Hydrogen-based shaft furnace high-temperature valve spline pair service performance analysis method based on fluid-thermal-solid coupling

Abstract

The invention discloses a service performance analysis method for a spline pair of a high-temperature valve of a hydrogen-based shaft furnace based on fluid-thermal-solid coupling, and belongs to the technical field of hydrogen metallurgy. Comprising the steps of establishing a three-dimensional model, a fluid calculation domain and a solid calculation domain of the three-dimensional model, carrying out attribute and parametric grid division on the fluid calculation domain and the solid calculation domain, constructing a steady-state simulation model, reduced iron particles and a motion model of the reduced iron particles, carrying out heat-flow coupling simulation, and solving a temperature field of the steady-state simulation model. And based on a temperature field combined temperature-stress coupling equation, solving a thermal deformation and stress field of the spline pair through a finite element method, analyzing service performance parameters of the high-temperature valve spline pair based on the temperature field, the thermal deformation and the stress field, and evaluating the service performance of the high-temperature valve spline pair. According to the method, rapid and accurate analysis of the service performance of the hydrogen-based shaft furnace high-temperature valve and rapid iteration verification of parameter optimization can be met, universality is high, and under the background that the hydrogen-based reduction ironmaking process is gradually popularized, the method is wide in application prospect.

Description

Technical Field

[0001] This invention relates to the field of hydrogen metallurgy technology, specifically to a method for analyzing the service performance of a high-temperature valve spline pair in a hydrogen-based vertical furnace based on fluid-thermal-solid coupling. Background Technology

[0002] The "hydrogen-based reduction ironmaking + electric arc furnace steelmaking" process boasts advantages such as a short process and low carbon emissions, and is considered a core direction for the green transformation of the steel industry. The high-temperature valve is a key component that continuously and stably delivers the direct reduced iron (DRI) particles generated in hydrogen-based reduction ironmaking to the electric arc furnace steelmaking process. During actual operation, the high-temperature valve spline assembly, which comes into contact with the DRI particles, is subjected to long-term thermo-coupled loads. These loads include the thermal load from the heat held by the reduced particles through heat conduction, and the mechanical load from motor torque and particle impact. This causes the spline assembly to exhibit certain deformation behavior, potentially leading to abrupt changes in the tooth surface contact state. This not only affects the working strength of the tooth surface but also exacerbates fatigue damage, seriously threatening the safe operation of the high-temperature valve and thus hindering the stable operation of the aforementioned process.

[0003] Existing research on the "hydrogen-based reduction ironmaking + electric arc furnace steelmaking" process mainly focuses on the analysis and optimization of its process parameters, without studying the service performance of its core components. Furthermore, research on splined pairs primarily focuses on fretting wear studies, neglecting performance studies under high-temperature conditions. Therefore, it is currently difficult to accurately assess whether the service performance of the high-temperature valve splined pair in a hydrogen-based vertical shaft furnace meets the requirements.

[0004] To address the aforementioned issues, there is an urgent need for a performance analysis method for high-temperature valve spline pairs in hydrogen-based vertical furnaces based on fluid-thermal-solid coupling, which can solve the problems existing in traditional methods and achieve self-balancing of pneumatic conveying flow. Summary of the Invention

[0005] The purpose of this invention is to provide a method for analyzing the service performance of splined pairs of high-temperature valves in hydrogen-based vertical shaft furnaces based on fluid-thermal-solid coupling. This method can meet the requirements of rapid and accurate analysis of the service performance of high-temperature valves in hydrogen-based vertical shaft furnaces and rapid iterative verification of parameter optimization. It has strong versatility and broad application prospects in the context of the gradual popularization of hydrogen-based reduction ironmaking processes.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for analyzing the service performance of a high-temperature valve spline pair in a hydrogen-based vertical furnace based on fluid-thermal-solid coupling includes: Step 1: Obtain the design parameters of the high-temperature valve spline pair of the hydrogen-based vertical shaft furnace, establish a three-dimensional model based on the design parameters, and establish the fluid computational domain and solid computational domain of the three-dimensional model; Step 2: Perform attribute and parameterized mesh generation on the fluid computational domain and the solid computational domain to generate a structured network for the overall 3D model and a refined network for key regions; Step 3: Construct a steady-state simulation model and a model of the iron particles and their motion based on the fluid computational domain and the solid computational domain; Step 4: Conduct thermal-fluid coupling simulation to solve the temperature field of the steady-state simulation model; Step 5: Based on the temperature field and temperature-stress coupling equation, solve the thermal deformation and stress field of the spline pair using the finite element method; Step 6: Analyze the service performance parameters of the high-temperature valve spline pair based on the temperature field, thermal deformation and stress field, and evaluate the service performance of the high-temperature valve spline pair.

