Air flow field simulation analysis method and device of air smoke system and electronic equipment

Abstract

The application relates to a flow field simulation analysis method and device of a wind smoke system and electronic equipment, wherein the method comprises the following steps: generating a three-dimensional structure model of the wind smoke system by using structure parameters and operation condition data of a fault equipment of a power plant, performing grid division, and determining a structured grid area and an unstructured grid area; loading corresponding customized boundary conditions, simulating a coal powder motion trajectory by using a preset gas-solid two-phase flow model, combining a preset turbulence model to capture flow field fluctuation characteristics, and solving a multi-physical field; extracting at least one flow field key parameter of the wind smoke system to establish a fault diagnosis index system, combining an orthogonal test design to optimize structure parameters, and outputting a flow field reconstruction scheme of the wind smoke system. Therefore, the problems of insufficient pertinence, low precision and poor engineering practicability caused by neglecting individualized structure parameters of power plant equipment, insufficient consideration of gas-solid two-phase flow coupling effects and only relying on experience formula to deduce fault reasons in the related art are solved.

Description

Technical Field

[0001] This application relates to the field of flow field simulation and fault analysis technology for flue gas systems, and in particular to a flow field simulation analysis method, device and electronic equipment for flue gas systems. Background Technology

[0002] Currently, the boiler flue gas system and its supporting pulverized coal feeding and sealing air subsystems are core components ensuring the safe and efficient operation of the unit. Their internal processes involve complex gas-solid two-phase flows, pressure distribution, and the coupling effects of structural parameters, directly impacting the continuous stability of fuel supply, system energy consumption, and the service life of critical equipment. In actual engineering operations, phenomena such as particle deposition and pulverized conveying during pulverized coal feeding, abnormal pressure drop in sealing air ducts deviating from design limits, and material bridging and stagnation at the pulverized coal silo outlet frequently occur, leading to prominent problems such as fluctuations in furnace combustion conditions, increased fan load, and decreased unit load control capabilities.

[0003] In related technologies, based on a generalized simulation model, a single gas phase flow simulation method is used to derive the flow field characteristics of the flue gas system and its supporting subsystems, and empirical formulas are combined to diagnose the causes of faults, providing analytical support for the operation of the flue gas system.

[0004] However, in related technologies, the generalized simulation models ignore the individual structural parameters of power plant equipment, resulting in a disconnect between the simulation results and the actual engineering situation. Because the simulation uses a single gas phase flow, it does not fully consider the coupling effect of gas and solid two-phase flow, and cannot accurately reproduce the coal powder transportation and deposition patterns. It relies solely on empirical formulas to deduce the causes of faults, resulting in low diagnostic accuracy and insufficient targeting of optimization solutions. At the same time, the simulation results are difficult to directly guide on-site modifications, leading to long fault treatment cycles and high costs, which urgently need to be improved. Summary of the Invention

[0005] This application provides a flow field simulation analysis method, device, and electronic equipment for a flue gas system to solve the problems in related technologies, such as neglecting the individual structural parameters of power plant equipment, not fully considering the coupling effect of gas-solid two-phase flow, relying only on empirical formulas to deduce the cause of faults, resulting in insufficient targeting of optimization schemes, low accuracy of fault diagnosis, poor engineering practicality, and difficulty in directly guiding on-site modifications with simulation results, leading to long fault treatment cycles and high costs.

[0006] The first aspect of this application provides a flow field simulation analysis method for a flue gas system, comprising the following steps: generating a three-dimensional structural model of the flue gas system using the structural parameters and operating condition data of faulty equipment in a power plant, performing mesh generation, and determining structured and unstructured mesh regions; applying corresponding customized boundary conditions to the structured and unstructured mesh regions, simulating the coal powder movement trajectory using a preset gas-solid two-phase flow model, and capturing the flow field pulsation characteristics using a preset turbulence model to solve for the multiphysics field; extracting at least one key flow field parameter of the flue gas system based on the multiphysics field, establishing a fault diagnosis index system based on the at least one key flow field parameter, optimizing the structural parameters using orthogonal experimental design, and outputting a flow field modification scheme for the flue gas system.

[0007] Through the aforementioned technical means, the embodiments of this application can construct a three-dimensional model of the flue gas system that recreates the actual equipment structure and operating conditions of a power plant. By combining differentiated mesh generation, customized boundary conditions, and coupled simulation of gas-solid two-phase flow and turbulence, multi-physics fields can be accurately solved. At the same time, a fault diagnosis system can be established through key flow field parameters, and structural parameters can be optimized by orthogonal experiments. A flow field modification scheme that can directly guide on-site construction can be output, thereby accurately capturing the abnormal flow field characteristics corresponding to the fault, realizing accurate diagnosis and targeted optimization of core equipment faults, adapting to typical special faults of power plant boilers, providing direct guidance for on-site modification, improving the practicality of the project, shortening the fault treatment cycle, reducing treatment costs, and ensuring operational stability and economy.

[0008] Optionally, in one embodiment of this application, the step of performing mesh division and determining structured and unstructured mesh regions includes: determining target and non-target fault regions of the three-dimensional structural model based on the key conditions of the air and smoke system; using the three-dimensional structural model, performing structured mesh densification on the target fault region with a first preset size to determine the structured mesh region; and using the three-dimensional structural model, performing unstructured mesh densification on the non-target fault region with a second preset size to determine the unstructured mesh region.

[0009] Through the above-mentioned technical means, the embodiments of this application can perform structured mesh densification on the target fault area, which can accurately capture complex flow phenomena such as local strong eddies, particle collisions and separations. Unstructured mesh densification is performed on non-target fault areas. While ensuring the overall flow field calculation accuracy, the amount of computation is greatly reduced and the simulation cycle is shortened. Thus, a balance between simulation accuracy and computational efficiency is achieved through differentiated mesh division, laying the foundation for accurate solution of subsequent multiphysics fields.

[0010] Optionally, in one embodiment of this application, the step of simulating the coal powder trajectory using a preset gas-solid two-phase flow model and capturing the flow field pulsation characteristics using a preset turbulence model to solve the multiphysics field includes: simulating insufficient coal feeding stability of the flue gas system based on the customized boundary conditions and the preset gas-solid two-phase flow model to solve the flow field velocity distribution of the flue gas system; simulating abnormal sealing air pressure drop of the flue gas system based on the customized boundary conditions and the preset turbulence model to capture the flow field pulsation characteristics, and solving the pressure loss of the flue gas system based on the flow field pulsation characteristics; simulating poor coal feeding in the small coal bins of the flue gas system based on the customized boundary conditions, the preset gas-solid two-phase flow model, and a preset pressure-velocity coupling strategy to solve the coal powder trajectory of the flue gas system; and determining the multiphysics field based on the flow field velocity distribution, the pressure loss, and the coal powder trajectory.

[0011] Through the above-mentioned technical means, the embodiments of this application can simulate fault scenarios such as insufficient coal feeding stability, abnormal sealing air pressure drop, and poor coal feeding in small coal bins, and accurately solve key parameters such as flow field velocity distribution, pressure loss, and coal powder movement trajectory. This breaks the limitations of single gas phase flow simulation and can accurately restore the abnormal flow field patterns corresponding to different faults, providing accurate and comprehensive data flow support for the establishment of subsequent fault diagnosis index system.

[0012] Optionally, in one embodiment of this application, establishing a fault diagnosis index system based on the at least one key flow field parameter includes: identifying at least one fault feature of the air and smoke system based on the at least one key flow field parameter; establishing a correspondence between the at least one key flow field parameter and the at least one fault feature; and establishing the fault diagnosis index system based on the correspondence.

