Performance optimization method and system for ultra-large natural ventilation cooling tower

Abstract

The invention provides a performance optimization method and system for an ultra-large natural ventilation cooling tower, and the method comprises the steps: obtaining the operation state data of the cooling tower and the distribution characteristics of an air flow field in the tower, and analyzing the influence of the aging and design defects of a tower core part on the air-water distribution; based on the operation state data and the flow field distribution characteristics, constructing a three-dimensional multi-physical field coupling model of the cooling tower so as to calculate various physical field distributions; dynamically adjusting a water distribution system and a filler arrangement scheme according to the distribution of various physical fields, so as to generate a nonlinear matching relationship between the filler mounting height and the airflow velocity field; and based on the angle adjustment of the splashing device and the structural optimization of the dehydrator, iteratively calculating wind-water matching parameters by adopting a closed-loop optimization algorithm until the preset optimal value of the tower outlet water temperature of the circulating water and the preset cooling capacity improvement target value are reached. According to the method, the thermal performance of the ultra-large natural ventilation cooling tower can be remarkably improved, the temperature reduction of circulating water flowing out of the tower is reduced, the cooling capacity is improved, and uneven gas-water distribution is effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cooling tower efficiency improvement and energy saving, and particularly relates to a performance optimization method and system for a super-large natural draft cooling tower. BACKGROUND

[0002] At present, as a core device of a circulating water system of a power plant, the cooling performance of a cooling tower directly affects the safe and stable operation of a steam turbine unit and the energy saving and consumption reduction level. The demand for energy saving and consumption reduction in the thermal power industry is increasingly urgent. However, due to problems such as aging and damage of internal fillers, uneven water distribution of a water distribution system, and unreasonable matching of air and water, the cooling tower often has a high outlet water temperature in summer and a poor condenser vacuum, which leads to an increase in coal consumption of the unit and a decrease in energy utilization efficiency.

[0003] In related technologies, the transformation of the cooling tower is mostly concentrated in the replacement of a single component or the optimization of a traditional uniform arrangement mode, and there are problems such as weak transformation pertinence, unstable matching degree of air and water, and unstable transformation effect. The cooling capacity of some cooling towers is not obviously improved after transformation, and the main reason is that the actual distribution characteristics of the airflow field in the tower are not accurately mastered, and there is a lack of systematic closed-loop optimization design and collaborative transformation scheme.

[0004] Therefore, how to realize efficiency improvement and energy saving transformation that can accurately match the air and water distribution in the tower and combine new materials and an optimized arrangement mode has become a problem to be solved at present. SUMMARY

[0005] The present application aims to at least partly solve one of the problems in the related art.

[0006] To this end, a first object of the present application is to provide a performance optimization method for a super-large natural draft cooling tower, which can significantly improve the thermal performance of the super-large natural draft cooling tower, reduce the outlet temperature of circulating water, improve the cooling capacity, and effectively improve the uneven distribution of air and water, thereby solving the problems of poor pertinence, insignificant improvement of cooling efficiency, and poor energy saving effect of the existing cooling tower transformation.

[0007] A second object of the present application is to provide a performance optimization system for a super-large natural draft cooling tower.

[0008] A third object of the present application is to provide an electronic device.

[0009] A fourth object of the present application is to provide a computer readable storage medium.

[0010] To achieve the above objects, a first aspect of the present application provides a performance optimization method for a super-large natural draft cooling tower, comprising the following steps: Obtain cooling tower operation state data and tower airflow flow field distribution characteristics, analyze the influence of tower core component aging and design defects on air-water distribution; Based on the operation state data and the flow field distribution characteristics, a three-dimensional multi-physical field coupling model of the cooling tower is constructed to calculate a plurality of physical field distributions, wherein the plurality of physical field distributions include velocity field, temperature field, pressure field, humidity field and cooling water quantity field distributions of wet air and circulating water; According to the plurality of physical field distributions, the water distribution system and the filler arrangement scheme are dynamically adjusted to generate a nonlinear matching relationship between the filler installation height and the airflow velocity field, and to eliminate the water distribution blind area; Based on the angle adjustment of the splash device and the structure optimization of the water separator, a closed-loop optimization algorithm is used to iteratively calculate the air-water matching parameters until the preset optimal circulating water outlet temperature value and the cooling capacity improvement target value are reached.

[0011] Optionally, the obtaining of the cooling tower operation state data and the tower airflow flow field distribution characteristics, and the analysis of the influence of the tower core component aging and the design defects on the air-water distribution, comprises: three-dimensional modeling of the tower airflow velocity field is performed by CFD simulation software to obtain airflow velocity distribution data in each region; the clogging rate of the filler is quantitatively analyzed by using a material aging detection device, and the water distribution uniformity coefficient is evaluated in combination with the splash device failure rate data.

[0012] Optionally, the construction of the three-dimensional multi-physical field coupling model of the cooling tower based on the operation state data and the flow field distribution characteristics comprises: setting grid accuracy by using the heat and mass transfer numerical analysis module of the Fluent software to perform multi-physical field synchronous calculation; and the coupling relationship of each physical field is obtained by iterative calculation, wherein the coupling relationship includes the correlation function of the velocity field and the temperature field and the influence coefficient of the humidity field on the cooling water quantity field.

[0013] Optionally, the dynamic adjustment of the water distribution system and the filler arrangement scheme according to the plurality of physical field distributions comprises: using a nonlinear non-equal-height arrangement method to control the filler installation height gradient within a preset height range; and the water distribution uniformity coefficient is increased to a target uniformity coefficient value by adjusting the angle adjustment structure of the splash device.

[0014] Optionally, the iterative calculation of the air-water matching parameters by using the closed-loop optimization algorithm based on the angle adjustment of the splash device and the structure optimization of the water separator until the preset optimal circulating water outlet temperature value and the cooling capacity improvement target value are reached comprises: using a TDE-I reflective centrifugal mixing type splash device to replace the original splash device to make the splash effect uniformity meet the design requirements; and using a non-equidistant variable-speed high-efficiency water separator to reduce the resistance coefficient of the water separator to below the target resistance coefficient.