[0007] Furthermore, in step 1, a three-dimensional model is established based on the design parameters, and the fluid computational domain and solid computational domain of the three-dimensional model are established, specifically as follows: Step 101: Based on the design parameters of the spline pair of the high-temperature valve of the hydrogen-based vertical furnace, draw the involute of the tooth surface in the 3D modeling software, generate the tooth profile of the spline pair, complete the assembly of the main components of the high-temperature valve, and establish a 3D model of the main structure of the high-temperature valve, including the rotor, shaft and bearing. Step 102: In the 3D modeling software, the non-interference evaluation method is used to ensure that the model has no self-interference phenomenon, and the model preprocessing check and surface repair work are carried out in the finite element simulation software to complete the construction of the solid computational domain; Step 103: Establish the boundary model of the fluid computation domain. The fluid computation domain is created by volume extraction or Boolean operation. The fluid computation domain includes the coolant fluid domain and the high-temperature particulate fluid domain. Step 104: Establish wall boundary conditions, where the wall includes the left and right tooth surfaces of the spline pair, the outer wall of the shaft, the inner wall of the cooling channel, the outer wall of the particulate fluid domain, and the remaining walls of the rotor. Set the heat transfer type of the wall according to the actual service conditions, where the heat transfer type includes heat transfer and heat convection. Step 105: Set the inlet and outlet of the coolant fluid domain and the high-temperature particulate fluid domain, and delete the boundary model of the fluid computation domain at the inlet and outlet locations.

[0008] Furthermore, in step 2, attribute- and parameterized meshes are generated for the fluid computational domain and the solid computational domain to produce a structured network for the overall 3D model and a refined network for key regions. Specifically: Step 201: Based on Lagrange coordinates, mesh the fluid computational domain and the solid computational domain. The shaft and coolant fluid domain are meshed using hexahedral dominant mesh, while the rotor and high-temperature particulate fluid domain are meshed using tetrahedral mesh. Step 202: Refine the mesh in the critical areas, which include the left and right tooth surfaces of the spline pair and the coolant flow domain.

[0009] Furthermore, in step 3, a steady-state simulation model and a model of the reduced iron particles and their motion are constructed based on the fluid computational domain and the solid computational domain, specifically as follows: Step 301: Set up a k-ε turbulent viscosity model for the fluid computation domain to simulate the turbulent motion of the fluid domain. The turbulent motion of the fluid domain corresponds to the coolant fluid domain and the high-temperature particle fluid domain, respectively setting up coolant turbulence and reduced particle transport. Step 302: Create discrete phase and injection source, and randomly generate reduced iron particles that meet the size range; Step 303: For the particle reduction process, select the transport model of the component model, the reaction type of volume reaction and particle surface reaction, and define the reaction equation.

[0010] Furthermore, the k-ε turbulent viscosity model defines the eddy current viscosity as: In the formula, Eeddy current viscosity, It is a constant. For fluid viscosity, For turbulent kinetic energy, The turbulent dissipation rate; The k-ε turbulent viscosity model defines the transport equation as follows: In the formula, These are the average velocity components in different directions. Molecular viscosity The turbulent viscosity coefficient, turbulent kinetic energy k The generated terms, , , , These are model constants.

[0011] Furthermore, the reaction equation mainly includes: Further, in step 4, a heat-fluid coupling simulation is performed to solve the temperature field of the steady-state simulation model, specifically as follows: Step 401: Set the material properties of the fluid computing domain, the solid computing domain, and the mixed components, wherein the material properties include density, specific heat capacity, and thermal conductivity; set the unit region conditions, i.e., the properties corresponding to the fluid computing domain and the solid computing domain, to control the fluid flow, reduction process, and conjugate heat transfer between the fluid computing domain and the solid computing domain. Step 402: Set boundary conditions to limit the solution range of the flow field, including defining the properties of the inlet, outlet, wall and internal interface, where the velocity inlet and pressure outlet are selected to satisfy the continuity equation, momentum equation and energy equation. Step 403: Select the pressure-velocity coupling algorithm, set the relaxation factor and convergence criterion; use standard initialization, set the number of iterations and interval time, and solve the continuity equation, momentum equation, energy equation, and... k-ε The turbulence model is used to monitor the residual curves until the convergence condition is met, thus obtaining the heat transfer equations involved in the heat-fluid coupling.

[0012] Furthermore, the continuity equation and the Navier-Stokes equation are as follows: In the formula, , , , These are the dimensionless gradient operator, velocity, pressure, and volume force, respectively. Re It is the Reynolds number; The momentum equation and energy equation are as follows: In the formula, U For the velocity tensor, p The pressure on the fluid element. δ For unit tensors, S M For mass force, µ l For fluid viscosity, S E As an internal heat source, K Thermal conductivity, h hot For total enthalpy, The work done by viscous forces. T For temperature; The heat transfer equations involved in the heat-fluid coupling are as follows: In the formula, K s , K f Thermal conductivity of solids and fluids, respectively. Let T be the Laplace operator for temperature T. Q For thermal power, ρ For fluid density, C p The specific heat capacity at constant pressure of the fluid. This is a convection term.

