Thermal field simulation method, equipment and media for high-pressure acid leaching reactor of laterite nickel ore

Abstract

This application proposes a method, equipment, and medium for simulating the thermal field of a high-pressure acid leaching reactor for laterite nickel ore. The method includes: constructing a hybrid discrete mesh based on the reactor's geometric model; setting thermal field simulation boundary conditions based on the hybrid discrete mesh; calculating the thermal resistance parameters of the scale layer on the reactor wall using an equivalent thermal resistance thin-shell element model; loading the scale layer thermal resistance parameters onto the boundary conditions; performing iterative calculations on the hybrid discrete mesh using a flow field solver and an energy equation solver; exchanging full-field data between the background and foreground meshes; periodically extracting full-field temperature distribution data; statistically analyzing the volume percentage of temperature intervals based on the full-field temperature distribution data; calculating the global thermal information entropy evolution stability index; and outputting the thermal field simulation results when the stability index meets the convergence criteria. This method replaces the solid scale layer model with an equivalent thermal resistance thin-shell element model, avoiding a surge in the number of meshes and improving the accuracy and reliability of the thermal field simulation results.

Description

Technical Field

[0001] This application relates to the field of thermodynamic simulation technology, and in particular to a method, equipment and medium for simulating the thermal field of a high-pressure acid leaching reactor for laterite nickel ore. Background Technology

[0002] Currently, high-pressure acid leaching of laterite nickel ore, as a core process for extracting nickel and cobalt in hydrometallurgy, is widely used in the preparation of raw materials for new energy batteries. With the development of chemical process intensification technologies, related simulation technologies, through the collaborative operation of computational fluid dynamics, multiphase flow stirring, and heat transfer models, have constructed a comprehensive system covering geometric discretization, boundary setting, and equation solving. Specifically, this system includes key aspects such as static and dynamic domain mesh generation, multiphysics coupling iteration, and thermal field distribution prediction, aiming to optimize the thermal efficiency of the reactor.

[0003] However, the thermal field simulation schemes in related technologies directly use the assumption of ideal smooth walls for calculation, without considering the hindering effect of mineral slurry deposits and scale layers on the heat transfer coefficient. This may lead to a simulated temperature field that is significantly higher than the actual operating conditions, resulting in misjudgments of the thermal equilibrium state. If a solid geometric model is forced for extremely thin scale layers, the large aspect ratio of the mesh will cause an exponential increase in the number of meshes, severely dragging down computational efficiency and even causing solution divergence, thus restricting the accurate application of simulation technology in complex industrial scenarios. Summary of the Invention

[0004] This application aims to at least partially address one of the technical problems in the related art.

[0005] Therefore, the first objective of this application is to propose a thermal field simulation method for a high-pressure acid leaching reactor for laterite nickel ore.

[0006] The second objective of this application is to propose an electronic device.

[0007] The third objective of this application is to provide a computer-readable storage medium.

[0008] To achieve the above objectives, the first aspect of this application is to propose a thermal field simulation method for a high-pressure acid leaching reactor for laterite nickel ore, comprising the following steps:

[0009] Obtain the geometric model of the reactor, and construct a hybrid discrete mesh containing a static domain background mesh and a moving domain foreground mesh based on the geometric model; Based on the hybrid discrete mesh, the thermal field simulation boundary conditions are set, and the thermal resistance parameters of the scale layer on the reactor wall are calculated using the equivalent thermal resistance thin shell element model. The thermal resistance parameters of the scale layer are then loaded into the thermal field simulation boundary conditions. Initialize the flow field solver and the energy equation solver, and use the flow field solver and the energy equation solver to perform iterative calculations on the hybrid discrete grid. During the iterative calculation process, full-field data exchange is performed between the background grid and the foreground grid according to a preset period. During the iterative calculation process, the temperature distribution data of the entire field is periodically extracted. Based on the temperature distribution data, the volume ratio of the temperature interval is statistically analyzed, and the global thermal information entropy evolution stability index is calculated according to the volume ratio of the temperature interval. When the global thermal information entropy evolution stability index meets the preset convergence judgment condition, the thermal field simulation result is output.

[0010] To achieve the above objectives, a second aspect of this application also provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the thermal field simulation method for a high-pressure acid leaching reactor for laterite nickel ore as described in any one of the first aspects above.

[0011] To achieve the above objectives, the third aspect of this application also proposes a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the thermal field simulation method for the high-pressure acid leaching reactor of laterite nickel ore as described in any one of the first aspects.

[0012] The technical solution provided by the embodiments of this application brings at least the following beneficial effects: This application replaces the solid fouling layer model with an equivalent thermal resistance thin-shell element model, which restores the physical reality of wall heat transfer attenuation while avoiding a surge in the number of meshes. Furthermore, combined with the convergence criterion based on the global thermal information entropy evolution stability index, it effectively overcomes the local one-sidedness of traditional residual criteria, and improves the computational efficiency and accuracy of complex thermal field simulations.

[0013] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0014] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating a thermal field simulation method for a high-pressure acid leaching reactor for laterite nickel ore, as proposed in an embodiment of this application. Figure 2 This is a schematic diagram illustrating the principle of a thermal field simulation method for a high-pressure acid leaching reactor for laterite nickel ore proposed in this application embodiment; Figure 3This is a schematic diagram of the thermal field simulation system of a high-pressure acid leaching reactor for laterite nickel ore proposed in an embodiment of this application. Detailed Implementation

[0015] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0016] The following description, with reference to the accompanying drawings, describes a thermal field simulation method, equipment, and medium for a high-pressure acid leaching reactor for laterite nickel ore, as proposed in an embodiment of this application.

[0017] Example 1 Figure 1 This is a flowchart illustrating a thermal field simulation method for a high-pressure acid leaching reactor for laterite nickel ore, as proposed in this application embodiment. Figure 1 As shown, the method includes the following steps: Step S101: Obtain the geometric model of the reactor, and construct a hybrid discrete mesh containing a static background mesh and a moving foreground mesh based on the geometric model.

[0018] Specifically, the geometric model of the reactor is obtained and a hybrid discrete mesh is constructed based on it, aiming to solve the mesh adaptability problem in the coupled calculation of dynamic and static flow fields under complex stirring conditions. The core of this step lies in decoupling the computational domain inside the reactor topologically into a relatively static domain and a dynamic domain that moves with the stirring components, defined as a background mesh and a foreground mesh, respectively. Through this domain decomposition strategy, the background mesh is used to discretize the fluid space containing fixed structures such as the reactor wall and baffles, while the foreground mesh is specifically used to discretize the dynamic space including the stirring blades and their sweeping range. The two form a specific overlapping region in spatial location to establish a data transfer channel. Thus, this construction method avoids the mesh reconstruction and quality degradation caused by large deformations in traditional dynamic meshing techniques, allowing for independent partitioning of the dynamic and static meshes and the exchange of physical quantities in the overlapping region through interpolation algorithms.

[0019] As one possible implementation, the analytical geometric model can identify the reactor cylinder region, the baffle structure region, and the stirring blade region. Based on the cylinder and baffle, a grid topology of the static domain is generated and discretized into a background grid. At the same time, a bounding box is established based on the stirring blade region to determine the boundary of the motion domain. The space within this range is discretized into a foreground grid, and the foreground grid and the background grid maintain the same grid size in the overlapping area. If necessary, the boundary of the foreground grid is locally refined to match the density of the background grid, thereby completing the construction of the hybrid discrete grid.

