data and three-dimensional blade grid data;

[0009] combined with the two-dimensional blade grid data and the three-dimensional blade grid data, using a first meteorological condition to perform air fluid simulation and gas-liquid coupling simulation on the wind turbine blade to obtain the air fluid simulation result and the gas-liquid coupling simulation result corresponding to the wind turbine blade; the first meteorological condition includes wind speed, temperature and humidity;

[0010] Based on the air fluid simulation result and the gas-liquid coupling simulation result, icing processes of the fan blade in a non-rotating state and a rotating state are simulated respectively to obtain first icing simulation result and second icing simulation result corresponding to the first meteorological condition; the first icing simulation result is an icing simulation result of the fan blade in a non-rotating state; and the second icing simulation result is an icing simulation result of the fan blade in a rotating state.

[0011] According to the first icing simulation result and the second icing simulation result, an icing analysis result under the first meteorological condition is obtained.

[0012] As an optional implementation of the embodiment of the application, the two-dimensional grid simulation and the three-dimensional grid simulation of any fan blade of the wind turbine generator set are performed to generate two-dimensional blade grid data and three-dimensional blade grid data, including:

[0013] Obtaining blade airfoil coordinates corresponding to the fan blade;

[0014] Determining two-dimensional flow field domains and three-dimensional flow field domains corresponding to the two-dimensional grid simulation and the three-dimensional grid simulation respectively;

[0015] Combining the blade airfoil coordinates and the two-dimensional flow field domains and the three-dimensional flow field domains, the two-dimensional blade grid data and the three-dimensional blade grid data are generated.

[0016] As an optional implementation of the embodiment of the application, after the two-dimensional blade grid data and the three-dimensional blade grid data are generated by combining the blade airfoil coordinates and the two-dimensional flow field domains and the three-dimensional flow field domains, the method further includes:

[0017] When the quality of the two-dimensional blade grid data and the three-dimensional blade grid data is unqualified, smoothing transition processing and encrypted grid processing are performed on the two-dimensional blade grid data and the three-dimensional blade grid data.

[0018] As an optional implementation of the embodiment of the application, the air fluid simulation and the gas-liquid coupling simulation of the fan blade are performed using the first meteorological condition based on the two-dimensional blade grid data and the three-dimensional blade grid data to obtain air fluid simulation results and gas-liquid coupling simulation results corresponding to the fan blade, including:

[0019] Obtaining a target energy model, a target viscosity model and a target boundary condition;

[0020] performing air fluid simulation calculation on the fan blade based on the target energy model, the target viscosity model, the target boundary condition and the first meteorological condition, to obtain an air fluid simulation result;

[0021] obtain droplet physical condition; parameters in the droplet physical condition are used to describe the physical state of the droplet;

[0022] perform gas-liquid coupling simulation calculation on the fan blade based on the target boundary condition and the first meteorological condition, and the droplet physical condition, in combination with the air fluid simulation result, to obtain a gas-liquid coupling simulation result.

[0023] As an optional implementation of the embodiment of the application, the icing process of the fan blade in a non-rotating state and a rotating state is simulated based on the air fluid simulation result and the gas-liquid coupling simulation result, respectively, to obtain a first icing simulation result and a second icing simulation result corresponding to the first meteorological condition, including:

[0024] obtain icing physical condition; the icing physical condition includes recovery factor, relative humidity, target icing model and ice density;

[0025] For the fan blade in a non-rotating state, the icing process of the fan blade in a non-rotating state is simulated based on the air fluid simulation result and the gas-liquid coupling simulation result, through the icing physical condition, to obtain the first icing simulation result;

[0026] obtain blade rotation parameters and periodic boundary condition in a rotating state;

[0027] For the fan blade in a rotating state, the icing process of the fan blade in a rotating state is simulated based on the air fluid simulation result, the gas-liquid coupling simulation result, the blade rotation parameters and the periodic boundary condition in a rotating state, through the icing physical condition, to obtain the second icing simulation result.

[0028] As an optional implementation of the embodiment of the application, the icing analysis result under the first meteorological condition includes flow field velocity distribution, liquid water content distribution, droplet collection coefficient distribution and icing thickness distribution.

[0029] As an optional implementation of the embodiment of the application, the method further includes:

[0030] The air flow simulation and the gas-liquid coupling simulation are performed on the fan blade under the first meteorological condition by combining the two-dimensional blade grid data and the three-dimensional blade grid data, so as to obtain the air flow simulation result and the gas-liquid coupling simulation result corresponding to the fan blade, and the icing analysis result under the first meteorological condition is obtained according to the first icing simulation result and the second icing simulation result.

[0031] The icing law of the fan blade under different meteorological conditions is generated through the plurality of icing analysis results.

[0032] In a second aspect, the embodiments of the present application provide a fan blade icing simulation method and device, which comprises:

[0033] The grid simulation unit is configured to perform two-dimensional grid simulation and three-dimensional grid simulation on any fan blade of the wind turbine generator set, and generate two-dimensional blade grid data and three-dimensional blade grid data.

[0034] The processing unit is configured to combine the two-dimensional blade grid data and the three-dimensional blade grid data, and perform air flow simulation and gas-liquid coupling simulation on the fan blade under a first meteorological condition, so as to obtain the air flow simulation result and the gas-liquid coupling simulation result corresponding to the fan blade; the first meteorological condition comprises wind speed, temperature and humidity.

[0035] The icing process simulation unit is configured to simulate the icing process of the fan blade in a non-rotating state and a rotating state based on the air flow simulation result and the gas-liquid coupling simulation result, and obtain a first icing simulation result and a second icing simulation result corresponding to the first meteorological condition; the first icing simulation result is the icing simulation result of the fan blade in the non-rotating state; and the second icing simulation result is the icing simulation result of the fan blade in the rotating state.

[0036] The icing analysis result acquisition unit is configured to obtain the icing analysis result under the first meteorological condition according to the first icing simulation result and the second icing simulation result.

[0037] As an optional implementation manner of the embodiments of the present application, the grid simulation unit is specifically configured to obtain blade airfoil coordinates corresponding to the fan blade; determine a two-dimensional flow field and a three-dimensional flow field corresponding to the two-dimensional grid simulation and the three-dimensional grid simulation respectively; and combine the blade airfoil coordinates and the two-dimensional flow field and the three-dimensional flow field, so as to generate the two-dimensional blade grid data and the three-dimensional blade grid data.

[0038] As an optional implementation of the embodiment of the present application, the grid simulation unit is further configured to perform smoothing transition processing and encrypted grid processing on the two-dimensional blade grid data and the three-dimensional blade grid data when the quality of the two-dimensional blade grid data and the three-dimensional blade grid data is unqualified.

[0039] As an optional implementation of the embodiment of the present application, the processing unit is specifically configured to obtain a target energy model, a target viscosity model and a target boundary condition; perform air fluid simulation calculation on the fan blade based on the target energy model, the target viscosity model, the target boundary condition and the first meteorological condition to obtain an air fluid simulation result; obtain a droplet physical condition; a parameter in the droplet physical condition is used to describe a physical state of a droplet; and perform gas-liquid coupling simulation calculation on the fan blade based on the target boundary condition and the first meteorological condition and the droplet physical condition in combination with the air fluid simulation result to obtain a gas-liquid coupling simulation result.

