Semi-analytical CFD-DEM spray gun blockage simulation method and system based on viscosity correction

Abstract

The invention discloses a semi-analytic CFD-DEM spray gun blockage simulation method and system based on viscosity correction, and aims to improve spatial distribution correlation between viscosity and particle concentration by reducing volume flow of magnesium oxide particles added into a feed port of a spray gun in a simulation manner, correcting viscosity of a control body unit and simulating interaction between particles and fluid at the same time. A semi-analytic CFD-DEM method is adopted, a background flow field is reconstructed through a kernel function, the limitation of the size of a fluid grid is broken through, and the calculation efficiency and precision are remarkably improved. According to the method, the problem of blockage of the spray gun in the spraying process is effectively solved, the spraying process can be optimized, and the production efficiency and the spraying quality are improved.

Description

Technical Field

[0001] The present application relates to the technical field of oriented silicon steel spraying, and in particular to a solution and system for spray gun blockage. Background Art

[0002] Applying a layer of special inhibitor on the end surface of the steel coil can inhibit the coarsening of the edge grains, thereby improving the material plasticity of the edge of the oriented silicon steel and significantly reducing the occurrence of edge cracks.

[0003] In the existing silicon steel spraying technology, the oriented silicon steel end face inhibitor spraying device uses a spray gun for spraying, and the problem of spray gun clogging is common, especially in the spraying process of oriented silicon steel end face inhibitor containing magnesium oxide and boric acid particles. This spraying process has high requirements on the working performance of the spray gun, because the presence of magnesium oxide and boric acid particles may increase the viscosity of the fluid, and the flow resistance of high viscosity fluid in the spray gun increases, affecting the normal operation of the spray gun, thereby increasing the risk of spray gun clogging.

[0004] At the same time, magnesium oxide and boric acid particles may be deposited inside the spray gun, especially at the spray gun outlet. These deposits will gradually accumulate and eventually cause the nozzle to be blocked.

[0005] When using fluids containing high concentrations of particles, the design and use of the spray gun may require special adjustments to prevent clogging. The semi-analytical CFD-DEM method (Computational Fluid Dynamics-DiscreteElement Method) based on viscosity correction simulates fluids containing particles by adjusting the viscosity model of the fluid. This method can more accurately optimize the design of the spray gun fluid domain and predict the flow behavior of the fluid in the spray gun, including flow velocity, pressure distribution, and particle distribution, thereby predicting and preventing spray gun clogging. In this way, the design and operating conditions of the spray gun can be optimized, the possibility of clogging can be reduced, and the stability and reliability of the spraying device can be improved.

[0006] The traditional analytical CFD-DEM method has a large amount of calculation and is difficult to simulate effectively; while the unanalytical CFD-DEM method is affected by the size of boric acid particles, resulting in a rough flow field mesh and affecting the simulation accuracy. Summary of the invention

[0007] The technical problem to be solved by the present application is to provide a semi-analytical CFD-DEM spray gun clogging simulation method and system based on viscosity correction, so as to improve the problem that the existing simulation method is not targeted and has poor simulation accuracy. It has high calculation efficiency, effectively solves the clogging problem of the spray gun during the spraying process, and improves production efficiency and spraying quality.

[0008] The embodiment of the present application is implemented as follows:

[0009] A semi-analytical CFD-DEM spray gun clogging simulation method based on viscosity correction, characterized by comprising the following steps:

[0010] Step S1: According to the obtained spray gun structure, a CFD solver is established in the CFD analysis software for simulation, and the spray fluid domain between the spray gun feed and the discharge port is meshed and encrypted to form a refined mesh flow field, and each refined mesh is used as a control volume unit;

[0011] Step S2: reducing the volume flow rate of magnesium oxide particles added from the feed port of the spray gun during the simulation process, and correcting the viscosity of the control volume unit to obtain a corrected viscosity, so as to ensure that each control volume unit is occupied by at least one magnesium oxide particle;

[0012] Step S3: transmitting the particle data to the CFD solver to obtain the DEM particle volume fraction information;

