Cooling tower through-flow structure optimization method and device, equipment and storage medium

Abstract

The embodiment of the invention discloses a cooling tower through-flow structure optimization method and device, equipment and a storage medium. According to the method, a simulation model of a target cooling tower is determined, and the simulation model has corresponding boundary conditions and multiple physical parameters. And with a preset temperature parameter as an input condition, a physical parameter as a decision variable and the optimal working efficiency of the target cooling tower as a target, solving an optimal solution based on a boundary condition of the simulation model to obtain the optimal working efficiency of the target cooling tower. And determining a through-flow structure optimization scheme of the target cooling tower according to the plurality of physical parameter values corresponding to the optimal working efficiency. According to the embodiment of the invention, the optimal physical parameters are accurately matched for the cooling tower by solving the optimal solution to generate the optimization scheme for optimizing the through-flow structure of the cooling tower, so that the operation efficiency of the cooling tower is improved, and the purpose of effectively saving water is achieved.

Description

Technical Field

[0001] This application relates to the field of thermal power generation, and includes, but is not limited to, a method, apparatus, equipment, and storage medium for optimizing the flow structure of a cooling tower. Background Technology

[0002] In the cold-end system of thermal power plants, cooling towers are key equipment for ensuring the efficient operation of the units, and their performance directly affects the overall operating efficiency and energy consumption of the units. Currently, the optimization design of cooling towers relies heavily on engineers' experience. However, different thermal power generating units vary greatly in terms of power and parameters. For example, large units have high heat loads and large cooling water requirements, while smaller units have relatively lower requirements. This experience-based optimization approach struggles to accurately adapt to the characteristics of different units, resulting in suboptimal optimization effects. Different power plant locations create vastly different climatic conditions for cooling towers. In cold northern regions, winter temperatures are extremely low, requiring extremely high antifreeze properties for cooling towers; in hot and humid southern regions, summer temperatures are high and humidity are high, requiring cooling towers to still dissipate heat efficiently under these conditions. Optimization methods relying solely on experience cannot fully consider these complex and variable climatic factors, causing cooling towers to fail to achieve optimal performance under different climatic conditions. Summary of the Invention

[0003] In view of this, the cooling tower flow structure optimization method, device, equipment, and storage medium provided in the embodiments of this application can control the transmission mechanism to accelerate transmission when preset conditions are triggered, so as to avoid the thin strip steel head arching and scrapping when it is accelerated immediately after biting the steel, thereby making the production process safe and smooth.

[0004] The cooling tower flow structure optimization method, apparatus, equipment, and storage medium provided in this application embodiment are implemented as follows: One aspect of this application provides a method for optimizing the flow structure of a cooling tower, the method comprising: Determine the simulation model of the target cooling tower, which has corresponding boundary conditions and multiple physical parameters; Using preset temperature parameters as input conditions, physical parameters as decision variables, and the optimal working efficiency of the target cooling tower as the objective, the optimal solution is obtained based on the boundary conditions of the simulation model to obtain the optimal working efficiency of the target cooling tower. Based on the multiple physical parameter values ​​corresponding to the optimal working efficiency, the flow structure optimization scheme of the target cooling tower is determined.

[0005] In one possible implementation, a simulation model of the target cooling tower is determined, including: Determine the corresponding geometric model and multiple physical parameters based on the actual structure of the target cooling tower; Based on the operating principle of the target cooling tower, the corresponding boundary conditions are matched to the geometric model to obtain the simulation model.

[0006] In one possible implementation, based on the operating principles of the target cooling tower, corresponding boundary conditions are matched to the geometric model to obtain a simulation model, including: Based on the operating principle of the target cooling tower, match at least one corresponding physical model to the geometric model; Based on the unit parameters of the thermal power generating unit where the target cooling tower is located, the boundary conditions corresponding to each physical model are determined to obtain the simulation model.

[0007] In one possible implementation, the physical parameters include at least one of the following: packing structure parameters, packing arrangement, flow guiding device parameters, and water distribution system piping arrangement parameters.

[0008] In one possible implementation, the algorithm for finding the optimal solution is the annealing algorithm.

[0009] In one possible implementation, optimal work efficiency includes at least one optimal work parameter; The method also includes: Determine at least one optimal operating parameter based on the historical meteorological data corresponding to the target cooling tower. The historical meteorological data includes at least one of temperature, humidity, wind speed, and wind direction.

[0010] In one possible implementation, the optimization scheme for the flow structure of the target cooling tower is determined based on multiple physical parameter values ​​corresponding to the optimal working efficiency, including: Simulation verification was performed based on multiple physical parameter values ​​corresponding to the optimal working efficiency. If the simulation verification results are accurate, the flow structure optimization scheme of the target cooling tower is determined based on the multiple physical parameter values ​​corresponding to the optimal working efficiency. If the simulation verification results are inaccurate, the simulation model is corrected.