[0020] Therefore, this step effectively decouples the high-frequency flow field changes in the complex topological space inside the reactor by constructing a hybrid discrete architecture that includes a static background mesh and a moving foreground mesh. It avoids the mesh distortion and negative volume risks caused by traditional dynamic mesh reconstruction and significantly improves the flux transfer accuracy and computational stability at complex fluid-structure interaction interfaces.

[0021] Step S102: Set the thermal field simulation boundary conditions based on the hybrid discrete mesh, calculate the thermal resistance parameters of the scale layer on the reactor wall using the equivalent thermal resistance thin shell element model, and load the scale layer thermal resistance parameters into the thermal field simulation boundary conditions.

[0022] Specifically, the process of setting boundary conditions for thermal field simulation based on hybrid discrete meshes is essentially about quantifying the hindering effect of the fouling layer on the inner wall of the reactor on the heat transfer process through an equivalent physical model, and mapping this effect to the boundary constraints of the simulation calculation. This step uses an equivalent thermal resistance thin-shell element model as a technical means to avoid solid geometric modeling and meshing of the extremely thin fouling layer, thereby mathematically constructing a virtual thermal resistance interface attached to the wall.

[0023] Specifically, this method obtains parameters characterizing the physical properties of the scale layer, calculates the thermal resistance per unit area based on the one-dimensional heat conduction principle, and then corrects the wall heat transfer coefficient to equivalently replace the existence of the solid scale layer.

[0024] As one possible implementation, data on the average thickness of the scale layer and the thermal conductivity of the scale material in a high-pressure acid leaching reactor for laterite nickel ore under actual operating conditions can be obtained. A virtual, non-physical thin-shell geometry adhering to the inner wall of the reactor can then be established. Based on the average scale layer thickness and thermal conductivity data, a one-dimensional heat conduction equation can be constructed, for example using the formula... The thermal resistance parameters of the scale layer were calculated, where, This represents the average thickness of the scale layer. The thermal conductivity of the scale material is given. Subsequently, the thermal resistance parameter of this scale layer is mapped onto the mesh nodes of the non-solid thin-shell geometry. The geometric modeling process of the solid scale layer model is equivalently replaced by modifying the wall heat transfer coefficient in the thermal field simulation boundary conditions. For example, the original wall composite heat transfer coefficient is adjusted. Revised to And load it into the solver.

[0025] Therefore, this step, by introducing an equivalent thermal resistance thin-shell element model, effectively solves the problem of increased mesh count and solution divergence caused by the extremely fine geometric features of the scale layer, significantly reducing computational resource consumption. Simultaneously, without increasing the complexity of the solid mesh, this method realistically reproduces the physical process of wall heat conduction efficiency decay under long-term operating conditions, ensuring the accuracy and engineering reliability of the temperature field distribution data in the thermal simulation results.

[0026] Step S103: Initialize the flow field solver and the energy equation solver, and use the flow field solver and the energy equation solver to perform iterative calculations on the hybrid discrete grid. During the iterative calculation process, the flow field solver and the energy equation solver are used to perform full-field data exchange between the background grid and the foreground grid according to a preset period.

[0027] Specifically, this step aims to reconstruct the unsteady thermal field evolution process within the reactor on a hybrid discrete grid containing a static background grid and a moving foreground grid by coupling the solution of fluid dynamics and thermodynamic equations. Its core lies in establishing a collaborative iterative mechanism between the flow field solver and the energy equation solver. The flow field solver analyzes momentum conservation and continuity constraints to obtain the fluid velocity vector field, which is then input as a convection transport term into the energy equation solver to update the overall temperature scalar distribution. For the non-conformal interface between the background and foreground grids caused by relative motion, this step employs a data mapping strategy based on spatial topology. A full-field data exchange operation is triggered at a preset data exchange cycle, interpolating and transferring physical quantities between different grid domains. This achieves flux conservation and information continuity across grid interfaces while ensuring geometric decoupling of the computational domain.

[0028] As one possible implementation, a separate solution strategy can be initiated. At the current time step, the flow field solver is activated first to obtain the velocity vector field and pressure field. Then, the flow field state is frozen and the energy equation solver is activated to update the temperature field. When the number of iterations reaches a preset period, the overlapping region nodes are traversed using the inverse distance weighted interpolation algorithm. The weight coefficients are calculated based on the Euclidean distance between the donor grid cell and the target node and then weighted summation is performed to complete the bidirectional update of fluid physical quantities between the background grid and the foreground grid.

[0029] Therefore, through the above-mentioned technical means, this step effectively solves the problem of multi-physics coupling solution under complex dynamic boundary conditions between rotating components and stationary walls. It avoids the risk of mesh distortion caused by traditional dynamic mesh reconstruction, and ensures the accurate transmission of heat flow information at the dynamic-static interface through periodic full-field data exchange, which significantly improves the numerical stability and convergence efficiency of unsteady thermal field simulation calculation.

[0030] Step S104: During the iterative calculation, the temperature distribution data of the entire field is periodically extracted. Based on the temperature distribution data, the volume ratio of the temperature interval is statistically analyzed, and the global thermal information entropy evolution stability index is calculated according to the volume ratio of the temperature interval. When the global thermal information entropy evolution stability index meets the preset convergence judgment condition, the thermal field simulation result is output.

[0031] Specifically, during the iterative calculation process, statistical characteristic quantities characterizing the macroscopic evolution of the thermal field are constructed by periodically collecting temperature distribution data across the entire field. The convergence of the simulation calculation is then determined based on the dynamic trend of these characteristic quantities. The core of this step lies in mapping the temperature scalar field of discrete grid cells to a continuous probability distribution model. By statistically analyzing the proportion of fluid volume in different temperature ranges, the degree of order or disorder in the distribution of thermal energy inside the reactor is quantified. Finally, a global thermal information entropy evolution stability index is constructed based on information entropy theory.

[0032] Among them, the global thermal information entropy evolution stability index serves as a criterion for measuring whether the system has reached a macroscopic thermal equilibrium state. It can effectively overcome the locality and one-sidedness of traditional criteria based on single-point monitoring or single physical equation residuals, and accurately identify steady-state moments from the perspective of global energy distribution evolution.

[0033] As one possible implementation, all effective computational cells in the hybrid discrete mesh can be traversed to obtain the average temperature and volume of each cell. The temperature range is divided into several continuous and non-overlapping temperature statistical intervals. The cumulative volume of effective computational cells falling into each interval is counted, and their proportion to the total fluid domain volume is calculated. The entropy value of the entire field thermal information is calculated using the Shannon entropy definition formula. Combined with the entropy value records of historical iteration steps and the simulation time interval, the result is obtained using the formula... The global thermal information entropy evolution stability index is calculated. When the index is less than the preset convergence judgment threshold for multiple consecutive statistical periods and the physical residual meets the requirements, the iteration is judged to be converged and the result is output.

[0034] Therefore, by introducing a convergence criterion based on the global thermal information entropy evolution stability index, this step can more accurately capture the macroscopic thermal equilibrium state in the complex multiphase flow stirring process, avoid misjudgment or premature termination of calculation due to local numerical fluctuations, and thus effectively shorten the iteration convergence cycle and improve the efficiency of predicting the evolution of complex thermal fields in the reactor while ensuring the physical credibility of the thermal field simulation results.

