A method, device and equipment for predicting temperature of a rapid cooling process of a bar

Abstract

This invention provides a method, apparatus, and device for predicting the temperature during the rapid cooling process of bar stock. The method includes: acquiring at least one bar stock rolling information and the shape of the bar stock cross-section after rolling; constructing a finite element non-uniform mesh model corresponding to the shape of the bar stock cross-section after rolling; dividing the finite element non-uniform mesh model into regions and acquiring the coordinate information of at least one node within at least one region; obtaining the heat transfer coefficients of each processing stage during the rapid cooling process based on the rolling information; the processing stages include an air cooling stage and a water cooling stage; and obtaining the predicted temperature distribution of the bar stock during the rapid cooling process based on the coordinate information, the heat transfer coefficient of the air cooling stage, the heat transfer coefficient of the water cooling stage, and a preset temperature prediction model. This invention enables the determination of the core temperature of the bar stock during the rapid cooling process.

Description

Technical Field

[0001] This invention relates to the field of rolling technology, and in particular to a method, apparatus, and equipment for predicting the temperature during the rapid cooling process of bar stock. Background Technology

[0002] Temperature is crucial in the hot rolling process. As one of the most important influencing factors in the plastic deformation and heat treatment of metals, temperature directly affects the deformation resistance of metals and the setting of key parameters in the rolling process, thereby affecting the bar rolling specifications, process parameters, and product performance and quality.

[0003] In the past, commonly used temperature measurement methods mostly employed on-site measurement and calculation methods based on traditional models. While on-site temperature measurement is convenient and quick, it is affected by many factors, which can easily lead to inaccurate temperature data. In some cases, it may even require obtaining the temperature through other rolling parameters. Furthermore, on-site temperature measurements are mostly surface temperatures, and the core temperature of the bar cannot be measured, resulting in inaccurate temperatures. Traditional model methods include empirical formulas and finite difference methods. Although these methods can be applied online and reduce experimental costs, the accuracy of temperature calculations still needs to be improved for hot rolling, which has a significant impact on temperature. Summary of the Invention

[0004] This invention provides a method, apparatus, and equipment for predicting the temperature during the rapid cooling process of bars. It enables the determination of the core temperature of the bar during rapid cooling.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] A method for predicting the temperature during a rapid cooling process of a bar stock, the method comprising:

[0007] Obtain at least one bar rolling information and the shape of the bar cross section after rolling;

[0008] Construct a finite element non-uniform mesh model corresponding to the shape of the cross-section of the rolled bar;

[0009] The finite element non-uniform mesh generation model is divided into regions to obtain the coordinate information of at least one node in at least one region;

[0010] Based on the rolling information, the heat transfer coefficients of each processing stage during the rapid cooling process are obtained; the processing stages include: air cooling stage and water cooling stage;

[0011] Based on the coordinate information, the heat transfer coefficient of the air-cooling stage, the heat transfer coefficient of the water-cooling stage, and the preset temperature prediction model, the predicted temperature distribution of the rapid cooling process of the bar is obtained.

[0012] Optionally, the node is the vertex of the element mesh in at least one region obtained after dividing the finite element non-uniform mesh generation model into regions.

[0013] Optionally, the finite element non-uniform mesh generation model is divided into regions to obtain the coordinate information of at least one node within at least one region, including:

[0014] According to the preset number of regions, the preset finite element non-uniform mesh generation model is divided into a first region, a second region, and a third region.

[0015] Obtain the number of width cell grids and the number of thickness cell grids in the first region;

[0016] Obtain the radial cell grid number of the second region and the third region;

[0017] Based on the number of width cell grids and the number of thickness cell grids, the coordinate information of the nodes of the cell grids in the first region is obtained;

[0018] Based on the number of width cell grids and the number of radial cell grids, the coordinate information of the nodes of the cell grids in the second region is obtained;

[0019] Based on the number of thickness element grids and the number of radial element grids, the coordinate information of the nodes of the element grids in the third region is obtained;

[0020] The first region and the second region have the same number of width cell grids; the first region and the third region have the same number of thickness cell grids; and the second region and the third region have the same number of radial cell grids.

[0021] Optionally, based on the coordinate information, the heat transfer coefficient of the air-cooling stage, the heat transfer coefficient of the water-cooling stage, and a preset temperature prediction model, the predicted temperature distribution of the rapid cooling process of the bar is obtained, including:

[0022] Based on the heat transfer coefficient of the air-cooling stage, the heat transfer coefficient of the water-cooling stage, the coordinate information, and the preset temperature prediction model, a set of linear equations for temperature prediction of the rapid cooling process of the bar is obtained.

[0023] Based on the set of linear equations for temperature prediction, the predicted temperature distribution of the rapid cooling process of the bar is obtained.

[0024] Optionally, the heat transfer coefficient of the air-cooling stage is calculated according to the following formula:

[0025]

[0026] Wherein, h rThe heat transfer coefficient is σ, where σ is the Stefan-Boltzmann constant, ε is the emissivity coefficient, and T is the instantaneous temperature. ∞ This represents the extreme temperature value.

[0027] The first heat transfer coefficient of the water-cooling stage is calculated according to the following formula:

[0028]

[0029] Wherein, h1 is the first heat transfer coefficient of the water-cooling stage, and T d The boiling point of cooling water, T R The surface temperature of the bar, T f Where is the cooling water temperature, D is the cooling water pipe diameter, d is the rod diameter, and λ is... w Let Re be the thermal conductivity of water, and Re be the Reynolds number.

[0030] The second heat transfer coefficient of the water-cooling stage is calculated according to the following formula:

[0031] h2=(Nu·λ α ) / d;

[0032] Wherein, h2 is the second heat transfer coefficient of the water-cooling stage, Nu is the Nusselt number, and λ is the... α Where d is the thermal conductivity of air, and d is the diameter of the rod.