[0013] Furthermore, in step 5, based on the temperature field and temperature-stress coupling equations, the thermal deformation and stress field of the spline pair are solved using the finite element method, specifically as follows: Step 501: Define the model material properties, connect the fluid-static structure module, transfer temperature data, and conduct steady-state thermal stress analysis; Step 502: Establish the spline tooth surface contact pair, turn off the small sliding state, turn on the large deflection state, process the contact interface, and detect the initial contact state to ensure that it conforms to the actual working conditions of the model. Step 503: Apply the steady-state temperature field obtained from the thermal-fluid coupling calculation as a thermal load to each solid computational domain, set the load application time, and apply the corresponding load boundary conditions; Step 504: Use a nonlinear control solution model, which includes two load steps. The first load step only acts as a mechanical load, while the second load step activates the temperature load to achieve the coupling effect of mechanical load and temperature load. Step 505: Combine the temperature-stress coupling equations and iteratively solve for thermal deformation, equivalent stress, tooth flank clearance, and fatigue life distribution until convergence is achieved.

[0014] Furthermore, the temperature-stress coupling equation is: In the formula, µ Let be the nodal displacement vector. For the node velocity vector, K Here is the stiffness matrix. F It is a force vector. Q This includes the applied nodal forces and the forces caused by thermal deformation.

[0015] Furthermore, in step 6, the service performance parameters of the high-temperature valve spline pair are analyzed based on the temperature field, thermal deformation, and stress field to evaluate the service performance of the high-temperature valve spline pair, specifically as follows: Step 601: Conduct a mesh independence test to determine the optimal mesh cell size and number; Step 602: Conduct shaft deformation behavior analysis, solve for the overall shaft deformation and X, Y, and Z direction deformation, and determine whether the spline pair meets the service requirements; Step 603: Conduct a backlash analysis of the spline pair teeth, solve the changes in the contact state of the spline pair tooth surfaces under thermal stress, and determine the trend of the backlash change and whether it meets the service requirements. Step 604: Conduct a strength analysis of the shaft tooth surface, solve for the equivalent stress on the tooth surface, analyze the stress distribution on the tooth surface, and determine whether the maximum equivalent stress meets the service requirements; Step 605: Calculate the fatigue life of the spline pair shaft tooth surface using the nominal stress method and predict the mean life of the spline pair under random loads using Miner's linear cumulative damage theory.

[0016] In summary, the present invention has at least one of the following beneficial technical effects: 1. Breaking through the limitations of traditional analysis, this invention achieves high-fidelity simulation of real working conditions. Traditional spline pair performance analysis is mostly based on geometric parameters and room temperature mechanical theory, without considering the multi-physical field coupling effects such as high temperature, particle erosion, and coolant flow in actual service. This invention constructs a fluid-thermal-solid fully coupled simulation model, which integrates the heat transfer of high-temperature particles, the flow of reducing gas and solid phases, the convective heat transfer of coolant, and the structural stress and deformation for integrated solution. This realistically reproduces the complex service environment of the high-temperature valve spline pair of the hydrogen-based vertical furnace, significantly improving the accuracy and reliability of performance evaluation.

[0017] 2. Achieving comprehensive and quantitative evaluation of service performance: This invention not only analyzes the deformation and stress of spline pairs, but also systematically evaluates multi-dimensional performance indicators such as tooth flank clearance changes, tooth surface contact state, equivalent stress distribution, and fatigue life, forming a complete service performance evaluation system. By quantitatively outputting key parameters such as deformation, peak stress, and fatigue cycle count, it provides direct data support for the strength verification, life prediction, and safety margin assessment of spline pairs.

[0018] 3. Significantly improves design optimization efficiency and supports rapid iteration. By adopting parametric modeling and automated simulation processes, designers can quickly predict the performance of different schemes by modifying spline pair geometric parameters (such as module, pressure angle, tooth root fillet, etc.), cooling channel layout, or operating boundary conditions. This enables a closed-loop iteration of "design-simulation-evaluation", greatly shortens the R&D cycle, reduces the cost of physical prototyping, and provides an efficient tool for the lightweight and high-reliability design of high-temperature valves.

[0019] 4. Reveal key failure mechanisms and guide structural improvements. Through simulation, the stress concentration area, contact state evolution trend and fatigue damage accumulation process of spline pairs under thermo-mechanical coupling can be clearly identified, and weak parts such as tooth root transition area and tooth surface contact edge can be identified.

