Dynamic water distribution method and system for natural ventilation cooling tower

Abstract

The invention discloses a dynamic water distribution method and system for a natural ventilation cooling tower, and the method comprises the following steps: testing the natural ventilation cooling tower, obtaining the performance parameters of the cooling tower, constructing a CFD simulation model of the natural ventilation cooling tower, and calculating the performance parameters of the cooling tower according to the CFD simulation model. The performance parameters of the cooling tower serve as boundary conditions of a CFD simulation model of the natural ventilation cooling tower to be calculated, a sample point response value is obtained, a Kriging agent model is constructed, the sample point response value is adopted for training, the trained model is adopted for collecting data of a water distribution execution mechanism of the cooling tower, the data are processed, and the performance parameters of the cooling tower are obtained. And the adjusted initial circulating water flow of the fan-shaped area is obtained, and water distribution adjustment is conducted on a cooling tower water distribution executing mechanism based on the adjusted initial circulating water flow of the fan-shaped area. Dynamic water distribution of the natural ventilation cooling tower according to environmental conditions is achieved, and the influence of environmental crosswind on the heat exchange performance of the natural ventilation cooling tower is reduced.

Description

Technical Field

[0001] The invention relates to the technical field of medium and large-sized natural ventilation cooling towers, and in particular to a dynamic water distribution method and system for a natural ventilation cooling tower. Background Art

[0002] The natural ventilation cooling tower is the main cooling equipment of the circulating water system of the thermal power plant, and its cooling efficiency seriously affects the economic efficiency of the thermal power unit. When there is a crosswind in the environment around the natural ventilation cooling tower, the air flow field in the tower is unevenly distributed, causing the heat exchange on the windward side to deteriorate and the heat exchange on the leeward side to increase. However, since the circulating water flow rate in each area of ​​the natural ventilation cooling tower in the prior art is consistent, the cooling potential of the leeward side is not fully utilized, resulting in a weakened overall heat exchange effect in the tower. Summary of the invention

[0003] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a natural ventilation cooling tower dynamic water distribution method and system, which solve the problems mentioned in the background technology.

[0004] To achieve the above object, the present invention provides the following technical solution: a natural ventilation cooling tower dynamic water distribution method, comprising the following steps: Step S1: Performing thermal performance and resistance performance tests on a natural ventilation cooling tower to obtain performance parameters of the cooling tower. The natural ventilation cooling tower includes a cooling tower water distribution actuator. The performance parameters of the cooling tower are processed to obtain thermal performance and resistance performance. Step S2: performing three-dimensional modeling on the natural ventilation cooling tower to obtain a three-dimensional model of the natural ventilation cooling tower calculation domain, and processing the three-dimensional model of the natural ventilation cooling tower calculation domain to obtain a grid model of the natural ventilation cooling tower calculation domain; Step S3: constructing a CFD simulation model of a natural ventilation cooling tower based on the computational domain grid model, thermal performance and resistance performance of the natural ventilation cooling tower, calculating the performance parameters of the cooling tower as the boundary conditions of the CFD simulation model of the natural ventilation cooling tower, obtaining a simulated value of the outlet water temperature of the natural ventilation cooling tower, comparing the simulated value of the outlet water temperature of the natural ventilation cooling tower with the performance parameters of the cooling tower, and obtaining a CFD simulation model of a natural ventilation cooling tower that meets the requirements; Step S4: using a natural ventilation cooling tower CFD simulation model that meets the requirements and combining with Latin hypercube experimental design to process the performance parameters of the cooling tower, obtaining sample point response values, constructing a Kriging proxy model, and using the sample point response values ​​to train the Kriging proxy model to obtain a trained Kriging proxy model; Step S5: collecting the average wind speed of the fan-shaped area of ​​the cooling tower water distribution actuator through the trained Kriging proxy model, calculating the average wind speed of the fan-shaped area, and obtaining the adjusted initial circulating water flow of the fan-shaped area; Step S6: adjusting the water distribution of the cooling tower water distribution actuator based on the adjusted initial circulating water flow rate of the sector area.

[0005] Furthermore, the performance parameters of the cooling tower in step S1 are divided into input performance parameters and output performance parameters, and the input performance parameters include: circulating water flow , Cooling tower inlet water temperature , Ambient temperature , relative humidity , crosswind speed , air flow rate at the water-spraying filler , atmospheric pressure , water filling resistance ; represents the kth value in the circulating water flow sequence; represents the kth value in the cooling tower inlet water temperature sequence; represents the kth value in the ambient temperature sequence; Represents the kth value in the ambient relative humidity sequence; represents the kth value in the crosswind speed sequence; It represents the kth value in the flow velocity sequence of air at the water-spraying filler; represents the kth value in the atmospheric pressure sequence; represents the kth value in the water-spraying filler resistance sequence; Output performance parameters include: cooling tower outlet water temperature , Outlet air temperature ; represents the kth value in the cooling tower outlet water temperature sequence; Represents the kth value in the tower air temperature sequence.