[0033] Optionally, based on the heat transfer coefficient of the air-cooling stage, the heat transfer coefficient of the water-cooling stage, the coordinate information, and the preset temperature prediction model, a set of linear equations for temperature prediction of the rapid cooling process of the bar is obtained, including:

[0034] Based on the heat transfer coefficient of the air-cooling stage, the heat transfer coefficient of the rolling stage, and the coordinate information, a differential equation for heat conduction is constructed according to the first law of thermodynamics.

[0035] Based on the variational principle of heat conduction problems, the first-order partial derivatives of the heat conduction differential equation are calculated and set to zero to obtain the equivalent functional equation for each element grid.

[0036] Based on the aforementioned heat conduction differential equation and finite element combination method, discrete elements are assembled to obtain the preset temperature prediction model;

[0037] The temperature partial derivative with respect to time in the preset temperature prediction model is expressed as a two-point backward difference scheme;

[0038] Substituting the two-point backward difference scheme into the temperature prediction model, we obtain the linear equations for predicting the temperature of the bar during rapid cooling.

[0039] Optionally, based on the temperature prediction linear equations, the predicted temperature distribution of the rapid cooling process of the bar is obtained, including:

[0040] The temperature prediction linear equations are solved by using a one-dimensional variable bandwidth storage method to obtain the temperature of each node in the first, second, and third regions on the cross-section of the bar.

[0041] The calculation of the temperature of the node is iterated until the calculation of the temperature of the node in the current processing stage reaches a preset number of times.

[0042] If the total time for the rapid cooling process has not been reached, the next round of acquiring at least one bar rolling information and the shape of the bar cross-section after rolling is continued; a finite element non-uniform mesh model corresponding to the shape of the bar cross-section after rolling is constructed; the finite element non-uniform mesh model is divided into regions, and the coordinate information of at least one node in at least one region is obtained; based on the coordinate information, the bar rolling information, and the preset temperature prediction model, the predicted temperature distribution of the rapid cooling process of the bar is obtained until the total time for the rapid cooling process is reached.

[0043] The present invention also provides a device for predicting the temperature of a bar stock during rapid cooling, the device comprising:

[0044] The acquisition module is used to acquire at least one bar rolling information and the shape of the bar cross section after rolling.

[0045] The processing module is used to construct a finite element non-uniform mesh model corresponding to the shape of the cross-section of the rolled bar; divide the finite element non-uniform mesh model into regions and obtain the coordinate information of at least one node in at least one region; obtain the heat transfer coefficient of each processing stage in the rapid cooling process based on the rolling information; the processing stages include: air cooling stage and water cooling stage; and obtain the predicted temperature distribution of the rapid cooling process of the bar based on the coordinate information, the heat transfer coefficient of the air cooling stage, the heat transfer coefficient of the water cooling stage, and a preset temperature prediction model.

[0046] The present invention also provides a computing device, comprising: a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described above.

[0047] The present invention also provides a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described above.

[0048] The above-described solution of the present invention has at least the following beneficial effects:

[0049] The above-described solution of the present invention obtains at least one bar rolling information and the shape of the bar cross-section after rolling; constructs a finite element non-uniform mesh model corresponding to the shape of the bar cross-section after rolling; divides the finite element non-uniform mesh model into regions and obtains the coordinate information of at least one node in at least one region; obtains the heat transfer coefficient of each processing stage in the rapid cooling process based on the rolling information; the processing stages include an air cooling stage and a water cooling stage; and obtains the predicted temperature distribution of the bar during the rapid cooling process based on the coordinate information, the heat transfer coefficient of the air cooling stage, the heat transfer coefficient of the water cooling stage, and a preset temperature prediction model. This enables the measurement of the core temperature of the bar during the rapid cooling process. Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating the method for predicting the temperature during the rapid cooling process of a bar provided in this embodiment of the invention.

[0051] Figure 2 This is a schematic diagram of a finite element non-uniform mesh generation model corresponding to the shape of the cross-section of the bar after rolling, according to an embodiment of the present invention.

[0052] Figure 3 This is a schematic diagram of the partitioning of a finite element non-uniform mesh generation model corresponding to the shape of the cross-section of the bar after rolling, according to an embodiment of the present invention.

[0053] Figure 4 This is another specific flowchart illustrating the method for predicting the temperature during the rapid cooling process of bars according to an embodiment of the present invention.

[0054] Figure 5 This is a schematic diagram of the temperature distribution at the end of the rapid cooling process according to an embodiment of the present invention;

[0055] Figure 6 This is a schematic diagram showing the temperature drop at the end of the rapid cooling process according to an embodiment of the present invention;

[0056] Figure 7 This is a schematic diagram of a module for predicting the temperature of a bar material during rapid cooling, according to an embodiment of the present invention. Detailed Implementation

[0057] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0058] like Figure 1 As shown, an embodiment of the present invention provides a method for predicting the temperature during the rapid cooling process of a bar stock, the method comprising:

[0059] Step 11: Obtain at least one bar rolling information and the shape of the bar cross section after rolling;

[0060] Step 12: Construct a finite element non-uniform mesh generation model corresponding to the shape of the cross-section of the rolled bar.

[0061] Step 13: Divide the finite element non-uniform mesh generation model into regions and obtain the coordinate information of at least one node in at least one region; here, the node is the vertex of the cell mesh in at least one region obtained after dividing the finite element non-uniform mesh generation model into regions.

[0062] Step 14: Based on the rolling information, obtain the heat transfer coefficients of each processing stage during the rapid cooling process; the processing stages include: air cooling stage and water cooling stage;

[0063] Step 15: Based on the coordinate information, the heat transfer coefficient of the air-cooling stage, the heat transfer coefficient of the water-cooling stage, and the preset temperature prediction model, obtain the predicted temperature distribution of the rapid cooling process of the bar.