[0020] 5. Promoting the localization and reliability improvement of key equipment in hydrogen metallurgy: This invention fills the gap in the performance analysis method of spline pairs of high-temperature valves in hydrogen-based vertical furnaces under high-temperature and multi-field coupling environments, providing key technical support for the independent development of high-performance high-temperature valves in China. Through forward-looking simulation evaluation, potential failure risks can be identified in advance, guiding material selection, cooling system design, and optimization of operating parameters, ensuring long-term stable operation of equipment, and facilitating the large-scale safe application of hydrogen metallurgy processes. Attached Figure Description

[0021] Figure 1This is a schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of the main structure of a high-temperature valve; Figure 3 Schematic diagrams of the fluid computational domain and the solid computational domain; Figure 4 This is a schematic diagram of the mesh generation for the simulation model; Figure 5 This is a schematic diagram of the results of the grid irrelevance test; Figure 6 This is a schematic diagram of the deformation of the shaft in the X direction. Figure 7 This is a schematic diagram of the deformation along the Y-axis of the rotation axis; Figure 8 This is a schematic diagram of the deformation of the shaft in the Z direction; Figure 9 This is a schematic diagram of the total deformation of the rotating shaft; Figure 10 This is a schematic diagram of the initial contact state of the spline pair; Figure 11 This is a schematic diagram of the working contact state of the spline pair; Figure 12 This is a schematic diagram of the equivalent stress on the tooth surface of the rotating shaft; Figure 13 This is a schematic diagram of tooth surface fatigue life and damage. Figure 14 This is a schematic diagram of the safety factor for the tooth surface. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0023] like Figure 1 As shown, this invention provides a method for analyzing the service performance of a high-temperature valve spline pair in a hydrogen-based vertical furnace based on fluid-thermal-solid coupling, comprising: Step 1: Obtain the design parameters of the high-temperature valve spline pair of the hydrogen-based vertical shaft furnace, establish a three-dimensional model based on the design parameters, and establish the fluid computational domain and solid computational domain of the three-dimensional model; Step 2: Perform attribute and parameterized mesh generation on the fluid computational domain and the solid computational domain to generate a structured network for the overall 3D model and a refined network for key regions; Step 3: Construct a steady-state simulation model and a model of the iron particles and their motion based on the fluid computational domain and the solid computational domain; Step 4: Conduct thermal-fluid coupling simulation, that is, determine the boundary conditions of the temperature field of the thermal-fluid calculation model (steady-state simulation model) and solve the temperature field of the steady-state simulation model; Step 5: Based on the temperature field and temperature-stress coupling equation, solve the thermal deformation and stress field of the spline pair using the finite element method; Step 6: Analyze the service performance parameters of the high-temperature valve spline pair based on the temperature field, thermal deformation and stress field, and evaluate the service performance of the high-temperature valve spline pair.

[0024] Next, this invention takes a certain type of high-temperature valve for a hydrogen-based vertical furnace as the research object and elaborates on the above method. The geometric parameters of the spline pair of the high-temperature valve are shown in Table 1, and the structure of the high-temperature valve is as follows. Figure 2 As shown.

[0025] Table 1 Geometric parameters of rotor shaft spline pair

[0026] In step 1, a three-dimensional model is established based on the design parameters, and the fluid computational domain and solid computational domain of the three-dimensional model are established, specifically as follows: Step 101: Based on the design parameters of the spline pair of the high-temperature valve of the hydrogen-based vertical furnace, draw the involute of the tooth surface in the 3D modeling software, generate the spline pair tooth profile, complete the assembly of the main components of the high-temperature valve, and establish a 3D model of the main structure of the high-temperature valve, including the rotor, shaft and bearings, etc. Step 102: In the 3D modeling software, the non-interference evaluation method is used to ensure that the model has no self-interference phenomenon, and the model preprocessing check and surface repair work are carried out in the finite element simulation software to complete the construction of the solid computational domain; Step 103: Establish the boundary model of the fluid computation domain to provide boundary conditions for subsequent fluid domain extraction. The fluid computation domain is created by volume extraction or Boolean operation. The fluid computation domain includes the coolant fluid domain and the high-temperature particulate fluid domain. Step 104: Establish wall boundary conditions, where the wall includes the left and right tooth surfaces of the spline pair, the outer wall of the shaft, the inner wall of the cooling channel, the outer wall of the particulate fluid domain, and the remaining walls of the rotor, etc. Set the heat transfer type of the wall according to the actual service conditions, where the heat transfer type includes heat transfer and heat convection, etc. Step 105: Set the inlet_1 and outlet_2 of the coolant fluid domain and the high-temperature particulate fluid domain, and delete the boundary model of the fluid calculation domain at the inlet and outlet positions.

[0027] Based on step 1, establish as follows Figure 3 The fluid computational domain and solid computational domain of the three-dimensional model shown.

[0028] In step 2, attribute- and parameterized meshes are generated for the fluid and solid computational domains to produce a structured network for the overall 3D model and a refined network for key regions. Specifically: Step 201: Based on Lagrange coordinates, mesh the fluid computational domain and the solid computational domain. The shaft and coolant fluid domain are meshed using hexahedral dominant mesh, while the rotor and high-temperature particulate fluid domain are meshed using tetrahedral mesh. Step 202: Refine the mesh in the critical areas, which include the left and right tooth surfaces of the spline pair and the coolant flow domain.