[0006] Further, the thermal performance and resistance performance in step S1 are specifically as follows: The performance parameters of the cooling tower are fitted by the Merkel equation to obtain the thermal performance ,in , ; Indicates the thermal performance parameters of the cooling tower; Indicates ventilation density; Indicates water density; Represents the functional relationship between two variables ga and qw; Indicates the air density; Indicates the cross-sectional area of ​​the cooling tower fill; Fit the performance parameters of the cooling tower to obtain the resistance performance ; Represents two variables The functional relationship between qw and qw.

[0007] Further, in step S3, a natural ventilation cooling tower CFD simulation model is constructed, and the specific process is as follows: By calculating the thermal performance, the heat transfer coefficient per unit volume of filler is obtained ,Will As a mass transfer model for the packing area, As a heat transfer model of the packing area, the natural draft cooling tower includes the packing area, and the mass transfer model and the heat transfer model are loaded into the packing area of ​​the natural draft cooling tower in the form of user-defined functions through CFD software; c p,a is the specific heat capacity of moist air, Le f is the Lewis factor of moist air; By calculating the resistance performance, the viscous resistance coefficient C1 and inertial resistance coefficient C2 of the filler area of ​​the natural ventilation cooling tower are obtained. Based on the viscous resistance coefficient C1, inertial resistance coefficient C2 and the computational domain grid model of the natural ventilation cooling tower, a CFD simulation model of the natural ventilation cooling tower is constructed.

[0008] Furthermore, the basic unit size of the grid in the three-dimensional model of the natural ventilation cooling tower computational domain is d; In step S3, a CFD simulation model of a natural ventilation cooling tower that meets the requirements is obtained. The specific process is as follows: Output the i-th cooling tower outlet water temperature in the performance parameter Calculate the boundary conditions of the natural ventilation cooling tower CFD simulation model to obtain the simulated value of the water temperature at the outlet of the natural ventilation cooling tower ; ; The simulated value of the water temperature at the outlet of the natural ventilation cooling tower And the output performance parameter of the i-th cooling tower outlet water temperature For comparison, when the simulated value of the water temperature at the outlet of the natural ventilation cooling tower is And the output performance parameter of the i-th cooling tower outlet water temperature The relative error Error≥5% between them means that the accuracy of the CFD simulation model of the natural ventilation cooling tower does not meet the requirements. Go to step S2 and adjust the basic unit size d of the grid to 0.9d until the relative error Error≤5%. When the simulation value of the outlet water temperature of the natural ventilation cooling tower is And the output performance parameter of the i-th cooling tower outlet water temperature The relative error between them is less than 5%, and the accuracy of the natural ventilation cooling tower CFD simulation model meets the requirements, that is, a natural ventilation cooling tower CFD simulation model that meets the requirements is obtained.

[0009] Furthermore, the cooling tower water distribution actuator includes a water distribution surface, a vertical shaft, an electric gate, a branch water tank, a first branch pipe and a second branch pipe; the water distribution surface is composed of A1, A2, A3, A4, A5, A6, A7, and A8 areas, and a plurality of first branch pipes and second branch pipes are arranged on the water distribution surface; a branch water tank is arranged on the vertical shaft, and the branch water tank is composed of a first branch water tank, a second branch water tank, a third branch water tank and a fourth branch water tank; the first branch water tank is arranged between A1 and A2; the second branch water tank is arranged between A3 and A4; the third branch water tank is arranged between A5 and A6; the fourth branch water tank is arranged between A7 and A8; and the first branch water tank, the second branch water tank, the third branch water tank and the fourth branch water tank are all provided with electric gates.

[0010] Further, in step S4, the trained Kriging proxy model is obtained, and the specific process is as follows: Take circulating water flow , Cooling tower inlet water temperature , Ambient temperature , relative humidity , crosswind speed , atmospheric pressure To optimize the variables, Latin hypercube experimental design is used to sample within the optimized variable interval and calculate the sample points ; Represents the nth sample element; The sample points are input into the CFD simulation model of the natural ventilation cooling tower that meets the requirements for calculation, and the sample point response value is obtained, that is, the average wind speed in the fan-shaped area. ; Indicates the average wind speed in the nth sector area; Construct a Kriging surrogate model, which means: , the regression function of the Kriging surrogate model selects a quadratic function, and the covariance function of the Kriging surrogate model selects a cubic spline function; Represents the predicted output of the Kriging surrogate model; is the polynomial matrix of x; x represents the vector of input variables; is the regression coefficient; represents the mean of y(x); represents a Gaussian random process with mean 0; The mean square error is used to evaluate the accuracy of the Kriging surrogate model, which means: , is the predicted value of the i-th sample point; It represents the response value of the i-th sampling point obtained by calculating the CFD simulation model of the natural ventilation cooling tower that meets the requirements; n represents the number of sample points; MSE represents the mean square error; when MSE≤0.25, it is considered that the prediction accuracy of the Kriging proxy model meets the requirements; when MSE>0.25, it is considered that the prediction accuracy of the Kriging proxy model does not meet the requirements, and 6 sample points are added, that is, n=n+6, and the response values ​​of the 6 sample points are calculated again, and the Kriging proxy model is rebuilt. The prediction accuracy of the Kriging proxy model is evaluated by the mean square error until the prediction accuracy of the Kriging proxy model meets the requirements, and the trained Kriging proxy model is obtained.