[0011] Another aspect of this application embodiment provides a cooling tower flow structure optimization device, the device comprising: The simulation module is used to determine the simulation model of the target cooling tower. The simulation model has corresponding boundary conditions and multiple physical parameters. The optimization module is used to find the optimal solution based on the boundary conditions of the simulation model, with preset temperature parameters as input conditions, physical parameters as decision variables, and the optimal working efficiency of the target cooling tower as the objective, so as to obtain the optimal working efficiency of the target cooling tower. The scheme determination module is used to determine the optimal flow structure scheme of the target cooling tower based on multiple physical parameter values ​​corresponding to the optimal working efficiency.

[0012] In one possible implementation, the simulation module is further used for: Determine the corresponding geometric model and multiple physical parameters based on the actual structure of the target cooling tower; Based on the operating principle of the target cooling tower, the corresponding boundary conditions are matched to the geometric model to obtain the simulation model.

[0013] In one possible implementation, the simulation module is further used for: Based on the operating principle of the target cooling tower, match at least one corresponding physical model to the geometric model; Based on the unit parameters of the thermal power generating unit where the target cooling tower is located, the boundary conditions corresponding to each physical model are determined to obtain the simulation model.

[0014] In one possible implementation, the physical parameters include at least one of the following: packing structure parameters, packing arrangement, flow guiding device parameters, and water distribution system piping arrangement parameters.

[0015] In one possible implementation, the algorithm for finding the optimal solution is the annealing algorithm.

[0016] In one possible implementation, optimal work efficiency includes at least one optimal work parameter; The device also includes: The parameter determination module is used to determine at least one optimal operating parameter based on the historical meteorological data corresponding to the target cooling tower. The historical meteorological data includes at least one of temperature, humidity, wind speed, and wind direction.

[0017] In one possible implementation, the scheme determination module is further used for: Simulation verification was performed based on multiple physical parameter values ​​corresponding to the optimal working efficiency. If the simulation verification results are accurate, the flow structure optimization scheme of the target cooling tower is determined based on the multiple physical parameter values ​​corresponding to the optimal working efficiency. If the simulation verification results are inaccurate, the simulation model is corrected.

[0018] The electronic device provided in this application includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the program, it implements the method described in this application.

[0019] The computer-readable storage medium provided in this application embodiment stores a computer program thereon, which, when executed by a processor, implements the method provided in this application embodiment.

[0020] In this embodiment, the cooling tower flow structure optimization method determines a simulation model of the target cooling tower. The simulation model has corresponding boundary conditions and multiple physical parameters. Using preset temperature parameters as input conditions, physical parameters as decision variables, and the optimal operating efficiency of the target cooling tower as the objective, the optimal solution is obtained based on the boundary conditions of the simulation model, yielding the optimal operating efficiency of the target cooling tower. Based on the multiple physical parameter values ​​corresponding to the optimal operating efficiency, an optimization scheme for the flow structure of the target cooling tower is determined. This embodiment accurately matches the optimal physical parameters to the cooling tower by finding the optimal solution, generating an optimization scheme for optimizing the cooling tower's flow structure, thereby improving the cooling tower's operating efficiency and achieving effective water conservation. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A flowchart is shown below illustrating a method for optimizing the flow path of a cooling tower according to an embodiment of this application; Figure 2 A schematic diagram illustrating a cooling tower flow structure optimization process according to an embodiment of this application is shown; Figure 3 A schematic diagram of a cooling tower flow structure optimization device according to an embodiment of this application is shown; Figure 4 A schematic diagram of an electronic device according to an embodiment of this application is shown. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the specific technical solutions of this application will be further described in detail below with reference to the accompanying drawings of the embodiments of this application. The following embodiments are used to illustrate this application, but are not intended to limit the scope of this application.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0025] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0026] It should be noted that the terms "first, second, third" used in the embodiments of this application are used to distinguish similar or different objects and do not represent a specific order of objects. It can be understood that "first, second, third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0027] The cooling tower flow structure optimization method of this application embodiment can be executed by any electronic device capable of controlling the hot-rolled strip steel system. This electronic device may include, but is not limited to, mobile phones, wearable devices (such as smartwatches, smart bracelets, smart glasses, etc.), tablet computers, laptops, vehicle terminals, PCs (Personal Computers), etc. The functions implemented by this method can be achieved by a processor in the electronic device calling program code. Of course, the program code can be stored in a computer storage medium. Therefore, the electronic device includes at least a processor and a storage medium.

[0028] The cooling tower flow structure optimization method of this application embodiment can be used in application scenarios where the structure of cooling towers in thermal power generation systems is optimized.

[0029] The relevant technologies lack a scientific and systematic analytical method for optimizing the flow structure of cooling towers. In most cases, advanced modern algorithms and analytical tools are not fully utilized, and relying solely on experience makes it difficult to comprehensively consider the complex physical processes inside the cooling tower, such as the heat and mass exchange between air and water, and the flow distribution of airflow. This makes it difficult to achieve optimal configuration of key design parameters such as the structure and distribution of the cooling tower packing material, and the packing arrangement. For example, an unreasonable packing structure can lead to insufficient air-water contact and reduced cooling efficiency; an inappropriate packing arrangement may cause local airflow short-circuiting, affecting the overall heat dissipation effect.