[0029] Based on step 2, we obtain the following: Figure 4 The grid division is shown.

[0030] In step 3, a steady-state simulation model and a model of reduced iron particles and their motion are constructed based on the fluid computational domain and the solid computational domain, specifically as follows: Step 301: Set up a k-ε turbulent viscosity model for the fluid computation domain to simulate the turbulent motion of the fluid domain. The turbulent motion of the fluid domain corresponds to the coolant fluid domain and the high-temperature particle fluid domain, respectively setting up coolant turbulence and reduced particle transport. Step 302: Create discrete phase and injection source, randomly generate reduced iron particles that meet the size range, including defining particle release surface, particle type, particle size distribution and temperature, etc. It should be noted that the discrete phase here refers to a method of particle generation and flow used in the process of establishing the aforementioned steady-state temperature field simulation model, which is named discrete phase model in the steady-state simulation model. Step 303: For the particle reduction process, select the transport model of the component model, the reaction type of volume reaction and particle surface reaction, and define the reaction equation to ensure solution convergence. It should be noted that the component model is a type of calculation model used in the process of establishing a steady-state temperature field simulation model to simulate the transport and reaction process of chemical components in multi-component fluids, while the transport model is one of the methods of the component model. It is also named component model and transport model in the steady-state simulation model.

[0031] In step 301, the k-ε turbulent viscosity model defines the eddy current viscosity as: (1) In the formula, Eeddy current viscosity, It is a constant. For fluid viscosity, For turbulent kinetic energy, The turbulent dissipation rate; The k-ε turbulent viscosity model defines the transport equation as follows: (2) Furthermore, the reaction equation mainly includes: .

[0032] In the formula, These are the average velocity components in different directions. Molecular viscosity The turbulent viscosity coefficient, turbulent kinetic energy k The generated terms, , , , These are model constants.

[0033] In step 4, a heat-fluid coupling simulation is performed to solve the temperature field of the steady-state simulation model, specifically as follows: Step 401: Set the material properties of the fluid computing domain, the solid computing domain, and the mixed components, wherein the material properties include density, specific heat capacity, and thermal conductivity; set the unit region conditions, i.e., the properties corresponding to the fluid computing domain and the solid computing domain, to control the fluid flow, reduction process, and conjugate heat transfer between the fluid computing domain and the solid computing domain. Step 402: Set boundary conditions to limit the solution range of the flow field, including defining the properties of the inlet, outlet, wall and internal interface, where the velocity inlet and pressure outlet are selected to satisfy the continuity equation, momentum equation and energy equation. Step 403: Select the pressure-velocity coupling algorithm, set the relaxation factor and convergence criterion; use standard initialization, set the number of iterations and interval time, and solve the continuity equation, momentum equation, energy equation, and... k-ε The turbulence model is used to monitor the residual curves until the convergence condition is met, thus obtaining the heat transfer equations involved in the heat-fluid coupling.

[0034] In step 402, the continuity equation and the Navier-Stokes equation are: (3) (4) In the formula, , , , These are the dimensionless gradient operator, velocity, pressure, and volume force, respectively. Re It is the Reynolds number; The momentum equation and energy equation are as follows: (5) (6) In the formula, U For the velocity tensor, p The pressure on the fluid element. δFor unit tensors, S M For mass force, µ l For fluid viscosity, S E As an internal heat source, K Thermal conductivity, h hot For total enthalpy, The work done by viscous forces. T For temperature; In step 403, the heat transfer equation involved in the heat-fluid coupling is: (7) In the formula, K s , K f Thermal conductivity of solids and fluids, respectively. Let T be the Laplace operator for temperature T. Q For thermal power, ρ For fluid density, C p The specific heat capacity at constant pressure of the fluid. This is a convection term.

[0035] In step 5, based on the temperature field and temperature-stress coupling equations, the thermal deformation and stress field of the spline pair are solved using the finite element method, specifically as follows: Step 501: Define the model material properties, connect the fluid-static structure module, transmit temperature data, and conduct steady-state thermal stress analysis. It should be noted that the fluid-static structure module refers to using the calculation results of thermal-fluid coupling (temperature field) as the thermal load condition for thermal-solid coupling simulation. That is, fluid-thermal-solid coupling uses "heat" as the connection condition. At this time, directly connecting the "fluid-static structure module" can realize fluid-thermal-solid coupling. The temperature data refers to the temperature field data obtained by solving based on the aforementioned steady-state temperature field simulation model. Step 502: Establish the spline tooth surface contact pair, turn off the small sliding state, turn on the large deflection state, process the contact interface, and detect the initial contact state to ensure that it conforms to the actual working conditions of the model. Step 503: Apply the steady-state temperature field obtained from the heat-fluid coupling calculation as a thermal load to each solid computational domain, set the load application time, and apply the corresponding load boundary conditions. The steady-state temperature field is the result obtained based on the heat transfer equation. Step 504: Use a nonlinear control solution model, which includes two load steps. The first load step only acts as a mechanical load, while the second load step activates the temperature load to achieve the coupling effect of mechanical load and temperature load. Step 505: Combine the temperature-stress coupling equations and iteratively solve for thermal deformation, equivalent stress, tooth flank clearance, and fatigue life distribution until convergence is achieved.