[0013] Step S4: based on the obtained DEM particle volume fraction information, the viscosity is corrected in combination with the viscosity correction method;

[0014] Step S5: expanding the particle background fluid domain to adjacent simulation control volume units with a set smoothing distance;

[0015] Step S6: reconstructing the background flow field information of the particles based on the kernel function, and the reconstructed background flow field information of the particles is used to calculate the interaction force between the fluid and the particles;

[0016] Step S7: Correcting the fluid volume fraction in the particle background flow field to calculate the interaction force between the fluid and the particles;

[0017] Step S8: Mapping the momentum to the source item of the control volume unit to which the particle belongs according to the position of the particle;

[0018] Step S9: adding the corrected viscosity and momentum source term of particle motion to solve the fluid control equation, and solving the fluid control equation corresponding to the refined grid;

[0019] Step S10: Transmitting the fluid information obtained after solving to the DEM solver;

[0020] Step S11: Calculate particle motion in the DEM solver and update particle position and velocity;

[0021] Step S12: The updated particle data is transmitted to the CFD solver, and the above steps are repeated to obtain the optimal particle position and velocity information.

[0022] In the above technical solution, step S2 reduces the volume of magnesium oxide particles added by the inlet simulation based on the dichotomy method; the specific reduction in the volume of magnesium oxide particles added by the inlet simulation is subject to the specific fluid domain structure, the size of the control body unit (the size of the control body unit in the fluid domain is different and cannot be the same), the simulation conditions, etc., and it is necessary to observe through conventional simulation debugging whether the control body units in the particle flow area are all occupied by at least one magnesium oxide particle.

[0023] In the above technical solution, step S2 includes the following specific steps:

[0024] Step S21: calculating the percentage of particles in the unit based on the ratio of the volume of the control volume unit to the volume of the actual particles in the control volume unit;

[0025] Step S22: Calculating the percentage of omitted particles in the control volume unit based on the calculation formula for the percentage of omitted particles in the control volume unit;

[0026] Step S23: Correct the viscosity of the liquid-solid two-phase mixed system based on the classical viscosity prediction model, and calculate the corrected viscosity of the particle flow in the control volume unit.

[0027] In the above technical solution, step S2 also includes:

[0028] By reducing the number of magnesium oxide particles and correcting the viscosity of liquid-solid multiphase flow at the same time, the calculation efficiency is improved and the corrected viscosity of the particle flow in the control volume unit is obtained.

[0029] The amount of magnesium oxide is reduced under the premise of truly reflecting the flow of fluid particles. For example, if the particles are reduced too much, the true law of the effect of particles on blockage cannot be obtained, and if the particles are reduced too little, it will not play much role in reducing the amount of calculation.

[0030] In the above technical solution, the particle ratio in the control volume unit is adopted, and at least one classical prediction model is used to correct the viscosity of the liquid-solid two-phase mixed system.

[0031] In the above technical solutions, the classical prediction models all obtain the particle flow viscosity through the functional relationship between the particle-free flow viscosity and the percentage of particles.

[0032] In the above technical solution, one of the classic prediction models of Einstein, Brinkman, Batchelor, Thomas and Muthukumar, Vakili-Nezhaad and Dorany is used to obtain the corrected viscosity of the liquid-solid two-phase mixed system.

[0033] In the above technology, in step S1, the amount of magnesium oxide particles to be added is selected according to the working conditions, and the amount of magnesium oxide particles is introduced as the actual amount of magnesium oxide particles for input.

[0034] Preferably, a plurality of operating modes are set, and each operating mode matches the amount of magnesium oxide particles that need to be added with a certain ratio value.

[0035] For example, if there are n magnesium oxide particles in a certain working condition, the actual simulation should also add n magnesium oxide particles, but the magnesium oxide particles are too small and too many, which requires more computing resources. Therefore, the actual simulation needs to add n magnesium oxide particles, and the simulation only adds some magnesium oxide particles, such as n / 2 magnesium oxide particles.

[0036] It is also possible to set 2n / 3, or 3n / 4, or set a ratio in the range of n / 4 to 3n / 4 according to other operating modes.