[0013] Through the above-mentioned technical means, the embodiments of this application can achieve precise correlation between key flow field parameters and fault characteristics, so as to establish a standardized fault diagnosis index system, avoid the limitations of traditional empirical formula derivation, improve the scientificity and reliability of fault diagnosis, and quickly and accurately identify fault types and root causes, providing clear directional guidance for subsequent structural optimization.

[0014] Optionally, in one embodiment of this application, the step of optimizing structural parameters using orthogonal experimental design and outputting a flow field modification scheme for the flue gas system includes: selecting at least one structural parameter of the flue gas system to be optimized based on the fault diagnosis index system, and determining the horizontal range of the at least one structural parameter to be optimized to determine an orthogonal experimental table; calculating flow field indices under different combinations of structural parameters according to the orthogonal experimental table, and determining the optimal combination of structural parameters according to the flow field indices and preset optimization conditions; and outputting the flow field modification scheme based on the optimal combination of structural parameters.

[0015] Through the above-mentioned technical means, the embodiments of this application can screen out the structural parameters to be optimized and their reasonable level ranges that significantly affect the flow field characteristics from the flue gas system based on the fault diagnosis index system. By using orthogonal experimental design, the optimal combination of structural parameters can be efficiently screened, thereby significantly reducing the number of calculations while ensuring simulation accuracy. This allows for the rapid generation of targeted and feasible flow field modification schemes, significantly improving the efficiency and effectiveness of flue gas system modification, further shortening the fault treatment cycle and reducing treatment costs.

[0016] A second aspect of this application provides a flow field simulation and analysis device for a flue gas system, comprising: a determination module, used to generate a three-dimensional structural model of the flue gas system using structural parameters and operating condition data of faulty equipment in a power plant, for meshing, and determining structured and unstructured mesh regions; a solution module, used to load corresponding customized boundary conditions according to the structured and unstructured mesh regions, simulate the coal powder movement trajectory using a preset gas-solid two-phase flow model, and capture the flow field pulsation characteristics using a preset turbulence model, in order to solve for the multiphysics field; and an analysis module, used to extract at least one key flow field parameter of the flue gas system based on the multiphysics field, establish a fault diagnosis index system based on the at least one key flow field parameter, optimize the structural parameters by combining orthogonal experimental design, and output a flow field modification scheme for the flue gas system.

[0017] Through the aforementioned technical means, the embodiments of this application can construct a three-dimensional model of the flue gas system that recreates the actual equipment structure and operating conditions of a power plant. By combining differentiated mesh generation, customized boundary conditions, and coupled simulation of gas-solid two-phase flow and turbulence, multi-physics fields can be accurately solved. At the same time, a fault diagnosis system can be established through key flow field parameters, and structural parameters can be optimized by orthogonal experiments. A flow field modification scheme that can directly guide on-site construction can be output, thereby accurately capturing the abnormal flow field characteristics corresponding to the fault, realizing accurate diagnosis and targeted optimization of core equipment faults, adapting to typical special faults of power plant boilers, providing direct guidance for on-site modification, improving the practicality of the project, shortening the fault treatment cycle, reducing treatment costs, and ensuring operational stability and economy.

[0018] Optionally, in one embodiment of this application, the determining module includes: a first determining unit, configured to determine the target fault region and non-target fault region of the three-dimensional structural model based on the key conditions of the air and smoke system; a second determining unit, configured to use the three-dimensional structural model to perform structured mesh densification on the target fault region using a first preset size to determine the structured mesh region; and a third determining unit, configured to use the three-dimensional structural model to perform unstructured mesh densification on the non-target fault region using a second preset size to determine the unstructured mesh region.

[0019] Through the above-mentioned technical means, the embodiments of this application can perform structured mesh densification on the target fault area, which can accurately capture complex flow phenomena such as local strong eddies, particle collisions and separations. Unstructured mesh densification is performed on non-target fault areas. While ensuring the overall flow field calculation accuracy, the amount of computation is greatly reduced and the simulation cycle is shortened. Thus, a balance between simulation accuracy and computational efficiency is achieved through differentiated mesh division, laying the foundation for accurate solution of subsequent multiphysics fields.

[0020] Optionally, in one embodiment of this application, the solving module includes: a first solving unit, used to simulate insufficient coal feeding stability of the flue gas system based on the customized boundary conditions and the preset gas-solid two-phase flow model, in order to solve the flow field velocity distribution of the flue gas system; a second solving unit, used to simulate abnormal sealing air pressure drop of the flue gas system based on the customized boundary conditions and the preset turbulence model, in order to capture flow field pulsation characteristics, and solve the pressure loss of the flue gas system according to the flow field pulsation characteristics; a third solving unit, used to simulate poor coal feeding in the small coal bin of the flue gas system based on the customized boundary conditions, the preset gas-solid two-phase flow model and the preset pressure-velocity coupling strategy, in order to solve the coal powder movement trajectory of the flue gas system; and a fourth determining unit, used to determine the multiphysics field based on the flow field velocity distribution, the pressure loss and the coal powder movement trajectory.

[0021] Through the above-mentioned technical means, the embodiments of this application can simulate fault scenarios such as insufficient coal feeding stability, abnormal sealing air pressure drop, and poor coal feeding in small coal bins, and accurately solve key parameters such as flow field velocity distribution, pressure loss, and coal powder movement trajectory. This breaks the limitations of single gas phase flow simulation and can accurately restore the abnormal flow field patterns corresponding to different faults, providing accurate and comprehensive data flow support for the establishment of subsequent fault diagnosis index system.

[0022] Optionally, in one embodiment of this application, the analysis module includes: an identification unit, configured to identify at least one fault feature of the air and smoke system based on the at least one key flow field parameter; and an establishment unit, configured to establish a correspondence between the at least one key flow field parameter and the at least one fault feature, and establish the fault diagnosis index system based on the correspondence.

[0023] Through the above-mentioned technical means, the embodiments of this application can achieve precise correlation between key flow field parameters and fault characteristics, so as to establish a standardized fault diagnosis index system, avoid the limitations of traditional empirical formula derivation, improve the scientificity and reliability of fault diagnosis, and quickly and accurately identify fault types and root causes, providing clear directional guidance for subsequent structural optimization.

[0024] Optionally, in one embodiment of this application, the analysis module includes: a fifth determining unit, configured to select at least one structural parameter of the flue gas system to be optimized based on the fault diagnosis index system, and determine the horizontal range of the at least one structural parameter to be optimized, so as to determine an orthogonal experimental table; a sixth determining unit, configured to calculate the flow field index under different combinations of structural parameters according to the orthogonal experimental table, and determine the optimal combination of structural parameters according to the flow field index and preset optimization conditions; and a generating unit, configured to output the flow field modification scheme based on the optimal combination of structural parameters.

[0025] Through the above-mentioned technical means, the embodiments of this application can screen out the structural parameters to be optimized and their reasonable level ranges that significantly affect the flow field characteristics from the flue gas system based on the fault diagnosis index system. By using orthogonal experimental design, the optimal combination of structural parameters can be efficiently screened, thereby significantly reducing the number of calculations while ensuring simulation accuracy. This allows for the rapid generation of targeted and feasible flow field modification schemes, significantly improving the efficiency and effectiveness of flue gas system modification, further shortening the fault treatment cycle and reducing treatment costs.

[0026] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the flow field simulation analysis method for a flue gas system as described in the above embodiments.