[0011] Further, in step S5, the adjusted initial circulating water flow rate of the sector area is obtained, and the specific process is as follows: The circulating water flow The initial circulating water flow rate of the cooling tower water distribution actuator area is evenly distributed to the cooling tower water distribution actuator area. , ; represents the initial circulating water flow rate of the area allocated to the j-th cooling tower water distribution actuator; The average wind speed in the fan-shaped area is normalized, and the normalization formula is: , is the normalized average wind speed in the fan-shaped area; represents the original average wind speed in the jth sector area; According to the normalized average wind speed ratio of the fan-shaped area, the initial circulating water flow rate of the cooling tower water distribution actuator is adjusted, and the adjusted initial circulating water flow rate of the fan-shaped area is obtained as follows: .

[0012] Further, in step S6, water distribution adjustment is performed in the following specific process: Circulating water flow Enter the shaft and adjust the circulating water flow of the branch water tank by controlling the opening of the electric gate according to the adjusted initial circulating water flow of the fan-shaped area. , circulating water flow Then it flows into the second branch pipe through the first branch pipe, and finally completes the water distribution regulation of the cooling tower water distribution actuator.

[0013] Furthermore, a natural ventilation cooling tower dynamic water distribution system is provided, which is used for the natural ventilation cooling tower dynamic water distribution method, and comprises: The data acquisition module is used to test the thermal performance and resistance performance of the natural ventilation cooling tower to obtain the performance parameters of the cooling tower. The natural ventilation cooling tower includes a cooling tower water distribution actuator, and the performance parameters of the cooling tower are processed to obtain the thermal performance and resistance performance; The three-dimensional modeling and mesh processing module is used to perform three-dimensional modeling on the natural ventilation cooling tower to obtain a three-dimensional model of the natural ventilation cooling tower calculation domain, and to process the three-dimensional model of the natural ventilation cooling tower calculation domain to obtain a mesh model of the natural ventilation cooling tower calculation domain; The CFD simulation model construction and correction module is used to construct a CFD simulation model of a natural ventilation cooling tower based on the computational domain grid model, thermal performance and resistance performance of the natural ventilation cooling tower, calculate the performance parameters of the cooling tower as the boundary conditions of the CFD simulation model of the natural ventilation cooling tower, obtain the simulated value of the water temperature at the outlet of the natural ventilation cooling tower, compare the simulated value of the water temperature at the outlet of the natural ventilation cooling tower with the performance parameters of the cooling tower, and obtain a CFD simulation model of the natural ventilation cooling tower that meets the requirements; The offline training and proxy model building module is used to process the performance parameters of the cooling tower by using the natural ventilation cooling tower CFD simulation model that meets the requirements and the Latin hypercube experimental design, obtain the sample point response value, build the Kriging proxy model, and use the sample point response value to train the Kriging proxy model to obtain the trained Kriging proxy model; The online calculation and flow dynamic allocation module is used to collect the average wind speed in the fan-shaped area of ​​the cooling tower water distribution actuator through the trained Kriging agent model, calculate the average wind speed in the fan-shaped area, and obtain the adjusted initial circulating water flow in the fan-shaped area; The water distribution adjustment execution module is used to adjust the water distribution of the cooling tower water distribution actuator based on the adjusted initial circulating water flow in the fan-shaped area.

[0014] Compared with the existing technology, the present invention has the following beneficial effects: (1) The present invention realizes dynamic water distribution of a natural ventilation cooling tower according to environmental conditions, reduces the influence of environmental side wind on the heat exchange performance of the natural ventilation cooling tower, effectively improves the cooling efficiency of the natural ventilation cooling tower, and reduces the energy consumption of thermal power units.

[0015] (2) The dynamic water distribution method and system for natural ventilation cooling towers proposed in the present invention avoids the hysteresis of water distribution adjustment caused by online calculation of the CFD simulation model of natural ventilation cooling towers by using offline training and proxy model construction modules, making the water distribution of natural ventilation cooling towers more real-time. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of the present invention.

[0017] Figure 2 It is a schematic diagram of the fan-shaped partition structure of the lower surface of the filler of the present invention.

