Numerical simulation method for droplet erosion of metal wall surface coupled with liquid film effect

Abstract

This application discloses a numerical simulation method for droplet erosion of metal walls coupled with liquid film effects, belonging to the field of computational fluid dynamics and equipment life prediction technology. The invention first constructs a flow field computational domain based on the geometry of the equipment under test and performs mesh generation to obtain a mesh model; then, it establishes a basic flow field solution model coupling continuous and discrete phases, and obtains convergent basic flow field results through iterative solution; it couples the Eulerian liquid film model and the Oka erosion rate calculation model, determines the absorption, rebound, and sputtering interaction process between the droplet and the metal wall through droplet impact energy parameters, and performs iterative calculations to obtain the metal wall erosion rate data; finally, it calculates the annualized thinning amount and distribution of the wall by converting the metal wall material density. This invention overcomes the shortcomings of traditional methods that ignore the liquid film effect and secondary erosion, achieving high-precision prediction of erosion damage in shielded areas, and providing a digital basis for the erosion-resistant design of heat exchange equipment.

Description

Technical Field

[0001] This application relates to the fields of computational fluid dynamics and material damage prediction technology, specifically to a numerical simulation method for droplet erosion of a metal wall coupled with liquid film effect. Background Technology

[0002] In many industrial sectors such as energy, chemical, and aerospace, the walls of metal equipment are often exposed to the erosion of high-speed airflow containing liquid droplets. For example, components such as the last-stage blades of steam turbines, tube bundles of heat exchange equipment, pipe elbows, and compressors of aero engines may experience erosion and wear due to repeated impacts from high-speed liquid droplets, leading to gradual thinning of the wall surface. In severe cases, this can cause perforation, leakage, or even equipment failure, seriously affecting the safety and economy of system operation.

[0003] Currently, research on multiphase flow erosion mainly focuses on gas-solid two-phase flow or liquid-solid two-phase flow, while research on the erosion mechanism of gas-liquid two-phase flow on metal walls is relatively limited. Traditional experimental research methods are time-consuming, costly, and difficult to observe the internal details of the flow field and the dynamic interaction between droplets and the wall.

[0004] With the development of computational fluid dynamics, numerical simulation has become an important tool for erosion prediction. However, existing CFD erosion simulation methods still have room for improvement: most methods use simplified wall boundary conditions, treating droplet impact as simple capture or reflection, ignoring the dynamic evolution of the liquid film after droplet impact on the wall; due to the lack of consideration of the liquid film effect, the prediction of secondary erosion in shielded areas or complex flow channels is distorted, and the simulation results deviate significantly from the actual physical process; existing erosion rate calculation models are mostly empirical models, which cannot directly reflect the influence of metallic material properties, such as hardness and density, on erosion behavior, and the models lack universality.

[0005] Therefore, there is an urgent need for a high-precision numerical simulation method that can accurately reflect the dynamic evolution of the liquid film after a droplet impacts a wall and its secondary impact on erosion. Summary of the Invention

[0006] This application addresses the problem in existing numerical simulations of droplet erosion on metal walls, where the droplet impact boundary is typically simplified to a simple capture or reflection process, neglecting the evolution of the liquid film after the droplet impact. This makes it difficult to effectively simulate the impact of secondary droplets generated by droplet sputtering on the shielded area, resulting in distorted and low-accuracy predictions of erosion on metal walls in complex flow channels. The application provides a numerical simulation method for droplet erosion on metal walls that couples with the liquid film effect.

[0007] To achieve the above objectives, this application adopts the following technical solution: In a first aspect, this application provides a numerical simulation method for droplet erosion of a metal wall coupled with a liquid film effect, comprising the following steps: S1. Construct the flow field computation domain and perform mesh generation based on the geometry of the device under test. Then, refine the mesh of the metal wall and near-wall region under test to obtain a mesh model for numerical calculation. S2. Based on the grid model, establish a basic flow field solution model that couples the continuous phase and the discrete phase, set the continuous phase boundary conditions, discrete phase injection parameters and discrete phase boundary conditions, perform the first iteration solution, and obtain the converged basic flow field results. S3, based on the converged basic flow field results, coupled the Euler liquid film model and the Oka erosion rate calculation model, simulated the evolution of the liquid film after the droplet impacts the wall using the Euler liquid film model, and determined the absorption, rebound and sputtering interaction process between the droplet and the metal wall based on the droplet impact energy parameters. At the same time, the Oka erosion rate calculation model was used to calculate the erosion rate of the metal wall, and the second iteration was performed until the erosion rate of the metal wall converged, thus obtaining the erosion rate data of the metal wall. S4. The erosion rate data of the metal wall is converted into thinning amount by the density of the metal wall material to obtain the annualized thinning amount and distribution of the wall, thereby realizing the prediction of erosion and thinning damage of the metal wall.