[0027] A fourth aspect of this application provides a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described flow field simulation analysis method for a flue gas system.

[0028] A fifth aspect of this application provides a computer program product that stores a computer program that, when executed by a processor, implements the above-described method for simulating and analyzing the flow field of a flue gas system.

[0029] This application's embodiments can construct a 3D model of the flue gas system that accurately reflects the actual equipment structure and operating conditions of a power plant. By combining differentiated mesh generation, customized boundary conditions, and simulation of gas-solid two-phase flow coupled with turbulence, it accurately solves multiphysics fields. Simultaneously, it establishes a fault diagnosis system through key flow field parameters and optimizes structural parameters using orthogonal experiments, outputting flow field modification schemes that can directly guide on-site construction. This accurately captures the abnormal flow field characteristics corresponding to faults, achieving precise diagnosis and targeted optimization of core equipment faults. It adapts to typical specific faults of power plant boilers, providing direct guidance for on-site modifications, improving engineering practicality, shortening fault treatment cycles, reducing treatment costs, and ensuring operational stability and economy. Therefore, it solves the problems in related technologies where neglecting the personalized structural parameters of power plant equipment and failing to fully consider the gas-solid two-phase flow coupling effect, relying solely on empirical formulas to deduce fault causes, leads to insufficient targeting of optimization schemes, low fault diagnosis accuracy, poor engineering practicality, and the inability to directly guide on-site modifications with simulation results, resulting in long fault treatment cycles and high costs.

[0030] Additional aspects and advantages of this application 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 this application. Attached Figure Description

[0031] 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 of a flow field simulation analysis method for a flue gas system according to an embodiment of this application; Figure 2 This is a schematic diagram of a pulverized coal feed pipeline for a power plant according to an embodiment of this application; Figure 3 The figure shows the modeling and flow field simulation results of the pulverized coal feeding pipeline of Power Plant No. 1 according to an embodiment of this application; Figure 4 This is a simulation result diagram of the flow field of a power plant No. 2 according to an embodiment of this application; Figure 5 This is a schematic diagram of the pulverized coal feeding pipeline of the No. 3 power plant small pulverized coal silo according to one embodiment of this application; Figure 6 The figure shows the modeling and flow field simulation results of the pulverized coal feeding pipeline of the No. 3 power plant according to an embodiment of this application; Figure 7 This is a schematic diagram of the structure of a flow field simulation analysis device for a flue gas system according to an embodiment of this application; Figure 8 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application.

[0032] Figure label: 10-Flow field simulation analysis device for air and smoke system; 100-Determination module, 200-Solution module, 300-Analysis module; 801-Memory, 802-Processor, 803-Communication interface. Detailed Implementation

[0033] The embodiments of this application are described in detail below. Examples of these embodiments are shown 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 this application, and should not be construed as limiting this application.

[0034] The following description, with reference to the accompanying drawings, outlines a flow field simulation analysis method, apparatus, and electronic equipment for a flue gas system according to embodiments of this application. In response to the aforementioned background technologies, which neglect the individual structural parameters of power plant equipment and fail to fully consider the coupling effect of gas-solid two-phase flow, relying solely on empirical formulas to deduce fault causes, the optimization schemes lack specificity, fault diagnosis accuracy is low, and engineering practicality is poor. Furthermore, simulation results are difficult to directly guide on-site modifications, resulting in long fault management cycles and high costs. This application provides a flow field simulation analysis method for flue gas systems. This method constructs a three-dimensional model of the flue gas system that recreates the actual equipment structure and operating conditions of a power plant. Combined with differentiated mesh generation, customized boundary conditions, and coupled simulation of gas-solid two-phase flow and turbulence, it accurately solves multiphysics fields. Simultaneously, it establishes a fault diagnosis system through key flow field parameters and optimizes structural parameters using orthogonal experiments, outputting flow field modification schemes that can directly guide on-site construction. This accurately captures the abnormal flow field characteristics corresponding to faults, achieving precise diagnosis and targeted optimization of core equipment faults. It adapts to typical specific faults of power plant boilers, providing direct guidance for on-site modifications, improving engineering practicality, shortening fault management cycles, reducing management costs, and ensuring operational stability and economy. This solves the problems in related technologies where, due to neglecting the individual structural parameters of power plant equipment and failing to fully consider the coupling effect of gas-solid two-phase flow, the fault causes are derived solely from empirical formulas, resulting in insufficient targeting of optimization schemes, low fault diagnosis accuracy, poor engineering practicality, and the inability of simulation results to directly guide on-site modifications, leading to long fault management cycles and high costs.

[0035] Specifically, Figure 1 This is a flowchart illustrating a flow field simulation analysis method for a flue gas system provided in an embodiment of this application.

[0036] like Figure 1 As shown, the flow field simulation analysis method for this flue gas system includes the following steps: In step S101, a three-dimensional structural model of the flue gas system is generated using the structural parameters and operating condition data of the faulty equipment in the power plant, in order to perform mesh division and determine the structured mesh region and the unstructured mesh region.

[0037] It is understood that the structural parameters in the embodiments of this application may include the diameter of the pulverized coal feeding pipe, the diameter change angle, the taper of the small pulverized coal silo, and the size of the sealing air guide plate. The operating condition data may include the pulverized coal feed rate, flue gas velocity, sealing air pressure, and pulverized coal particle size distribution.

[0038] In actual implementation, the embodiments of this application can collect structural parameters of faulty equipment in the power plant (including the diameter and diameter change angle of the pulverized coal feeding pipe, the taper of the small pulverized coal silo, and the size of the sealing air guide plate) and operating condition data (including pulverized coal feed rate, flue gas velocity, sealing air pressure, and pulverized coal particle size distribution). The three-dimensional structural model is restored through three-dimensional computer-aided design technology, and abnormal operating condition data is removed using data cleaning technology to ensure modeling accuracy.

[0039] For example, this application embodiment can obtain detailed design drawings of the faulty equipment, extract key dimensional parameters, including but not limited to the diameter change angle of the pulverized coal feeding pipe, the taper of the small pulverized coal silo, and the size of the sealing air guide plate. It can also record operating parameters under fault conditions, including but not limited to the pulverized coal feeding rate, the sealing air inlet pressure, the material level height of the small pulverized coal silo, and the flue gas velocity. Simultaneously, it can collect physical property parameters such as pulverized coal particle size distribution and fluid medium density. This application embodiment can use three-dimensional computer-aided design technology to restore the three-dimensional structure of the equipment according to the actual drawings, remove non-fluid field related auxiliary structures to simplify the model, and import the simplified model into the mesh generation module. The size of the structured mesh area is controlled within 5mm, and the unstructured mesh area uses a 10-20mm mesh. After completing the mesh independence verification, the final mesh scheme is determined to ensure a balance between computational efficiency and accuracy.

[0040] The embodiments of this application can construct an accurate three-dimensional structural model based on the actual structural parameters and operating condition data of the faulty equipment. By implementing a partitioned meshing strategy, the three-dimensional model is highly consistent with the actual engineering situation, providing a foundation for the accuracy of subsequent flow field simulation. At the same time, the differentiated meshing method takes into account both simulation accuracy and computational efficiency, avoiding insufficient accuracy or computational redundancy caused by a single mesh type, and laying a solid foundation for the accurate solution of subsequent multiphysics fields.

[0041] In step S102, customized boundary conditions are applied to the structured and unstructured grid regions, and a preset gas-solid two-phase flow model is used to simulate the coal powder's trajectory. A preset turbulence model is also used to capture the flow field's pulsation characteristics in order to solve the multiphysics field.

