Fan blade icing simulation method and device

Abstract

The embodiment of the invention provides a fan blade icing simulation method and device, and relates to the technical field of data processing.The method specifically comprises the steps that two-dimensional grid simulation and three-dimensional grid simulation are conducted on any fan blade of a wind generating set, and two-dimensional blade grid data and three-dimensional blade grid data are generated; in combination with the two-dimensional blade grid data and the three-dimensional blade grid data, performing air fluid simulation and gas-liquid coupling simulation on the fan blade by using a first meteorological condition to obtain an air fluid simulation result and a gas-liquid coupling simulation result corresponding to the fan blade; based on the air fluid simulation result and the gas-liquid coupling simulation result, the icing process of the fan blade in the non-rotating state and the icing process of the fan blade in the rotating state are simulated, a first icing simulation result and a second icing simulation result corresponding to the first meteorological condition are obtained, and an icing analysis result under the first meteorological condition is obtained. According to the invention, the icing process of the fan blade can be simulated, so that the safety of wind power plant equipment is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of data processing, and in particular to a method and device for simulating ice coating on wind turbine blades. Background Art

[0002] Wind turbine blades are one of the key components for the normal operation of wind turbines, and their performance directly affects the stability of the entire machine. In the low temperature environment of winter, ice often forms on the surface of the blades, which changes the shape and aerodynamic characteristics of the blades, affects the power generation of the wind turbine, and may also cause unplanned shutdowns, increase the burden on grid dispatch, and threaten the safety and stability of the grid. Therefore, the icing of wind turbine blades will be simulated in advance to predict blade icing and its impact on the output of the unit, which can help wind farms formulate anti-icing strategies in advance, reduce losses and improve economic benefits. The dispatching department can also optimize the operation plan according to the icing situation to ensure the safe and efficient operation of the grid.

[0003] At present, wind tunnel experiments and numerical simulation methods are mostly used to simulate the icing of wind turbine blades. Among them, wind tunnel experiments usually control meteorological conditions in the wind tunnel to observe the blade icing process in order to obtain the type and intensity of icing and its impact on aerodynamic characteristics. However, wind tunnel experiments are costly and time-consuming, and are limited by changing meteorological conditions.

[0004] As a result, existing research on icing of wind turbine blades mostly refers to the icing results of power transmission lines and aircraft wings. There is still a lack of research on the icing process of wind turbine blades, and it is difficult to accurately describe the changes in surface temperature and moisture of wind turbine blades after icing.

[0005] Therefore, how to simulate ice covering on wind turbine blades has become an urgent problem to be solved. Summary of the invention

[0006] In view of this, an embodiment of the present application provides a method and device for simulating icing of wind turbine blades, which can simulate the icing process of wind turbine blades to improve the safety of wind farm equipment.

[0007] In a first aspect, an embodiment of the present application provides a method for simulating ice coating on wind turbine blades, comprising:

[0008] Performing two-dimensional mesh simulation and three-dimensional mesh simulation on any wind turbine blade of the wind turbine generator set to generate two-dimensional blade mesh data and three-dimensional blade mesh data;

[0009] Combining the two-dimensional blade mesh data and the three-dimensional blade mesh data, using a first meteorological condition to perform air fluid simulation and gas-liquid coupling simulation on the fan blade to obtain an air fluid simulation result and a gas-liquid coupling simulation result corresponding to the fan 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, the icing process of the fan blade in the non-rotating state and the rotating state is simulated 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 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;

[0011] An ice covering analysis result under the first meteorological condition is obtained according to the first ice covering simulation result and the second ice covering simulation result.

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

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

[0014] 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;

[0015] The two-dimensional blade mesh data and the three-dimensional blade mesh data are generated in combination with the blade airfoil coordinates and based on the two-dimensional flow field and the three-dimensional flow field.

[0016] As an optional implementation of the embodiment of the present application, after combining the blade airfoil coordinates and generating the two-dimensional blade mesh data and the three-dimensional blade mesh data based on the two-dimensional flow field and the three-dimensional flow field, the method further includes:

[0017] When the quality of the two-dimensional blade mesh data and the three-dimensional blade mesh data is unqualified, smooth transition processing and mesh encryption processing are performed on the two-dimensional blade mesh data and the three-dimensional blade mesh data.

[0018] As an optional implementation of the embodiment of the present application, the combining the two-dimensional blade mesh data and the three-dimensional blade mesh data, using the first meteorological condition to perform air fluid simulation and gas-liquid coupling simulation on the fan blade to obtain the air fluid simulation result and gas-liquid coupling simulation result corresponding to the fan blade, includes:

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

[0020] 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;

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

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

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

[0024] Acquiring icing physical conditions; the icing physical conditions include recovery factor, relative humidity, target icing model and ice density;

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

[0026] Obtain blade rotation parameters and periodic boundary conditions under the rotating state;

[0027] For 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 conditions in the rotating state, the icing process of the fan blade in the rotating state is simulated through the icing physical conditions to obtain the second icing simulation result.

[0028] As an optional implementation of the embodiment of the present application, the ice cover analysis results under the first meteorological conditions include flow field velocity distribution, liquid water content distribution, droplet collection coefficient distribution and ice cover thickness distribution.

