Insulator dirt accumulation dynamic simulation wind tunnel system based on multi-field coupling

Abstract

The invention relates to the technical field of insulator pollution accumulation simulation, in particular to an insulator pollution accumulation dynamic simulation wind tunnel system based on multi-field coupling, which comprises the steps of S1, virtual wind tunnel modeling; s2, constructing a fluid solution model; s3, setting boundary conditions and initial conditions; s4, flow field and particle motion coupling calculation; and S5, outputting and visualizing a simulation result. Through a multi-field coupling numerical simulation wind tunnel platform, the technical problems that a traditional experiment is long in period and high in cost, and a microdynamics rule is difficult to obtain are solved.

Description

Technical Field

[0001] This invention relates to the field of insulator contamination simulation technology, specifically to a wind tunnel system for dynamic simulation of insulator contamination based on multi-field coupling. Background Technology

[0002] In high-voltage transmission systems, insulators are critical components for maintaining the electrical insulation and mechanical support of lines, and the cleanliness of their surfaces directly affects the operational safety of the system. During long-term operation, the surface of insulators is susceptible to the deposition of pollutants such as suspended particles, salt spray, and water vapor in the atmosphere, forming a conductive contaminant layer. When this surface contaminant layer becomes damp or wet, its leakage current increases significantly, easily triggering flashover accidents, causing power outages or equipment damage.

[0003] To study the pollution accumulation characteristics of insulators, traditional methods mainly include natural exposure tests and physical wind tunnel tests. Natural exposure tests are time-consuming, highly susceptible to environmental influences, and difficult to control and repeat. In contrast, while physical wind tunnel tests can control wind speed, humidity, and dust concentration to some extent, they suffer from high equipment costs, long testing cycles, and limitations in data acquisition. More importantly, in actual pollution accumulation processes, the movement, collision, adhesion, and resuspension behavior of pollution particles are extremely complex, making it difficult to obtain their microscopic dynamic laws through experimental methods alone.

[0004] With the development of computational fluid dynamics technology, numerical simulation-based methods have become an important means of studying gas-solid two-phase flow and particle deposition behavior. Currently, some studies have attempted to apply gas-solid two-phase flow models to fields such as dust deposition, spraying, and filtration, but their application in the problem of insulator contamination in power systems is still limited, and a systematic and modular simulation platform is lacking.

[0005] Existing numerical studies on insulator contamination largely focus on single-parameter analyses, such as the impact of airflow velocity or particle size on deposition, lacking a unified simulation framework that couples multiple parameters. Furthermore, the flow field distribution varies significantly under airflow conditions for different insulator structures (e.g., disc, rod, and composite insulators), making it difficult for existing models to balance geometric complexity with computational accuracy. In addition, key issues such as particle adhesion probability, resuspension mechanisms, and saturation contamination patterns on insulator surfaces still lack quantitative research. These limitations highlight the urgent need to develop a dedicated simulation platform.

[0006] Therefore, there is an urgent need for a platform capable of multiphysics-based simulation of airflow and particle motion in a virtual wind tunnel environment. This platform would allow for the systematic study of the effects of different wind speeds, humidity levels, particle concentrations, and insulator structural parameters on pollution accumulation characteristics without the need for physical experiments. Through this platform, the pollution accumulation rate, saturated pollution accumulation, and pollution distribution patterns can be numerically quantified, visualized, and reproducibly analyzed, providing a scientific basis for the design, operation, and maintenance of insulators to prevent flashover. Summary of the Invention

[0007] To address the aforementioned issues, a dynamic simulation wind tunnel system for insulator contamination based on multi-field coupling is provided. This system solves the technical problems of long experimental cycles, high costs, and difficulty in obtaining microscopic dynamic laws in insulator contamination research by using a multi-field coupling numerical simulation wind tunnel platform.

