Anti-abrasion and anti-explosion detection early warning method and system for boiler

Abstract

The invention discloses a boiler anti-wear and anti-explosion detection early warning method and system, and relates to the technical field of boiler detection, and the method comprises the steps: arranging a sensor to collect boiler operation data; calculating a temperature field in the boiler through a heat flow coupling equation; calculating a flow velocity field in the boiler by using a fluid mechanics equation; constructing a damage degree equation based on the temperature field and the flow velocity field in combination with the stress field distribution of the boiler component in the working environment, and evaluating the damage condition of the boiler component; based on the calculation result of the damage degree equation, the damage risk level of the boiler component is judged, risk early warning operation is executed, and operation parameters of the boiler are adjusted. According to the anti-abrasion and anti-explosion detection early warning method for the boiler, the multi-physical field effect in the boiler is comprehensively considered, and the damage state and potential risks of boiler parts can be evaluated more accurately. A finite element method is adopted to finely solve a temperature field and a flow velocity field, stress field distribution is combined, the damage degree of the boiler is dynamically evaluated, and the health state of boiler parts is reflected in real time.

Description

Technical Field

[0001] The invention relates to the technical field of boiler detection, and in particular to a boiler anti-wear and explosion-proof detection and early warning method and system. Background Art

[0002] Boilers play a vital role in various industrial production, especially in power generation, chemical industry, metallurgy and other fields. The long-term operation of boilers faces a variety of harsh environments, such as high temperature, high pressure and corrosive fluids. These factors will cause wear, cracks, corrosion and other damage to boiler components, such as furnace tubes, combustion chambers, heat exchangers, etc., and even affect the safety and stability of the boiler. At present, boiler monitoring technology mainly relies on basic parameter monitoring of equipment such as temperature sensors and pressure sensors, as well as regular manual inspections, non-destructive testing technologies and other methods.

[0003] However, traditional temperature and pressure monitoring can only provide some operating parameters of the boiler, and cannot fully reflect the damage of the internal components of the boiler. Especially under extreme conditions such as high temperature and high pressure, traditional methods are prone to miss or fail to detect minor damage.

[0004] The existing manual inspection method is limited by the experience of the inspectors and the inspection cycle, making it difficult to achieve real-time and dynamic monitoring, and the comprehensiveness and accuracy of the inspection are poor.

[0005] Although non-destructive testing techniques have been used in boiler damage assessment, these methods still require manual intervention and cannot provide comprehensive real-time data.

[0006] In summary, the existing boiler monitoring technology is unable to conduct a comprehensive assessment of the various operating conditions of the boiler, and it is difficult to accurately determine the damage of boiler components in real time. Summary of the invention

[0007] In view of the above-mentioned problems, the present invention is proposed.

[0008] Therefore, the technical problem solved by the present invention is that the prior art cannot comprehensively evaluate the damage status of boiler components, and only relies on the calculation of a single physical quantity, lacks coupling analysis and comprehensive consideration of the damage mechanism of boiler components.

[0009] In order to solve the above technical problems, the present invention provides the following technical solutions: a boiler anti-wear and explosion-proof detection and early warning method, comprising: Arrange sensors to collect boiler operation data; Calculate the temperature field inside the boiler through the heat-flux coupling equation; Calculate the flow velocity field inside the boiler using fluid mechanics equations; Based on the temperature field and velocity field combined with the stress field distribution of boiler components in the working environment, a damage degree equation is constructed to evaluate the damage status of boiler components. Based on the calculation results of the damage degree equation, the damage risk level of boiler components is determined, risk warning operations are performed, and the operating parameters of the boiler are adjusted; The calculation of the temperature field inside the boiler includes discretizing the spatial domain of the boiler, dividing it into a plurality of finite elements based on the geometric shape of the boiler components, and each element is connected by nodes to form a discretized grid; Based on the physical characteristics of the boiler, the finite element method is used as the numerical solution of the heat-flow coupling equation to convert the continuous temperature field into the temperature value of discrete points and calculate at each node; Setting temperature field boundary conditions, wherein the temperature field boundary conditions include heat source boundary conditions, convection heat transfer boundary conditions and initial temperature conditions; According to the discretized grid structure, the heat flow coupling equation of each unit is constructed, and the temperature value of each node at each time step is solved by iterative calculation through the temperature distribution of the node; Through the time domain analysis of the finite element method, the temperature field is iteratively calculated. In each time step, the temperature value of each node is updated according to the temperature distribution information of the current time step to obtain the dynamic temperature field of the boiler in the entire working cycle.

[0010] As a preferred solution of the boiler anti-wear and explosion-proof detection and early warning method of the present invention, wherein: the heat flow coupling equation is expressed as: , in, Indicates a location inside the boiler and time Temperature at It represents the thermal conductivity coefficient, which describes the thermal conductivity characteristics of the material; Represents the second-order gradient of the temperature field, describing the change of temperature in space; The coefficient that represents the coupling between fluid flow and temperature field, characterizing the effect of fluid flow on heat conduction; Represents the velocity field of the fluid.

[0011] As a preferred solution of the boiler anti-wear and explosion-proof detection and early warning method of the present invention, the fluid mechanics equation is expressed as: , in, Represents the fluid velocity field, describing the velocity of the fluid in space and time Distribution of Indicates the density of the fluid; Indicates the pressure of the fluid; Indicates the viscosity of the fluid; Indicates the coefficient of influence of temperature on fluid flow rate.