[0042] It is understood that the customized boundary conditions in the embodiments of this application can be understood as a non-uniform combination of boundary parameters set at the inlet and outlet of the computational domain, reflecting the actual flow state, for different fault types and different operating conditions of the flue gas system. The preset gas-solid two-phase flow model can be a mathematical and physical model framework used to describe the momentum, energy, and mass exchange between gas and solid particles. For example, the DPM (Discrete Phase Model) gas-solid two-phase flow model can be selected. The preset gas-solid two-phase flow model can be set by those skilled in the art according to the actual situation, and no specific restrictions are imposed here. The preset turbulence model can be a simulation model used to simulate the turbulent motion of airflow within the flue gas system. For example, the RNG (Renormalization Group) k-ε turbulence model can be selected. The preset turbulence model can be set by those skilled in the art according to the actual situation, and no specific restrictions are imposed here. The flow field pulsation characteristics can be understood as the instantaneous fluctuation state of parameters such as airflow velocity and pressure within the flue gas system. Multiphysics can be understood as a comprehensive simulation field that integrates various parameters such as flow velocity, pressure, temperature, coal powder concentration, and motion trajectory. It is the core data support for subsequent fault diagnosis and structural optimization.

[0043] In actual implementation, the embodiments of this application can load customized boundary conditions (such as setting the inlet of the pulverized coal feeding system as the mass flow rate boundary and the outlet as the pressure boundary; setting the inlet of the sealing air system as the pressure boundary and the outlet as the flow rate boundary; setting the inlet of the small pulverized coal silo as the free surface boundary and the outlet as the velocity boundary), using a gas-solid two-phase flow model to simulate the pulverized coal motion trajectory, combining a turbulence model to capture the flow field pulsation characteristics, and coupling a pressure-velocity coupling algorithm for the pressure drop anomaly problem to improve the solution accuracy and solve the multiphysics field.

[0044] The embodiments of this application can apply customized boundary conditions to different regions, and perform coupled simulation by combining gas-solid two-phase flow model and turbulence model to solve multi-physics field. It fully considers the coupling effect of gas-solid two-phase flow and flow field pulsation characteristics, and can accurately reflect the complex flow characteristics inside the flue gas system, providing high-precision flow field data support for fault diagnosis.

[0045] In step S103, based on multiphysics, at least one key flow field parameter of the flue gas system is extracted, and a fault diagnosis index system is established based on at least one key flow field parameter. The structural parameters are optimized by combining orthogonal experimental design, and the flow field modification scheme of the flue gas system is output.

[0046] It is understood that at least one key flow field parameter in the embodiments of this application may include velocity uniformity, pressure loss coefficient, particle deposition rate, etc. Orthogonal experimental design can be understood as a statistical method that arranges experiments using orthogonal arrays to obtain the optimal parameter combination with fewer trials.

[0047] In actual implementation, the embodiments of this application can extract key flow field parameters through the numerical simulation post-processing module, establish a fault diagnosis index system (such as the powder feeding stability judgment threshold and the pressure drop abnormality critical value), optimize structural parameters by combining orthogonal experimental design, and output the flow field modification scheme of the flue gas system.

[0048] For example, embodiments of this application can extract flow field parameters under fault conditions and identify abnormal flow field characteristics corresponding to the fault, including but not limited to excessive velocity gradient in the variable diameter section of the powder feeding system, excessive pressure loss in the vortex zone behind the sealing air guide plate, and excessive particle retention and deposition rate in the cone of the small powder bin. Specifically, embodiments of this application can use simulation post-processing software to divide the computational domain into sections and sample data, calculate the ratio of the velocity standard deviation to the average velocity of each section to obtain the velocity uniformity, obtain the pressure loss coefficient by the ratio of the total pressure difference between the inlet and outlet to the dynamic pressure, and obtain the particle deposition rate by the ratio of the mass of particles deposited on the wall to the total mass of particles at the inlet.

[0049] Furthermore, embodiments of this application can establish a correspondence between "fault phenomena and abnormal thresholds of flow field parameters" based on a large number of historical fault cases and simulation data. Embodiments of this application can employ orthogonal experimental design methods to design multiple sets of experimental schemes for the structural parameters corresponding to the root cause of the fault. The optimal combination of structural parameters is selected through simulation solutions, and a secondary simulation is performed on the optimized model to verify whether the fault characteristics have been eliminated, ensuring that the flow field parameters meet design and operational requirements. Based on the optimal parameter combination, a flow field modification scheme for the flue gas system is output to draw modification construction drawings, clarifying the modification locations, dimensional requirements, and construction techniques.

[0050] The embodiments of this application can extract key parameters of the flow field based on multi-physics fields, establish a standardized fault diagnosis index system, optimize structural parameters through orthogonal experimental design, and output flow field modification schemes, thereby accurately locating the root cause of the fault and outputting modification schemes that can directly guide on-site construction, achieving efficient treatment of specific faults and ensuring the safe and efficient operation of the unit.

[0051] Optionally, in one embodiment of this application, mesh division is performed to determine structured and unstructured mesh regions, including: determining target and non-target fault regions in a three-dimensional structural model based on the key conditions of the air and smoke system; using the three-dimensional structural model, the target fault region is densified with a first preset size to determine the structured mesh region; using the three-dimensional structural model, the non-target fault region is densified with a second preset size to determine the unstructured mesh region.

[0052] It is understood that the key conditions in the embodiments of this application can be understood as the criteria for judging areas in the flue gas system with high failure rates and high requirements for simulation accuracy, including failure frequency, flow field complexity, and equipment structural importance. The target failure area can be understood as an area in the flue gas system with high failure rates, complex flow fields, and high requirements for simulation accuracy, and is also the core area for subsequent fault diagnosis and optimization, such as the diameter change section and bends of the powder feeding pipe, the area around the sealing air guide plate, the cone part of the small powder silo, and the outlet of the small powder silo. Non-target failure areas can be understood as areas in the flue gas system with low failure rates, relatively simple flow fields, and lower requirements for simulation accuracy. The first preset size can be understood as the size of the grid cells used for mesh refinement set for the target failure area. For example, the first preset size can be 5mm. The first preset size can be set by those skilled in the art according to actual conditions, and no specific limitations are made here. The second preset size can be understood as the size of the grid cell used for mesh densification, which is set for non-target fault areas. The size is larger than the first preset size. The second preset size can be understood as 10-20mm. The second preset size can be set by those skilled in the art according to the actual situation, and no specific restrictions are made here.

[0053] In actual implementation, the embodiments of this application can perform preprocessing based on the three-dimensional structural model of the flue gas system. Based on the key conditions of the flue gas system, the high-incidence areas of failure, complex flow field regions, and key parts of the equipment structure are identified, and the target and non-target failure areas of the three-dimensional structural model are determined. The target areas (the diameter change section and bends of the powder feeding pipe, the area around the sealing air guide plate, the cone part of the small powder silo, and the outlet of the small powder silo) are densified with structured mesh, and the mesh size is controlled within 5mm. The remaining areas are non-target failure areas, and unstructured mesh with a size of 15mm is used.

[0054] Furthermore, the range of grid parameters is determined based on the principle of balancing computational accuracy and computational efficiency, and is matched with the characteristic scale of gas-solid two-phase flow in the powder feeding system. This enables the accurate capture of complex flow phenomena such as local strong eddies, particle collisions and separations. The grid distortion rate is controlled at <5%, which can effectively avoid numerical discretization errors caused by grid distortion and ensure the convergence and stability of the flow field calculation.