[0008] To address the problems of existing technologies, this invention provides a wind tunnel system for dynamic simulation of insulator pollution accumulation based on multi-field coupling, comprising: Step S1: Virtual wind tunnel modeling. Construct the wind tunnel computational domain and insulator model, define the inlet, outlet and wall boundary conditions, and perform mesh generation to generate computational nodes; Step S2: Constructing the fluid solution model. Construct a fluid solution model to achieve bidirectional information exchange and simultaneous solution between airflow and particles; Step S3: Setting boundary and initial conditions. Based on the real-world scenario, set the initial and boundary conditions; Step S4: Coupled calculation of flow field and particle motion. Perform flow field iterative calculations and iteratively calculate particle trajectories. Determine particle adhesion information on insulator surface based on particle collision and contamination criteria, and calculate contamination amount and distribution. Step S5: Simulation results output and visualization. Based on the calculation results of step S4, the airflow, static pressure, and pollution accumulation distribution of the insulator, as well as the pollution accumulation characteristics under different wind speeds, humidity, particle concentrations, and particle diameters, are output, thus enabling the visualization of the results.

[0009] As a specific embodiment of the present invention, in step S1, the insulator model is directly called from the standard insulator module library or imported after being modeled by the user. The user can revise the size of the wind tunnel calculation domain according to the size of the insulator model. The left and right sides of the wind tunnel calculation domain are the inlet and outlet of the flow field, respectively, and the wall of the wind tunnel calculation domain is the x-axis.

[0010] In a specific embodiment of the present invention, in step S1, a tetrahedral meshing method is used to perform mesh generation.

[0011] In a specific embodiment of the present invention, in step S1, the wall of the wind tunnel calculation domain adopts an expansion boundary for smooth transition.

[0012] As a specific embodiment of the present invention, in step S2, the Eulerian gas-solid two-phase flow model is used to solve the bidirectional information exchange between the airflow and the particles. One phase is the gas phase and the other phase is the solid particle phase. The material, concentration and diameter of the particles can be set by the user.

[0013] In a specific embodiment of the present invention, in step S2, in order to realize bidirectional information exchange between airflow and particles, the Euler gas-solid two-phase flow model is used for simultaneous solution: the gas phase is the continuous phase, the solid particle phase is the discrete phase, and the material, concentration, and diameter parameters of the particles can be customized by the user; at the same time, considering the viscosity characteristics of the airflow, the airflow field solution is controlled by the RNG Ke turbulence model. By solving the gas phase momentum conservation equation and the turbulence energy equation, the accuracy of flow field prediction under different wind speed conditions is improved, providing high-fidelity flow field data for subsequent particle motion trajectory calculation.

[0014] In a specific embodiment of the present invention, in step S2, the influence of relative humidity on the degree of contamination on the surface of the insulator is taken into account. A mapping relationship between the humidity field and the wall state is established through the Euler liquid film model: the capillary force and van der Waals force formed by the liquid bridge on the surface of the insulator are calculated based on the relative humidity parameter, and used as a correction term for the particle adhesion criterion; the liquid film model and the gas-solid two-phase flow model form a multi-physics coupling, and by sharing the flow field calculation results and particle motion parameters, the dynamic influence of humidity on the contamination process is simulated.

[0015] In a specific embodiment of the present invention, in step S3, the collision-accumulation criterion is defined as follows: when the collision velocity of solid particles is greater than the critical trapping velocity, their kinetic energy is sufficient to overcome the adhesion potential energy between the particles and the insulator surface, causing the particles to fail to adhere and to reflect. The critical trapping velocity is expressed as: , In the formula, ρ p Represents the density of solid particles. k * This represents the interfacial energy between solid particles and the surface of an insulator. R p Indicates the radius of solid particles. E * This represents the elastic modulus of the particles. s s This indicates the yield strength of the insulator.

[0016] As a specific embodiment of the present invention, the output results in step S4 include a flow field vector diagram, a windward static pressure diagram, a leeward static pressure diagram, a pollution distribution diagram on the top surface of the insulator, a pollution distribution diagram on the ground surface of the insulator, and a pollution average volume fraction-time curve of the insulator.

[0017] As a specific embodiment of the present invention, the output results in step S4 also include the dynamic evolution process of the insulator flow field and pollution distribution using three-dimensional rendering and streamline animation.

[0018] A data processing device, comprising: Memory, used to store computer programs; A processor is used to implement a dynamic simulation wind tunnel system for insulator pollution accumulation based on multi-field coupling when executing the computer program.

[0019] A computer-readable storage medium storing a computer program that, when executed by a processor, implements a dynamic simulation wind tunnel system for insulator contamination based on multi-field coupling.