[0012] As a preferred solution of the boiler anti-wear and explosion-proof detection and early warning method of the present invention, wherein: the calculation of the flow velocity field inside the boiler includes discretizing the spatial domain of the boiler, dividing it into a plurality of finite units based on the geometric shape of the boiler components, and each unit is connected by nodes to form a discretized grid; Based on the physical characteristics of the boiler, the finite element method is used as a numerical solution to the fluid mechanics equations to convert the fluid velocity field into velocity values ​​at discrete points and perform calculations at each node; Setting velocity field boundary conditions, wherein the velocity field boundary conditions include velocity boundary conditions at the fluid inlet, temperature boundary conditions at the fluid inlet, and fluid pressure boundary conditions at the outlet; According to the discretized grid structure, the fluid mechanics equation of each unit is constructed to obtain the velocity field distribution of each node; Through the time domain analysis of the finite element method, the velocity field is iteratively calculated. In each time step, the fluid velocity field is updated according to the current temperature field and fluid characteristics until the convergence conditions are met, and the dynamic velocity field of the boiler in the entire working cycle is obtained.

[0013] As a preferred solution of the boiler anti-wear and explosion-proof detection and early warning method described in the present invention, the damage degree equation is expressed as: , in, Indicates the boiler components at time The damage situation at the time; Indicates time The stress you are under all the time; Indicates the maximum bearing stress of the material; Indicates the maximum operating temperature of the material; Indicates the maximum flow rate; They represent the sensitivity indexes of stress, temperature and flow rate to material damage respectively.

[0014] As a preferred solution of the boiler anti-wear and explosion-proof detection and early warning method of the present invention, wherein: the damage risk level of the boiler components is judged including when Less than or equal to the first damage threshold, it is judged as a low risk level. If the damage level is greater than the first damage threshold and less than the second damage threshold, it is judged as a medium risk level. If it is greater than or equal to the second damage threshold, it is judged as a high risk level.

[0015] As a preferred solution of the boiler anti-wear and explosion-proof detection and early warning method of the present invention, when it is judged to be a low risk level, the current operating parameters of the boiler are maintained; When it is judged to be a medium risk level, the discretized grid is optimized and the grid density is increased; Use the refined discretized grid to update the temperature field, velocity field and damage degree equation; Get the damage degree distribution under mesh encryption, identify abnormal meshes, and adjust the operating parameters of boiler components in the abnormal meshes; When it is judged to be a high risk level, the area that needs to be shut down is determined according to the damage distribution, and a local shutdown operation is performed through a zoning control strategy; In the shutdown area, acoustic emission detection technology is used to monitor the wear of boiler components and identify the worn parts; at the same time, infrared thermal imaging technology is used to monitor temperature anomalies in the combustion area, heat exchange area and cooling area; and vibration monitoring technology is used to evaluate the dynamic state of boiler components; Based on the test results, identify areas with wear or explosion hazards; Wear-resistant coating spraying or welding repair is carried out on the worn parts, support reinforcement and sealing treatment is carried out on the explosion-proof potential parts, and the fluid flow rate, temperature control and pressure parameters are adjusted; after the repair is completed, restart the boiler.

[0016] Another object of the present invention is to provide a boiler anti-wear and explosion-proof detection and early warning system, which can solve the problem that the existing boiler anti-wear and explosion-proof monitoring method lacks effective coupling analysis between the temperature field, velocity field and damage degree, resulting in the inability to accurately assess the damage risk of boiler components. At the same time, the existing method has low computational efficiency when dealing with complex physical field coupling problems.

[0017] To solve the above technical problems, the present invention provides the following technical solutions: a boiler anti-wear and explosion-proof detection and early warning system, comprising: a data acquisition module, used to arrange sensors to collect boiler operation data; a temperature analysis module, used to calculate the temperature field inside the boiler through a thermal-flux coupling equation; a flow rate analysis module, used to calculate the flow rate field inside the boiler using a fluid mechanics equation; a damage analysis module, used to construct a damage degree equation based on the temperature field and the flow rate field combined with the stress field distribution of boiler components in the working environment, and evaluate the damage status of boiler components; and an early warning adjustment module, used to judge the damage risk level of boiler components based on the calculation results of the damage degree equation, execute risk early warning operations, and optimize and adjust the operating parameters of the boiler.

[0018] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned boiler anti-wear and explosion-proof detection and early warning method when executing the computer program.

[0019] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned boiler anti-wear and explosion-proof detection and early warning method.

[0020] Beneficial effects of the present invention: The boiler anti-wear and explosion-proof detection and early warning method provided by the present invention optimizes the boiler anti-wear and explosion-proof detection and early warning process by introducing a damage assessment method based on the coupling of temperature field, velocity field and stress field. Compared with the traditional single physical field analysis, the present invention comprehensively considers the multi-physical field effects inside the boiler, and can more accurately assess the damage status and potential risks of boiler components. By using the finite element method to accurately solve the temperature field and velocity field, combined with the stress field distribution, the damage degree of the boiler is dynamically assessed, and the health status of the boiler components can be reflected in real time.

[0021] In terms of risk assessment, the present invention proposes a multi-level risk classification method based on damage degree. At a low risk level, the existing operating status of the boiler is maintained to avoid unnecessary operational intervention; at a medium risk level, the local grid encryption optimization scheme is used to improve the calculation accuracy, optimize the boiler operating parameters, and reduce potential damage; at a high risk level, combined with the risk assessment results, local shutdowns are carried out and repairs are implemented to prevent serious failures of boiler equipment due to damage.

