Method and device for dynamically visualizing hydrodynamic characteristics of heating surface of boiler reheater

Abstract

The invention relates to the technical field of hydrodynamic characteristic analysis, in particular to a boiler reheater heating surface hydrodynamic characteristic dynamic visualization method and device, and the method comprises the steps: collecting various parameters of a reheater and a boiler where the reheater is located, the method comprises the following steps: generating a fluid flow-heat transfer two-way coupling three-dimensional numerical simulation model of a reheater to construct a fluid flow-heat transfer two-way coupling three-dimensional numerical simulation model after a low-temperature reheater and a final-stage reheater are added to the reheater, generating simulated operation parameters of the reheater in a simulated steam flow state, and constructing a hydrodynamic characteristic prediction model by combining the simulated operation parameters and operation parameters of a boiler; and generating a hydrodynamic characteristic dynamic visualization result of the heating surface of the reheater by utilizing the temperature field, the flow velocity distribution and the steam flow track of the heating surface of the reheater predicted by the dynamic characteristic prediction model. The basic data of the hydrodynamic characteristics of the heating surface of the reheater can be obtained in real time, the hydrodynamic characteristics of the heating surface of the boiler reheater can be monitored and predicted in real time and visually and dynamically displayed in real time, and visual guidance is provided for boiler operation optimization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water power characteristics analysis, in particular to a dynamic visualization method and device for water power characteristics of a reheater heating surface of a boiler. BACKGROUND

[0002] In the related art, in the traditional boiler design and operation stage, the water power characteristics of the reheater heating surface are usually estimated by relying on the thermodynamic calculation manual and the empirical formula. The monitoring system developed later relies on discrete point measurement (such as sparse wall temperature measurement points) to estimate the water power characteristics of the reheater heating surface.

[0003] However, in the related art, although the traditional thermodynamic calculation manual and the empirical formula can provide basic data support, when the boiler load changes greatly, the fuel properties change, or the reheater heating surface appears abnormal conditions such as ash deposition and slagging, it is difficult to establish a correlation model between the structural parameters and the water power characteristics, and it is difficult to quickly and accurately reflect the real-time changes of the water power characteristics of the reheater. It is difficult to provide effective guidance for the optimized operation of the boiler; the discrete point measurement cannot comprehensively reflect the three-dimensional water power state of the reheater heating surface, and it is difficult to effectively realize online evaluation and real-time regulation of the water power characteristics, which needs to be solved urgently. SUMMARY

[0004] The present application provides a dynamic visualization method and device for water power characteristics of a reheater heating surface of a boiler, to solve the problems in the related art that the traditional thermodynamic calculation manual and the empirical formula can provide basic data support, but when the boiler load changes abnormally, it is difficult to establish a correlation model between the structural parameters and the water power characteristics, and it is difficult to quickly and accurately reflect the real-time changes of the water power characteristics of the reheater. It is difficult to provide effective guidance for the optimized operation of the boiler; the discrete point measurement cannot comprehensively reflect the three-dimensional water power state of the reheater heating surface, and it is difficult to realize online evaluation and real-time regulation of the water power characteristics.

[0005] The first aspect embodiment of the present application provides a dynamic visualization method for water power characteristics of a reheater heating surface of a boiler, comprising the following steps: collecting the operating parameters and environmental parameters of the boiler where the reheater is located and the operating parameters and structural parameters of the reheater, and based on the operating parameters and environmental parameters of the boiler and the operating parameters and structural parameters of the reheater, constructing a fluid flow-heat transfer bidirectional coupling three-dimensional numerical simulation model of the reheater after adding a low-temperature reheater and a final-stage reheater; based on the fluid flow-heat transfer bidirectional coupling three-dimensional numerical simulation model, generating simulation operating parameters of the reheater in a simulated steam flow state, to construct a water power characteristics prediction model in combination with the simulation operating parameters and the operating parameters of the boiler; and generating a dynamic visualization result of the water power characteristics of the reheater heating surface by using the temperature field, the flow velocity distribution and the steam flow trajectory of the reheater heating surface predicted by the water power characteristics prediction model.

[0006] Optionally, in an embodiment of the present application, the fluid flow-heat two-way coupling three-dimensional numerical simulation model of the reheater after adding the low-temperature reheater and the final-stage reheater is constructed based on the operating parameters and environmental parameters of the boiler and the operating parameters and structural parameters of the reheater, including: based on the operating parameters and environmental parameters of the boiler and the operating parameters and structural parameters of the reheater, a target turbulence model is used to construct the steam side model in the reheater tube, and a target radiation model is used to construct the flue gas side model outside the reheater tube; the fluid flow-heat two-way coupling three-dimensional numerical simulation model is constructed by combining the steam side model in the reheater tube and the flue gas side model outside the reheater tube.

[0007] Optionally, in an embodiment of the present application, the fluid flow-heat two-way coupling three-dimensional numerical simulation model of the reheater after adding the low-temperature reheater and the final-stage reheater is constructed based on the operating parameters and environmental parameters of the boiler and the operating parameters and structural parameters of the reheater, including: setting dynamic boundary conditions and grid references for constructing the fluid flow-heat two-way coupling three-dimensional numerical simulation model; the fluid flow-heat two-way coupling three-dimensional numerical simulation model is constructed based on the dynamic boundary conditions and the grid references.

[0008] Optionally, in an embodiment of the present application, the simulation operating parameters of the reheater in the simulated steam flow state are generated based on the fluid flow-heat two-way coupling three-dimensional numerical simulation model, including: extracting the hydrodynamic characteristic parameters, heat transfer characteristic parameters, and oxide skin risk correlation parameters of the reheater in the simulated steam flow state; determining the simulation operating parameters according to the hydrodynamic characteristic parameters, heat transfer characteristic parameters, and oxide skin risk correlation parameters.

[0009] Optionally, in an embodiment of the present application, after generating the hydrodynamic characteristic dynamic visualization result of the reheater heating surface, it further includes: generating a control strategy of the reheater according to the temperature field, flow velocity distribution, and steam flow trajectory; optimizing the operating parameters and / or structural parameters of the reheater according to the control strategy.

[0010] The second aspect embodiment of the present application provides a boiler reheater heating surface hydrodynamic characteristic dynamic visualization device, comprising: an acquisition module configured to acquire operating parameters and environmental parameters of a boiler in which a reheater is located, operating parameters and structural parameters of the reheater, and construct a fluid flow-heat bidirectional coupling three-dimensional numerical simulation model of the reheater after a low-temperature reheater and a final-stage reheater are added to the reheater based on the operating parameters and environmental parameters of the boiler and the operating parameters and structural parameters of the reheater; a simulation module configured to generate simulation operating parameters of the reheater in a simulated steam flow state based on the fluid flow-heat bidirectional coupling three-dimensional numerical simulation model, and construct a hydrodynamic characteristic prediction model by combining the simulation operating parameters and the operating parameters of the boiler; and a first generation module configured to generate a hydrodynamic characteristic dynamic visualization result of the reheater heating surface by using a temperature field, a flow velocity distribution, and a steam flow trajectory of the reheater heating surface predicted by the hydrodynamic characteristic prediction model.

[0011] Optionally, in an embodiment of the present application, the acquisition module comprises: a first construction unit configured to construct a steam side model in a reheater tube by using a target turbulent flow model based on the operating parameters and environmental parameters of the boiler and the operating parameters and structural parameters of the reheater, and construct a flue gas side model outside the reheater tube by using a target radiation model; and a second construction unit configured to construct the fluid flow-heat bidirectional coupling three-dimensional numerical simulation model by combining the steam side model in the reheater tube and the flue gas side model outside the reheater tube.

[0012] Optionally, in an embodiment of the present application, the acquisition module comprises: a setting unit configured to set a dynamic boundary condition and a grid reference for constructing the fluid flow-heat bidirectional coupling three-dimensional numerical simulation model; and a third construction unit configured to construct the fluid flow-heat bidirectional coupling three-dimensional numerical simulation model based on the dynamic boundary condition and the grid reference.

[0013] Optionally, in an embodiment of the present application, the simulation module comprises: an extraction unit configured to extract a hydrodynamic characteristic parameter, a heat transfer characteristic parameter, and a scale risk correlation parameter of the reheater in the simulated steam flow state; and a determination unit configured to determine the simulation operating parameters according to the hydrodynamic characteristic parameter, the heat transfer characteristic parameter, and the scale risk correlation parameter.

[0014] Optionally, in an embodiment of the present application, further comprising: a second generation module configured to generate a regulation and control strategy of the reheater according to the temperature field, the flow velocity distribution, and the steam flow trajectory after the hydrodynamic characteristic dynamic visualization result of the reheater heating surface is generated; and an optimization module configured to optimize the operating parameters and / or the structural parameters of the reheater according to the regulation and control strategy.

