Boiler heat transfer inertia determination, boiler heat transfer inertia regulation analysis method and device

Abstract

The present application relates to the technical field of physical engine, specifically to a boiler heat transfer inertia determination and a boiler heat transfer inertia regulation and analysis method and device, comprising the following steps: according to the geometric structure of the boiler and the material physical property parameters, the superheater tube wall and the header are discretized into Lagrange material points carrying mass, temperature and stress information. In the present application, by analyzing the geometric structure of the boiler and the material physical property parameters, the heated components are discretized into material points carrying mass, temperature and stress information, and coupling processing is realized in the grid covering the fluid domain and the solid domain, so that the heat transfer process can be continuously tracked on a global scale. By performing mapping between the material point information and the grid nodes under variable working conditions, the distribution changes of momentum and energy can be obtained synchronously, and these change results are updated to the material points in real time, realizing the dynamic interaction of position, temperature and stress, and making the time sequence evolution of heat transfer inertia more accurate.

Description

Technical Field

[0001] This invention relates to the field of physical engine technology, and in particular to a method and apparatus for determining boiler heat transfer inertia and for controlling and analyzing boiler heat transfer inertia. Background Technology

[0002] A boiler is a device that converts the chemical energy of fuel into the internal energy of steam. The energy transfer process is as follows: the chemical energy of the fuel entering the boiler is converted into the thermal energy of the flue gas / bed material; the flue gas / bed material transfers the thermal energy to the heating surface metal through the refractory material (or directly); the heating surface metal transfers the thermal energy to the working fluid; and the working fluid, with its increased internal energy, is output from the boiler. There are many techniques for controlling the heat transfer inertia of a boiler, such as changing the heat transfer coefficient on the flue gas / working fluid side, changing the total amount of flue gas, bed material, refractory material, or heating surface metal, and changing the thermophysical properties of the refractory material / heating surface metal. When one or more of these factors change, the heat transfer inertia may increase or decrease accordingly; this is the basic principle of heat transfer inertia control technology.

[0003] In existing technologies, when dealing with complex and variable operating conditions, it is difficult to describe the independent contribution of each component in the heat transfer process, and it is also difficult to track the time-series characteristics of dynamic response. This leads to a one-sided understanding of system inertia. When abnormal heat transfer inertia occurs, the only solutions are to increase or decrease the overall material or adjust the heat transfer coefficient, which can easily lead to increased energy consumption or response lag. For example, under high load fluctuations, simply relying on changing the heat transfer coefficient on the flue gas side often cannot effectively alleviate the temperature response lag of certain water-cooled walls. As a result, although adjustments are made, the heat transfer inertia problem at critical locations still exists. Therefore, improvements are needed. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the existing technology and to propose a method and apparatus for determining boiler heat transfer inertia and controlling boiler heat transfer inertia.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for determining boiler heat transfer inertia and analyzing boiler heat transfer inertia control, comprising the following steps: Based on the boiler's geometry and material properties, the superheater tube wall and header are discretized into Lagrangian material points carrying mass, temperature, and stress information. A background Eulerian grid covering the fluid and solid domains is established to generate a coupled simulation multiphysics discretization model. Based on the coupled simulation multiphysics discretization model, the material point information is mapped to the background grid nodes under the variable working condition boundary. The momentum and energy equations of the grid nodes are solved to obtain the node increment and heat flow vector field. Then, the node increment and heat flow vector field are fed back to the material points to update the position, temperature and stress state, and to obtain the global heat flux path and structural stress time series set. Based on the global heat flux path and structural stress time series, the main steam temperature data is extracted to determine the response time and identify the key water-cooled wall heat flux path, thereby obtaining the system's baseline effective response time constant and a list of key components. The thermophysical parameters of each component in the system's baseline effective response time constant and the list of key components are perturbed and calculated one by one to establish a component inertial response sensitivity sequence table. Based on the component inertial response sensitivity sequence table, the system response time change and parameter disturbance of each component are called to calculate the component disturbance response combination term. The component disturbance response combination term is arranged and selected to determine the target point for boiler heat transfer inertial control optimization.

[0006] Preferably, the steps for obtaining the coupled simulation multiphysics discrete model are as follows: Based on the boiler's geometric structure and material properties, the superheater tube wall and header are spatially discretized. The geometric units of the superheater tube wall and header are divided according to the tube diameter, wall thickness, length and node position. The physical properties of each geometric unit are decomposed into mass value, temperature value and stress value, forming Lagrange material points carrying mass value, temperature value and stress value. Based on the Lagrange material points carrying mass, temperature and stress values, they are mapped to the Eulerian background space, and an Eulerian background grid covering the fluid and solid domains is divided. The mass, temperature and stress values ​​corresponding to each Lagrange material point are superimposed on the grid nodes to generate a background Eulerian grid covering the fluid and solid domains. Based on the background Eulerian grid covering the fluid and solid domains, the Lagrangian matter points are bidirectionally coupled with the Eulerian background grid, and the physical quantities of the matter points and the physical quantities of the grid nodes are iteratively transferred and updated to generate a coupled simulation multiphysics discrete model.

[0007] Preferably, the steps for obtaining the node increment and the heat flux vector field are as follows: Based on the coupled simulation multiphysics discretization model, the variable working condition boundary sequence is read, and the inlet velocity value, outlet pressure value, wall temperature value and wall heat transfer coefficient value are expanded in time order. According to the spatial correspondence between the material point coordinates and the background grid node coordinates, nearest neighbor matching and volume allocation are performed. The mass value, temperature value and stress value of each material point are allocated to the corresponding background grid node according to the mapping weight to obtain the material point information mapping state of the background grid node. Based on the mapping status of the material point information of the background grid nodes, the inlet velocity value, outlet pressure value, wall temperature value and wall heat transfer coefficient value in the variable working condition boundary sequence are called according to the same time index. Momentum balance and energy balance are calculated for each node. Flux aggregation and gradient evaluation are completed by combining the topological connection order of adjacent nodes. The velocity increment, pressure increment and temperature increment of each node are identified and organized into vector form to obtain the node increment and heat flow vector field.

[0008] Preferably, the steps for obtaining the global heat flux path and structural stress time series set are as follows: Based on the node increment and heat flux vector field, the velocity increment, pressure increment, and temperature increment are transmitted back to the material point according to the mapping weight of the background grid node to the material point. The position value, temperature value, and stress value are updated for each material point. The connection path of the heat flux vector is tracked along the time sequence and spliced ​​into a single continuous path. At the same time, the updated stress value sequence is recorded in the time sequence to generate a global heat flux path and structural stress time series set.