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

[0030] For multiple different meteorological conditions, by 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 air fluid simulation results and gas-liquid coupling simulation results corresponding to the fan blade, to the step of obtaining an icing analysis result under the first meteorological condition according to the first icing simulation result and the second icing simulation result, and obtaining icing analysis results corresponding to multiple different meteorological conditions respectively;

[0031] The icing patterns of the wind turbine blades under a plurality of different meteorological conditions are generated through the plurality of icing analysis results.

[0032] In a second aspect, an embodiment of the present application provides a method and device for simulating ice coating on wind turbine blades, comprising:

[0033] A grid simulation unit is used to 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;

[0034] a processing unit, configured to combine the two-dimensional blade mesh data and the three-dimensional blade mesh data, and use a first meteorological condition to perform air fluid simulation and gas-liquid coupling simulation on the fan blade to obtain an air fluid simulation result and a gas-liquid coupling simulation result corresponding to the fan blade; the first meteorological condition includes: wind speed, temperature and humidity;

[0035] an icing process simulation unit, configured to simulate the icing process of the fan blade in a non-rotating state and a rotating state respectively based on the air fluid 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 an icing simulation result of the fan blade in a non-rotating state; the second icing simulation result is an icing simulation result of the fan blade in a rotating state;

[0036] An icing analysis result acquisition unit is used to acquire an 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 of the embodiment of the present application, the grid simulation unit is specifically used to obtain the blade airfoil coordinates corresponding to the wind turbine blade; determine the two-dimensional flow field and the three-dimensional flow field corresponding to the two-dimensional grid simulation and the three-dimensional grid simulation respectively; combine the blade airfoil coordinates, and generate the two-dimensional blade grid data and the three-dimensional blade grid data based on the two-dimensional flow field and the three-dimensional flow field.

[0038] As an optional implementation of the embodiment of the present application, the mesh simulation unit is also used to perform smooth transition processing and mesh encryption processing on the two-dimensional blade mesh data and the three-dimensional blade mesh data when the quality of the two-dimensional blade mesh data and the three-dimensional blade mesh data is unqualified.

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

[0040] As an optional implementation manner of the embodiment of the present application, the icing simulation result acquisition unit is specifically used to obtain icing physical conditions; the icing physical conditions include recovery factor, relative humidity, target icing model and ice density; for the fan blades in the non-rotating state, based on the air fluid simulation results and the gas-liquid coupling simulation results, the icing process of the fan blades in the non-rotating state is simulated through the icing physical conditions to obtain the first icing simulation result; obtain blade rotation parameters and periodic boundary conditions in the rotating state; for the fan blades in the 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 the rotating state, the icing process of the fan blades in the rotating state is simulated through the icing physical conditions to obtain the second icing simulation result.

[0041] As an optional implementation of the embodiment of the present application, the ice cover analysis results under the first meteorological conditions include flow field velocity distribution, liquid water content distribution, droplet collection coefficient distribution and ice cover thickness distribution.

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

[0043] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a memory and a processor, wherein the memory is used to store a computer program; and the processor is used to enable the electronic device to implement the wind turbine blade icing simulation method described in any of the above embodiments when executing the computer program.

[0044] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a computing device, the computing device implements the wind turbine blade icing simulation method described in any one of the above embodiments.

[0045] 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 a first meteorological condition to perform air fluid simulation and gas-liquid coupling simulation on the wind turbine blade to obtain an air fluid simulation result and a gas-liquid coupling simulation result corresponding to the wind turbine blade; the first meteorological condition includes: wind speed, temperature and humidity; based on the air fluid simulation result and the gas-liquid coupling simulation result, icing processes of the wind turbine blade in a non-rotating state and a rotating state are simulated 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; according to the first icing simulation result and the second icing simulation result, an icing analysis result under the first meteorological condition is obtained. The present application implements icing simulation on 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 then 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. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings required in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0048] Figure 1 One of the step flow charts of the wind turbine blade icing simulation method provided in the embodiment of the present application;

[0049] Figure 2 A second flow chart of the steps of the wind turbine blade icing simulation method provided in the embodiment of the present application;

[0050] Figure 3 A schematic diagram of the structure of a wind turbine blade icing simulation method and device provided in an embodiment of the present application;

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

[0052] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme 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, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0054] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way. In addition, in the description of the embodiments of the present application, unless otherwise specified, the meaning of "multiple" refers to two or more.

[0055] It should be noted that, in this article, the term "comprises" 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 includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0056] The present application provides a method for simulating ice coating on wind turbine blades. Figure 1 As shown, the wind turbine blade icing simulation method includes the following steps S101-S104:

[0057] S101, 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.

[0058] In some embodiments, when simulating the icing process of wind turbine blades, it is necessary to first perform two-dimensional mesh simulation and three-dimensional mesh simulation on any wind turbine blade of the wind turbine generator set, because the two-dimensional blade mesh data is the basis for numerical calculations in the two-dimensional plane, which provides a discretized calculation area for solving physical equations, and is convenient for using numerical methods to calculate physical quantities related to air flow and water droplet motion. It also helps to focus on the key physical phenomena of the blade cross section, clearly showing the motion trajectory and impact position of water droplets in the plane, as well as heat transfer, water droplet spreading and freezing at the initial stage of icing.

[0059] The three-dimensional grid data can accurately restore the geometric shape of the wind turbine blades in three-dimensional space and the surrounding flow field environment, fully present the ice growth on the blade surface and the three-dimensional motion trajectory of water droplets, and can especially accurately simulate the ice morphology of special parts such as the blade tip. It is the key to accurately calculate the three-dimensional aerodynamic performance of the blade after ice coating, and it supports the comprehensive simulation of multiple coupled physical processes, which can consider the interaction between air flow, water droplet movement and ice growth in three-dimensional space, truly reflect the actual situation, and provide support for the study of ice coating mechanism and anti-icing and de-icing strategies.

