A method for collaborative optimization of the thermal performance of a natural draft cooling tower

Abstract

The present invention discloses a method for collaborative optimization of the thermal performance of a natural draft cooling tower, which includes the following steps: performing performance tests on the packing of the natural draft cooling tower to obtain the performance parameters of the natural draft cooling tower; establishing a model based on the performance parameters of the natural draft cooling tower, and extracting the packing layout form parameters and water distribution structure parameters of the natural draft cooling tower; using an optimization algorithm and experimental design to obtain the optimal uniformity sample points of each parameter; obtaining the outlet water temperature of the natural draft cooling tower at the optimal uniformity sample points through CFD calculation; constructing a radial basis surrogate model based on the data obtained from the CFD calculation; and using an optimization algorithm to optimize and solve the radial basis surrogate model to obtain the packing layout form parameters and water distribution structure parameters corresponding to the lowest outlet water temperature of the natural draft cooling tower. The present invention achieves the purpose of energy conservation and consumption reduction of thermal power units by obtaining the optimal design of the packing layout and water distribution structure and minimizing the outlet water temperature of the natural draft cooling tower to the greatest extent.

Description

Technical Field

[0001] The present invention relates to the technical field of medium and large natural draft cooling towers, and provides a method for collaborative optimization of the thermal performance of natural draft cooling towers. Background Art

[0002] Natural draft cooling towers are one of the main equipment in the cold end system of thermal power plants. The decline of their cooling efficiency will cause the vacuum of condensers to deteriorate, which will further lead to an increase in the coal consumption of thermal power units, affecting the economy and safety of thermal power units. According to relevant literature data, for a 300MW thermal power unit, when the outlet water temperature of the natural draft cooling tower rises by 1°C, the unit efficiency will decrease by 0.23%, resulting in an increase in coal consumption of 0.8gce / kW.h. Therefore, improving the thermal performance of natural draft cooling towers is very important for energy conservation and consumption reduction.

[0003] Currently, the main measures to improve the thermal performance of natural draft cooling towers are to adjust the packing layout form or the water distribution structure. Most of the existing technologies optimize the cooling performance of cooling towers separately from the packing layout form or the water distribution structure. However, in actual projects, the influence of the packing layout form and the water distribution structure on the performance of cooling towers is often interrelated. Optimizing the water distribution structure or the packing layout form alone, it is difficult to achieve the best optimization effect of the thermal performance of the cooling tower; in addition, the existing technologies for optimizing the cooling performance of cooling towers mostly adopt the method of repeated adjustment and selection, with extremely large workload and unable to accurately describe the relationship between the packing layout parameters, the water distribution structure parameters and the cooling performance of natural draft cooling towers, resulting in difficulty in achieving the optimal design of the packing layout and the water distribution structure of natural draft cooling towers. Summary of the Invention

[0004] In order to overcome the above defects of the prior art, the embodiments of the present invention provide a method for collaborative optimization of the thermal performance of natural draft cooling towers, which can quickly and effectively realize the collaborative optimization of the packing layout mode and the water distribution structure of natural draft cooling towers, obtain the best collaborative combination of the packing layout form and the water distribution structure, and solve the problem of energy conservation and consumption reduction of cooling towers.

[0005] To achieve the above object, the present invention provides the following technical solution: A method for collaborative optimization of the thermal performance of natural draft cooling towers, comprising the following steps:

[0006] Step S1: Test the thermal performance and resistance performance of the packing of the natural draft cooling tower to obtain the thermal performance, resistance performance of the packing, and the structural parameters and working parameters of the natural draft cooling tower;

[0007] Step S2: Based on the thermal performance and resistance performance of the packing, as well as the structural parameters and operating parameters of the natural draft cooling tower, parametrically establish the three-dimensional model, grid model, and CFD simulation model of the natural draft cooling tower. Extract the packing of the natural draft cooling tower through the three-dimensional model, grid model, and CFD simulation model of the natural draft cooling tower to obtain the packing layout form parameters and water distribution structure parameters of the natural draft cooling tower;

[0008] Step S3: Based on the packing layout form parameters and water distribution structure parameters of the natural draft cooling tower, use the multi-dimensional Latin hypercube experimental design to construct a sampling space under the given constraints, generate sample points from the sampling space, and then combine the locust optimization algorithm to iteratively optimize the sample points to obtain the optimized sample points. Use the maximum-minimum distance evaluation criterion to conduct a uniformity test on the optimized sample points, and regenerate the optimized sample points that do not meet the evaluation criteria until the best uniformity sample points are obtained; conduct a simulation calculation on the best uniformity sample points through the CFD simulation model to obtain the outlet water temperature of the natural draft cooling tower corresponding to the best uniformity sample points;