[0064] It should be noted that the bar rolling information may include rolling parameters of the rapid cooling process, specifically including but not limited to: air temperature, rapid cooling water temperature, air-cooling distance between water tanks, water flow rate, water pressure, and rolling speed; the bar rolling information may also include initial information of the rolled piece, specifically including but not limited to: steel grade of the rolled piece, carbon content of the rolled piece, manganese content of the rolled piece, width of the rolled piece, thickness of the rolled piece, and temperature of the rolled piece; the bar rolling information may also include unit grid division information, specifically including but not limited to: number of width units in the first region, number of thickness units in the first region, number of radial units in the second region, and number of radial units in the third region; the bar rolling information can be collected by sensors installed on the bar rolling equipment, and the bar rolling information can be selected and used according to the actual predicted temperature calculation requirements during the rapid cooling process of the bar;

[0065] It should be noted that when predicting the temperature of the rapid cooling process of the bar based on the finite element non-uniform mesh generation model, since the rolling dimension is much larger than the width and thickness dimensions during the rolling process, heat conduction in the rolling direction can be ignored.

[0066] In addition, such as Figure 2As shown, when constructing the finite element non-uniform mesh model corresponding to the shape of the bar cross section after rolling, since the boundary heat transfer conditions and geometry of the bar in the width and thickness directions are symmetrical, it is possible to construct only the finite element non-uniform mesh model corresponding to the shape of one-quarter of the bar rolling cross section.

[0067] In this embodiment of the invention, the bar rolling section is divided into regions based on the finite element non-uniform mesh generation model; then, the coordinate information of the nodes of at least one unit mesh on the divided bar rolling section is obtained; and based on the coordinate information, the heat transfer coefficient of the air cooling stage, the heat transfer coefficient of the water cooling stage, and the preset temperature prediction model, the predicted temperature corresponding to the node of the mesh unit on the bar rolling section is obtained; further, based on the predicted temperature corresponding to each node, the predicted temperature distribution of the bar during the entire rapid cooling process is obtained; the preset temperature prediction model can be constructed based on the coordinate information and the rolling information; thus, the core temperature of the bar can be measured during the rapid cooling process.

[0068] In an optional embodiment of the present invention, step 13 may include:

[0069] Step 131: Divide the preset finite element non-uniform mesh generation model into a first region, a second region, and a third region according to the preset number of regions.

[0070] Step 132: Obtain the number of width cell grids and the number of thickness cell grids in the first region;

[0071] Step 133: Obtain the radial cell grid number of the second region and the third region;

[0072] Step 134: Obtain the coordinate information of the nodes of the unit grid in the first region based on the number of width unit grids and the number of thickness unit grids;

[0073] Step 135: Based on the number of width cell grids and the number of radial cell grids, obtain the coordinate information of the nodes of the cell grids in the second region;

[0074] Step 136: Based on the number of thickness unit grids and the number of radial unit grids, obtain the coordinate information of the nodes of the unit grids in the third region;

[0075] The first region and the second region have the same number of width cell grids; the first region and the third region have the same number of thickness cell grids; and the second region and the third region have the same number of radial cell grids.

[0076] In this embodiment, the finite element non-uniform mesh generation model is divided into regions, and the node coordinate information in each region is calculated. This allows for the effective calculation of the temperature of the core of the bar in the first region and significantly improves the accuracy of temperature calculation.

[0077] It should be noted that the number of regions in the preset finite element non-uniform mesh generation model can be determined according to actual needs, including but not limited to three regions. In this scheme, it is preferred to divide it into three regions.

[0078] like Figure 3 As shown, in another optional embodiment of the present invention, the node coordinates of the first region can be calculated according to the following formula:

[0079] x = (i-1) × db;

[0080] y = (j-1) × dh;

[0081] Wherein, x is the x-axis node coordinate, i is the column number of the node, db is the cell grid width, y is the y-axis node coordinate, j is the row number of the node, and dh is the cell grid thickness;

[0082] The node coordinates of the second region can be calculated using the following formula:

[0083] If the node is on the left boundary, then x = 0;

[0084]

[0085] Wherein, x is the x-axis node coordinate, r0 is the grid width of the first region, dr0 is the length on the boundary line between the second region and the third region, j is the row number of the node, cos is the cosine, i is the column number of the node, dbθ is the cell grid angle of the second region, π is pi, y is the y-axis node coordinate, and sin is the sine.

[0086] The node coordinates of the third region can be calculated using the following formula:

[0087]

[0088]

[0089] Wherein, x is the x-axis node coordinate, r0 is the grid width of the first region, i is the column number of the node, dr0 is the length on the boundary line between the second region and the third region, cos is the cosine, j is the row number of the node, dhθ is the unit grid angle of the third region, y is the y-axis node coordinate, and sin is the sine.

[0090] In this embodiment, the expression for the cell grid width db is:

[0091] Wherein, r0 is the grid width of the first region, and n2 is the number of width cell grids in the first region;

[0092] The expression for the unit grid thickness dh is:

[0093] Wherein, r0 is the grid width of the first region, and n1 is the number of thickness unit grids in the first region;

[0094] The expression for the grid width r0 of the first region is:

[0095] Wherein, n is a proportionality coefficient, and r is a radius;

[0096] The expression for the length dr0 on the boundary line between the second region and the third region is:

[0097] Wherein, r0 is the grid width of the first region, and n3 is the number of radial cell grids;

[0098] The expression for the cell grid angle dbθ in the second region is:

[0099] Wherein, π is the mathematical constant pi, and n1 is the number of thickness unit grids in the first region;

[0100] The expression for the unit grid angle dhθ of the third region is:

[0101] Wherein, π is the mathematical constant pi, and n2 is the number of width-unit grids in the first region.