[0022] The advantage of the present invention over traditional technologies is that it can not only more accurately assess the damage of boiler components, but also take targeted optimization measures according to different risk levels, thus avoiding untimely warnings or improper handling caused by over-reliance on traditional monitoring methods. By adopting the method of the present invention, the safety and efficiency of boiler operation are significantly improved, while the service life of the equipment is extended and downtime and maintenance costs are reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0024] Figure 1 An overall flow chart of a boiler anti-wear and explosion-proof detection and early warning method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0026] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0027] Example 1, reference Figure 1 , which is an embodiment of the present invention, provides a boiler anti-wear and explosion-proof detection and early warning method, comprising: Step 1: Arrange sensors to collect boiler operation data; Step 2: Calculate the temperature field inside the boiler through the heat flow coupling equation; Step 3: Calculate the flow velocity field inside the boiler using fluid mechanics equations; Step 4: Based on the temperature field and velocity field combined with the stress field distribution of boiler components in the working environment, a damage degree equation is constructed to evaluate the damage status of boiler components; Step 5: Based on the calculation results of the damage degree equation, determine the damage risk level of the boiler components, perform risk warning operations, and adjust the operating parameters of the boiler.

[0028] In step 2, the heat flow coupling equation is expressed as, , in, Indicates a location inside the boiler and time Temperature at It represents the thermal conductivity coefficient, which describes the thermal conductivity characteristics of the material; Represents the second-order gradient of the temperature field, describing the change of temperature in space; The coefficient that represents the coupling between fluid flow and temperature field, characterizing the effect of fluid flow on heat conduction; Represents the velocity field of the fluid.

[0029] It should be noted that, in this embodiment, a local heat source model is first constructed to describe the distribution of heat source density inside the boiler, which is expressed as: , in, A location inside the boiler and time The heat source density at time represents the heat energy generation density at that position and time. is the initial heat source density of the boiler, which indicates the heat source distribution of the boiler under standard working conditions and is obtained through boiler design parameters or experimental data. A location inside the boiler and time The temperature change at time , which represents the temperature difference between this position and the target temperature, is defined as ,in is the actual temperature at that location, is the target temperature at that location, It is the maximum operating temperature of the boiler, which is determined by the material and design limitations of the boiler and is usually the maximum safe temperature for boiler operation.

[0030] Furthermore, calculating the temperature field inside the boiler includes discretizing the spatial domain of the boiler, dividing it into a plurality of finite elements based on the geometric shapes of the boiler components, and each element is connected by nodes to form a discretized grid; Based on the physical characteristics of the boiler, the finite element method is used as the numerical solution of the heat-flow coupling equation to convert the continuous temperature field into the temperature value of discrete points and calculate at each node; Set the temperature field boundary conditions, which include heat source boundary conditions, convection heat transfer boundary conditions and initial temperature conditions. The heat source boundary conditions reflect the heat input inside the boiler, and the cooling area is modeled by the convection heat transfer boundary conditions. According to the discretized grid structure, the heat flow coupling equation of each unit is constructed, and the temperature value of each node at each time step is solved by iterative calculation through the temperature distribution of the node; Through the time domain analysis of the finite element method, the temperature field is iteratively calculated. In each time step, the temperature value of each node is updated according to the temperature distribution information of the current time step to obtain the dynamic temperature field of the boiler in the entire working cycle.

[0031] It should be noted that the thermal conductivity It represents the thermal conductivity of the material under the action of heat flow, which determines the speed of temperature propagation inside the boiler. In order to calculate the thermal conductivity coefficient, the local heat source model and the temperature difference of the temperature field are combined to derive it, which is expressed as: , in, A location inside the boiler and time The thermal conductivity coefficient at this time indicates the thermal conductivity of this position. A location inside the boiler and time The temperature difference at that time indicates the difference between the actual temperature at that location and the target temperature.

[0032] To further optimize the heat transfer coefficient, the least square method is introduced to adjust the heat transfer coefficient by minimizing the error between the internal temperature field of the boiler and the target temperature field, which is expressed as: , in, is the temperature field error, which represents the sum of the errors between the target temperature field and the actual temperature field. The target temperature field is preset based on boiler design or experimental data. is the actual temperature field.

[0033] By minimizing the error, more accurate thermal conductivity is obtained , expressed as: , in, is the optimized thermal conductivity coefficient, is the initial heat transfer coefficient, is the learning rate.

[0034] Coupling coefficient It reflects the coupling intensity between fluid velocity and temperature field, which can be expressed as: , in, A location inside the boiler and time The coupling coefficient when is the temperature difference, which represents the difference between the actual temperature at that location and the target temperature.

[0035] Use particle swarm optimization algorithm to optimize the coupling coefficient , to obtain the optimal coupling effect, expressed as: , in, is the coupling coefficient error, is the target coupling coefficient.

[0036] Through the optimization process, the following optimal coupling coefficient is finally obtained, expressed as: , Specifically, the purpose of boiler spatial domain discretization is to transform the continuous physical system into a discrete mathematical model for numerical calculation. The specific steps are as follows: First, a 3D geometric model of the boiler is established. This model can be designed using a 3D modeling tool. The geometric model should include the combustion area, heat exchange area, and cooling area. Through this model, the characteristics of different areas can be identified, so that the mesh density can be arranged reasonably.

[0037] In the combustion zone, since the temperature gradient during the combustion process is very steep, a higher calculation accuracy is required. In order to ensure the calculation accuracy, a refined grid is used. At this time, the side length of each grid unit will be relatively small, for example, set to 5cm or less. In the heat exchange zone, the temperature field changes relatively uniformly in this part, and a coarser grid can be used to reduce the amount of calculation. The grid density can be set to 10cm to 15cm. In the cooling zone, the temperature field in the cooling zone changes less, so the grid spacing can be further increased, and the grid density is set to about 20cm.