[0040] As an optional implementation of the embodiment of the present application, the icing simulation result obtaining unit is specifically configured to obtain a icing physical condition; the icing physical condition includes a recovery factor, a relative humidity, a target icing model and an ice density; perform simulation on an icing process of the fan blade in a non-rotating state based on the air fluid simulation result and the gas-liquid coupling simulation result through the icing physical condition for the fan blade in the non-rotating state to obtain a first icing simulation result; obtain a blade rotation parameter and a periodic boundary condition in a rotating state; perform simulation on an icing process of the fan blade in the rotating state based on the air fluid simulation result, the gas-liquid coupling simulation result, the blade rotation parameter and the periodic boundary condition in the rotating state through the icing physical condition for the fan blade in the rotating state to obtain a second icing simulation result.

[0041] As an optional implementation of the embodiment of the present application, the icing analysis result in the first meteorological condition includes a flow field velocity distribution, a liquid water content distribution, a droplet collection coefficient distribution and an icing thickness distribution.

[0042] As an optional implementation of the embodiment of the present application, the icing simulation result obtaining unit is further configured to: for a plurality of different meteorological conditions, perform air fluid simulation and gas-liquid coupling simulation on the fan blade under a first meteorological condition by combining the two-dimensional blade grid data and the three-dimensional blade grid data, to obtain air fluid simulation results and gas-liquid coupling simulation results corresponding to the fan blade; obtain icing analysis results corresponding to the plurality of different meteorological conditions respectively according to the first icing simulation result and the second icing simulation result; and generate icing rules of the fan blade under the plurality of different meteorological conditions according to the plurality of icing analysis results.

[0043] In a third aspect, the embodiments of the present application provide an electronic device, including a memory and a processor, the memory is configured to store a computer program, and the processor is configured to cause the electronic device to implement the fan blade icing simulation method according to any one of the above embodiments when executing the computer program.

[0044] In a fourth aspect, the embodiments of the present application provide a computer readable storage medium, and the computer readable storage medium stores a computer program, and the computer program causes a computing device to implement the fan blade icing simulation method according to any one of the above embodiments when the computer program is executed by the computing device.

[0045] The fan blade icing simulation method provided in the embodiments of the application specifically comprises: performing two-dimensional grid simulation and three-dimensional grid simulation on any fan blade of a wind turbine generator set to generate two-dimensional blade grid data and three-dimensional blade grid data; combining the two-dimensional blade grid data and the three-dimensional blade grid data, using a first meteorological condition to perform air fluid simulation and gas-liquid coupling simulation on the fan blade to obtain air fluid simulation results and gas-liquid coupling simulation results corresponding to the fan blade; the first meteorological condition comprises wind speed, temperature and humidity; based on the air fluid simulation results and the gas-liquid coupling simulation results, the icing process of the fan blade in a non-rotating state and a rotating state is simulated respectively to obtain first icing simulation results and second icing simulation results corresponding to the first meteorological condition; the first icing simulation results are the icing simulation results of the fan blade in the non-rotating state; the second icing simulation results are the icing simulation results of the fan blade in the rotating state; and the icing analysis results under the first meteorological condition are obtained according to the first icing simulation results and the second icing simulation results. The above steps are used to simulate the icing of the fan blade, obtain the first icing simulation results in the non-rotating state and the second icing simulation results in the rotating state, and then simulate the icing of the fan blade under the first meteorological condition, including the formation speed, distribution characteristics of the icing and the dynamic influence on the performance of the fan, thereby making up for the lack of research on the icing process of the fan blade in the prior art, and then accurately describing the surface temperature and moisture change of the fan blade after icing through the icing analysis results, which is helpful for subsequent development of corresponding anti-icing strategies. BRIEF DESCRIPTION OF DRAWINGS

[0046] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and serve to explain the principles of the application together with the specification.

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, below will briefly introduce the drawings needed to be invoked in the embodiments or the prior art description. Obviously, for those skilled in the art, other drawings can also be obtained from these drawings without any creative labor.

[0048] Figure 1 One of the step flow charts of the fan blade icing simulation method provided in the embodiments of the application;

[0049] Figure 2 The second step flow chart of the fan blade icing simulation method provided in the embodiments of the application;

[0050] Figure 3 The structural schematic diagram of the fan blade icing simulation method device provided in the embodiments of the application;

[0051] Figure 4 A hardware structure schematic diagram of an electronic device provided in an embodiment of the present application is shown in the following. DETAILED DESCRIPTION

[0052] In order to more clearly understand the above-mentioned purposes, features and advantages of the present disclosure, the solutions of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0053] In the following description, a large number of specific details are set forth in order to facilitate a thorough understanding of the present disclosure, but the present disclosure can also be implemented in other manners different from those described herein; obviously, the embodiments described in the specification are only a part of the embodiments of the present disclosure, and not all the embodiments.

[0054] In the embodiments of the present application, the words such as "exemplary" or "for example" are used to mean serving as an example, instance, or illustration. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or having more advantages than other embodiments or design schemes. Rather, the words such as "exemplary" or "for example" are intended to present the relevant concept in a specific manner. In addition, in the description of the embodiments of the present application, the meaning of "plurality" is two or more, unless otherwise specified.

[0055] It should be noted that in this document, the term "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or other elements inherent to such a process, method, article or device. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or device including the element.

[0056] The embodiments of the present application provide a fan blade icing simulation method, referring to FIG. 1, the fan blade icing simulation method includes the following steps S101-S104: Figure 1

[0057] S101, performing two-dimensional grid simulation and three-dimensional grid simulation on any fan blade of a wind turbine generator set, to generate two-dimensional blade grid data and three-dimensional blade grid data.

[0058] ​In some embodiments, when simulating the icing process of the wind turbine blade, it is necessary to first perform two-dimensional grid simulation and three-dimensional grid simulation on any wind turbine blade of the wind turbine generator set, because the two-dimensional blade grid data is the basis for numerical calculation in a two-dimensional plane, which provides a discrete calculation area for solving physical equations, facilitating the calculation of air flow and water droplet motion related physical quantities by numerical methods. And it helps to focus on the key physical phenomena of the blade section, clearly showing the motion trajectory of water droplets in the plane, the impact position, and the heat transfer, water droplet spreading and freezing in the initial stage of icing, etc.

[0059] Three-dimensional grid data can accurately restore the geometric shape of the wind turbine blade in three-dimensional space and the surrounding flow field environment, fully present the icing growth of the blade surface and the three-dimensional motion trajectory of water droplets, especially accurately simulate the icing shape of special parts such as the blade tip. It is also the key to accurately calculating the three-dimensional aerodynamic performance of the blade after icing, and it supports the comprehensive simulation of multiple coupled physical processes, can consider the interaction of air flow, water droplet motion and icing growth in three-dimensional space, and truly reflect the actual situation, providing support for studying icing mechanism and deicing strategy.

[0060] Further, it is necessary to first perform two-dimensional grid simulation and three-dimensional grid simulation on any wind turbine blade of the wind turbine generator set to generate two-dimensional blade grid data and three-dimensional blade grid data.

[0061] Specifically, the specific steps of performing two-dimensional grid simulation and three-dimensional grid simulation on any wind turbine blade of the wind turbine generator set to generate two-dimensional blade grid data and three-dimensional blade grid data include the following steps 1 to 3:

[0062] Step 1, obtaining the blade airfoil coordinates corresponding to the wind turbine blade.

[0063] In some embodiments, the blade airfoil coordinates are coordinate data of the blade airfoil, which are usually stored in a text file in the form of (X, Y, Z) coordinates. The coordinate data should be arranged in a certain order, generally recording the coordinate values of each point along the airfoil profile in sequence, and ensuring the accuracy and integrity of the data.