[0015] Optionally, after the wind-water matching parameters are iteratively calculated by the closed-loop optimization algorithm based on the splash device angle adjustment and the water remover structure optimization, the method further comprises: based on the multiple physical field distributions, selecting a hydrophilic S wave filler with anti-aging performance meeting the specifications to replace the original filler, and reinforcing the water distribution system by using a glass steel support structure.

[0016] Optionally, the non-equidistant variable-speed high-efficiency water remover is formed by one-time extrusion molding of polyvinyl chloride material, and the upper and lower edges of the non-equidistant variable-speed high-efficiency water remover are provided with a roll edge structure.

[0017] To achieve the above-mentioned purpose, the second aspect of the present application further proposes a performance optimization system of a super-large natural draft cooling tower, comprising the following modules: An acquisition module is configured to acquire cooling tower operating state data and tower internal airflow flow field distribution characteristics, and analyze the influence of tower core component aging and design defects on air-water distribution; A calculation module is configured to construct a full three-dimensional multi-physical field coupling model of the cooling tower based on the operating state data and the flow field distribution characteristics, and calculate multiple physical field distributions, wherein the multiple physical field distributions include velocity field, temperature field, pressure field, humidity field and cooling water quantity field distributions of wet air and circulating water; An adjustment module is configured to dynamically adjust a water distribution system and a filler arrangement scheme according to the distribution results of the multiple physical fields, so as to generate a nonlinear matching relationship between the filler installation height and the airflow velocity field, and eliminate the water distribution blind area; An iteration module is configured to iteratively calculate wind-water matching parameters by a closed-loop optimization algorithm based on the splash device angle adjustment and the water remover structure optimization, until a preset circulating water outlet tower water temperature optimal value and a cooling capacity improvement target value are reached.

[0018] To achieve the above-mentioned purpose, the third aspect of the present application further proposes an electronic device, comprising: at least one processor; and a memory communicatively connected with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the performance optimization method of the super-large natural draft cooling tower according to any one of the first aspect.

[0019] To achieve the above-mentioned purpose, the fourth aspect of the present application further proposes a computer readable storage medium having a computer program stored thereon, and the computer program is executed by a processor to implement the performance optimization method of the super-large natural draft cooling tower according to any one of the first aspect.

[0020] The technical solution provided by the embodiments of this application brings at least the following beneficial effects: This application adopts closed-loop optimization combined with air-water matching to enhance heat transfer technology. Combined with full three-dimensional modeling and numerical analysis of heat and mass transfer, it can accurately grasp the airflow field distribution characteristics within the tower, achieving optimal matching between cooling air and circulating water, significantly improving the cooling efficiency of the cooling tower, and solving the problem of insufficient targeting in traditional retrofits. Furthermore, it adopts a nonlinear unequal height arrangement of the water-spraying packing, combined with a TDE-I type reflective centrifugal hybrid spray device and a new type of hydrophilic S-wave packing, effectively eliminating water distribution blind zones, promoting uniform air-water contact, and significantly improving the heat transfer effect. The selected new tower core components have good anti-aging performance and mechanical stability, extending the equipment's service life and reducing equipment maintenance costs and replacement frequency. Therefore, this application, through closed-loop optimization and air-water matching to enhance heat transfer technology, achieves improved uniformity of air-water distribution in the cooling tower, significantly reduces the outlet water temperature and increases cooling capacity, effectively improves thermal performance, reduces unit coal consumption, and enhances the economy and safety of cooling tower operation.

[0021] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0022] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating a performance optimization method for an ultra-large natural draft cooling tower proposed in an embodiment of this application; Figure 2 This is a flowchart illustrating a specific data feature analysis and processing method proposed in an embodiment of this application; Figure 3 This is a flowchart illustrating a specific physical field analysis and calculation method proposed in an embodiment of this application; Figure 4 This is a flowchart illustrating a specific method for dynamically adjusting optimization parameters as proposed in an embodiment of this application; Figure 5 This is a flowchart illustrating a specific method for iterative optimization of Feng Shui matching parameters proposed in an embodiment of this application; Figure 6 This is a schematic diagram of a strain measurement system for a large structural component of a heavy-duty gas turbine, as proposed in an embodiment of this application. Detailed Implementation

[0023] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0024] The following description, with reference to the accompanying drawings, illustrates a method and system for optimizing the performance of an ultra-large natural draft cooling tower, as proposed in an embodiment of this application.

[0025] Figure 1 This is a flowchart illustrating a performance optimization method for an ultra-large natural draft cooling tower proposed in an embodiment of this application, as shown below. Figure 1 As shown, the method includes the following steps: Step S101: Obtain cooling tower operating status data and airflow field distribution characteristics inside the tower, and analyze the impact of tower core component aging and design defects on air-water distribution.

[0026] Specifically, this step is a crucial preliminary step in this technical solution to achieve closed-loop optimization and enhanced heat transfer through airflow matching. This step combines multi-source data acquisition with CFD (Computational Fluid Dynamics) modeling to achieve precise control over the internal thermodynamic and fluid dynamic states of the cooling tower.

[0027] As one possible approach, acquiring operational status data involves real-time monitoring of key parameters such as inlet and outlet water temperatures, circulating water flow rate, ambient temperature, humidity, and wind speed. This is typically achieved by integrating a distributed sensor network with a DCS system to ensure data continuity and accuracy. Simultaneously, a full 3D model of the cooling tower is created using CFD simulation software such as Fluent to simulate the distribution of velocity, temperature, pressure, humidity, and cooling water flow fields within the tower. Boundary conditions, such as inlet wind speed, need to be set during the modeling process. Water temperature Ambient temperature etc., to reflect the actual operating conditions.