[0035] Example 2 Based on the above embodiments, this embodiment provides a detailed description of the specific implementation of step S101, "obtaining the geometric model of the reactor and constructing a hybrid discrete mesh containing a static domain background mesh and a moving domain foreground mesh based on the geometric model".

[0036] In this embodiment, a hybrid discrete mesh comprising a static domain background mesh and a moving domain foreground mesh is constructed based on a geometric model. This includes: analyzing the topology of the geometric model to identify the reactor vessel region, baffle structure region, and agitator blade region; generating a static domain mesh topology based on the reactor vessel region and baffle structure region, and discretizing the static domain mesh topology into a static domain background mesh; generating a moving domain mesh topology based on the agitator blade region; determining the boundary range of the moving domain by establishing bounding boxes around the agitator blade region; discretizing the space within the boundary range of the moving domain into the moving domain foreground mesh, wherein the moving domain foreground mesh overlaps with the static domain background mesh in spatial location; checking the mesh size consistency within the overlapping region; and performing local densification processing on the boundary mesh of the moving domain foreground mesh located in the overlapping region to match the mesh density of the static domain background mesh at the corresponding location, thus completing the construction of the hybrid discrete mesh.

[0037] Specifically, this embodiment first reads the geometric feature data from the imported computer-aided design model file as the input source, and uses curvature analysis algorithm or surface adjacency relationship recognition algorithm to perform processing actions. The part with curvature change rate less than the preset plane threshold and position coordinates located on the outer envelope surface is marked as the reactor body area. The sheet-like structure located inside the body and with regular array arrangement characteristics is identified as the baffle structure area. The set of curved surfaces located near the central axis and with spiral twisting characteristics is identified as the stirring blade area. The divided functional area identifiers are output.

[0038] Next, using the reactor vessel shell region and baffle structure region as input, an unstructured tetrahedral mesh generation strategy is employed to generate the mesh topology of the static domain and discretize it into a static domain background mesh. Boundary layer meshes are simultaneously set at the baffle edges and reactor wall. Using the agitator blade region as input, the maximum sweep radius of the agitator blade during rotation is calculated. A cylindrical virtual bounding box with a radius of 1.1 to 1.2 times this maximum sweep radius is constructed. The axial length of this bounding box covers both ends of the blade and extends outwards to 10% to 20% of the blade height. The space within this boundary is discretized into a moving domain foreground mesh. The physical coordinates of the outermost boundary nodes of the moving domain foreground mesh are ensured to be located within the fluid computational domain of the static domain background mesh, thus outputting a dynamic and static dual-domain mesh with overlapping areas.

[0039] Then, the static domain background mesh cells and the moving domain foreground mesh cells within the overlapping zone are traversed, and the volume ratio between the two is calculated as input. If the ratio exceeds the allowable range of 0.8 to 1.2, it is marked as a size mismatch zone. The boundary mesh of the moving domain foreground mesh located in the overlapping area is recursively segmented using an octree subdivision algorithm until the average size difference between the mesh size and the static domain background mesh is less than 10%. Finally, a hybrid discrete mesh with matched mesh density is output.

[0040] Therefore, this implementation method effectively decouples the mesh generation process of stationary and rotating components by accurately identifying geometric topological features and establishing a hybrid discrete mesh architecture with overlapping areas, thus avoiding the risk of mesh distortion caused by traditional dynamic mesh reconstruction. At the same time, by performing strict size consistency checks and local densification processing on the overlapping areas, the interpolation error caused by abrupt changes in mesh scale is eliminated, ensuring the conservation of flow field and temperature field data exchange and computational accuracy at the dynamic-static interface.

[0041] Example 3 Based on the above embodiments, this embodiment provides a detailed description of the specific implementation of step S102, which involves setting thermal field simulation boundary conditions based on the hybrid discrete grid, calculating the thermal resistance parameters of the scale layer on the reactor wall using the equivalent thermal resistance thin-shell element model, and loading the scale layer thermal resistance parameters into the thermal field simulation boundary conditions.

[0042] In this embodiment, the thermal resistance parameters of the scale layer on the reactor wall are calculated using an equivalent thermal resistance thin-shell element model. This includes: obtaining the average thickness data of the scale layer and the thermal conductivity data of the scale material in the high-pressure acid leaching reactor of laterite nickel ore under actual operating conditions, and establishing a virtual, non-physical thin-shell geometric surface attached to the inner wall of the reactor; constructing a one-dimensional heat conduction equation based on the average thickness data of the scale layer and the thermal conductivity data of the scale material, calculating the hindering effect of the scale layer on heat flow transfer through the one-dimensional heat conduction equation, and obtaining the thermal resistance parameters of the scale layer; mapping the thermal resistance parameters of the scale layer onto the mesh nodes of the non-physical thin-shell geometric surface, and modifying the wall heat transfer coefficient in the thermal field simulation boundary conditions to equivalently replace the geometric modeling process of the physical scale layer model.

[0043] Specifically, the data input sources for this embodiment are the average thickness of the scale layer and the thermal conductivity data of the scale material in the high-pressure acid leaching reactor of laterite nickel ore under actual operating conditions. The average thickness of the scale layer can be obtained by referring to historical maintenance records or by collecting the scale deposition thickness after long-term operation using a multi-point ultrasonic thickness gauge deployed on the inner wall of the reactor. The average value is obtained after removing the maximum and minimum values ​​from the multiple sets of thickness values. The thermal conductivity data of the scale material is matched in the material property database based on the component analysis of the laterite nickel ore scale sample in the laboratory or determined by steady-state heat flow method.

[0044] The processing involves creating a virtual, thin-shell geometry attached to the inner wall of the reactor. Specifically, the interface between the fluid domain and the solid wall of the reactor is extracted using mesh generation software. Instead of directly stretching a solid mesh onto this interface, a virtual, thickness-free attribute marker is assigned to it, defining it as a set of thin-shell elements used for thermal resistance calculations. The output is a wall interface model with the virtual attribute marker, serving as the geometric basis for subsequent thermal resistance calculations.

[0045] Next, using the average thickness data of the scale layer obtained above... Thermal conductivity data of scale substances The input source is used as the processing method to construct a thermal resistance calculation model based on the one-dimensional heat conduction principle. Specifically, it simplifies the calculation formula of one-dimensional thermal resistance according to Fourier's law of heat conduction. ,in Thermal resistance per unit area, in units of , The unit is , The unit is This formula is used to calculate the hindering effect of scale on heat transfer. The output is a quantified thermal resistance parameter of the scale. .

[0046] Finally, the calculated thermal resistance parameters of the scale layer are used. Combined heat transfer coefficient with the original wall surface The input source is a thin-shell geometry, and the processing involves mapping thermal resistance parameters to mesh nodes and correcting boundary conditions. Specifically, this involves traversing each computational node on the thin-shell geometry and... The numerical values ​​are stored in custom user variables of the nodes. If the fouling thickness varies in different regions, the corresponding thermal resistance value is assigned based on spatial coordinate interpolation. Subsequently, in the boundary condition settings of the solver, the original wall composite heat transfer coefficient is used. Revised to ,in, This represents the corrected overall wall heat transfer coefficient. The output is a thermal field simulation boundary condition with the equivalent fouling thermal resistance effect applied. This condition uses the corrected heat transfer coefficient to participate in the subsequent energy equation iteration, thereby simulating the thermal insulation effect of the fouling layer without increasing the number of solid meshes.