[0008] Furthermore, in step S1, multiple boundary layer meshes are added to the metal wall surface, the number of boundary layer meshes is more than 5, and the mesh size of the metal wall surface is refined to 1.0 mm to obtain a computational mesh.

[0009] Furthermore, in step S2, the continuous phase is the gas phase, which is solved using the RNG k-ε model; the discrete phase is the droplet, which is solved using the DPM model, and the discrete phase is injected through a particle injector in the manner of a jet source, with the droplet being regarded as a uniform spherical particle.

[0010] Furthermore, the formula for calculating the impact energy is as follows:

[0011] The impact energy is , For the density of the liquid, The velocity of the droplet perpendicular to the metal wall. Where is the droplet diameter, For liquid surface tension, This represents the height of the liquid film.

[0012] Furthermore, the specific criteria for determining the interaction mechanism based on the impact energy E are as follows: When E < 16, it is determined that the droplet is absorbed by the liquid film on the metal wall. When 16≤E<57.7, the droplet is determined to have bounced, and the bounce velocity is calculated based on the normal restitution coefficient. When E≥57.7, it is determined that the droplet has sputtered and generated a secondary droplet.

[0013] Furthermore, when it is determined that the droplet has bounced, the expression for the normal recovery coefficient is:

[0014] in, The normal restitution coefficient, The impact angle is measured from the metal wall surface.

[0015] Furthermore, in the Oka erosion rate calculation model, the erosion rate expression is:

[0016] Where ER is the erosion rate. For target material density, To standardize the particle impact velocity, For particle impact velocity, The Vickers hardness of the material. Particle size, To standardize particle size, , , These are erosion parameters.

[0017] Furthermore, the formula for calculating the annualized wall thinning is as follows:

[0018] Where D represents the annualized wall thinning. The density of the metal wall material.

[0019] Furthermore, the distribution results include erosion rate cloud maps, droplet trajectory maps, and annualized wall thinning distribution maps.

[0020] Secondly, this application also provides a numerical simulation system for droplet erosion of a metal wall coupled with a liquid film effect, used to implement a numerical simulation method for droplet erosion of a metal wall coupled with a liquid film effect, comprising: The mesh model construction module is used to construct the flow field computation domain and perform mesh generation based on the geometry of the device under test. The mesh is refined on the metal wall and near-wall region under test to obtain a mesh model for numerical calculation. The basic flow field solution module is used to establish a basic flow field solution model that couples continuous and discrete phases based on the grid model, set the continuous phase boundary conditions, discrete phase injection parameters and discrete phase boundary conditions, perform the first iteration solution, and obtain the converged basic flow field results. The erosion rate calculation module is used to couple the Euler liquid film model and the Oka erosion rate calculation model based on the converged basic flow field results. The Euler liquid film model simulates the evolution of the liquid film after the droplet impacts the wall, and determines the absorption, rebound and sputtering interaction process between the droplet and the metal wall based on the droplet impact energy parameters. At the same time, the Oka erosion rate calculation model is used to calculate the erosion rate of the metal wall. The second iteration is performed until the erosion rate of the metal wall converges, and the erosion rate data of the metal wall is obtained. The thinning amount conversion module is used to convert the metal wall erosion rate data into thinning amount based on the metal wall material density, so as to obtain the annualized thinning amount and distribution results of the wall, and realize the prediction of metal wall erosion thinning damage.