[0028] Among the acquired parameters, the analysis of the airflow field needs to focus on the cooling coefficient. Volumetric mass transfer coefficient drag coefficient Core thermodynamic performance parameters, including cooling number. The formula used to measure the heat exchange capacity of a cooling tower is as follows: ,in For water flow rate, This refers to the gas flow rate. By analyzing the correlation between these parameters and the condition of internal components, problems such as uneven gas-water distribution caused by packing aging, splash device failure, or dewatering device deformation can be identified.

[0029] In practical applications, this step is mainly used for performance diagnosis and optimization design of natural draft counterflow wet cooling towers. Accurate modeling and analysis of the flow field within the tower provides a scientific basis for subsequent considerations such as nonlinear unequal height arrangement of packing and selection of TDE-I reflective centrifugal mixing spray devices, thereby improving cooling efficiency, reducing outlet water temperature, improving condenser vacuum, and ultimately achieving the unit's energy-saving and consumption-reducing goals.

[0030] Step S102: Based on the operating status data and flow field distribution characteristics, construct a three-dimensional multi-physics coupling model of the cooling tower to calculate the distribution of various physical fields, including the velocity field, temperature field, pressure field, humidity field and cooling water flow field of humid air and circulating water.

[0031] Specifically, this step is a key technical step in optimizing the thermal performance of the cooling tower. This step integrates real-time monitoring data during the operation of the cooling tower (such as inlet water temperature, outlet water temperature, air temperature, humidity, wind speed, etc.) with CFD simulation analysis to construct a multi-physics coupling model that includes heat transfer, mass transfer, and momentum exchange between humid air and circulating water, thereby accurately calculating the distribution of various physical fields within the tower.

[0032] One possible approach is to first use CFD simulation software such as Fluent to geometrically model the cooling tower. The modeling accuracy needs to reach millimeter-level reproduction of the actual structure, including key areas such as the packing zone, water distribution system, scavenger, and air inlet. The interaction between humid air and circulating water in the model is described using a multiphase flow model (such as the VOF model), combined with heat and mass transfer models (such as evaporative cooling models and convective heat transfer models) to achieve thermodynamic coupling calculations. In the boundary condition settings, parameters such as the inlet temperature and flow rate of the circulating water, the Sauter Mean Diameter (SMD) of the spray device, and the temperature, humidity, and wind speed of the ambient air must all be input based on actual operating data to ensure the physical realism of the model.

[0033] At the parameter level, the model needs to calculate the velocity field of humid air and circulating water. ), temperature field ( ), pressure field ( ), humidity field ( ) and cooling water flow field ( The three-dimensional distribution of the temperature field is calculated. The velocity field calculation requires consideration of turbulence models (such as the RNG k-ε model), the temperature field must satisfy the energy conservation equation, and the humidity field needs to incorporate water vapor diffusion and evaporation rate models. The distribution of the cooling water flow field is determined by the water balance equation and the water distribution uniformity index of the spray device (such as the water distribution coefficient). An evaluation will be conducted.

[0034] In practical applications, this step primarily focuses on optimizing the thermal performance of ultra-large natural draft cooling towers. Especially in cases of aging tower core components, uneven water distribution, and decreased heat exchange efficiency, 3D modeling analysis can provide a scientific basis for packing arrangement and water distribution system modifications. For example, by modeling and analyzing the flow field of cooling tower No. 1, problems such as uneven airflow distribution and low heat exchange efficiency in the packing area can be identified, thus guiding the subsequent selection of nonlinear unequal-height packing arrangement and TDE-I reflective centrifugal hybrid spray device.

[0035] Step S103: Based on the distribution of various physical fields, dynamically adjust the water distribution system and the packing arrangement scheme to generate a nonlinear matching relationship between the packing installation height and the airflow velocity field, and eliminate water distribution blind zones.

[0036] Specifically, this step is based on the multi-physics field coupling analysis of humid air and circulating water inside the cooling tower, including velocity field, temperature field, pressure field, humidity field and cooling water flow field, etc. CFD technology is used to perform full three-dimensional modeling and numerical simulation of the airflow distribution inside the tower, thereby revealing the flow characteristics and cooling capacity differences of airflow at different heights and regions.

[0037] As one possible approach, CFD simulation results are first used to identify areas within the tower with low or uneven airflow velocities, i.e., potential water distribution blind zones. Then, based on the principle of airflow-water matching, the layout of the spray devices and distribution pipes in the water distribution system is optimized to establish a nonlinear matching relationship between the circulating water distribution density and the airflow velocity field. For example, in areas with high airflow velocities, the packing height is appropriately reduced to avoid excessive disturbance of the water flow by the airflow, which could lead to a decrease in evaporation efficiency; while in areas with low airflow velocities, the packing height is increased to enhance the water-air contact time and heat exchange efficiency.

[0038] Regarding parameter settings, the packing installation height is typically adjusted within the range of 0.5 to 2 meters, and the typical range of the airflow velocity field is 0.5 to 2 m / s. Through iterative optimization, the packing arrangement and airflow distribution are optimized to achieve the best match, thereby improving the overall heat exchange efficiency, reducing the outlet water temperature by 2 degrees Celsius or more, and meeting the design target of cooling capacity of not less than 115%.

[0039] In practical applications, this procedure is suitable for energy-saving retrofits of ultra-large natural draft cooling towers, especially when the tower core components are aging, water distribution is uneven, and thermal performance is declining. Through nonlinear matching design, cooling efficiency can be effectively improved, condenser vacuum losses reduced, and the unit's operational economy enhanced. Its technical value lies in achieving synergistic enhancement of air-water distribution through precise physical field analysis and dynamic optimization design, providing a scientific basis and engineering implementation path for the efficient operation of cooling towers.

[0040] Step S104: Based on the angle adjustment of the spray device and the structure optimization of the water separator, a closed-loop optimization algorithm is used to iteratively calculate the air-water matching parameters until the preset optimal value of the circulating water outlet temperature and the target value of the cooling capacity improvement are achieved.

[0041] Specifically, in the closed-loop optimization process, this step takes the optimal solution for air-water matching and the optimal value of the circulating water outlet temperature as the objective function. By iteratively adjusting the spray angle of the spray device, the density of the nozzle arrangement, and the structural parameters of the water separator (such as the arc blade spacing and the rolled edge height), the coordinated optimization of air-water flow is achieved.