[0047] Therefore, this implementation replaces the solid fouling layer model with an equivalent thermal resistance thin-shell element model, avoiding the problem of exponential increase in the number of meshes and deterioration of mesh quality caused by the geometric characteristics of the extremely thin fouling layer. It significantly reduces the computational memory overhead and prevents solution divergence. At the same time, while ensuring simulation efficiency, it realistically reproduces the physical process of the decay of wall heat conduction efficiency under long-term operating conditions, and improves the accuracy of thermal field simulation results.

[0048] Example 4 Based on the above embodiments, this embodiment provides a detailed description of the specific implementation of step S103 above, which involves "initializing the flow field solver and the energy equation solver, performing iterative calculations on the hybrid discrete grid using the flow field solver and the energy equation solver, and exchanging full-field data between the background grid and the foreground grid according to a preset period during the iterative calculation process".

[0049] In this embodiment, iterative calculations are performed on the hybrid discrete grid using a flow field solver and an energy equation solver. This includes: running a separate solution strategy, prioritizing the activation of the flow field solver at the current time step, and using the flow field solver to solve the momentum equation and continuity equation to obtain the fluid velocity vector field and pressure field distribution in the hybrid discrete grid; temporarily freezing the calculation state of the flow field solver, activating the energy equation solver, using the fluid velocity vector field as the convection term input, and solving the energy conservation equation to update the temperature scalar field in the hybrid discrete grid; checking whether the current iteration step has reached the preset data exchange cycle; if not, proceeding to the next time step; if the data exchange cycle has been reached, triggering a full-field data exchange operation, and using an inverse distance weighted interpolation algorithm to update the fluid physical quantities in the overlapping region of the static background grid and the moving foreground grid.

[0050] Specifically, this embodiment first initiates a separate solution strategy, prioritizing the activation of the flow field solver at the current time step. The input consists of the current pressure field prediction and boundary conditions. The discrete solutions of the momentum equation and continuity equation are processed, and the intermediate velocity field and pressure field are corrected through a pressure-velocity coupling algorithm. The output is the fluid velocity vector field and pressure field distribution in the hybrid discrete grid.

[0051] Then, after obtaining the fluid velocity vector field, the calculation state of the flow field solver is temporarily frozen, the velocity field data is locked and no longer updated, and the energy equation solver is activated instead. The previously frozen fluid velocity vector field is input as the convection term into the energy conservation equation, and the equation is solved to update the temperature scalar field in the hybrid discrete grid, and new temperature distribution data is output.

[0052] Next, check if the current iteration step has reached the preset data exchange cycle. If not, proceed directly to the calculation loop of the next time step; if the preset data exchange cycle has been reached, trigger the full data exchange operation.

[0053] Specifically, the fluid physical quantities in the overlapping region of the static domain background grid and the moving domain foreground grid are updated using an inverse distance weighted interpolation algorithm. This process first traverses each target grid node in the overlapping region as input, searching for the nearest donor grid cells in either the static domain background grid or the moving domain foreground grid, which serve as the data source. Then, the Euclidean distance between the geometric centers of the target grid node and the several donor grid cells is calculated. Based on the Euclidean distance, the interpolation weight coefficient for each donor grid cell is calculated, where the interpolation weight coefficient is inversely proportional to the Euclidean distance. Specifically, this is obtained by constructing a weight function with a power of 2 and normalizing it. Finally, the physical quantity values ​​on the several donor grid cells are weighted and summed with their corresponding interpolation weight coefficients to obtain the interpolated physical quantity of the target grid node. This interpolated physical quantity is then assigned to the target grid node, thus completing the continuous transfer of physical quantities such as velocity, pressure, and temperature at the static-dynamic interface, ensuring the accuracy of the initial field data calculated in the next time step.

[0054] Therefore, this implementation method effectively reduces the numerical stiffness of multiphysics coupling calculations and improves iterative stability by using a separate solution strategy and a frozen coupling mechanism. At the same time, it combines the inverse distance weighted interpolation algorithm to exchange data across the entire field, eliminating the flux non-conservation problem at the interface between dynamic and static grids, and significantly improving the calculation accuracy and convergence efficiency of thermal field simulation under complex stirred flow fields.

[0055] Example 5 Based on the above embodiments, this embodiment provides a detailed description of the specific implementation of step S104: "Periodically extract the full-field temperature distribution data during the iterative calculation process, statistically analyze the volume ratio of the temperature interval based on the full-field temperature distribution data, calculate the global thermal information entropy evolution stability index based on the volume ratio of the temperature interval, and output the thermal field simulation result when the global thermal information entropy evolution stability index meets the preset convergence judgment condition."

[0056] In this embodiment, the method of calculating the volume ratio of temperature intervals based on the full-field temperature distribution data includes: traversing all effective computational cells in the hybrid discrete grid, obtaining the average temperature value and volume value of each effective computational cell to form a full-field temperature dataset; determining the highest and lowest temperature values ​​in the full-field temperature dataset, and dividing the temperature range from the lowest to the highest temperature value into... A series of continuous and non-overlapping temperature statistical intervals are defined. The cumulative volume of all effective computational units falling within each temperature statistical interval is calculated. The cumulative volume is divided by the total fluid domain volume of the hybrid discrete grid to obtain the volume ratio of each temperature statistical interval. The volume ratio of the temperature interval is used as a probability distribution feature characterizing the thermal uniformity of the entire field.

[0057] Specifically, this embodiment first traverses all valid computational cells in the hybrid discrete mesh. The input source is the average temperature value and cell volume value of each valid computational cell stored in the solver's memory. The processing action is to read the temperature scalar stored at the cell center through the post-processing interface. Unit volume obtained by geometric calculation and all units The data pairs are loaded into a dynamic array, and the output is a full-field temperature dataset that covers the entire fluid domain.

[0058] Next, the highest and lowest temperature values ​​in the entire temperature dataset are determined. The input source is the temperature column data in the aforementioned entire temperature dataset. The processing action is to traverse, compare, or sort the temperature data to find the maximum temperature. and minimum temperature And according to the preset number of intervals Calculate the interval step size Thus from arrive The temperature range is divided into The output consists of a series of continuous and non-overlapping temperature statistical intervals, and the results are a series of well-defined temperature statistical interval boundaries.

[0059] Subsequently, the cumulative volume of all valid computational units falling within each temperature statistical interval is calculated. The input sources are the temperature and volume values ​​of each unit in the full-field temperature dataset, as well as the predefined temperature statistical intervals. The processing action is to initialize the intervals to a length of... Accumulate the volume data into an array, traverse the entire temperature dataset, and determine the temperature of each unit. The interval number to which it belongs The corresponding unit volume Accumulated to the first element of the accumulation array In the term, the total fluid domain volume is obtained by summing the volumes of all elements. The output result is the cumulative volume value corresponding to each temperature statistical interval.

[0060] Finally, the cumulative volume is divided by the total fluid domain volume of the hybrid discrete grid, with the input source being the cumulative volume and the total fluid domain volume for each temperature statistical interval. The processing action is to divide each item in the volume accumulation array by . After normalization, the output is the volume percentage of each temperature statistical interval. This proportion serves as a probability distribution characteristic representing the overall thermal uniformity of the field.

[0061] Based on this, when calculating the global thermal entropy evolution stability index, the Shannon entropy definition formula is used to calculate the global thermal entropy value at the current simulation moment. ,in, This refers to the volume percentage of the temperature range obtained above, combined with the entropy values ​​recorded in previous iterations. Calculate the global thermal information entropy evolution stability index ,in, The simulation time interval between two statistical moments. This represents the current cumulative number of iterations. This index is used to quantify the stability of thermal field evolution in order to correct for the damping coefficient.