[0015] The third aspect of the embodiments of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the program to implement the method for dynamically visualizing water dynamic characteristics of a reheater heating surface of a boiler as described in the above embodiments.

[0016] The fourth aspect of the embodiments of the present application provides a computer readable storage medium storing a computer program, which is executed by a processor to implement the method for dynamically visualizing water dynamic characteristics of a reheater heating surface of a boiler as described above.

[0017] The fifth aspect of the embodiments of the present application provides a computer program product comprising a computer program, which is executed to implement the method for dynamically visualizing water dynamic characteristics of a reheater heating surface of a boiler as described above.

[0018] Additional aspects and advantages of the present application will be made apparent by the following description and the accompanying drawings.

[0019] The embodiments of the present application can collect various parameters of the reheater and the boiler in which the reheater is located to construct a fluid flow-heat two-way coupling three-dimensional numerical simulation model of the reheater after a new low-temperature reheater and a final-stage reheater are added, generate simulation operation parameters of the reheater in a simulation steam flow state, and thus construct a water dynamic characteristic prediction model to predict a temperature field, a flow velocity distribution, and a steam flow trajectory of the reheater heating surface and generate a visualized result in combination with the simulation operation parameters and the operation parameters of the boiler. Thus, the water dynamic characteristic basic data of the reheater heating surface are obtained through the three-dimensional numerical simulation model and the numerical simulation, the comprehensive water dynamic characteristic prediction model is established by deeply mining and learning the historical operation data, the real-time monitoring and prediction of the water dynamic characteristics of the reheater heating surface of the boiler and the real-time and intuitive dynamic display are realized, the intuitive guidance for the boiler operation optimization is provided, and the water dynamic characteristic analysis and optimization design of the supercritical unit boiler reheater in the heating modification are applicable. Thus, the problems in the related art that the traditional thermal calculation manual and the empirical formula can provide basic data support, but when the boiler load is in an abnormal condition, the correlation model between the structural parameters and the water dynamic characteristics cannot be established, it is difficult to quickly and accurately reflect the real-time changes of the water dynamic characteristics of the reheater, and the effective guidance for the optimized operation of the boiler cannot be provided, and the discrete point measurement cannot comprehensively reflect the three-dimensional water dynamic state of the reheater heating surface, and it is difficult to realize the online evaluation and real-time regulation of the water dynamic characteristics are solved. BRIEF DESCRIPTION OF DRAWINGS

[0020] The above and / or additional aspects and advantages of the present application will become apparent and more readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which: Figure 1A flow chart of a method for dynamically visualizing water dynamic characteristics of a boiler reheater heating surface according to an embodiment of the present application is provided. Figure 2 A structural schematic diagram of a device for dynamically visualizing water dynamic characteristics of a boiler reheater heating surface according to an embodiment of the present application is provided. Figure 3 A structural schematic diagram of an electronic device according to an embodiment of the present application is provided.

[0021] Reference Signs: 10 - a device for dynamically visualizing water dynamic characteristics of a boiler reheater heating surface; 100 - a collection module, 200 - a simulation module, and 300 - a first generation module; 301 - a memory, 302 - a processor, and 303 - a communication interface. DETAILED DESCRIPTION

[0022] Embodiments of the present application are described in detail below with reference to the attached drawings, which are meant to be exemplary and are not intended to limit the present application.

[0023] The method and device for dynamically visualizing water dynamic characteristics of a reheater heating surface of a boiler according to the embodiments of the present application are described below with reference to the accompanying drawings. In the related art mentioned in the background, although the traditional thermodynamic calculation manual and empirical formula can provide basic data support, when the boiler load is in an abnormal condition, a correlation model of the structural parameters and the water dynamic characteristics cannot be established, and it is difficult to quickly and accurately reflect the real-time changes of the water dynamic characteristics of the reheater, and the related art cannot provide effective guidance for the optimized operation of the boiler. The discrete point measurement cannot comprehensively reflect the three-dimensional water dynamic state of the reheater heating surface, and it is difficult to realize online evaluation and real-time regulation of the water dynamic characteristics. The present application provides a method for dynamically visualizing water dynamic characteristics of a reheater heating surface of a boiler. In the method, the fluid flow-heat two-way coupling three-dimensional numerical simulation model of the reheater after the addition of the low-temperature reheater and the final reheater can be constructed by collecting various parameters of the reheater and the boiler in which the reheater is located, the simulated operating parameters of the reheater under the simulated steam flow state can be generated, and the temperature field, the flow velocity distribution, and the steam flow trajectory of the reheater heating surface can be predicted by combining the simulated operating parameters and the operating parameters of the boiler and constructing a water dynamic characteristic prediction model, and the visualized results can be generated. Thus, the basic data of the water dynamic characteristics of the reheater heating surface are obtained through the three-dimensional numerical simulation model and numerical simulation, the comprehensive water dynamic characteristic prediction model is established by deeply mining and learning the historical operating data, the real-time monitoring and prediction of the water dynamic characteristics of the reheater heating surface of the boiler are realized, and the real-time and intuitive dynamic display is realized, which provides intuitive guidance for the optimized operation of the boiler and is suitable for the water dynamic characteristic analysis and optimized design of the supercritical unit boiler reheater in the heating modification. Thus, the problems in the related art are solved, that is, although the traditional thermodynamic calculation manual and empirical formula can provide basic data support, when the boiler load is in an abnormal condition, a correlation model of the structural parameters and the water dynamic characteristics cannot be established, and it is difficult to quickly and accurately reflect the real-time changes of the water dynamic characteristics of the reheater, and the related art cannot provide effective guidance for the optimized operation of the boiler. The discrete point measurement cannot comprehensively reflect the three-dimensional water dynamic state of the reheater heating surface, and it is difficult to realize online evaluation and real-time regulation of the water dynamic characteristics.

[0024] Specifically, Figure 1 A flowchart of the method for dynamically visualizing water dynamic characteristics of a reheater heating surface of a boiler according to the embodiments of the present application is shown in FIG. 1.

[0025] As Figure 1 shown, the method for dynamically visualizing water dynamic characteristics of a reheater heating surface of a boiler includes the following steps: In step S101, the operating parameters and environmental parameters of the boiler in which the reheater is located and the operating parameters and structural parameters of the reheater are collected, and based on the operating parameters and environmental parameters of the boiler and the operating parameters and structural parameters of the reheater, a fluid flow-heat two-way coupling three-dimensional numerical simulation model of the reheater after the addition of the low-temperature reheater and the final reheater is constructed.

[0026] In some embodiments, in order to better analyze the complex correlation between the reheater heating surface and the internal flow and heat transfer between the boiler, the application can, but is not limited to, collect the operating parameters and environmental parameters of the boiler where the reheater is located and the operating parameters and structural parameters of the reheater, so as to construct a fluid flow-heat transfer two-way coupling model after the low-temperature reheater and the last-stage reheater are newly added on the basis of the original reheater of the boiler according to the operating parameters and environmental parameters of the boiler where the reheater is located and the operating parameters and structural parameters of the reheater.

[0027] Among them, the operating parameters of the boiler include but are not limited to: main steam pressure, main steam temperature, boiler load change, desuperheating water flow, etc., which are also the core indicators of the overall operation of the boiler and will directly affect the boiler thermal cycle and steam supply state; the environmental parameters of the boiler include but are not limited to the flue gas temperature (300-600℃) inside the boiler and the environmental temperature (20-30℃), etc., wherein the flue gas is the product after the boiler burns, and its temperature directly affects the external heat exchange condition of the reheater; the environmental temperature is the external environmental basic data of the boiler operation.

[0028] The operating parameters of the reheater include but are not limited to reheated steam flow, reheated steam temperature, steam extraction flow, steam extraction pressure, etc., which are different from the operating parameters of the boiler. The operating parameters of the reheater are the key parameters of the reheater itself, and can directly reflect the steam flow and heat exchange effect of the reheater; the structural parameters of the reheater include but are not limited to the number of reheater tube rows, the position of the steam extraction port, the arrangement area and other parameters, which are inherent design parameters of the reheater and can determine the fluid flow path and heat exchange area distribution of the reheater.

[0029] After obtaining these parameters, in order to ensure the reliability and effectiveness of the parameters, the application embodiments can also preprocess these parameters.

[0030] For example, the application can, but is not limited to, filter abnormal data such as abnormal wall temperature and abnormal flow rate in the parameters by adopting the normal distribution 3σ principle; and then fill in the missing values based on the forward difference method combined with LSTM interpolation.