[0055] The embodiments of this application can perform structured mesh densification on the target fault area, which can accurately capture complex flow phenomena such as local strong eddies, particle collisions and separations. Unstructured mesh densification is performed on the non-target fault area. While ensuring the overall flow field calculation accuracy, the amount of computation is greatly reduced and the simulation cycle is shortened. Thus, a balance between simulation accuracy and computational efficiency is achieved through differentiated mesh division, laying the foundation for accurate solutions of subsequent multiphysics fields.

[0056] Optionally, in one embodiment of this application, a preset gas-solid two-phase flow model is used to simulate the coal powder movement trajectory, and a preset turbulence model is combined to capture the flow field pulsation characteristics in order to solve the multiphysics field. This includes: simulating insufficient coal feeding stability of the flue gas system based on customized boundary conditions and the preset gas-solid two-phase flow model to solve the flow field velocity distribution of the flue gas system; simulating abnormal sealing air pressure drop of the flue gas system based on customized boundary conditions and the preset turbulence model to capture the flow field pulsation characteristics, and solving the pressure loss of the flue gas system based on the flow field pulsation characteristics; simulating poor coal feeding in the small coal bin of the flue gas system based on customized boundary conditions, the preset gas-solid two-phase flow model, and the preset pressure-velocity coupling strategy to solve the coal powder movement trajectory of the flue gas system; and determining the multiphysics field based on the flow field velocity distribution, pressure loss, and coal powder movement trajectory.

[0057] It is understood that the flow field velocity distribution in this embodiment can be understood as the distribution of the magnitude and direction of airflow velocity at different locations within the flue gas system. Its uniformity directly affects the stability of coal feeding, and uneven velocity distribution can lead to coal powder deposition or conveying pulsation. Pressure loss can be understood as the amount of pressure attenuation caused by pipeline resistance, equipment obstruction, etc., during the airflow process within the flue gas system, and is a core indicator for judging abnormal pressure drop in the sealed air system. Coal powder trajectory can be understood as the movement path of coal powder particles within the flue gas system, and its distribution pattern directly reflects the smoothness of coal feeding in the small coal bin. Abnormal trajectory can lead to poor coal feeding. The preset pressure-velocity coupling strategy can be the SIMPLEC pressure-velocity coupling algorithm. The preset pressure-velocity coupling strategy can be set by those skilled in the art according to the actual situation, and no specific restrictions are imposed here.

[0058] In actual implementation, the embodiments of this application can be based on customized boundary conditions and a preset gas-solid two-phase flow model, setting the inlet as the mass flow boundary and the outlet as the pressure boundary to simulate the insufficient stability of the coal feeding system. The DPM discrete phase model is used to inject coal powder particles, and the collision and adhesion models between particles and the pipe wall are defined to solve the velocity distribution and particle trajectory of the gas-solid two-phase flow field.

[0059] The embodiments of this application can simulate the abnormal sealing pressure drop of the flue gas system based on customized boundary conditions and preset turbulence models. The inlet is set as the pressure boundary and the outlet as the flow boundary. The RNG k-ε turbulence model is enabled to capture the flow field pulsation characteristics in order to calculate the pressure loss along the flow path and the local pressure loss under different guide vane layouts.

[0060] The embodiments of this application can be based on customized boundary conditions, a preset gas-solid two-phase flow model and a preset pressure-velocity coupling strategy, setting the inlet of the silo as a free surface boundary and the outlet as a velocity boundary, simulating the poor coal feeding in the small coal silo of the flue gas system, considering the gravity of coal powder and the drag force of airflow, simulating the coal powder flow state in the silo and the outlet discharge speed, so as to solve the coal powder movement trajectory of the flue gas system.

[0061] For example, in the embodiments of this application, customized boundary conditions can be loaded, and the powder feeding system and the small powder silo can be simulated using the DPM gas-solid two-phase flow model and the RNG k-ε turbulence model. The sealing air system can be simulated using the RNG k-ε turbulence model and the SIMPLEC pressure-velocity coupling algorithm to solve for the flow field velocity distribution, pressure loss and particle motion trajectory.

[0062] Specifically, the DPM gas-solid two-phase flow model is suitable for low volume fraction (<10%) pulverized coal-air two-phase flow, accurately tracking the trajectory, collision, and deposition behavior of individual pulverized coal particles, consistent with the actual working conditions of pulverized coal conveying in the feeding system. The RNG k-ε turbulence model improves the turbulence dissipation rate equation based on the standard k-ε model, achieving higher simulation accuracy for complex turbulence such as strong swirling and separated flows, and accurately capturing the eddy characteristics around the sealed air guide plate and at the outlet of the small pulverized coal silo. The SIMPLEC algorithm accelerates the convergence speed of pressure-velocity coupling by correcting the coefficients of the pressure correction equation, and is particularly suitable for calculating the steady-state flow field of incompressible fluids. The specific solution process in this embodiment is as follows: first, initialize the flow field, solve the continuity and momentum equations to obtain the initial velocity field, then correct the pressure and velocity using the pressure correction equation, iteratively solve until the residuals converge (continuity residual <1e-3, velocity residual <1e-4), and finally load the DPM model to solve for particle phase motion.

[0063] The embodiments of this application can simulate fault scenarios such as insufficient coal feeding stability, abnormal sealing air pressure drop, and poor coal feeding in small coal bins. They can accurately solve key parameters such as flow field velocity distribution, pressure loss, and coal powder movement trajectory, breaking the limitations of single gas phase flow simulation. They can accurately restore the abnormal flow field patterns corresponding to different faults, providing accurate and comprehensive data flow support for the establishment of subsequent fault diagnosis index system.

[0064] Optionally, in one embodiment of this application, a fault diagnosis index system is established based on at least one key flow field parameter, including: identifying at least one fault feature of the air and smoke system based on at least one key flow field parameter; establishing a correspondence between at least one key flow field parameter and at least one fault feature; and establishing a fault diagnosis index system based on the correspondence.

[0065] It is understood that at least one fault feature in the embodiments of this application may include excessive velocity gradient in the variable diameter section of the powder feeding system, excessive pressure loss in the vortex zone behind the sealing air guide plate, and excessive particle retention and deposition rate in the cone of the small powder bin.

[0066] For example, embodiments of this application can identify at least one fault feature corresponding to a fault based on at least one key flow field parameter, establish a correspondence based on a large number of historical fault cases and simulation data, and establish a fault diagnosis index system based on the correspondence. For example, insufficient powder feeding stability corresponds to a powder feeding pipe velocity uniformity of <80%, abnormal sealing air pressure drop corresponds to a sealing air system pressure loss coefficient >1.2 times the design value, and poor powder feeding corresponds to a small powder silo particle deposition rate >10%.

[0067] The embodiments of this application can achieve precise correlation between key flow field parameters and fault characteristics, so as to establish a standardized fault diagnosis index system, avoid the limitations of traditional empirical formula derivation, improve the scientificity and reliability of fault diagnosis, and quickly and accurately identify fault types and root causes, providing clear directional guidance for subsequent structural optimization.

[0068] Optionally, in one embodiment of this application, the flow field modification scheme of the flue gas system is output by combining orthogonal experimental design to optimize structural parameters, including: selecting at least one structural parameter of the flue gas system to be optimized based on the fault diagnosis index system, and determining the horizontal range of at least one structural parameter to be optimized to determine the orthogonal experimental table; calculating the flow field index under different combinations of structural parameters according to the orthogonal experimental table, and determining the optimal combination of structural parameters according to the flow field index and preset optimization conditions; and outputting the flow field modification scheme based on the optimal combination of structural parameters.