[0037] As the number of particles increases, the flow viscosity increases, so the reduction in the amount of magnesium oxide introduced by the simulation needs to be balanced by increasing the fluid viscosity.

[0038] In the above technical solution, in step S2, the calculation formula for the percentage of magnesium oxide particles omitted in the control unit is as follows:

[0039]

[0040] Where: To control the percentage of omitted magnesium oxide particles in the volume unit, V 控制体单元体积 is the volume of the control unit, V 控制体单元内氧化镁颗粒体积 is the volume of magnesium oxide particles in the control unit, V 实际 V is the volume flow rate of magnesium oxide particles input from the feed port under actual working conditions, 仿真 is the volume flow rate of magnesium oxide particles input from the feed inlet during the simulation process.

[0041] In the above technical solution, in step S2, the corrected viscosity of the control unit is calculated based on the classical viscosity prediction model:

[0042]

[0043] Where: η 0 is the viscosity of the flow without particles, η 控制体单元修正黏度 Corrected viscosity for control volume elements.

[0044] In the above technical solution, the continuity equation, momentum equation, and particle force of the interaction between fluid and particles are solved successively to ensure that the total mass of the fluid remains unchanged in any given area, and obtain particle forces including drag force, virtual mass force, Basset force, Magnus force and Saffman force.

[0045] In the above technical solution, step S4: expand the particle background flow field in the fluid domain to the control volume unit with a smoothing distance of kd, d is the particle diameter, k is the smoothing distance coefficient. According to research by scholars, the solution accuracy is higher when the smoothing distance coefficient is 3.

[0046] In the above technical solution, the side length of the fluid grid is between one tenth and three times the diameter of the particle.

[0047] In the above technical solution, step S5 uses a Gaussian function as a kernel function to weight the properties of the fluid at different positions in space. For each calculation point, the fluid units around it are considered, and the kernel function is used to calculate the weighted average of the fluid properties of these units.

[0048] In the above technical solution, in step S5, a Gaussian function is used as a kernel function to weight the background flow field information of each particle, which also includes the corrected viscosity of the control volume unit. The background flow field attribute solution formula is as follows:

[0049] Where: is the background flow field property expression, η J is the flow field property in unit J, α J is the fluid volume of unit J, V cell，J is the volume of unit J, k i，J (r i -r J ) is r i -r J is the relative position of the particle to the unit position. In step S6, the interaction forces (drag force, virtual mass force, Basset force, Magnus force and Saffman force) between the particle and the fluid are calculated using the corrected fluid volume fraction information, and the viscosity term in the interaction force between the particle and the fluid is replaced by the particle background viscosity flow field information weighted by the Gaussian function.

[0050] In the above technical solution, in step S6, the interaction force between the particle and the fluid (drag force, virtual mass force, Basset force, Magnus force and Saffman force) is calculated using the corrected fluid volume fraction information, and the viscosity term in the interaction force between the particle and the fluid is replaced by the particle background viscosity flow field information weighted by the Gaussian function.

[0051] The present invention also provides an electronic device or a storage medium, in which a computer program is stored, and when the computer program is executed, it is used to implement the steps of the above method.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] Since the diameter of the spray gun outlet is only 1 to 2 mm, the traditional analytical CFD-DEM method is too computationally intensive to be effectively simulated. The unanalyzed CFD-DEM method is affected by the size of the boric acid particles, resulting in a rough flow field mesh and affecting the simulation accuracy.

[0054] The semi-analytical CFD-DEM method proposed in the present invention simulates the interaction between particles and fluids by introducing some magnesium oxide particles and correcting the fluid viscosity formula. Since magnesium oxide particles have different densities from water, they will produce spatial concentration differences in the flow field due to inertial forces, so the improved viscosity formula is related to the spatial distribution of particle concentration. The semi-analytical CFD-DEM method is used to reconstruct the background flow field through the kernel function, breaking through the limitation of the fluid grid size and significantly improving the calculation efficiency and accuracy. This method effectively solves the problem of spray gun clogging during the spraying process, can optimize the spraying process, and improve production efficiency and spraying quality.