[0042] For example, the optimization of feng shui matching parameters includes, but is not limited to, airflow speed. Water droplet diameter Water film thickness Heat transfer coefficient Optimization of key variables, such as the angle of the spray device, is crucial. Fine-tuning of the spray device angle is typically performed within the range of 30 to 60 degrees to improve the atomization and uniformity of water droplet distribution, thereby enhancing heat exchange efficiency. Optimization of the demister structure involves adjusting the geometric parameters of the arc blades to reduce the ventilation resistance coefficient. Improve water removal efficiency This reduces water droplet entrainment losses.

[0043] In practical applications, this step also needs to be considered in conjunction with the operating conditions of the cooling tower, such as ambient temperature. wet-bulb temperature Circulating water flow rate Numerical simulations and parameter iterations were performed under multiple operating conditions. Through a closed-loop feedback mechanism, the system continuously corrected the arrangement of the splash device and the water separator until the circulating water outlet temperature was adjusted. The temperature should be reduced by more than 2°C, and the cooling capacity should be increased by no less than 115% to meet the performance requirements under the designed meteorological conditions.

[0044] Therefore, this application can effectively optimize the air-water distribution in the cooling tower, improve heat exchange efficiency, reduce the outlet water temperature by more than 2°C, significantly improve the unit's economy, and extend the service life of the tower core components.

[0045] In summary, the performance optimization method for ultra-large natural draft cooling towers in this application adopts closed-loop optimization combined with air-water matching to enhance heat transfer technology. Combined with full 3D modeling and numerical analysis of heat and mass transfer, it can accurately grasp the airflow field distribution characteristics within the tower, achieving optimal matching of cooling air and circulating water, significantly improving the cooling tower's cooling efficiency, and solving the problem of insufficient targeting in traditional retrofits. Furthermore, the use of a nonlinear, unequal-height arrangement of the water-spraying packing, along with a TDE-I type reflective centrifugal hybrid spray device and novel hydrophilic S-wave packing, effectively eliminates water distribution blind zones, promotes uniform air-water contact, and significantly improves heat transfer efficiency. The selected novel tower core components possess excellent anti-aging properties and mechanical stability, extending equipment lifespan and reducing maintenance costs and replacement frequency. Therefore, this method, through closed-loop optimization and air-water matching to enhance heat transfer technology, improves the uniformity of air-water distribution in the cooling tower, significantly reduces the outlet water temperature and increases cooling capacity, effectively improving thermal performance, reducing unit coal consumption, and enhancing the economic efficiency and safety of cooling tower operation.

[0046] Based on the above embodiments, in order to more clearly illustrate the specific implementation process of this application in obtaining cooling tower operating status data and airflow field distribution characteristics inside the tower, and analyzing the impact of tower core component aging and design defects on air-water distribution, the following is an exemplary description of a specific feature analysis and processing method proposed in one embodiment of this application. Figure 2 A flowchart illustrating a specific data feature analysis and processing method proposed in this application embodiment is shown below. Figure 2 As shown, the method includes the following steps: Step S201: Use CFD simulation software to perform three-dimensional modeling of the airflow velocity field inside the tower and obtain airflow velocity distribution data for each region.

[0047] Specifically, this step aims to accurately obtain airflow velocity distribution data for each area inside the cooling tower, providing a scientific basis for subsequent packing arrangement and water distribution system optimization. As an implementation method, Fluent simulation software is used for full 3D modeling. Based on the cooling tower's geometry, boundary conditions, and operating conditions, a complete flow field model is constructed, including the air inlet, water spray zone, packing zone, desiccant zone, and air outlet. The interaction between humid air and circulating water in the model is numerically solved using heat and mass transfer coupling equations. The calculation of the airflow velocity field relies on the combined application of the Navier-Stokes equations and turbulence models (such as the k-ε model or RANS model). For mesh generation, a hybrid unstructured hexahedral and tetrahedral mesh is used, with localized refinement of key areas (such as the packing zone and near the spray device) to ensure the accuracy of the velocity field calculation.

[0048] During the simulation process, key input parameters need to be set, including ambient temperature. Circulating water flow rate Air intake speed thermal resistance coefficient of packing air density and viscosity The output includes the airflow velocity distribution in different regions within the tower. Its spatial resolution is typically controlled between 0.1 and 0.5 m to meet engineering accuracy requirements. The calculation error of the velocity field must be controlled within the accuracy requirements for CFD verification and validation in relevant standards.

[0049] In practical applications, this step is typically performed before cooling tower retrofitting to assess airflow distribution defects in the existing structure. For example, for Cooling Tower No. 1, CFD simulation revealed localized excessively low or high airflow velocities in its packing zone, leading to uneven heat exchange efficiency. The simulation results can guide subsequent nonlinear unequal height arrangement of the packing, selection of splashing devices, and adjustment of the water distribution system, thereby achieving uniform air-water distribution and improving overall thermal performance.

[0050] Step S202: The clogging rate of the packing is quantitatively analyzed using material aging testing equipment, and the water distribution uniformity coefficient is evaluated in combination with the failure rate data of the splashing device.

[0051] Specifically, this step uses non-contact or contact testing methods to quantitatively assess aging phenomena such as scaling, clogging, and damage on the packing surface, thereby providing data support for subsequent packing selection and layout optimization.

[0052] As an example, material aging testing equipment typically integrates modules such as infrared thermal imaging, laser scanning, image recognition, and conductivity measurement to perform multi-dimensional analysis of the physical state of the filler surface. For instance, infrared thermal imaging technology can identify localized temperature anomalies on the filler surface caused by blockage, and combined with image recognition algorithms, pixel-level statistics of the damaged areas of the filler can be performed to calculate the blockage rate. It is defined as the ratio of the area of ​​the blocked area to the total effective area of ​​the packing.