[0062] Therefore, this implementation method discretizes the continuous temperature field into probability distribution features and uses information entropy theory to accurately identify the thermal equilibrium state from the perspective of global energy distribution ordering. This overcomes the local bias of traditional single-point monitoring or single residual criteria and can effectively shorten the iteration convergence cycle while ensuring the physical credibility of the simulation results.

[0063] Example 6 This embodiment will describe in detail the complete implementation of the thermal field simulation method of the high-pressure acid leaching reactor for laterite nickel ore of this application, focusing on the specific implementation path from geometric model processing, hybrid mesh construction, equivalent thermal resistance loading, separate iterative solution to convergence determination based on global thermal information entropy.

[0064] like Figure 2 As shown, in this embodiment, the geometric model of the high-pressure acid leaching reactor for laterite nickel ore is first obtained, and a hybrid discrete mesh containing static and dynamic domains is constructed accordingly. Specifically, the topological structure of the geometric model is analyzed to identify the reactor cylinder region, baffle structure region, and agitator blade region. Geometric feature data from the imported computer-aided design model file is read, and the model surface is divided into different functional regions using curvature analysis algorithms or surface adjacency recognition algorithms. Regions with a curvature change rate less than a preset plane threshold (e.g., 0.01) and whose position coordinates are on the outer envelope are marked as the reactor cylinder region. Sheet-like structures located inside the cylinder with a regular array arrangement are identified as baffle structure regions. A set of curved surfaces located near the central axis and exhibiting helical twisting characteristics is identified as the agitator blade region. The static domain's mesh topology is generated based on the reactor vessel shell and baffle structure regions. An unstructured tetrahedral meshing strategy is used to spatially discretize the static domain, setting the global base size to L (e.g., 1 / 50 of the reactor diameter D). Boundary layer meshes are set at the baffle edges and reactor walls, with 5 to 10 boundary layer layers and a growth rate controlled between 1.1 and 1.2. This discretizes the static domain's mesh topology into a background mesh. The dynamic domain's mesh topology is then generated based on the impeller region. The boundary of the dynamic domain is determined by establishing bounding boxes around the impeller region. The maximum sweep radius of the impeller during rotation is calculated. (That is, the vertical distance from the outermost end of the stirring blade to the center of rotation), construct a radius of 1.1 to 1.2 times. A cylindrical virtual bounding box is constructed, with its axial length covering both ends of the blade and extending outwards to 10% to 20% of the blade height. The space within the boundary of the motion domain is discretized into a foreground mesh, ensuring that the foreground mesh overlaps with the background mesh in spatial location. A refinement strategy requiring rotational periodic boundary conditions or sliding mesh techniques is employed when generating the foreground mesh, ensuring that the physical coordinates of the outermost boundary nodes of the foreground mesh are located within the fluid computational domain of the background mesh, forming an overlap band with a width of at least 3 to 5 mesh cell sizes. The mesh size consistency within the overlap region is then checked by traversing both background and foreground mesh cells within the overlap band and calculating their volume ratio. If the ratio exceeds the allowable range of 0.8 to 1.2, the region is marked as a size mismatch area. Local refinement processing is applied to the boundary mesh of the foreground mesh located in the overlap region. An octree subdivision algorithm is used to recursively subdivide the foreground mesh marked as a size mismatch area until the average size difference between the foreground mesh and the background mesh is less than 10%, thus matching the mesh density of the background mesh at the corresponding location and completing the construction of the hybrid discrete mesh.

[0065] Furthermore, the thermal field simulation boundary conditions were set based on a hybrid discrete mesh, and the thermal resistance parameters of the scale layer on the reactor wall were calculated using an equivalent thermal resistance thin-shell element model. Specifically, the average thickness data of the scale layer and the thermal conductivity data of the scale material were obtained from the high-pressure acid leaching reactor for laterite nickel ore under actual operating conditions. The scale deposition thickness after long-term operation was collected by reviewing historical maintenance records or by using a multi-point ultrasonic thickness gauge deployed on the inner wall of the reactor. The average thickness data of the scale layer was obtained by removing the maximum and minimum values ​​from the collected thickness values. At the same time, based on the compositional analysis of the laterite nickel ore scale samples in the laboratory (e.g., the main components are calcium sulfate or hematite), the corresponding thermal conductivity was matched in the material property database or the thermal conductivity at a specific temperature was determined by steady-state heat flow method as the thermal conductivity data of the scale material. A virtual, thin-shell geometry, without any solid structure, is established attached to the inner wall of the reactor. The interface between the fluid domain and the solid wall is extracted using mesh generation software. Instead of directly stretching a solid mesh onto this interface, a virtual, thickness-free attribute is assigned to it, defining it as a set of thin-shell elements used for thermal resistance calculation. A one-dimensional heat conduction equation is constructed based on the average thickness data of the scale layer and the thermal conductivity data of the scale material. For example, assuming the heat flow direction is perpendicular to the wall and the temperature distribution inside the scale layer is linear, the one-dimensional thermal resistance calculation formula is simplified using Fourier's law of heat conduction. ,in Thermal resistance per unit area (unit: ), The average thickness of the scale layer obtained above (unit: m). The thermal conductivity of the aforementioned scale material (unit: The hindering effect of scale on heat transfer is calculated using the one-dimensional heat conduction equation, yielding the scale thermal resistance parameters. These parameters are then mapped onto mesh nodes of a thin-shell geometry without a solid body. By traversing each computational node on the thin-shell geometry, the calculated values ​​are... Numerical values ​​are stored in custom user variables at the nodes. If the scale thickness varies in different regions, corresponding thermal resistance values ​​are assigned based on spatial coordinate interpolation. The geometric modeling process of the solid scale layer model is equivalently replaced by modifying the wall heat transfer coefficient in the thermal field simulation boundary conditions. In the solver's boundary condition settings, the original wall composite heat transfer coefficient is changed. Revised to The modified heat transfer coefficient is used in subsequent energy equation iterations to simulate the insulation effect of the fouling layer without increasing the number of solid meshes.

[0066] Furthermore, the flow field solver and energy equation solver are initialized, and iterative calculations are performed on the hybrid discrete grid. Specifically, a separate solver strategy is initiated. During the initialization phase, a pressure-based solver is configured, and either the SIMPLE or PISO pressure-velocity coupling algorithm is selected. The first-order or second-order upwind scheme of the momentum equation is set as the discretization scheme. At the current time step, the flow field solver is activated first, while the energy equation remains closed or suspended. The momentum and continuity equations are solved using the flow field solver. Based on the current pressure field prediction, the momentum conservation equation in the Navier-Stokes equations is solved to obtain the intermediate velocity field. The pressure correction equation is constructed using the continuity equation, and the pressure and velocity fields are updated. This internal iterative process is repeated until the flow field residual decreases below 1e-4, obtaining the fluid velocity vector field and pressure field distribution in the hybrid discrete grid. After obtaining the fluid velocity vector field, the calculation state of the flow field solver is temporarily frozen, locking the data for the fluid velocity vector field and pressure field so that they remain unchanged in subsequent energy calculation steps, and the momentum equation is no longer updated. Activate the energy equation solver, input the fluid velocity vector field as the convection term, substitute the frozen velocity vector into the convection term of the energy conservation equation to reflect the heat transport effect of fluid flow, solve the energy conservation equation to update the temperature scalar field in the hybrid discrete grid, and perform several sub-iterations until the temperature residual meets the convergence criterion (e.g., 1e-6). Check whether the current iteration step has reached the preset data exchange cycle, read the current global time step counter, and perform a modulo operation with the preset exchange interval N (e.g., every 10 time steps or the number of steps dynamically determined according to the Courant number CFL condition). If the target is not reached, the calculation proceeds directly to the next time step, skipping the data interaction stage to save computational resources. If the preset data exchange cycle is reached, i.e., the modulus operation result is 0, the full-field data exchange operation is triggered, the solution process of each physical field is paused, the current calculation data is locked, and the fluid physical quantities in the overlapping area of ​​the background and foreground grids are updated using the inverse distance weight interpolation algorithm. The physical quantities such as velocity, pressure, and temperature calculated by the foreground grid are transferred to the overlapping nodes of the background grid, and vice versa, to ensure the continuity of physical quantities at the dynamic-static interface.