[0031] Among them, the normal distribution 3σ principle filters abnormal data here refers to that the distribution of wall temperature, flow rate and other data under normal working conditions conforms to the normal distribution, most of the data are concentrated near the average value, 3σ represents the range of mean value ± 3 times standard deviation, and the application embodiments can determine the data exceeding this range as abnormal values, such as distorted data caused by sensor failure and instantaneous working condition mutation, and eliminate them, so as to avoid the interference of extreme abnormal values on the subsequent model and make the data more consistent with the normal law of actual operation of the equipment.

[0032] The forward difference method combined with LSTM (Long Short-Term Memory, a deep learning model specially designed to process time-series data such as continuously collected operating parameters) interpolates missing values. Here, missing values may occur due to transmission interruptions or temporary sensor malfunctions during data collection. The embodiments of the present application can estimate these missing values using interpolation methods and insert them. The forward difference method refers to a simple estimation based on the trend of adjacent data before the missing value. Meanwhile, the embodiments of the present application can capture long-term changes in data by combining LSTM, thereby ensuring that the inserted values accurately match the actual fluctuation trend of the data, ensuring the integrity of the data set, and providing continuous and reliable data support for subsequent model training and simulation analysis.

[0033] Further, to adapt to the actual working conditions of adding low-temperature reheaters and final reheaters to the reheater heat surface during the water dynamic characteristic analysis process, the embodiments of the present application can, but are not limited to, construct a fluid flow-heat two-way coupling three-dimensional numerical simulation model after adding low-temperature reheaters and final reheaters to the reheater, that is, construct a fluid flow-heat two-way coupling three-dimensional numerical simulation model after adding low-temperature reheaters and final reheaters to the original reheater, thereby accurately capturing the complex characteristics of the reheater after adding new equipment and providing a reliable basis for water dynamic characteristic analysis.

[0034] Specifically, the addition of new equipment changes the original steam flow path (such as extending the flow channel and increasing the branch) and the heat exchange environment (such as the change in the heat exchange area between the newly added heating surface and the flue gas), resulting in more complex interactions between the in-pipe steam flow rate, pressure distribution, and the out-pipe flue gas heat transfer conditions: the steam flow state (such as the reduced flow rate downstream of the extraction port) changes the heat transfer efficiency, and the steam temperature and pressure changes caused by heat transfer in turn affect the flow (such as the change in flow rate due to density change).

[0035] Based on this, the embodiments of the present application adopt a two-way coupling model, which can truly restore the interaction between flow and heat transfer, and the three-dimensional characteristics can accurately present the spatial distribution differences (such as local flow rate mutation and temperature gradient) in key areas such as elbow pipes and extraction ports, avoiding errors of simplified models.

[0036] The embodiments of the present application can provide high-quality data support for constructing a fluid flow-heat two-way coupling model after adding low-temperature final reheaters and final reheaters by comprehensively collecting key parameters of boiler and reheater operation, environment, structure, filtering, and missing value repair, etc. preprocessing process, ensuring that the model construction can accurately match the actual working conditions, thereby providing a reliable basis for risk prediction and optimization adjustment of the actual reheater.

[0037] Optionally, in an embodiment of the present application, based on the operating parameters and environmental parameters of the boiler and the operating parameters and structural parameters of the reheater, a fluid flow-heat two-way coupling three-dimensional numerical simulation model of the reheater after adding the low-temperature reheater and the final-stage reheater is constructed, including: based on the operating parameters and environmental parameters of the boiler and the operating parameters and structural parameters of the reheater, a target turbulence model is used to construct a reheater steam-side model in the pipe, and a target radiation model is used to construct a reheater flue gas-side model outside the pipe; combining the reheater steam-side model in the pipe and the reheater flue gas-side model outside the pipe, a fluid flow-heat two-way coupling three-dimensional numerical simulation model is constructed.

[0038] It can be understood that the target turbulence model here refers to a numerical model for accurately simulating the turbulent flow characteristics of the steam in the reheater pipe, such as the SST k-ω turbulence model, etc. The model can describe the turbulent state of the steam in the pipe (such as flow velocity distribution, turbulent intensity, shear force, etc.) through mathematical equations, so as to capture the complex turbulent behavior of the steam in the reheater pipe during flow, and provide accurate fluid dynamics basis for the flow-heat coupling analysis of the steam side in the pipe, ensuring that the simulation results of the flow characteristics of the steam in the pipe are consistent with the actual operating state.

[0039] The target radiation model here refers to a numerical model for simulating the radiation heat transfer process between the flue gas outside the reheater pipe and the pipe wall, such as the DO radiation model, etc. Since the flue gas temperature in the boiler is high (300-600℃), radiation is an important way for the flue gas to transfer heat to the pipe wall, and the radiation model can calculate the transfer and absorption of flue gas radiation energy through the discrete coordinate method, accurately quantify the radiation heat transfer contribution of the flue gas outside the pipe to the pipe wall, and after being combined with the turbulent flow model of the steam side in the pipe, the two-way coupling process of “flue gas radiation + convection heat transfer outside the pipe → pipe wall heat transfer → pipe steam flow heat transfer” can be completely restored, ensuring the accuracy of the heat transfer characteristic simulation.

[0040] In actual implementation, when constructing the fluid flow-heat two-way coupling three-dimensional numerical simulation model of the reheater after adding the low-temperature reheater and the final-stage reheater based on the operating parameters and environmental parameters of the boiler and the operating parameters and structural parameters of the reheater, the present application can but is not limited to be realized by combining the reheater steam-side model in the pipe and the reheater flue gas-side model outside the pipe.

[0041] In this embodiment of the application, the target turbulence model used for the steam side model inside the reheater tubes may be, but is not limited to, the SST k-ω turbulence model (hear stress transport k-ω model). The SST k-ω turbulence model is mainly used to simulate the turbulent flow state of steam inside the reheater tubes. It can accurately capture parameters such as steam velocity distribution and turbulence intensity. It can take into account the calculation accuracy of near-wall and far-field flow, so it is extremely suitable for scenarios such as reheater tube steam with shear flow and velocity changes (such as the condition of reduced velocity downstream of the extraction port).

[0042] The target radiation model used in the reheater tube external flue gas side model in this application embodiment may, but is not limited to, the DO radiation model (Discrete Ordinates Radiation Model). The DO radiation model is mainly used to simulate the radiative heat transfer process of the flue gas outside the reheater tubes. It can quantify the heat transferred from the flue gas to the tube wall, accurately calculate the radiative heat transfer of high-temperature flue gas (300-600℃), and closely match the actual heat transfer environment of the reheater in the boiler flue, thereby ensuring the accuracy of the heat transfer calculation.

[0043] By combining the steam-side model inside the reheater tubes and the flue gas-side model outside the reheater tubes, this embodiment of the application can construct a two-way coupled three-dimensional numerical simulation model of the reheater's fluid flow and heat transfer. That is, the two-way coupled three-dimensional numerical simulation model of the reheater's fluid flow and heat transfer can be understood here as simulating the three-dimensional operating state after the addition of a low-temperature reheater and a final-stage reheater by coupling the interaction between the steam flow inside the reheater tubes and the heat transfer of the flue gas outside the tubes. It outputs key parameters such as velocity distribution, tube wall temperature field, and pressure loss to support the model for analyzing the reheater's hydrodynamic characteristics, heat transfer efficiency, and potential risks.

[0044] The embodiments of this application can accurately simulate the interaction between steam flow and heat transfer on the reheater heating surface under varying operating conditions by constructing a two-way coupled three-dimensional numerical simulation model of the steam side model inside the reheater tubes and the flue gas side model outside the reheater tubes. This effectively captures the impact of flow changes on heat transfer and equipment risks (such as oxide scale shedding), providing realistic quantitative support for hydrodynamic characteristic analysis.

[0045] Optionally, in one embodiment of this application, a three-dimensional numerical simulation model of fluid flow-heat transfer bidirectional coupling is constructed based on the boiler's operating parameters and environmental parameters, as well as the reheater's operating parameters and structural parameters. This includes: setting dynamic boundary conditions and mesh references for constructing the fluid flow-heat transfer bidirectional coupling three-dimensional numerical simulation model; and constructing the fluid flow-heat transfer bidirectional coupling three-dimensional numerical simulation model based on the dynamic boundary conditions and mesh references.

[0046] Based on the descriptions of other embodiments, it is understood that this application can combine the steam side model inside the reheater tubes and the flue gas side model outside the reheater tubes to construct a three-dimensional numerical simulation model of fluid flow-heat transfer bidirectional coupling after the addition of a low-temperature reheater and a final stage reheater in the reheater.