[0053] Furthermore, the failure rate of the spray device The failure rate is calculated by statistically analyzing the ratio of the number of failures to the total operating time within the operating cycle. This failure rate data, combined with the clogging rate, is used to evaluate the water distribution uniformity coefficient. Its computational model can be expressed as:

[0054] Among them, the water distribution uniformity coefficient The larger the value, the more uniform the water distribution on the packing surface and the better the thermal performance. The evaluation result of this coefficient will directly affect the decision on the packing arrangement and the selection of the splashing device, such as whether to adopt a nonlinear unequal height arrangement or replace it with a TDE-I reflective centrifugal mixing splashing device.

[0055] In practical applications, this step is typically performed during cooling tower shutdown and maintenance. The testing equipment is installed above the packing layer and uses a moving platform or fixed support to achieve full coverage scanning of the packing surface. The test data is uploaded to the control system in real time and compared with historical operating data to provide a basis for closed-loop optimization.

[0056] Based on the above embodiments, in order to more clearly illustrate the specific implementation process of constructing a three-dimensional multiphysics coupling model of a cooling tower based on operating status data and flow field distribution characteristics to calculate various physical field distributions, the following is an exemplary illustration of a specific physical field analysis and calculation method proposed in one embodiment of this application. Figure 3 A flowchart illustrating a specific physical field analysis and calculation method proposed in this application is shown below. Figure 3 As shown, the method includes the following steps: Step S301: Set the mesh accuracy using the heat and mass transfer numerical analysis module of Fluent software and perform multiphysics synchronous calculations.

[0057] Specifically, this step utilizes the heat and mass transfer numerical analysis module of Fluent software to simultaneously calculate the multiphysics field inside the ultra-large natural draft cooling tower, thereby optimizing the design of the cooling tower's thermal performance. This step involves establishing a full three-dimensional geometric model of the cooling tower and importing actual operating parameters (such as circulating water flow rate, inlet water temperature, ambient temperature, humidity, etc.). Reasonable boundary and initial conditions are then set in Fluent to simulate the heat and mass transfer process between humid air and circulating water.

[0058] In model construction, the mesh accuracy was strictly controlled to within 0.1m to ensure high-resolution capture of complex flow fields within the tower (such as velocity, temperature, pressure, humidity, and cooling water flow). Unstructured meshing techniques were employed, combined with local refinement strategies, to refine key areas (such as the packing zone, water distribution zone, and air inlet) and improve computational accuracy. Simultaneously, in the multiphysics coupling calculations, the VOF (Volume of Fluid) method was used to simulate the interaction between water droplets and air, and a turbulence model was combined to describe the airflow motion, achieving a high-fidelity simulation of the gas-water two-phase flow.

[0059] In practical applications, this step addresses issues such as aging of internal components, uneven water distribution, and poor air-water matching in cooling towers. Through numerical simulation, it reveals bottlenecks in the tower's thermal performance, providing a scientific basis for subsequent optimization of packing arrangement, selection of spray devices, and adjustment of the water distribution system. For example, by analyzing the distribution of humid air cooling capacity in different areas within the tower, areas of air-water mismatch can be identified, thus guiding the optimized design of air-water matching.

[0060] Step S302: The coupling relationship of each physical field is obtained through iterative calculation. The coupling relationship includes the correlation function between the velocity field and the temperature field, and the influence coefficient of the humidity field on the cooling water flow field.

[0061] Specifically, this step obtains the coupling relationship of various physical fields inside the cooling tower through iterative calculation, including the correlation function between the velocity field and the temperature field, as well as the influence coefficient of the humidity field on the cooling water flow field. This is the core link to realize the closed-loop optimization of the cooling tower and the air-water matching to enhance heat transfer.

[0062] As one possible approach, the distribution data of the velocity field, temperature field, humidity field, and cooling water flow field within the tower are first obtained through numerical analysis. Subsequently, a multiphysics coupling model is established, using the correlation function between the velocity field and the temperature field... For reference, the coupling relationships between various physical fields are iteratively solved by combining the enthalpy change of moist air with the evaporation rate of water droplets. Among them, the correlation function between the velocity field and the temperature field is used to describe the dynamic response of airflow velocity to changes in water temperature, while the influence coefficient of the humidity field on the cooling water flow field reflects the inhibitory or enhancing effect of air humidity on the water evaporation rate.

[0063] During the iterative calculation process, key control parameters need to be set, such as the airflow velocity range. Water temperature change threshold Humidity field change step size and convergence judgment In addition, the volumetric mass transfer coefficient of the cooling tower must also be considered. drag coefficient Equip the thermodynamic performance parameters to ensure that the iteration results conform to the actual operating conditions.

[0064] In practical applications, this step is mainly used in the energy-saving retrofit design of natural draft counterflow wet cooling towers. By accurately identifying the coupling characteristics of various physical fields within the tower, a scientific basis can be provided for optimizing the packing arrangement, selecting splash devices, and adjusting the water distribution system, thereby eliminating water distribution blind spots and improving heat exchange efficiency.

[0065] Based on the above embodiments, in order to more clearly illustrate the specific implementation process of this application in dynamically adjusting the water distribution system and the packing arrangement scheme according to various physical field distributions, so as to generate a nonlinear matching relationship between the packing installation height and the airflow velocity field and eliminate the water distribution blind zone, the following is an exemplary description of a specific optimization parameter dynamic adjustment method proposed in one embodiment of this application. Figure 4 This is a flowchart illustrating a specific method for dynamically adjusting optimization parameters proposed in an embodiment of this application, as shown below. Figure 4 As shown, the method includes the following steps: Step S401: A non-linear, unequal height arrangement method is adopted to control the packing installation height gradient within a preset height range.

[0066] Specifically, this step employs a nonlinear, unequal-height arrangement to gradient-control the installation height of the water-spraying packing within the cooling tower, ensuring the packing installation height is controlled within the range of 0.5-1.2m. This arrangement is based on the non-uniform distribution characteristics of the airflow field inside the cooling tower. By setting differentiated packing installation heights in different areas, the contact area between the packing and the airflow, as well as the heat exchange path, are spatially optimized, thereby enhancing the heat and mass transfer efficiency between the air and water.