[0020] The advantages of this invention compared to existing technologies are as follows: By constructing a multi-field coupled numerical simulation wind tunnel platform, this invention achieves a systematic and refined study of the insulator contamination process. This platform overcomes the limitations of traditional experimental methods in terms of environmental controllability and result repeatability, accurately reproducing the multi-physics coupling behavior of airflow, particle motion, and surface deposition in a virtual environment, thereby deeply revealing the microscopic dynamics of contaminant particles. By integrating the Eulerian gas-solid two-phase flow model, the RNG Ke turbulence model, and the Eulerian liquid film model, this invention achieves cross-scale analysis from macroscopic flow field distribution to microscopic particle adhesion mechanisms, providing a unified simulation framework for studying contamination characteristics under different insulator structures and meteorological conditions. Its core value lies in its ability to systematically explore the influence mechanisms of multiple parameters such as wind speed, humidity, and particle characteristics on the contamination process without relying on physical experimental equipment, significantly reducing research costs and shortening the development cycle. Simultaneously, it provides a scientific and intuitive theoretical basis for optimizing insulator anti-pollution flashover design and formulating operation and maintenance strategies. Attached Figure Description

[0021] Figure 1 This is a flowchart of a wind tunnel system for dynamic simulation of insulator contamination based on multi-field coupling, according to the present invention.

[0022] Figure 2 This is a detailed flowchart of a wind tunnel system for dynamic simulation of insulator contamination based on multi-field coupling, according to the present invention.

[0023] Figure 3 This is a list of insulator geometric parameters.

[0024] Figure 4 This is a three-dimensional model diagram of an insulator.

[0025] Figure 5 This is a schematic diagram of the location around the insulator in a wind tunnel system for dynamic simulation of insulator contamination based on multi-field coupling, according to the present invention.

[0026] Figure 6 This invention relates to a mesh partitioning diagram of the wind tunnel computational domain and the insulator model in a wind tunnel system for dynamic simulation of insulator contamination based on multi-field coupling.

[0027] Figure 7 This invention relates to a dynamic simulation wind tunnel system for insulator contamination accumulation based on multi-field coupling, showing the static pressure distribution cloud map of the windward and leeward sides of an insulator. Figure 8 This invention relates to a vector diagram of the airflow field distribution on the surface of an insulator in a wind tunnel system for dynamic simulation of insulator contamination based on multi-field coupling. Figure 9 The present invention relates to a dynamic simulation of insulator contamination distribution on the surface of an insulator in a wind tunnel system based on multi-field coupling. Detailed Implementation

[0028] To further understand the features, technical means, and specific objectives and functions achieved by the present invention, the present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments.

[0029] Reference Figure 1 and Figure 2 As shown, a wind tunnel system for dynamic simulation of insulator pollution accumulation based on multi-field coupling includes: Step S1: Virtual wind tunnel modeling. Construct the wind tunnel computational domain and insulator model, define the inlet, outlet and wall boundary conditions, and perform mesh generation to generate computational nodes.

[0030] Step S2: Constructing the fluid solution model. Constructing the fluid solution model enables bidirectional information exchange and simultaneous solution between airflow and particles.

[0031] Step S3: Set boundary and initial conditions. Based on the real-world scenario, set the initial and boundary conditions.

[0032] Step S4: Coupled calculation of flow field and particle motion. Perform iterative calculation of flow field and iterative calculation of particle motion trajectory. Determine the attachment information of particles on the insulator surface based on the particle collision and contamination criterion, and calculate the amount and distribution of contamination.

[0033] Step S5: Simulation results output and visualization. Based on the calculation results of step S4, the airflow, static pressure, and pollution accumulation distribution of the insulator, as well as the pollution accumulation characteristics under different wind speeds, humidity, particle concentrations, and particle diameters are output, and the results are visualized.

[0034] In step S1, the insulator model is directly called from the standard insulator module library or imported after being modeled by the user. The user can revise the size of the wind tunnel calculation domain according to the size of the insulator model. The left and right sides of the wind tunnel calculation domain are the inlet and outlet of the flow field, respectively, and the wall of the wind tunnel calculation domain is the x-axis.

[0035] In step S1, a tetrahedral meshing method is used to generate the mesh.

[0036] In step S1, the wind tunnel computational domain wall adopts an expansion boundary for smooth transition.