[0009] Step S4: Based on the outlet water temperature of the natural draft cooling tower corresponding to the best uniformity sample points, construct a radial basis surrogate model, and establish the mapping relationship between the packing layout parameters, water distribution structure parameters of the natural draft cooling tower, and the outlet water temperature of the natural draft cooling tower through the radial basis surrogate model;

[0010] Step S5: Evaluate the prediction accuracy of the constructed radial basis surrogate model. If the prediction accuracy evaluation meets the requirements, go to Step S6; if the prediction accuracy evaluation does not meet the requirements, go to Step S3, resample the best uniformity sample points, and reconstruct the radial basis surrogate model until the radial basis surrogate model meets the prediction accuracy evaluation requirements;

[0011] Step S6: Use the locust optimization algorithm to optimize and solve the mapping relationship between the packing layout parameters, water distribution structure parameters of the natural draft cooling tower, and the outlet water temperature of the natural draft cooling tower in the radial basis surrogate model after the prediction accuracy evaluation to obtain the packing layout form parameters and water distribution structure parameters corresponding to the lowest outlet water temperature of the natural draft cooling tower.

[0012] Furthermore, the thermal performance of the packing in Step S1 is , and the resistance performance is , represents the mass transfer coefficient per unit volume of the packing; Δp represents the pressure drop of the packing; α represents the constant coefficient of the thermal performance; β is the exponent of the ventilation density g a in the thermal performance; γ represents the exponent of the water spray density q q in the thermal performance; A 0 is the constant coefficient of the resistance performance; M represents the exponent of v z in the resistance performance; g aDenote the ventilation density as q q The water spraying density is q a The air density is ρ z The air velocity in the vertical direction is v; g represents the acceleration due to gravity.

[0013] Furthermore, the structural parameters of the natural draft cooling tower in step S1 are the geometric dimensions of the tower barrel of the natural draft cooling tower, the elevation of the packing layer, the elevation of the spraying device, and the thickness of the packing layer; the operating parameters are the moisture content A of the ambient air, the dry bulb temperature T g , the circulating cooling water flow rate Q w , the inlet temperature T of the circulating cooling water in , the atmospheric pressure P a , the water spraying density q w .

[0014] Furthermore, the parameters of the packing layout form in step S2 specifically include: the number of packing partitions n of the natural draft cooling tower f , the outer circle radius of the partition packing of the natural draft cooling tower , the thickness of the partition packing of the natural draft cooling tower , and the constraint conditions of the parameters of the packing layout form of the natural draft cooling tower are respectively: , , ; represents the outer circle radius of the first partition; represents the outer circle radius of the second partition; represents the outer circle radius of the third partition; represents the outer circle radius of the (n - 1)th partition; represents the outer circle radius of the nth partition; represents the thickness of the partition packing of the nth natural draft cooling tower.

[0015] Furthermore, the parameters of the water distribution structure in step S2 specifically include: the number of partitions n of the water distribution surface of the natural draft cooling tower w , and the water spraying density of the partitioned water distribution of the natural draft cooling tower ; the number of partitions n of the water distribution surface of the natural draft cooling tower w and the outer circle radius of the partition packing of the natural draft cooling tower are consistent with the number of packing partitions n of the natural draft cooling tower f , and the constraint condition of the water spraying density of the partitioned water distribution of the natural draft cooling tower is ; are respectively the areas of the th partitioned water distribution surfaces; represents the water spraying density of the nth partitioned water distribution of the natural draft cooling tower.

[0016] Furthermore, the specific process of obtaining the optimal uniformity sample points in step S3 is as follows:

[0017] Step S31: Based on the packing layout form parameters and water distribution structure parameters of the natural draft cooling tower, use the multi-dimensional Latin hypercube experimental design to construct a sampling space under the given constraints, generate sample points from the sampling space, and mark the generated sample points as the initial population;

[0018] Step S32: Use the locust optimization algorithm to perform iterative optimization on the initial population to obtain the optimized sample points;

[0019] Step S33: Check the sample points on the boundary of the multi-dimensional Latin hypercube experimental design interval to prevent the generated sample points from exceeding the boundary;

[0020] Step S34: Conduct a uniformity test on the optimized sample points by using the maximum-minimum distance evaluation criterion, and regenerate the optimized sample points that do not meet the evaluation criterion until the best uniformity sample points are obtained ; represents the k-th best uniformity sample point.