[0067] As one possible implementation, this step, based on the simulation analysis results of the three-dimensional model in the above embodiments, determines the differences in airflow density and heat exchange capacity in each region, and then designs the gradient distribution of the packing installation height. In actual operation, the packing installation height gradient is controlled within the range of 0.5-1.2m. This range is based on the comprehensive optimization results of the cooling tower structural characteristics, airflow distribution law, and packing thermodynamic performance. An excessively large gradient will cause airflow short-circuiting, affecting heat exchange uniformity; an excessively small gradient will not effectively improve air-water matching, making it difficult to achieve the expected energy-saving target.

[0068] The setting of the packing installation height gradient must meet the following conditions: First, the change in packing height should be inversely proportional to the airflow velocity field, meaning that the packing should be installed at a higher height in areas with lower airflow velocity to prolong the water-air contact time. Second, the change in packing height should ensure the continuity of water flow between packing layers, avoiding uneven water flow distribution or local blockage caused by abrupt height changes. Furthermore, the installation height of the packing must also consider its structural strength and anti-aging performance to ensure that it does not collapse or deform during long-term operation.

[0069] In practical applications, this arrangement is suitable for ultra-large natural draft counterflow wet cooling towers, especially when the packing material inside the tower is aging, the airflow distribution is uneven, and the thermal performance is declining. By using a non-linear unequal height arrangement, the problem of unreasonable packing material arrangement in the original design can be effectively corrected, water distribution blind spots can be eliminated, and the overall heat exchange efficiency of the packing material can be improved.

[0070] Step S402: By adjusting the angle adjustment structure of the spray device, the water distribution uniformity coefficient is increased to above the target uniformity coefficient value.

[0071] Specifically, this step adjusts the angle of the spray device to improve the water distribution uniformity coefficient to over 0.93. In some embodiments, this step optimizes the installation angle of the spray device based on the above CFD simulation analysis results and the airflow field distribution characteristics within the tower, thereby improving the uniformity of circulating water distribution in the packing zone and increasing heat exchange efficiency.

[0072] As one possible implementation, the angle adjustment structure of the splash device typically includes a rotatable nozzle support, an angle adjustment screw, and a positioning and locking mechanism. By adjusting the installation angle of the nozzle, the splashing direction and coverage area of ​​the water flow can be changed, resulting in a more uniform water film distribution on the packing surface. In practice, the airflow velocity and direction in each region need to be determined based on the velocity, temperature, and humidity field distributions within the tower simulated using Fluent simulation software, thereby designing the optimal angle configuration of the splash device. During actual installation, the installation angle of the splash device is usually controlled within the range of 15 to 30 degrees to ensure good dispersion and coverage of the water flow in the vertical direction.

[0073] The uniformity coefficient (UC) is a crucial parameter for evaluating the water distribution effect of a splashing device. It is defined as the ratio of the actual water density to the ideal uniform water density. A higher UC value indicates more uniform water distribution. This step optimizes the splashing device angle to increase the UC value to above 0.93, meeting the recommended standards for water distribution uniformity in relevant indicators. Furthermore, parameters such as the atomization effect, splash radius, and nozzle flow coefficient of the splashing device must also comply with relevant design specifications to ensure sufficient contact between the water flow and air, thereby improving evaporative cooling efficiency.

[0074] In practical applications, this step is suitable for the modification of the water distribution system of natural draft counterflow wet cooling towers, especially when the tower core components are aging, the splashing effect is poor, and the water distribution is uneven. By adjusting the angle of the splashing device, local blind spots in water distribution within the tower can be effectively eliminated, the thermal performance of the packing area can be improved, thereby reducing the outlet water temperature and improving the unit's operating efficiency.

[0075] Based on the above embodiments, in order to more clearly illustrate the specific implementation process of this application, which uses a closed-loop optimization algorithm to iteratively calculate the air-water matching parameters based on the angle adjustment of the splashing device and the structure optimization of the water separator, until the preset optimal value of the circulating water outlet temperature and the target value of the cooling capacity are achieved, the following is an exemplary description of a specific air-water matching parameter iterative optimization method proposed in one embodiment of this application. Figure 5 This is a flowchart illustrating a specific method for iterative optimization of Feng Shui matching parameters proposed in an embodiment of this application, as follows: Figure 5 As shown, the method includes the following steps: Step S501: Replace the original spraying device with a TDE-I reflective centrifugal mixing spraying device to ensure that the atomization uniformity of the spraying effect meets the design requirements.

[0076] Specifically, in this embodiment, the splashing device achieves efficient dispersion and uniform water distribution of circulating water before it enters the packing zone through the synergistic effect of centrifugal force and reflective structure, thereby significantly improving the thermal performance of the cooling tower.

[0077] As one possible implementation, the TDE-I type splashing device in this embodiment adopts a structural design combining multi-layer reflectors and centrifugal nozzles. When circulating water enters the splashing device through the distribution pipe, it first forms a high-speed rotating water flow under the action of the centrifugal nozzles, generating centrifugal force to uniformly throw water droplets radially. Subsequently, the water flow impacts the reflectors, further breaking them into finer droplets, forming an atomization effect. This structure can effectively reduce the concentrated water flow area, avoid the occurrence of localized over-wetting or dry areas, thereby improving the air-water contact efficiency in the packing area.

[0078] The atomization uniformity of the TDE-I splashing device must meet design requirements, typically evaluated using splash coverage area, droplet size distribution (DSD), and water distribution uniformity coefficient (UC). The designed splash coverage area should reach at least 95% of the total area of ​​the packing zone, the droplet size should be controlled within the range of 0.5~2.0 mm, and the water distribution uniformity coefficient (UC) should not be less than 0.85. Furthermore, the splashing device is made of engineering plastic (ABS), possessing good corrosion resistance and aging resistance, meeting the requirements of relevant standards.