[0037] In step S2, the Eulerian gas-solid two-phase flow model is used to solve the bidirectional information exchange between the airflow and the particles. One phase is the gas phase and the other phase is the solid particle phase. The material, concentration, and diameter of the particles can be set by the user.

[0038] In step S2, to achieve bidirectional information exchange between airflow and particles, the Euler gas-solid two-phase flow model is used for simultaneous solution: the gas phase is the continuous phase, and the solid particle phase is the discrete phase. The material, concentration, and diameter parameters of the particles can be customized by the user. At the same time, considering the viscosity characteristics of the airflow, the airflow field solution is controlled by the RNG Ke turbulence model. By solving the momentum conservation equation of the gas phase and the turbulence energy equation, the accuracy of flow field prediction under different wind speed conditions is improved, providing high-fidelity flow field data for subsequent particle trajectory calculation.

[0039] In step S2, considering the influence of relative humidity on the degree of contamination on the insulator surface, a mapping relationship between the humidity field and the wall state is established using the Euler liquid film model: the capillary force and van der Waals force formed by the liquid bridge on the insulator surface are calculated based on the relative humidity parameter, and used as a correction term for the particle adhesion criterion; the liquid film model and the gas-solid two-phase flow model form a multi-physics coupling, and by sharing the flow field calculation results and particle motion parameters, the dynamic influence of humidity on the contamination process is simulated.

[0040] In step S3, the collision-induced contamination criterion is defined as follows: when the collision velocity of a solid particle is greater than the critical trapping velocity, its kinetic energy is sufficient to overcome the adhesive potential energy between the particle and the insulator surface, causing the particle to fail to adhere and reflect. The critical trapping velocity is expressed as: , In the formula, ρ p Represents the density of solid particles. k * This represents the interfacial energy between solid particles and the surface of an insulator. R p Indicates the radius of solid particles. E * This represents the elastic modulus of the particles.s s This indicates the yield strength of the insulator.

[0041] The output results in step S4 include the flow field vector diagram, the static pressure diagram of the windward side, the static pressure diagram of the leeward side, the pollution distribution diagram of the top surface of the insulator, the pollution distribution diagram of the ground surface of the insulator, and the average volume fraction of pollution of the insulator versus time curve.

[0042] The output of step S4 also includes the dynamic evolution of the insulator flow field and pollution distribution using 3D rendering and streamline animation.

[0043] From a microscopic perspective, insulator contamination is a dynamic process of suspended particles driven by electric field force, drag force, and gravity. Specifically, airborne pollutant particles migrate to the insulator surface and collide and adhere under the coupling of multiple fields. Therefore, by calculating the distribution of the flow field around the insulator and the trajectory of the pollutant particles, the contamination characteristics of the insulator under different environmental parameters can be well simulated.

[0044] Step 1: Use the Geometry module to establish the wind tunnel computational domain and insulator model, define the inlet, outlet and wall boundary conditions, and use Mesh to generate computational nodes through mesh generation.

[0045] In this invention, the virtual wind tunnel adopts a rectangular channel structure, with its length, width, and height all set to 1 m by default. The left and right sides of the wind tunnel computational domain are the inlet and outlet of the flow field, respectively (e.g., ...). Figure 5 The wind tunnel inlet is set as a velocity inlet boundary condition, the outlet is a pressure outlet, the wall is a stationary wall, and the tunnel size and boundary conditions can be manually adjusted by the user according to the actual situation.

[0046] Users can create a 3D geometric model of the insulator using the built-in software or other modeling software and then import it into the simulation platform. This module supports automatic mesh generation; the airflow region uses an unstructured tetrahedral mesh (e.g., ...). Figure 6 The insulator surface is locally densified to capture the flow field characteristics near the wall. The wind tunnel computational domain wall uses an expanding boundary for smooth transition. This patent case uses an LXY-70 glass insulator sheet as the simulation example; its dimensions and 3D model are as follows: Figure 3 and Figure 4 As shown.

[0047] Step 2: Build a fluid solution model in the Fluent module to achieve bidirectional information exchange and simultaneous solution of airflow and particles.