[0030] Furthermore, current cooling tower design and optimization processes do not fully utilize climate information from different sites. While some optimizations consider climate factors, they fail to incorporate a comprehensive and in-depth analysis of historical meteorological parameters. The long-term variation patterns of climate elements such as temperature, humidity, wind speed, and wind direction have not been studied, resulting in optimized cooling towers being unable to adapt to seasonal or even annual climate changes and failing to maintain consistently high-efficiency operation during actual operation.

[0031] It is evident that existing cooling tower optimization methods, due to their over-reliance on experience, lack of scientific analysis methods, and insufficient utilization of climate information, cannot accurately match the climate conditions of different units and different sites, which seriously affects the performance of cooling towers and thus restricts the overall efficiency and economy of the cold-end system of thermal power generation.

[0032] Therefore, it can be seen that the technical problem solved by the embodiments of this application is how to achieve precision.

[0033] The following describes in detail the optimized flow structure scheme of the cooling tower according to the embodiments of this application, with reference to the accompanying drawings.

[0034] Figure 1 A flowchart illustrating a method for optimizing the flow path of a cooling tower according to an embodiment of this application is shown. Figure 1 As shown, the cooling tower flow structure optimization method of this application embodiment may include the following steps S10-S30.

[0035] For ease of description, the cooling tower flow structure optimization method of this application embodiment is described using an electronic device as the execution subject. It should be understood that the execution subject of this application embodiment can also be a processor or chip in an electronic device, and this application embodiment does not impose any limitations.

[0036] Step S10: The electronic device determines the simulation model of the target cooling tower.

[0037] In one possible implementation, electronic equipment performs simulation modeling on the cooling tower requiring flow structure optimization, obtaining a corresponding simulation model. The flow structure of the cooling tower refers to the path and spatial organization of cooling water and air flowing, contacting, and exchanging heat and mass within the tower, which determines the cooling tower's efficiency, energy consumption, footprint, and applicable scenarios. It can include internal and external flow guiding devices. The internal flow guiding device guides the internal airflow direction, preventing airflow short-circuiting and enhancing the heat and mass exchange effect between air and water. The external flow guiding device pre-processes the airflow entering the cooling tower based on changes in external wind direction and speed, improving the uniformity and stability of the incoming airflow. Therefore, optimizing the flow structure of the cooling tower can improve its operating efficiency and reduce energy consumption.

[0038] Optionally, the simulation model determined by the electronic device includes a geometric model characterizing the physical structure of the target cooling tower, and a physical model characterizing the operating principle of the target cooling tower. The structural model may include multiple physical parameters, and the physical model may have corresponding boundary conditions. That is, the process of the electronic device determining the simulation model of the target cooling tower may include determining the corresponding geometric model and multiple physical parameters based on the actual structure of the target cooling tower. Then, based on the operating principle of the target cooling tower, corresponding boundary conditions are matched to the geometric model to obtain the simulation model.

[0039] In some embodiments, the geometric model can be constructed using professional CFD modeling software, such as ANSYS Fluent, based on the actual physical structural dimensions of the target cooling tower. The geometric model may include key components of the target cooling tower, such as the tower cylinder, air inlet, water spraying device, packing layer, and water collection tank. For complex component structures, this embodiment can employ appropriate meshing techniques, such as unstructured meshing. This meshing method can improve computational efficiency while maintaining computational accuracy. For example, in the packing layer region, due to its complex internal structure, this embodiment can set a denser mesh to accurately simulate the heat and mass exchange process between air and water.

[0040] For example, embodiments of this application can determine at least one parameter corresponding to the flow structure in the geometric model as a physical parameter. Changes in this physical parameter directly affect the flow structure, thereby affecting the working efficiency of the target cooling tower. The parameter corresponding to the flow structure may include at least one of the following: packing structure parameters, packing arrangement method, parameters of the flow guiding device, and layout parameters of the water distribution system pipes. The packing structure parameters may further include the shape, size, and spacing of the packing. The packing arrangement method may include layered or staggered arrangement. The parameters of the flow guiding device may further include the angle, length, and number of guide plates. The layout parameters of the water distribution system pipes may further include pipe diameter, direction, and nozzle spacing.