[0021] Compared with the prior art, this application has the following beneficial effects: This application provides a numerical simulation method for droplet erosion of metal walls coupled with liquid film effects. The method refines the mesh of the metal wall and its near-wall region, providing a mesh foundation for accurate analysis of near-wall physical processes. A basic flow field solution model coupling continuous and discrete phases is established. A first iteration yields a converged basic flow field result, ensuring the accuracy of input parameters such as droplet impact velocity and impact angle. Based on the converged basic flow field result, an Eulerian liquid film model and an Oka erosion rate calculation model are coupled. The droplet impact energy parameter determines the absorption, rebound, and sputtering interactions between the droplet and the metal wall, and a second iteration is performed until the erosion rate converges. This allows secondary droplets generated by sputtering on the surface of the front tube bundle to be carried by the gas phase flow field to the leeward side of the rear tube bundle, thus enabling quantitative prediction of secondary erosion in the shielded area. This method can accurately characterize droplet impact, liquid film evolution, and secondary droplet interaction processes, improving the accuracy of predicting metal wall erosion and thinning in complex flow channels, and providing a basis for erosion resistance design and operational parameter optimization. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1This is a flowchart of a numerical simulation method for droplet erosion of a metal wall coupled with liquid film effect provided in an embodiment of this application.

[0024] Figure 2 This is a cloud map showing the annualized thinning of the titanium alloy heat exchanger tube bundle wall in the embodiments of this application.

[0025] Figure 3 This diagram illustrates the effect of different droplet flow rates on the maximum annual thinning of each row of alloy heat exchanger tubes in the embodiments of this application.

[0026] Figure 4 This diagram illustrates the effect of different droplet sizes on the maximum annual thinning of each pipe in the embodiments of this application.

[0027] Figure 5 This is a diagram showing the effect of different steam humidity on the maximum annual thinning of each pipe in the embodiments of this application. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0029] See Figure 1 This application provides a numerical simulation method for droplet erosion of a metal wall coupled with the liquid film effect, comprising the following steps: S1. Construct the flow field computation domain and perform mesh generation based on the geometry of the device under test. Then, refine the mesh of the metal wall and near-wall region under test to obtain a mesh model for numerical calculation. In step S1, multiple boundary layer meshes are added to the metal wall surface. The number of boundary layer meshes is more than 5, and the mesh size of the metal wall surface is refined to 1.0 mm to obtain a computational mesh.

[0030] In another embodiment provided in this application, step S1 specifically includes the following process: The computational domain for the flow field is determined based on the actual geometry of the device under test. Taking a power plant condenser heat exchanger tube as an example, a flow field model is constructed containing three rows of metal tube bundles arranged in an equilateral triangle. The outer diameter of the tubes is 26 mm, the center-to-center distance is 32.5 mm, and the tube length is 200 mm. The axial distance between the inlet section and the first row of tube bundles, and the distance between the outlet section and the last row of tube bundles, are both 33.85 mm. The selection of the computational domain should ensure that the upstream inlet boundary conditions are not affected by the downstream wall, while the downstream outlet boundary conditions should ensure the full development of the flow field.

[0031] The computational domain for the flow field was meshed. Meshing software was used, employing a region-based meshing method. The main flow region was meshed using an unstructured grid, with a base grid size of 3.5 mm. Mesh refinement was applied to the measured metal wall and near-wall regions: local refinement was performed on the fluid injection surface, with the grid size fined to 2.0 mm; local refinement was also performed on the metal pipe wall, with the grid size fined to 1.0 mm. Five boundary layer meshes were added to the measured metal wall to analyze near-wall flow and liquid film evolution characteristics. The total number of meshes was approximately 5.53 million. Mesh quality was comprehensively evaluated using tilt and orthogonality quality. The mesh tilt and orthogonality were found to be excellent, meeting the computational requirements.

[0032] Through the above operations, a grid model for numerical calculation is obtained.

[0033] S2. Based on the grid model, establish a basic flow field solution model that couples the continuous phase and the discrete phase, set the continuous phase boundary conditions, discrete phase injection parameters and discrete phase boundary conditions, perform the first iteration solution, and obtain the converged basic flow field results. In another embodiment provided in this application, step S2 specifically includes the following process: Based on the aforementioned mesh model, a fundamental flow field solution model coupling the continuous and discrete phases is established. The continuous phase is the gas phase, solved using the RNG k-ε model (renormalization group k-ε turbulence model); the discrete phase is the liquid droplets, solved using the DPM model (discrete phase model). The continuous phase medium is water vapor with a density of 0.05116 kg / m³ and a dynamic viscosity of 9.62 × 10⁻⁶. -6 Pa·s; the discrete phase is a droplet particle phase, which is solved using the DPM model. The droplets are considered as uniform spherical particles with a volume fraction of less than 10%, and the density of the discrete phase medium is 992.2 kg / m³.