[0102] In another optional embodiment of the present invention, step 15 may include:

[0103] Step 151: Based on the heat transfer coefficient of the air cooling stage, the heat transfer coefficient of the water cooling stage, the coordinate information, and the preset temperature prediction model, obtain the temperature prediction linear equation set for the rapid cooling process of the bar.

[0104] Step 152: Based on the temperature prediction linear equation set, obtain the predicted temperature distribution of the rapid cooling process of the bar.

[0105] In this embodiment, the heat exchange stage in the rapid cooling process of the bar stock may include, but is not limited to, an air cooling stage and a water cooling stage. In the air cooling stage, the heat of the rolled bar stock is mainly dissipated through thermal radiation. During hot rolling of the bar stock, the temperature of the rolled bar stock is much higher than the ambient temperature in most stages of the entire production process, resulting in a large amount of heat radiation loss. Air cooling heat exchange is the main form of heat exchange in the rapid cooling process of the bar stock. In addition, the rolled bar stock can also exchange heat with the environment through thermal convection. The heat transfer coefficient between the rolled bar stock and natural air convection can be taken as 5 W / (m²). 2 ·K).

[0106] In another optional embodiment of the present invention, in step 151, the heat transfer coefficient of the air-cooling stage is calculated according to the following formula:

[0107]

[0108] Wherein, h r The heat transfer coefficient for the air-cooled stage is given by σ, where σ is the Stefan-Boltzmann constant, σ = 5.67 × 10⁻⁶. -8 W / (m 2 ·K 4 ), where ε is the emissivity coefficient, and the relationship between the emissivity coefficient ε and temperature is: ε = 0.125 (T / 1000). 2 -0.38(T / 1000)+1.1, where T is the instantaneous temperature. ∞ This represents the extreme temperature value.

[0109] The first heat transfer coefficient of the water-cooling stage is calculated according to the following formula:

[0110]

[0111] Wherein, h1 is the first heat transfer coefficient of the water-cooling stage, and T d The boiling point of cooling water, T R The surface temperature of the bar, T f The temperature of the cooling water, T d T R and T f The units for all values ​​are °C, D is the cooling water pipe diameter, is the rod diameter, the units for D and d are meters, is the thermal conductivity of water, and λ is... w The unit is W / (m 2 ·℃), where Re is the Reynolds number. It is a dimensionless number;

[0112] The second heat transfer coefficient of the water-cooling stage is calculated according to the following formula:

[0113] h2=(Nu·λ α ) / d;

[0114] Wherein, h2 is the second heat transfer coefficient of the water-cooling stage, i.e., the air convection heat transfer coefficient, with units of W / (m²). 2 ℃), where Nu is the Nusselt number, a dimensionless number, and λ α The thermal conductivity of air is expressed in W / (m²). 2 (℃), where d is the diameter of the bar, in meters.

[0115] In this embodiment, the water cooling stage may include: high-pressure water descaling, pre-cooling before finishing rolling, and post-finish rolling water spray cooling. Cooling water is sprayed from a nozzle under a certain pressure and exchanges heat with the rolled bar through convection, thereby reducing the temperature of the rolled bar. Relevant studies have shown that the value of the heat transfer coefficient is related to different temperatures and water flow densities. Therefore, the relationship between the heat transfer coefficient and the temperature range and water flow density range is as follows:

[0116] When t0 = 50℃ and 280 < w < 12600, the second heat transfer coefficient of the water-cooling stage is calculated based on h2 = 225.9w. 0.048 ×1.163 obtained;

[0117] When 200℃≤t0≤500℃ and 5≤w≤2000, the second heat transfer coefficient of the water-cooling stage is based on get;

[0118] When t0 ≥ 50℃ and 100 ≤ w ≤ 2000, the second heat transfer coefficient of the water-cooling stage is based on Obtained; wherein, t0 is the surface temperature of the rolled bar, and w is the water flow density, in L / (min·m 2 ).

[0119] In another optional embodiment of the present invention, step 151 may include:

[0120] Step 1511: Based on the heat transfer coefficient of the air-cooling stage, the heat transfer coefficient of the rolling stage, and the coordinate information, construct the differential equation of heat conduction according to the first law of thermodynamics;

[0121] Step 1512: Based on the variational principle of the heat conduction problem, calculate the first-order partial derivatives of the heat conduction differential equation and set them to zero to obtain the equivalent functional equation for each cell grid.

[0122] Step 1513: Assemble the discrete elements according to the heat conduction differential equation and the finite element combination method to obtain the preset temperature prediction model;

[0123] Step 1514: Express the temperature partial derivative with respect to time in the preset temperature prediction model as a two-point backward difference scheme;

[0124] Step 1515: Substitute the two-point backward difference scheme into the temperature prediction model to obtain the linear equations for temperature prediction of the rapid cooling process of the bar.

[0125] In this embodiment, a differential equation for heat conduction is established based on the first law of thermodynamics. Assuming that the material has isotropic thermal conductivity, the differential equation for heat conduction is:

[0126] Wherein, T is the instantaneous temperature in K, and ρ is the material density in kg / m³. 3 Where c is the specific heat of the material, in J / (kg·K), t is time, in seconds, and k is the thermal conductivity coefficient, in W / (m·K). Internal heat source intensity, unit: J / m 3 Where x is the coordinate of the x-axis node and y is the coordinate of the y-axis node;

[0127] Using the Euler equations under given boundary and initial conditions, and based on the variational principle of the heat conduction problem, the first-order partial derivatives of the equivalent functional equation of the element are calculated and set to zero, thus transforming the heat conduction problem into an extremum problem of the equivalent functional equation of the element.

[0128] The equivalent functional equation for each unit is expressed as:

[0129]

[0130] Wherein, the I (e) The energy functional for each cell grid, where S is the area of ​​each cell grid, l is the edge length boundary value of each cell grid, h is the heat transfer coefficient, T is the instantaneous temperature of the rod, ρ is the density of the rod, c is the specific heat of the rod, t is time, and k is the thermal conductivity coefficient. The intensity of the internal heat source is given by x, where x is the coordinate of the x-axis node and y is the coordinate of the y-axis node.