[0036] In step 505, the temperature-stress coupling equation is: (8) In the formula, µ Let be the nodal displacement vector. For the node velocity vector, K Here is the stiffness matrix. F It is a force vector. Q This includes the applied nodal forces and the forces caused by thermal deformation; The equivalent stress has six components, including three normal stresses and three shear stresses, and its matrix form is as follows: (9) The stress variation matrix is ​​in the form of: (10) The thermal deformation of the spline pair is: (11) In the formula, ε It is the strain vector; α The coefficient of thermal expansion of the material; △T This represents the temperature difference between the actual temperature of the model and the reference temperature.

[0037] In step 6, the service performance parameters of the high-temperature valve spline pair are analyzed based on the temperature field, thermal deformation, and stress field to evaluate the service performance of the high-temperature valve spline pair. Specifically: Step 601: Conduct a mesh independence test to determine the optimal mesh cell size and number, specifically: Using the total deformation of the shaft with different grid numbers as an indicator, the deformation was calculated for 1.2 million, 1.6 million, 2 million, 2.4 million, and 2.8 million grid nodes respectively. Figure 5 As shown, the total deformation of the shaft tends to stabilize when the number of grid nodes is 2 million. Further increasing the number of grid nodes has a greater impact on the calculation accuracy. Therefore, a number of grid nodes of 2 million is selected for subsequent calculations, which ensures both the accuracy of the results and the efficiency of the calculation. Step 602: Conduct shaft deformation behavior analysis, solve for the overall shaft deformation and X, Y, and Z-axis deformation, and determine whether the spline pair meets the service requirements. Specifically: like Figure 6 , Figure 7 , Figure 8 and Figure 9As shown, the maximum radial deformation of the shaft is 0.138 mm and 0.145 mm in the X and Y directions, respectively, and the maximum axial deformation is 0.634 mm in the Z direction. The axial deformation rate is only 0.023% compared to the total length of the shaft. The deformation rates in the X and Y directions are 0.048% and 0.050% respectively compared to the tooth tip circle diameter of the shaft, which are both small deformations. Therefore, considering only the total deformation, the rotor shaft spline pair meets the design requirements. Step 603: Conduct a backlash analysis of the spline pair teeth, solve for the changes in the contact state of the spline pair tooth surfaces under thermal stress, and determine the trend of the backlash change and whether it meets the service requirements. Specifically: like Figure 10 and Figure 11 As shown, compared to the initial state, the tooth surface contact state of the spline pair during operation changes from "adhesion and sliding" to "approaching". That is, under the action of thermal stress, the spline pair as a whole changes from the initial "tight fit" to a relatively "loose fit", showing a tendency to increase the gap, but still does not separate, meeting the service requirements. Step 604: Conduct a strength analysis of the shaft tooth surface, solve for the equivalent stress on the tooth surface, analyze the stress distribution on the tooth surface, and determine whether the maximum equivalent stress meets the service requirements. Specifically: like Figure 12 As shown, the maximum equivalent stresses of the splines on the left and right sides of the shaft are 315.000 MPa and 311.780 MPa, respectively. The yield strength of the shaft material is approximately 335 MPa. Therefore, the equivalent stresses of the tooth surfaces on the left and right sides of the spline pair meet the requirements and are in line with service requirements. Step 605: Calculate the fatigue life of the splined shaft tooth surface using the nominal stress method and predict the mean life of the splined pair under random loads using Miner's linear cumulative damage theory. Specifically: Tooth surface fatigue life, damage and safety factor, such as Figure 13 and Figure 14 As shown. At this point, the minimum fatigue life of the tooth surface is 1.852 × 10⁻⁶. 5 After [number] cycles, the maximum damage value was 53.996 > 0.1, and the minimum safety factor was 0.764 < 1. The main reason for fatigue damage was stress concentration at the tooth root. Therefore, it is necessary to reduce the equivalent stress at the tooth root and improve fatigue life by adjusting the displacement coefficient and the tooth root transition fillet.

[0038] In step 605, the linear cumulative damage theory is calculated as follows: In the formula, D This represents the cumulative fatigue damage. S j For the j-th stress, N j For stressS j Fatigue life under action.

[0039] Embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0040] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0041] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0042] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0043] Contents not described in detail in this specification are prior art known to those skilled in the art. It is hereby indicated that the above description is intended to help those skilled in the art understand this invention, but does not limit the scope of protection of this invention. Any equivalent substitutions, modifications, improvements, or simplifications of the above descriptions that do not depart from the essential content of this invention fall within the scope of protection of this invention.