[0064] Step 2, determining the two-dimensional flow field domain and the three-dimensional flow field domain corresponding to the two-dimensional grid simulation and the three-dimensional grid simulation respectively.

[0065] In some embodiments, when performing the two-dimensional grid simulation and the three-dimensional grid simulation on the fan blade, the respective corresponding calculation range and boundary conditions need to be determined in advance to ensure that the subsequent air flow process and icing process can be accurately simulated. The two-dimensional flow field region includes the complete physical region around the two-dimensional cross section of the fan blade, including the incoming flow region, the blade vicinity region and the downstream region. The meteorological conditions of the incoming flow region are crucial for simulating the process of water droplets moving towards the blade under the action of air flow. The blade vicinity region is the core region of the icing process, where water droplets impact the blade and undergo phase change. The two-dimensional flow field region needs to accurately cover this region to facilitate the study of the motion trajectory, impact position and icing initiation of water droplets on the blade cross section. The downstream region helps to observe the state change of the fluid after passing through the blade, such as the influence of the wake on the subsequent water droplet motion. The three-dimensional flow field region needs to construct a complete three-dimensional calculation space related to the fan blade, including the blade and sufficient space around it, so as to truly simulate the whole process of air flowing around the blade, water droplets impacting the blade in three-dimensional space and icing on the blade surface.

[0066] For example, the two-dimensional flow field region and the three-dimensional flow field region can be C-shaped two-dimensional flow field region and C-shaped three-dimensional flow field region,

[0067] Because in actual operation, the fluid (such as air) around the fan blade flows from a distance, passes through the blade and then flows to a distance. The C-shaped two-dimensional flow field region and the C-shaped three-dimensional flow field region can well simulate the physical process of such fluid flow. It includes the incoming flow region, the near-field region around the blade (including the boundary layer and the wake region) and the outflow region downstream, which completely covers the whole process of fluid interaction with the blade. For example, when simulating the aerodynamic performance and icing process of the fan blade, the C-shaped flow field region can accurately capture how air with water droplets approaches the blade, produces complex flow phenomena (such as boundary layer separation, vortex formation, etc.) on the blade surface, and changes the flow state after leaving the blade.

[0068] At the same time, the C-shaped flow field region has clear inlet and outlet boundaries, which makes it convenient to set the inlet and outlet conditions of the fluid when performing grid simulation. At the inlet boundary, the physical parameters of the incoming flow can be set according to the actual situation (such as the environmental conditions of the fan operation, the design working condition, etc.), including velocity (size and direction), temperature, pressure, liquid water content, etc. These parameters provide initial conditions for the simulation of the whole flow field, determining how the fluid and water droplets enter the calculation region and interact with the blade. The outlet boundary condition can be set in different forms such as free outflow, given pressure outlet, etc. according to the nature of the flow field and the requirements of the simulation, to simulate the state of the fluid after leaving the flow field region, making the simulation more consistent with the physical reality.

[0069] Step 3, combining the blade airfoil coordinates and the two-dimensional flow field domain and the three-dimensional flow field domain, generating the two-dimensional blade grid data and the three-dimensional blade grid data.

[0070] In some embodiments, the specific implementation method of combining the blade airfoil coordinates and the two-dimensional flow field domain and the three-dimensional flow field domain to generate the two-dimensional blade grid data and the three-dimensional blade grid data can be: inputting the blade airfoil coordinates into the ICEM tool in the ANSYS software system, and performing two-dimensional unstructured grid division on the fan blade by ICEM (Integrated Computer-Aided Engineering and Manufacturing). Due to the complex geometry and special flow phenomena of the tip portion of the fan blade, the unstructured grid division function of ICEM can well adapt to this area and accurately simulate the fluid flow and icing conditions near the blade tip.

[0071] ANSYS is a large general-purpose engineering simulation software, widely used in many engineering fields, including aerospace, automobile, machinery, electronics, energy, etc. It provides a complete engineering simulation solution, covering simulation analysis of multiple physical disciplines such as structural mechanics, fluid mechanics, electromagnetism, and heat conduction. ICEM is mainly used as a powerful pre-processing tool in the ANSYS software system, focusing on meshing.

[0072] S102, combining the two-dimensional blade grid data and the three-dimensional blade grid data, using the first meteorological condition to perform air fluid simulation and gas-liquid coupling simulation on the fan blade to obtain the corresponding air fluid simulation result and gas-liquid coupling simulation result of the fan blade.

[0073] The first meteorological condition includes wind speed, temperature, and humidity.

[0074] In the study of fan blades, air fluid simulation is used to analyze the aerodynamic performance of the blade. By simulating the flow process of air around the blade, information such as pressure distribution and velocity distribution on the blade surface can be obtained. This helps to evaluate the lift and drag characteristics of the blade, and further optimizes the geometry of the blade to improve the efficiency of the fan. For example, when designing new fan blades, air fluid simulation can be used to compare the influence of different airfoils, different twist angles, and other factors on the performance of the blade, so as to select the optimal design scheme.

[0075] The gas-liquid coupling simulation can deeply understand the formation mechanism of icing by simulating the two-phase flow of air and water droplets, and the processes of water droplet impact, spreading, freezing, etc. on the blade surface. This is very important for predicting the location, thickness, shape, etc. of icing, and can provide a basis for the development of anti-icing and de-icing strategies. For example, the icing of the blade under different meteorological conditions (such as wind speed, liquid water content, temperature, etc.) can be simulated, and the effects of different anti-icing measures (such as heating, coating, etc.) can be evaluated.

[0076] For example, the wind speed in the first meteorological condition can be set to 10 meters per second, the temperature to 270K, and the humidity to 100%.

[0077] S103, based on the air flow simulation result and the gas-liquid coupling simulation result, simulate the icing process of the fan blade in a non-rotating state and a rotating state respectively, and obtain a first icing simulation result and a second icing simulation result corresponding to the first meteorological condition.

[0078] The first icing simulation result is the icing simulation result of the fan blade in a non-rotating state; and the second icing simulation result is the icing simulation result of the fan blade in a rotating state.

[0079] In some embodiments, in order to more comprehensively obtain the icing simulation process of the fan blade, considering that the fan blade has two states of rotation and non-rotation, the embodiments of the present application simulate the icing process of the fan blade in a non-rotating state and a rotating state respectively, and obtain a first icing simulation result and a second icing simulation result corresponding to the first meteorological condition.

[0080] Specifically, by simulating the icing process of the fan blade in a non-rotating state, the impact, spreading, and freezing of water droplets on the blade surface can be simulated, and the initial conditions and key factors of icing formation under different meteorological conditions (such as wind speed, temperature, and humidity) can be determined. Since the blade is in a non-rotating state, it is easier to analyze the heat exchange process between the blade and the surrounding air and water droplets, and to simulate the shape of the icing on the blade surface, such as the flatness, thickness variation, and presence or absence of local accumulation of the ice layer. These icing shape characteristics are important for evaluating the impact of icing on the aerodynamic performance of the blade, as different icing shapes will change the surface roughness and geometry of the blade, thereby affecting the performance of the fan; providing basic data for evaluating the impact of icing on the aerodynamic performance of the blade. Although the blade is rotating when the fan is running, the shape change and surface roughness change caused by icing in the non-rotating state will also affect the aerodynamic performance, such as reduced lift coefficient and increased drag coefficient. These data can be used as a reference for subsequent performance evaluation in the rotating state, and help to preliminarily screen anti-icing strategies.