[0067] Furthermore, the specific process of full-field data exchange between the background and foreground grids using the inverse distance weighted interpolation algorithm is as follows: Traverse each target grid node in the overlapping region, and use an ADT (Alternating Digital Tree) or KD-tree spatial search structure to locate all nodes located in the overlapping area of ​​the background and foreground grids. Mark the nodes whose physical quantities are to be updated as receiving points. Search for several donor grid cells in the background or foreground grid, which serves as the data source, that are spatially closest to the target grid nodes. Construct a search sphere with each receiving point as its center and a preset search radius (e.g., twice the average grid size). Select vertices or cell center points in the source grid that fall within this search sphere, and choose the M closest points (e.g., ...). or ( ) is used as the donor point. The Euclidean distance between the geometric centers of the target mesh node and several donor mesh cells is calculated using the distance formula between two points. Calculate the receiving point to the th The straight-line distance between the donor points, where... Indicates the target mesh node and the first The Euclidean distance between the centers of the donor grid cells These represent the X, Y, and Z coordinate components of the target mesh node in the Cartesian coordinate system. They represent the first The X, Y, and Z coordinate components of each donor grid cell center in Cartesian coordinates are calculated. The interpolation weighting coefficients for each donor grid cell are calculated based on the Euclidean distance, and a weighting function is constructed. ,in Indicates the first Initial weights of each donor grid cell, It is the power exponent (usually taken as 2 to reflect the inverse square relationship). Use extremely small positive numbers (such as 1e-10) to prevent the denominator from being zero. Calculate the normalized weights. ,in, Represents the normalized interpolation weight coefficients. To sum the index variables (from 1 to ... ), The total number of donor mesh cells is represented by the interpolation weighting coefficient, which is inversely proportional to the Euclidean distance. The physical quantity values ​​on several donor mesh cells are weighted and summed with their corresponding interpolation weighting coefficients. For scalar or vector data such as temperature and velocity components, the formula is used... Perform calculations, where This represents the interpolated physical quantity value of the target mesh node. Indicates the first The physical quantity values ​​on the donor grid cells are used to obtain the interpolated physical quantity of the target grid node, and the interpolated physical quantity is assigned to the target grid node to complete a bidirectional data update, enabling fluid information to be transmitted across discontinuous grid interfaces.

[0068] Furthermore, after every N time steps of iterative computation, temperature distribution data of the entire field mesh is extracted, and the volume percentage of each temperature interval is statistically analyzed based on this data. Specifically, all valid computational cells in the hybrid discrete mesh are traversed, and the list of all mesh cells containing the fluid domain is accessed through the solver's post-processing interface. Solid domain and invalid dead zone meshes are excluded, and the average temperature and volume of each valid computational cell are obtained, forming the entire field temperature dataset. The temperature scalar stored at the cell center is then read. Unit volume obtained by geometric calculation to all units The data is loaded into a dynamic array or list in memory. The highest and lowest temperature values ​​in the entire temperature dataset are determined by sorting the temperature data columns or performing a single pass through and comparing them to find the maximum temperature within the entire fluid domain. and minimum temperature The temperature range from the lowest to the highest temperature value will be divided into K consecutive and non-overlapping temperature statistical intervals. The number of intervals K will be set according to preset resolution requirements or empirical values ​​(e.g., ), calculate interval step size ,in This indicates the width of each temperature statistical interval. and These represent the maximum and minimum temperatures obtained above, respectively, with K being the total number of intervals. A series of intervals are then constructed. Where i is the interval index (from 1 to K). Calculate the cumulative volume of all valid computational units falling within each temperature statistical interval, initialize a volume summation array of length K, iterate through the entire temperature dataset, and determine the temperature of each unit. To determine the corresponding unit volume for each interval i. The accumulated volume is added to the i-th element of the volume accumulation array. The accumulated volume is then divided by the total fluid domain volume of the hybrid discrete mesh. The total fluid domain volume is obtained by summing the volumes of all elements. Then, divide each item in the accumulation array by... The volume percentage of each temperature interval is obtained, and the volume percentage of each temperature interval is used as a probability distribution feature to characterize the thermal uniformity of the entire field. A normalized probability distribution histogram is generated for subsequent entropy calculation.

[0069] In this embodiment, the global thermal entropy evolution stability index at the current moment is calculated based on the volume ratio of the temperature range. Specifically, the Shannon entropy definition formula is used to calculate the global thermal entropy value at the current simulation moment, and the global thermal entropy evolution stability index is calculated by combining the entropy value records of historical iteration steps. The formula for calculating the global thermal entropy value is: ,in, Indicates the current number The entropy value of the overall thermal information calculated in the next iteration step is obtained by performing a logarithmic operation on the volume proportions of all intervals obtained in the previous step and then summing them with weights. It is a dimensionless scalar used to reflect the uniformity or disorder of the temperature distribution within the reactor at the current moment. The larger the value, the more uniform the temperature distribution. i is the index variable of the temperature statistical interval, ranging from 1 to K. This represents the volume percentage of the i-th temperature statistical interval. This parameter is derived from the normalized volume distribution data obtained in the previous step, and its value ranges from 0 to 1. The sum equals 1. K represents the total number of temperature statistical intervals. This parameter is set by the user during the initialization phase according to the required temperature resolution, and is usually between 50 and 200. This represents the natural logarithm operation. The formula for calculating the global thermal information entropy evolution stability index is: ,in, This represents the global thermal information entropy evolution stability index. It is a comprehensive convergence evaluation indicator that integrates the rate of entropy change and the inertial damping of the calculation process, used to determine whether the thermal field has reached a macroscopic steady state. The meaning is the same as above. This represents the entropy value of the entire field's thermal information at the last statistical moment. This value is stored in the historical record variable and is the entropy result obtained when this calculation step was performed last time. It is used to calculate the rate of change of entropy over time. This parameter represents the simulation time interval between two statistical moments. It is equal to the physical time step set in the solver multiplied by the number of iteration steps between the two statistical moments (i.e., the data exchange period N), and is expressed in seconds. It is used to convert the change in entropy into a rate of change. This indicates the current cumulative number of iterations. This parameter is counted from the start of the simulation and increases continuously as the calculation progresses. It is used to provide a larger correction factor in the early stages of the calculation. This represents the corrected damping coefficient due to inertia during iterative convergence. This parameter is a preset empirical constant, typically set based on the reactor size and fluid viscosity, for example, a value between 100 and 500, intended to facilitate convergence in the early stages of iteration. When the instability index is small, it is amplified to prevent misjudgment of convergence due to small local fluctuations in the early stage of calculation. As the number of iterations increases, the influence of this item gradually weakens.