[0069] It is understood that, in the embodiments of this application, at least one structural parameter to be optimized refers to a geometric parameter within the flue gas system that is related to faults and can be optimized through structural adjustments, such as the diameter change angle of the powder feeding pipe, the angle of the sealing air guide plate, the taper of the small powder hopper, and the number of internal guide grooves. The horizontal range refers to the reasonable adjustment interval of the structural parameter to be optimized, determined based on equipment design standards, on-site construction conditions, and fault diagnosis results, ensuring that the adjusted parameters meet the actual engineering requirements. The orthogonal experimental table refers to an experimental scheme table formulated according to the number and horizontal range of the structural parameters to be optimized, based on the principles of orthogonal experimental design, used to reasonably arrange the number of experiments and efficiently screen the optimal parameter combination. Flow field indicators refer to the core parameters obtained through flow field simulation under different combinations of structural parameters, used to evaluate flow field performance, such as powder feeding speed uniformity, sealing air pressure drop, and small powder hopper particle deposition rate. Preset optimization conditions can be powder feeding speed uniformity ≥90%, sealing air pressure drop meeting design values, and small powder hopper particle deposition rate <5%. These preset optimization conditions can be set by those skilled in the art according to actual conditions, and no specific restrictions are imposed here.

[0070] In actual implementation, the embodiments of this application can select key structural parameters that affect the flow field characteristics as experimental factors, determine the level range of each factor, design orthogonal experimental tables, obtain flow field indicators under different parameter combinations through simulation calculations, determine the influence weight of each factor on the indicators through range analysis and variance analysis, screen out the optimal combination of structural parameters, draw modification construction drawings based on the optimal parameter combination, clarify the modification location, size requirements and construction technology, and verify the fault treatment effect through actual measurement.

[0071] Among them, at least one of the structural parameters to be optimized has a horizontal range including: the diameter change angle of the pulverized coal feeding pipe (30°-50°), the angle of the sealing air guide plate (10°-25°), the taper of the small pulverized coal silo (45°-60°), and the number of internal guide grooves (0-6). Specifically, based on the fluid dynamics of the diffuser pipe, an angle that is too small will increase the pipe length and resistance, while an angle that is too large will cause fluid separation and the formation of eddies. The diameter change angle of the pulverized coal feeding pipe is 30°-50°, which is the optimal angle range for the diffuser pipe in engineering, and can effectively reduce the pressure loss and velocity unevenness in the diameter change section. Combining the previous simulation and field test, the angle of the sealing air guide plate (10°-25°) can guide the sealing air to uniformly cover the sealing surface, which avoids the poor sealing air adhesion effect caused by an angle that is too small, and also prevents the generation of eddies and additional resistance caused by an angle that is too large. According to the characteristics of the angle of repose in powder mechanics, the angle of repose of pulverized coal is about 35°-45°. The taper of the small pulverized coal silo (45°-60°) can ensure that the pulverized coal slides smoothly, avoids arching and accumulation, and at the same time takes into account the effective volume of the silo. The range of 0-6 internal guide channels covers working conditions from no guide channels to multiple guide channels. The optimal number of guide channels can be determined through orthogonal experiments, which effectively improves the flow field uniformity at the outlet of the small powder bin and reduces particle deposition.

[0072] The preset optimization conditions can be: uniformity of coal feeding speed ≥90%, sealing air pressure drop meeting design value, and particle deposition rate in the small coal silo <5%. Specifically, the industry standard for coal feeding system operation requires that the deviation of coal feeding rate of burners in the same layer be <10%, and the corresponding uniformity of coal feeding pipe speed must be ≥90%, which can ensure uniform heat load of each burner and avoid uneven burning in the furnace and overheating of the heating surface; the pressure drop of the sealing air system directly affects the sealing effect and fan energy consumption. Too high a pressure drop will increase the plant power consumption, while too low a pressure drop will lead to coal powder leakage. Meeting the design value can balance sealing reliability and economy; too high a particle deposition rate will lead to a reduction in the effective volume of the small coal silo, poor coal feeding, or even coal blockage. A deposition rate of <5% can ensure the long-term stable operation of the small coal silo and reduce the number of cleaning and maintenance times.

[0073] The embodiments of this application can screen out the structural parameters to be optimized and their reasonable level ranges that significantly affect the flow field characteristics from the flue gas system based on the fault diagnosis index system. By using orthogonal experimental design, the optimal combination of structural parameters can be efficiently screened, thereby greatly reducing the number of calculations while ensuring simulation accuracy. This allows for the rapid generation of targeted and feasible flow field modification schemes, significantly improving the efficiency and effectiveness of flue gas system modification, further shortening the fault treatment cycle and reducing treatment costs.

[0074] Specifically, it can be combined with Figures 2 to 6 As shown, the working principle of the flow field simulation analysis method of the flue gas system in this application is explained in detail with a specific embodiment.

[0075] like Figure 2 and Figure 3 As shown, the No. 1 power plant's coal feeding system has the following characteristics: nominal pipe diameter DN200, original angle of the diameter-changing section 30°, coal feeding rate 20-30t / h, coal powder particle size 10-80μm, and air velocity 12-18m / s.

[0076] like Figure 4 As shown, the sealing air system of Power Plant No. 2 has a pipeline length of 15m, a guide plate angle of 10°, an inlet pressure of 0.25-0.35MPa, and a design flow rate of 8000m³ / h.

[0077] like Figure 5 and Figure 6 As shown, the No. 3 power plant's small pulverized coal silo has a body diameter of 3m, an original taper of 60°, an outlet diameter of DN150, a material level height of 2-4m, and a pulverized coal bulk density of 1200kg / m³.

[0078] Specifically, this application embodiment can employ three-dimensional computer-aided design technology to reconstruct the three-dimensional models of the three types of faulty equipment according to the actual equipment drawings, removing non-flow field-related auxiliary structures (such as supports and flanges) to simplify the model and avoid interference from non-core structures on the calculation results. The simplified three-dimensional model is imported into the mesh generation module. For critical fault areas such as the diameter change section and bends of the powder feeding pipeline, the area around the sealing air guide plate, the cone part of the small powder silo, and the outlet of the small powder silo, a structured mesh with a size of 5mm is used for densification, while the remaining areas use an unstructured mesh with a size of 15mm. After completing the mesh generation, mesh independence is verified, and the final mesh count for a single equipment model is controlled between 800,000 and 1,200,000.

[0079] Furthermore, the DPM gas-solid two-phase flow model and the RNG k-ε turbulence model were used to simulate the powder feeding system and the small powder silo, while the RNG k-ε turbulence model and the SIMPLEC algorithm were used to simulate the sealing air system. Specifically, the powder feeding system inlet was set to a mass flow rate boundary (25 t / h), and the outlet was set to a pressure boundary (0.1 MPa); the sealing air system inlet was set to a pressure boundary (0.3 MPa), and the outlet was set to a flow rate boundary (8000 m³ / h); the small powder silo inlet was set to a free surface boundary, and the outlet was set to a velocity boundary (1 m / s). Physical property settings: air density 1.2 kg / m³, dynamic viscosity 1.8 × 10⁻⁶. -5 Pa s, coal powder density 2500kg / m³, particle-to-pipe wall collision recovery coefficient 0.8, adhesion probability 10%.