[0034] Continuous phase boundary conditions are set. The continuous phase inlet is set as a velocity inlet, which is 200 m / s in this embodiment, and the outlet is a pressure outlet. The metal wall uses the standard wall function method and no-slip boundary conditions.

[0035] Configure the discrete phase injection parameters and boundary conditions. The discrete phase is injected via a jet source through a particle injector, with an inlet velocity consistent with the continuous phase (200 m / s) and a particle size of 200 μm. Enable the stochastic discrete walk model to account for the stochastic influence of turbulence on the droplet trajectory. Set the discrete phase outlet boundary type to escape.

[0036] The first iteration is performed. The solver uses a pressure-velocity coupled algorithm. The pressure term and other terms are discretized using a first-order upwind scheme. The step size of the iteration variables is adjusted by setting a sub-relaxation factor until each residual curve stabilizes within the set allowable range. The calculation is then considered convergent, and the converged continuous and discrete phase basic flow field results are obtained.

[0037] Even with a physically-based erosion model in existing technologies, erosion prediction results will still be distorted if the input basic flow field data, such as droplet impact velocity and impact angle, are inaccurate. By establishing a two-way coupled basic flow field solution model of continuous and discrete phases, this step ensures high-precision calculation of droplet trajectory, impact position, impact velocity, and impact angle, providing accurate input parameters for the Oka erosion rate calculation model in subsequent step S3, and solving the problem of erosion prediction deviation caused by inaccurate basic flow field calculations.

[0038] S3. Based on the converged basic flow field results, the Euler liquid film model and the Oka erosion rate calculation model are coupled. The Euler liquid film model is used to simulate the evolution of the liquid film after the droplet impacts the wall. The absorption, rebound and sputtering interaction process between the droplet and the metal wall is determined according to the droplet impact energy parameters. At the same time, the Oka erosion rate calculation model is used to calculate the erosion rate of the metal surface. The second iteration is performed until the erosion rate of the metal wall converges, and the erosion rate data of the metal wall are obtained. In another embodiment provided in this application, step S3 specifically includes the following process: Based on the convergent basic flow field results, a coupled Eulerian Wall Film Model is used to simulate the physical model of liquid film formation and evolution after droplet impact on the wall. The wall boundary conditions are defined, and the Eulerian Wall Film Model is activated to simulate the dynamic evolution process of the liquid film formed after droplet impact on the metal wall.

[0039] Calculate the droplet impact energy parameter. The impact energy E is a dimensionless parameter, and its calculation formula is as follows:

[0040] The impact energy is , For the density of the liquid, The velocity of the droplet perpendicular to the metal wall. Where is the droplet diameter, For liquid surface tension, This represents the height of the liquid film.

[0041] The specific criteria for determining the interaction mechanism based on the impact energy E are as follows: When E < 16, it is determined that the droplet is absorbed by the liquid film on the metal wall. When 16≤E<57.7, the droplet is determined to have bounced, and the bounce velocity is calculated based on the normal restitution coefficient. When E≥57.7, it is determined that the droplet has sputtered and generated secondary droplets. These secondary droplets will be carried by the gas phase flow field, bypass the obstacles behind, and collide with the shielded area.

[0042] In the rebound state, the velocity of the rebounding droplet is given by the following formula:

[0043] in, and These are the tangential and normal components of the rebound velocity, respectively. It is the tangential component of the impact velocity. It is the normal component of the impact velocity; When it is determined that the droplet has bounced, the expression for the normal recovery coefficient is:

[0044] in, The normal restitution coefficient, The impact angle is measured from the metal wall surface, in radians.

[0045] When it is determined that droplet sputtering occurs, this embodiment uses the Stanton-Rutland model (a physical model used to simulate the secondary droplet generated after a droplet hits a wall) to simulate droplet sputtering and generate secondary droplets.