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

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

[0062] Step 1: Obtain the blade airfoil coordinates corresponding to the fan blade.

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

[0064] Step 2: Determine the two-dimensional flow field and the three-dimensional flow field corresponding to the two-dimensional grid simulation and the three-dimensional grid simulation respectively.

[0065] In some embodiments, when performing two-dimensional grid simulation and three-dimensional grid simulation on the fan blade, it is necessary to first determine the corresponding calculation range and boundary conditions to ensure that the air flow process and ice coating process can be accurately simulated later. The two-dimensional flow field includes the complete physical area around the two-dimensional cross-section of the fan blade, including the incoming flow area, the area near the blade and the downstream area; wherein, the meteorological conditions in the incoming flow area are critical to simulating the process of water droplets moving toward the blade driven by the airflow; the area near the blade is the core area of ​​the ice coating process, where water droplets hit the blade and undergo phase change, and the two-dimensional flow field needs to accurately cover this area to facilitate the study of the motion trajectory, impact position and ice coating of water droplets on the blade cross section; the downstream area is helpful to observe the state changes of the fluid after passing through the blade, such as the influence of the wake on the subsequent water droplet movement. The three-dimensional flow field needs to construct a complete three-dimensional calculation space related to the fan blade, including the blade and enough space around it, so as to truly simulate the whole process of air bypassing the blade, water droplets hitting the blade in three-dimensional space and ice coating on the blade surface.

[0066] Exemplarily, the two-dimensional flow field and the three-dimensional flow field may be a C-shaped two-dimensional flow field and a C-shaped three-dimensional flow field.

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

[0068] At the same time, the C-shaped flow field has clear inlet and outlet boundaries, which makes it easy 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, design conditions, etc.), including speed (magnitude and direction), temperature, pressure, liquid water content, etc. These parameters provide the initial conditions for the simulation of the entire flow field and determine how the fluid and water droplets enter the calculation area and interact with the blades. The outlet boundary conditions can be set to different forms such as free outflow and given pressure outlet according to the properties of the flow field and the requirements of the simulation to simulate the state of the fluid after leaving the flow field, making the simulation more in line with physical reality.

[0069] Step 3: Combining the blade airfoil coordinates and based on the two-dimensional flow field and the three-dimensional flow field, generate the two-dimensional blade mesh data and the three-dimensional blade mesh data.

[0070] In some embodiments, the specific implementation method of combining the blade airfoil coordinates and generating the two-dimensional blade mesh data and the three-dimensional blade mesh data based on the two-dimensional flow field and the three-dimensional flow field may be: inputting the blade airfoil coordinates into the ICEM tool in the ANSYS software system, and performing two-dimensional unstructured meshing on the wind turbine blade through ICEM (Integrated Computer-Aided Engineering and Manufacturing); since the tip part of the wind turbine blade usually has a complex geometric shape and special flow phenomena, the unstructured meshing function of ICEM can adapt to this area well and accurately simulate the fluid flow and icing conditions near the blade tip.

[0071] Among them, ANSYS is a large-scale general-purpose engineering simulation software, which is widely used in multiple engineering fields, including aerospace, automobile, machinery, electronics, energy, etc. It provides a complete set of engineering simulation solutions, covering simulation analysis of multiple physical disciplines such as structural mechanics, fluid mechanics, electromagnetism, heat conduction, etc. 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 mesh data and the three-dimensional blade mesh data, using the 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.

[0073] Wherein, the first meteorological condition includes: wind speed, temperature and humidity.

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

[0075] The gas-liquid coupling simulation can provide a deep understanding of the formation mechanism of ice by simulating the two-phase flow of air and water droplets, as well as the impact, spreading, and freezing of water droplets on the blade surface. This is very important for predicting the location, thickness, shape, and other information of ice, which can provide a basis for the formulation of anti-icing and de-icing strategies. For example, the ice coverage of blades under different meteorological conditions (such as wind speed, liquid water content, temperature, etc.) can be simulated to evaluate the effects of different anti-icing measures (such as heating, coating, etc.).

[0076] Exemplarily, 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 fluid 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 an icing simulation result when the fan blade is in a non-rotating state; the second icing simulation result is an icing simulation result when the fan blade is in a rotating state.

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

[0080] Specifically, by simulating the icing process of wind turbine blades in a non-rotating state, the impact, spreading, and freezing of water droplets on the blade surface can be simulated, and then it can be determined how water droplets interact with the blades under different meteorological conditions (such as wind speed, temperature, and humidity), thereby capturing the initial conditions and key factors of icing formation. Since the blades are in a non-rotating state, it is also easier to analyze the heat exchange process between the blades and the surrounding air and water droplets; and to simulate the morphology of ice on the blade surface, such as the flatness of the ice layer, thickness changes, and whether there is local accumulation. These icing morphological characteristics are of great significance for evaluating the impact of icing on the aerodynamic performance of the blades, because different icing morphologies will change the surface roughness and geometry of the blades, thereby affecting the performance of the wind turbine; and provide basic data for evaluating the impact of icing on the aerodynamic performance of the blades. Although the blades are rotating when the actual wind turbine is running, the changes in blade shape and surface roughness caused by icing in a non-rotating state will also affect the aerodynamic performance, such as a decrease in lift coefficient and an increase in drag coefficient. These data can be used as a reference for subsequent performance evaluation in a rotating state and help to preliminarily screen anti-icing strategies.