[0048] It should be noted that the Fluent module is a computational fluid dynamics (CFD) solver in the ANSYS Workbench software suite, serving as the "flow field and particle motion calculation engine" in the numerical simulation platform of this patent. Its core function is to receive the 3D model constructed by the Geometry module and the mesh data generated by the Mesh module, and calculate the airflow distribution around the insulator and the trajectory of pollutant particles by solving the fluid dynamics control equations, providing crucial data support for subsequent calculations of pollution accumulation.

[0049] To achieve bidirectional information exchange between airflow and particles, and considering the interactions between airflow and particles, an Eulerian gas-solid two-phase flow model is used for solution. One phase is the gas phase, and the other is the solid particle phase. The material, concentration, and diameter of the particles can be set by the user. This model is used to numerically simulate the contamination characteristics of pollutants on the insulator surface. During the model solution process, the solid particles are treated as a continuous phase, thus establishing the mass and momentum conservation equations for the gas and solid phases, expressed as follows: , , This model needs to take into account the effects of turbulence and the viscosity of the fluid, and uses RNG. ke A turbulence model is used for calculations to improve the accuracy of flow field predictions at different wind speeds. The governing equations of the model are as follows: .

[0050] In high-humidity environments, moving solid particles adsorb and condense moisture from the air on their surfaces. When these particles collide with the insulator surface, liquid bridges form at the contact interface, generating capillary forces. Simultaneously, when the solid particles are very close to the insulator surface, van der Waals forces arise between molecules or atoms due to transient dipoles, permanent dipoles, and dipole-induced interactions. These van der Waals forces require extremely small distances to generate; larger distances are negligible. Both capillary forces and van der Waals forces are major components of the adhesion between solid particles and the insulator surface.

[0051] The formula for calculating capillary force is: ; The formula for calculating van der Waals force is: , In the above formula, c Indicates the contact coefficient. r Indicates the Kelvin radius. D This indicates the distance between the solid particles and the surface of the insulator. γ w This represents the surface tension coefficient of the liquid bridge. q1 represents the contact angle between the insulator surface and the droplet. q 2 represents the contact angle between the insulator surface and the solid particles. V m Represents the molar volume of a liquid. R g Represents the molar constant of a gas. e The potential well coefficient is represented by T, the open temperature by Z, and the intermolecular distance by z. R 1 represents the molecular radius.

[0052] To account for the influence of relative humidity on the degree of contamination on the insulator surface, and considering the capillary effect of liquid bridges on the insulator surface, the Euler liquid film model is used to account for the influence of relative humidity on the insulator wall.

[0053] Step 3: Set initial and boundary conditions based on the real-world scenario.

[0054] The wind tunnel geometry model includes the flow field inlet, flow field outlet, flow field boundary wall, and insulator wall. All walls are set to static, standard roughness by default, but the specific roughness can be set by the user according to the actual conditions. For the flow field inlet, the user can set the wind speed, particle concentration, and a certain humidity ratio of water.

[0055] Within the Mesh module, the boundary named inlet will be the flow field inlet by default in the subsequent Fluent module, and the boundary named outlet will be the flow field outlet by default. The flow field boundaries of the remaining wind tunnel computation domain will be the standard walls by default. Users who import insulator models need to manually define the names of each part of the walls to facilitate setting boundary conditions in Fluent.

[0056] Step 4: Perform flow field iterative calculation and iteratively calculate the motion trajectory of the particles. Based on the particle collision and contamination criteria, determine the adhesion information of the particles on the insulator surface and calculate the amount and distribution of contamination.

[0057] During the collision between solid particles and the surface of an insulator, if the velocity of the particles is higher than the critical capture velocity between them, their kinetic energy is sufficient to overcome the adhesion potential energy, thus causing the particles to fail to adhere to the surface and reflect.

[0058] The critical capture velocity of solid particles can be expressed as: , In the formula, ρ p Represents the density of solid particles. k * This represents the interfacial energy between solid particles and the surface of an insulator. R p Indicates the radius of solid particles.E * This represents the elastic modulus of the particles. s s This indicates the yield strength of the insulator.

[0059] Step 5: Based on the calculation results of Step 4, output the airflow, static pressure, and pollution accumulation distribution of the insulator, as well as the pollution accumulation characteristics under different wind speeds, humidity, particle concentrations, and particle diameters, to visualize the results.