[0055] Unanalyzed CFD-DEM requires the fluid grid side length to be three times the particle diameter, so the fluid grid at the spray gun outlet is too coarse and cannot truly reflect the flow field information; the fully-analyzed CFD-DEM fluid grid side length reaches one-tenth of the particle diameter, which can truly reflect the flow field information, but the fluid grid is too small, and the momentum and energy exchange between the particles and the fluid needs to be performed by surface integration, which is inefficient. The present invention takes into account the narrow channel of the spray gun outlet and adopts a semi-analyzed CFD-DEM method to eliminate the dependence of the fluid grid size on the boric acid particles and the magnesium oxide particles.

[0056] The present invention uses a kernel function to reconstruct the viscosity information in the background flow field. The particle ratio in the control volume unit is combined with a classical prediction model to correct the viscosity of the solid-liquid two-phase mixed system. The simulation of viscosity is closer to the real scene.

[0057] Considering that CFD-DEM tracks the information of each particle through the Lagrangian method, there is a problem of large calculation for the actual problem of a large number of particles. Replacing the actual magnesium oxide particles with some magnesium oxide particles, modifying the continuous phase viscosity and simplifying the model can effectively reduce the amount of calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0059] Figure 1 This is the semi-analytical CFD-DEM solution process based on viscosity correction for this application.

[0060] Figure 2 This is the schematic diagram of the semi-analytical CFD-DEM method based on viscosity correction for this application.

[0061] Figure 3 A comparison of the Euler-Lagrangian model system of this application with two other solution methods.

[0062] Figure 4 This application expands the background fluid domain from the local unit to the adjacent unit schematic.

[0063] Figure 5 Schematic diagram of the volume criterion of magnesium oxide particles added in the binary reduction simulation for this application.

[0064] Figure 6 This is a schematic diagram of the spray simulation model structure for this application. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0066] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0067] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0068] In the present application, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may include that the first and second features are in direct contact, or may include that the first and second features are not in direct contact but are in contact through another feature between them. Moreover, a first feature being "above", "above" and "above" a second feature includes that the first feature is directly above and obliquely above the second feature, or simply indicates that the first feature is higher in level than the second feature. A first feature being "below", "below" and "below" a second feature includes that the first feature is directly below and obliquely below the second feature, or simply indicates that the first feature is lower in level than the second feature.

[0069] The features and performance of the present application are further described in detail below in conjunction with the embodiments.

[0070] Example 1

[0071] The semi-analytical CFD-DEM spray gun clogging research method based on viscosity correction implemented in the present invention is as follows Figure 1-6 As shown, the following steps are included:

[0072] Step S1, establish a simulation control volume unit in CFD analysis software, introduce part of the magnesium oxide particle representation, instead of the actual magnesium oxide particle; cfd fluid software is used to calculate the fluid, and the current OpenFOAM can be used, and the discrete element software is used to calculate particles such as liggghts. The two softwares are coupled through the cfdem software. The establishment of the simulation control volume unit is performed by meshing through a blockmesh command in OpenFOAM.

[0073] like Figure 4-6 According to the obtained spray gun structure, a CFD solver is established in the CFD analysis software for simulation, and the spray fluid domain between the spray gun feed and the discharge port is meshed and encrypted to form a refined mesh flow field, and each refined mesh is used as a control volume unit.

[0074] Step S2, reducing the volume flow rate of magnesium oxide particles added by inlet simulation, and correcting the viscosity of the control unit at the same time, wherein the volume flow rate of magnesium oxide particles added by inlet simulation is the volume flow rate of magnesium oxide particles input from the inlet feed port during the simulation process;

[0075] The percentage of particles within a cell is solved based on the following formula:

[0076]

[0077] Where: is the percentage of particles in the control volume unit, V 控制体单元体积 is the volume of the control unit, V 控制体单元内颗粒的体积 is the volume of the particle in the control unit;

[0078] The percentage of omitted particles in the control volume element is calculated based on the following formula:

[0079]