[0021] Furthermore, the objective function expression of the radial basis surrogate model in Step S4 is:

[0022] (1);

[0023] In the formula, represents the outlet water temperature corresponding to the best uniformity sample point x; i represents the i-th best uniformity sample point; represents the weight coefficient; represents the basis function; represents the Euclidean distance between two points; represents the center point of the i-th basis function.

[0024] Furthermore, the prediction accuracy evaluation of the constructed radial basis surrogate model in Step S5 includes: the multiple correlation coefficient, the relative mean absolute error, and the relative maximum absolute error. The closer the multiple correlation coefficient is to 1, the higher the prediction accuracy of the radial basis surrogate model, and the smaller the relative mean absolute error and the relative maximum absolute error; the higher the prediction accuracy of the radial basis surrogate model, the smaller the relative maximum absolute error, and the higher the local prediction accuracy of the radial basis surrogate model.

[0025] Furthermore, the packing layout form parameters and water distribution structure parameters corresponding to the lowest outlet water temperature of the natural draft cooling tower in Step S6 are as follows:

[0026] Step S61: The initial population position , the maximum number of iterations and the linearly decreasing parameter c of the locust optimization algorithm; is the d-th dimension of the i-th locust, and D is the search space dimension;

[0027] Step S62: Calculate the fitness of each individual in the initial population position of the locust optimization algorithm, and obtain the position of the current best fitness individual;

[0028] Step S63: Update the linearly decreasing parameter c through the formula ; where c max is the maximum value of the linearly decreasing parameter c, c min is the minimum value of the linearly decreasing parameter c, and N iter is the current iteration number;

[0029] Step S64: Update the position of the current best fitness individual, and calculate the position of the updated current best fitness individual and compare it with the position of the historical best fitness individual. When the position of the updated current best fitness individual is better than the position of the historical best fitness individual, update it. When the position of the updated current best fitness individual is not better than the position of the historical best fitness individual, do not update it. The position update formula of the current best fitness individual is expressed as:

[0030] (2);

[0031] In the formula, is the d-th dimension of the i-th locust at the N-th iter iteration, is the d-th dimension of the j-th locust at the N-th iter iteration, N is the population size of the locusts, , ub d is the upper boundary of the locust position in the d-th dimension, lb d is the lower boundary of the locust position in the d-th dimension, d ij (N iter ) is the distance between the i-th locust and the j-th locust in the locust population at the N-th iter iteration, is the d-th dimension of the optimal locust position; represents the activation function; , f is the attraction intensity between locusts; l is the attraction range between locusts;

[0032] Step S65: Judge whether the current iteration number N iter in the position of the updated current best fitness individual reaches the maximum iteration number N iter,max . When it does not reach the maximum iteration number N iter,max , return to Step S62 to re-obtain the position of the current best fitness individual. When it reaches the maximum iteration number N iter,maxIf so, the optimization ends, and the position of the currently best fitness individual after calculation update is the packing layout form parameter and water distribution structure parameter corresponding to the lowest outlet water temperature of the natural draft cooling tower.

[0033] Compared with the existing technologies, the present invention has the following beneficial effects:

[0034] (1) The present invention can realize the collaborative optimization of the packing layout form and water distribution structure of the natural draft cooling tower, obtain the packing layout form parameter and water distribution structure parameter corresponding to the lowest outlet water temperature of the natural draft cooling tower, overcome the shortcoming that the existing technologies cannot realize the optimal design of the packing layout form and water distribution structure, and minimize the outlet water temperature of the natural draft cooling tower to achieve the purpose of energy conservation and consumption reduction of thermal power units.

[0035] (2) Compared with the traditional thermal performance optimization method of natural draft cooling towers, the thermal performance collaborative optimization method of natural draft cooling towers proposed by the present invention has a significant reduction in the calculation amount of the CFD simulation model, making it more convenient for practical engineering applications. Description of the Drawings

[0036] Figure 1 It is a flow chart of a thermal performance collaborative optimization method for a natural draft cooling tower of the present invention. Detailed Embodiments

[0037] As Figure 1 shown, the present invention provides a technical solution: a thermal performance collaborative optimization method for a natural draft cooling tower, including the following steps:

[0038] Step S1: Test the thermal performance and resistance performance of the packing of the natural draft cooling tower to obtain the thermal performance, resistance performance, structural parameters and working parameters of the natural draft cooling tower.

[0039] Step S2: Based on the thermal performance and resistance performance of the packing and the structural parameters and working parameters of the natural draft cooling tower, parametrically establish the three-dimensional model, grid model and CFD simulation model of the natural draft cooling tower, extract the packing of the natural draft cooling tower through the three-dimensional model, grid model and CFD simulation model of the natural draft cooling tower, and obtain the packing layout form parameter and water distribution structure parameter of the natural draft cooling tower.