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

[0082] The rotation of the blades will cause dynamic changes in the flow field around the blades, such as periodic air flow velocity and pressure distribution. This dynamic flow field will affect the trajectory of water droplets and the way they hit the blades. By simulating the icing process in a rotating state, we can deeply study the impact of dynamic factors on icing; due to the rotational motion of the blades, icing will not only change the surface shape and roughness of the blades, but also affect the blade's angle of attack, torque and other parameters. By simulating the icing process in a rotating state, we can accurately evaluate the changes in these parameters, and then accurately analyze the impact of icing on performance indicators such as wind turbine output power, efficiency, and stability, providing key data for the safe operation and performance optimization of wind turbines.

[0083] Furthermore, by simulating the icing process of the fan blades in the non-rotating state and the rotating state, the first icing simulation result and the second icing simulation result corresponding to the first meteorological condition are obtained. Through the first icing simulation result and the second icing simulation result, the first icing simulation result can provide a basis for further optimizing and verifying the anti-icing strategy under a more complex rotating state. Through the second icing simulation result, the icing situation of the fan blades under the first gas phase condition can be reflected, including the formation speed, distribution characteristics and dynamic impact of icing on the fan performance, which is helpful for the subsequent designation of the corresponding anti-icing strategy.

[0084] S104: Obtain an ice covering analysis result under the first meteorological condition according to the first ice covering simulation result and the second ice covering simulation result.

[0085] In some embodiments, the first meteorological condition is used to simulate the blade icing process in a non-rotating state and a rotating state to obtain the flow field velocity distribution, liquid water content distribution, droplet collection coefficient distribution and ice thickness distribution on the wind turbine blades under the current first meteorological condition, and then generate an icing analysis result under the first meteorological condition, so as to grasp the blade icing condition under the current first meteorological condition through the icing analysis result.

[0086] It should be noted that in the embodiment of the present application, after the execution of the above-mentioned step S104 is completed, different meteorological conditions can also be set to execute the above-mentioned steps S102 to S104 for multiple different meteorological conditions to obtain the icing analysis results of the wind turbine blades under different meteorological conditions, and then generate the icing rules of the wind turbine blades under multiple different meteorological conditions.

[0087] At the same time, the wind turbine blade icing analysis results under different meteorological conditions can be saved to generate a wind turbine blade icing database, which provides a reliable data reference for the subsequent wind turbine blade anti-icing design and is highly intuitive. This will help wind farms 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 situation 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 a first meteorological condition to perform air fluid simulation and gas-liquid coupling simulation on the wind turbine blade to obtain an air fluid simulation result and a gas-liquid coupling simulation result corresponding to the wind turbine blade; the first meteorological condition includes: wind speed, temperature and humidity; based on the air fluid simulation result and the gas-liquid coupling simulation result, icing processes of the wind turbine blade in a non-rotating state and a rotating state are simulated 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; according to the first icing simulation result and the second icing simulation result, an icing analysis result under the first meteorological condition is obtained. The present application implements icing simulation on 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 then 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, obtaining a target energy model, a target viscosity model, and a target boundary condition.

[0092] In the above step S102, the two-dimensional blade mesh data and the three-dimensional blade mesh data are combined, and the air flow simulation of the fan blade is performed using the first meteorological condition. The specific implementation method can be: import the two-dimensional blade mesh data and the three-dimensional blade mesh data into Fluent (Fluent is the computational fluid dynamics (CFD) software in the ANSYS software system), and enable the energy model based on the mesh data in the boundary conditions to activate the solution of the energy equation, deduce the heat transfer and temperature change in the flow field, and select the viscosity model and enable viscous heating to infer the heat generated by the viscosity of the fluid. Therefore, it is necessary to first obtain the target energy model, target viscosity model and target boundary conditions.

[0093] Specifically, the target energy model can be a multiphase flow energy model, because the two-phase flow of air and water droplets is involved in the icing process of wind turbine blades. 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, energy equations are established for the air phase and the liquid phase respectively, and the energy coupling relationship between the two phases is described by the interphase energy transfer terms (such as interphase heat conduction, latent heat exchange, etc.). This model can accurately simulate the complex energy interaction process between the gas and liquid phases, which 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-ω model and the k-ε model. The k-ω model can simulate the turbulence in the boundary layer well in the near-wall area, but there may be some problems in the free flow area far away from the wall; the k-ε model has advantages in dealing with turbulence in the free flow area, but its accuracy in the near-wall area is slightly worse. The k-omega-SST model cleverly combines the advantages of these two models and adopts a hybrid function to realize the use of different models in different areas, thereby improving the simulation accuracy of the entire flow field (especially the complex flow field including the boundary layer and the mainstream area).

[0095] Specifically, the target boundary conditions include: inlet boundary conditions, blade wall boundary conditions, and outlet boundary conditions; the physical environment boundaries of the calculation area are defined by the target boundary conditions; wherein the inlet boundary conditions are used to set the physical state of the air when it enters the calculation area, and the inlet boundary conditions may include the inlet wind speed, temperature, humidity, etc. In an embodiment of the present application, the inlet wind speed, temperature, and humidity can be set to the wind speed size in the first meteorological condition, for example: the wind speed is 10m / s, the temperature is 270K, 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 embodiment of the present application, the influence of the temperature of the fan blade surface on the icing process is mainly considered, and then the blade temperature in the blade wall boundary condition is set, for example, it can be set to 280K.