[0018] Figure 3 It is a schematic diagram of the structure of the cooling tower water distribution actuator of the present invention.

[0019] Figure 4 It is a system flow chart of the present invention.

[0020] Figure numerals: 1. vertical shaft; 2. electric gate; 3. branch water tank; 4. first branch pipe; 5. second branch pipe. DETAILED DESCRIPTION

[0021] like Figure 1 As shown, the present invention provides a technical solution: a natural ventilation cooling tower dynamic water distribution method, comprising the following steps: Step S1: Performing thermal performance and resistance performance tests on a natural ventilation cooling tower to obtain performance parameters of the cooling tower. The natural ventilation cooling tower includes a cooling tower water distribution actuator. The performance parameters of the cooling tower are processed to obtain thermal performance and resistance performance. Step S2: performing three-dimensional modeling on the natural ventilation cooling tower to obtain a three-dimensional model of the natural ventilation cooling tower calculation domain, and processing the three-dimensional model of the natural ventilation cooling tower calculation domain to obtain a grid model of the natural ventilation cooling tower calculation domain; Step S3: constructing a CFD simulation model of a natural ventilation cooling tower based on the computational domain grid model, thermal performance and resistance performance of the natural ventilation cooling tower, calculating the performance parameters of the cooling tower as the boundary conditions of the CFD simulation model of the natural ventilation cooling tower, obtaining a simulated value of the outlet water temperature of the natural ventilation cooling tower, comparing the simulated value of the outlet water temperature of the natural ventilation cooling tower with the performance parameters of the cooling tower, and obtaining a CFD simulation model of a natural ventilation cooling tower that meets the requirements; Step S4: using a natural ventilation cooling tower CFD simulation model that meets the requirements and combining with Latin hypercube experimental design to process the performance parameters of the cooling tower, obtaining sample point response values, constructing a Kriging proxy model, and using the sample point response values ​​to train the Kriging proxy model to obtain a trained Kriging proxy model; Step S5: collecting the average wind speed of the fan-shaped area of ​​the cooling tower water distribution actuator through the trained Kriging proxy model, calculating the average wind speed of the fan-shaped area, and obtaining the adjusted initial circulating water flow of the fan-shaped area; Step S6: adjusting the water distribution of the cooling tower water distribution actuator based on the adjusted initial circulating water flow rate of the sector area.

[0022] Among them, the performance parameters of the cooling tower in step S1 are divided into input performance parameters and output performance parameters. The input performance parameters include: circulating water flow rate , Cooling tower inlet water temperature , Ambient temperature , relative humidity , crosswind speed , air flow rate at the water-spraying filler , atmospheric pressure , water filling resistance ; represents the kth value in the circulating water flow sequence; represents the kth value in the cooling tower inlet water temperature sequence; represents the kth value in the ambient temperature sequence; Represents the kth value in the ambient relative humidity sequence; represents the kth value in the crosswind speed sequence; It represents the kth value in the flow velocity sequence of air at the water-spraying filler; represents the kth value in the atmospheric pressure sequence; represents the kth value in the water-spraying filler resistance sequence; Output performance parameters include: cooling tower outlet water temperature , Outlet air temperature ; represents the kth value in the cooling tower outlet water temperature sequence; Represents the kth value in the tower air temperature sequence.

[0023] The specific process of thermal performance and resistance performance in step S1 is as follows: The performance parameters of the cooling tower are fitted by the Merkel equation to obtain the thermal performance ,in , ; Indicates the thermal performance parameters of the cooling tower; Indicates ventilation density; Indicates water density; Represents the functional relationship between two variables ga and qw; Indicates the air density; Indicates the cross-sectional area of ​​the cooling tower fill; Fit the performance parameters of the cooling tower to obtain the resistance performance ; Represents two variables The functional relationship between qw and qw.

[0024] Among them, in step S3, a natural ventilation cooling tower CFD simulation model is constructed, and the specific process is as follows: By calculating the thermal performance, the heat transfer coefficient per unit volume of filler is obtained ,Will As a mass transfer model for the packing area, As a heat transfer model of the packing area, the natural draft cooling tower includes the packing area, and the mass transfer model and the heat transfer model are loaded into the packing area of ​​the natural draft cooling tower in the form of user-defined functions through CFD software; c p,ais the specific heat capacity of moist air, Le f is the Lewis factor of moist air; By calculating the resistance performance, the viscous resistance coefficient C1 and inertial resistance coefficient C2 of the filler area of ​​the natural ventilation cooling tower are obtained. Based on the viscous resistance coefficient C1, inertial resistance coefficient C2 and the computational domain grid model of the natural ventilation cooling tower, a CFD simulation model of the natural ventilation cooling tower is constructed.