[0080] This application embodiment can employ a hybrid initialization method, setting the initial flow field velocity (15 m / s for the powder feeding system and 20 m / s for the sealing air system) and initial pressure (0.1 MPa). This application embodiment can set the iteration step count to 5000 steps and the residual convergence criterion to 1×10⁻⁶. -4 (Continuous phase), 1×10 - ³ (discrete phase), the calculation results are saved every 100 steps to monitor the trend of flow field parameter changes. The embodiments of this application can extract key parameters under fault conditions: the velocity deviation coefficient of the variable diameter section of the coal feeding system in Power Plant No. 1 is 0.45, the total pressure drop of the sealing air system in Power Plant No. 2 is 0.06MPa (exceeding the design value of 0.04MPa), and the particle flow fluctuation coefficient at the outlet of the small coal silo in Power Plant No. 3 is 0.3.

[0081] This application's embodiments can design orthogonal tests for three types of faults: the diameter change angle of the coal feed pipe in Power Plant 1 (30°, 40°, 45°, 50°), the angle of the sealing air guide plate in Power Plant 2 (10°, 15°, 20°, 25°), and the taper of the small coal silo in Power Plant 3 (45°, 50°, 55°, 60°) and the number of internal guide grooves (0, 2, 4, 6). Simulations are performed for each set of test parameters, and the optimal parameter combination is selected: a 45° diameter change angle for the coal feed pipe in Power Plant 1, a 15° angle for the sealing air guide plate in Power Plant 2, a 45° taper for the small coal silo in Power Plant 3, and 4 internal guide grooves. The optimized simulation shows that the velocity deviation coefficient of the coal feed system in Power Plant 1 is reduced to 0.12, the total pressure drop of the sealing air system in Power Plant 2 is reduced to 0.038 MPa, and the particle flow fluctuation coefficient at the outlet of the small coal silo in Power Plant 3 is reduced to 0.08, all meeting the design requirements.

[0082] This application's embodiments can generate flow field velocity cloud maps, pressure cloud maps, and particle trajectory maps through a numerical simulation post-processing module, clearly identifying the core root causes of three types of faults: excessively small diameter change angles in the feed pipe leading to sudden changes and uneven velocity distribution; unreasonable angles of the sealing air guide plates causing local eddies and additional resistance losses; and excessively large cones in the small powder silo causing particle retention and bridging. It outputs equipment modification drawings (diameter change angle adjustment, guide plate angle optimization, and internal guide channel addition) and construction technical requirements. After guiding the power plant to complete the modification, actual measurement data shows that the feed stability of Power Plant No. 1 increased to 92%, the energy consumption of the sealing air system in Power Plant No. 2 decreased by 8%, and the feed interruption fault in the small powder silo of Power Plant No. 3 was eliminated.

[0083] The flow field simulation analysis method for flue gas systems proposed in this application can construct a three-dimensional model of the flue gas system that accurately reflects the actual equipment structure and operating conditions of a power plant. By combining differentiated mesh generation, customized boundary conditions, and coupled simulation of gas-solid two-phase flow and turbulence, multi-physics fields can be accurately solved. Simultaneously, a fault diagnosis system is established through key flow field parameters, and structural parameters are optimized using orthogonal experiments. The method outputs flow field modification schemes that can directly guide on-site construction, thereby accurately capturing the abnormal flow field characteristics corresponding to faults. This enables precise diagnosis and targeted optimization of core equipment faults, adapting to typical specific faults of power plant boilers, providing direct guidance for on-site modifications, improving engineering practicality, shortening fault treatment cycles, reducing treatment costs, and ensuring operational stability and economy. This solves the problems in related technologies where neglecting the personalized structural parameters of power plant equipment and failing to fully consider the coupling effect of gas-solid two-phase flow, relying solely on empirical formulas to deduce fault causes, leads to insufficient targeting of optimization schemes, low fault diagnosis accuracy, poor engineering practicality, and difficulty in directly guiding on-site modifications with simulation results, resulting in long fault treatment cycles and high costs.

[0084] Next, with reference to the accompanying drawings, the flow field simulation analysis device for the flue gas system proposed in the embodiments of this application is described.

[0085] Figure 7 This is a schematic diagram of the flow field simulation analysis device for the flue gas system according to an embodiment of this application.

[0086] like Figure 7 As shown, the flow field simulation analysis device 10 of the air and smoke system includes: a determination module 100, a solution module 200, and an analysis module 300.

[0087] The determination module 100 is used to generate a three-dimensional structural model of the flue gas system using the structural parameters and operating condition data of the faulty equipment in the power plant, so as to perform mesh division and determine the structured mesh area and the unstructured mesh area.

[0088] The solver module 200 is used to load corresponding customized boundary conditions according to the structured grid region and the unstructured grid region, simulate the coal powder movement trajectory using a preset gas-solid two-phase flow model, and capture the flow field pulsation characteristics in combination with a preset turbulence model to solve the multiphysics field.

[0089] Analysis module 300 is used to extract at least one key flow field parameter of the flue gas system based on multi-physics field, establish a fault diagnosis index system based on at least one key flow field parameter, optimize structural parameters by combining orthogonal experimental design, and output a flow field modification scheme for the flue gas system.

[0090] Optionally, in one embodiment of this application, the determining module 100 includes: a first determining unit, a second determining unit, and a third determining unit.

[0091] The first determining unit is used to determine the target fault area and non-target fault area of ​​the three-dimensional structural model based on the key conditions of the air and smoke system.

[0092] The second determining unit is used to refine the structured mesh of the target fault area using a three-dimensional structural model and a first preset size, so as to determine the structured mesh area.

[0093] The third determining unit is used to refine the unstructured mesh of the non-target fault area using a three-dimensional structural model and a second preset size, so as to determine the unstructured mesh area.

[0094] Optionally, in one embodiment of this application, the solving module 200 includes: a first solving unit, a second solving unit, a third solving unit, and a fourth determining unit.

[0095] The first solving unit is used to simulate the insufficient powder feeding stability of the flue gas system based on customized boundary conditions and a preset gas-solid two-phase flow model, so as to solve the flow field velocity distribution of the flue gas system.

[0096] The second solver unit is used to simulate the abnormal sealing air pressure drop of the flue gas system based on customized boundary conditions and a preset turbulence model, in order to capture the flow field pulsation characteristics and solve the pressure loss of the flue gas system according to the flow field pulsation characteristics.

[0097] The third solution unit is used to simulate poor coal feeding in the small coal bin of the flue gas system based on customized boundary conditions, a preset gas-solid two-phase flow model and a preset pressure-velocity coupling strategy, so as to solve the coal powder movement trajectory of the flue gas system.

[0098] The fourth determining unit is used to determine the multiphysics field based on the flow velocity distribution, pressure loss, and pulverized coal movement trajectory.

[0099] Optionally, in one embodiment of this application, the analysis module 300 includes an identification unit and an establishment unit.

[0100] The identification unit is used to identify at least one fault feature of the flue gas system based on at least one key flow field parameter.

[0101] Establish a unit to establish a correspondence between at least one key flow field parameter and at least one fault feature, and establish a fault diagnosis index system based on the correspondence.

[0102] Optionally, in one embodiment of this application, the analysis module 300 includes: a fifth determining unit, a sixth determining unit, and a generating unit.

[0103] The fifth determining unit is used to select at least one structural parameter of the flue gas system to be optimized based on the fault diagnosis index system, and to determine the horizontal range of at least one structural parameter to be optimized in order to determine the orthogonal test table.

[0104] The sixth determining unit is used to calculate the flow field indices under different combinations of structural parameters based on the orthogonal experimental table, and to determine the optimal combination of structural parameters based on the flow field indices and preset optimization conditions.