[0009] Preferably, the steps for obtaining the system baseline effective response time constant and the list of key components are as follows: Based on the global heat flux path and structural stress time series, the main steam temperature data sequence is extracted, the stable interval before the start of the variable operating condition is located and the stable mean value is calculated as the starting temperature value, the stable interval after the end of the variable operating condition is located and the stable mean value is calculated as the ending temperature value, the numerical differential of the main steam temperature data is calculated point by point, the temperature change rate in the entire response interval is obtained, the maximum value of the temperature change rate is identified, and the main steam temperature data and change rate characteristics are obtained. Based on the main steam temperature data and its rate of change characteristics, the effective response time constant is calculated; Based on the effective response time constant, time alignment is performed on all water-cooled wall heat flux paths, and water-cooled wall channels that maintain correlation within the effective response time constant range are selected to generate a system baseline effective response time constant and a list of key components.

[0010] Preferably, the step of obtaining the component inertial response sensitivity sequence list is as follows: Based on the system baseline effective response time constant and the list of key components, the thermal conductivity, specific heat capacity, and density values ​​of each key component are read one by one. A perturbation of a set amplitude is applied to each type of thermophysical parameter while keeping other parameters unchanged. The effective response time constant is recalculated using the perturbed thermophysical parameters. The new system effective response time constant corresponding to each perturbation is recorded and compared with the system baseline effective response time constant to form a component inertial response sensitivity sequence table composed of the corresponding differences of the key components.

[0011] Preferably, the step of obtaining the component disturbance response combination item is as follows: Based on the component inertial response sensitivity sequence table, the system reference effective response time constant, the system effective response time constant after disturbance, and the corresponding reference thermophysical parameter values ​​and the thermophysical parameter values ​​after disturbance for each key component are read and organized one by one into a combination of component parameter disturbance and system effective response time constant to generate component disturbance response combination item.

[0012] Preferably, the step of obtaining the target point for boiler heat transfer inertia regulation optimization is as follows: Based on the component disturbance response combination, a comprehensive bottleneck index is calculated and summarized to form a comprehensive bottleneck index set. Based on the comprehensive bottleneck index set, all comprehensive bottleneck indices are sorted in descending order, and the component name and corresponding thermophysical parameter name at the top of the sort are taken as priority control objects to generate boiler heat transfer inertia control optimization target points.

[0013] The present invention also provides an analysis apparatus, comprising: The discrete modeling module is used to discretize the superheater tube wall and header into Lagrange material points carrying mass, temperature and stress information based on the boiler geometry and material properties, establish a background Eulerian mesh covering the fluid and solid domains, and generate a coupled simulation multiphysics discrete model. The simulation solution module is used to map the material point information to the background grid nodes under the variable working condition boundary according to the coupled simulation multiphysics discrete model, solve the momentum and energy equations of the grid nodes, obtain the node increment and heat flow vector field, and then return the node increment and heat flow vector field to the material points to update the position, temperature and stress state, and obtain the global heat flux path and structural stress time series set. The response analysis module is used to extract the main steam temperature data to determine the response time and identify the key water-cooled wall heat flux path based on the global heat flux path and structural stress time series set, obtain the system reference effective response time constant and key component list, apply perturbation to the thermophysical parameters of each component in the system reference effective response time constant and key component list and calculate them, and establish a component inertial response sensitivity sequence table. The regulation and optimization module is used to call the system response time change and parameter disturbance of each component according to the component inertial response sensitivity sequence table, calculate the component disturbance response combination item, arrange and select the component disturbance response combination item, and determine the boiler heat transfer inertial regulation and optimization target point.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by analyzing the boiler's geometry and material properties, the heated components are discretized into material points carrying mass, temperature, and stress information. Coupled processing is achieved within a grid covering both the fluid and solid domains, enabling continuous tracking of the heat transfer process on a global scale. By mapping the material point information to grid nodes under varying operating conditions, the distribution changes of momentum and energy can be synchronously obtained, and these changes are fed back to the material points in real time, achieving dynamic interaction of position, temperature, and stress. This allows for a more precise description of the temporal evolution of heat transfer inertia. Simultaneously, main steam temperature data is extracted, key heat transfer paths are identified, and combined with the baseline effective response time constant, expanding a single result into a set of key components matching the global heat flux path. This ensures that the evaluation results are no longer limited to macroscopic averages but can focus on specific components. By applying perturbations to the parameters of each key component and obtaining response times under different conditions, a sensitivity sequence table is formed, achieving a layer-by-layer analysis from overall behavior to local contributions. Furthermore, it can identify the bottleneck object with the most significant impact on heat transfer inertia among numerous factors, forming a quantitative control target for boiler heat transfer inertia. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the steps of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0017] Please see Figure 1 This invention provides a technical solution for determining boiler heat transfer inertia and analyzing boiler heat transfer inertia control, comprising the following steps: Based on the boiler's geometry and material properties, the superheater tube wall and header are discretized into Lagrangian material points carrying mass, temperature, and stress information. A background Eulerian grid covering the fluid and solid domains is established to generate a coupled simulation multiphysics discretization model. Based on the coupled simulation multiphysics discretization model, the material point information is mapped to the background grid nodes under the variable working condition boundary. The momentum and energy equations of the grid nodes are solved to obtain the node increment and heat flow vector field. Then, the node increment and heat flow vector field are fed back to the material points to update the position, temperature and stress state, and the global heat flux path and structural stress time series set are obtained. Based on the global heat flux path and structural stress time series, the main steam temperature data is extracted to determine the response time and identify the key water-cooled wall heat flux path. The system reference effective response time constant and key component list are obtained. The thermophysical parameters of each component in the system reference effective response time constant and key component list are perturbed and calculated one by one to establish the component inertial response sensitivity sequence table. Based on the component inertial response sensitivity sequence table, the system response time change and parameter disturbance of each component are called to calculate the component disturbance response combination term. The component disturbance response combination term is arranged and selected to determine the target point for boiler heat transfer inertial control optimization.

[0018] The steps for obtaining the discrete model of a multiphysics field in coupled simulation are as follows: Based on the boiler's geometric structure and material properties, the superheater tube wall and header are spatially discretized. The geometric units of the superheater tube wall and header are divided according to the tube diameter, wall thickness, length and node position. The physical properties of each geometric unit are decomposed into mass value, temperature value and stress value, forming Lagrange material points carrying mass value, temperature value and stress value. Based on the Lagrangian material points carrying mass, temperature and stress values, they are mapped to the Eulerian background space, and an Eulerian background grid covering the fluid and solid domains is divided. The mass, temperature and stress values ​​corresponding to each Lagrangian material point are superimposed on the grid nodes to generate a background Eulerian grid covering the fluid and solid domains. Based on the background Eulerian grid covering the fluid and solid domains, the Lagrangian material points are bidirectionally coupled with the Eulerian background grid. The physical quantities of the material points and the physical quantities of the grid nodes are iteratively transferred and updated to generate a coupled simulation multiphysics discrete model.