[0047] In actual implementation, this application can also set dynamic boundary conditions and mesh references for constructing a three-dimensional numerical simulation model of fluid flow-heat transfer bidirectional coupling.

[0048] In this context, dynamic boundary conditions refer to numerical simulation boundaries that closely match the actual operating conditions of the final stage reheater and reflect the dynamic changes of parameters. These include, but are not limited to, the flow rate, temperature, and turbulence intensity at the main steam inlet, the mass flow rate and pressure at the extraction steam outlet, and key parameters such as wall material and thermal conductivity. These parameters can provide input data that conforms to actual operation for the two-way coupled simulation of fluid flow and heat transfer.

[0049] The grid benchmark here refers to the grid generation standard for building numerical simulation models, thereby providing a high-quality computational grid foundation for subsequent extraction of hydrodynamic parameters such as outlet velocity distribution and pipe wall temperature field.

[0050] Specifically, in this application embodiment, a basic parametric geometric model (fluid flow-heat transfer bidirectional coupling model) can be constructed using ANSYS Meshing based on the structural parameters of the reheater (such as serpentine tube bank, extraction port location, arrangement area, etc.).

[0051] Then, the embodiments of this application can perform fine mesh processing on key areas of flow and heat transfer, and set mesh benchmarks: set 3 layers of densification zone at the bend (to capture sudden changes in flow velocity), and control the mesh growth rate to 1.1 near the steam extraction port (to balance calculation accuracy and efficiency), to ensure that the overall mesh quality is >0.7 and there are no negative volume errors, providing a reliable discretized geometric basis for subsequent calculations.

[0052] To address the core characteristics of steam flow inside the reheater tubes and heat transfer of flue gas outside the tubes, appropriate physical models were selected: the SST k-ω turbulence model was used on the steam side inside the tubes to accurately simulate the turbulent flow state of the steam (such as velocity distribution and turbulence intensity); the DO radiation model was used on the flue gas side outside the tubes to quantify the radiative heat transfer process of high-temperature flue gas (300-600℃) and to fit the heat exchange environment of the reheater and the flue gas.

[0053] Next, based on the collected operating parameters, the boundary conditions of the model are set to ensure that the simulation closely matches the actual operating state of the reheater. Among them, the inlet conditions mainly set the main steam flow rate, temperature, and 5% turbulence intensity (matching the initial state of the steam in the tube); the outlet conditions are mainly for the extraction steam outlet, using the mass flow rate outlet boundary, and inputting the extraction steam flow rate and pressure (simulating the steam splitting characteristics); and the reheater wall conditions are set as follows: the tube wall material is set to SA-213S30432, and its thermal conductivity is dynamically adjusted according to λ=0.03T+10 (T is the tube wall temperature), while a non-slip adiabatic wall is set (reproducing the interaction between the tube wall and the fluid).

[0054] Finally, the coupled solution (flow and heat transfer related calculation) method is configured. In this embodiment, the SIMPLE algorithm can be used to realize the pressure-velocity coupling between the steam flow inside the pipe and the heat transfer of the flue gas outside the pipe. The flow state affects the heat transfer efficiency, and the temperature and pressure changes caused by heat transfer react to the flow in a two-way coupled logic. The coupling equations of the flow field and the temperature field are solved to ensure that the interaction between flow and heat transfer is accurately restored.

[0055] After the calculation converges, the three-dimensional numerical simulation model of fluid flow-heat transfer bidirectional coupling outputs the quantitative results of the hydrodynamic characteristics of the reheater, including but not limited to key parameters such as the velocity distribution at the reheater outlet, the pipe wall temperature field, and the pressure loss distribution.

[0056] This application embodiment can provide an accurate geometric discretization basis for hydrodynamic characteristic analysis by performing fine mesh processing on key areas of the reheater and ensuring mesh quality. Then, by using certain turbulence models and certain radiation models, the steam turbulence inside the tube and the heat transfer of flue gas outside the tube are accurately simulated respectively. Finally, by combining dynamic wall conditions and solution algorithms, a two-way coupled solution is achieved. The extracted parameters such as velocity distribution, tube wall temperature field, and pressure loss can accurately reflect the flow uniformity, heat transfer matching and system resistance characteristics of the reheater heating surface, providing a realistic quantitative basis for hydrodynamic characteristic analysis and ensuring the reliability of the analysis results.

[0057] Step S102: Based on the fluid flow-heat transfer bidirectional coupled three-dimensional numerical simulation model, generate simulated operating parameters of the reheater under simulated steam flow conditions, and combine the simulated operating parameters with the boiler operating parameters to construct a hydrodynamic characteristic prediction model. Those skilled in the art will understand that, in recent years, numerical simulation technology has made significant progress in the application of boiler technology. In the study of the hydrodynamic characteristics of boiler reheater heating surfaces, numerical simulation can provide high-precision data on the distribution of flow, temperature, and pressure fields, enabling a deeper understanding of the flow and heat transfer mechanisms inside the reheater.

[0058] However, on the one hand, numerical simulation requires a lot of computing resources and time, and its response speed is difficult to meet the actual needs of online monitoring and rapid decision-making applications with high real-time requirements; on the other hand, the accuracy of numerical simulation models is highly dependent on the establishment of the model, the setting of boundary conditions, and the selection of physical parameters, while its precision is difficult to determine in actual operation, which may affect the reliability of numerical simulation results.

[0059] Meanwhile, traditional numerical simulation methods (such as CFD analysis based on steady-state flow fields) cannot dynamically respond to load fluctuations, resulting in significant deviations between simulation results and actual operating data. Furthermore, existing technologies do not integrate real-time operating data (such as steam flow rate and desuperheating water usage in DCS systems) with numerical simulations, making it impossible to achieve online evaluation of hydrodynamic characteristics.

[0060] However, machine learning technology can automatically learn inherent patterns and rules from large amounts of historical data, establish a mapping relationship between input and output, and achieve prediction and classification of unknown data. Regarding the hydrodynamic characteristics of boiler reheater heating surfaces, machine learning methods can fully utilize the large amount of historical data accumulated during boiler operation, uncover hidden patterns behind the data, establish fast and accurate prediction models, and achieve real-time prediction and evaluation of the hydrodynamic characteristics of reheater heating surfaces.

[0061] Considering that during reheater operation under varying conditions, the downstream steam velocity at the reheater extraction port decreases under extraction conditions, leading to a decrease in the Nusselt number and heat transfer coefficient, an increase in tube wall temperature, and an increased risk of oxide scale shedding, and that the thermal conductivity of the reheater material also dynamically changes with temperature, affecting heat transfer characteristics, this application embodiment can generate simulated operating parameters of the reheater under simulated steam flow conditions by simulating hydrodynamic parameters (flow velocity, pressure, wall temperature, etc.) under varying conditions based on a constructed three-dimensional numerical simulation model of fluid flow-heat transfer bidirectional coupling of the reheater. This allows for the construction of a hydrodynamic characteristic prediction model, identifying risks such as uneven flow, local overpressure, or overheating that may be caused by the addition of equipment. This provides quantitative support for optimizing the hydrodynamic characteristics of the reheater heating surface (such as flow distribution and pressure loss control), ensuring the safe and efficient operation of the system.

[0062] For example, this application may, but is not limited to, merge the preprocessed real-time operating data (mainly the preprocessed boiler operating parameters) with the simulated operating data to form a complete training dataset, and divide the complete training dataset into a 70% training set containing different load conditions, a 20% validation set for model tuning, and a 10% test set for evaluating the model's generalization ability.

[0063] The input features of this hydrodynamic characteristic prediction model are the boiler's operating parameters: main steam pressure, temperature, extraction steam flow rate, boiler load, desuperheating water flow rate, etc.; the output targets are: reheater wall temperature, steam velocity, pressure distribution, and other target parameters.

[0064] The basic architecture of the hydrodynamic characteristic prediction model can be, but is not limited to, a two-layer long short-term memory network (LSTM): each with 128 neurons. Dropout can be used to prevent overfitting, and the end is connected to a fully connected layer to output the predicted value. The hyperparameters can be, but are not limited to, a learning rate of 0.001, a batch size of 32, 500 iterations, and finally, the mean squared error (MSE) is used as the loss function, and the Adam optimizer is used for iterative training.

[0065] Training can employ, but is not limited to, an early stopping strategy: the process terminates when the validation set MSE shows no decrease for 10 consecutive rounds. Ultimately, on the test set, this can achieve prediction results with a wall temperature prediction error of ±2℃, a flow velocity error of ≤3%, and a single-sample inference time of ≤100ms. This ensures that the final trained hydrodynamic characteristic prediction model has good generalization ability for different load fluctuations and extraction steam flow changes, with a prediction delay of <500ms under sudden operating conditions such as a surge in extraction steam, demonstrating significantly better dynamic adaptability than traditional steady-state CFD simulations.