[0038] The combustion zone mesh uses tetrahedral elements, which have the advantage of being able to adapt to complex geometries. In the combustion zone, the accuracy of details can be increased by adaptive mesh encryption technology. The heat exchange zone mesh uses hexahedral elements, which can provide higher computational efficiency under regular geometric structures. The cooling zone mesh can use tetrahedral or hexahedral elements, depending on the specific geometry.

[0039] For areas with large temperature field changes in the combustion zone, an adaptive mesh refinement method is used. This method adjusts the mesh density based on the preliminary calculation results to ensure the accuracy of important areas.

[0040] Calculate temperature gradient: Calculate the rate of change of the temperature field in each grid cell. If the gradient value exceeds the threshold, refine the grid.

[0041] , in, Representing temperature field The gradient at a certain position in space describes the rate of change of the temperature field along the spatial direction. Indicates the temperature value at the next node of the current grid cell, usually the location The temperature at Indicates the temperature value at the current node of the current grid cell, usually the position The temperature at Represents the spatial distance between the current grid cells.

[0042] In areas with large gradients, mesh refinement is performed. For example, in the combustion zone, if the temperature gradient is greater than the set threshold, the number of meshes in this area is increased to ensure the accuracy of the numerical solution.

[0043] To avoid computational errors due to mesh quality issues such as distortion or degradation, mesh quality standards need to be followed during mesh generation: the minimum angle of the mesh should be greater than 30 degrees. The shape factor of the mesh should be close to 1 to avoid distorted meshes.

[0044] Furthermore, the boiler space domain is divided into grids It needs to be discretized into multiple small units. According to the actual geometry of the boiler, select the appropriate mesh type, such as tetrahedron, hexahedron, etc. The density of the mesh should be selected according to the calculation accuracy requirements: for high temperature gradient areas, the mesh density should be increased; for areas with relatively gentle temperature changes, the mesh density can be appropriately reduced. For a single small unit, the temperature field The shape functions at the nodes are interpolated within each element.

[0045] Shape function selection: choose first-order linear shape function Temperature field Interpolation is performed, expressed as: , in, Indicates the spatial location of the boiler and time The temperature at Indicates The shape function of the nodes satisfies , that is, at the node At, shape function is 1, and other nodes are 0. Indicates Nodes at time The temperature at that time.

[0046] Laplace Operator in the Heat-Flow Coupled Equation It is necessary to discretize and use the finite element method to calculate the stiffness matrix of each element. The discretization of the Laplace operator is: , in, is the element stiffness matrix, representing the node and nodes The heat conduction relationship between Represents the second-order gradient of the shape function.

[0047] The specific calculation method of the stiffness matrix is: , Fluid coupling term in the thermal-fluid coupling equation Describes the effect of fluid flow on the temperature field. Discretize this item to get the fluid coupling matrix: , in, Represents the fluid coupling matrix, which describes the coupling effect between fluid flow and temperature field. The calculation needs to consider the fluid velocity field , discretized by numerical methods such as weighted least squares.

[0048] In order to improve stability and accurately describe the evolution of the temperature field over time, this embodiment uses the backward Euler method for time discretization. In the backward Euler method, the time derivative term At the current time step Discretize it and get: , in, Indicates the current time step The temperature field at the moment, Represents the previous time step The temperature field at the moment, Represents the time step.

[0049] The advantage of the backward Euler method is its good stability, which is especially effective when dealing with large temperature gradients.

[0050] According to the heat-flow coupling equation, the time derivative of the temperature field is discretized using the backward Euler method to obtain: , After finishing, we get: , Among them, K represents the stiffness matrix, which contains the stiffness matrices of all small units. Represents the fluid coupling matrix, which contains the fluid coupling terms of all small units. Indicates the current time step The temperature vector contains the temperatures of all nodes. F represents the load vector, which includes the influence of the heat source term.

[0051] Through time discretization, the heat flow coupling equation is transformed into a system of linear algebraic equations. In order to efficiently solve the system of equations, the conjugate gradient method is used, which includes: Initialization: Initial temperature field Set as the initial condition, usually a uniform temperature field. Initial residual . Initial search direction .

[0052] Iterative calculation: In each iteration, the step size is calculated : , in, represents the residual vector of the kth iteration, Indicates the search direction for the kth iteration.

[0053] Update temperature field : , in, represents the updated temperature field, Represents the temperature field of the previous iteration.

[0054] Update residual : , in, represents the residual vector of the k+1th iteration.

[0055] Update search direction : , in: , Termination condition: When the residual When it is small enough or the number of iterations reaches the maximum value, stop the iteration.

[0056] Through the above steps, the heat-flow coupling equations are discretized based on the finite element method, and the implicit time discretization method (backward Euler method) and conjugate gradient method are used to solve the temperature field T(x, t) of the boiler at different time steps, which can more accurately describe the dynamic changes of the boiler temperature field.

[0057] In step three, the fluid dynamics equation is expressed as, , in, Represents the fluid velocity field, describing the velocity of the fluid in space and time Distribution of Indicates the density of the fluid; Indicates the pressure of the fluid; Indicates the viscosity of the fluid; Indicates the coefficient of influence of temperature on fluid flow rate.