[0041] In other embodiments, after determining the geometric model, the electronic device of this application can match at least one corresponding physical model to the geometric model based on the operating principles of the target cooling tower. Furthermore, based on the unit parameters of the thermal power generating unit where the target cooling tower is located, the boundary conditions corresponding to each physical model are determined to obtain the simulation model. Different physical models can be selected based on different physical operating conditions within the target cooling tower. For example, for airflow, a turbulence model, such as the k-ε model or k-ω model, can be used to accurately describe the turbulent flow characteristics. For heat and mass exchange between air and water, an appropriate heat and mass transfer model, such as the enthalpy-humidity model, is selected to consider processes such as water evaporation and sensible heat transfer. The boundary conditions set by the electronic device based on the operating conditions of the physical model can include the air velocity, temperature, and humidity at the air inlet, as well as the water temperature and flow rate of the water spray device, making the simulation model as close as possible to the actual operating conditions.

[0042] For example, the boundary conditions of the simulation model in this application embodiment can be determined based on historical empirical parameters. That is, the electronic equipment can pre-obtain the unit parameters of the thermal power generating unit where the target cooling tower is located, including but not limited to unit power, rated heat load, cooling water volume, and inlet / outlet water temperature. These parameters form the basis for subsequent analysis and optimization; different unit parameters determine the required cooling capacity and performance requirements of the target cooling tower. For example, for high-power units, which have larger heat loads, the target cooling tower needs to have higher heat dissipation efficiency and greater flow capacity.

[0043] Step S20: Using preset temperature parameters as input conditions, physical parameters as decision variables, and the optimal working efficiency of the target cooling tower as the objective, the optimal solution is obtained based on the boundary conditions of the simulation model to obtain the optimal working efficiency of the target cooling tower.

[0044] In one possible implementation, after determining the simulation model, the electronic device couples the simulation model with the optimal solution algorithm to determine the optimal flow structure scheme of the target cooling tower through optimal solution calculation. This coupling method may include defining parameters for the optimal solution algorithm based on the simulation model of the target cooling tower. These parameters include input conditions, decision variables, and the objective, and then calculating the optimal solution to obtain the optimal objective. The optimal solution algorithm selected in this embodiment can be the annealing algorithm, a powerful and widely popular optimization algorithm. Its main advantage lies in its strong global search capability, particularly adept at handling complex problems that are difficult to solve using traditional optimization methods.

[0045] In some embodiments, the present application can define optimal working efficiency as the objective, preset temperature parameters as input conditions, and physical parameters as decision variables during the parameter definition process. Working efficiency may include the cooling tower's cooling efficiency (e.g., cooling water temperature drop, cooling efficiency coefficient, etc.) or water saving rate. Decision variables are at least one physical parameter corresponding to the simulation model described above, and temperature parameters may include key temperature parameters such as initial temperature, cooling rate, and termination temperature. The design of these temperature parameters ensures that the initial temperature is a relatively high value to guarantee that the algorithm can fully search the solution space. The cooling rate should be moderate, ensuring that the algorithm converges quickly while avoiding missing the global optimum.

[0046] In other embodiments, optimal operating efficiency includes at least one optimal operating parameter, such as cooling efficiency and water saving rate. To further improve the reliability of the optimization results, embodiments of this application may also obtain historical meteorological data of the environment where the target cooling tower is located, and determine at least one optimal operating parameter based on the historical meteorological data corresponding to the target cooling tower. The historical meteorological data includes at least one of temperature, humidity, wind speed, and wind direction.

[0047] Optionally, electronic devices can acquire historical meteorological data by first determining the location of the target cooling tower, and then obtaining years of historical meteorological data from the local meteorological department or professional meteorological data agency, covering temperature, humidity, wind speed, wind direction, etc. Simultaneously, meteorological monitoring equipment can be installed at the target cooling tower location to monitor climate data under typical operating conditions in real time. For example, during the high-temperature period in summer, the focus is on changes in temperature and humidity; in windy areas, the impact of different wind directions and speeds on the air intake of the cooling tower is analyzed. Through comprehensive analysis of historical and real-time data, typical climate conditions under different seasons and weather conditions can be determined.

[0048] Based on the aforementioned historical meteorological data, the electronic equipment can be further enhanced with at least one corresponding optimal operating parameter. This allows the optimization process to consider both performance optimization under specific operating conditions and comprehensive performance under different climatic conditions. For example, under high temperature and high humidity conditions, the electronic equipment also considers optimizing the heat dissipation capacity of the target cooling tower; under low temperature and low humidity conditions, the electronic equipment also focuses on the antifreeze performance of the target cooling tower.

[0049] In some embodiments, this application can couple the simulated annealing algorithm with the CFD simulation model using the Python language. Python has rich scientific computing libraries, such as NumPy for efficient numerical computation, SciPy for optimization algorithm tools, and Matplotlib for result visualization. Using these libraries, the electronic device can implement the annealing algorithm to find the optimal solution through solution space exploration, objective function calculation, and result visualization. The solution space exploration process uses NumPy to generate random numbers to explore the solution space, and randomly generates new solutions based on the defined range of decision variables as new solutions to try in the simulated annealing algorithm iterations. The objective function calculation process can be implemented using functions in SciPy to define and calculate the objective function. The objective function is determined based on at least one physical model of the simulation model and boundary conditions, with at least one physical parameter of the simulation model serving as the decision variable for the objective function. In each calculation process, the electronic device substitutes the preset temperature parameters and the current physical parameters into the objective function to obtain the corresponding candidate efficiency. After multiple iterations, one of the candidate efficiency is determined as the optimal solution, i.e., the target efficiency. The results visualization process can use Matplotlib to plot the optimization process curve of the simulated annealing algorithm, with the number of iterations as the horizontal axis and the target efficiency as the vertical axis, to show how the target efficiency changes during the optimization process, making it easier to observe the convergence of the algorithm.