[0066] Based on the hydrodynamic characteristic prediction model trained by this model, after inputting certain boiler operating parameters such as main steam pressure, temperature, extraction steam flow rate, boiler load, and desuperheating water flow rate, the model can generate in real time the operating parameters of the reheater under the corresponding steam flow state, such as reheater wall temperature, steam velocity, and pressure distribution.

[0067] This application embodiment can construct a hydrodynamic characteristic prediction model by merging real-time operation data and numerical simulation data to cover multiple operating conditions of the boiler. This enables the rapid and accurate prediction of key parameters such as reheater tube wall temperature and flow velocity using the constructed hydrodynamic characteristic prediction model. At the same time, it improves the generalization of operating conditions such as load fluctuations and sudden increases in steam extraction, solving the problem of slow dynamic response in traditional CFD simulation. It provides efficient quantitative support for real-time analysis, risk prediction (such as local overheating), and parameter optimization of the hydrodynamic characteristics of the reheater heating surface.

[0068] Optionally, in one embodiment of this application, based on a three-dimensional numerical simulation model of fluid flow-heat transfer bidirectional coupling, simulated operating parameters of the reheater under simulated steam flow conditions are generated, including: extracting hydrodynamic characteristic parameters, heat transfer characteristic parameters, and oxide scale risk correlation parameters of the reheater under simulated steam flow conditions; and determining the simulated operating parameters based on the hydrodynamic characteristic parameters, heat transfer characteristic parameters, and oxide scale risk correlation parameters.

[0069] Based on the descriptions of other embodiments, it is understood that when constructing the hydrodynamic prediction model, this application may, but is not limited to, use the simulated operating parameters of the reheater under simulated steam flow conditions generated by the fluid flow-heat transfer bidirectional coupled three-dimensional numerical simulation model as part of the data basis.

[0070] In actual implementation, the simulated operating parameters of the reheater under simulated steam flow conditions generated by the three-dimensional numerical simulation model of fluid flow-heat transfer bidirectional coupling in this application mainly include, but are not limited to, the hydrodynamic characteristic parameters, heat transfer characteristic parameters, and oxide scale risk-related parameters of the reheater under simulated steam flow conditions.

[0071] The simulated operating parameters of the reheater generated in this application embodiment under simulated steam flow conditions are mainly based on the influence of the velocity change of the last-stage reheater on the hydrodynamic characteristics under extraction steam conditions, as well as the hydrodynamic characteristic parameters (velocity distribution, pressure loss, flow distribution), heat transfer characteristic parameters (tube wall temperature field, temperature gradient), and oxide scale risk-related parameters (temperature fluctuation amplitude, velocity pulsation frequency) extracted from the oxide scale shedding risk analysis.

[0072] Among them, the formation and shedding of oxide scale are closely related to pipe wall temperature and steam flow rate (high temperature easily leads to thickening of oxide scale, and drastic temperature fluctuations or flow rate pulsations will exacerbate oxide scale shedding). Therefore, parameters such as pipe wall temperature fluctuation amplitude (the degree of temperature change over time) and flow rate pulsation frequency (the number of rapid flow rate fluctuations) can be directly extracted from the simulated operation data and used to quantitatively assess the risk of oxide scale shedding.

[0073] This application embodiment can focus the simulated operating parameters on key parameters such as the tube wall temperature fluctuation amplitude and flow velocity pulsation frequency extracted from the hydrodynamic characteristics of the reheater under extraction steam conditions and the risk of oxide scale shedding. These parameters are then used as data to supplement the hydrodynamic prediction model, thereby ensuring that the finally trained hydrodynamic prediction model can not only accurately predict hydrodynamic parameters, but also analyze the risks related to oxide scale formation. This enables the coordinated assessment of hydrodynamic characteristics and safety risks, providing more comprehensive and practical quantitative support for the operation optimization and risk prevention of the reheater heating surface.

[0074] Step S103: Using the temperature field, velocity distribution and steam flow trajectory of the reheater heating surface predicted by the hydrodynamic characteristic prediction model, a dynamic visualization result of the hydrodynamic characteristics of the reheater heating surface is generated.

[0075] As one possible approach, after using the trained hydrodynamic characteristic prediction model to predict and generate hydrodynamic characteristic data such as temperature field, velocity distribution and steam flow trajectory of the reheater heating surface, this application can, but is not limited to, visualize these hydrodynamic characteristic data to generate dynamic visualization results of the hydrodynamic characteristics of the reheater heating surface.

[0076] For example, this application may, but is not limited to, construct a web-based 3D visualization interface and use GPU acceleration to dynamically render hydrodynamic characteristics.

[0077] In this application, embodiments may, but are not limited to, use color mapping to visually display the temperature field. For example, red (>580℃), orange (560℃-580℃), and blue (<560℃) may be set to correspond to over-temperature, warning, and safe zones, respectively.

[0078] Meanwhile, the embodiments of this application can also present the steam flow trajectory with streamline animation, where the streamline density is proportional to the flow velocity, clearly showing the steam flow state inside the reheater.

[0079] Furthermore, embodiments of this application can support axial / radial slice interaction in the visualization interface, allowing users to view cross-sectional pressure cloud diagrams and wall shear stress distribution, thus assisting in the analysis of local hydrodynamic characteristics.

[0080] The interface is compatible with mainstream browsers and can achieve high-performance graphics rendering without the need for plugins, ensuring smooth loading and dynamic updates of 3D models.

[0081] Based on this visualization interface, users can input boiler operating parameters to trigger dynamic calculations. For example, they can input and adjust the extraction steam flow rate to adapt to the adjustable high-pressure heating conditions; and link the main steam flow rate and temperature parameters under different loads. After parameter changes, the hydrodynamic characteristic prediction model can complete the prediction update within 1 second, and the 3D visualization interface can simultaneously refresh the hydrodynamic characteristic maps such as temperature field and flow velocity streamlines.

[0082] Additionally, the system can be configured in the background to support both keyboard input and slider adjustment modes. Users can set threshold verification in the parameter input box (such as prompting when the steam extraction flow rate exceeds the range) to ensure safe and compliant operating condition settings and meet the dynamic analysis needs of operators for hydrodynamic characteristics under different operating conditions.

[0083] This visual interface allows for real-time monitoring of boiler reheater operating parameters and triggers multi-level warnings when abnormal parameters occur. For example, when the local wall temperature of the reheater exceeds 580℃ (the critical value for oxidation resistance of SA-213S30432 material) for 10 consecutive minutes, the corresponding area flashes red in the 3D interface; when the steam flow rate is below 15m / s (the critical value for scale carrying), the streamline animation slows down and the interface displays "Insufficient flow rate, increased risk of scale shedding," simultaneously recommending an increase in desuperheating water flow; when the reheater system pressure loss fluctuates by more than 20% from the baseline value, areas that may have accumulated ash or become blocked are automatically marked, generating an optimization strategy of "suggesting a check of the cleanliness of the heated surfaces." Furthermore, warnings can be accompanied by audible alerts and risk level indicators (high / medium / low) to help operators quickly locate abnormal operating conditions.

[0084] It should be noted that the specific settings of the visualization interface and the specific settings of the multi-level warning can be set by professionals in this field according to the actual situation. The embodiments in this application are only illustrative and do not impose any specific limitations.

[0085] This application embodiment can use a three-dimensional visualization scheme to present the hydrodynamic characteristics of the reheater temperature field and steam flow with intuitive color mapping and streamline animation. It comprehensively reflects the three-dimensional hydrodynamic state of the reheater heating surface, including velocity distribution, pressure distribution, and temperature distribution. This overcomes the limitations of existing monitoring systems that rely on discrete point measurements, and provides a more comprehensive and intuitive display of hydrodynamic characteristics. This allows operators to intuitively understand the real-time status of the reheater heating surface, making it easier to identify potential problems and take optimization measures in a timely manner.

[0086] Furthermore, the visualization interface in this embodiment supports slice-based interactive analysis of local parameters and can trigger multi-level early warnings and provide optimization suggestions based on material critical values. It adapts to complex operating conditions and flexible control, including different loads and different steam extraction flows, providing flexible control methods for boiler operation optimization. By inputting different operating parameters, it generates corresponding hydrodynamic characteristic display results in real time, supporting operators to make flexible adjustments according to actual needs. This allows operators to quickly grasp the hydrodynamic status under different operating conditions, locate abnormal risks, and efficiently meet the needs of dynamic analysis and safety control.