[0058] Specifically, calculating the velocity field inside the boiler includes discretizing the spatial domain of the boiler, dividing it into multiple finite elements based on the geometric shapes of the boiler components, and each element is connected by nodes to form a discretized grid; Based on the physical characteristics of the boiler, the finite element method is used as a numerical solution to the fluid mechanics equations to convert the fluid velocity field into velocity values ​​at discrete points and perform calculations at each node; Setting velocity field boundary conditions, which include velocity boundary conditions at the fluid inlet, temperature boundary conditions at the fluid inlet, and fluid pressure boundary conditions at the outlet, wherein the velocity and temperature at the fluid inlet reflect the initial working state of the boiler; According to the discretized grid structure, the fluid mechanics equation of each unit is constructed to obtain the velocity field distribution of each node; Through the time domain analysis of the finite element method, the velocity field is iteratively calculated. In each time step, the fluid velocity field is updated according to the current temperature field and fluid characteristics until the convergence conditions are met, and the dynamic velocity field of the boiler in the entire working cycle is obtained.

[0059] Specifically, under steady-state or unsteady-state flow conditions, temperature changes will affect the density and flow rate of the fluid, thereby affecting the movement of the fluid. Specifically, an increase in temperature usually leads to a decrease in the density of the fluid, which in turn leads to an increase in the buoyancy effect. The coefficient of the effect of temperature on the flow rate of the fluid is expressed as: , in, represents density, The specific heat capacity of the fluid Represents the thermal expansion coefficient of the fluid.

[0060] In step 4, the damage degree equation is expressed as, , in, Indicates the boiler components at time The damage situation at the time; Indicates time The stress you are under all the time; Indicates the maximum bearing stress of the material; Indicates the maximum operating temperature of the material; Indicates the maximum flow rate; They represent the sensitivity indexes of stress, temperature and flow rate to material damage respectively.

[0061] It should be noted that in the actual operation of the boiler, the material will experience periodic load changes, which have a direct impact on the fatigue damage of the material. Fatigue testing is an effective way to obtain the effect of stress on material damage. Using standard fatigue test methods, the material is placed under different stress amplitudes for multiple loading and unloading to simulate the stress changes of boiler components in actual operation. Fatigue life of test materials (i.e., the time it takes for a material to rupture or fail at different stress levels) and the amount of deformation of a material at different stress amplitudes.

[0062] Design several different stress amplitudes , testing the fatigue life of the material under these stress amplitudes .

[0063] Fitting SN curve according to test results, that is, fatigue life of the material and stress amplitude The relationship between: , in, is a constant, is the stress sensitivity index.

[0064] Through the SN curve fitted by the above experiment, the stress sensitivity index can be obtained The sensitivity index is calculated based on the experimental data at different stress levels. For example, by using regression analysis method, fatigue life data under different stress amplitudes can be fitted to obtain .

[0065] Temperature is another important factor that affects the performance of boiler materials. The performance of materials in high temperature environments often changes significantly, so the effect of temperature on materials needs to be quantified through experiments. In order to obtain the temperature sensitivity index , follow these steps: High temperature creep is a common phenomenon in boiler operation, especially in the high temperature part of the boiler, where the long-term high temperature load on the material may cause creep damage. Creep test can effectively measure the effect of temperature on material damage.

[0066] Constant stress is applied in a high temperature environment, and the creep behavior of the material is tested by measuring the deformation. Usually, the high temperature environment commonly seen in boilers is selected for creep testing, such as testing the creep rate of materials under high temperature conditions such as 300℃, 500℃, and 700℃.

[0067] Conduct creep tests at multiple temperatures (e.g. 300°C, 500°C, 700°C) and record the deformation rate of the material .

[0068] Use the formula to fit the relationship between temperature and creep rate: , in, is a constant, is the temperature, is the maximum operating temperature of the material, is the temperature sensitivity index.

[0069] The temperature sensitivity index is obtained by fitting the relationship between temperature and creep rate through creep test Through creep test data at different temperatures, the effect of temperature on material damage is calculated, and the temperature sensitivity index is fitted. .

[0070] Too high a flow rate may cause wear on the material surface, especially in the steam pipes and heat exchangers of the boiler. The fluid wear test simulates the flow of fluid inside the boiler to evaluate the effect of flow rate on material damage.

[0071] Select several typical flow rates (e.g. Wear tests were performed at these flow rates. At each flow rate, the wear depth or mass loss of the test material was measured. The relationship between flow rate and damage was fitted based on the wear data: , in, is a constant, is the flow rate, is the maximum flow rate, is the flow rate sensitivity index.

[0072] The flow rate sensitivity index was fitted by analyzing the effect of flow rate on material damage through flow rate test data. .

[0073] In step 5, the damage risk level of boiler components is determined including Less than or equal to the first damage threshold, it is judged as a low risk level. If the damage level is greater than the first damage threshold and less than the second damage threshold, it is judged as a medium risk level. If it is greater than or equal to the second damage threshold, it is judged as a high risk level.

[0074] When it is judged as a low risk level, the current boiler operating parameters are maintained.

[0075] When it is judged to be a medium risk level, the discretized grid is optimized and the grid density is increased; the temperature field, velocity field and damage degree equation are updated using the encrypted discretized grid; the damage degree distribution under mesh encryption is obtained, the abnormal grid is determined, and the operating parameters of the boiler components in the abnormal grid are adjusted; It should be noted that the discretized grid is locally optimized, and an adaptive grid generation algorithm is used to identify high-gradient areas of the temperature field, velocity field, and stress field, and the grids are encrypted for these areas. Based on the encrypted grid, the temperature field, velocity field, and damage degree equation are recalculated. Specifically, based on the spatial resolution of the encrypted grid, the temperature, velocity, and damage degree values ​​of each node are updated to ensure the calculation accuracy. According to the optimized damage degree distribution, abnormal grids are identified, and abnormal grids are defined as areas where the damage degree exceeds the preset threshold. By comparing with the temperature, velocity, and stress field data, the most severely damaged areas are determined. For the areas where the identified abnormal grids are located, the operating parameters of the boiler components are adjusted. The adjustments include optimizing fluid flow rate, temperature control, and pressure adjustment to reduce or avoid potential component damage and ensure the safe operation of the boiler.