[0050] To further improve the accuracy of the optimization results, this application embodiment can also consider the equipment aging factors during the long-term operation of the cooling tower. By establishing an equipment aging model, the impact of packing performance degradation, pipe wear, etc. on the cooling tower performance can be simulated, and the aging factors can be incorporated into the objective function or constraints.

[0051] Optionally, embodiments of this application can achieve interaction between the Python language and the target cooling tower simulation model through TUI (Text User Interface) script interaction or FluentPython API interaction.

[0052] TUI script interaction is achieved by writing TUI script files (usually with a ".jou" suffix) that contain Fluent operation commands. For example, here is a simple TUI script example used to start Fluent, load the mesh file, and set solver parameters: ( / file / read-case"cooling_tower.msh") ( / solve / set / time-step0.01) ( / solve / iterate1000) ( / file / write-case-data"result.cas"result.dat") In Python code, the "subprocess" module is used to call the TUI script mentioned above. Example code is as follows: import subprocess fluent_command = r'"C:\Program Files\ANSYS Inc\vXX\fluent\ntbin\win64\fluent" 3d -t4 -g -i cooling_tower.jou' The above paths need to be adjusted according to the actual ANSYS installation path and script names. try: subprocess.run(fluent_command, shell=True, check=True) print("Fluent simulation completed successfully.") except subprocess.CalledProcessError as e: print(f"Error occurred during Fluent simulation: {e}") The FluentPythonAPI interaction method requires that the PyFluent plugin be installed in your Python environment. If you are using a custom Python environment, you need to add the ANSYSFluent Python library path to the system environment variable "PYTHONPATH". For example, add "ANSYSInc\vXX\fluent\ntbin\win64\pyfluent" to "PYTHONPATH". Below is a simple example code using the FluentPythonAPI to start Fluent, load the mesh file, and set solver parameters: Python fromansys.fluent.coreimportlaunch_fluent Start Fluent session=launch_fluent(mode='solver',processor_count=4) Load grid file session.file.read_case('cooling_tower.msh') Set solver parameters session.solve.set.time_step(0.01) session.solve.iterate.iterate(1000) Save results session.file.write_case_data('result.cas','result.dat') Close Fluent session session.exit() Through the interaction between Python and the simulation model described above, this embodiment of the application can update the parameters of the CFD simulation model based on the current combination of decision variables in each iteration of the annealing algorithm, and perform simulation calculations to obtain the corresponding cooling tower performance indicators. According to the criteria of the simulated annealing algorithm, it is determined whether to accept a new solution. If the new solution improves the objective function value, it is accepted; otherwise, a worse solution is accepted with a certain probability. This probability gradually decreases as the temperature decreases, thus giving the algorithm a chance to escape local optima and search for the global optimum.

[0053] Step S30: Determine the optimal flow structure scheme of the target cooling tower based on the multiple physical parameter values ​​corresponding to the optimal working efficiency.

[0054] In one possible implementation, after completing the optimization process and obtaining the optimal operating efficiency, the electronic device can determine the optimal flow structure scheme for the target cooling tower based on multiple physical parameter values ​​corresponding to the optimal operating efficiency. The optimal flow structure scheme can include multiple physical parameter values, each indicating how to set the corresponding physical parameters.

[0055] For example, the packing structure parameters in the physical parameters of this application embodiment include the shape, size, spacing, and material properties of the packing. Optimizing these physical parameter values ​​allows for the selection of materials with different thermal conductivity and hydrophilic properties, thus optimizing the air-water heat and mass exchange efficiency. The packing arrangement parameters include layered arrangement, staggered arrangement, etc. Optimizing these physical parameter values ​​can optimize the arrangement to avoid local airflow short-circuiting and enhance heat and mass exchange. The flow guiding device parameters include the angle, length, and number of guide plates. Optimizing these physical parameter values ​​can improve the internal airflow state of the target cooling tower and the incoming airflow. The water spraying system piping layout parameters include pipe diameter, direction, and nozzle arrangement. Optimizing these physical parameter values ​​ensures that circulating water is evenly sprayed onto the packing, increasing the water-air contact area and time. When arranging the water spraying system piping of the target cooling tower, the pressure loss and flow distribution characteristics of the water flow inside the pipes can be considered. Based on fluid mechanics principles, computational fluid dynamics methods are used to simulate and analyze the pipe network to determine the optimal pipe layout and pipe diameter combination.