[0081] By simulating the icing process of the fan blade in a rotating state, the actual operating conditions of the fan can be simulated; in actual applications, the fan blade is in a high-speed rotating state, and rotation will have many complex effects on the icing process, such as centrifugal force and Coriolis force. By simulating icing in the rotating state, the actual situation of fan icing in the natural environment can be more realistically reflected, including the formation speed, distribution characteristics of icing, and dynamic impact on fan performance.

[0082] Since the rotation of the blade will cause dynamic changes in the flow field around the blade, such as periodic air flow velocity and pressure distribution. This dynamic flow field will affect the motion trajectory of water droplets and the way they impact the blade. By simulating the icing process in the rotating state, the impact of dynamic factors on icing can be further studied; due to the rotational motion of the blade, icing will not only change the surface shape and roughness of the blade, but also affect the parameters such as the angle of attack and torque of the blade. By simulating the icing process in the rotating state, the changes in these parameters can be accurately evaluated, and the impact of icing on the performance indicators of the fan, such as output power, efficiency, and stability, can be accurately analyzed, providing key data for the safe operation and performance optimization of the fan.

[0083] Further, by simulating the icing process of the fan blade in the non-rotating state and the rotating state under the first meteorological condition, a first icing simulation result and a second icing simulation result corresponding to the first meteorological condition are obtained. The first icing simulation result can provide a basis for further optimizing and verifying the anti-icing strategy in a more complex rotating state through the first icing simulation result and the second icing simulation result. The second icing simulation result can reflect the icing condition of the fan blade under the first meteorological condition, including the formation speed of the icing, the distribution characteristics, and the dynamic influence on the performance of the fan, which is helpful for subsequent specification of the corresponding anti-icing strategy.

[0084] In some embodiments, the first meteorological condition is used to simulate the icing process of the blade in the non-rotating state and the rotating state to obtain the flow field velocity distribution, the liquid water content distribution, the droplet collection coefficient distribution, and the icing thickness distribution on the fan blade under the current first meteorological condition, and further generate the icing analysis result under the first meteorological condition, so as to master the icing condition of the blade under the current first meteorological condition through the icing analysis result.

[0085] In some embodiments, the first meteorological condition is used to simulate the icing process of the blade in the non-rotating state and the rotating state to obtain the flow field velocity distribution, the liquid water content distribution, the droplet collection coefficient distribution, and the icing thickness distribution on the fan blade under the current first meteorological condition, and further generate the icing analysis result under the first meteorological condition, so as to master the icing condition of the blade under the current first meteorological condition through the icing analysis result.

[0086] It should be noted that after the execution of the above step S104 ends, different meteorological conditions can be set to execute the above steps S102 to S104 for a plurality of different meteorological conditions to obtain the icing analysis result of the fan blade under different meteorological conditions, and further generate the icing law of the fan blade under a plurality of different meteorological conditions.

[0087] Meanwhile, the icing analysis result of the fan blade under different meteorological conditions can be saved to generate an icing database of the fan blade, which provides reliable data reference for subsequent anti-icing design of the fan blade, and has strong intuitiveness. Further, it is helpful for the wind farm to formulate anti-icing, de-icing, and operation control strategies in advance, reduce economic losses, and improve economic benefits; the dispatching department can also optimize the operation plan according to the icing condition to ensure the safe and efficient operation of the power grid.

[0088] The wind turbine blade icing simulation method provided in the embodiment of the present application is specifically as follows: performing two-dimensional grid simulation and three-dimensional grid simulation on any wind turbine blade of a wind turbine generator set to generate two-dimensional blade grid data and three-dimensional blade grid data; combining the two-dimensional blade grid data and the three-dimensional blade grid data, using first meteorological conditions to perform air fluid simulation and gas-liquid coupling simulation on the wind turbine blade to obtain air fluid simulation results and gas-liquid coupling simulation results corresponding to the wind turbine blade; the first meteorological condition includes: wind speed, temperature and humidity; based on the air fluid simulation results and the gas-liquid coupling simulation results, simulating the icing process of the wind turbine blade in a non-rotating state and a rotating state respectively, to obtain a first icing simulation result and a second icing simulation result corresponding to the first meteorological condition; the first icing simulation result is an icing simulation result of the wind turbine blade in a non-rotating state; the second icing simulation result is an icing simulation result of the wind turbine blade in a rotating state; based on the first icing simulation result and the second icing simulation result, obtaining an icing analysis result under the first meteorological condition. The present application realizes icing simulation of wind turbine blades through the above steps, obtains a first icing simulation result in a non-rotating state and a second icing simulation result in a rotating state, and thus can simulate the icing situation of wind turbine blades under first meteorological conditions, including the formation speed and distribution characteristics of icing and the dynamic impact on wind turbine performance, thereby making up for the lack of research on the icing process of wind turbine blades in the prior art, and then accurately describes the surface temperature and moisture changes of wind turbine blades after icing through the icing analysis results, which is helpful for the subsequent formulation of corresponding anti-icing strategies.

[0089] As a refinement and expansion of the above embodiment, refer to Figure 2 As shown, the wind turbine blade icing simulation method includes the following steps S201-S210:

[0090] S201 , performing two-dimensional mesh simulation and three-dimensional mesh simulation on any wind turbine blade of a wind turbine generator set to generate two-dimensional blade mesh data and three-dimensional blade mesh data.

[0091] S202: Obtain a target energy model, a target viscosity model, and target boundary conditions.

[0092] The method for combining the two-dimensional blade grid data and the three-dimensional blade grid data in step S102 and performing air flow simulation on the fan blade using the first meteorological condition can be: importing the two-dimensional blade grid data and the three-dimensional blade grid data into Fluent (Fluent is a computational fluid dynamics (CFD) software in the ANSYS software system), and enabling an energy model based on the grid data in the boundary condition to activate the solution of the energy equation, derive the heat transfer and temperature change in the flow field, and select a viscosity model and enable viscosity heating to infer the heat generated by the viscosity of the fluid. Therefore, the target energy model, the target viscosity model, and the target boundary condition need to be obtained first.

[0093] Specifically, the target energy model can be a multiphase flow energy model, because the icing process of the fan blade involves two-phase flow of air and water droplets. In addition to considering the energy equation of each phase itself, this model also needs to consider the energy transfer between phases. For example, in the Euler-Euler multiphase flow model, an energy equation is established for the air phase and the liquid phase, and the energy coupling relationship between the two phases is described through interphase energy transfer terms (such as interphase heat conduction, latent heat exchange, etc.). This model can accurately simulate the complex energy interaction process between gas and liquid two phases, and is crucial for the simulation of the icing process.

[0094] Specifically, the target viscosity model can be a k-omega-SST viscosity model to calculate the heat generated by the viscosity of the fluid, which is developed on the basis of the k-omega model and the k-epsilon model. The k-omega model can well simulate the turbulent flow in the boundary layer, but may have some problems in the free flow area far from the wall; the k-epsilon model has an advantage in handling turbulent flow in the free flow area, but has slightly poor accuracy in the near-wall region. The k-omega-SST model combines the advantages of these two models by using a blending function to use different models in different regions, thereby improving the simulation accuracy of the entire flow field (especially complex flow fields containing boundary layers and main flow regions).