[0131] It should be noted that the boundary conditions may include: a boundary S1 given a temperature value, T(x,y,z,t)=T0; (for t>0, on S1);

[0132] The initial condition can be: T(x,y,z,t=0)=T0(x,y,z) (within V);

[0133] Wherein, T is the instantaneous temperature, x is the x-axis node coordinate, y is the y-axis node coordinate, z is the z-axis node coordinate, t is time, T0 is the temperature distribution state at t=0, and V is the volume region;

[0134] Based on the variational principle of heat conduction problems, after taking the first-order partial derivatives of the equivalent functional expression of the element and setting them to zero, the equivalent functional equation for each element mesh is obtained as follows:

[0135]

[0136] The equivalent functional equation for each of the above cell grids can be transformed into the following form:

[0137]

[0138] in, The I (e) The energy for each cell grid, S is the area of ​​each cell grid, l is the boundary value of the side length of each cell grid, and N is... i The shape function of node i, the N j Let be the shape function of node j, where subscripts i and j both represent node numbers. Based on the fundamental principles of the finite element method, the shape function of the element mesh can be calculated; T is the instantaneous temperature of the bar, ρ is the density of the bar, c is the specific heat of the bar, t is time, and k is the thermal conductivity coefficient. The internal heat source intensity is given by x, which is the x-axis node coordinate, y, which is the y-axis node coordinate, and z, which is the length of the node in the rolling direction. Since the preset finite element non-uniform mesh generation model only considers nodes in one-dimensional and two-dimensional directions, the value of coordinate z can be ignored in the specific calculation; V is the volume of the deformation zone.

[0139] Based on existing finite element combination methods and the temperature function of each element mesh, discrete element meshes can be assembled, and the stiffness matrices of the element meshes can be assembled into a global stiffness matrix to obtain the preset temperature prediction model, i.e.:

[0140]

[0141] Wherein, the [K T [ ] represents the overall temperature stiffness matrix, the [K3] is the overall temperature stiffness matrix. The {p} is a constant term. E represents the energy functional of the entire rolling section;

[0142] The temperature partial derivative with respect to time in the preset temperature prediction model is expressed as a two-point backward difference scheme, namely: Wherein, Δt is the time interval value during the rolling process;

[0143] Substituting the two-point backward difference scheme into the preset temperature prediction model, the linear equation system for temperature prediction is obtained, namely:

[0144] In another optional embodiment of the present invention, step 152 may include:

[0145] Step 1521: Solve the temperature prediction linear equations using the one-dimensional variable bandwidth storage method to obtain the temperature of each node in the first, second, and third regions on the cross-section of the bar.

[0146] Step 1522: Iterate the number of times the temperature of the node is calculated until the number of times the temperature of the node in the current processing stage is calculated reaches a preset number;

[0147] Step 1523: If the total time of the rapid cooling process has not been reached, continue to the next round of acquiring at least one bar rolling information and the shape of the bar cross-section after rolling; construct a finite element non-uniform mesh generation model corresponding to the shape of the bar cross-section after rolling; divide the finite element non-uniform mesh generation model into regions and acquire the coordinate information of at least one node in at least one region; based on the coordinate information, the bar rolling information and the preset temperature prediction model, obtain the predicted temperature distribution of the rapid cooling process of the bar until the total time of the rapid cooling process is reached.

[0148] In this embodiment, for each bar rolling stage, the temperature of the nodes on the rolling section is calculated once for each rolling of the bar until the rolling time of that rolling stage or the entire rolling process ends, thereby obtaining the predicted temperature distribution of the bar throughout the entire rolling process. At the same time, each predicted temperature obtained corresponds to coordinate information. The one-dimensional variable bandwidth storage method is used to solve the linear equation of temperature prediction, which can save storage space and improve computational efficiency.

[0149] The loan storage method may specifically include: for the general form KX = B of the temperature prediction linear equation system, storing the elements starting from the first column of non-zero elements and continuing until the diagonal elements, where K is a symmetric sparse matrix, and the symmetric sparse matrix K is shown below:

[0150]

[0151] Stored as a one-dimensional matrix:

[0152] K = [k11 k 12 k 22 k 23 k 33 k 14 k 24 0 k 44 k 35 0 k 55 … k n-1,n k nn ];

[0153] Wherein, k is a matrix element, the first subscript indicates the row number, and the second subscript indicates the column number; the temperature of each node in the first, second, and third regions on the rolled section corresponds to a matrix element.

[0154] In a specific embodiment of the present invention, the hot rolling process of a steel grade is selected as the calculation object, and the temperature evolution law from the end of bar rolling to the end of the entire rapid cooling process is analyzed using the finite element method; some rolling information is shown in Table 1 below:

[0155]

[0156]

[0157] Table 1

[0158] like Figure 4 As shown in Table 1 above, the temperature evolution during the rapid cooling process of rolled bars from a certain steel mill is calculated and analyzed using the rolling information. The specific calculation process may include:

[0159] Step 41: Obtain rolling information and the shape of the bar rolling section; the rolling process is based on the fact that the rolling dimension is much larger than the width and thickness dimensions, so heat conduction in the rolling direction can be ignored; the heat transfer conditions and geometry at the boundary of the bar width and thickness are symmetrical, so a quarter section is considered;

[0160] Step 411: Collect control parameters, rolling section unit mesh generation information, and rolling information, as shown in Table 1;

[0161] Step 412: Collect rolling parameters for different rolling stages, as shown in Table 1;

[0162] Step 42: Obtain the coordinate information of at least one node of the rolling section according to the preset finite element non-uniform mesh generation model corresponding to the shape of the rolling section;

[0163] Step 421: Divide the rolling section element mesh, establish the finite element model, and calculate the node coordinates;

[0164] Step 422: Determine the heat transfer coefficients for different rolling stages;

[0165] Step 43: Based on the coordinate information, the heat transfer coefficient of the air-cooling stage, the heat transfer coefficient of the water-cooling stage, and the preset temperature prediction model, obtain the predicted temperature distribution of the rapid cooling process of the bar.