[0097] The outlet boundary condition is used to describe the situation after the air leaves the calculation area. Common outlet boundary conditions include pressure outlet and velocity outlet. In an embodiment of the present 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, and simulate the pressure state of the air fluid after it flows out of the calculation area. When air naturally flows out of an area to an external environment with relatively stable pressure, the pressure state at its outlet has an important influence on the flow characteristics of the entire flow field. For fan blades, the use of pressure outlet conditions can more realistically reflect the state of the air after it flows out of the calculation area under the push of the blades.

[0098] S203 . Based on the target energy model, the target viscosity model, the target boundary condition and the first meteorological condition, perform air fluid simulation calculation on the wind turbine blade to obtain the air fluid simulation result.

[0099] It should be noted that when using Fluent software for air fluid simulation, a separable solver is used to solve the continuity equation and motion equation one by one, and the variable values ​​are obtained through the linear solution of adjacent units; and it is assumed that there are only unsteady two-phase flows of air and supercooled water droplets in the flow field, and the gravity and heat conduction of the air are not considered. The near-wall treatment adopts the efficient and practical standard wall function method, and the pressure-velocity coupling adopts the SIMPLE algorithm to solve the Navier-Stokes equations on the staggered grid, and the momentum and turbulent kinetic energy are processed using the second-order upwind scheme.

[0100] Among them, the separate solver is the target solver, and the target solver can use a coupled solver. Coupled solvers are suitable for complex flow field problems, especially those involving strong coupling relationships (such as the coupling of pressure and velocity). It can usually converge to a stable solution faster. The coupled solver simultaneously solves the continuity equation (related to the conservation of mass, which contains the relationship between velocity and density) and the momentum equation (describing the relationship between velocity and pressure), which can handle the pressure changes caused by such velocity changes and the reaction of pressure changes to velocity well, thereby more realistically simulating the actual flow of air.

[0101] When performing air fluid simulation calculations on the fan blades based on the target energy model, the target viscosity model, the target boundary conditions and the first meteorological conditions, it is necessary to perform standard initialization and set the number of iteration steps to start the simulation calculation to calculate the velocity-related data, pressure-related data, turbulence characteristic data, boundary layer-related data, etc. of each grid. After the simulation calculation is completed (reaching the set number of iteration steps or satisfying the convergence conditions), the flow field simulation results can be saved for subsequent analysis; the flow field simulation results will save all simulation result data including the geometric information of the calculation domain, grid information, flow field variables, etc.

[0102] It should be noted that when performing fluid simulation calculations, the solver requires an initial flow field state to start the iterative solution process. The standard initialization is to provide an initial guess value for the flow field variables (such as velocity, pressure, temperature, etc.) in the entire calculation area. Through standard initialization, reasonable initial velocity and pressure guess values ​​can be assigned to the areas near and far from the blades, respectively, and the first step of calculation can be performed, and then iterated step by step; the size of the iteration step usually needs to be adjusted according to the complexity of the problem, the quality of the grid, and the convergence requirements. Generally speaking, you can first try a smaller number of iteration steps to observe the convergence trend of the solution. If the solution does not converge, you can gradually increase the number of iteration steps.

[0103] S204, obtaining the physical conditions of the droplet.

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

[0105] In some embodiments, the droplet physical conditions may include: liquid water content, droplet diameter, monodisperse distribution, droplet resistance model, etc.; wherein, the setting of liquid water content (LWC) is crucial for accurately simulating the degree of icing on wind turbine blades. Under actual meteorological conditions, the amount of liquid water in the air directly determines the thickness of the ice layer that can accumulate on the blade surface. For example, when the LWC is higher, it means that more water droplets can hit and adhere to the blades, and then freeze to form a thicker layer of ice; 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 0.001kg / m 3 .

[0106] Droplets of different diameters behave very differently after hitting the blades. Smaller droplets have less inertia and are more likely to follow the airflow around the blades or spread evenly on the blade surface; larger droplets, due to their greater inertia, are more likely to hit the blades directly and may form localized water accumulation areas after impact. These water accumulation areas are more likely to freeze in low temperature environments, thus affecting the shape and location of ice. For example, the droplet diameter can be set to 20 microns.

[0107] Assuming that the droplets are monodisperse allows the calculation to focus on the basic behavior of the droplets and their interaction with the blades without being disturbed by the complex changes in the droplet size distribution. At the same time, under certain actual meteorological conditions, the droplet size distribution may 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 drag model can accurately calculate the movement trajectory of droplets in the airflow, which is critical for determining the location and distribution of ice on the blades. The airflow around the fan blades is complex, and the movement trajectory of droplets in this complex airflow is affected by many factors, including airflow speed, blade rotation (if the blade is in a rotating state), and the resistance of the droplets themselves. Through an accurate droplet drag model, it is possible to simulate how droplets move in the airflow and eventually hit the blades, thereby determining which areas on the blades are more susceptible to droplet impact and, in turn, more prone to ice.

[0109] S205. In combination with the air fluid simulation result, based on the target boundary condition, the first meteorological condition, and the droplet physical condition, perform gas-liquid coupling simulation calculation on the fan blade to obtain the gas-liquid coupling simulation result.