[0080] Where: is the percentage of omitted particles in the control volume unit, V 控制体单元体积 is the volume of the control unit, V 控制单元内氧化镁颗粒的体积 is the volume of the magnesium oxide particles in the control unit, V 实际的氧代镁颗粒体积 is the actual volume of magnesium oxide particles, V 仿真加入的氧代镁颗粒体积The volume of magnesium oxide particles added for simulation;

[0081] The corrected viscosity of the particle flow in the control volume unit is calculated based on the classical viscosity prediction model:

[0082]

[0083] Where: η 0 is the viscosity of the flow without particles, η 控制体单元修正黏度 Corrected viscosity for control volume elements;

[0084] The above formula introduces the purpose of improving the calculation efficiency by reducing the number of magnesium oxide particles and correcting the viscosity of liquid-solid multiphase flow.

[0085] Step S3: transmitting the particle data to the CFD solver to obtain information such as the DEM particle volume fraction;

[0086] Step S4: based on the obtained DEM particle volume fraction information, the viscosity is corrected in combination with the viscosity correction method;

[0087] Step S5: Figure 4 , expand the particle background flow field to the control volume unit with a smoothing distance of kd, draw a circle with the center of the particle as the center and kd as the radius, and the grids involved in the circle constitute the background flow field area; d is the particle diameter, k is the smoothing distance coefficient. According to research by scholars, the solution accuracy is higher when the smoothing distance coefficient is 3.

[0088] Step S6: reconstructing the background flow field information of the particles based on the kernel function, and using the reconstructed background flow field information of the particles to calculate the interaction force between the fluid and the particles;

[0089] Step S7: Correcting the fluid volume fraction in the particle background flow field to calculate the interaction force between the fluid and the particles;

[0090] Step S8: Mapping the momentum to the source item of the control volume unit according to the position of the particle;

[0091] Step S9: adding the corrected viscosity and the momentum source term of the particle motion to solve the fluid control equation, and solving the fluid control equation;

[0092] Step S10: transmitting the solved fluid information to the DEM solver;

[0093] Step S11: Calculate particle motion in the DEM solver and update particle position and velocity;

[0094] Step S12: Transmit the updated particle data to the CFD solver, and repeat the above steps.

[0095] Furthermore, in step S1, considering that CFD-DEM tracks the information of each particle through the Lagrangian method, there is a problem of large calculation for the actual problem of a large number of particles. Using some magnesium oxide particles to replace the actual magnesium oxide particles, modifying the continuous phase viscosity and simplifying the model can effectively reduce the amount of calculation.

[0096] Furthermore, in the step S1, the viscosity of the solid-liquid two-phase mixed system is corrected by controlling the particle ratio in the volume unit in combination with the classical prediction model in Table 1.

[0097] Table 1 Classic viscosity prediction model

[0098]

[0099] Furthermore, in step S2, by reducing the volume flow of magnesium oxide particles added by inlet simulation and correcting the viscosity of the control unit, a large number of magnesium oxide particles can be avoided from being tracked, thereby improving the calculation efficiency. Specifically, step S2 includes the following specific steps:

[0100] Step S21: based on the binary method, the volume of magnesium oxide particles added by the inlet simulation is reduced. The basic idea of ​​the binary method is to continuously reduce the search interval by half until the target value is found or the interval is reduced to zero. The specific reduction in the volume of magnesium oxide particles added by the inlet simulation is subject to the specific fluid domain structure, the size of the control body unit (the size of the control body unit in the fluid domain is different and cannot be the same), the simulation conditions, etc. It is necessary to observe whether the control body units in the particle flow area are occupied by at least one magnesium oxide particle through conventional simulation debugging, and reduce the volume of magnesium oxide particles added by the inlet simulation based on the binary method;

[0101] Step S22: Calculate the percentage of magnesium oxide particles omitted in the control volume unit based on the calculation formula for the percentage of magnesium oxide particles omitted in the control volume unit.

[0102] Step S23: Based on the percentage of omitted magnesium oxide particles in the control volume unit Calculation of corrected viscosity η of control volume unit using classic viscosity prediction model 控制体单元修正粘度 .