[0076] When it is judged to be a high-risk level, the area that needs to be shut down is determined based on the damage distribution, and a local shutdown operation is performed through a zoning control strategy; in the shutdown area, acoustic emission detection technology is used to monitor the wear of boiler components and identify worn areas; at the same time, infrared thermal imaging technology is used to monitor temperature anomalies in the combustion area, heat exchange area and cooling area; and the dynamic state of boiler components is evaluated through vibration monitoring technology; based on the test results, the areas of wear or explosion-proof hazards are identified; the worn areas are repaired by spraying wear-resistant coatings or welding, and the areas with explosion-proof hazards are supported, reinforced and sealed, and the fluid flow rate, temperature control and pressure parameters are adjusted; after the repair is completed, the boiler is restarted.

[0077] Specifically, firstly, by analyzing the distribution of damage degree, determine the area in the boiler that needs to be shut down. This step compares the damage degree value of each area of ​​the boiler with the preset risk threshold. Through damage degree analysis, identify the area where the damage degree exceeds the high risk threshold, and combine multiple physical field data such as heat flow and stress to adopt a partition control strategy for local shutdown. The specific method is: for each boiler component (such as heat exchanger, burner, pipeline, etc.), spatial discretization is performed according to the changes in parameters such as damage degree value, temperature, pressure, etc., priority shutdown is performed according to the level of damage degree, and the operation process is optimized. During shutdown, gradually reduce the fluid flow rate, temperature and pressure in the boiler to reduce the mechanical load and prevent further damage.

[0078] In the shutdown area, acoustic emission detection technology is first used to monitor the wear of boiler components. Acoustic emission sensors are installed at key locations of boiler components (such as heat exchangers, burners, etc.). By collecting weak acoustic wave signals on the surface of boiler components in real time, cracks, wear or deformation on the surface or inside of the material can be identified. By analyzing the frequency, amplitude and waveform of the acoustic wave signal, the location and degree of wear can be accurately determined. The analysis of acoustic emission signals can be combined with damage degree data to accurately locate the worn parts and provide a basis for subsequent repair operations.

[0079] At the same time, infrared thermal imaging technology is used to monitor temperature anomalies in the combustion area, heat exchange area and cooling area of ​​the boiler. The surface temperature distribution of boiler components is an important indicator for monitoring the operating status of the boiler. By installing infrared thermal imaging equipment at various key locations of the boiler (such as burners, heat exchangers, pipelines, etc.), the temperature changes on the surface of the boiler can be monitored in real time. By capturing thermal radiation and generating temperature images of boiler components, the infrared thermal imager can clearly show possible temperature anomaly areas, such as local overheating or insufficient cooling. These temperature anomaly areas are usually related to equipment wear, corrosion, blockage and other problems, so infrared thermal imaging can effectively detect potential risks in boiler operation.

[0080] Vibration monitoring technology is used to evaluate the dynamic state of boiler components. Vibration sensors are installed at key parts of the boiler to collect vibration signals of boiler components in real time. The frequency and amplitude of the vibration signal reflect the dynamic characteristics of boiler components, such as whether resonance, looseness or deformation occurs during operation. In particular, abnormal vibration under high temperature and high pressure environments is often closely related to wear, cracks or structural damage. Based on the analysis of vibration signals and combined with other test data, the damage state of boiler components can be identified, and the location of wear or explosion hazards can be further confirmed.

[0081] According to the results of the above detection technology, after the parts with wear or explosion-proof hidden dangers are identified, the repair phase begins. For the worn parts, wear-resistant coating spraying or welding repair is adopted. The specific operation includes: firstly, the surface of the worn parts is cleaned to remove impurities such as rust and oxides. Then, according to the degree of wear, appropriate wear-resistant coating materials (such as ceramic coatings, alloy coatings, etc.) are selected, and the wear-resistant coating is evenly applied to the worn parts by spraying technology. For severely worn parts, welding repair is adopted. First, the damaged parts are cut or cleaned, and then the damaged areas are repaired using the corresponding welding technology to ensure that the parts restore their original strength and durability.

[0082] For explosion-proof potential areas, support reinforcement, sealing treatment and other measures are taken for repair. Support reinforcement usually increases the load-bearing capacity by installing support frames or adding reinforcing ribs on the outside of boiler components (such as pipes, furnace walls, etc.). Sealing treatment uses materials such as explosion-proof sealants and high-temperature resistant gaskets to seal the high-pressure and high-temperature areas of the boiler to prevent leakage or explosion accidents. Reinforcement and sealing treatment are particularly important in high-risk areas of the boiler (such as the combustion area), which can effectively improve the safety and stability of boiler components.

[0083] After completing the wear repair and explosion-proof hidden danger treatment, adjust the fluid flow rate, temperature control and pressure parameters of the boiler according to the working status of the repaired part. After the repair is completed, first conduct a low-power test run to gradually restore the normal operation of the boiler. During the operation, closely monitor the temperature, pressure, flow rate and other parameters of the repaired part, and adjust the operating parameters of the boiler according to the monitoring data to ensure that it operates in a safe and stable state.