[0056] In some embodiments, this application can further determine multiple physical parameter values ​​corresponding to the optimal working efficiency, and perform simulation verification based on these multiple physical parameter values. If the simulation verification result is accurate, an optimized flow structure scheme for the target cooling tower is determined based on the multiple physical parameter values ​​corresponding to the optimal working efficiency. If the simulation verification result is inaccurate, the simulation model is corrected, and the optimal working efficiency is re-solved after the correction is completed.

[0057] Furthermore, the simulation verification process in this application embodiment can be based on the optimized parameters to create a small-scale experimental model or conduct localized modification tests on an actual cooling tower. The actual performance parameters of the cooling tower, such as cooling water temperature and outlet air humidity, are measured through experiments and compared with the simulation results. If there are deviations between the experimental and simulation results, the causes of the deviations are analyzed, and the simulation model and optimization scheme are corrected until the experimental results match the simulation results, ensuring the reliability and effectiveness of the optimization scheme.

[0058] Meanwhile, considering the actual operating cost of the target cooling tower, the electronic equipment can not only measure the performance parameters of the target cooling tower during the simulation verification process, but also assess the environmental impact of the optimized target cooling tower. Assessment indicators include, but are not limited to, noise emissions and the impact on surrounding air quality. The simulation verification process can also perform an economic evaluation of the optimization results, ensuring that the optimized solution has good economic benefits while improving performance. Furthermore, this application embodiment can also establish an environmental impact assessment model and feed the assessment results back into the optimization process, ensuring that the optimized solution meets environmental protection requirements while improving cooling tower performance.

[0059] Figure 2 A schematic diagram illustrating a cooling tower flow structure optimization process according to an embodiment of this application is shown. Figure 2 As shown in the embodiment of this application, in the process of optimizing the flow structure of the target cooling tower, data can first be collected on the unit parameters of the thermal power generating unit where the target cooling tower is located and the historical meteorological data of its location. Then, a simulation model is obtained by simulating and modeling the target cooling tower based on the unit parameters. Next, an annealing algorithm is executed based on the simulation model and the historical meteorological data of the location to obtain multiple physical parameter values ​​corresponding to the optimal working efficiency of the target cooling tower. The accuracy of the determined optimal working efficiency is then determined through simulation verification. If the optimal working efficiency is accurate, an optimization scheme for the flow structure of the target cooling tower is determined based on the multiple physical parameter values ​​corresponding to the optimal working efficiency. If the optimal working efficiency is inaccurate, at least one of the optimization algorithm and the simulation model can be optimized.

[0060] Based on the aforementioned technical features, this application embodiment, through the coupling of annealing algorithm and CFD simulation analysis, can accurately explore the optimal combination of physical parameter values ​​such as the target cooling tower packing structure and distribution, packing arrangement, and water spray system piping layout. This generates an optimized scheme for optimizing the cooling tower's flow structure, thereby improving the cooling tower's operating efficiency and achieving effective water conservation. Regarding the packing structure, the optimized packing shape and size maximize the air-water contact area, resulting in more thorough heat and mass exchange between air and water. The problem of insufficient air-water contact caused by unreasonable packing structure in traditional methods is solved, significantly improving cooling efficiency. In terms of packing arrangement, the optimized layered or staggered arrangement effectively avoids local airflow short-circuiting, making the airflow distribution inside the cooling tower more uniform and further enhancing the heat and mass exchange effect. The optimized water spray system piping layout ensures that circulating water is evenly sprayed onto the packing, increasing the contact area and time between water and air, and improving the cooling effect. Through these optimizations, the cooling tower can achieve a higher cooling water temperature drop under the same cooling water volume and air intake conditions, thereby improving the overall cooling efficiency and providing better cooling effect for thermal power generating units, ensuring that the units can operate stably and efficiently under various operating conditions.

[0061] Meanwhile, by improving the cooling efficiency of the cooling tower based on this solution, the amount of cooling water required to meet the same cooling demand can be reduced. For example, where a large amount of water spray was originally needed to achieve a certain cooling effect, optimization can reduce the amount of spray water while maintaining the same cooling effect through a more reasonable packing structure and arrangement, effective airflow guidance by the flow guiding device, and optimized layout of the water spray system pipeline, thus achieving water conservation. On the other hand, by fully utilizing and comprehensively analyzing the climate information of different sites, the operating parameters of the cooling tower can be dynamically adjusted according to the actual local climate conditions, avoiding water waste caused by climate changes. For example, in areas with high humidity, the amount of spray water can be appropriately reduced because the water vapor carried in the air can assist in part of the cooling process, achieving precise water conservation and effectively reducing water consumption in thermal power generation, which is in line with the requirements of sustainable development.