[0103] In step S22, the calculation formula for the percentage of omitted magnesium oxide particles in the control volume unit is as follows:

[0104]

[0105] Where: To control the percentage of omitted magnesium oxide particles in the volume unit, V 控制体单元体积 is the volume of the control unit, V 控制体单元内氧化镁颗粒体积 is the volume of magnesium oxide particles in the control unit, V inlet实际加入的氧化镁颗粒体积流量V is the volume flow rate of magnesium oxide particles input from the inlet under actual working conditions. inlet仿真加入的氧化镁颗粒体积流量 In order to reduce the volume flow rate of magnesium oxide particles input from the inlet feed port after the magnesium oxide particles are reduced due to simplified calculation during the simulation process, the volume flow rate of magnesium oxide particles added to the simulated inlet is reduced by dichotomy.

[0106] In step S23, the corrected viscosity of the control volume unit is calculated based on the classical viscosity prediction model:

[0107]

[0108] Where: η 控制体单元修正粘度 is the corrected viscosity of the control volume element, η 0 It is the viscosity of the flow without particles.

[0109] In step S23, based on the percentage of magnesium oxide particles omitted in the control volume unit The corrected viscosity η of the control volume unit is calculated using one of the classic prediction models of Einstein, Brinkman, Batchelor, Thomas and Muthukumar, Vakili-Nezhaad and Dorany. 控制体单元修正粘度 .

[0110] In step S3, the uncorrected viscosity term in the fluid control equation is replaced by the corrected viscosity of the control volume unit. That is, the fluid viscosity μ is the corrected viscosity η of the control volume unit. 控制体单元修正黏度 .

[0111] In step S5, a Gaussian function is used as a kernel function to weight the background flow field information of each particle, which also includes the corrected viscosity of the control volume unit. The formula for solving the background flow field properties is as follows:

[0112] Where: is the background flow field property expression, η J is the flow field property in unit J, α J is the fluid volume of unit J, V cell，J is the volume of unit J, k i，J (r i -r J ) is r i -r J is the relative position of the particle to the unit position.

[0113] In step S6, the interaction forces (drag force, virtual mass force, Basset force, Magnus force and Saffman force) between the particles and the fluid are calculated using the corrected fluid volume fraction information, and the viscosity term in the interaction force between the particles and the fluid is replaced by the particle background viscosity flow field information weighted by the Gaussian function.

[0114] Step S9: Solve the fluid control equation based on the refined grid. The fluid control equation is as follows:

[0115] Continuity equation:

[0116]

[0117] Where: t is time, ρ is fluid density, α is fluid volume fraction, and U is fluid velocity vector;

[0118] Momentum equation:

[0119]

[0120] Where: p is pressure, g is gravitational acceleration, F p is the force exerted by the particle on the fluid, μ is the fluid viscosity, and μ t is the turbulent viscosity;

[0121]

[0122] Where: V cell is the volume of the FVM unit, n is the number of particles, i is the particle number, f d，i is the drag force, f vm，i is the virtual mass force, f Bas，i is the Basset force, f Mag，i is the Magnus force, f Saff，i For Saffman force;

[0123] For relatively dense granular flow, the drag force is selected as the Gidaspow model:

[0124]

[0125] Where: is the fluid background velocity, v i is the particle velocity, V i is the particle volume, β is the drag coefficient;

[0126]

[0127] Where: d i is the particle diameter, C d ,ω d is the coefficient, which is determined by the fluid volume fraction α and the particle Reynolds number Re i Decide;

[0128]

[0129]

[0130] For a single particle or a dilute particle flow, the drag force is:

[0131]

[0132] Virtual mass force:

[0133]

[0134] Basset force:

[0135]

[0136] Where: t′ is the integral variable;

[0137] Magnus force:

[0138]

[0139] Where: C l is the coefficient, and the particle Reynolds number Re i , particle vorticity and relative velocity Decision, i is the particle angular velocity;

[0140] Saffman force:

[0141]