[0060] The output includes a flow field vector diagram, a static pressure diagram of the windward side, a static pressure diagram of the leeward side, a pollution distribution diagram of the top surface of the insulator, a pollution distribution diagram of the ground surface of the insulator, a pollution average volume fraction-time curve of the insulator, and a dynamic evolution process of the flow field and pollution distribution of the insulator using 3D rendering and streamline animation, so as to more intuitively study the pollution accumulation process of the insulator in a virtual environment and the influence of different environmental parameters on the pollution accumulation characteristics of the insulator.

[0061] The environmental conditions used in the simulation case of this patent are an average particle diameter of 10μm, a wind speed of 1m / s, and a standard humidity of 40%.

[0062] 1. Analysis of the static pressure state on the surface of the insulator Static pressure is the pressure generated by the collision of flowing air with the insulator. The greater the static pressure on the wall, the greater the probability of it being encapsulated with solid particles. Analyzing the static pressure distribution on the wall can provide a preliminary indication of the contamination distribution pattern of the insulator. The static pressure distribution cloud maps of the windward and leeward sides of the insulator surface are shown in the figure. Figure 7 As shown.

[0063] Under the influence of airflow, the windward side of the insulator is under positive pressure and has the highest static pressure. Air molecules collide most frequently with the insulator surface in this area, making it easier for pollutants to adhere directly to the windward side. In contrast, the leeward side exhibits negative pressure characteristics. When airflow passes around the insulator, it creates vortices and suction effects near the leeward side, causing pollutants in the air to be drawn in and deposited in this negative pressure area.

[0064] 2. Analysis of the flow velocity around the insulator Vector diagram of airflow field distribution around the insulator as follows Figure 8 As shown.

[0065] The airflow velocity is highest on the side of the insulator, increasing the probability of solid particles carried by the airflow colliding with the wall. However, due to the higher velocity, the collision speed and drag force of the solid particles are also greater, making them more prone to reflection or peeling off from the wall. Therefore, at high wind speeds, the side is actually the area with the fewest solid particles, while the windward side has the most solid particles due to the higher static pressure and moderate airflow velocity. Meanwhile, although the insulator provides some shielding against airflow, low-speed vortex zones form on its leeward side and bottom region under the influence of turbulent diffusion effects.

[0066] 3. Analysis of Pollution Distribution Characteristics of Insulators The distribution of surface contaminants when the insulator is saturated is as follows Figure 9 As shown.

[0067] The volume fraction of pollutants is relatively high on the windward side and in some areas on the ground. After the airflow passes through the insulator, due to the turbulence effect, backflow or local eddies often occur on the leeward side and bottom of the insulator, thus forming local pollution distribution in areas where the airflow cannot flow directly, such as the leeward side and the ground.

[0068] In summary, numerical simulation methods provide an important technical approach for the analysis and mechanism research of insulator contamination characteristics. Based on the above design ideas, this patent uses Ansys simulation software and computational fluid dynamics to perform numerical simulation to study the distribution of the flow field around the insulator, the trajectory of contaminant solid particles, and the collision and adhesion process of particles on the insulator surface.

[0069] A data processing device, comprising: Memory, used to store computer programs; A processor is used to implement a dynamic simulation wind tunnel system for insulator pollution accumulation based on multi-field coupling when executing the computer program.

[0070] A computer-readable storage medium storing a computer program that, when executed by a processor, implements a dynamic simulation wind tunnel system for insulator contamination based on multi-field coupling.

[0071] The above embodiments only illustrate one or more implementations of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of the present invention should be determined by the appended claims.

Claims

1. A wind tunnel system for dynamic simulation of insulator pollution accumulation based on multi-field coupling, characterized in that, include: Step S1: Virtual wind tunnel modeling. Construct the wind tunnel computational domain and insulator model, define the inlet, outlet and wall boundary conditions, and perform mesh generation to generate computational nodes; Step S2: Constructing the fluid solution model. Construct a fluid solution model to achieve bidirectional information exchange and simultaneous solution between airflow and particles; Step S3: Setting boundary and initial conditions. Based on the real-world scenario, set the initial and boundary conditions; Step S4: Coupled calculation of flow field and particle motion. Perform flow field iterative calculations and iteratively calculate particle trajectories. Determine particle adhesion information on insulator surface based on particle collision and contamination criteria, and calculate contamination amount and distribution. Step S5: Simulation results output and visualization. Based on the calculation results of step S4, the airflow, static pressure, and pollution accumulation distribution of the insulator, as well as the pollution accumulation characteristics under different wind speeds, humidity, particle concentrations, and particle diameters, are output, thus enabling the visualization of the results.