[0166] Step 431: Construct a temperature prediction model based on the basic principles of finite element method, as well as node coordinate information and rolling information;

[0167] Step 432: Based on the temperature prediction model, form a set of linear equations for the finite element solution of the temperature field;

[0168] Step 433: Solve the linear equation system using the one-dimensional variable bandwidth storage method to obtain the predicted temperature value of the node;

[0169] Step 434: Determine whether the node temperature calculation in any rolling stage has ended; if the node temperature calculation in that rolling stage has not ended, increase the number of iterations and continue the calculation; otherwise, determine whether the temperature calculation of the nodes in the entire rolling process has ended; if the temperature calculation of the nodes in the entire rolling process has not ended, collect rolling information and perform node calculation for the next rolling stage; otherwise, stop the node temperature calculation.

[0170] Predicted temperature calculation results are as follows Figure 5 and Figure 6 ,in, Figure 5 This is a temperature distribution diagram at the end of the rapid cooling process. Figure 6 This is a graph showing the temperature drop at the end of the rapid cooling process.

[0171] In the above embodiments of the present invention, the method for predicting the temperature of the rapid cooling process of the bar can obtain the temperature distribution at any location during the rapid cooling process of the rolled bar, with detailed and accurate information. It can measure the core temperature of the bar during the rapid cooling process and can develop a dedicated calculation program using FORTRAN language to be applied to the hot rolling process of the bar, optimize the hot rolling process parameters, and further improve the accuracy and efficiency of temperature prediction.

[0172] like Figure 7 As shown, embodiments of the present invention also provide a device 70 for predicting the temperature of a bar during rapid cooling, the device 70 comprising:

[0173] The acquisition module 71 is used to acquire at least one bar rolling information and the shape of the bar cross section after rolling.

[0174] Processing module 72 is used to construct a finite element non-uniform mesh generation model corresponding to the shape of the cross-section of the rolled bar; divide the finite element non-uniform mesh generation model into regions and obtain the coordinate information of at least one node in at least one region; obtain the heat transfer coefficient of each processing stage in the rapid cooling process based on the rolling information; the processing stages include: air cooling stage and water cooling stage; and obtain the predicted temperature distribution of the rapid cooling process of the bar based on the coordinate information, the heat transfer coefficient of the air cooling stage, the heat transfer coefficient of the water cooling stage, and the preset temperature prediction model.

[0175] Optionally, the node is the vertex of the element mesh in at least one region obtained after dividing the finite element non-uniform mesh generation model into regions.

[0176] Optionally, the finite element non-uniform mesh generation model is divided into regions to obtain the coordinate information of at least one node within at least one region, including:

[0177] According to the preset number of regions, the preset finite element non-uniform mesh generation model is divided into a first region, a second region, and a third region.

[0178] Obtain the number of width cell grids and the number of thickness cell grids in the first region;

[0179] Obtain the radial cell grid number of the second region and the third region;

[0180] Based on the number of width cell grids and the number of thickness cell grids, the coordinate information of the nodes of the cell grids in the first region is obtained;

[0181] Based on the number of width cell grids and the number of radial cell grids, the coordinate information of the nodes of the cell grids in the second region is obtained;

[0182] Based on the number of thickness element grids and the number of radial element grids, the coordinate information of the nodes of the element grids in the third region is obtained;

[0183] The first region and the second region have the same number of width cell grids; the first region and the third region have the same number of thickness cell grids; and the second region and the third region have the same number of radial cell grids.

[0184] Optionally, based on the coordinate information, the heat transfer coefficient of the air-cooling stage, the heat transfer coefficient of the water-cooling stage, and a preset temperature prediction model, the predicted temperature distribution of the rapid cooling process of the bar is obtained, including:

[0185] Based on the heat transfer coefficient of the air cooling stage, the heat transfer coefficient of the water cooling stage, the coordinate information, and the preset temperature prediction model, a set of linear equations for temperature prediction of the rapid cooling process of the bar is obtained.

[0186] Based on the set of linear equations for temperature prediction, the predicted temperature distribution of the rapid cooling process of the bar is obtained.

[0187] Optionally, the heat transfer coefficient of the air-cooling stage is calculated according to the following formula:

[0188]

[0189] Wherein, h r The heat transfer coefficient is σ, where σ is the Stefan-Boltzmann constant, ε is the emissivity coefficient, and T is the instantaneous temperature. ∞ This represents the extreme temperature value.

[0190] The first heat transfer coefficient of the water-cooling stage is calculated according to the following formula:

[0191]

[0192] Wherein, h1 is the first heat transfer coefficient of the water-cooling stage, and T d The boiling point of cooling water, T R The surface temperature of the bar, T f Where is the cooling water temperature, D is the cooling water pipe diameter, d is the rod diameter, and λ is... w Let Re be the thermal conductivity of water, and Re be the Reynolds number.

[0193] The second heat transfer coefficient of the water-cooling stage is calculated according to the following formula:

[0194] h2=(Nu·λ α ) / d;

[0195] Wherein, h2 is the second heat transfer coefficient of the water-cooling stage, Nu is the Nusselt number, and λ is the... α Where d is the thermal conductivity of air, and d is the diameter of the rod.

[0196] Optionally, based on the heat transfer coefficient of the air-cooling stage, the heat transfer coefficient of the water-cooling stage, the coordinate information, and the preset temperature prediction model, a set of linear equations for temperature prediction of the rapid cooling process of the bar is obtained, including:

[0197] Based on the heat transfer coefficient of the air-cooling stage, the heat transfer coefficient of the rolling stage, and the coordinate information, a differential equation for heat conduction is constructed according to the first law of thermodynamics.