[0110] Specifically, in combination with the air fluid simulation results, based on the target boundary conditions and the first meteorological conditions, and the droplet physical conditions, the specific implementation method of performing air-liquid coupling simulation calculation on the fan blades may be: importing the air fluid simulation results into the Fluent Icing module, which is a functional module in the Fluent software specifically used to simulate the icing process. It is built on the basis of Fluent's powerful computational fluid dynamics (CFD) and provides a special solution for studying physical phenomena involving ice accumulation.

[0111] Then, based on the physical conditions of the droplets and using the same target boundary conditions as those used in the air fluid simulation to match the flow field of the air fluid, a particle solution method is selected, and based on the CFL number, the number of iterations, the artificial viscosity coefficient, and the residual cutoff value, a gas-liquid coupling simulation calculation is performed on the fan blade to obtain the gas-liquid coupling simulation result.

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

[0113] The artificial viscosity coefficient is a viscosity term artificially introduced in numerical calculations in order to deal with some physical phenomena (such as simulating discontinuous phenomena such as shock waves) or to improve the stability of numerical calculations; 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 processed during the simulation process (such as simulating shock waves in high-speed airflow), a smaller artificial viscosity coefficient may help to smooth these discontinuous phenomena to a certain 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 stronger viscosity to handle, resulting in unstable calculations or inability to accurately simulate certain physical phenomena.

[0114] The residual cutoff value refers to the error measure between the current solution and the exact solution obtained after each iterative calculation in the numerical solution process. The residual cutoff value is a standard set to determine whether the iteration can be stopped; for example, the residual cutoff value can be 1e-8. When the residual is less than the residual cutoff value, it can be considered that the calculation has converged to a solution that meets the accuracy requirements, and then by setting the residual cutoff value, a calculation result that meets the accuracy requirements can be obtained.

[0115] It should be noted that in the solution process of the gas-liquid coupling simulation, it is necessary to assume that the supercooled water droplets are evenly 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, their temperature is consistent with the ambient temperature, and no heat exchange occurs with the air; during the movement, the water droplets are affected by the combined effects of their own gravity, air resistance, and buoyancy.

[0116] S206: Obtaining physical conditions for icing.

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

[0118] In some embodiments, the recovery factor in the icing physical condition is mainly used to consider the influence of factors such as surface roughness on the airflow. In numerical simulation, it can be used to correct the momentum equation of the boundary layer. When the airflow bypasses the blade surface, the energy loss and recovery of the airflow will change due to surface characteristics (such as roughness). For example, the recovery factor can be set to 0.9.

[0119] The relative humidity is a key indicator to measure the water vapor content in the air. A relative humidity of 100% means that the air is saturated. In this environment, water vapor is very easy to condense on the cold blade surface, providing sufficient water source for ice formation. It is one of the material basic conditions for the formation of ice, because without sufficient water vapor, a large amount of ice cannot be produced.

[0120] The target icing model is mainly used to simulate the type and process of icing. In the embodiment of the present application, the target icing model can be a Glaze icing model, which takes into account the movement of water droplets on the blade surface (such as the formation of water droplets rolling or staying) and the heat and mass transfer process. 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, which plays a key role in predicting the shape and distribution of ice.

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

[0122] In combination 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 hitting 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 air-liquid coupling simulation result, and through the icing physical condition, 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 blades in a non-rotating state, it is necessary to first obtain information such as the air flow velocity, pressure and temperature distribution around the blades from the air fluid simulation results. This information determines the position and angle of the water droplets hitting the blades. At the same time, the blade surface temperature boundary conditions are set to the temperature obtained from the air fluid simulation to provide basic conditions for the icing simulation. Then, based on the dynamic behavior of water droplets on the blade surface in the gas-liquid coupling simulation results, including collision, aggregation, rebound and spreading, the initial distribution and motion state of the water droplets are determined. And the heat and mass transfer information therein is used to calculate the freezing rate of the water droplets. Finally, based on the physical conditions of ice formation, the volume and mass changes of ice are calculated, and the accumulation process of ice on the blade surface is gradually simulated, thereby completing the simulation of the icing process.

[0125] S208. Obtain blade rotation parameters and periodic boundary conditions in a rotating state.

[0126] In some embodiments, unlike the icing simulation of the fan blades in a non-rotating state, the icing simulation of the fan blades in a rotating state requires setting blade rotation parameters and 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. The rotation speed of the blade will directly affect the relative motion between the blade and the surrounding air and water droplets. A 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 impact on the icing process. Different rotation directions will cause differences in the impact distribution of airflow and water droplets on the blade surface, which in turn affects the morphology and distribution of ice. 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 around which it rotates. Its position and direction affect the relative motion relationship between various parts of the blade and the airflow and water droplets.

[0128] Specifically, the periodic boundary conditions under the rotation state include: spatial periodicity and time periodicity; wherein, spatial periodicity refers to the periodic characteristics of blade rotation, and by setting appropriate spatial periodic boundary conditions, for example, the rotation of the blade is regarded as a periodically repeated process during simulation, so that only one or several cycles can be simulated, rather than a lengthy simulation of the entire continuous rotation process, which can save computing resources and reflect the overall ice coverage characteristics.