[0095] Specifically, the target boundary condition includes: an inlet boundary condition, a blade wall boundary condition, and an outlet boundary condition; the target boundary condition is used to define the physical environment boundary of the calculation region; wherein the inlet boundary condition is used to set the physical state of air entering the calculation region, and the inlet boundary condition can include the wind speed, temperature, humidity, etc. of the inlet, and in the embodiments of the present application, the wind speed, temperature, and humidity of the inlet can be set as the wind speed in the first meteorological condition, for example: the wind speed is 10 m / s, the temperature is 270 K, and the humidity is 100%.

[0096] The blade wall boundary condition is used to define the interaction between air and the surface of the fan blade. In the embodiments of the application, the influence of the temperature of the surface of the fan blade on the icing process is mainly considered, and the temperature of the blade in the blade wall boundary condition is set, for example, can be set to 280K.

[0097] The outlet boundary condition is used to describe the situation after the air leaves the calculation region. Common outlet boundary conditions include pressure outlet and velocity outlet. In the embodiments of the application, the outlet boundary condition can be set to a pressure outlet condition to assume the pressure state at the outlet according to the actual situation, so as to simulate the pressure state of the air fluid flowing out of the calculation region. When the air naturally flows out of one region to an external environment with relatively stable pressure, the pressure state at the outlet has an important influence on the flow characteristics of the entire flow field. For the fan blade, the pressure outlet condition can more truly reflect the state of the air flowing out of the calculation region under the push of the blade.

[0098] S203, based on the target energy model, the target viscosity model, the target boundary condition and the first meteorological condition, performing air fluid simulation calculation on the fan blade to obtain the air fluid simulation result.

[0099] It should be noted that when the Fluent software is used for air fluid simulation, a segregated solver is used to solve the continuity equation and the motion equation one by one, and the variable value is obtained through linear solution of adjacent elements; and the non-steady two-phase flow of only air and supercooled water droplets in the flow field is set, and the gravity and heat conduction of the air are not considered. The near-wall treatment adopts the high-efficiency and practical standard wall function method, and the pressure-velocity coupling adopts the SIMPLE algorithm, and the Navier-Stokes equation is solved on the staggered grid, and the second-order upwind format is used to process momentum and turbulent kinetic energy.

[0100] The segregated solver is the target solver, and the target solver can use a coupled solver. The coupled solver is suitable for complex flow field problems, especially in the case of strong coupling relationship (such as the coupling of pressure and velocity). It can converge to a stable solution faster. The coupled solver simultaneously solves the continuity equation (related to mass conservation, which includes the relationship between velocity and density) and the momentum equation (describing the relationship between velocity and pressure), which can well handle the pressure change caused by the change of velocity and the reaction of velocity to the change of pressure, thereby more truly simulating the actual flow of air.

[0101] In the air flow simulation calculation of the fan blade based on the target energy model, the target viscosity model, the target boundary condition and the first meteorological condition, standard initialization and iteration step number setting are needed to start the simulation calculation to calculate the speed related data, pressure related data, turbulence characteristic data, boundary layer related data and the like of each grid, and then the flow field simulation result can be saved for subsequent analysis after the simulation calculation is completed (the iteration step number is set or the convergence condition is met). All simulation result data including the geometric information of the calculation domain, the grid information and the flow field variable are saved in the flow field simulation result.

[0102] It should be noted that when performing fluid simulation calculation, the solver needs an initial flow field state to start the iterative solving process. The standard initialization is to provide an initial guess value for the flow field variables (such as speed, pressure, temperature, etc.) in the entire calculation region. Through standard initialization, reasonable initial speed and pressure guess values can be assigned to the areas near and far from the blade for the first step of calculation, and then gradually iterated; the size of the iteration step number usually needs to be adjusted according to the complexity of the problem, the grid quality and the convergence requirement. Generally, a smaller iteration step number can be tried first, and the convergence trend of the solution can be observed. If the solution does not converge, the iteration step number can be gradually increased.

[0103] S204, acquire a droplet physical condition.

[0104] The parameters in the droplet physical condition are used to describe the physical state of the droplet.

[0105] In some embodiments, the droplet physical condition can include liquid water content, droplet diameter, monodisperse distribution, droplet resistance model, etc.; wherein the setting of the liquid water content (LWC) is crucial for accurately simulating the icing degree of the fan blade. Under actual meteorological conditions, the amount of liquid water in the air directly determines the thickness of the ice layer that can be accumulated on the blade surface. For example, when the LWC is high, it means that more water droplets can hit and adhere to the blade, and then freeze to form a thicker ice layer; while a lower LWC will result in a relatively slow icing process and a thinner ice layer. Exemplarily, the liquid water content (LWC) can be set to 0.001 kg / m 3 .

[0106] Different diameters of droplets have very different behaviors after hitting the blade. Smaller droplets have smaller inertia and are more likely to bypass the blade with the airflow or spread evenly on the blade surface; while larger droplets are more likely to directly hit the blade due to their large inertia, and may form local water accumulation areas after hitting. These water accumulation areas are more likely to freeze in a low temperature environment, thereby affecting the shape and position of the ice, exemplarily, the droplet diameter can be set to 20 microns,

[0107] Assuming a monodisperse distribution of droplets allows the calculation to focus on the fundamental behavior of the droplets and their interaction with the blade, without being disturbed by the complex variations of the droplet size distribution. At the same time, under certain meteorological conditions, the droplet size distribution can be relatively concentrated, and the assumption of monodisperse distribution can be used as an approximation to simulate this situation, providing a basis for understanding and analyzing the icing process.

[0108] The droplet resistance model can accurately calculate the trajectory of the droplets in the airflow, which is crucial for determining the location and distribution of icing on the blade. The airflow around the fan blade is complex, and the trajectory of the droplets in this complex airflow is influenced by various factors, including airflow velocity, blade rotation (if the blade is in a rotating state), and droplet resistance. Through an accurate droplet resistance model, it can be simulated how droplets move in the airflow and eventually impact the blade, determining which areas of the blade are more likely to be impacted by droplets and thus more prone to icing.

[0109] S205, in combination with the air fluid simulation result, based on the target boundary condition and the first meteorological condition, and the droplet physical condition, the gas-liquid coupling simulation calculation of the fan blade is carried out, and the gas-liquid coupling simulation result is obtained.

[0110] Specifically, in combination with the air fluid simulation result, based on the target boundary condition and the first meteorological condition, and the droplet physical condition, the gas-liquid coupling simulation calculation of the fan blade can be: import the air fluid simulation result into Fluent Icing module, Fluent Icing module is a functional module in Fluent software specially used for simulating icing process. It is built on the basis of Fluent's powerful computational fluid dynamics (CFD), providing a special solution for studying physical phenomena involving ice accumulation.

[0111] Then, based on the droplet physical condition, and using the same target boundary condition as the air fluid simulation, to match the flow field of the air fluid, the particle (Particles) solving method is selected, based on the CFL number, the number of iterations, the artificial viscosity coefficient, and the residual truncation value, the gas-liquid coupling simulation calculation of the fan blade is carried out, and the gas-liquid coupling simulation result is obtained.

[0112] Particles solving method is usually used in scenarios involving interaction between discrete particles (such as liquid droplets in gas-liquid coupling simulation) and continuous phase (such as air). It can track the trajectory of particles in the flow field, the interaction with the surrounding environment (such as momentum and energy exchange with the fluid), simulate the behavior of particles in the entire calculation area by analyzing the force acting on each particle and solving its motion equation, and thus more accurately reflect the dynamic characteristics of particles in the actual physical process. CFL (Courant-Friedrichs-Lewy) number is a dimensionless number used to measure the stability and accuracy of numerical solution in computational fluid dynamics. It is defined based on the relationship between the velocity of fluid flow, time step and space step.