[0025] Among them, the basic unit size of the grid of the three-dimensional model of the natural ventilation cooling tower calculation domain is d; In step S3, a CFD simulation model of a natural ventilation cooling tower that meets the requirements is obtained. The specific process is as follows: Output the i-th cooling tower outlet water temperature in the performance parameter Calculate the boundary conditions of the natural ventilation cooling tower CFD simulation model to obtain the simulated value of the water temperature at the outlet of the natural ventilation cooling tower ; ; The simulated value of the water temperature at the outlet of the natural ventilation cooling tower And the output performance parameter of the i-th cooling tower outlet water temperature For comparison, when the simulated value of the water temperature at the outlet of the natural ventilation cooling tower is And the output performance parameter of the i-th cooling tower outlet water temperature The relative error Error≥5% between them means that the accuracy of the CFD simulation model of the natural ventilation cooling tower does not meet the requirements. Go to step S2 and adjust the basic unit size d of the grid to 0.9d until the relative error Error≤5%. When the simulation value of the outlet water temperature of the natural ventilation cooling tower is And the output performance parameter of the i-th cooling tower outlet water temperature The relative error between them is less than 5%, and the accuracy of the natural ventilation cooling tower CFD simulation model meets the requirements, that is, a natural ventilation cooling tower CFD simulation model that meets the requirements is obtained.

[0026] like Figure 2-Figure 3 As shown, the cooling tower water distribution actuator includes a water distribution surface, a shaft 1, an electric gate 2, a branch water tank 3, a first branch pipe 4 and a second branch pipe 5; the water distribution surface is composed of A1, A2, A3, A4, A5, A6, A7, and A8 areas, and a plurality of first branch pipes 4 and second branch pipes 5 are arranged on the water distribution surface; a branch water tank 3 is arranged on the shaft 1, and the branch water tank 3 is composed of a first branch water tank, a second branch water tank, a third branch water tank and a fourth branch water tank; the first branch water tank is arranged between A1 and A2; the second branch water tank is arranged between A3 and A4; the third branch water tank is arranged between A5 and A6; the fourth branch water tank is arranged between A7 and A8; and the first branch water tank, the second branch water tank, the third branch water tank and the fourth branch water tank are all provided with an electric gate 2.

[0027] Among them, in step S4, the trained Kriging proxy model is obtained, and the specific process is: Take circulating water flow , Cooling tower inlet water temperature , Ambient temperature , relative humidity , crosswind speed , atmospheric pressure To optimize the variables, Latin hypercube experimental design is used to sample within the optimized variable interval and calculate the sample points ; Represents the nth sample element; The sample points are input into the CFD simulation model of the natural ventilation cooling tower that meets the requirements for calculation, and the sample point response value is obtained, that is, the average wind speed in the fan-shaped area. ; Indicates the average wind speed in the nth sector area; Construct a Kriging surrogate model, which means: , the regression function of the Kriging surrogate model selects a quadratic function, and the covariance function of the Kriging surrogate model selects a cubic spline function; Represents the predicted output of the Kriging surrogate model; is the polynomial matrix of x; x represents the vector of input variables; is the regression coefficient; represents the mean of y(x); represents a Gaussian random process with mean 0; The mean square error is used to evaluate the accuracy of the Kriging surrogate model, which means: , is the predicted value of the i-th sample point; It represents the response value of the i-th sample point obtained by calculating the CFD simulation model of the natural ventilation cooling tower that meets the requirements; n represents the number of sample points; MSE represents the mean square error; when MSE≤0.25, it is considered that the prediction accuracy of the Kriging proxy model meets the requirements; when MSE>0.25, it is considered that the prediction accuracy of the Kriging proxy model does not meet the requirements. In order to improve the prediction accuracy, 6 sample points are added, that is, n=n+6, and the response values ​​of the 6 sample points are calculated, and the Kriging proxy model is rebuilt. The prediction accuracy of the Kriging proxy model is evaluated by the mean square error until the prediction accuracy of the Kriging proxy model meets the requirements, and the trained Kriging proxy model is obtained.

[0028] Among them, in step S5, the adjusted initial circulating water flow rate of the sector area is obtained, and the specific process is: The circulating water flow The initial circulating water flow rate of the cooling tower water distribution actuator area is evenly distributed to the cooling tower water distribution actuator area. , ; represents the initial circulating water flow rate of the area allocated to the j-th cooling tower water distribution actuator; The average wind speed in the fan-shaped area is normalized to eliminate the dimension effect. The normalization formula is: , is the normalized average wind speed in the fan-shaped area; represents the original average wind speed in the jth sector area; According to the normalized average wind speed ratio of the fan-shaped area, the initial circulating water flow rate of the cooling tower water distribution actuator is adjusted, and the adjusted initial circulating water flow rate of the fan-shaped area is obtained as follows: .