[0198] Based on the variational principle of heat conduction problems, the first-order partial derivatives of the heat conduction differential equation are calculated and set to zero to obtain the equivalent functional equation for each element grid.

[0199] Based on the aforementioned heat conduction differential equation and finite element combination method, discrete elements are assembled to obtain the preset temperature prediction model;

[0200] The temperature partial derivative with respect to time in the preset temperature prediction model is expressed as a two-point backward difference scheme;

[0201] Substituting the two-point backward difference scheme into the temperature prediction model, we obtain the linear equations for predicting the temperature of the bar during rapid cooling.

[0202] Optionally, based on the temperature prediction linear equations, the predicted temperature distribution of the rapid cooling process of the bar is obtained, including:

[0203] The temperature prediction linear equations are solved by using a one-dimensional variable bandwidth storage method to obtain the temperature of each node in the first, second, and third regions on the cross-section of the bar.

[0204] The calculation of the temperature of the node is iterated until the calculation of the temperature of the node in the current processing stage reaches a preset number of times.

[0205] If the total time for the rapid cooling process has not been reached, the next round of acquiring at least one bar rolling information and the shape of the bar cross-section after rolling is continued; a finite element non-uniform mesh model corresponding to the shape of the bar cross-section after rolling is constructed; the finite element non-uniform mesh model is divided into regions, and the coordinate information of at least one node in at least one region is obtained; based on the coordinate information, the bar rolling information, and the preset temperature prediction model, the predicted temperature distribution of the rapid cooling process of the bar is obtained until the total time for the rapid cooling process is reached.

[0206] It should be noted that this device is the same as the method described above. All implementations in the above method embodiments are applicable to the embodiments of this device and can achieve the same technical effect.

[0207] Embodiments of the present invention also provide a computing device, including: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0208] Embodiments of the present invention also provide a computer-readable storage medium including instructions that, when executed on a computer, cause the computer to perform the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0209] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0210] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0211] In the embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0212] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0213] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0214] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0215] Furthermore, it should be noted that in the apparatus and method of the present invention, it is obvious that the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered equivalent solutions of the present invention. Moreover, the steps performing the above-described series of processes can naturally be executed in the order described, but are not necessarily required to be executed in chronological order; some steps can be executed in parallel or independently of each other. Those skilled in the art will understand that all or any step or component of the method and apparatus of the present invention can be implemented in any computing device (including processors, storage media, etc.) or network of computing devices, in hardware, firmware, software, or a combination thereof. This is something that those skilled in the art can achieve by using their basic programming skills after reading the description of the present invention.

[0216] Therefore, the object of the present invention can also be achieved by running a program or a set of programs on any computing device. The computing device can be a known general-purpose device. Therefore, the object of the present invention can also be achieved simply by providing a program product containing program code implementing the method or apparatus. That is, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any known storage medium or any storage medium developed in the future. It should also be noted that in the apparatus and method of the present invention, it is obvious that the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered equivalent to the present invention. Furthermore, the steps performing the above series of processes can naturally be performed in the order described, but are not necessarily required to be performed in chronological order. Some steps can be performed in parallel or independently of each other.

[0217] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method of predicting the temperature of a bar during a rapid cooling process, characterized by, The method includes: Obtain at least one bar rolling information and the shape of the bar cross section after rolling; Construct a finite element non-uniform mesh model corresponding to the shape of the cross-section of the rolled bar; The finite element non-uniform mesh generation model is divided into regions to obtain the coordinate information of at least one node in at least one region; Based on the rolling information, the heat transfer coefficients of each processing stage during the rapid cooling process are obtained; the processing stages include: air cooling stage and water cooling stage; Based on the coordinate information, the heat transfer coefficient of the air-cooling stage, the heat transfer coefficient of the water-cooling stage, and the preset temperature prediction model, the predicted temperature distribution of the rapid cooling process of the bar is obtained. The process includes dividing the finite element non-uniform mesh model into regions and obtaining the coordinate information of at least one node within at least one region, including: According to the preset number of regions, the preset finite element non-uniform mesh generation model is divided into a first region, a second region, and a third region. Obtain the number of width cell grids and the number of thickness cell grids in the first region; Obtain the radial cell grid number of the second region and the third region; Based on the number of width cell grids and the number of thickness cell grids, the coordinate information of the nodes of the cell grids in the first region is obtained; Based on the number of width cell grids and the number of radial cell grids, the coordinate information of the nodes of the cell grids in the second region is obtained; Based on the number of thickness element grids and the number of radial element grids, the coordinate information of the nodes of the element grids in the third region is obtained; Wherein, the first region and the second region have the same number of width cell grids; the first region and the third region have the same number of thickness cell grids; and the second region and the third region have the same number of radial cell grids. The node coordinates of the first region are calculated according to the following formula: ； ； in, The coordinates of the x-axis nodes. The column number of the node. The width of the cell grid. The coordinates of the y-axis node are... The row number of the node. The thickness of the unit grid; The node coordinates of the second region are calculated according to the following formula: If the node is on the left boundary, then ; ； in, The grid width of the first region. The length on the boundary line between the second region and the third region. For cosine, For the unit grid angle of the second region, Pi It is the sine; The node coordinates of the third region are calculated according to the following formula: ； ； in, The angle of the unit grid in the third region.

2. The method for predicting the temperature during the rapid cooling process of a bar according to claim 1, characterized in that, The node is the vertex of the element mesh in at least one region obtained after dividing the finite element non-uniform mesh generation model into regions.