[0087] Optionally, in one embodiment of this application, after generating the dynamic visualization results of the hydrodynamic characteristics of the reheater heating surface, the method further includes: generating a control strategy for the reheater based on the temperature field, velocity distribution, and steam flow trajectory; and optimizing the operating parameters and / or structural parameters of the reheater based on the control strategy.

[0088] In other embodiments, after generating the dynamic visualization results of the hydrodynamic characteristics of the reheater heating surface, this application can generate a reheater control strategy based on the hydrodynamic characteristics of the reheater heating surface, such as the temperature field, velocity distribution, and steam flow trajectory, thereby optimizing the operating parameters and / or structural parameters of the reheater according to the reheater control strategy.

[0089] For example, this application can generate intelligent control strategies by monitoring hydrodynamic parameters in real time: when the wall temperature in a certain area of ​​the reheater reaches 575℃ and the flow velocity is 20m / s, the hydrodynamic characteristic prediction model automatically calculates the optimal adjustment scheme, such as increasing the opening of the corresponding desuperheating water nozzle by 15%, and predicting 5 minutes in advance that the wall temperature can be reduced to 565℃ after adjustment (error ±3℃).

[0090] This strategy adjusts parameters such as steam flow rate and wall shear stress to reduce the amount of desuperheating water used, thereby reducing heat efficiency loss and avoiding the risk of desuperheater cracking caused by excessive water spraying, achieving a synergistic optimization of economy and safety.

[0091] Furthermore, based on the dynamic visualization results of hydrodynamic characteristics, the embodiments of this application can also generate accurate steam extraction location optimization schemes for different steam extraction conditions.

[0092] Taking the 340t / h extraction steam condition as an example, this application embodiment can analyze the steam velocity distribution of the last stage reheater and recommend adjusting the extraction steam outlet from the original design of 40 rows to 30 rows.

[0093] This adjustment increases the steam velocity in the extraction zone from 18 m / s to 22 m / s, effectively enhancing the scale-carrying capacity and reducing the risk of scale accumulation due to insufficient flow velocity. Simultaneously, considering the placement of the new low-temperature reheater and the matching of the steam flow field, numerical simulation combined with machine learning ensures that the reheater system resistance loss is reduced by 12% and wall temperature uniformity is improved by 18% after the extraction location adjustment, avoiding localized overheating and guaranteeing the safe and efficient operation of the unit after the heating system upgrade.

[0094] This application's embodiments can optimize the control strategy of the desuperheating water system, optimize the spray volume based on the real-time flow field state, avoid control lag or over-control, reduce equipment wear and damage caused by abnormal hydrodynamic characteristics, extend equipment service life, reduce equipment maintenance costs, and improve the economic efficiency and safety of unit operation. Simultaneously, by combining the design of the newly added intermediate header and desuperheating water system, the control logic can be optimized to ensure the best desuperheating effect under different operating conditions, further improving the stability and reliability of boiler operation. As a result, operators can adjust boiler operating parameters in a timely manner, reduce unnecessary energy consumption, thereby reducing operating costs and improving operating efficiency, achieving the goal of energy conservation and consumption reduction.

[0095] The following is a detailed explanation of the dynamic visualization method for the hydrodynamic characteristics of the boiler reheater heating surface in this application, using a specific embodiment.

[0096] Taking a heating system renovation project of a supercritical unit (2×350MW) as the application scenario, this paper analyzes the hydrodynamic characteristics of its final stage reheater (original pipe diameter Φ51mm, material SA-213TP347H) and the newly added low-temperature reheater (located above the tail flue to adapt to the unit's heating needs and frequent load adjustments). The specific implementation steps are as follows: Real-time data acquisition: Real-time data is acquired from the unit's DCS system at a sampling frequency of 1Hz, including: Main steam parameters: pressure 24.2MPa, temperature 566℃; Reheat steam parameters: flow rate 1685t / h (100% THA), temperature 566℃; Extraction steam parameters: flow rate 200t / h, pressure 4.0MPa; Wall temperature data: 48 wall temperature measuring points in the last-stage reheater (the highest measured wall temperature was 570℃). Sensor configuration: K-type thermocouples, vortex flow meters, etc. are used.

[0097] Data preprocessing: Outliers are removed by setting thresholds and filtered using the normal distribution 3σ principle; for missing data during the data acquisition process, forward difference method combined with long short-term memory network (LSTM) interpolation is used (e.g., when steam extraction flow is missing, it is predicted using the data from the previous 10 minutes); parameters such as pressure (0~30MPa) and temperature (0~600℃) are normalized to the [0, 1] interval.

[0098] Numerical simulation: A three-dimensional numerical simulation model was constructed, including the newly added low-temperature reheater and the final-stage reheater. Boundary conditions were input to simulate the steam flow state, and hydrodynamic parameters such as steam velocity, pressure distribution, and temperature field distribution were extracted, as detailed below: The geometric model was built using ANSYS Fluent, featuring a Wade serpentine tube bank (120 rows) for the final stage reheater with a tube diameter of Φ44.5mm and a bend radius of 1.5D. A new cryogenic reheater was added, located in the tail flue (12m wide × 8m deep × 6m high), using Φ51mm tubes arranged in parallel. A structured mesh was generated using ANSYS Meshing, with finer mesh refinement on the final stage reheater wall and a mesh quality >0.7.

[0099] Then set the boundary conditions: Inlet conditions: main steam flow rate 1885 t / h (100% THA), temperature 566℃, turbulence intensity 5%; Extraction steam outlet conditions: flow rate 200 t / h, pressure 4.0 MPa, using mass flow rate outlet boundary; Wall conditions: SA-213S30432 material, thermal conductivity 25 W / (m²). K), non-slip thermal insulation wall.

[0100] The SST k-ω turbulence model was adopted, and the SIMPLE algorithm was used for pressure-velocity coupling. Hydrodynamic parameters such as the outlet velocity distribution, tube wall temperature field, and pressure loss distribution of the final stage reheater were extracted from the calculation results.

[0101] A hydrodynamic characteristic prediction model was constructed by training the preprocessed runtime data and numerical simulation parameters using machine learning algorithms, as detailed below: The dataset was constructed by collecting 8,760 hours of boiler unit operation data from January to December, which was divided into: a training set of 6,000 hours (70%), containing 47.7% to 100% THA load conditions; a validation set of 1,760 hours (20%), used for model tuning; and a test set of 1,000 hours (10%), used to evaluate the model's generalization ability.

[0102] The input features (12 dimensions) are main steam pressure, temperature, extraction steam flow rate, boiler load, desuperheating water flow rate, etc.; the output targets are the final stage reheater wall temperature (48 points), steam velocity, and pressure distribution (30 monitoring points).

[0103] A two-layer Long Short-Term Memory (LSTM) network with 128 neurons was used, with Dropout (0.2) to prevent overfitting, and a fully connected layer at the end to output the predicted value. The hyperparameters were set to a learning rate of 0.001, a batch size of 32, and 500 iterations. The mean squared error (MSE) was used as the loss function, and the Adam optimizer was used for iterative training.

[0104] The input includes 12-dimensional features such as main steam pressure and temperature, and the output includes target parameters such as the wall temperature of the final reheater and the steam flow rate. An early stopping strategy is used during training; the process terminates when the MSE on the validation set shows no decrease for 10 consecutive rounds. Ultimately, on the test set, the system achieves a wall temperature prediction error of ±2℃, a flow rate error of ≤3%, and a single-sample inference time of ≤100ms.

[0105] The model's performance was validated on the test set: the prediction error of the final stage reheater wall temperature was controlled within ±2℃, a 60% improvement over traditional methods; the relative error of the steam velocity prediction was ≤3%, accurately reflecting the actual velocity. Based on an NVIDIA RTX 3090 graphics card, the real-time inference time per sample was ≤100ms, meeting the requirements for online monitoring response. The model showed good generalization ability for load fluctuations of 47.7%-100% THA and changes in extraction steam flow rate of 50-340t / h. Under sudden operating conditions such as a surge in extraction steam, the prediction latency was <500ms, and its dynamic adaptability was significantly better than traditional steady-state CFD simulation.

[0106] The hydrodynamic characteristic prediction model is lightweighted and embedded into edge computing devices (such as industrial gateways) to achieve local real-time inference with a latency of ≤50ms. Key hydrodynamic parameters processed at the edge (such as coordinates of wall temperature overheating areas and abnormal flow velocity thresholds) are uploaded to the cloud dynamic display module via the MQTT protocol. Local caching and anomaly warning are supported in the offline mode to ensure the system's continuous operation capability when communication is interrupted.