[0113] The artificial viscosity coefficient is a viscous term artificially introduced in numerical calculation to handle some physical phenomena (such as simulation of discontinuous phenomena such as shock waves) or to improve the stability of numerical calculation; for example, the artificial viscosity coefficient can be set to 1e-5, and a smaller artificial viscosity coefficient means that the introduced artificial viscosity is relatively weak. If there are discontinuous phenomena that need to be handled in the simulation process (such as simulating shock waves in high-speed air flow), a smaller artificial viscosity coefficient may help to smooth the handling of these discontinuous phenomena to some extent, while minimizing the impact on the original flow field and particle motion characteristics. However, if the artificial viscosity coefficient is set too small, it may not be able to effectively handle some situations that require strong viscosity to handle, resulting in unstable calculations or inaccurate simulation of certain physical phenomena.

[0114] The residual truncation value refers to the error measure between the current solution and the exact solution after each iteration in the numerical solution process. The residual truncation value is a standard for determining whether the iteration can stop; for example, the residual truncation value can be 1e-8, and when the residual is less than the residual truncation value, it can be considered that the calculation has converged to a solution that meets the accuracy requirements, and thus the residual truncation value can be set to obtain a calculation result that meets the accuracy requirements.

[0115] It should be noted that in the solving process of gas-liquid coupling simulation, it is necessary to set that the supercooled water droplets are uniformly distributed in the air, and the air flow is not affected by the movement of the water droplets; the physical properties of the water droplets remain unchanged during the movement process, the temperature is consistent with the environment temperature, and no heat exchange occurs with the air; during the movement process, the water droplets are subjected to the combined action of their own gravity, air resistance and buoyancy.

[0116] S206, obtaining icing physical conditions.

[0117] The icing physical conditions include a recovery factor, a relative humidity, a target icing model, and an ice density.

[0118] In some embodiments, the recovery factor in the icing physics condition is primarily used to account for the effects of factors such as surface roughness on airflow. In numerical simulations, it can be used to modify the boundary layer momentum equation. As airflow passes over a blade surface, surface characteristics (such as roughness) alter the energy loss and recovery of the airflow. For example, the recovery factor can be set to 0.9.

[0119] Relative humidity is a key indicator of the water vapor content in the air. A relative humidity of 100% means the air is saturated. Under these conditions, water vapor easily condenses on cold blade surfaces, providing an ample source of water for ice formation. This is one of the fundamental conditions for ice formation; without sufficient water vapor, significant ice cannot form.

[0120] The target icing model is primarily used to simulate the type and process of icing. In this embodiment, the target icing model can be a glaze icing model, which takes into account the motion of water droplets on the blade surface (such as rolling or stagnant water droplets) as well as heat and mass transfer processes. This model can more realistically simulate the microscopic process of blade icing, such as how water droplets freeze and accumulate on the blade surface under different temperature and airflow conditions. This plays a key role in predicting ice shape and distribution.

[0121] Ice density is an important physical parameter. In ice simulation, it is used to calculate physical quantities such as ice mass and volume. Once the ice density is known, combined with the simulated ice volume change (such as the increase in thickness), the mass of ice on the blade can be calculated. The commonly used ice density is 917 kg / m 3 .

[0122] Combined with the above-mentioned physical conditions for icing, based on the first meteorological conditions and target boundary conditions consistent with the air fluid simulation, the Fluent software will first calculate the flow field, then simulate the process of droplets impacting the blades, and finally calculate the ice coverage on the blades based on the liquid water distribution.

[0123] S207. For the fan blade in the non-rotating state, based on the air fluid simulation result and the gas-liquid coupling simulation result, and through the icing physical conditions, simulate the icing process of the fan blade in the non-rotating state to obtain the first icing simulation result.

[0124] Specifically, for the fan blade in the non-rotating state, the airflow velocity, pressure and temperature distribution around the blade and other information need to be obtained from the air fluid simulation results, which determine the position and angle of water droplet impact on the blade, etc. At the same time, the blade surface temperature boundary condition and the like are set to the temperature obtained by the air fluid simulation, providing a basic condition for the icing simulation. Then, according to the dynamic behavior of water droplets on the blade surface in the gas-liquid coupling simulation results, including collision, coalescence, rebound and spreading, etc., the initial distribution and motion state of the water droplets are determined. And the heat and mass transfer information is used to calculate the freezing rate of the water droplets. Finally, based on the icing physical conditions, the volume and mass change of ice are calculated, and the ice accumulation process on the blade surface is gradually simulated, so as to complete the simulation of the icing process.

[0125] S208, obtaining blade rotation parameters and periodic boundary conditions in the rotating state.

[0126] In some embodiments, unlike the icing simulation of the fan blade in the non-rotating state, the icing simulation of the fan blade in the rotating state needs to set the blade rotation parameters and the periodic boundary conditions in the rotating state.

[0127] Specifically, the blade rotation parameters include the rotation speed, rotation direction and rotation axis of the blade, and the rotation speed of the blade directly affects the relative motion between the blade and the surrounding air and water droplets. Higher rotation speed will change the collision frequency and relative speed between the blade surface and the air and water droplets, which has an important influence on the icing process. Different rotation directions will cause differences in the impact distribution of the airflow and water droplets on the blade surface, thereby affecting the shape and distribution of the icing. The rotation axis defines the central axis of the blade rotation. For a typical horizontal axis fan, the rotation axis is horizontal, which determines the geometric position of the blade rotation around it. Its position and direction affect the relative motion relationship between each part of the blade and the airflow and water droplets.

[0128] Specifically, the periodic boundary conditions in the rotating state include spatial periodicity and temporal periodicity. The spatial periodicity refers to the periodicity of the blade rotation, and appropriate spatial periodic boundary conditions are set. For example, the rotation of the blade is regarded as a periodically repeated process during simulation, so that only one period or a few periods can be simulated, rather than a lengthy simulation of the entire continuous rotation process, which not only saves computing resources but also reflects the overall icing characteristics.

[0129] The temporal periodicity is to set the boundary conditions based on the periodic characteristics of the blade rotation. It is determined how the state of the blade surface (such as temperature, airflow velocity, etc.) at each time in a rotation period is connected with the next period, to ensure the coherence and accuracy of the simulation, so as to accurately present the evolution of the icing in the continuous rotation process.

[0130] S209, for the fan blade in the rotating state, based on the air flow simulation result, the gas-liquid coupling simulation result, the blade rotation parameter, and the periodic boundary condition in the rotating state, the icing physical condition is used to simulate the icing process of the fan blade in the rotating state, and the second icing simulation result is obtained.

[0131] Specifically, for the fan blade in the rotating state, the air flow simulation result and the gas-liquid coupling simulation result also need to be combined; the previously obtained air flow simulation result is used, but at this time, the new situation brought by blade rotation needs to be considered. For example, rotation will cause dynamic changes in the airflow velocity distribution on the surface of the blade, which is no longer the case in the static state. The airflow velocity needs to be corrected and reanalyzed according to the rotation parameter to accurately determine the dynamic trajectory of the water droplets impacting the blade. Combined with the gas-liquid coupling simulation result, the new dynamic behavior of the water droplets on the blade surface in the rotating state is analyzed. Rotation can change the collision, coalescence, rebound, and spreading of water droplets, and these processes need to be reevaluated according to the new conditions, and the heat and mass transfer conditions also need to be reconsidered, because rotation will affect the relative motion and contact time between water droplets and the blade surface, and thus affect the freezing rate of water droplets.