[0019] Specifically, based on the boiler's geometry and material properties, the vertex coordinates, topological connections, and surface normal vectors of the superheater tube walls and headers are first extracted from the 3D geometric model file. Simultaneously, material properties are retrieved from the material database, including the density, specific heat capacity, thermal conductivity, Young's modulus, and Poisson's ratio of each component at different temperatures. Then, the superheater tube walls and headers are spatially discretized using an octree-based adaptive meshing method. An initial meshing unit size is set; for example, the entire geometric model is enclosed in a cube with a side length of 1 meter, resulting in an initial unit size of 1 meter. Subsequently, the units containing the geometric entities are recursively subdivided. The subdivision terminates based on a comparison between the unit size and local geometric features. Specifically, subdivision stops when the size of a unit is less than a preset feature length threshold. This feature length threshold is set with reference to the minimum tube wall thickness; for example, for a tube wall with a thickness of 20 mm, the feature length threshold... The value is set to 5mm to ensure that at least 4 layers of discrete elements can be generated in the thickness direction. After the meshing is completed, the center point of each final geometric element is defined as the location of a Lagrangian material point. Then, the physical properties of each geometric element are decomposed and assigned to the corresponding Lagrangian material point. The mass value is calculated by multiplying the volume of the geometric element by the material density value of the corresponding location obtained from the material database. The temperature value is assigned according to the temperature field distribution of the initial steady-state condition. If there is no specific initial field, it is uniformly set to the ambient temperature, such as 25 degrees Celsius. The stress value is set to a zero tensor in the initial state, indicating that the structure is in a stress-free state. By traversing all the meshed geometric elements and completing the above calculations and assignments, a data set containing all material points is finally formed. Each material point carries a unique ID, three-dimensional spatial coordinates, mass value, temperature value, and six independent stress component values ​​as a Lagrangian material point.

[0020] Based on the Lagrange matter points carrying mass, temperature, and stress values, the mapping process to the Eulerian background space is initiated. First, the coverage and resolution of the Eulerian background mesh are determined. Its coverage must completely encompass the maximum space occupied by all Lagrange matter points. A safety boundary is then extended outwards from this boundary, with the distance set to 1.5 times the maximum expected particle displacement. For example, if the maximum particle movement is expected to be 0.1 meters in a single time step, the safety boundary is set to 0.15 meters. The mesh resolution, i.e., the size of the mesh cells, is set to twice the average spacing of all Lagrange matter points, ensuring that each mesh cell contains an average of 8 matter points. This setting balances computational accuracy with efficiency. Subsequently, a uniform Cartesian mesh is generated within the defined spatial range, forming the Eulerian background mesh covering both the fluid and solid domains. Next, the physical quantities carried by each Lagrange point are superimposed onto the Eulerian grid nodes where it resides. This process uses shape function interpolation. Specifically, for each Lagrange point, the background grid cell in which it resides is located, and the eight corner nodes of that cell are identified. Then, using a trilinear shape function based on the relative coordinates of the point within the cell, the weight coefficients of each physical quantity with respect to these eight nodes are calculated. For example, the mass, momentum, temperature, and stress tensor values ​​of a point are allocated and accumulated to the corresponding grid nodes according to these weight coefficients. This interpolation and superposition calculation is repeated for all Lagrange points to complete the mapping of all physical quantities from the Lagrange points to the Eulerian background grid nodes. Finally, a background Eulerian grid covering the fluid and solid domains is generated, carrying the initial mass field, momentum field, temperature field, and stress field.

[0021] Based on the background Eulerian grid covering both the fluid and solid domains, a bidirectional coupling relationship is established between Lagrangian matter points and the Eulerian background grid. This coupling is achieved through a time-stepped iterative loop. At the beginning of each iteration, a mapping from matter points to the grid is first performed. The updated mass, momentum, temperature, and stress values ​​of each Lagrangian matter point are superimposed onto the background grid nodes of its corresponding Eulerian element using trilinear interpolation, forming the field variables of the grid nodes. Subsequently, the governing equations are solved on the Eulerian background grid to calculate the velocity and temperature increments of each grid node. After the calculation is completed, a reverse mapping from the grid to the matter points is performed, transferring the calculated physical quantity increments from the grid nodes back to the Lagrangian matter points. Specifically, for each Lagrangian matter point, its relative position within the grid element is used again... The system uses a trilinear shape function to calculate the weighted average of the velocity and temperature increments of the eight corner nodes of the element, thus obtaining the velocity and temperature increments of the material point itself. Then, it uses these increments to update the state of the material point. For example, the new velocity of the material point is equal to the old velocity plus the velocity increment, the new position is equal to the old position plus the product of the new velocity and the time step, and the new temperature is equal to the old temperature plus the temperature increment. At the same time, the stress state of the material point is updated based on the velocity gradient field. This complete process of "mapping-solving-reverse mapping-updating" constitutes an iterative transfer and update. After each time step, all physical quantities on the background mesh nodes are reset to zero to prepare for the mapping of the next time step. By repeatedly executing this series of iterative steps, a dynamic computational framework with bidirectional information flow is established, and finally, a coupled simulation multiphysics discrete model is generated.

[0022] The steps for obtaining the node increment and heat flux vector field are as follows: Based on the coupled simulation multiphysics discretization model, the variable working condition boundary sequence is read, and the inlet velocity value, outlet pressure value, wall temperature value and wall heat transfer coefficient value are expanded in time order. According to the spatial correspondence between the material point coordinates and the background grid node coordinates, nearest neighbor matching and volume allocation are performed. The mass value, temperature value and stress value of each material point are allocated to the corresponding background grid node according to the mapping weight, and the material point information mapping state of the background grid node is obtained. Based on the mapping status of material point information of background grid nodes, the inlet velocity value, outlet pressure value, wall temperature value and wall heat transfer coefficient value in the variable working condition boundary sequence are called according to the same time index. Momentum balance and energy balance are calculated for each node. Flux aggregation and gradient evaluation are completed by combining the topological connection order of adjacent nodes. The velocity increment, pressure increment and temperature increment of each node are identified and organized into vector form to obtain the node increment and heat flow vector field.