[0079] In practical applications, this spray device is suitable for the retrofitting of water distribution systems in natural draft counterflow cooling towers, especially when there is uneven airflow distribution within the tower or the original spray device is aging or has insufficient performance. Through CFD simulation analysis, combined with the airflow field distribution characteristics within the tower, the installation position, angle, and spacing of the spray device can be optimized to achieve the best air-water matching effect.

[0080] Step S502: Use an unequal-pitch variable-speed high-efficiency water separator to reduce the water separator resistance coefficient to below the target resistance coefficient.

[0081] Specifically, this step employs a variable-speed, high-efficiency water separator with unequal pitch. Its core objective is to significantly reduce the airflow resistance coefficient by optimizing the separator's structural design. To reduce the concentration of water droplets to below 0.05, thereby improving the overall thermal performance of the cooling tower. As a crucial component at the top of the cooling tower, the water eliminator's primary function is to remove water droplets entrained in humid air, preventing water loss and equipment corrosion. However, traditional water eliminators are prone to fin deformation under high humidity and temperature conditions during long-term operation, increasing ventilation resistance and affecting airflow distribution and heat exchange efficiency.

[0082] In this embodiment, the variable-speed high-efficiency water separator is manufactured using a one-time extrusion molding process with polyvinyl chloride (PVC) material. The top and bottom edges of the separator are designed with rolled edges. Specifically, this variable-speed high-efficiency water separator is made of PVC material and achieves adaptive adjustment of airflow under non-uniform distribution at different heights and in different regions by improving the geometry and arrangement of the arc blades. Its design is based on aerodynamic principles; by setting different spacing and arc blade tilt angles in different airflow velocity regions, the water separator has stronger water removal capacity in high-wind-speed areas and reduces resistance in low-wind-speed areas, thereby optimizing the overall drag coefficient. During actual installation, the arc blade spacing of the water separator is arranged non-linearly according to the airflow velocity field distribution within the tower, ensuring that a low drag coefficient is maintained under different heights and airflow conditions. value.

[0083] Furthermore, the resistance coefficient of this water separator Verification was conducted through wind tunnel testing and CFD simulation. The numerical simulation results show that, under the design conditions, the water separator... The resistance level can be stably controlled below 0.05, meeting the resistance requirements for high-efficiency water separators in relevant standards. This step plays a crucial role in the energy-saving renovation of cooling towers. By reducing ventilation resistance and improving airflow distribution within the tower, it helps to increase the heat exchange efficiency of the packing zone, ultimately achieving a reduction in circulating water temperature of 2°C or more, improving unit economy and reducing coal consumption.

[0084] Based on the above embodiments, in one embodiment of this application, after iteratively calculating the feng shui matching parameters using a closed-loop optimization algorithm based on the angle adjustment of the splashing device and the structure optimization of the water separator, the method further includes: replacing the original packing with hydrophilic S-wave packing that meets the anti-aging performance specifications based on multiple physical field distributions, and reinforcing the water distribution system with a fiberglass support structure.

[0085] Specifically, based on the above multi-physics field distribution results, this embodiment selects hydrophilic S-wave packing with anti-aging performance that meets the specifications to replace the original packing, and uses a fiberglass support structure to reinforce the water distribution system, thereby improving the thermal performance of the packing by 22% and ensuring a service life of not less than 20 years.

[0086] As one possible approach, based on multi-physics field data such as velocity, temperature, humidity, and cooling water flow within the tower obtained from CFD simulation analysis, and combined with the principle of air-water matching, the original packing system is optimized and its structure reinforced. Among these, the hydrophilic S-wave packing possesses excellent thermodynamic properties and anti-aging characteristics. Its surface structure design effectively enhances the water film's unfolding area and air contact efficiency, thereby improving heat and mass transfer efficiency. The packing's thermodynamic performance is improved by 22%, and its anti-aging performance must meet the relevant standards for weather resistance, UV resistance, and chemical corrosion resistance under long-term operating conditions, ensuring a service life of no less than 20 years under high humidity, high temperature, and corrosive circulating water conditions.

[0087] In practical applications, the original packing material is replaced section by section based on its breakage rate, scaling degree, and thermal performance degradation. The installation height and spacing of the new packing material are arranged nonlinearly and unequally according to the airflow distribution characteristics of each region in the CFD simulation to achieve uniform matching of air and water flow. Simultaneously, a fiberglass reinforced plastic (FRP) support structure is used to reinforce the water distribution system. Its tensile strength is not less than 300 MPa, and its corrosion resistance meets the standard for "Fiberglass Reinforced Plastic Pultruded Profiles," effectively preventing structural failure of the support due to corrosion or fatigue during long-term operation, thereby ensuring the stable operation of the water distribution system.

[0088] Therefore, through the synergistic optimization of the packing and support, not only is the thermal performance of the cooling tower improved, but its structural stability and durability are also significantly enhanced, providing a solid guarantee for achieving the expected goals.

[0089] To achieve the above embodiments, this application also proposes a performance optimization system for ultra-large natural draft cooling towers. Figure 6 This is a schematic diagram of the structure of a performance optimization system for an ultra-large natural draft cooling tower proposed in an embodiment of this application, as shown below. Figure 6 As shown, the system includes: The acquisition module 100 is used to acquire cooling tower operating status data and airflow field distribution characteristics inside the tower, and to analyze the impact of tower core component aging and design defects on air-water distribution.

[0090] The calculation module 200 is used to construct a full three-dimensional multi-physics coupling model of the cooling tower based on the operating status data and flow field distribution characteristics, and to calculate various physical field distributions, including the velocity field, temperature field, pressure field, humidity field and cooling water flow field distribution of humid air and circulating water.

[0091] The adjustment module 300 is used to dynamically adjust the water distribution system and the packing arrangement scheme according to the distribution results of the multiphysics field, so as to generate a nonlinear matching relationship between the installation height of the packing and the airflow velocity field and eliminate the water distribution blind zone.

[0092] The iteration module 400 is used to iteratively calculate the air-water matching parameters based on the angle adjustment of the splash device and the structure optimization of the water separator, using a closed-loop optimization algorithm until the preset optimal value of the circulating water outlet temperature and the target value of the cooling capacity improvement are achieved.