[0107] Dynamic display: A web-based 3D visualization interface is built upon a network framework, utilizing GPU acceleration for dynamic rendering of hydrodynamic characteristics. Color mapping is employed to intuitively display the temperature field, with red (>580℃), orange (560-580℃), and blue (<560℃) corresponding to overheating, warning, and safe zones, respectively. Streamline animations depict the steam flow trajectory, with streamline density proportional to flow velocity, clearly showing the steam flow state within the Φ44.5mm final-stage reheater. Axial / radial slice interaction is supported, allowing users to view cross-sectional pressure contour maps and wall shear stress distribution, aiding in the analysis of local hydrodynamic characteristics. The interface is compatible with mainstream browsers, requiring no plugins, and utilizes WebGL for high-performance graphics rendering, ensuring smooth loading and dynamic updates of the 3D model.

[0108] The interface features a real-time operating condition configuration area, allowing users to input parameters to trigger dynamic calculations. The extraction steam flow rate can be adjusted from 50-340 t / h in 5 t / h increments, adapting to adjustable high-pressure heating conditions. The load percentage can be set between 47.7% and 100% THA, linked to the main steam flow rate (900-1885 t / h) and temperature parameters. After parameter changes, the hydrodynamic characteristic prediction model updates its predictions within 1 second, and the 3D visualization interface simultaneously refreshes hydrodynamic characteristic maps such as temperature field and flow velocity streamlines. It supports both keyboard input and slider adjustment modes, and the parameter input box includes threshold verification (such as prompts when the extraction steam flow rate exceeds the range) to ensure safe and compliant operating condition settings, meeting the dynamic analysis needs of operators for hydrodynamic characteristics under different operating conditions.

[0109] The system incorporates hydrodynamic risk identification rules, triggering multi-level warnings through real-time parameter monitoring: when the local wall temperature of the final stage reheater exceeds 580℃ (the critical value for oxidation resistance of SA-213S30432 material) for 10 consecutive minutes, the corresponding area flashes red on the 3D interface; when the steam flow rate is below 15m / s (the critical value for scale carrying), the streamline animation slows down and the interface displays "Insufficient flow rate, increased risk of scale shedding," simultaneously recommending an increase in desuperheating water flow; when the reheater system pressure loss fluctuates by more than 20% from the baseline value, areas that may have accumulated dust or blockages are automatically marked, generating an optimization strategy of "suggesting a check of the cleanliness of the heated surfaces." Warnings are accompanied by audible alerts and risk level indicators (high / medium / low) to help operators quickly locate abnormal operating conditions.

[0110] Optimize decision-making: Desuperheating water control optimization: Intelligent control strategies are generated through real-time monitoring of hydrodynamic parameters. When the wall temperature in a certain area of ​​the final reheater reaches 575℃ and the flow velocity is 20m / s, the machine learning model automatically calculates the optimal control scheme. For example, increasing the opening of the corresponding desuperheating water nozzle by 15% and predicting 5 minutes in advance that the wall temperature can be reduced to 565℃ after the adjustment (error ±3℃). Based on parameters such as steam velocity and wall shear stress, this strategy reduces the desuperheating water consumption from 10.53t / h to 6.2t / h, reducing thermal efficiency loss and avoiding the risk of desuperheater cracking caused by excessive water spraying, achieving synergistic optimization of economy and safety.

[0111] Steam extraction location optimization: Based on the dynamic display results of hydrodynamic characteristics, precise steam extraction location optimization schemes are generated for different steam extraction conditions. Taking the 340t / h steam extraction condition as an example, by analyzing the steam velocity distribution of the final stage reheater, it is recommended to adjust the steam extraction port from the 40th row of the original design to the 30th row. This adjustment can increase the steam velocity in the extraction area from 18m / s to 22m / s, effectively enhancing the oxide scale carrying capacity and reducing the risk of oxide scale accumulation due to insufficient flow velocity. At the same time, considering the arrangement location of the newly added low-temperature reheater and the matching of the steam flow field, through the combination of numerical simulation and machine learning, it is ensured that after the steam extraction location adjustment, the reheater system resistance loss is reduced by 12%, the wall temperature uniformity is improved by 18%, local overheating problems are avoided, and the safe and efficient operation of the unit after the heating system renovation is guaranteed.

[0112] The dynamic visualization method for the hydrodynamic characteristics of the boiler reheater heating surface proposed in this application can collect various parameters of the reheater and the boiler it is located in to construct a three-dimensional numerical simulation model of fluid flow-heat transfer bidirectional coupling after the addition of a low-temperature reheater and a final-stage reheater. This model generates simulated operating parameters of the reheater under simulated steam flow conditions. By combining these simulated operating parameters with the boiler's operating parameters, a hydrodynamic characteristic prediction model is constructed to predict the temperature field, velocity distribution, and steam flow trajectory of the reheater heating surface, and the visualization results are generated. This method achieves the acquisition of basic data on the hydrodynamic characteristics of the reheater heating surface through a three-dimensional numerical simulation model and numerical simulation. By deeply mining and learning from historical operating data, a comprehensive hydrodynamic characteristic prediction model is established, enabling real-time monitoring and prediction of the hydrodynamic characteristics of the boiler reheater heating surface, as well as real-time, intuitive dynamic display. This provides intuitive guidance for boiler operation optimization and is applicable to the hydrodynamic characteristic analysis and optimization design of supercritical unit boiler reheaters in heating system retrofits. This solves the problems in related technologies, such as the inability of traditional thermal calculation manuals and empirical formulas to establish a correlation model between structural parameters and hydrodynamic characteristics when abnormal boiler load occurs, making it difficult to quickly and accurately reflect the real-time changes in the hydrodynamic characteristics of the reheater and provide effective guidance for the optimized operation of the boiler; and the inability of discrete point measurements to fully reflect the three-dimensional hydrodynamic state of the reheater heating surface, making it difficult to achieve online evaluation and real-time control of hydrodynamic characteristics.

[0113] Next, referring to the accompanying drawings, a dynamic visualization device for the hydrodynamic characteristics of the boiler reheater heating surface according to an embodiment of this application is described.

[0114] Figure 2 This is a schematic diagram of the structure of the dynamic visualization device for the hydrodynamic characteristics of the boiler reheater heating surface according to an embodiment of this application.

[0115] like Figure 2 As shown, the dynamic visualization device 10 for the hydrodynamic characteristics of the boiler reheater heating surface includes: a data acquisition module 100, a simulation module 200, and a first generation module 300.

[0116] The acquisition module 100 is used to acquire the operating parameters and environmental parameters of the boiler where the reheater is located, as well as the operating parameters and structural parameters of the reheater. Based on the operating parameters and environmental parameters of the boiler, as well as the operating parameters and structural parameters of the reheater, a three-dimensional numerical simulation model of fluid flow-heat transfer bidirectional coupling after the addition of a low-temperature reheater and a final-stage reheater is constructed.

[0117] The simulation module 200 is used to generate simulated operating parameters of the reheater under simulated steam flow conditions based on a three-dimensional numerical simulation model that is based on a two-way coupled fluid flow-heat transfer model. This simulated operating parameters are then combined with the boiler's operating parameters to construct a hydrodynamic characteristic prediction model.

[0118] The first generation module 300 is used to generate dynamic visualization results of the hydrodynamic characteristics of the reheater heating surface by using the temperature field, velocity distribution and steam flow trajectory predicted by the hydrodynamic characteristic prediction model.

[0119] Optionally, in one embodiment of this application, the acquisition module 100 includes: a first construction unit and a second construction unit.

[0120] The first building unit is used to construct the steam side model inside the reheater tubes using a target turbulence model and the flue gas side model outside the reheater tubes using a target radiation model, based on the boiler's operating parameters and environmental parameters, as well as the reheater's operating parameters and structural parameters.

[0121] The second building unit is used to combine the steam side model inside the reheater tubes and the flue gas side model outside the reheater tubes to build a two-way coupled three-dimensional numerical simulation model of fluid flow and heat transfer.

[0122] Optionally, in one embodiment of this application, the acquisition module 100 includes: a setting unit and a third construction unit.

[0123] The setting unit is used to set the dynamic boundary conditions and mesh reference for constructing a two-way coupled three-dimensional numerical simulation model of fluid flow and heat transfer.

[0124] The third building block is used to construct a three-dimensional numerical simulation model of fluid flow-heat transfer bidirectional coupling based on dynamic boundary conditions and grid references.

[0125] Optionally, in one embodiment of this application, the simulation module 200 includes an extraction unit and a determination unit.

[0126] The extraction unit is used to extract the hydrodynamic characteristic parameters, heat transfer characteristic parameters, and oxide scale risk-related parameters of the reheater under simulated steam flow conditions.

[0127] The determination unit is used to determine the simulation operation parameters based on hydrodynamic characteristic parameters, heat transfer characteristic parameters, and oxide scale risk correlation parameters.