[0023] Specifically, based on the coupled simulation multiphysics discrete model, a predefined variable operating condition boundary sequence is first read. This sequence is a dataset containing multiple timestamps, each timetamp corresponding to a set of boundary condition values. These values ​​include the fluid velocity distribution at the boiler inlet section, the pressure at the outlet section, the surface temperature of the water-cooled wall directly in contact with the combustion chamber flame, and the convective heat transfer coefficient between the superheater tube wall and the flue gas. Then, the inlet velocity, outlet pressure, wall temperature, and wall heat transfer coefficient values ​​from this sequence are loaded into memory in time-stamp order, ready to be called during simulation time steps. Next, for each Lagrange-Hebrew material point in the simulation domain, its spatial location is determined in the background Eulerian grid based on its current three-dimensional coordinates, identifying its unique background grid cell. This location process is accelerated using spatial hashing or octree search algorithms to avoid global traversal. Once the grid cell containing the material point is determined, it is identified. The eight corner nodes of the unit are formed, and the mapping weight of the material point relative to these eight corner nodes is calculated. The mapping weight is calculated based on the trilinear interpolation shape function. Specifically, the local coordinates of the material point in the grid cell (the value ranges from 0 to 1) are substituted into the shape function to obtain its weight value with respect to the eight corner nodes. The sum of all weight values ​​is always 1. The magnitude of the weight value reflects the spatial proximity between the material point and the node. After the weight calculation of all material points is completed, each Lagrange-Hebrew material point is traversed, and its mass value, temperature value, and six independent stress tensor component values ​​are multiplied by its mapping weight with respect to the eight corner nodes of the unit. The results are accumulated to the corresponding physical quantities of the corresponding background grid nodes. By performing this weighted allocation process on all material points, the comprehensive representation of all material point information on the background grid at the current time step is finally obtained, that is, the mapping state of material point information of the background grid nodes.

[0024] Based on the mapping state of the material point information of the background grid nodes, i.e., the accumulated mass, momentum, energy, and stress information on each grid node, the solution phase begins. First, according to the current simulation time step, the corresponding inlet velocity, outlet pressure, wall temperature, and wall heat transfer coefficient values ​​are indexed and retrieved from the loaded variable boundary condition sequence, and these values ​​are applied to the background grid nodes located on the model boundary. Then, all non-boundary grid nodes within the computational domain are traversed, and the discretized momentum and energy conservation equations are solved for each node. In the momentum budget calculation, for a central node, the pressure gradient force acting on the control volume surface is calculated using the pressure difference between it and its six adjacent nodes (in Cartesian coordinates), and the viscous force is calculated using the velocity difference. These forces are combined with the mass information stored on the node to calculate the velocity increment of that node in the current time step. In the energy budget calculation… In the calculation, the conductive heat flux is calculated using the temperature difference between the central node and adjacent nodes, and the convective heat flux is calculated using the convection term. All heat fluxes are summed to obtain the net heat change of the node. This is then divided by the node's mass and specific heat capacity to obtain the node's temperature increment. Gradient evaluation in this process, such as the temperature gradient, is calculated using the central difference scheme, which divides the temperature difference between two adjacent nodes by the distance between them. Flux aggregation involves algebraically summing all momentum and heat fluxes flowing out of or into a node's control volume. After completing the calculations for all internal nodes, the three components of the calculated velocity increment, pressure increment, and temperature increment for each node are combined into a five-dimensional vector. At the same time, the calculated inter-node heat fluxes are also organized into vector form, with the direction pointing towards the heat flow direction and the magnitude being the heat flux density. All these vectors from all nodes are combined to finally obtain the node increment and heat flux vector field.

[0025] The steps for obtaining the global heat flux path and structural stress time series set are as follows: Based on the node increment and heat flux vector field, the velocity increment, pressure increment, and temperature increment are back to the material point according to the mapping weight of the background grid node to the material point. The position value, temperature value, and stress value are updated for each material point. The connection path of the heat flux vector is tracked along the time sequence and spliced ​​into a single continuous path. At the same time, the updated stress value sequence is recorded in the time sequence to generate the global heat flux path and structural stress time series set.

[0026] Specifically, based on the node increments and heat flux vector field, information is transferred from the background mesh to the Lagrange-Hebrew material point. For each Lagrange-Hebrew material point, its background mesh cell is first determined. Then, the mapping weights of the material point relative to the eight corner nodes of the cell, calculated and stored in the first step, are called. The velocity increment, pressure increment, and temperature increment of these eight corner nodes are weighted and averaged according to these mapping weights to calculate the velocity increment, pressure increment, and temperature increment of the material point itself in the current time step. Subsequently, the physical state of each material point is updated one by one. Its position value is updated by multiplying the current velocity by the time step and adding it to the old position. Its velocity value is updated by adding the calculated velocity increment to the old velocity. Its temperature value is updated by adding the temperature increment to the old temperature. For the stress value update, the velocity gradient tensor at the material point's location is first obtained by interpolation from the mesh nodes using the same mapping weights. Then, based on the constitutive relations of the material (e.g., elastoplastic model), the strain rate tensor caused by the velocity gradient is calculated, and the stress tensor value is updated. While updating the state of all material points, the tracking of the heat flux path is initiated. A set of starting material points is selected from the preset key heat source region (e.g., the surface of the water-cooled wall with the highest heat load). At each time step, based on the local heat flux direction obtained by interpolating the heat flux vector field at the location of these material points, a short distance is traced forward to determine the path point location of the next time step. These continuous path points are connected in chronological order to form the trajectory of heat flux from the wall to the internal fluid, i.e., a single continuous path. At the same time, a time series data structure is maintained for each material point. After updating its physical state at each time step, the latest six stress component values ​​are appended to the sequence. Finally, by summarizing all tracked heat flux paths and the stress change history of all material points, a global heat flux path and structural stress time series set is generated.

[0027] The steps for obtaining the system baseline effective response time constant and the list of key components are as follows: Based on the global heat flux path and structural stress time series, the main steam temperature data sequence is extracted, the stable interval before the start of the variable operating condition is located and the stable mean value is calculated as the starting temperature value, the stable interval after the end of the variable operating condition is located and the stable mean value is calculated as the ending temperature value, the numerical derivative of the main steam temperature data is calculated point by point, the temperature change rate in the entire response interval is obtained, the maximum value of the temperature change rate is identified, and the main steam temperature data and change rate characteristics are obtained. Based on the main steam temperature data and its rate of change characteristics, the effective response time constant is calculated using the following formula: ; in, For the effective response time constant, The moment when the response begins in the main steam temperature data. The moment when the main steam temperature reaches a 63% temperature rise. This is the starting temperature value. For a moment Temperature value, To terminate the temperature value, Main steam temperature data in the range The maximum rate of change within, System morphology influencing factors; Based on the effective response time constant, time alignment is performed on all water-cooled wall heat flux paths, and water-cooled wall channels that maintain correlation within the effective response time constant range are selected to generate a system baseline effective response time constant and a list of key components.