[0084] After the repair is completed and the parameters are adjusted, restart the boiler. When restarting, gradually increase the temperature and pressure to avoid excessive pressure and temperature difference causing new damage to the boiler components. After the boiler resumes normal operation, continue to monitor various operating parameters to ensure safe and stable operation of the boiler.

[0085] Embodiment 2, an embodiment of the present invention, provides a boiler anti-wear and explosion-proof detection and early warning system, comprising: A data acquisition module, used to arrange sensors to collect boiler operation data; Temperature analysis module, used to calculate the temperature field inside the boiler through heat-flow coupling equations; Flow rate analysis module, used to calculate the flow rate field inside the boiler using fluid mechanics equations; Damage analysis module, which is used to construct damage degree equations based on temperature field and velocity field combined with stress field distribution of boiler components in the working environment to evaluate the damage status of boiler components; The early warning adjustment module is used to determine the damage risk level of boiler components based on the calculation results of the damage degree equation, perform risk early warning operations, and optimize and adjust the operating parameters of the boiler.

[0086] Embodiment 3, an embodiment of the present invention, is different from the first two embodiments in that: If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0087] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0088] More specific examples of computer-readable media (a non-exhaustive list) include the following representations of electrical connections with one or more wirings (electronic devices), portable computer disk cartridges (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0089] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following techniques known in the art or their combination can be used to implement a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit with a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0090] Example 4, an embodiment of the present invention, provides a boiler anti-wear and explosion-proof detection and early warning method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0091] The environmental conditions of the experiment were set as follows: the fluid in the boiler simulation area was water vapor, the average operating temperature was 500°C, and the pressure was set to 30 MPa. The boiler components were made of high-temperature resistant alloy steel with a maximum bearing stress of 400 MPa. The flow rate range was 5-10 m / s, and the temperature fluctuated between 300°C and 600°C. Sensors were arranged inside the boiler to collect temperature, flow rate and pressure information in real time. The traditional method relies on simplified thermodynamic formulas and empirical models, and only considers temperature and pressure data. The method of the present invention uses numerical calculations of thermal-fluid coupling equations and fluid mechanics equations, combined with damage degree equations, to conduct a comprehensive damage assessment.

[0092] In the experiment, the traditional method and the method of the present invention were first used to conduct risk assessment under high load (70%), medium load (50%) and low load (30%). The traditional method evaluates the damage of boiler components by combining temperature and fluid pressure data with empirical formulas. The specific steps include: collecting sensor data, estimating temperature field and flow velocity field using empirical formulas, calculating the degree of damage according to simplified evaluation formulas, and finally judging the risk level. The method of the present invention first collects real-time data of the boiler through sensors, calculates the temperature field and flow velocity field in the boiler using thermal-fluid coupling equations and fluid mechanics equations, discretizes each node through the finite element method, and evaluates the damage of each node in combination with the damage degree equation. Finally, the risk level is judged according to the calculation results, and early warning operations are performed to adjust the boiler operating parameters. The experimental results are shown in Table 1.

[0093] Table 1 Comparison of experimental results , Traditional methods usually rely on simplified physical models and empirical formulas, most of which ignore the coupling between the complex temperature field, velocity field and stress field inside the boiler. The calculation process of traditional methods often simplifies or calculates these factors independently, resulting in the inability to fully and accurately reflect the actual damage of boiler components under different working conditions. Especially under high load and low load conditions, the fluid dynamics inside the boiler change more complexly, and traditional methods fail to fully capture this change, resulting in deviations in the evaluation results.

[0094] In contrast, the present invention considers the multi-dimensional coupling effects of temperature, flow rate and stress through the joint calculation of thermal-fluid coupling equations and fluid mechanics equations. This comprehensive calculation method can reflect the actual working environment inside the boiler, thereby providing a more accurate risk assessment under complex working conditions such as high load and low load. Specifically, the method of the present invention uses more sophisticated numerical calculation and discretization technology, which can dynamically iterate the temperature field and flow rate field in multiple time steps, accurately capture the temperature fluctuations, fluid flow and stress distribution of boiler components, and avoid the errors that may be caused by the simplified processing of traditional methods.

[0095] In addition, the present invention introduces a damage degree equation based on the coupled analysis of temperature field and velocity field, quantifying the influence of temperature, velocity and stress on boiler component damage as a comprehensive evaluation index, thereby achieving accurate risk judgment in boiler operation. The innovation of this method is that it not only integrates the influence of multiple physical fields, but also optimizes and adjusts the boiler operation parameters according to the actual calculation results, effectively reducing the damage risk of boiler components and ensuring the safe and efficient operation of the boiler.

[0096] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A boiler anti-wear and explosion-proof detection and early warning method, characterized in that: include: Arrange sensors to collect boiler operation data; Calculate the temperature field inside the boiler through the heat-flux coupling equation; Calculate the flow velocity field inside the boiler using fluid mechanics equations; Based on the temperature field and velocity field combined with the stress field distribution of boiler components in the working environment, a damage degree equation is constructed to evaluate the damage status of boiler components; Based on the calculation results of the damage degree equation, the damage risk level of boiler components is determined, risk warning operations are performed, and boiler operating parameters are adjusted; The calculation of the temperature field inside the boiler includes discretizing the spatial domain of the boiler into a plurality of finite elements based on the geometric shapes of the boiler components, wherein each element is connected by nodes to form a discretized grid; Based on the physical characteristics of the boiler, the finite element method is used as the numerical solution of the heat-flow coupling equation to convert the continuous temperature field into discrete point temperature values ​​and perform calculations at each node; Setting temperature field boundary conditions, wherein the temperature field boundary conditions include heat source boundary conditions, convection heat transfer boundary conditions and initial temperature conditions; According to the discretized grid structure, the heat flow coupling equation of each unit is constructed, and the temperature value of each node at each time step is solved by iterative calculation through the temperature distribution of the node; Through the time domain analysis of the finite element method, the temperature field is iteratively calculated. In each time step, the temperature value of each node is updated according to the temperature distribution information of the current time step to obtain the dynamic temperature field of the boiler during the entire working cycle.