Claims

1. A method for analyzing the service performance of a high-temperature valve spline pair in a hydrogen-based vertical furnace based on fluid-thermal-solid coupling, characterized in that, include: Step 1: Obtain the design parameters of the high-temperature valve spline pair of the hydrogen-based vertical shaft furnace, establish a three-dimensional model based on the design parameters, and establish the fluid computational domain and solid computational domain of the three-dimensional model; Step 2: Perform attribute and parameterized mesh generation on the fluid computational domain and the solid computational domain to generate a structured network for the overall 3D model and a refined network for key regions; Step 3: Construct a steady-state simulation model and a model of the iron particles and their motion based on the fluid computational domain and the solid computational domain; Step 4: Conduct thermal-fluid coupling simulation to solve the temperature field of the steady-state simulation model; Step 5: Based on the temperature field and temperature-stress coupling equation, solve the thermal deformation and stress field of the spline pair using the finite element method; Step 6: Analyze the service performance parameters of the high-temperature valve spline pair based on the temperature field, thermal deformation and stress field, and evaluate the service performance of the high-temperature valve spline pair.

2. The method for analyzing the service performance of a high-temperature valve spline pair in a hydrogen-based vertical furnace based on fluid-thermal-solid coupling, as described in claim 1, is characterized in that... In step 1, a three-dimensional model is established based on the design parameters, and the fluid computational domain and solid computational domain of the three-dimensional model are established, specifically as follows: Step 101: Based on the design parameters of the spline pair of the high-temperature valve of the hydrogen-based vertical furnace, draw the involute of the tooth surface in the 3D modeling software, generate the tooth profile of the spline pair, complete the assembly of the main components of the high-temperature valve, and establish a 3D model of the main structure of the high-temperature valve, including the rotor, shaft and bearing. Step 102: In the 3D modeling software, the non-interference evaluation method is used to ensure that the model has no self-interference phenomenon, and the model preprocessing check and surface repair work are carried out in the finite element simulation software to complete the construction of the solid computational domain; Step 103: Establish the boundary model of the fluid computation domain. The fluid computation domain is created by volume extraction or Boolean operation. The fluid computation domain includes the coolant fluid domain and the high-temperature particulate fluid domain. Step 104: Establish wall boundary conditions, where the wall includes the left and right tooth surfaces of the spline pair, the outer wall of the shaft, the inner wall of the cooling channel, the outer wall of the particulate fluid domain, and the remaining walls of the rotor. Set the heat transfer type of the wall according to the actual service conditions, where the heat transfer type includes heat transfer and heat convection. Step 105: Set the inlet and outlet of the coolant fluid domain and the high-temperature particulate fluid domain, and delete the boundary model of the fluid computation domain at the inlet and outlet locations.

3. The method for analyzing the service performance of a high-temperature valve spline pair in a hydrogen-based vertical furnace based on fluid-thermal-solid coupling, as described in claim 2, is characterized in that... In step 2, attribute- and parameterized meshes are generated for the fluid and solid computational domains to produce a structured network for the overall 3D model and a refined network for key regions. Specifically: Step 201: Based on Lagrange coordinates, mesh the fluid computational domain and the solid computational domain. The shaft and coolant fluid domain are meshed using hexahedral dominant mesh, while the rotor and high-temperature particulate fluid domain are meshed using tetrahedral mesh. Step 202: Refine the mesh in the critical areas, which include the left and right tooth surfaces of the spline pair and the coolant flow domain.

4. The method for analyzing the service performance of a high-temperature valve spline pair in a hydrogen-based vertical furnace based on fluid-thermal-solid coupling, as described in claim 3, is characterized in that... In step 3, a steady-state simulation model and a model of reduced iron particles and their motion are constructed based on the fluid computational domain and the solid computational domain, specifically as follows: Step 301: Set up a k-ε turbulent viscosity model for the fluid computation domain to simulate the turbulent motion of the fluid domain. The turbulent motion of the fluid domain corresponds to the coolant fluid domain and the high-temperature particle fluid domain, respectively setting up coolant turbulence and reduced particle transport. Step 302: Create discrete phase and injection source, and randomly generate reduced iron particles that meet the size range; Step 303: For the particle reduction process, select the transport model of the component model, the reaction type of volume reaction and particle surface reaction, and define the reaction equation.

5. The method for analyzing the service performance of a high-temperature valve spline pair in a hydrogen-based vertical furnace based on fluid-thermal-solid coupling, as described in claim 4, is characterized in that... The k-ε turbulent viscosity model defines eddy current viscosity as: In the formula, Eeddy current viscosity, It is a constant. For fluid viscosity, For turbulent kinetic energy, The turbulent dissipation rate; The k-ε turbulent viscosity model defines the transport equation as follows: In the formula, These are the average velocity components in different directions. Molecular viscosity The turbulent viscosity coefficient, turbulent kinetic energy k The generated terms, , , , These are model constants.