3. The method for predicting the temperature during the rapid cooling process of a bar according to claim 1, characterized in that, Based on the coordinate information, the heat transfer coefficient of the air-cooling stage, the heat transfer coefficient of the water-cooling stage, and the preset temperature prediction model, the predicted temperature distribution of the rapid cooling process of the bar is obtained, including: Based on the heat transfer coefficient of the air cooling stage, the heat transfer coefficient of the water cooling stage, the coordinate information, and the preset temperature prediction model, a set of linear equations for temperature prediction of the rapid cooling process of the bar is obtained. Based on the set of linear equations for temperature prediction, the predicted temperature distribution of the rapid cooling process of the bar is obtained.

4. The method for predicting the temperature during the rapid cooling process of a bar according to claim 3, characterized in that, The heat transfer coefficient of the air-cooling stage is calculated according to the following formula: ； Among them, the The heat transfer coefficient for the air-cooling stage is... The Stefan-Boltzmann constant is stated as follows. The blackness coefficient is the value of the blackness coefficient. For instantaneous temperature, the This represents the extreme temperature value. The first heat transfer coefficient of the water-cooling stage is calculated according to the following formula: ； Among them, the The first heat transfer coefficient in the water-cooling stage, the The boiling point of cooling water, the The surface temperature of the bar is... The cooling water temperature, the The diameter of the cooling water pipe is... Where is the diameter of the bar, the The thermal conductivity of water is... It is the Reynolds number; The second heat transfer coefficient of the water-cooling stage is calculated according to the following formula: ； Among them, the The second heat transfer coefficient in the water-cooling stage, the For the Nuschelt standard number, the stated The thermal conductivity of air is... The diameter is the bar diameter.

5. The method for predicting the temperature during the rapid cooling process of a bar according to claim 3, characterized in that, Based on the heat transfer coefficients of the air-cooling stage and the water-cooling stage, the coordinate information, and the preset temperature prediction model, a set of linear equations for temperature prediction of the rapid cooling process of the bar is obtained, including: Based on the heat transfer coefficient of the air-cooling stage, the heat transfer coefficient of the water-cooling stage, and the coordinate information, a differential equation for heat conduction is constructed according to the first law of thermodynamics. Based on the variational principle of heat conduction problems, the first-order partial derivatives of the heat conduction differential equation are calculated and set to zero to obtain the equivalent functional equation for each element grid. Based on the aforementioned heat conduction differential equation and finite element combination method, discrete elements are assembled to obtain the preset temperature prediction model; The temperature partial derivative with respect to time in the preset temperature prediction model is expressed as a two-point backward difference scheme; Substituting the two-point backward difference scheme into the temperature prediction model, we obtain the linear equations for predicting the temperature of the bar during rapid cooling.

6. The method for predicting the temperature during the rapid cooling process of a bar according to claim 3, characterized in that, Based on the aforementioned temperature prediction linear equations, the predicted temperature distribution of the rapid cooling process of the bar is obtained, including: The temperature prediction linear equations are solved by using a one-dimensional variable bandwidth storage method to obtain the temperature of each node in the first, second, and third regions on the cross-section of the bar. The calculation of the temperature of the node is iterated until the calculation of the temperature of the node in the current processing stage reaches a preset number of times. If the total time for the rapid cooling process has not been reached, the next round of acquiring at least one bar rolling information and the shape of the bar cross-section after rolling is continued; a finite element non-uniform mesh model corresponding to the shape of the bar cross-section after rolling is constructed; the finite element non-uniform mesh model is divided into regions, and the coordinate information of at least one node in at least one region is obtained; based on the coordinate information, the bar rolling information, and the preset temperature prediction model, the predicted temperature distribution of the rapid cooling process of the bar is obtained until the total time for the rapid cooling process is reached.

7. A device for predicting the temperature during the rapid cooling process of a bar stock, characterized in that, The device includes: The acquisition module is used to acquire at least one bar rolling information and the shape of the bar cross section after rolling. The processing module is used to construct a finite element non-uniform mesh model corresponding to the shape of the cross-section of the rolled bar; divide the finite element non-uniform mesh model into regions and obtain the coordinate information of at least one node in at least one region; obtain the heat transfer coefficient of each processing stage in the rapid cooling process based on the rolling information; the processing stages include: air cooling stage and water cooling stage; and obtain the predicted temperature distribution of the rapid cooling process of the bar based on the coordinate information, the heat transfer coefficient of the air cooling stage, the heat transfer coefficient of the water cooling stage, and a preset temperature prediction model. The process includes dividing the finite element non-uniform mesh model into regions and obtaining the coordinate information of at least one node within at least one region, including: According to the preset number of regions, the preset finite element non-uniform mesh generation model is divided into a first region, a second region, and a third region; the number of width element meshes and the number of thickness element meshes in the first region are obtained; the number of radial element meshes in the second region and the third region are obtained; the coordinate information of the nodes of the element meshes in the first region is obtained based on the number of width element meshes and the number of thickness element meshes; the coordinate information of the nodes of the element meshes in the second region is obtained based on the number of width element meshes and the number of radial element meshes; the coordinate information of the nodes of the element meshes in the third region is obtained based on the number of thickness element meshes and the number of radial element meshes; wherein, the number of width element meshes in the first region and the second region are the same; the number of thickness element meshes in the first region and the third region are the same; the number of radial element meshes in the second region and the third region are the same. The node coordinates of the first region are calculated according to the following formula: ； ； in, The coordinates of the x-axis nodes. The column number of the node. The width of the cell grid. The coordinates of the y-axis node are... The row number of the node. The thickness of the unit grid; The node coordinates of the second region are calculated according to the following formula: If the node is on the left boundary, then ; ； in, The grid width of the first region. The length on the boundary line between the second region and the third region. For cosine, For the unit grid angle of the second region, Pi It is the sine; The node coordinates of the third region are calculated according to the following formula: ； ； in, The angle of the unit grid in the third region.

8. A computing device, characterized in that, include: A processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method as described in any one of claims 1-6.

9. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the method as described in any one of claims 1-6.

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