[0093] It should be noted that the explanation of the aforementioned embodiment of the performance optimization method for ultra-large natural draft cooling towers also applies to the system of this embodiment, and will not be repeated here.

[0094] In summary, the performance optimization system for the ultra-large natural draft cooling tower in this application embodiment improves the uniformity of air-water distribution in the cooling tower through closed-loop optimization and enhanced heat exchange technology that matches airflow and water flow. This significantly reduces the outlet water temperature and increases cooling capacity, effectively improving thermal performance, reducing unit coal consumption, and enhancing the economy and safety of cooling tower operation.

[0095] To implement the above embodiments, this application also proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the performance optimization method for the ultra-large natural draft cooling tower described in any one of the first aspect embodiments above.

[0096] To implement the above embodiments, this application also proposes a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the performance optimization method for an ultra-large natural draft cooling tower as described in any one of the first aspects of the embodiments above.

[0097] 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.

[0098] 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, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0099] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more 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.

[0100] 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, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0101] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination 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.

[0102] 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.

[0103] 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.

[0104] 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 performance optimization of a hyper large natural draft cooling tower, characterized in that, The method comprises the following steps: Obtain cooling tower operating state data and airflow flow field distribution characteristics in the tower, and analyze the influence of tower core component aging and design defects on air-water distribution; Based on the operating state data and the flow field distribution characteristics, a three-dimensional multi-physical field coupling model of the cooling tower is constructed to calculate a plurality of physical field distributions, wherein the plurality of physical field distributions include velocity field, temperature field, pressure field, humidity field and cooling water quantity field distributions of wet air and circulating water; Based on the plurality of physical field distributions, the water distribution system and the filler arrangement scheme are dynamically adjusted to generate a nonlinear matching relationship between the filler installation height and the airflow velocity field, and to eliminate the water distribution blind area; Based on the angle adjustment of the splash device and the structure optimization of the water separator, a closed-loop optimization algorithm is used to iteratively calculate the air-water matching parameters until the preset optimal circulating water outlet temperature value and the cooling capacity improvement target value are reached.

2. The method of claim 1, wherein, The obtaining of the cooling tower operating state data and the airflow flow field distribution characteristics in the tower, and the analysis of the influence of tower core component aging and design defects on air-water distribution, comprises: A three-dimensional model of the airflow velocity field in the tower is established by using CFD simulation software to obtain airflow velocity distribution data in each region; The blocking rate of the filler is quantitatively analyzed by using a material aging detection device, and the water distribution uniformity coefficient is evaluated in combination with the failure rate data of the splash device.

3. The method of claim 1, wherein, The construction of the three-dimensional multi-physical field coupling model of the cooling tower based on the operating state data and the flow field distribution characteristics comprises: The grid accuracy is set by using the heat and mass transfer numerical analysis module of the Fluent software, and multi-physical field synchronous calculation is performed; The coupling relationship of each physical field is obtained by iterative calculation, wherein the coupling relationship includes the correlation function of the velocity field and the temperature field, and the influence coefficient of the humidity field on the cooling water quantity field.

4. The method of claim 1, wherein, The dynamic adjustment of the water distribution system and the filler arrangement scheme based on the plurality of physical field distributions comprises: The filler installation height is gradiently controlled within a preset height range by using a nonlinear unequal height arrangement method; The water distribution uniformity coefficient is increased to a value above the target uniformity coefficient value by adjusting the angle adjustment structure of the splash device.

5. The method of claim 1, wherein, The closed-loop optimization algorithm is used to iteratively calculate the air-water matching parameters based on the angle adjustment of the splash device and the structure optimization of the water separator until the preset optimal circulating water outlet temperature value and the cooling capacity improvement target value are reached, which comprises: A TDE-I reflective centrifugal mixing type splash device is used to replace the original splash device to make the splash effect uniformity meet the design requirements; An unequal distance variable speed high efficiency water separator is used to reduce the resistance coefficient of the water separator to a value below the target resistance coefficient.

6. The method of claim 1, wherein, After the closed-loop optimization algorithm is used to iteratively calculate the air-water matching parameters based on the angle adjustment of the splash device and the structure optimization of the water separator, the following steps are further included: Based on the plurality of physical field distributions, a hydrophilic S wave filler with aging resistance performance meeting the specifications is selected to replace the original filler, and a glass steel support structure is used to reinforce the water distribution system.

7. The method of claim 5, wherein, The unequal distance variable speed high efficiency water separator is made of polyvinyl chloride material by one-time extrusion molding, and the upper and lower edges of the unequal distance variable speed high efficiency water separator are provided with a rolled edge structure.

8. A performance optimization system for a hyper-large natural draft cooling tower, characterized in that, The following modules are included: An acquisition module is configured to acquire cooling tower operation state data and airflow flow field distribution characteristics in the tower, and analyze the influence of tower core component aging and design defects on air-water distribution; A calculation module is configured to construct a full three-dimensional multi-physical field coupling model of the cooling tower based on the operation state data and the flow field distribution characteristics, and calculate a plurality of physical field distributions, wherein the plurality of physical field distributions include velocity field, temperature field, pressure field, humidity field and cooling water amount field distributions of wet air and circulating water; An adjustment module is configured to dynamically adjust a water distribution system and a filler arrangement scheme according to the distribution results of the plurality of physical fields, so as to generate a nonlinear matching relationship between the filler installation height and the airflow velocity field, and eliminate the water distribution blind area; An iteration module is configured to iteratively calculate air-water matching parameters by using a closed-loop optimization algorithm based on splash device angle adjustment and water remover structure optimization, until a preset circulating water outlet tower water temperature optimal value and a cooling capacity improvement target value are reached.

9. An electronic device comprising: at least one processor; and a memory connected with the at least one processor in communication; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the performance optimization method of the super-large natural draft cooling tower according to any one of claims 1-7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the performance optimization method of the super-large natural draft cooling tower according to any one of claims 1-7.

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