[0028] Specifically, based on the global heat flux path and structural stress time series set, the temperature values ​​of all material points located at the main steam outlet section of the boiler are first extracted from this time series set. By spatially averaging the temperature values ​​of these material points at each time step, a single time series, namely the main steam temperature data series, is obtained. This series is then analyzed to locate stable intervals. This process uses a sliding time window with a width of 120 seconds. Starting from the beginning of the series, the time window moves forward second by second, and the standard deviation of all temperature data points within the window is calculated. When the standard deviation of three consecutive time windows is less than a preset stability threshold of 0.1 degrees Celsius, the region is determined to be a stable interval. This stability threshold is based on... The statistical distribution of temperature fluctuations under steady-state conditions in historical operating data is determined. Using the upper limit of its 95% confidence interval, this method is employed to find the last stable interval in the period before the issuance of the variable operating condition command, and calculate the arithmetic mean of all temperature data points within that interval to obtain the initial temperature value. Similarly, in the period after the variable operating condition response process has basically ended, the first stable interval is found, and its arithmetic mean is calculated to obtain the final temperature value. Then, within the entire response interval between the time corresponding to the initial temperature value and the time corresponding to the final temperature value, the main steam temperature data sequence is numerically differentiated point-by-point to calculate the instantaneous temperature change rate at each time point. This calculation uses the central difference method, i.e., for each time point... The rate of temperature change, by Temperature at time minus The temperature at time 1 is divided by the time interval between two time points. The temperature change rate values ​​are iterated through the entire response interval to find the maximum absolute value. Finally, the complete main steam temperature data sequence, the calculated starting temperature value, the ending temperature value, and the maximum value of the identified temperature change rate are combined to obtain the main steam temperature data and change rate characteristics.

[0029] formula: The above formula improves the traditional method for calculating the time constant of a first-order system by introducing a correction term, thereby more accurately quantifying the dynamic response characteristics of complex thermal systems such as boilers. The part within parentheses on the left side of the formula is the classical time constant calculation formula derived based on the ideal exponential response curve, while the correction term within parentheses on the right side quantifies the deviation between the actual response process and the ideal first-order response. The numerator term... This characterizes the difference between the temperature rise extrapolated linearly at the maximum rate of change and the actual temperature rise during the initial stage of the response. This difference reflects complex effects such as nonlinearity, higher-order dynamics, and pure time delay in the internal heat transfer process of the system, as well as the influence factor of system morphology. The deviation is then weighted to account for the influence of the boiler’s specific structure on its dynamic characteristics.

[0030] The effective response time constant is a core indicator for measuring the thermal inertia of a boiler. It is measured in seconds (s) and represents the characteristic scale of the time required for the main steam temperature to transition from an initial steady state to a new steady state after being subjected to a step disturbance. Its value reflects the boiler's response speed to load changes.

[0031] The starting time of the response in the main steam temperature data, measured in seconds (s), is obtained from the "Main Steam Temperature Data and Rate of Change Characteristics" acquired in the previous step. Specifically, it is the end point of the stable interval before the start of the variable operating condition. This moment marks the beginning of the system's departure from the initial steady state and entry into the dynamic response phase. For example, by analyzing the main steam temperature data sequence, it is determined that the system was in a stable state before 1200s, and the temperature began to rise after 1200s. The value is 1200s.

[0032] The time when the main steam temperature reaches a 63.2% temperature rise, expressed in seconds (s), is determined by first calculating the target temperature value. The target temperature value equals the initial temperature value plus 63.2% of the total temperature rise. Then, the data is iterated through from... The main steam temperature data sequence, starting from a specific time, is used to find the precise moment when the temperature first reaches the target temperature value through linear interpolation. For example, if the starting temperature is 540℃ and the ending temperature is 560℃, then the total temperature rise is 20℃, and the temperature corresponding to a 63.2% temperature rise is... The temperature was found to be 552.60℃ at 1349s and 552.68℃ at 1350s in the temperature series. Therefore, it was calculated using linear interpolation. s.

[0033] This is the initial temperature value, expressed in degrees Celsius (°C). This parameter is directly derived from the initial temperature value obtained in the previous step, specifically from the "Main Steam Temperature Data and Rate of Change Characteristics." It is obtained by averaging the temperature data within the stable range before the start of the variable operating condition and represents the initial thermodynamic state of the system before response. For example, if the initial temperature value calculated using the aforementioned method is 540°C, then... The value is 540℃.

[0034] For a moment The temperature value is expressed in degrees Celsius (°C). This parameter's value is based on the parameter... The definition is directly determined, namely the temperature value at which the total temperature rise reaches 63.2%, and its calculation process is already described in [the original text]. The acquisition steps are described in detail, for example, based on the aforementioned example, The value is 552.64℃.

[0035] The termination temperature is expressed in degrees Celsius (°C). This parameter is also directly derived from the termination temperature value obtained in the previous step, specifically from the "Main Steam Temperature Data and Rate of Change Characteristics." It is obtained by averaging the temperature data within the stable range after the end of the variable operating condition, representing the new thermodynamic equilibrium state reached by the system after the response. For example, if the termination temperature value calculated using the aforementioned method is 560°C, then... The value is 560℃.

[0036] Main steam temperature data in the range The maximum rate of change within a given time interval, expressed in degrees Celsius per second (°C / s), is obtained from the temperature change rate sequence contained in the "Main Steam Temperature Data and Rate of Change Characteristics" obtained in the previous step. This is achieved by searching within the time interval... Find the maximum value among all instantaneous temperature change rate values ​​within the specified range. For example, if the maximum value within this range is 0.2℃ / s, then... The value is 0.2℃ / s.

[0037] The system morphology influence factor is a dimensionless parameter used to quantify the degree of influence of the boiler's specific physical structure (such as the relative dimensions of each component and material ratio) on the dynamic characteristics of its temperature response. The value of this factor is obtained through multiple regression analysis of simulation or experimental data from a series of standard boiler models, and its calculation formula can be expressed as: ,in This is the ratio of the total volume of the superheater and reheater to the total volume of the water-cooled wall system. It is the ratio of the total mass of the pressure-bearing components to the total mass of the internal working fluid. and For a 300MW subcritical coal-fired boiler, the regression coefficients can be obtained through database queries and regression analysis. , If a certain boiler , ,but .

[0038] Calculations based on parameters: Substitute the parameter values ​​obtained from the above example into the formula: s; s; ℃; ℃; ℃; ℃ / s; ; The calculation process is as follows: ; ; ; ; ; ; ; The results indicate that the baseline effective response time constant of this boiler system is 308.31 seconds, representing the equivalent first-order system time constant of the time required for the system temperature to reach 63.2% of the total change. This is a relatively small... A higher value means a faster system response and less thermal inertia, while a lower value means a slower response and greater thermal inertia.