[0062] Furthermore, the flow structure optimization method of this application possesses excellent adaptability and versatility. It is not dependent on a specific unit type or location; whether it is a large thermal power generating unit or a small unit, whether it is located in a cold region, a hot region, or other different climatic conditions, this method can be used for targeted optimization by collecting relevant unit parameters and climate data. This breaks through the limitations of traditional experience-based optimization methods in terms of adaptability, making this method widely applicable and capable of providing a scientific and effective solution for cooling tower optimization throughout the thermal power generation industry, promoting technological progress in the industry, and improving overall economic and environmental benefits.

[0063] It should be understood that although the steps in the above flowcharts are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the above flowcharts may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0064] Based on the foregoing embodiments, this application provides a cooling tower flow structure optimization device, which includes various modules and units included in each module, and can be implemented by a processor; of course, it can also be implemented by specific logic circuits; in the implementation process, the processor can be a central processing unit (CPU), microprocessor (MPU), digital signal processor (DSP) or field programmable gate array (FPGA), etc.

[0065] Figure 3 A schematic diagram of a cooling tower flow structure optimization device according to an embodiment of this application is shown. Figure 3 As shown, the cooling tower flow structure optimization device in this application embodiment includes: Simulation module 30 is used to determine the simulation model of the target cooling tower. The simulation model has corresponding boundary conditions and multiple physical parameters. The optimization module 31 is used to obtain the optimal working efficiency of the target cooling tower by taking the preset temperature parameters as input conditions, the physical parameters as decision variables, and the optimal working efficiency of the target cooling tower as the objective, and finding the optimal solution based on the boundary conditions of the simulation model. The scheme determination module 32 is used to determine the flow structure optimization scheme of the target cooling tower based on multiple physical parameter values ​​corresponding to the optimal working efficiency.

[0066] In one possible implementation, simulation module 30 is further used for: Determine the corresponding geometric model and multiple physical parameters based on the actual structure of the target cooling tower; Based on the operating principle of the target cooling tower, the corresponding boundary conditions are matched to the geometric model to obtain the simulation model.

[0067] In one possible implementation, simulation module 30 is further used for: Based on the operating principle of the target cooling tower, match at least one corresponding physical model to the geometric model; Based on the unit parameters of the thermal power generating unit where the target cooling tower is located, the boundary conditions corresponding to each physical model are determined to obtain the simulation model.

[0068] In one possible implementation, the physical parameters include at least one of the following: packing structure parameters, packing arrangement, flow guiding device parameters, and water distribution system piping arrangement parameters.

[0069] In one possible implementation, the algorithm for finding the optimal solution is the annealing algorithm.

[0070] In one possible implementation, optimal work efficiency includes at least one optimal work parameter; The device also includes: The parameter determination module is used to determine at least one optimal operating parameter based on the historical meteorological data corresponding to the target cooling tower. The historical meteorological data includes at least one of temperature, humidity, wind speed, and wind direction.

[0071] In one possible implementation, the scheme determination module 32 is further used for: Simulation verification was performed based on multiple physical parameter values ​​corresponding to the optimal working efficiency. If the simulation verification results are accurate, the flow structure optimization scheme of the target cooling tower is determined based on the multiple physical parameter values ​​corresponding to the optimal working efficiency. If the simulation verification results are inaccurate, the simulation model is corrected.

[0072] The description of the above device embodiments is similar to that of the above method embodiments, and has similar beneficial effects. For technical details not disclosed in the device embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

[0073] It should be noted that, in the embodiments of this application... Figure 3 The module division of the cooling tower flow structure optimization device shown is illustrative and represents only one logical functional division; in actual implementation, other division methods may be used. Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, exist as separate physical units, or have two or more units integrated into one unit. The integrated units can be implemented in hardware, as software functional units, or a combination of both.

[0074] It should be noted that, in the embodiments of this application, if the above-described methods are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), magnetic disks, or optical disks. Thus, the embodiments of this application are not limited to any specific hardware and software combination.

[0075] Figure 4 A schematic diagram of an electronic device according to an embodiment of this application is shown. For example... Figure 4 As shown in the figure, this application provides an electronic device, which can be a server, and its internal structure diagram can be as follows. Figure 4 As shown, the electronic device includes a processor 420, a memory, and a transceiver 440 connected via a system bus 410. The processor 420 provides computing and control capabilities. The memory includes a non-volatile storage medium 431 and internal memory 432. The non-volatile storage medium 431 stores an operating system, computer programs, and a database. The internal memory 432 provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium 431. The database stores data. The transceiver 440 communicates with external terminals via a network connection. The computer program is executed by the processor 420 to implement the aforementioned methods.

[0076] This application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor 420, implements the steps of the method provided in the above embodiments.

[0077] This application provides a computer program product containing instructions that, when run on a computer, cause the computer to perform the steps in the method provided in the above-described method embodiments.

[0078] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0079] In one possible implementation, the shooting prompting device provided in this application can be implemented as a computer program, which can be configured as follows: Figure 4 The device operates on the electronic device shown. The memory of the electronic device can store the various program modules that make up the above-described apparatus. The computer program composed of the various program modules causes the processor 420 to execute the steps of the methods in the various embodiments of this application described in this specification.