[0142] Step S08: Reconstruct the background fluid information based on the kernel function, including the background fluid viscosity information, and the solution formula is as follows:

[0143]

[0144] Where: is the background flow field property expression, η J is the flow field property in unit J, α J is the fluid volume of unit J, V cell，J is the volume of unit J, k i，J (r i -r J ) is r i -r J is the relative position of the particle to the unit position, and the Gaussian formula is usually used as the kernel function;

[0145] Step S9: Correct the fluid volume fraction in the background flow field. The correction formula is as follows:

[0146]

[0147] Where: is the volume fraction calculated using particle-fluid force transmission;

[0148] Step S11, calculate the particle movement and update the particle position and velocity, the calculation formula is as follows:

[0149]

[0150] Where: m i is the particle mass, V i is the particle volume, are the pressure and viscosity terms, τ is the deviatoric stress tensor, and f i，j 、f i，w is the collision force between particles and between particles and the wall;

[0151]

[0152] Where: I i is the moment of inertia matrix, T r , T t is the rolling friction torque and moment caused by the tangential force of particles or walls.

[0153] Figure 2 This indicates that the boric acid particles are relatively large, with a particle size of up to 20 microns, and all of them can be added to the simulated flow field without being omitted.

[0154] Figure 3 In the comparison of the solution methods, in step S9, considering the narrow channel of the spray gun outlet, the unresolved CFD-DEM requires the fluid grid side length to reach three times the particle diameter. Therefore, the fluid grid of the spray gun outlet is too coarse and cannot truly reflect the flow field information; the fully resolved CFD-DEM fluid grid side length reaches one tenth of the particle diameter, which can truly reflect the flow field information, but the fluid grid is too small, and the momentum and energy exchange between the particles and the fluid needs to be carried out through surface integration, which is inefficient.

[0155] In step S9, considering the narrow channel of the spray gun discharge port, a semi-analytical CFD-DEM method is used. The side length of the semi-analytical CFD-DEM fluid grid is greater than or less than three times the particle diameter. Therefore, the grid can be appropriately encrypted and the grid size is appropriate. It can truly reflect the flow field information and eliminate the dependence of the fluid grid size on the boric acid particles and magnesium oxide particles.

[0156] The above-mentioned semi-analytical CFD-DEM method is used to reconstruct the background flow field through the kernel function, breaking through the limitation of the fluid grid size and significantly improving the calculation efficiency and accuracy. This method can effectively solve the problem of spray gun clogging during the spraying process, optimize the spraying process, and improve production efficiency and spraying quality.

[0157] The embodiments described above are part of the embodiments of the present application, rather than all of the embodiments. The detailed description of the embodiments of the present application is not intended to limit the scope of the present application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

Claims

1. A semi-analytical CFD-DEM spray gun clogging simulation method based on viscosity correction, characterized in that The steps include: Step S1: According to the obtained spray gun structure, a CFD solver is established in the CFD analysis software for simulation, and the spray fluid domain between the spray gun feed and the discharge port is meshed and encrypted to form a refined mesh flow field, and each refined mesh is used as a control volume unit; Step S2: reducing the volume flow rate of magnesium oxide particles added from the feed port of the spray gun during the simulation process, and correcting the viscosity of the control volume unit to obtain a corrected viscosity, so as to ensure that each control volume unit is occupied by at least one magnesium oxide particle; Step S3: transmitting the particle data to the CFD solver to obtain the DEM particle volume fraction information; Step S4: based on the obtained DEM particle volume fraction information, the viscosity is corrected in combination with the viscosity correction method; Step S5: expanding the particle background fluid domain to adjacent simulation control volume units with a set smoothing distance; Step S6: reconstructing the background flow field information of the particles based on the kernel function, and the reconstructed background flow field information of the particles is used to calculate the interaction force between the fluid and the particles; Step S7: Correcting the fluid volume fraction in the particle background flow field to calculate the interaction force between the fluid and the particles; Step S8: Mapping the momentum to the source item of the control volume unit to which the particle belongs according to the position of the particle; Step S9: adding the corrected viscosity and the momentum source term of the particle motion to solve the fluid control equation, and solving the fluid control equation; Step S10: Transmitting the fluid information obtained after solving to the DEM solver; Step S11: Calculate particle motion in the DEM solver and update particle position and velocity; Step S12: The updated particle data is transmitted to the CFD solver, and the above steps are repeated to obtain the optimal particle position and velocity information.