[0029] The specific process of water distribution adjustment in step S6 is as follows: Circulating water flow Enter shaft 1, and control the opening of electric gate 2 to adjust the circulating water flow of branch water tank 3 according to the adjusted initial circulating water flow of the fan-shaped area. , circulating water flow Then it flows into the second branch pipe 5 through the first branch pipe 4, and finally completes the water distribution regulation of the cooling tower water distribution actuator.

[0030] like Figure 4 As shown, a natural ventilation cooling tower dynamic water distribution system is used for a natural ventilation cooling tower dynamic water distribution method, comprising: The data acquisition module is used to test the thermal performance and resistance performance of the natural ventilation cooling tower to obtain the performance parameters of the cooling tower. The natural ventilation cooling tower includes a cooling tower water distribution actuator, and the performance parameters of the cooling tower are processed to obtain the thermal performance and resistance performance; The three-dimensional modeling and mesh processing module is used to perform three-dimensional modeling on the natural ventilation cooling tower to obtain a three-dimensional model of the natural ventilation cooling tower calculation domain, and to process the three-dimensional model of the natural ventilation cooling tower calculation domain to obtain a mesh model of the natural ventilation cooling tower calculation domain; The CFD simulation model construction and correction module is used to construct a CFD simulation model of a natural ventilation cooling tower based on the computational domain grid model, thermal performance and resistance performance of the natural ventilation cooling tower, calculate the performance parameters of the cooling tower as the boundary conditions of the CFD simulation model of the natural ventilation cooling tower, obtain the simulated value of the water temperature at the outlet of the natural ventilation cooling tower, compare the simulated value of the water temperature at the outlet of the natural ventilation cooling tower with the performance parameters of the cooling tower, and obtain a CFD simulation model of the natural ventilation cooling tower that meets the requirements; The offline training and proxy model building module is used to process the performance parameters of the cooling tower by using the natural ventilation cooling tower CFD simulation model that meets the requirements and the Latin hypercube experimental design, obtain the sample point response value, build the Kriging proxy model, and use the sample point response value to train the Kriging proxy model to obtain the trained Kriging proxy model; The online calculation and flow dynamic allocation module is used to collect the average wind speed in the fan-shaped area of ​​the cooling tower water distribution actuator through the trained Kriging agent model, calculate the average wind speed in the fan-shaped area, and obtain the adjusted initial circulating water flow in the fan-shaped area; The water distribution adjustment execution module is used to adjust the water distribution of the cooling tower water distribution actuator based on the adjusted initial circulating water flow in the fan-shaped area.

[0031] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A natural ventilation cooling tower dynamic water distribution method, characterized in that: The following steps are involved: Step S1: Performing thermal performance and resistance performance tests on a natural ventilation cooling tower to obtain performance parameters of the cooling tower. The natural ventilation cooling tower includes a cooling tower water distribution actuator. The performance parameters of the cooling tower are processed to obtain thermal performance and resistance performance. Step S2: performing three-dimensional modeling on the natural ventilation cooling tower to obtain a three-dimensional model of the natural ventilation cooling tower calculation domain, and processing the three-dimensional model of the natural ventilation cooling tower calculation domain to obtain a grid model of the natural ventilation cooling tower calculation domain; Step S3: constructing a CFD simulation model of a natural ventilation cooling tower based on the computational domain grid model, thermal performance and resistance performance of the natural ventilation cooling tower, calculating the performance parameters of the cooling tower as the boundary conditions of the CFD simulation model of the natural ventilation cooling tower, obtaining a simulated value of the outlet water temperature of the natural ventilation cooling tower, comparing the simulated value of the outlet water temperature of the natural ventilation cooling tower with the performance parameters of the cooling tower, and obtaining a CFD simulation model of a natural ventilation cooling tower that meets the requirements; Step S4: using a natural ventilation cooling tower CFD simulation model that meets the requirements and combining with Latin hypercube experimental design to process the performance parameters of the cooling tower, obtaining sample point response values, constructing a Kriging proxy model, and using the sample point response values ​​to train the Kriging proxy model to obtain a trained Kriging proxy model; Step S5: collecting the average wind speed of the fan-shaped area of ​​the cooling tower water distribution actuator through the trained Kriging proxy model, calculating the average wind speed of the fan-shaped area, and obtaining the adjusted initial circulating water flow of the fan-shaped area; Step S6: adjusting the water distribution of the cooling tower water distribution actuator based on the adjusted initial circulating water flow rate of the sector area.