[0046] In one specific embodiment provided in this application, based on the converged basic flow field results, the erosion rate of the metal wall is calculated using the Oka erosion rate calculation model based on droplet impact velocity, droplet size, impact angle function, and titanium alloy material parameters. In this Oka erosion model, the erosion rate expression is:

[0047] Where ER is the erosion rate. For target material density, To standardize the particle impact velocity, For particle impact velocity, The Vickers hardness of the material. Particle size, To standardize particle size, , , These are erosion parameters.

[0048] The impact angle dependence of the erosion rate is given by the following equation:

[0049] in, and These are erosion parameters.

[0050] The second iteration is performed based on the metal wall erosion rate until the metal wall erosion rate converges, thus obtaining the metal wall erosion rate data.

[0051] To address the problem of unpredictable secondary erosion effects leading to severe distortion in predictions of the obstructed area, this embodiment simulates the transport process of secondary droplets in the flow field and their impact on the obstructed area through sputtering determination and the generation and tracking of secondary droplets. In a tube bundle heat exchanger, the first row of tubes is directly impacted by droplets and sputtered. The resulting secondary droplets are carried by the airflow around the rear, impacting the leeward side of the second and third rows of tubes. This physical process is completely ignored in traditional methods, but can be accurately captured in this embodiment.

[0052] S4. The erosion rate data of the metal wall is converted into thinning amount by the density of the metal wall material to obtain the annualized thinning amount and distribution of the wall, thereby realizing the prediction of erosion and thinning damage of the metal wall.

[0053] In one specific embodiment provided in this application, step S4 includes the following process: Extract the metal wall erosion rate data ER obtained in step S3; Density of metal wall material The erosion rate data of the metal wall is converted into a thinning amount, and the annualized thinning amount of the wall is calculated using the following formula:

[0054] Where D represents the annualized wall thinning. The density of the metal wall material.

[0055] See Figure 2 The image shows the annualized thinning of the titanium alloy heat exchanger tube bundle wall. The results of this embodiment show a maximum annualized wall thinning of 0.0219 mm / a. The output distribution results include an erosion rate cloud map, a droplet trajectory map, and a thinning distribution map. High-value areas in the erosion rate cloud map correspond to high erosion risk areas, and the droplet trajectory map reflects the complex spatial distribution characteristics of droplets around the tube bundle.

[0056] In traditional methods without coupled Eulerian liquid film models, droplet impact on the wall is simplified to capture or reflection, without simulating the liquid film formation and sputtering processes. Simulation results show that areas blocked by the preceding tube bundles, such as the leeward side of the second and third rows of tube bundles, show no erosion. However, in the method of this application, which couples the Eulerian liquid film model and the Oka erosion rate calculation model, simulation results show that, due to the accurate capture of secondary droplets generated by droplet sputtering, these secondary droplets are carried by the airflow around the rear and impact the blocked area, resulting in slight but not negligible erosion in that area. Comparative experiments demonstrate that the method of this application can predict secondary erosion effects that traditional methods cannot predict, and the prediction results are more consistent with physical reality.

[0057] Example 2 See Figure 3 In Example 2, the effect of different droplet velocities on the erosion rate was investigated. This example is basically the same as Example 1, except that the droplet velocity in the discrete phase injection parameters was changed. Four sets of velocities were set: 100 m / s, 150 m / s, 200 m / s, and 250 m / s, with a particle size of 200 micrometers and a humidity of 10%, corresponding to mass flow rates of 3.07 × 10⁻⁶ m / s and 3.07 × 10⁻⁶ m / s, respectively. - ³ kg / s, 4.60 × 10⁶ - ³ kg / s, 6.14 × 10⁶ - ³ kg / s, 7.67 × 10⁶ - The simulation results show that the annualized wall thinning of each tube bundle increases significantly with increasing flow velocity, and the erosion rate exhibits a power-law positive correlation with the flow velocity, consistent with the physical laws of the Oka erosion rate calculation model. This verifies that the method in this application can accurately reflect the influence of flow velocity on erosion, providing a basis for determining safe operating flow velocities in engineering.