[0080] It should be noted that the descriptions of the storage medium and device embodiments above are similar to the descriptions of the method embodiments above, and have similar beneficial effects. For technical details not disclosed in the storage medium, storage medium, and device embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0081] It should be understood that the phrases "one embodiment," "an embodiment," or "some embodiments" mentioned throughout the specification mean that a specific feature, structure, or characteristic related to an embodiment is included in at least one embodiment of this application. Therefore, phrases such as "in one possible implementation," "in one embodiment," or "in some embodiments" appearing throughout the specification do not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above-described embodiments are merely for descriptive purposes and do not represent the superiority or inferiority of the embodiments. The descriptions of the various embodiments above tend to emphasize the differences between the various embodiments; their similarities or commonalities can be referred to mutually, and for the sake of brevity, they will not be repeated here.

[0082] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three kinds of relationships. For example, object A and / or object B can represent three situations: object A exists alone, object A and object B exist simultaneously, and object B exists alone.

[0083] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0084] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple modules or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or modules can be electrical, mechanical, or other forms.

[0085] The modules described above as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules. They may be located in one place or distributed across multiple network units. Some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs.

[0086] In addition, each functional module in the various embodiments of this application can be integrated into one processing unit, or each module can be a separate unit, or two or more modules can be integrated into one unit; the integrated modules can be implemented in hardware or in the form of hardware plus software functional units.

[0087] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, read-only memory (ROM), magnetic disks, or optical disks.

[0088] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0089] The methods disclosed in the several method embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments.

[0090] The features disclosed in the several product embodiments provided in this application can be arbitrarily combined without conflict to obtain new product embodiments.

[0091] The features disclosed in the several method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method or device embodiments.

[0092] The above description is merely an embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for optimizing the flow path structure of a cooling tower, characterized in that, The method includes: A simulation model of the target cooling tower is determined, wherein the simulation model has corresponding boundary conditions and multiple physical parameters; Using preset temperature parameters as input conditions, physical parameters as decision variables, and the optimal working efficiency of the target cooling tower as the objective, the optimal solution is obtained based on the boundary conditions of the simulation model to obtain the optimal working efficiency of the target cooling tower. Based on the multiple physical parameter values ​​corresponding to the optimal working efficiency, the flow structure optimization scheme of the target cooling tower is determined.

2. The method according to claim 1, characterized in that, The simulation model for determining the target cooling tower includes: The corresponding geometric model and multiple physical parameters are determined based on the actual structure of the target cooling tower; Based on the operating principle of the target cooling tower, the corresponding boundary conditions are matched to the geometric model to obtain the simulation model.

3. The method according to claim 2, characterized in that, The step of matching corresponding boundary conditions to the geometric model to obtain a simulation model based on the operating principle of the target cooling tower includes: Based on the operating principle of the target cooling tower, at least one corresponding physical model is matched for the geometric model; Based on the unit parameters of the thermal power generating unit where the target cooling tower is located, the boundary conditions corresponding to each physical model are determined to obtain the simulation model.

4. The method according to claim 2, characterized in that, The physical parameters include at least one of the following: packing structure parameters, packing arrangement method, parameters of the flow guiding device, and layout parameters of the water spraying system pipeline.

5. The method according to claim 1, characterized in that, The algorithm for finding the optimal solution is the annealing algorithm.

6. The method according to any one of claims 1-5, characterized in that, The optimal working efficiency includes at least one optimal working parameter; The method further includes: The at least one optimal operating parameter is determined based on the historical meteorological data corresponding to the target cooling tower, wherein the historical meteorological data includes at least one of temperature, humidity, wind speed, and wind direction.

7. The method according to claim 1, characterized in that, The step of determining the optimized flow structure scheme of the target cooling tower based on multiple physical parameter values ​​corresponding to the optimal working efficiency includes: Simulation verification was performed based on multiple physical parameter values ​​corresponding to the optimal working efficiency. If the simulation verification results are accurate, the flow structure optimization scheme of the target cooling tower is determined based on the multiple physical parameter values ​​corresponding to the optimal working efficiency. If the simulation verification results are inaccurate, the simulation model shall be corrected.

8. A cooling tower flow structure optimization device, characterized in that, The device includes: The simulation module is used to determine the simulation model of the target cooling tower, which has corresponding boundary conditions and multiple physical parameters; The optimization module is used to obtain the optimal working efficiency of the target cooling tower by taking the preset temperature parameters as input conditions, the physical parameters as decision variables, and the optimal working efficiency of the target cooling tower as the objective, and finding the optimal solution based on the boundary conditions of the simulation model. The scheme determination module is used to determine the flow structure optimization scheme of the target cooling tower based on multiple physical parameter values ​​corresponding to the optimal working efficiency.

9. An electronic device comprising a memory and a processor, the memory storing a computer program executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 7.

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