2. A natural ventilation cooling tower dynamic water distribution method according to claim 1, characterized in that: The performance parameters of the cooling tower in step S1 are divided into input performance parameters and output performance parameters. The input performance parameters include: circulating water flow rate , Cooling tower inlet water temperature , Ambient temperature , relative humidity , crosswind speed , air flow rate at the water-spraying filler , atmospheric pressure , water filling resistance ; represents the kth value in the circulating water flow sequence; represents the kth value in the cooling tower inlet water temperature sequence; represents the kth value in the ambient temperature sequence; Represents the kth value in the ambient relative humidity sequence; represents the kth value in the crosswind speed sequence; It represents the kth value in the flow velocity sequence of air at the water-spraying filler; represents the kth value in the atmospheric pressure sequence; represents the kth value in the water-spraying filler resistance sequence; Output performance parameters include: cooling tower outlet water temperature , Outlet air temperature ; represents the kth value in the cooling tower outlet water temperature sequence; Represents the kth value in the tower air temperature sequence.

3. A natural ventilation cooling tower dynamic water distribution method according to claim 2, characterized in that: The specific process of thermal performance and resistance performance in step S1 is as follows: The performance parameters of the cooling tower are fitted by the Merkel equation to obtain the thermal performance ,in , ; Indicates the thermal performance parameters of the cooling tower; Indicates ventilation density; Indicates water density; Represents the functional relationship between two variables ga and qw; Indicates the air density; Indicates the cross-sectional area of ​​the cooling tower fill; Fit the performance parameters of the cooling tower to obtain the resistance performance ; Represents two variables The functional relationship between qw and qw.

4. A natural ventilation cooling tower dynamic water distribution method according to claim 3, characterized in that: In step S3, a natural ventilation cooling tower CFD simulation model is constructed, and the specific process is as follows: By calculating the thermal performance, the heat transfer coefficient per unit volume of filler is obtained ,Will As a mass transfer model for the packing area, As a heat transfer model of the packing area, the natural draft cooling tower includes the packing area, and the mass transfer model and the heat transfer model are loaded into the packing area of ​​the natural draft cooling tower through CFD software; c p,a is the specific heat capacity of moist air, Le f is the Lewis factor of moist air; By calculating the resistance performance, the viscous resistance coefficient C1 and inertial resistance coefficient C2 of the filler area of ​​the natural ventilation cooling tower are obtained. Based on the viscous resistance coefficient C1, inertial resistance coefficient C2 and the computational domain grid model of the natural ventilation cooling tower, a CFD simulation model of the natural ventilation cooling tower is constructed.

5. A natural ventilation cooling tower dynamic water distribution method according to claim 4, characterized in that: The basic unit size of the grid in the three-dimensional model of the natural draft cooling tower computational domain is d; In step S3, a CFD simulation model of a natural ventilation cooling tower that meets the requirements is obtained. The specific process is as follows: Output the i-th cooling tower outlet water temperature in the performance parameter Calculate the boundary conditions of the natural ventilation cooling tower CFD simulation model to obtain the simulated value of the water temperature at the outlet of the natural ventilation cooling tower ; ; The simulated value of the water temperature at the outlet of the natural ventilation cooling tower And the output performance parameter of the i-th cooling tower outlet water temperature For comparison, when the simulated value of the water temperature at the outlet of the natural ventilation cooling tower is And the output performance parameter of the i-th cooling tower outlet water temperature The relative error Error≥5% between them means that the accuracy of the CFD simulation model of the natural ventilation cooling tower does not meet the requirements. Go to step S2 and adjust the basic unit size d of the grid to 0.9d until the relative error Error≤5%. When the simulation value of the outlet water temperature of the natural ventilation cooling tower is And the output performance parameter of the i-th cooling tower outlet water temperature The relative error between them is less than 5%, and the accuracy of the natural ventilation cooling tower CFD simulation model meets the requirements, that is, a natural ventilation cooling tower CFD simulation model that meets the requirements is obtained.

6. A natural ventilation cooling tower dynamic water distribution method according to claim 5, characterized in that: The cooling tower water distribution actuator includes a water distribution surface, a vertical shaft, an electric gate, a branch water tank, a first branch pipe and a second branch pipe; the water distribution surface is composed of A1, A2, A3, A4, A5, A6, A7 and A8 areas, and a plurality of first branch pipes and second branch pipes are arranged on the water distribution surface; a branch water tank is arranged on the vertical shaft, and the branch water tank is composed of a first branch water tank, a second branch water tank, a third branch water tank and a fourth branch water tank; the first branch water tank is arranged between A1 and A2; the second branch water tank is arranged between A3 and A4; the third branch water tank is arranged between A5 and A6; the fourth branch water tank is arranged between A7 and A8; and electric gates are arranged on the first branch water tank, the second branch water tank, the third branch water tank and the fourth branch water tank.