[0128] Optionally, in one embodiment of this application, it further includes: a second generation module and an optimization module.

[0129] The second generation module is used to generate a control strategy for the reheater based on the temperature field, velocity distribution, and steam flow trajectory after generating a dynamic visualization result of the hydrodynamic characteristics of the reheater heating surface.

[0130] The optimization module is used to optimize the operating parameters and / or structural parameters of the reheater according to the control strategy.

[0131] It should be noted that the explanation of the above-mentioned embodiment of the dynamic visualization method for the hydrodynamic characteristics of the boiler reheater heating surface also applies to the dynamic visualization device for the hydrodynamic characteristics of the boiler reheater heating surface in this embodiment, and will not be repeated here.

[0132] The dynamic visualization device for the hydrodynamic characteristics of the boiler reheater heating surface proposed in this application can collect various parameters of the reheater and the boiler it is located in to construct a three-dimensional numerical simulation model of fluid flow-heat transfer bidirectional coupling after the addition of a low-temperature reheater and a final-stage reheater. This model generates simulated operating parameters of the reheater under simulated steam flow conditions. By combining these simulated operating parameters with the boiler's operating parameters, a hydrodynamic characteristic prediction model is constructed to predict the temperature field, velocity distribution, and steam flow trajectory of the reheater heating surface, and the visualization results are generated. Thus, it achieves the acquisition of basic data on the hydrodynamic characteristics of the reheater heating surface through a three-dimensional numerical simulation model and numerical simulation. By deeply mining and learning from historical operating data, a comprehensive hydrodynamic characteristic prediction model is established, enabling real-time monitoring and prediction of the hydrodynamic characteristics of the boiler reheater heating surface, as well as real-time, intuitive dynamic display. This provides intuitive guidance for boiler operation optimization and is applicable to the hydrodynamic characteristic analysis and optimization design of supercritical unit boiler reheaters in heating system retrofits. This solves the problems in related technologies, such as the inability of traditional thermal calculation manuals and empirical formulas to establish a correlation model between structural parameters and hydrodynamic characteristics when abnormal boiler load occurs, making it difficult to quickly and accurately reflect the real-time changes in the hydrodynamic characteristics of the reheater and provide effective guidance for the optimized operation of the boiler; and the inability of discrete point measurements to fully reflect the three-dimensional hydrodynamic state of the reheater heating surface, making it difficult to achieve online evaluation and real-time control of hydrodynamic characteristics.

[0133] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 301, the processor 302, and the computer program stored on the memory 301 and capable of running on the processor 302.

[0134] When the processor 302 executes the program, it implements the dynamic visualization method for the hydrodynamic characteristics of the boiler reheater heating surface provided in the above embodiments.

[0135] Furthermore, electronic devices also include: Communication interface 303 is used for communication between memory 301 and processor 302.

[0136] The memory 301 is used to store computer programs that can run on the processor 302.

[0137] The memory 301 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0138] If the memory 301, processor 302, and communication interface 303 are implemented independently, then the communication interface 303, memory 301, and processor 302 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0139] Optionally, in a specific implementation, if the memory 301, processor 302, and communication interface 303 are integrated on a single chip, then the memory 301, processor 302, and communication interface 303 can communicate with each other through an internal interface.

[0140] Processor 302 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0141] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for dynamic visualization of the hydrodynamic characteristics of the boiler reheater heating surface.

[0142] This application also provides a computer program product, including a computer program that can run computer instructions. When the computer instructions are executed by a processor, they implement the dynamic visualization method for the hydrodynamic characteristics of the boiler reheater heating surface provided in this application.

[0143] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0144] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0145] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0146] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0147] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0148] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0149] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0150] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for dynamic visualization of the hydrodynamic characteristics of a boiler reheater heating surface, characterized in that, Includes the following steps: The operating and environmental parameters of the boiler where the reheater is located, as well as the operating and structural parameters of the reheater, are collected. Based on the operating and environmental parameters of the boiler and the operating and structural parameters of the reheater, a three-dimensional numerical simulation model of fluid flow-heat transfer bidirectional coupling is constructed after the addition of a low-temperature reheater and a final-stage reheater to the reheater. Based on the fluid flow-heat transfer bidirectional coupled three-dimensional numerical simulation model, simulated operating parameters of the reheater under simulated steam flow conditions are generated. Combined with the simulated operating parameters and the operating parameters of the boiler, a hydrodynamic characteristic prediction model is constructed. The temperature field, velocity distribution, and steam flow trajectory of the reheater heating surface are predicted using the hydrodynamic characteristic prediction model, and dynamic visualization results of the hydrodynamic characteristics of the reheater heating surface are generated.

2. The method according to claim 1, characterized in that, Based on the boiler's operating and environmental parameters, as well as the reheater's operating and structural parameters, a three-dimensional numerical simulation model of fluid flow and heat transfer coupling after the addition of a low-temperature reheater and a final-stage reheater is constructed, including: Based on the operating and environmental parameters of the boiler and the operating and structural parameters of the reheater, a target turbulence model is used to construct the steam side model inside the reheater tubes, and a target radiation model is used to construct the flue gas side model outside the reheater tubes. By combining the steam side model inside the reheater tubes and the flue gas side model outside the reheater tubes, a three-dimensional numerical simulation model of fluid flow-heat transfer bidirectional coupling is constructed.

3. The method according to claim 1, characterized in that, Based on the boiler's operating and environmental parameters, as well as the reheater's operating and structural parameters, a three-dimensional numerical simulation model of fluid flow and heat transfer coupling after the addition of a low-temperature reheater and a final-stage reheater is constructed, including: Set the dynamic boundary conditions and mesh reference for constructing the two-way coupled three-dimensional numerical simulation model of fluid flow and heat transfer; The fluid flow-heat transfer bidirectional coupled three-dimensional numerical simulation model is constructed based on the dynamic boundary conditions and the mesh reference.

4. The method according to claim 1, characterized in that, The simulation operating parameters of the reheater under simulated steam flow conditions are generated based on the fluid flow-heat transfer bidirectional coupled three-dimensional numerical simulation model, including: Extract the hydrodynamic characteristic parameters, heat transfer characteristic parameters, and oxide scale risk correlation parameters of the reheater under simulated steam flow conditions; The simulation operation parameters are determined based on the hydrodynamic characteristic parameters, heat transfer characteristic parameters, and oxide scale risk correlation parameters.

5. The method according to claim 1, characterized in that, After generating the dynamic visualization results of the hydrodynamic characteristics of the reheater heating surface, the process also includes: The control strategy for the reheater is generated based on the temperature field, flow velocity distribution, and steam flow trajectory. The operating parameters and / or structural parameters of the reheater are optimized according to the control strategy.

6. A dynamic visualization device for the hydrodynamic characteristics of a boiler reheater heating surface, characterized in that, include: The acquisition module is used to acquire the operating parameters and environmental parameters of the boiler where the reheater is located, as well as the operating parameters and structural parameters of the reheater. Based on the operating parameters and environmental parameters of the boiler, as well as the operating parameters and structural parameters of the reheater, a three-dimensional numerical simulation model of fluid flow-heat transfer bidirectional coupling is constructed after the addition of a low-temperature reheater and a final-stage reheater to the reheater. The simulation module is used to generate simulated operating parameters of the reheater under simulated steam flow conditions based on the fluid flow-heat transfer bidirectional coupled three-dimensional numerical simulation model, so as to combine the simulated operating parameters and the operating parameters of the boiler to construct a hydrodynamic characteristic prediction model. The generation module is used to generate dynamic visualization results of the hydrodynamic characteristics of the reheater heating surface based on the temperature field, velocity distribution and steam flow trajectory predicted by the hydrodynamic characteristic prediction model.

7. The apparatus according to claim 6, characterized in that, The acquisition module includes: The first construction unit is used to construct the steam side model inside the reheater tubes using a target turbulence model and the flue gas side model outside the reheater tubes using a target radiation model, based on the operating parameters and environmental parameters of the boiler and the operating parameters and structural parameters of the reheater. The second building unit is used to combine the steam side model inside the reheater tubes and the flue gas side model outside the reheater tubes to build the fluid flow-heat transfer bidirectional coupled three-dimensional numerical simulation model.

8. An electronic device, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the dynamic visualization method for the hydrodynamic characteristics of the boiler reheater heating surface as described in any one of claims 1-5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the dynamic visualization method for the hydrodynamic characteristics of the boiler reheater heating surface as described in any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed, it is used to implement the dynamic visualization method for the hydrodynamic characteristics of the boiler reheater heating surface as described in any one of claims 1-5.

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