[0039] Based on the effective response time constant, the time series of all water-cooled wall heat flux paths are first time-aligned with the main steam temperature data series. Specifically, the time axis of each heat flux path data is shifted so that its zero point is aligned with the start time of the main steam temperature response. Align, then, in the time window Within the time frame from the start of the response to an effective response time constant, water-cooled wall channels that maintain a strong correlation with changes in the main steam temperature are selected. This selection process is achieved by calculating the Pearson correlation coefficient between the heat flux time series and the main steam temperature time series of each water-cooled wall channel. This coefficient measures the degree of linear correlation between the two variables, with a value ranging from -1 to 1. The closer the absolute value is to 1, the stronger the correlation. A correlation judgment threshold is set, which is based on statistical analysis of historical simulation data. This threshold is determined by comparing two groups of water-cooled walls that are known to have an impact on the main steam temperature and those that do not. The correlation coefficients of each channel were calculated. It was found that the correlation coefficients of the affected group were generally above 0.7, while those of the unaffected group were below 0.5. Therefore, the correlation threshold was set to 0.7. All water-cooled wall channels were iterated through, and the correlation coefficient between their heat flux sequence and main steam temperature sequence within a specified time window was calculated one by one. If the calculated correlation coefficient was greater than or equal to 0.7, the water-cooled wall channel was determined to be a critical component, and its unique identifier (e.g., channel number) was added to a list. After the iteration was complete, this list constituted the critical component list. Finally, the effective response time constant calculated in the previous step was... As a system baseline value, it is combined with the generated list of key components to obtain the system baseline effective response time constant and the list of key components.

[0040] The steps for obtaining the component inertial response sensitivity sequence list are as follows: Based on the system baseline effective response time constant and the list of key components, the thermal conductivity, specific heat capacity, and density values ​​of each key component are read one by one. A perturbation of a set amplitude is applied to each type of thermophysical parameter while keeping other parameters unchanged. The effective response time constant is recalculated using the perturbed thermophysical parameters. The new system effective response time constant corresponding to each perturbation is recorded and compared with the system baseline effective response time constant to form a component inertial response sensitivity sequence table composed of the corresponding differences of the key components.

[0041] Specifically, based on the system's baseline effective response time constant and the list of key components, the process first iterates through each component in the list, retrieving its baseline thermophysical parameters from the material database associated with the coupled simulation multiphysics discrete model. These parameters include thermal conductivity, specific heat capacity, and density. Subsequently, sensitivity analyses are performed sequentially for these three parameters. For example, thermal conductivity is analyzed first. For the first key component in the list, a positive perturbation and a negative perturbation of a set magnitude are applied to its baseline thermal conductivity value. The perturbation magnitude is set to 5% of the baseline value. This magnitude is chosen based on engineering experience, ensuring that it induces observable changes in the system response without causing numerical instability. While applying the perturbations, the specific heat capacity and density values ​​of this component, as well as all other thermophysical parameters of all key components, remain unchanged. The perturbed thermal conductivity value is then updated in the multiphysics discrete model. Finally, the same variable boundary condition sequence is used... Using the column as input, the complete coupled simulation calculation process is re-executed until a new main steam temperature data sequence is obtained. The new effective system response time constant is calculated according to the previously determined method. This new effective response time constant is subtracted from the system baseline effective response time constant to obtain a difference. The component name, the type of disturbance parameter (thermal conductivity), the direction of disturbance (positive or negative), and the corresponding difference in response time constant are recorded. After completing the disturbance analysis of one parameter of a component, its parameter is restored to the baseline value. Then, the same disturbance is applied to the next key component and the above process is repeated until the thermal conductivity of all key components has been disturbed and analyzed. Then, in the same way, the heat capacity and density parameters are compared and disturbed in turn. Finally, all the recorded difference data are grouped according to component name and parameter type, and sorted in descending order of the absolute value of the difference to form a component inertial response sensitivity sequence table composed of the difference arrangement of key components.

[0042] The steps for obtaining the component disturbance response combination item are as follows: Based on the component inertial response sensitivity sequence table, the system reference effective response time constant, the system effective response time constant after disturbance, and the corresponding reference thermophysical parameter values ​​and the thermophysical parameter values ​​after disturbance are read for each key component. These are then organized into component parameter disturbance and system effective response time constant combinations to generate component disturbance response combination items.

[0043] Specifically, based on the component inertial response sensitivity sequence list, a data processing and combination process is initiated, traversing each record in the sequence list. Each record contains a unique identifier for the critical component, the type of perturbed thermophysical parameter (thermal conductivity, specific heat capacity, or density), the effective response time constant of the new system after perturbation, and the difference from the baseline value. For each row in the sequence list, the identifier of the critical component is first read, and this is used as an index to retrieve the baseline thermophysical parameter value of the component from the material database, i.e., the original value before perturbation. For example, for component "water-cooled wall channel #101", its baseline thermal conductivity is read as 45 W / (m·K). Then, the effective response time constant of the system after perturbation is read from the current sequence list record, for example, 315.72 seconds. At the same time, based on the perturbation type and magnitude (e.g., thermal conductivity +5%), the value of the perturbed thermophysical parameter is calculated. W / (m·K), then, this information is structured and combined to form a data unit, which contains at least the following fields: component identifier ("water-cooled wall channel #101"), parameter type ("thermal conductivity"), reference parameter value (45), parameter value after disturbance (47.25), system reference effective response time constant (e.g., 308.31 seconds calculated above) and system effective response time constant after disturbance (315.72 seconds). The above reading, calculation and combination operations are repeated for all entries in the component inertial response sensitivity sequence list, and each entry is converted into such a structured data unit. Finally, all the generated data units are gathered together to form a complete set, namely the component disturbance response combination item.

[0044] The steps for obtaining the target point for boiler heat transfer inertia control optimization are as follows: Based on the component disturbance response combination terms, the comprehensive bottleneck index is calculated and summarized to form a comprehensive bottleneck index set. The calculation formula is as follows: ; in, For the first The overall bottleneck index of each component For the first The system's effective response time constant under component disturbances The system's baseline effective response time constant, For the first Thermophysical property parameters of each component under disturbance For the first The reference thermophysical property parameters of each component. For the set of all perturbation components, This is the maximum value among the absolute changes in the effective response time constant of the system under all component disturbances. To influence the weighting factors; Based on the comprehensive bottleneck index set, all comprehensive bottleneck indices are sorted in descending order. The component name and corresponding thermophysical parameter name at the top of the list are taken as the priority control objects, and the boiler heat transfer inertia control optimization target point is generated.