[0105] The generation unit is used to output flow field modification schemes based on the optimal combination of structural parameters.

[0106] It should be noted that the foregoing explanation of the flow field simulation analysis method embodiment for the flue gas system also applies to the flow field simulation analysis device for the flue gas system in this embodiment, and will not be repeated here.

[0107] The flow field simulation and analysis device for the flue gas system proposed in this application can construct a three-dimensional model of the flue gas system that recreates the actual equipment structure and operating conditions of a power plant. Combined with differentiated mesh generation, customized boundary conditions, and coupled simulation of gas-solid two-phase flow and turbulence, it accurately solves multiphysics fields. Simultaneously, it establishes a fault diagnosis system through key flow field parameters and optimizes structural parameters using orthogonal experiments, outputting flow field modification schemes that can directly guide on-site construction. This accurately captures the abnormal flow field characteristics corresponding to faults, achieving precise diagnosis and targeted optimization of core equipment faults. It adapts to typical specific faults of power plant boilers, providing direct guidance for on-site modifications, improving engineering practicality, shortening fault treatment cycles, reducing treatment costs, and ensuring operational stability and economy. Therefore, it solves the problems in related technologies where neglecting the personalized structural parameters of power plant equipment and failing to fully consider the coupling effect of gas-solid two-phase flow, relying solely on empirical formulas to deduce fault causes, leads to insufficient targeting of optimization schemes, low fault diagnosis accuracy, poor engineering practicality, and the inability to directly guide on-site modifications with simulation results, resulting in long fault treatment cycles and high costs.

[0108] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 801, the processor 802, and the computer program stored on the memory 801 and capable of running on the processor 802.

[0109] When the processor 802 executes the program, it implements the flow field simulation analysis method for the flue gas system provided in the above embodiments.

[0110] Furthermore, electronic devices also include: Communication interface 803 is used for communication between memory 801 and processor 802.

[0111] The memory 801 is used to store computer programs that can run on the processor 802.

[0112] The memory 801 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0113] If the memory 801, processor 802, and communication interface 803 are implemented independently, then the communication interface 803, memory 801, and processor 802 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized into address buses, data buses, control buses, etc. For ease of representation, Figure 8 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0114] Optionally, in a specific implementation, if the memory 801, processor 802, and communication interface 803 are integrated on a single chip, then the memory 801, processor 802, and communication interface 803 can communicate with each other through an internal interface.

[0115] The processor 802 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0116] This application also provides a non-volatile computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described flow field simulation analysis method for a flue gas system.

[0117] This application also provides a computer program product storing a computer program that, when executed by a processor, implements the above-described method for flow field simulation analysis of a flue gas system.

[0118] 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.

[0119] 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, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0120] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N 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.

[0121] 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 by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0122] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more 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.

[0123] 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.

[0124] 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.

[0125] 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 and analyzing the flow field of a flue gas system, characterized in that, Includes the following steps: A three-dimensional structural model of the flue gas system is generated using the structural parameters and operating condition data of the faulty equipment in the power plant, in order to perform mesh generation and determine the structured and unstructured mesh regions. Based on the structured grid region and the unstructured grid region, corresponding customized boundary conditions are applied. A preset gas-solid two-phase flow model is used to simulate the coal powder movement trajectory, and a preset turbulence model is combined to capture the flow field fluctuation characteristics in order to solve the multiphysics field. Based on the multiphysics field, at least one key flow field parameter of the flue gas system is extracted, and a fault diagnosis index system is established based on the at least one key flow field parameter. The structural parameters are optimized by combining orthogonal experimental design, and the flow field modification scheme of the flue gas system is output.

2. The method according to claim 1, characterized in that, The process of mesh generation, determining structured and unstructured mesh regions, includes: Based on the key conditions of the air and smoke system, the target fault area and non-target fault area of ​​the three-dimensional structural model are determined; Using the aforementioned three-dimensional structural model, the target fault region is densified with a structured mesh using a first preset size to determine the structured mesh region; Using the aforementioned three-dimensional structural model, an unstructured mesh is densified in the non-target fault region using a second preset size to determine the unstructured mesh region.

3. The method according to claim 1, characterized in that, The process employs a pre-defined gas-solid two-phase flow model to simulate the trajectory of pulverized coal, and combines this with a pre-defined turbulence model to capture the flow field fluctuation characteristics in order to solve for the multiphysics field, including: Based on the customized boundary conditions and the preset gas-solid two-phase flow model, the insufficient powder feeding stability of the flue gas system is simulated in order to solve the flow field velocity distribution of the flue gas system. Based on the customized boundary conditions and the preset turbulence model, the abnormal sealing air pressure drop of the flue gas system is simulated to capture the flow field pulsation characteristics, and the pressure loss of the flue gas system is solved according to the flow field pulsation characteristics. Based on the customized boundary conditions, the preset gas-solid two-phase flow model, and the preset pressure-velocity coupling strategy, the poor coal feeding in the small coal bin of the flue gas system is simulated to solve the coal powder movement trajectory of the flue gas system. The multiphysics field is determined based on the flow field velocity distribution, the pressure loss, and the coal powder movement trajectory.

4. The method according to claim 1, characterized in that, The establishment of a fault diagnosis index system based on at least one key flow field parameter includes: Identify at least one fault feature of the flue gas system based on the at least one key flow field parameter; A correspondence is established based on at least one key flow field parameter and at least one fault feature, and a fault diagnosis index system is established based on the correspondence.

5. The method according to claim 1, characterized in that, The optimization of structural parameters through orthogonal experimental design, and the output of the flow field modification scheme for the flue gas system, include: Based on the fault diagnosis index system, at least one structural parameter of the flue gas system to be optimized is selected, and the horizontal range of the at least one structural parameter to be optimized is determined in order to determine the orthogonal test table; The flow field indices under different combinations of structural parameters are calculated based on the orthogonal experimental table, and the optimal combination of structural parameters is determined based on the flow field indices and preset optimization conditions. Based on the optimal combination of structural parameters, the flow field modification scheme is output.

6. A flow field simulation and analysis device for a flue gas system, characterized in that, include: The determination module is used to generate a three-dimensional structural model of the flue gas system using the structural parameters and operating condition data of the faulty equipment in the power plant, so as to perform mesh generation and determine the structured mesh area and the unstructured mesh area. The solution module is used to load corresponding customized boundary conditions according to the structured grid region and the unstructured grid region, simulate the coal powder movement trajectory using a preset gas-solid two-phase flow model, and capture the flow field fluctuation characteristics in combination with a preset turbulence model to solve the multiphysics field. The analysis module is used to extract at least one key flow field parameter of the flue gas system based on the multiphysics field, establish a fault diagnosis index system based on the at least one key flow field parameter, optimize the structural parameters by combining orthogonal experimental design, and output the flow field modification scheme of the flue gas system.

7. The apparatus according to claim 6, characterized in that, The determining module includes: The first determining unit is used to determine the target fault area and non-target fault area of ​​the three-dimensional structural model based on the key conditions of the air and smoke system. The second determining unit is used to use the three-dimensional structural model to perform structured mesh densification on the target fault area using a first preset size, so as to determine the structured mesh area; The third determining unit is used to use the three-dimensional structural model to perform unstructured mesh densification on the non-target fault area using a second preset size, so as to determine the unstructured mesh area.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to implement the flow field simulation analysis method for a flue gas system as described in any one of claims 1-5.

9. A non-volatile computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the flow field simulation analysis method for the flue gas system as described in any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the flow field simulation analysis method for the flue gas system as described in any one of claims 1-5.

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