[0058] Example 3 See Figure 4 In Example 3, the influence of different droplet sizes on the erosion rate was investigated. This example is basically the same as Example 1, except that the droplet size in the discrete phase injection parameters was changed. Five groups of droplet sizes were set: 100 μm, 150 μm, 200 μm, 250 μm, and 300 μm. The flow rate was 200 m / s, and the humidity was 10%. As the droplet size increased, the droplet kinetic energy increased, leading to an increasing annualized thinning of the tube bundle wall in each row. This proves that large-diameter droplets are the main factor causing severe wear of the tube bundle. This verifies that the method of this application can accurately reflect the influence of droplet size on erosion, providing a reference for the requirements of droplet separation equipment in engineering design.

[0059] Example 4 See Figure 5In Example 4, the effect of different steam humidity levels on the erosion rate was investigated. This example is basically the same as Example 1, except that the humidity variable in the discrete phase injection parameters was changed and converted into droplet mass flow rate. Humidity values ​​of 6%, 8%, 10%, and 12% were set, corresponding to mass flow rates of 3.68 × 10⁻⁶. - ³ kg / s, 4.91 × 10⁻⁶ - ³ kg / s, 6.14 × 10⁶ - ³ kg / s, 7.37 × 10⁶ - The flow rate was 3 kg / s, with a particle size of 200 μm and a flow velocity of 200 m / s. The results showed that the annualized thinning of the pipe wall increased almost linearly with increasing humidity, indicating that in a high-speed flow environment, the total mass of droplets impacting the metal wall per unit time is the direct variable determining the degree of erosion damage. This verifies that the method in this application can accurately reflect the influence of humidity on erosion, providing quantitative guidance for humidity control in operating conditions.

[0060] Although the above embodiments have been described in detail using titanium alloy condenser heat exchange tubes as an example, those skilled in the art should understand that the prediction method of this application is also applicable to heat exchange equipment, pipe elbows, or turbine blades made of other metal materials such as stainless steel and carbon steel. All numerical simulations of metal wall erosion by droplets using the method of this application fall within the protection scope of this application.

[0061] In another embodiment provided in this application, a numerical simulation system for droplet erosion of a metal wall coupled with the liquid film effect is provided, for implementing a numerical simulation method for droplet erosion of a metal wall coupled with the liquid film effect, comprising: The mesh model construction module is used to construct the flow field computation domain and perform mesh generation based on the geometry of the device under test. The mesh is refined on the metal wall and near-wall region under test to obtain a mesh model for numerical calculation. The basic flow field solution module is used to establish a basic flow field solution model that couples continuous and discrete phases based on the grid model, set the continuous phase boundary conditions, discrete phase injection parameters and discrete phase boundary conditions, perform the first iteration solution, and obtain the converged basic flow field results. The erosion rate calculation module is used to couple the Euler liquid film model and the Oka erosion rate calculation model based on the converged basic flow field results. The Euler liquid film model simulates the evolution of the liquid film after the droplet impacts the wall, and determines the absorption, rebound and sputtering interaction process between the droplet and the metal wall based on the droplet impact energy parameters. At the same time, the Oka erosion rate calculation model is used to calculate the erosion rate of the metal wall. The second iteration is performed until the erosion rate of the metal wall converges, and the erosion rate data of the metal wall is obtained. The thinning amount conversion module is used to convert the metal wall erosion rate data into thinning amount based on the metal wall material density, so as to obtain the annualized thinning amount and distribution results of the wall, and realize the prediction of metal wall erosion thinning damage.

[0062] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application. These modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A numerical simulation method for the liquid drop erosion of a metal wall surface coupled with a liquid film effect, characterized in that, Includes the following steps: S1. Construct the flow field computation domain and perform mesh generation based on the geometry of the device under test. Then, refine the mesh of the metal wall and near-wall region under test to obtain a mesh model for numerical calculation. S2. Based on the grid model, establish a basic flow field solution model that couples the continuous phase and the discrete phase, set the continuous phase boundary conditions, discrete phase injection parameters and discrete phase boundary conditions, perform the first iteration solution, and obtain the converged basic flow field results. S3, based on the converged basic flow field results, coupled the Euler liquid film model and the Oka erosion rate calculation model, simulated the evolution of the liquid film after the droplet impacts the wall using the Euler liquid film model, and determined the absorption, rebound and sputtering interaction process between the droplet and the metal wall based on the droplet impact energy parameters. At the same time, the Oka erosion rate calculation model was used to calculate the erosion rate of the metal wall, and the second iteration was performed until the erosion rate of the metal wall converged, thus obtaining the erosion rate data of the metal wall. S4. The erosion rate data of the metal wall is converted into thinning amount by the density of the metal wall material to obtain the annualized thinning amount and distribution of the wall, thereby realizing the prediction of erosion and thinning damage of the metal wall.