[0045] Specifically, the formula: The above formula is used to identify the most critical components and their thermophysical parameters affecting the boiler's heat transfer inertia. This index consists of two weighted parts. The first part is the normalized sensitivity, which is the absolute value of the ratio of the relative change in the system's effective response time constant to the relative change in the parameter. It quantifies the degree of system response change caused by a unit parameter disturbance, reflecting the "efficiency" of the parameter's influence. The second part is the normalized absolute change in response, which is the ratio of the absolute change in the system response time constant caused by a single parameter disturbance to the largest absolute change among all disturbances. This quantifies the "magnitude" of the parameter's influence, determined by the influence weighting factor. By weighting and summing these two parts, the comprehensive bottleneck index can identify both parameters that affect high efficiency and those that can cause drastic changes in the system response.

[0046] For the first The comprehensive bottleneck index of each component is a dimensionless evaluation value used to measure the bottleneck of the first component. The larger the index value, the more critical the control target is for a particular thermophysical property parameter of a component on the heat transfer inertia of the entire boiler system or the likelihood that it will become a performance bottleneck.

[0047] For the first The effective system response time constant under component disturbance is expressed in seconds (s). This parameter originates from the "component disturbance response combination term" generated in the previous step and is specific to the [number]th component disturbance. The system response time constant value is obtained by recalculating the entire process simulation after applying a specific perturbation to a certain thermophysical property parameter of a component. For example, after applying a +5% perturbation to the thermal conductivity of "water-cooled wall channel #101", the corresponding value is read from the "component perturbation response combination item". The value is 315.72s.

[0048] The system baseline effective response time constant, in seconds (s), is the system effective response time constant obtained through initial simulation calculations with all components set to baseline thermophysical parameters. It serves as the benchmark for all sensitivity analyses, and its value has been calculated in previous steps, for example, based on the previous example. The value is 308.31s.

[0049] For the first The values ​​of thermal properties under component perturbation are determined by the parameter type. For example, thermal conductivity is expressed in W / (m·K). This parameter also originates from the "component perturbation response combination term" and is the parameter value after the perturbation is applied. For instance, if a +5% perturbation is applied to the thermal conductivity of "water-cooled wall channel #101", its baseline value is 45 W / (m·K). The value is W / (m·K).

[0050] For the first The reference thermophysical property values ​​of each component, in units of Similarly, this parameter also originates from the "component disturbance response combination term," which is the original parameter value when no disturbance is applied, such as the reference thermal conductivity of "water-cooled wall channel #101." The value is 45 W / (m·K).

[0051] This is the set of all perturbation components. It's a logical set containing all key components and their parameter combinations that have undergone perturbation analysis. It defines the range for subsequent maximization. For example, if the list of key components includes 10 water-cooled wall channels, each channel is analyzed with 3 parameters, and each parameter is subjected to positive and negative perturbations, then the set... It includes One disturbance term.

[0052] This is the maximum absolute change in the effective response time constant of the system under all component disturbances, expressed in seconds (s). Calculating this value requires iterating through all entries in the "Component Disturbance Response Combination Item" and calculating the value corresponding to each entry. Then, find the largest of these absolute differences. For example, after iterating through all 60 disturbance terms, it is found that the change in the response time constant caused by the -5% density disturbance of "superheater header #2" is the largest, with an absolute difference of 12.5s. The value is 12.5s.

[0053] The influencing weighting factor is a dimensionless parameter used to balance the importance of the sensitivity and absolute influence terms in the formula. Its value ranges from 0 to 1. The setting of this factor depends on the policy preference for regulation; if the preference is to find the parameter with the highest efficiency of change, then… Take a smaller value (e.g., 0.2); if the preference is to find the parameter that causes the largest change in response, then... Take a larger value (e.g., 0.8). In the absence of a specific preference, 0.5 is usually chosen, indicating that both are equally important. This setting is used here. It is 0.5.

[0054] Calculations based on parameters: Taking the thermal conductivity of "water-cooled wall channel #101" plus a 5% disturbance as an example, calculate its comprehensive bottleneck index by substituting the following parameters: s; s; W / (m·K); W / (m·K); s; ; The calculation process is as follows: ; ; ; ; ; The results indicate that the overall bottleneck index for the thermal conductivity parameter of "water-cooled wall channel #101" is 0.777. This is calculated by considering all perturbation terms. The values ​​can be used to obtain a set of comprehensive bottleneck indices. The distribution of values ​​in this set will reveal which components and which parameters constitute the main bottlenecks to the system's heat transfer inertia.

[0055] Based on the comprehensive bottleneck index set, all calculated comprehensive bottleneck index values ​​in the set are first sorted in descending order, with the largest value at the front and the smallest value at the back, forming an ordered list. Then, the first entry in this ordered list is extracted, which corresponds to the specific component-parameter combination with the largest comprehensive bottleneck index. The component name recorded in this entry, such as "superheater header #2", and the corresponding thermophysical parameter name, such as "density", are read. This pair of information, namely "density of superheater header #2", is determined as the primary priority control object under the current analysis conditions. This object is the key point with the greatest impact on boiler heat transfer inertia and the greatest control potential. Finally, this priority control object is output to generate the boiler heat transfer inertia control optimization target point.

[0056] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for determining boiler heat transfer inertia and analyzing boiler heat transfer inertia control, characterized in that, Includes the following steps: Based on the boiler's geometry and material properties, the superheater tube wall and header are discretized into Lagrangian material points carrying mass, temperature, and stress information. A background Eulerian grid covering the fluid and solid domains is established to generate a coupled simulation multiphysics discretization model. Based on the coupled simulation multiphysics discretization model, the material point information is mapped to the background grid nodes under the variable working condition boundary. The momentum and energy equations of the grid nodes are solved to obtain the node increment and heat flow vector field. Then, the node increment and heat flow vector field are fed back to the material points to update the position, temperature and stress state, and to obtain the global heat flux path and structural stress time series set. Based on the global heat flux path and structural stress time series, the main steam temperature data is extracted to determine the response time and identify the key water-cooled wall heat flux path, thereby obtaining the system's baseline effective response time constant and a list of key components. The thermophysical parameters of each component in the system's baseline effective response time constant and the list of key components are perturbed and calculated one by one to establish a component inertial response sensitivity sequence table. Based on the component inertial response sensitivity sequence table, the system response time change and parameter disturbance of each component are called to calculate the component disturbance response combination term. The component disturbance response combination term is arranged and selected to determine the target point for boiler heat transfer inertial control optimization.