[0129] Time periodicity is to set boundary conditions based on the periodic characteristics of blade rotation. It determines how the state of the blade surface (such as temperature, airflow velocity, etc.) at each moment in a rotation cycle connects with the next cycle, ensuring the coherence and accuracy of the simulation, so as to accurately present the evolution of ice cover during continuous rotation.

[0130] S209. For 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 conditions in the rotating state, the icing process of the fan blade in the rotating state is simulated through the icing physical conditions to obtain the second icing simulation result.

[0131] Specifically, for the fan blades in a rotating state, it is also necessary to combine the air-fluid simulation results and the gas-liquid coupling simulation results; use the previously obtained air-fluid simulation results, but at this time consider the new situation brought about by the rotation of the blades. For example, rotation will cause the airflow velocity distribution on the blade surface to change dynamically, and it is no longer the situation in a static state. The airflow velocity must be corrected and re-analyzed according to the rotation parameters to accurately determine the dynamic trajectory of the water droplets hitting the blades. And combined with the gas-liquid coupling simulation results, analyze the new dynamic behavior of water droplets on the blade surface in a rotating state. Rotation may change the collision, aggregation, rebound and spreading of water droplets. These processes must be re-evaluated according to the new conditions, and the heat and mass transfer must be reconsidered, because rotation will affect the relative motion and contact time between the water droplets and the blade surface, and thus affect the freezing rate of the water droplets.

[0132] Then, similar to the ice simulation in the non-rotating state, the volume and mass changes of ice should be calculated in combination with the icing physical conditions. In the rotating state, the accumulation and distribution of ice will change dynamically with the rotation of the blade. To accurately simulate this change, the state of ice is continuously updated according to the rotation parameters and periodic boundary conditions, so as to achieve accurate simulation of the blade icing process in the rotating state and obtain the second icing simulation result.

[0133] S210: Obtain an ice covering analysis result under the first meteorological condition according to the first ice covering simulation result and the second ice covering simulation result.

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

[0135] In the embodiment of the present application, the icing of the wind turbine blades in the non-rotating state and the rotating state is simulated to obtain the icing simulation result of the wind turbine blades in the non-rotating state, i.e., the first icing simulation result; and the icing simulation result of the wind turbine blades in the rotating state, i.e., the second icing simulation result, so as to fully grasp the icing condition of the blades under the first meteorological condition, which is helpful for the wind farm to formulate anti-icing, deicing 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 situation to ensure the safe and efficient operation of the power grid.

[0136] Based on the same inventive concept, as an implementation of the above method, an embodiment of the present application also provides a method device for simulating icing on wind blades. This embodiment corresponds to the above method embodiment. For ease of reading, this embodiment will no longer repeat the details of the above method embodiment one by one, but it should be clear that a method device for simulating icing on wind blades in this embodiment can correspond to all the contents of the above method embodiment.

[0137] The present application provides a method and device for simulating ice coating on wind turbine blades. Figure 3 FIG. 1 is a schematic diagram of the structure of the wind turbine blade icing simulation method device. Figure 3 As shown, the wind turbine blade icing simulation method device 300 includes:

[0138] A mesh simulation unit 301 is used to perform 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;

[0139] The processing unit 302 is used to combine the two-dimensional blade mesh data and the three-dimensional blade mesh data, and use the first meteorological condition to perform air fluid simulation and gas-liquid coupling simulation on the fan blade to obtain an air fluid simulation result and a gas-liquid coupling simulation result corresponding to the fan blade; the first meteorological condition includes: wind speed, temperature and humidity;

[0140] The icing process simulation unit 303 is used to simulate the icing process of the fan blade in the non-rotating state and the rotating state respectively based on the air fluid 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; 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 used to acquire an 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 used to obtain the blade airfoil coordinates corresponding to the wind turbine blade; determine the two-dimensional flow field and the three-dimensional flow field corresponding to the two-dimensional grid simulation and the three-dimensional grid simulation respectively; combine the blade airfoil coordinates, and generate the two-dimensional blade grid data and the three-dimensional blade grid data 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 mesh simulation unit 301 is also used to perform smooth transition processing and mesh encryption processing on the two-dimensional blade mesh data and the three-dimensional blade mesh data when the quality of the two-dimensional blade mesh data and the three-dimensional blade mesh data is unqualified.

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

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

[0146] As an optional implementation of the embodiment of the present application, the ice cover analysis results under the first meteorological conditions include flow field velocity distribution, liquid water content distribution, droplet collection coefficient distribution and ice cover thickness distribution.

[0147] As an optional implementation manner of the embodiment of the present application, the icing simulation result acquisition unit 304 is also used to perform air fluid simulation and gas-liquid coupling simulation on the fan blades according to a first meteorological condition by combining the two-dimensional blade grid data and the three-dimensional blade grid data for multiple different meteorological conditions, so as to obtain the air fluid simulation results and gas-liquid coupling simulation results corresponding to the fan blades, and to obtain the icing analysis results under the first meteorological condition according to the first icing simulation result and the second icing simulation result, and obtain the icing analysis results corresponding to the multiple different meteorological conditions respectively; and generate the icing rules of the fan blades under the multiple different meteorological conditions through the multiple icing analysis results.

[0148] Based on the same inventive concept, an embodiment of the present disclosure also provides an electronic device. Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure, such as Figure 4 As shown, the electronic device provided in this embodiment includes: a memory 401 and a processor 402, wherein the memory 401 is used to store a computer program; and the processor 402 is used to execute the audio data processing method provided in the above embodiment when executing the computer program.