[0040] Step S3: Based on the packing layout form parameter and water distribution structure parameter of the natural draft cooling tower, use the multi-dimensional Latin hypercube experimental design to form a sampling space under the given constraint conditions, generate sample points from the sampling space, then combine the locust optimization algorithm to iteratively optimize the sample points to obtain the optimized sample points, and use the maximum minimum distance evaluation criterion to perform a uniformity test on the optimized sample points, and regenerate the optimized sample points that do not meet the evaluation criterion until the best uniformity sample points are obtained. ; Through the CFD simulation model, for the optimal uniformity sample points carry out simulation calculations to obtain the outlet water temperature of the natural draft cooling tower at the optimal uniformity sample points ; represents the k-th optimal uniformity sample point; represents the outlet water temperature of the natural draft cooling tower at the k-th optimal uniformity sample point;

[0041] Step S4: Based on the outlet water temperature of the natural draft cooling tower at the optimal uniformity sample points construct a radial basis surrogate model, and establish the mapping relationship between the packing layout parameters, water distribution structure parameters of the natural draft cooling tower and the outlet water temperature of the natural draft cooling tower through the radial basis surrogate model;

[0042] Step S5: Evaluate the prediction accuracy of the constructed radial basis surrogate model. When the prediction accuracy evaluation meets the requirements, go to Step S6; when the prediction accuracy evaluation does not meet the requirements, go to Step S3, resample the optimal uniformity sample points, and reconstruct the radial basis surrogate model until the radial basis surrogate model meets the prediction accuracy evaluation requirements;

[0043] Step S6: Use the grasshopper optimization algorithm to optimize and solve the mapping relationship between the packing layout parameters, water distribution structure parameters of the natural draft cooling tower and the outlet water temperature of the natural draft cooling tower in the radial basis surrogate model after the prediction accuracy evaluation, and obtain the packing layout form parameters and water distribution structure parameters corresponding to the lowest outlet water temperature of the natural draft cooling tower.

[0044] Among them, the thermal performance of the packing in Step S1 is , and the resistance performance is , represents the mass transfer coefficient per unit volume of the packing; Δp represents the pressure drop of the packing; α represents the constant coefficient of the thermal performance; β is the exponent of the ventilation density g a in the thermal performance; γ represents the exponent of the water spraying density q q in the thermal performance; A 0 is the constant coefficient of the resistance performance; M represents the exponent of v z in the resistance performance; g a represents the ventilation density; q q is the water spraying density; ρ a is the air density; v z is the air velocity in the vertical direction; g represents the acceleration due to gravity.

[0045] Among them, the structure parameters of the natural draft cooling tower in Step S1 are the geometric dimensions of the tower barrel of the natural draft cooling tower, the elevation of the packing layer, the elevation of the spraying device, and the thickness of the packing layer; the working parameters are the moisture content A of the ambient air, the dry bulb temperature T g , the circulating cooling water flow rate Q w, the inlet temperature T of the circulating cooling water in , the atmospheric pressure P a , the water spraying density q w .

[0046] Among them, the packing layout form parameters in step S2 specifically include: the number of packing partitions n of the natural draft cooling tower f , the outer circle radius of the partitioned packing of the natural draft cooling tower , the thickness of the partitioned packing of the natural draft cooling tower . The constraint conditions of the packing layout form parameters of the natural draft cooling tower are respectively: , , ; represents the outer circle radius of the first partition; represents the outer circle radius of the second partition; represents the outer circle radius of the third partition; represents the outer circle radius of the (n - 1)th partition; represents the outer circle radius of the nth partition, that is, the radial radius of the entire packing layer, which is determined by the tower barrel structure of the cooling tower; represents the thickness of the partitioned packing of the nth natural draft cooling tower.

[0047] Among them, the water distribution structure parameters in step S2 specifically include: the number of partitions n of the water distribution surface of the natural draft cooling tower w , the water spraying density of the partitioned water distribution of the natural draft cooling tower ; the number of partitions n of the water distribution surface of the natural draft cooling tower w and the outer circle radius of the partitioned packing of the natural draft cooling tower are consistent with the number of packing partitions n of the natural draft cooling tower f . The constraint condition of the water spraying density of the partitioned water distribution of the natural draft cooling tower is ; are respectively the areas of the th partitioned water distribution surfaces; represents the water spraying density of the partitioned water distribution of the nth natural draft cooling tower.