[0132] Then, according to the icing simulation in the non-rotating state, the ice volume and mass change are calculated by combining the icing physical condition. In the rotating state, the accumulation and distribution of ice will dynamically change with the rotation of the blade. To accurately simulate this change, the state of the ice is constantly updated according to the rotation parameter and the periodic boundary condition, thereby realizing accurate simulation of the icing process of the blade in the rotating state to obtain the second icing simulation result.

[0133] S210, according to the first icing simulation result and the second icing simulation result, the icing analysis result under the first meteorological condition is obtained.

[0134] Specifically, according to the first icing simulation result and the second icing simulation result, the icing analysis result under the first meteorological condition includes the flow field velocity distribution, the liquid water content (LWC) distribution, the droplet collection coefficient distribution, and the icing thickness distribution on the fan blade in the rotating state and the non-rotating state. According to the icing analysis result of the fan blade under the current first meteorological condition, it can be obtained that the airflow velocity decreases sharply after passing through the blade, and the liquid water in the air is significantly reduced due to adhesion to the blade. The simulation result shows that icing mainly occurs in the middle and rear sections of the blade, especially at the blade tip position.

[0135] The embodiment of the application obtains the icing simulation result of the fan blade in the non-rotating state, i.e., the first icing simulation result, and the icing simulation result of the fan blade in the rotating state, i.e., the second icing simulation result, by simulating the icing of the fan blade in the non-rotating state and the rotating state, thereby comprehensively grasping the icing condition of the blade under the first meteorological condition, helping the wind farm to formulate the anti-icing, de-icing and operation control strategy in advance, reducing the economic loss and improving the economic benefit, and enabling the dispatching department to optimize the operation plan according to the icing condition, so as to guarantee the safe and efficient operation of the power grid.

[0136] Based on the same inventive concept, as an implementation of the above method, the embodiment of the application also provides a fan blade icing simulation method device, which corresponds to the foregoing method embodiment. For ease of reading, the details in the foregoing method embodiment will not be repeated here, but it should be clear that the fan blade icing simulation method device in this embodiment can correspondingly implement all the contents in the foregoing method embodiment.

[0137] The embodiment of the application provides a fan blade icing simulation method device, Figure 3 The structural diagram of the fan blade icing simulation method device is shown in Figure 3 The fan blade icing simulation method device 300 comprises:

[0138] The grid simulation unit 301 is configured to perform two-dimensional grid simulation and three-dimensional grid simulation on any fan blade of the wind turbine generator set, and generate two-dimensional blade grid data and three-dimensional blade grid data.

[0139] The processing unit 302 is configured to perform air fluid simulation and gas-liquid coupling simulation on the fan blade using the first meteorological condition in combination with the two-dimensional blade grid data and the three-dimensional blade grid data, so as to obtain the air fluid simulation result and the gas-liquid coupling simulation result corresponding to the fan blade; the first meteorological condition comprises wind speed, temperature and humidity.

[0140] The icing process simulation unit 303 is configured to simulate the icing process of the fan blade in the non-rotating state and the rotating state based on the air fluid simulation result and the gas-liquid coupling simulation result, so as to obtain the first icing simulation result and the second icing simulation result corresponding to the first meteorological condition; the first icing simulation result is the icing simulation result of the fan blade in the non-rotating state; the second icing simulation result is the icing simulation result of the fan blade in the rotating state.

[0141] The icing analysis result acquisition unit 304 is configured to acquire the icing analysis result under the first meteorological condition according to the first icing simulation result and the second icing simulation result.

[0142] As an optional implementation of the embodiment of the present application, the grid simulation unit 301 is specifically configured to obtain blade airfoil coordinates corresponding to the fan blade; determine a two-dimensional flow field and a three-dimensional flow field corresponding to the two-dimensional grid simulation and the three-dimensional grid simulation respectively; and generate the two-dimensional blade grid data and the three-dimensional blade grid data in combination with the blade airfoil coordinates and based on the two-dimensional flow field and the three-dimensional flow field.

[0143] As an optional implementation of the embodiment of the present application, the grid simulation unit 301 is further configured to perform smoothing transition processing and encrypted grid processing on the two-dimensional blade grid data and the three-dimensional blade grid data when the quality of the two-dimensional blade grid data and the three-dimensional blade grid data is unqualified.

[0144] As an optional implementation of the embodiment of the present application, the processing unit 302 is specifically configured to obtain a target energy model, a target viscosity model and a target boundary condition; perform air fluid simulation calculation on the fan blade based on the target energy model, the target viscosity model, the target boundary condition and the first meteorological condition, to obtain an air fluid simulation result; obtain a droplet physical condition; a parameter in the droplet physical condition is used to describe a physical state of a droplet; and perform gas-liquid coupling simulation calculation on the fan blade based on the target boundary condition and the first meteorological condition and the droplet physical condition in combination with the air fluid simulation result, to obtain a gas-liquid coupling simulation result.

[0145] As an optional implementation of the embodiment of the present application, the icing simulation result obtaining unit 303 is specifically configured to obtain icing physical conditions; the icing physical conditions include a recovery factor, a relative humidity, a target icing model and an ice density; perform simulation on an icing process of the fan blade in a non-rotating state based on the air fluid simulation result and the gas-liquid coupling simulation result through the icing physical conditions, to obtain a first icing simulation result for the fan blade in the non-rotating state; obtain blade rotation parameters and a periodic boundary condition in a rotating state; and perform simulation on an icing process of the fan blade in the rotating state based on the air fluid simulation result, the gas-liquid coupling simulation result, the blade rotation parameters and the periodic boundary condition in the rotating state through the icing physical conditions, to obtain a second icing simulation result for the fan blade in the rotating state.

[0146] As an optional implementation of the embodiment of the present application, the icing analysis result under the first meteorological condition includes a flow field velocity distribution, a liquid water content distribution, a droplet collection coefficient distribution and an icing thickness distribution.

[0147] As an optional implementation of the embodiment of the present application, the icing simulation result obtaining unit 304 is further configured to: for a plurality of different meteorological conditions, perform air fluid simulation and gas-liquid coupling simulation on the fan blade under a first meteorological condition by combining the two-dimensional blade grid data and the three-dimensional blade grid data, to obtain air fluid simulation results and gas-liquid coupling simulation results corresponding to the fan blade; and obtain icing analysis results corresponding to the plurality of different meteorological conditions respectively according to the first icing simulation result and the second icing simulation result; and generate icing rules of the fan blade under the plurality of different meteorological conditions based on the plurality of icing analysis results.

[0148] Based on the same inventive concept, the embodiment of the present disclosure further provides an electronic device. Figure 4 The structural schematic diagram of the electronic device provided by the embodiment of the present disclosure is shown in Figure 4 The electronic device provided by the embodiment of the present disclosure includes a memory 401 and a processor 402, the memory 401 is configured to store a computer program, and the processor 402 is configured to execute the computer program to execute the processing method of the audio data provided by the above-mentioned embodiment.

[0149] Based on the same inventive concept, the embodiment of the present disclosure further provides a computer readable storage medium, and the computer readable storage medium stores a computer program. When the computer program is executed by a processor, the computer device implements the fan blade icing simulation method provided by the above-mentioned embodiment.

[0150] 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 computer usable program code.