2. A boiler anti-wear and explosion-proof detection and early warning method according to claim 1, characterized in that: The heat flow coupling equation is expressed as, ， in, Indicates a location inside the boiler and time Temperature at Represents the thermal conductivity coefficient, which describes the thermal conductivity characteristics of the material; Represents the second-order gradient of the temperature field, describing the change of temperature in space; The coefficient that represents the coupling between fluid flow and temperature field, characterizing the effect of fluid flow on heat conduction; represents the velocity field of the fluid.

3. A boiler anti-wear and explosion-proof detection and early warning method according to claim 2, characterized in that: The fluid mechanics equation is expressed as, ， in, Represents the fluid velocity field, describing the velocity of the fluid in space and time distribution of Indicates the density of the fluid; Indicates the pressure of the fluid; Indicates the viscosity of the fluid; Indicates the coefficient of influence of temperature on fluid flow rate.

4. A boiler anti-wear and explosion-proof detection and early warning method according to claim 3, characterized in that: The calculation of the flow velocity field inside the boiler includes discretizing the spatial domain of the boiler into a plurality of finite elements based on the geometric shape of the boiler components, wherein each element is connected by nodes to form a discretized grid; Based on the physical characteristics of the boiler, the finite element method is used as a numerical solution to the fluid mechanics equations to convert the fluid velocity field into velocity values ​​at discrete points and perform calculations at each node. Setting velocity field boundary conditions, wherein the velocity field boundary conditions include velocity boundary conditions at the fluid inlet, temperature boundary conditions at the fluid inlet, and fluid pressure boundary conditions at the fluid outlet; According to the discretized grid structure, the fluid mechanics equation of each unit is constructed to obtain the velocity field distribution of each node; Through time-domain analysis using the finite element method, the velocity field is iteratively calculated. In each time step, the fluid velocity field is updated according to the current temperature field and fluid characteristics until the convergence conditions are met, and the dynamic velocity field of the boiler throughout the entire working cycle is obtained.

5. A boiler anti-wear and explosion-proof detection and early warning method according to claim 4, characterized in that: The damage degree equation is expressed as, ， in, Indicates the boiler components at time The damage situation at the time of Indicates time The stress you are constantly under; Indicates the maximum bearing stress of the material; Indicates the maximum operating temperature of the material; Indicates the maximum flow rate; They represent the sensitivity indexes of stress, temperature and flow rate to material damage respectively.

6. A boiler anti-wear and explosion-proof detection and early warning method according to claim 5, characterized in that: The determination of the damage risk level of boiler components includes: Less than or equal to the first damage threshold, it is judged as a low risk level. Greater than the first damage threshold and less than the second damage threshold, it is judged as a medium risk level. If the damage level is greater than or equal to the second damage threshold, it is judged as a high risk level.

7. A boiler anti-wear and explosion-proof detection and early warning method according to claim 6, characterized in that: When it is judged to be a low risk level, the current boiler operating parameters are maintained; When the risk level is judged to be medium, the discretized grid is optimized and the grid density is increased; Use the refined discretized grid to update the temperature field, velocity field and damage equation; Obtain the damage degree distribution under mesh encryption, identify abnormal meshes, and adjust the operating parameters of boiler components within the abnormal meshes; When the risk level is judged to be high, the area that needs to be shut down is determined based on the damage distribution, and a local shutdown operation is performed through a zoning control strategy; In the shutdown area, acoustic emission detection technology is used to monitor the wear of boiler components and identify worn areas. At the same time, infrared thermal imaging technology is used to monitor temperature anomalies in the combustion area, heat exchange area, and cooling area. Vibration monitoring technology is also used to evaluate the dynamic status of boiler components. Based on the test results, identify areas with wear or explosion hazards; Repair the worn parts by spraying wear-resistant coating or welding, support and reinforce the explosion-proof potential parts, seal them, and adjust the fluid flow rate, temperature control and pressure parameters; after the repair is completed, restart the boiler.

8. A boiler anti-wear and explosion-proof detection and early warning system, using a boiler anti-wear and explosion-proof detection and early warning method according to any one of claims 1 to 7, characterized in that: include: Data acquisition module, used to arrange sensors to collect boiler operation data; Temperature analysis module, used to calculate the temperature field inside the boiler through heat-flux coupling equations; Flow velocity analysis module, used to calculate the flow velocity field inside the boiler using fluid mechanics equations; A damage analysis module is used to construct a damage degree equation based on the temperature field and velocity field combined with the stress field distribution of boiler components in the working environment to evaluate the damage status of boiler components; and The early warning adjustment module is used to judge the damage risk level of boiler components based on the calculation results of the damage degree equation, perform risk early warning operations, and optimize and adjust the operating parameters of the boiler.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the boiler anti-wear and explosion-proof detection and early warning method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the boiler anti-wear and explosion-proof detection and early warning method according to any one of claims 1 to 7 are implemented.

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