[0070] In this embodiment, when the global thermal entropy evolution stability index meets the preset convergence judgment condition, the thermal field simulation result is output, including: real-time monitoring of the fluctuation curve of the global thermal entropy evolution stability index with the number of iteration steps, obtaining the flow field residual value and energy residual value in the iterative calculation; comparing the global thermal entropy evolution stability index with the preset convergence judgment threshold, if the global thermal entropy evolution stability index is less than the convergence judgment threshold for a consecutive preset number of statistical periods, and simultaneously meets the condition that the flow field residual value and energy residual value are lower than the preset physical residual limit, then it is confirmed that the thermal field simulation has reached the macroscopic thermal equilibrium state and the thermal field simulation result is output; if the global thermal entropy evolution stability index is greater than or equal to the convergence judgment threshold, or the flow field residual value or energy residual value is not lower than the physical residual limit, then it is determined that the current state is in a non-steady-state transition process, and the flow field solver and energy equation solver are controlled to continue to execute the calculation of the next time step until the convergence judgment condition is met.

[0071] That is, it determines whether the global thermal information entropy evolution stability index meets the preset convergence threshold to decide the termination of the iterative calculation. Specifically, it monitors the fluctuation curve of the global thermal information entropy evolution stability index with the number of iterations in real time, and calculates the global thermal information entropy evolution stability index at each statistical moment during the solver's operation. The values ​​are plotted as time series curves or recorded in log files. The first derivative or moving average of the index is calculated to observe its downward trend. The flow field residuals and energy residuals from the iterative calculations are obtained. The continuity residuals, momentum residuals (x, y, z components), and energy equation residuals for the current time step are directly read from the solver's residual monitoring module. These residuals are typically in the form of normalized root mean square error. The global thermal information entropy evolution stability index is compared with a preset convergence threshold to set a macroscopic thermal equilibrium convergence threshold. (e.g., 1e-5), determine the current... Is it strictly less than this threshold? If the global thermal information entropy evolution stability index is less than the convergence judgment threshold for M consecutive statistical periods, a counter is set to count the number of consecutive times the condition is met (e.g., 50 consecutive statistical steps). This can eliminate accidental numerical fluctuations and simultaneously satisfy the condition that the flow field residual value and energy residual value are lower than the preset physical residual limit. The numerical convergence standard of the physical field is set (e.g., the flow field residual is less than 1e-4 and the energy residual is less than 1e-6). Only when the macroscopic entropy index is stable and the microscopic numerical residual meets the standard at the same time, it is confirmed that the thermal field simulation has reached the macroscopic thermal equilibrium state. It is considered that the temperature field distribution in the reactor no longer changes significantly with time and has reached a steady state in engineering sense. The thermal field simulation results are then output. If the global thermal information entropy evolution stability index is greater than or equal to the convergence threshold, or the flow field residual value or energy residual value is not lower than the physical residual limit, then if any one of the above three conditions (entropy index meets the standard, continuity meets the standard, residual meets the standard) is not met, it is determined that the current state is in a non-steady-state transition process, indicating that the flow field or thermal field is still evolving violently. The flow field solver and energy equation solver are instructed to continue the calculation of the next time step, keeping the current solution settings and boundary conditions unchanged, and advancing the time step to... This continues until the convergence threshold is met.

[0072] Therefore, this embodiment effectively analyzes the high-frequency flow field variations caused by the high-speed rotation of the stirring impeller by constructing a hybrid discrete architecture that includes a static background mesh and a moving foreground mesh, avoiding the mesh distortion and negative volume risks caused by traditional dynamic mesh reconstruction techniques. Using an equivalent thermal resistance thin-shell element model to replace the solid fouling layer model significantly reduces the number of mesh discretizations and computational memory overhead caused by extremely thin geometric features, while restoring the physical reality of the decay of wall heat conduction efficiency under long-term operating conditions. An inverse distance weighted interpolation algorithm is used to perform full-field data exchange between the foreground and background meshes, ensuring the conservation and continuity of flow field and temperature field information transmission at the dynamic-static interface. In particular, by statistically analyzing the volume ratio of the entire temperature range and calculating the global thermal information entropy evolution stability index, a convergence criterion based on macroscopic thermodynamic statistical characteristics was established. This overcomes the local bias of single-point monitoring or single residual criterion, accurately identifies the thermal equilibrium state from the perspective of global energy distribution ordering, shortens the iteration convergence cycle while ensuring the physical credibility of the simulation results, and improves the prediction efficiency of the complex thermal field evolution in the high-pressure acid leaching reactor of laterite nickel ore.

[0073] To achieve the above embodiments, this application also proposes a thermal field simulation system for a high-pressure acid leaching reactor for laterite nickel ore. Figure 3 This is a schematic diagram of the thermal field simulation system for a high-pressure acid leaching reactor for laterite nickel ore, as proposed in an embodiment of this application. Figure 3 As shown, the system includes: Module 100 is used to obtain the geometric model of the reactor and construct a hybrid discrete mesh containing a static background mesh and a moving foreground mesh based on the geometric model.

[0074] The calculation module 200 is used to set the boundary conditions for thermal field simulation based on a hybrid discrete mesh, calculate the thermal resistance parameters of the scale layer on the reactor wall using an equivalent thermal resistance thin shell element model, and load the thermal resistance parameters of the scale layer into the boundary conditions for thermal field simulation.

[0075] The iteration module 300 is used to initialize the flow field solver and the energy equation solver, and to perform iterative calculations on the hybrid discrete grid using the flow field solver and the energy equation solver. During the iterative calculation process, the module performs full-field data exchange between the background grid and the foreground grid according to a preset period. The output module 400 is used to periodically extract the full-field temperature distribution data during the iterative calculation process, calculate the volume ratio of the temperature interval based on the full-field temperature distribution data, and calculate the global thermal information entropy evolution stability index based on the volume ratio of the temperature interval. When the global thermal information entropy evolution stability index meets the preset convergence judgment condition, the thermal field simulation results are output.

[0076] It should be noted that the explanation of the above-mentioned embodiment of the thermal field simulation method for the high-pressure acid leaching reactor of laterite nickel ore also applies to the system of this embodiment, and will not be repeated here.

[0077] In summary, the thermal field simulation system for the high-pressure acid leaching reactor of laterite nickel ore in this application replaces the solid scale layer model with an equivalent thermal resistance thin-shell element model, avoiding a surge in the number of meshes and improving the accuracy and reliability of the thermal field simulation results.

[0078] To implement the above embodiments, this application also proposes an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the thermal field simulation method for the high-pressure acid leaching reactor of laterite nickel ore as described in any of the first aspect embodiments above.

[0079] To implement the above embodiments, this application also proposes a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the thermal field simulation method for a high-pressure acid leaching reactor for laterite nickel ore as described in any one of the first aspects of the embodiments above.