2. The semi-analytical CFD-DEM spray gun clogging simulation method based on viscosity correction according to claim 1 is characterized in that In step S2, the volume of the magnesium oxide particles added in the simulation is reduced based on the dichotomy method, and the search interval is continuously halved until the target value is found or the interval is reduced to zero, so as to ensure that each control volume unit is occupied by at least one magnesium oxide particle.

3. The semi-analytical CFD-DEM spray gun clogging simulation method based on viscosity correction according to claim 1 is characterized in that In step S2: the percentage of omitted magnesium oxide particles in the control unit is calculated based on the calculation formula for the percentage of omitted magnesium oxide particles in the control unit; and the corrected viscosity of the control unit is calculated based on the percentage of omitted magnesium oxide particles in the control unit and the classical viscosity prediction model.

4. The semi-analytical CFD-DEM spray gun clogging simulation method based on viscosity correction according to claim 1 is characterized in that In step S2, the calculation formula for the percentage of omitted magnesium oxide particles in the control volume unit is as follows: Where: 控制体单元内省略氧化镁颗粒百分比 To control the percentage of omitted magnesium oxide particles in the volume unit, V 控制体单元体积 is the volume of the control unit, V 控制体单元内氧化镁颗粒体积 is the volume of magnesium oxide particles in the control unit, V 实际 V is the volume flow rate of magnesium oxide particles input from the feed port under actual working conditions, 仿真 is the volume flow rate of magnesium oxide particles input from the feed inlet during the simulation process.

5. The semi-analytical CFD-DEM spray gun clogging simulation method based on viscosity correction according to claim 1 is characterized in that In step S2, the corrected viscosity of the control volume unit is calculated based on the classical viscosity prediction model: Where: η 控制体单元修正粘度 is the corrected viscosity of the control volume unit, and η0 is the viscosity of the flow without particles.

6. The semi-analytical CFD-DEM spray gun clogging simulation method based on viscosity correction according to claim 1, characterized in that In step S2, based on the percentage of magnesium oxide particles omitted in the control volume unit The corrected viscosity η of the control volume unit is calculated using one of the classic prediction models of Einstein, Brinkman, Batchelor, Thomas and Muthukumar, Vakili-Nezhaad and Dorany. 控制体单元修正粘度 .

7. The semi-analytical CFD-DEM spray gun clogging simulation method based on viscosity correction according to claim 1 is characterized in that In step S3, the uncorrected viscosity term in solving the fluid control equation is replaced by the corrected viscosity of the control volume unit.

8. The semi-analytical CFD-DEM spray gun clogging simulation method based on viscosity correction according to claim 1, characterized in that In step S5, a Gaussian function is used as a kernel function to weight the background flow field information of each particle, which also includes the corrected viscosity of the control volume unit. The formula for solving the background flow field properties is as follows: Where: is the background flow field property expression, η J is the flow field property in unit J, α J is the fluid volume of unit J, V cell，J is the volume of unit J, k i，J (r i -r J ) is r i -r J is the relative position of the particle to the unit position.

9. The semi-analytical CFD-DEM spray gun clogging simulation method based on viscosity correction according to claim 1, characterized in that In step S6, the interaction force between the particle and the fluid is calculated using the corrected fluid volume fraction information, and the viscosity term in the interaction force between the particle and the fluid is replaced by the particle background viscosity flow field information weighted by the Gaussian function; the interaction force includes at least one of the drag force, virtual mass force, Basset force, Magnus force and Saffman force.

10. A semi-analytical CFD-DEM spray gun clogging simulation system based on viscosity correction, characterized in that: A computer program is stored therein, and when the computer program is executed, it is used to implement the steps of the method according to any one of claims 1 to 9.

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