2. The method for determining boiler heat transfer inertia and analyzing boiler heat transfer inertia control according to claim 1, characterized in that, The steps for obtaining the discrete multiphysics model for coupled simulation are as follows: Based on the boiler's geometric structure and material properties, the superheater tube wall and header are spatially discretized. The geometric units of the superheater tube wall and header are divided according to the tube diameter, wall thickness, length and node position. The physical properties of each geometric unit are decomposed into mass value, temperature value and stress value, forming Lagrange material points carrying mass value, temperature value and stress value. Based on the Lagrange material points carrying mass, temperature and stress values, they are mapped to the Eulerian background space, and an Eulerian background grid covering the fluid and solid domains is divided. The mass, temperature and stress values ​​corresponding to each Lagrange material point are superimposed on the grid nodes to generate a background Eulerian grid covering the fluid and solid domains. Based on the background Eulerian grid covering the fluid and solid domains, the Lagrangian matter points are bidirectionally coupled with the Eulerian background grid, and the physical quantities of the matter points and the physical quantities of the grid nodes are iteratively transferred and updated to generate a coupled simulation multiphysics discrete model.

3. The method for determining boiler heat transfer inertia and analyzing boiler heat transfer inertia control according to claim 1, characterized in that, The steps for obtaining the node increment and heat flux vector field are as follows: Based on the coupled simulation multiphysics discretization model, the variable working condition boundary sequence is read, and the inlet velocity value, outlet pressure value, wall temperature value and wall heat transfer coefficient value are expanded in time order. According to the spatial correspondence between the material point coordinates and the background grid node coordinates, nearest neighbor matching and volume allocation are performed. The mass value, temperature value and stress value of each material point are allocated to the corresponding background grid node according to the mapping weight to obtain the material point information mapping state of the background grid node. Based on the mapping status of the material point information of the background grid nodes, the inlet velocity value, outlet pressure value, wall temperature value and wall heat transfer coefficient value in the variable working condition boundary sequence are called according to the same time index. Momentum balance and energy balance are calculated for each node. Flux aggregation and gradient evaluation are completed by combining the topological connection order of adjacent nodes. The velocity increment, pressure increment and temperature increment of each node are identified and organized into vector form to obtain the node increment and heat flow vector field.

4. The method for determining boiler heat transfer inertia and analyzing boiler heat transfer inertia control according to claim 1, characterized in that, The steps for obtaining the global heat flux path and structural stress time series set are as follows: Based on the node increment and heat flux vector field, the velocity increment, pressure increment, and temperature increment are transmitted back to the material point according to the mapping weight of the background grid node to the material point. The position value, temperature value, and stress value are updated for each material point. The connection path of the heat flux vector is tracked along the time sequence and spliced ​​into a single continuous path. At the same time, the updated stress value sequence is recorded in the time sequence to generate a global heat flux path and structural stress time series set.

5. The method for determining boiler heat transfer inertia and analyzing boiler heat transfer inertia control according to claim 1, characterized in that, The steps for obtaining the system's baseline effective response time constant and the list of key components are as follows: Based on the global heat flux path and structural stress time series, the main steam temperature data sequence is extracted, the stable interval before the start of the variable operating condition is located and the stable mean value is calculated as the starting temperature value, the stable interval after the end of the variable operating condition is located and the stable mean value is calculated as the ending temperature value, the numerical differential of the main steam temperature data is calculated point by point, the temperature change rate in the entire response interval is obtained, the maximum value of the temperature change rate is identified, and the main steam temperature data and change rate characteristics are obtained. Based on the main steam temperature data and its rate of change characteristics, the effective response time constant is calculated; Based on the effective response time constant, time alignment is performed on all water-cooled wall heat flux paths, and water-cooled wall channels that maintain correlation within the effective response time constant range are selected to generate a system baseline effective response time constant and a list of key components.

6. The method for determining boiler heat transfer inertia and analyzing boiler heat transfer inertia control according to claim 1, characterized in that, The steps for obtaining the component inertial response sensitivity sequence list are as follows: Based on the system baseline effective response time constant and the list of key components, the thermal conductivity, specific heat capacity, and density values ​​of each key component are read one by one. A perturbation of a set amplitude is applied to each type of thermophysical parameter while keeping other parameters unchanged. The effective response time constant is recalculated using the perturbed thermophysical parameters. The new system effective response time constant corresponding to each perturbation is recorded and compared with the system baseline effective response time constant to form a component inertial response sensitivity sequence table composed of the corresponding differences of the key components.

7. The method for determining boiler heat transfer inertia and analyzing boiler heat transfer inertia control according to claim 1, characterized in that, The steps for obtaining the component disturbance response combination item are as follows: Based on the component inertial response sensitivity sequence table, the system reference effective response time constant, the system effective response time constant after disturbance, and the corresponding reference thermophysical parameter values ​​and the thermophysical parameter values ​​after disturbance for each key component are read and organized one by one into a combination of component parameter disturbance and system effective response time constant to generate component disturbance response combination item.

8. The method for determining boiler heat transfer inertia and analyzing boiler heat transfer inertia control according to claim 1, characterized in that, The steps for obtaining the target point for boiler heat transfer inertial control optimization are as follows: Based on the component disturbance response combination, a comprehensive bottleneck index is calculated and summarized to form a comprehensive bottleneck index set. Based on the comprehensive bottleneck index set, all comprehensive bottleneck indices are sorted in descending order, and the component name and corresponding thermophysical parameter name at the top of the sort are taken as priority control objects to generate boiler heat transfer inertia control optimization target points.

9. The analytical apparatus for determining boiler heat transfer inertia and analyzing boiler heat transfer inertia control according to any one of claims 1-8, characterized in that, include: The discrete modeling module is used to discretize the superheater tube wall and header into Lagrange material points carrying mass, temperature and stress information based on the boiler geometry and material properties, establish a background Eulerian mesh covering the fluid and solid domains, and generate a coupled simulation multiphysics discrete model. The simulation solution module is used to map the material point information to the background grid nodes under the variable working condition boundary according to the coupled simulation multiphysics discrete model, solve the momentum and energy equations of the grid nodes, obtain the node increment and heat flow vector field, and then return the node increment and heat flow vector field to the material points to update the position, temperature and stress state, and obtain the global heat flux path and structural stress time series set. The response analysis module is used to extract the main steam temperature data to determine the response time and identify the key water-cooled wall heat flux path based on the global heat flux path and structural stress time series set, obtain the system reference effective response time constant and key component list, apply perturbation to the thermophysical parameters of each component in the system reference effective response time constant and key component list and calculate them, and establish a component inertial response sensitivity sequence table. The regulation and optimization module is used to call the system response time change and parameter disturbance of each component according to the component inertial response sensitivity sequence table, calculate the component disturbance response combination item, arrange and select the component disturbance response combination item, and determine the boiler heat transfer inertial regulation and optimization target point.

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