[0080] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0081] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0082] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0083] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0084] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0085] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0086] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0087] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for simulating the thermal field of a high-pressure acid leaching reactor for laterite nickel ore, characterized in that, Includes the following steps: Obtain the geometric model of the reactor, and construct a hybrid discrete mesh containing a static domain background mesh and a moving domain foreground mesh based on the geometric model; Based on the hybrid discrete mesh, the thermal field simulation boundary conditions are set, and the thermal resistance parameters of the scale layer on the reactor wall are calculated using the equivalent thermal resistance thin shell element model. The thermal resistance parameters of the scale layer are then loaded into the thermal field simulation boundary conditions. Initialize the flow field solver and the energy equation solver, and use the flow field solver and the energy equation solver to perform iterative calculations on the hybrid discrete grid. During the iterative calculation process, full-field data exchange is performed between the background grid and the foreground grid according to a preset period. During the iterative calculation process, the temperature distribution data of the entire field is periodically extracted. Based on the temperature distribution data, the volume ratio of the temperature interval is statistically analyzed, and the global thermal information entropy evolution stability index is calculated according to the volume ratio of the temperature interval. When the global thermal information entropy evolution stability index meets the preset convergence judgment condition, the thermal field simulation result is output.

2. The method according to claim 1, characterized in that, The construction of a hybrid discrete mesh comprising a static domain background mesh and a moving domain foreground mesh based on the geometric model includes: The topology of the geometric model is analyzed to identify the reactor cylinder region, the baffle structure region, and the stirring blade region. The mesh topology of the static domain is generated based on the reactor cylinder region and the baffle structure region, and the mesh topology of the static domain is discretized into the background mesh of the static domain. The mesh topology of the motion domain is generated based on the stirring blade region. The boundary range of the motion domain is determined by establishing a bounding box around the stirring blade region. The space within the boundary range of the motion domain is discretized into the motion domain foreground mesh, wherein the motion domain foreground mesh overlaps with the static domain background mesh in spatial position. Check the mesh size consistency within the overlapping area, and perform local refinement processing on the boundary mesh of the moving domain foreground mesh located in the overlapping area to match the mesh density of the stationary domain background mesh at the corresponding position, thus completing the construction of the hybrid discrete mesh.

3. The method according to claim 1, characterized in that, The calculation of the thermal resistance parameters of the scale layer on the reactor wall using the equivalent thermal resistance thin-shell unit model includes: The average thickness of the scale layer and the thermal conductivity of the scale material were obtained under actual operating conditions in the high-pressure acid leaching reactor for laterite nickel ore. A virtual, non-physical thin-shell geometry was then established on the inner wall of the reactor. A one-dimensional heat conduction equation is constructed based on the average thickness data of the scale layer and the thermal conductivity data of the scale material. The hindering effect of the scale layer on heat flow transfer is calculated through the one-dimensional heat conduction equation to obtain the thermal resistance parameter of the scale layer. The thermal resistance parameters of the scale layer are mapped onto the mesh nodes of the non-solid thin-shell geometry. By modifying the wall heat transfer coefficient in the thermal field simulation boundary conditions, the geometric modeling process of the solid scale layer model is equivalently replaced.

4. The method according to claim 1, characterized in that, The iterative calculation performed on the hybrid discrete grid using the flow field solver and the energy equation solver includes: A separate solution strategy is employed, prioritizing the activation of the flow field solver at the current time step. The flow field solver is then used to solve the momentum equation and the continuity equation to obtain the fluid velocity vector field and pressure field distribution in the hybrid discrete grid. Temporarily freeze the computation state of the flow field solver, activate the energy equation solver, take the fluid velocity vector field as the convection term input, solve the energy conservation equation to update the temperature scalar field in the hybrid discrete grid; Check whether the current iteration step has reached the preset data exchange cycle. If the data exchange cycle has not been reached, proceed to the calculation of the next time step. If the data exchange cycle has been reached, trigger the full-field data exchange operation and use the inverse distance weight interpolation algorithm to update the fluid physical quantities in the overlapping area of ​​the static domain background grid and the moving domain foreground grid.

5. The method according to claim 4, characterized in that, The process of updating the fluid physical quantities in the overlapping region of the static domain background mesh and the moving domain foreground mesh using the inverse distance weighted interpolation algorithm includes: Traverse each target mesh node in the overlapping region and search for several donor mesh cells that are spatially closest to the target mesh node in the static background mesh or the moving foreground mesh, which serve as the data source. Calculate the Euclidean distance between the target mesh node and the geometric centers of the plurality of donor mesh cells, and calculate the interpolation weight coefficient of each donor mesh cell based on the Euclidean distance, wherein the interpolation weight coefficient is inversely proportional to the Euclidean distance; The physical quantity values ​​on the plurality of donor grid cells are weighted and summed with the corresponding interpolation weight coefficients to obtain the interpolated physical quantity of the target grid node, and the interpolated physical quantity is assigned to the target grid node.

6. The method according to claim 1, characterized in that, The method of calculating the volume percentage of temperature intervals based on the overall temperature distribution data includes: Traverse all valid computing cells in the hybrid discrete grid, obtain the cell average temperature value and cell volume value of each valid computing cell, and construct a full-field temperature dataset; Determine the highest and lowest temperature values ​​in the full-field temperature dataset, and divide the temperature range from the lowest to the highest temperature value into... A continuous, non-overlapping temperature statistical interval; The cumulative volume of all valid computational units falling within each of the temperature statistical intervals is calculated. The cumulative volume is divided by the total fluid domain volume of the hybrid discrete grid to obtain the volume ratio of each temperature statistical interval. The volume ratio of the temperature interval is then used as a probability distribution feature characterizing the overall thermal uniformity.

7. The method according to claim 6, characterized in that, The calculation of the global thermal information entropy evolution stability index includes: The global thermal information entropy value at the current simulation moment is calculated using the Shannon entropy definition formula, and the global thermal information entropy evolution stability index is calculated by combining the entropy value records of historical iteration steps. The formula for calculating the entropy value of the entire field thermal information is as follows: ； The formula for calculating the global thermal information entropy evolution stability index is as follows: ； in, Represents the global thermal information entropy evolution stability index. Indicates the current number The entropy value of the full-field thermal information is calculated in the next iteration step. This represents the entropy value of the total thermal information at the last statistical moment. Indicates the first The volume percentage of each temperature statistical interval This indicates the total number of temperature statistical intervals. This represents the simulation time interval between two statistical moments. This represents the current cumulative number of iterations. This represents the corrected damping coefficient due to inertia for iterative convergence.

8. The method according to claim 7, characterized in that, When the global thermal information entropy evolution stability index satisfies the preset convergence judgment condition, the thermal field simulation results are output, including: The fluctuation curve of the global thermal information entropy evolution stability index with the number of iteration steps is monitored in real time, and the flow field residual value and energy residual value in the iterative calculation are obtained. The global thermal information entropy evolution stability index is compared with a preset convergence judgment threshold. If the global thermal information entropy evolution stability index is less than the convergence judgment threshold for a consecutive preset number of statistical periods, and the flow field residual value and the energy residual value are both lower than the preset physical residual limit, then the thermal field simulation is confirmed to have reached the macroscopic thermal equilibrium state and the thermal field simulation result is output. If the global thermal information entropy evolution stability index is greater than or equal to the convergence determination threshold, or if the flow field residual value or the energy residual value is not lower than the physical residual limit, then it is determined that the current state is in a non-steady-state transition process, and the flow field solver and the energy equation solver are controlled to continue to execute the calculation of the next time step until the convergence determination condition is met.

9. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the thermal field simulation method for a high-pressure acid leaching reactor for laterite nickel ore as described in any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the thermal field simulation method for the high-pressure acid leaching reactor of laterite nickel ore as described in any one of claims 1-8.

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