2. The insulator pollution dynamic simulation wind tunnel system based on multi-field coupling according to claim 1, characterized in that, In step S1, the insulator model is directly called from the standard insulator module library or imported after being modeled by the user. The user can revise the size of the wind tunnel calculation domain according to the size of the insulator model. The left and right sides of the wind tunnel calculation domain are the inlet and outlet of the flow field, respectively, and the wall of the wind tunnel calculation domain is the x-axis.

3. The insulator pollution dynamic simulation wind tunnel system based on multi-field coupling according to claim 1, characterized in that, In step S1, a tetrahedral meshing method is used to generate the mesh.

4. The insulator pollution dynamic simulation wind tunnel system based on multi-field coupling according to claim 1, characterized in that, In step S1, the wind tunnel computational domain wall adopts an expansion boundary for smooth transition.

5. The insulator pollution dynamic simulation wind tunnel system based on multi-field coupling according to claim 1, characterized in that, In step S2, the Euler gas-solid two-phase flow model is used to solve the bidirectional information exchange between the airflow and the particles, where one phase is the gas phase and the other phase is the solid particle phase.

6. The insulator pollution dynamic simulation wind tunnel system based on multi-field coupling according to claim 1, characterized in that, In step S2, to achieve bidirectional information exchange between airflow and particles, the Euler gas-solid two-phase flow model is used for simultaneous solution: the gas phase is the continuous phase, and the solid particle phase is the discrete phase. The material, concentration, and diameter parameters of the particles can be customized by the user. At the same time, considering the viscosity characteristics of the airflow, the airflow field solution is controlled by the RNG Ke turbulence model. By solving the momentum conservation equation of the gas phase and the turbulence energy equation, the accuracy of flow field prediction under different wind speed conditions is improved, providing high-fidelity flow field data for subsequent particle trajectory calculation.

7. The insulator pollution dynamic simulation wind tunnel system based on multi-field coupling according to claim 6, characterized in that, In step S2, considering the influence of relative humidity on the degree of contamination on the insulator surface, a mapping relationship between the humidity field and the wall state is established using the Euler liquid film model: the capillary force and van der Waals force formed by the liquid bridge on the insulator surface are calculated based on the relative humidity parameter, and used as a correction term for the particle adhesion criterion; the liquid film model and the gas-solid two-phase flow model form a multi-physics coupling, and by sharing the flow field calculation results and particle motion parameters, the dynamic influence of humidity on the contamination process is simulated.

8. The insulator pollution dynamic simulation wind tunnel system based on multi-field coupling according to claim 1, characterized in that, In step S3, the collision-induced contamination criterion is defined as follows: when the collision velocity of a solid particle is greater than the critical trapping velocity, its kinetic energy is sufficient to overcome the adhesive potential energy between the particle and the insulator surface, causing the particle to fail to adhere and reflect. The critical trapping velocity is expressed as: ， In the formula, ρ p Represents the density of solid particles. k * This represents the interfacial energy between solid particles and the surface of an insulator. R p Indicates the radius of solid particles. E * This represents the elastic modulus of the particles. s s This indicates the yield strength of the insulator.

9. The insulator pollution dynamic simulation wind tunnel system based on multi-field coupling according to claim 1, characterized in that, The output results in step S4 include the flow field vector diagram, the static pressure diagram of the windward side, the static pressure diagram of the leeward side, the pollution distribution diagram of the top surface of the insulator, the pollution distribution diagram of the ground surface of the insulator, and the average volume fraction of pollution of the insulator versus time curve.

10. The insulator pollution dynamic simulation wind tunnel system based on multi-field coupling according to claim 1, characterized in that, The output of step S4 also includes the dynamic evolution of the insulator flow field and pollution distribution using 3D rendering and streamline animation.

11. A data processing device, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement, when executing the computer program, a dynamic simulation wind tunnel system for insulator contamination based on multi-field coupling as described in any one of claims 1 to 10.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements a dynamic simulation wind tunnel system for insulator contamination based on multi-field coupling as described in any one of claims 1 to 10.

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