[0149] Based on the same inventive concept, an embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the computing device implements the wind turbine blade icing simulation method provided in the above embodiment.

[0150] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media that include computer-usable program code.

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

[0152] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0153] Computer readable media include permanent and non-permanent, removable and non-removable storage media. Storage media can be implemented by any method or technology to store information, and 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 technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0154] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for simulating ice coating on fan blades, characterized in that: include: Performing two-dimensional mesh simulation and three-dimensional mesh simulation on any wind turbine blade of the wind turbine generator set to generate two-dimensional blade mesh data and three-dimensional blade mesh data; Combining the two-dimensional blade mesh data and the three-dimensional blade mesh data, using the first meteorological condition to perform air fluid simulation and gas-liquid coupling simulation on the fan blade, so as to obtain an air fluid simulation result and a gas-liquid coupling simulation result corresponding to the fan blade; The first meteorological conditions include: wind speed, temperature and humidity; Based on the air fluid simulation result and the gas-liquid coupling simulation result, the icing process of the fan blade in the non-rotating state and the rotating state is simulated 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 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; An ice covering analysis result under the first meteorological condition is obtained according to the first ice covering simulation result and the second ice covering simulation result.

2. The method according to claim 1, characterized in that The two-dimensional mesh simulation and the three-dimensional mesh simulation are performed on any wind turbine blade of the wind turbine generator set to generate two-dimensional blade mesh data and three-dimensional blade mesh data, including: Obtaining 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; The two-dimensional blade mesh data and the three-dimensional blade mesh data are generated in combination with the blade airfoil coordinates and based on the two-dimensional flow field and the three-dimensional flow field.

3. The method according to claim 2, characterized in that After combining the blade airfoil coordinates and generating the two-dimensional blade mesh data and the three-dimensional blade mesh data based on the two-dimensional flow field and the three-dimensional flow field, the method further includes: When the quality of the two-dimensional blade mesh data and the three-dimensional blade mesh data is unqualified, smooth transition processing and mesh encryption processing are performed on the two-dimensional blade mesh data and the three-dimensional blade mesh data.

4. The method according to claim 1, characterized in that: The combining the two-dimensional blade mesh data and the three-dimensional blade mesh data, using the first meteorological condition to perform air fluid simulation and gas-liquid coupling simulation on the fan blade to obtain an air fluid simulation result and a gas-liquid coupling simulation result corresponding to the fan blade, includes: Obtaining a target energy model, a target viscosity model, and target boundary conditions; 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; Acquiring a physical condition of a liquid droplet; the parameters in the physical condition of the liquid droplet are used to describe the physical state of the liquid droplet; In combination with the air fluid simulation result, based on the target boundary condition and the first meteorological condition, as well as the droplet physical condition, a gas-liquid coupling simulation calculation is performed on the fan blade to obtain the gas-liquid coupling simulation result.

5. The method according to claim 1, characterized in that 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: Acquiring icing physical conditions; the icing physical conditions include recovery factor, relative humidity, target icing model and ice density; For the fan blade in the non-rotating state, based on the air fluid simulation result and the gas-liquid coupling simulation result, the icing process of the fan blade in the non-rotating state is simulated through the icing physical condition to obtain the first icing simulation result; Obtain blade rotation parameters and periodic boundary conditions under the rotating state; For 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 conditions in the rotating state, the icing process of the fan blade in the rotating state is simulated through the icing physical conditions to obtain the second icing simulation result.

6. The method according to claim 1, characterized in that The ice analysis results under the first meteorological conditions include flow field velocity distribution, liquid water content distribution, droplet collection coefficient distribution and ice thickness distribution.

7. The method according to claim 1, characterized in that The method further comprises: For multiple different meteorological conditions, by 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 air fluid simulation results and gas-liquid coupling simulation results corresponding to the fan blade, to the step of obtaining an icing analysis result under the first meteorological condition according to the first icing simulation result and the second icing simulation result, and obtaining icing analysis results corresponding to multiple different meteorological conditions respectively; The icing patterns of the wind turbine blades under a plurality of different meteorological conditions are generated through the plurality of icing analysis results.

8. A method and device for simulating ice coating on fan blades, characterized in that: include: A grid simulation unit, used for performing two-dimensional grid simulation and three-dimensional grid simulation on any wind turbine blade of a wind turbine generator set, and generating two-dimensional blade grid data and three-dimensional blade grid data; a processing unit, configured to combine the two-dimensional blade mesh data and the three-dimensional blade mesh data, and use the first meteorological condition to perform air-fluid simulation and gas-liquid coupling simulation on the fan blade, so as to obtain an air-fluid simulation result and a gas-liquid coupling simulation result corresponding to the fan blade; The first meteorological conditions include: wind speed, temperature and humidity; an icing process simulation unit, configured to simulate the icing process of the fan blade in a non-rotating state and a rotating state respectively based on the air fluid 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 an icing simulation result of the fan blade in a non-rotating state; the second icing simulation result is an icing simulation result of the fan blade in a rotating state; An icing analysis result acquisition unit is used to acquire an icing analysis result under the first meteorological condition according to the first icing simulation result and the second icing simulation result.

9. An electronic device, characterized in that: include: A memory and a processor, wherein the memory is used to store a computer program; and the processor is used to enable the electronic device to implement the wind turbine blade icing simulation method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a computing device, the computing device implements the wind turbine blade icing simulation method according to any one of claims 1 to 7.

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