2. The method according to claim 1, wherein, In step S1, multiple boundary layer meshes are added to the metal wall surface. The number of boundary layer meshes is more than 5, and the mesh size of the metal wall surface is refined to 1.0 mm to obtain a computational mesh.

3. The method of claim 1, wherein the method is characterized by, In step S2, the continuous phase is the gas phase, which is solved using the RNG k-ε model; the discrete phase is the droplet, which is solved using the DPM model, and the discrete phase is injected through a particle injector in the form of a jet source, with the droplet being regarded as a uniform spherical particle.

4. The method of claim 1, wherein, The formula for calculating the impact energy is: where the impingement energy is , is the liquid density, is the velocity of the droplet normal to the metal wall, is the droplet diameter, is the liquid surface tension, is the liquid film height.

5. The method of claim 4, wherein the method is characterized by, The specific criteria for determining the interaction mechanism based on the impact energy E are as follows: When E < 16, it is determined that the droplet is absorbed by the liquid film on the metal wall. When 16≤E<57.7, the droplet is determined to have bounced, and the bounce velocity is calculated based on the normal restitution coefficient. When E≥57.7, it is determined that the droplet has sputtered and generated a secondary droplet.

6. The method of claim 5, wherein the method is characterized by, When it is determined that the droplet has bounced, the expression for the normal recovery coefficient is: wherein, is the normal restitution coefficient, is the angle of impact measured from the metal wall.

7. The numerical simulation method for droplet erosion of a metal wall surface coupled with liquid film effect according to claim 1, characterized in that, In the Oka erosion rate calculation model, the erosion rate expression is: Where ER is the erosion rate. For target material density, To standardize the particle impact velocity, For particle impact velocity, The Vickers hardness of the material. Particle size, To standardize particle size, , , These are erosion parameters.

8. The numerical simulation method for droplet erosion of a metal wall coupled with liquid film effect according to claim 1, characterized in that, The formula for calculating the annualized thinning of the wall surface is: wherein D is the annualized wall thinning, is the density of the metal wall material.

9. The method of claim 8, wherein the method is characterized by, The distribution results include erosion rate cloud maps, droplet trajectory maps, and annualized wall thinning distribution maps.

10. A numerical simulation system for coupling liquid film effect and metal wall surface droplet erosion, characterized in that, A numerical simulation method for simulating the droplet erosion of a metal wall surface coupled with a liquid film effect as described in any one of claims 1-9 includes: The mesh model construction module is used to construct the flow field computation domain and perform mesh generation based on the geometry of the device under test. The mesh is refined on the metal wall and near-wall region under test to obtain a mesh model for numerical calculation. The basic flow field solution module is used to establish a basic flow field solution model that couples continuous and discrete phases based on the grid model, set the continuous phase boundary conditions, discrete phase injection parameters and discrete phase boundary conditions, perform the first iteration solution, and obtain the converged basic flow field results. The erosion rate calculation module is used to couple the Euler liquid film model and the Oka erosion rate calculation model based on the converged basic flow field results. The Euler liquid film model simulates the evolution of the liquid film after the droplet impacts the wall, and determines the absorption, rebound and sputtering interaction process between the droplet and the metal wall based on the droplet impact energy parameters. At the same time, the Oka erosion rate calculation model is used to calculate the erosion rate of the metal wall. The second iteration is performed until the erosion rate of the metal wall converges, and the erosion rate data of the metal wall is obtained. The thinning amount conversion module is used to convert the metal wall erosion rate data into thinning amount based on the metal wall material density, so as to obtain the annualized thinning amount and distribution results of the wall, and realize the prediction of metal wall erosion thinning damage.

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