6. The method for analyzing the service performance of a high-temperature valve spline pair in a hydrogen-based vertical furnace based on fluid-thermal-solid coupling, as described in claim 5, is characterized in that... In step 4, a heat-fluid coupling simulation is performed to solve the temperature field of the steady-state simulation model, specifically as follows: Step 401: Set the material properties of the fluid computing domain, the solid computing domain, and the mixed components, wherein the material properties include density, specific heat capacity, and thermal conductivity; set the unit region conditions, i.e., the properties corresponding to the fluid computing domain and the solid computing domain, to control the fluid flow, reduction process, and conjugate heat transfer between the fluid computing domain and the solid computing domain. Step 402: Set boundary conditions to limit the solution range of the flow field, including defining the properties of the inlet, outlet, wall and internal interface, where the velocity inlet and pressure outlet are selected to satisfy the continuity equation, momentum equation and energy equation. Step 403: Select the pressure-velocity coupling algorithm, set the relaxation factor and convergence criterion; use standard initialization, set the number of iterations and interval time, and solve the continuity equation, momentum equation, energy equation, and... k-ε The turbulence model is used to monitor the residual curves until the convergence condition is met, thus obtaining the heat transfer equations involved in the heat-fluid coupling.

7. The method for analyzing the service performance of a high-temperature valve spline pair in a hydrogen-based vertical furnace based on fluid-thermal-solid coupling, as described in claim 6, is characterized in that... The continuity equation and the Navier-Stokes equation are as follows: In the formula, , , , These are the dimensionless gradient operator, velocity, pressure, and volume force, respectively. Re It is the Reynolds number; The momentum equation and energy equation are as follows: In the formula, U For the velocity tensor, p The pressure on a fluid element. δ For unit tensors, S M For mass force, µ l For fluid viscosity, S E As an internal heat source, K Thermal conductivity, h hot For total enthalpy, The work done by viscous forces. T For temperature; The heat transfer equations involved in the heat-fluid coupling are as follows: In the formula, K s , K f Thermal conductivity of solids and fluids, respectively. For the Laplace operator at temperature T, Q For thermal power, ρ For fluid density, C p The specific heat capacity at constant pressure of the fluid. This is a convection term.

8. The method for analyzing the service performance of a high-temperature valve spline pair in a hydrogen-based vertical furnace based on fluid-thermal-solid coupling, as described in claim 7, is characterized in that... In step 5, based on the temperature field and temperature-stress coupling equations, the thermal deformation and stress field of the spline pair are solved using the finite element method, specifically as follows: Step 501: Define the model material properties, connect the fluid-static structure module, transfer temperature data, and conduct steady-state thermal stress analysis; Step 502: Establish the spline tooth surface contact pair, turn off the small sliding state, turn on the large deflection state, process the contact interface, and detect the initial contact state to ensure that it conforms to the actual working conditions of the model. Step 503: Apply the steady-state temperature field obtained from the thermal-fluid coupling calculation as a thermal load to each solid computational domain, set the load application time, and apply the corresponding load boundary conditions; Step 504: Use a nonlinear control solution model, which includes two load steps. The first load step only acts as a mechanical load, while the second load step activates the temperature load to achieve the coupling effect of mechanical load and temperature load. Step 505: Combine the temperature-stress coupling equations and iteratively solve for thermal deformation, equivalent stress, tooth flank clearance, and fatigue life distribution until convergence is achieved.

9. The method for analyzing the service performance of a high-temperature valve spline pair in a hydrogen-based vertical furnace based on fluid-thermal-solid coupling, as described in claim 8, is characterized in that... The temperature-stress coupling equation is: In the formula, µ Let be the nodal displacement vector. For the node velocity vector, K Here is the stiffness matrix. F It is a force vector. Q This includes the applied nodal forces and the forces caused by thermal deformation.

10. The method for analyzing the service performance of a high-temperature valve spline pair in a hydrogen-based vertical furnace based on fluid-thermal-solid coupling, as described in claim 9, is characterized in that... In step 6, the service performance parameters of the high-temperature valve spline pair are analyzed based on the temperature field, thermal deformation, and stress field to evaluate the service performance of the high-temperature valve spline pair. Specifically: Step 601: Conduct a mesh independence test to determine the optimal mesh cell size and number; Step 602: Conduct shaft deformation behavior analysis, solve for the overall shaft deformation and X, Y, and Z direction deformation, and determine whether the spline pair meets the service requirements; Step 603: Conduct a backlash analysis of the spline pair teeth, solve the changes in the contact state of the spline pair tooth surfaces under thermal stress, and determine the trend of the backlash change and whether it meets the service requirements. Step 604: Conduct a strength analysis of the shaft tooth surface, solve for the equivalent stress on the tooth surface, analyze the stress distribution on the tooth surface, and determine whether the maximum equivalent stress meets the service requirements; Step 605: Calculate the fatigue life of the spline pair shaft tooth surface using the nominal stress method and predict the mean life of the spline pair under random loads using Miner's linear cumulative damage theory.

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