[0151] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

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

[0153] Computer readable media includes permanent and non-permanent, removable and non-removable media, which can be implemented by any method or technology for storing information, which 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, 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] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for simulating icing on a wind turbine blade, characterized in that, The method comprises the steps of: performing two-dimensional grid simulation and three-dimensional grid simulation on any wind blade of a wind turbine generator set to generate two-dimensional blade grid data and three-dimensional blade grid data; performing air fluid simulation and gas-liquid coupling simulation on the wind blade under the first meteorological condition by combining the two-dimensional blade grid data and the three-dimensional blade grid data to obtain air fluid simulation results and gas-liquid coupling simulation results corresponding to the wind blade; the first meteorological condition comprises wind speed, temperature and humidity; based on the air fluid simulation results and the gas-liquid coupling simulation results, simulating the icing process of the wind blade in a non-rotating state and a rotating state respectively to obtain first icing simulation results and second icing simulation results corresponding to the first meteorological condition; the first icing simulation results are the icing simulation results of the wind blade in a non-rotating state; the second icing simulation results are the icing simulation results of the wind blade in a rotating state; obtaining icing analysis results under the first meteorological condition according to the first icing simulation results and the second icing simulation results; the step of performing air fluid simulation and gas-liquid coupling simulation on the wind blade under the first meteorological condition by combining the two-dimensional blade grid data and the three-dimensional blade grid data comprises the steps of: obtaining a target energy model, a target viscosity model and a target boundary condition; performing air fluid simulation calculation on the wind blade based on the target energy model, the target viscosity model, the target boundary condition and the first meteorological condition to obtain the air fluid simulation results; obtaining liquid droplet physical conditions; parameters in the liquid droplet physical conditions are used to describe the physical state of the liquid droplets; performing gas-liquid coupling simulation calculation on the wind blade based on the target boundary condition and the first meteorological condition and the liquid droplet physical conditions in combination with the air fluid simulation results to obtain the gas-liquid coupling simulation results; the step of simulating the icing process of the wind blade in a non-rotating state and a rotating state respectively based on the air fluid simulation results and the gas-liquid coupling simulation results to obtain first icing simulation results and second icing simulation results corresponding to the first meteorological condition comprises the steps of: obtaining icing physical conditions; the icing physical conditions comprise a recovery factor, relative humidity, a target icing model and ice density; for the wind blade in a non-rotating state, simulating the icing process of the wind blade in a non-rotating state based on the air fluid simulation results and the gas-liquid coupling simulation results through the icing physical conditions to obtain the first icing simulation results; obtaining blade rotation parameters and periodic boundary conditions in a rotating state; for the wind blade in a rotating state, simulating the icing process of the wind blade in a rotating state based on the air fluid simulation results, the gas-liquid coupling simulation results, the blade rotation parameters and the periodic boundary conditions in a rotating state through the icing physical conditions to obtain the second icing simulation results.

2. The method of claim 1, wherein, The two-dimensional grid simulation and the three-dimensional grid simulation are performed on any wind blade of a wind turbine generator set to generate two-dimensional blade grid data and three-dimensional blade grid data, including: Obtaining blade airfoil coordinates corresponding to the wind blade; Determining a two-dimensional flow field domain and a three-dimensional flow field domain corresponding to the two-dimensional grid simulation and the three-dimensional grid simulation, respectively; Combining the blade airfoil coordinates and the two-dimensional flow field domain and the three-dimensional flow field domain to generate the two-dimensional blade grid data and the three-dimensional blade grid data.

3. The method of claim 2, wherein, After the combining the blade airfoil coordinates and the two-dimensional flow field domain and the three-dimensional flow field domain to generate the two-dimensional blade grid data and the three-dimensional blade grid data, the method further includes: When the quality of the two-dimensional blade grid data and the three-dimensional blade grid data is unqualified, performing smoothing transition processing and encrypted grid processing on the two-dimensional blade grid data and the three-dimensional blade grid data.

4. The method of claim 1, wherein, The icing analysis result under the first meteorological condition includes flow field velocity distribution, liquid water content distribution, droplet collection coefficient distribution, and icing thickness distribution.

5. The method of claim 1, wherein, The method further includes: For a plurality of different meteorological conditions, using a first meteorological condition to perform air fluid simulation and gas-liquid coupling simulation on the wind blade by combining the two-dimensional blade grid data and the three-dimensional blade grid data to obtain air fluid simulation results and gas-liquid coupling simulation results corresponding to the wind blade, and obtaining icing analysis results corresponding to the plurality of different meteorological conditions respectively according to the first icing simulation result and the second icing simulation result; Generating icing rules of the wind blade under a plurality of different meteorological conditions through a plurality of the icing analysis results.

6. A wind turbine blade icing simulation method apparatus characterized by, Including: A grid simulation unit for performing two-dimensional grid simulation and three-dimensional grid simulation on any wind blade of a wind turbine generator set to generate two-dimensional blade grid data and three-dimensional blade grid data; A processing unit for combining the two-dimensional blade grid data and the three-dimensional blade grid data, using a first meteorological condition to perform air fluid simulation and gas-liquid coupling simulation on the wind blade to obtain air fluid simulation results and gas-liquid coupling simulation results corresponding to the wind blade; The first meteorological condition includes wind speed, temperature, and humidity; An icing process simulation unit for simulating an icing process of the wind blade in a non-rotating state and a rotating state based on the air fluid simulation results and the gas-liquid coupling simulation results to obtain a first icing simulation result and a second icing simulation result corresponding to the first meteorological condition; the first icing simulation result is an icing simulation result of the wind blade in a non-rotating state; and the second icing simulation result is an icing simulation result of the wind blade in a rotating state; An icing analysis result acquisition unit for obtaining an icing analysis result under the first meteorological condition according to the first icing simulation result and the second icing simulation result; The processing unit is specifically configured to obtain a target energy model, a target viscosity model, and a target boundary condition; perform air fluid simulation calculation on the fan blade based on the target energy model, the target viscosity model, the target boundary condition, and the first meteorological condition, to obtain an air fluid simulation result; obtain a droplet physical condition; a parameter in the droplet physical condition is used to describe a physical state of a droplet; and perform gas-liquid coupling simulation calculation on the fan blade based on the target boundary condition and the first meteorological condition, and the droplet physical condition, in combination with the air fluid simulation result, to obtain a gas-liquid coupling simulation result. The icing simulation result obtaining unit is specifically configured to obtain a icing physical condition; the icing physical condition includes a recovery factor, a relative humidity, a target icing model, and an ice density; for the fan blade in a non-rotating state, perform simulation on an icing process of the fan blade in the non-rotating state based on the air fluid simulation result and the gas-liquid coupling simulation result, through the icing physical condition, to obtain a first icing simulation result; obtain a blade rotation parameter and a periodic boundary condition in a rotating state; and for the fan blade in the rotating state, perform simulation on an icing process of the fan blade in the rotating state based on the air fluid simulation result, the gas-liquid coupling simulation result, the blade rotation parameter, and the periodic boundary condition in the rotating state, through the icing physical condition, to obtain a second icing simulation result.

7. An electronic device, comprising: Comprise: A memory and a processor, the memory is used to store a computer program; the processor is used to make the electronic device realize the fan blade icing simulation method in any one of claims 1-5 when executing the computer program.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium has a computer program stored thereon, and when the computer program is executed by a computing device, the computing device realizes the fan blade icing simulation method in any one of claims 1-5.

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