7. A natural ventilation cooling tower dynamic water distribution method according to claim 6, characterized in that: In step S4, the trained Kriging proxy model is obtained. The specific process is as follows: Take circulating water flow , Cooling tower inlet water temperature , Ambient temperature , relative humidity , crosswind speed , atmospheric pressure To optimize the variables, Latin hypercube experimental design is used to sample within the optimized variable interval and calculate the sample points ; Represents the nth sample element; The sample points are input into the CFD simulation model of the natural ventilation cooling tower that meets the requirements for calculation, and the sample point response value is obtained, that is, the average wind speed in the fan-shaped area. ; Indicates the average wind speed in the nth sector area; Construct a Kriging surrogate model, which means: , the regression function of the Kriging surrogate model selects a quadratic function, and the covariance function of the Kriging surrogate model selects a cubic spline function; Represents the predicted output of the Kriging surrogate model; is the polynomial matrix of x; x represents the vector of input variables; is the regression coefficient; represents the mean of y(x); represents a Gaussian random process with mean 0; The mean square error is used to evaluate the accuracy of the Kriging surrogate model, which means: , is the predicted value of the i-th sample point; represents the response value of the i-th sampling point obtained by calculating the CFD simulation model of the natural ventilation cooling tower that meets the requirements; n represents the number of sampling points; Represents mean square error; when MSE≤0.25, it is considered that the prediction accuracy of the Kriging surrogate model meets the requirements; when MSE>0.25, it is considered that the prediction accuracy of the Kriging surrogate model does not meet the requirements, and 6 sample points are added, that is, n=n+6, and the response values ​​of the 6 sample points are calculated again, and the Kriging surrogate model is rebuilt. The prediction accuracy of the Kriging surrogate model is evaluated by the mean square error until the prediction accuracy of the Kriging surrogate model meets the requirements, and the trained Kriging surrogate model is obtained.

8. A natural ventilation cooling tower dynamic water distribution method according to claim 7, characterized in that: In step S5, the adjusted initial circulating water flow rate of the sector area is obtained, and the specific process is as follows: The circulating water flow The initial circulating water flow rate of the cooling tower water distribution actuator area is evenly distributed to the cooling tower water distribution actuator area. , ; represents the initial circulating water flow rate of the area allocated to the j-th cooling tower water distribution actuator; The average wind speed in the fan-shaped area is normalized, and the normalization formula is: , is the normalized average wind speed in the fan-shaped area; represents the original average wind speed in the jth sector area; According to the normalized average wind speed ratio of the fan-shaped area, the initial circulating water flow rate of the cooling tower water distribution actuator is adjusted, and the adjusted initial circulating water flow rate of the fan-shaped area is obtained as follows: .

9. A natural ventilation cooling tower dynamic water distribution method according to claim 8, characterized in that: The specific process of water distribution adjustment in step S6 is as follows: Circulating water flow Enter the shaft and adjust the circulating water flow of the branch water tank by controlling the opening of the electric gate according to the adjusted initial circulating water flow of the fan-shaped area. , circulating water flow Then it flows into the second branch pipe through the first branch pipe, and finally completes the water distribution regulation of the cooling tower water distribution actuator.

10. A natural ventilation cooling tower dynamic water distribution system, used in a natural ventilation cooling tower dynamic water distribution method according to any one of claims 1 to 9, characterized in that: include: The data acquisition module is used to test the thermal performance and resistance performance of the natural ventilation cooling tower to obtain the performance parameters of the cooling tower. The natural ventilation cooling tower includes a cooling tower water distribution actuator, and the performance parameters of the cooling tower are processed to obtain the thermal performance and resistance performance; The three-dimensional modeling and mesh processing module is used to perform three-dimensional modeling on the natural ventilation cooling tower to obtain a three-dimensional model of the natural ventilation cooling tower calculation domain, and to process the three-dimensional model of the natural ventilation cooling tower calculation domain to obtain a mesh model of the natural ventilation cooling tower calculation domain; The CFD simulation model construction and correction module is used to construct a CFD simulation model of a natural ventilation cooling tower based on the computational domain grid model, thermal performance and resistance performance of the natural ventilation cooling tower, calculate the performance parameters of the cooling tower as the boundary conditions of the CFD simulation model of the natural ventilation cooling tower, obtain the simulated value of the water temperature at the outlet of the natural ventilation cooling tower, compare the simulated value of the water temperature at the outlet of the natural ventilation cooling tower with the performance parameters of the cooling tower, and obtain a CFD simulation model of the natural ventilation cooling tower that meets the requirements; The offline training and proxy model building module is used to process the performance parameters of the cooling tower by using the natural ventilation cooling tower CFD simulation model that meets the requirements and the Latin hypercube experimental design, obtain the sample point response value, build the Kriging proxy model, and use the sample point response value to train the Kriging proxy model to obtain the trained Kriging proxy model; The online calculation and flow dynamic allocation module is used to collect the average wind speed in the fan-shaped area of ​​the cooling tower water distribution actuator through the trained Kriging agent model, calculate the average wind speed in the fan-shaped area, and obtain the adjusted initial circulating water flow in the fan-shaped area; The water distribution adjustment execution module is used to adjust the water distribution of the cooling tower water distribution actuator based on the adjusted initial circulating water flow in the fan-shaped area.

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