[0048] Among them, in step S3, obtaining the optimal uniformity sample points, the specific process is as follows:

[0049] Step S31: Based on the packing layout form parameters and water distribution structure parameters of the natural draft cooling tower, use the multi-dimensional Latin hypercube experimental design to construct a sampling space under the given constraint conditions, generate sample points from the sampling space, and mark the generated sample points as the initial population;

[0050] Step S32: Use the locust optimization algorithm to perform iterative optimization on the initial population to obtain the optimized sample points;

[0051] Step S33: Check the sample points on the boundary of the multi-dimensional Latin hypercube experimental design interval to prevent the generated sample points from going out of bounds;

[0052] Step S34: Conduct a uniformity test on the optimized sample points using the maximum-minimum distance evaluation criterion, and regenerate the optimized sample points that do not meet the evaluation criterion until the best uniformity sample points are obtained. 。

[0053] Among them, the objective function expression of the radial basis surrogate model in step S4 is:

[0054] (1);

[0055] In the formula, represents the outlet water temperature corresponding to the best uniformity sample point x; i represents the i-th best uniformity sample point; represents the weight coefficient; represents the basis function; represents the Euclidean distance between two points; represents the center point of the i-th basis function.

[0056] Among them, the prediction accuracy evaluation of the constructed radial basis surrogate model in step S5 includes: the multiple correlation coefficient , the relative mean absolute error , the relative maximum absolute error . The closer the multiple correlation coefficient R 2 is to 1, the higher the prediction accuracy of the radial basis surrogate model, and the smaller the relative mean absolute error RAAE and the relative maximum absolute error RMAE; the higher the prediction accuracy of the radial basis surrogate model, the smaller the relative maximum absolute error RMAE, and the higher the local prediction accuracy of the radial basis surrogate model; represents the actual value of the outlet water temperature of the i-th optimal sample point; represents the predicted value of the outlet water temperature of the i-th optimal sample point; represents the average value of the actual values of the outlet water temperatures of all optimal sample points; represents all the outlet water temperatures of the optimal sample points and the actual value of the outlet water temperature of the optimal sample point The maximum value of the absolute error between them.

[0057] Among them, the packing layout form parameters and water distribution structure parameters corresponding to the lowest outlet water temperature of the natural draft cooling tower in step S6 are as follows:

[0058] Step S61: Initialize the population position of the grasshopper optimization algorithm , the maximum number of iterations and the linearly decreasing parameter c; is the d-th dimension of the i-th locust, and D is the dimension of the search space;

[0059] Step S62: Calculate the fitness of each individual in the initial population position of the locust optimization algorithm, and obtain the position of the current best fitness individual;

[0060] Step S63: Update the linearly decreasing parameter c through the formula ; c max is the maximum value of the linearly decreasing parameter c, c min is the minimum value of the linearly decreasing parameter c, N iter is the current iteration number;

[0061] Step S64: Update the position of the current best fitness individual, and calculate the position of the updated current best fitness individual and compare it with the position of the historical best fitness individual. When the position of the updated current best fitness individual is better than the position of the historical best fitness individual, update it. When the position of the updated current best fitness individual is not better than the position of the historical best fitness individual, do not update it. The position update formula of the current best fitness individual is expressed as:

[0062] (2);

[0063] In the formula, is the d-th dimension of the i-th locust at the N iter -th iteration, is the d-th dimension of the j-th locust at the N iter -th iteration, N is the population size of the locusts, , ub d is the upper boundary of the locust position in the d-th dimension, lb d is the lower boundary of the locust position in the d-th dimension, d ij (N iter ) is the distance between the i-th locust and the j-th locust in the locust population at the N iter -th iteration, is the d-th dimension of the optimal locust position; represents the activation function; , f is the attraction intensity between locusts; l is the attraction range between locusts;

[0064] Step S65: Judge whether the current iteration number N iter in the position of the updated current best fitness individual reaches the maximum iteration number N iter,max . When it does not reach the maximum iteration number N iter,max , return to Step S62 to re-obtain the position of the current best fitness individual. When it reaches the maximum iteration number N iter,max, the optimization ends, and the position of the currently best fitness individual after calculation update is the form parameters of the packing layout and the distribution structure parameters corresponding to the lowest outlet water temperature of the natural draft cooling tower.

[0065] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for collaborative optimization of thermal performance of a natural ventilation cooling tower, characterized in that: The following steps are involved: Step S1: Performing thermal performance and resistance performance tests on the filler of the natural ventilation cooling tower to obtain the thermal performance, resistance performance of the filler and the structural parameters and working parameters of the natural ventilation cooling tower; Step S2: based on the thermal performance and resistance performance of the filler and the structural parameters and working parameters of the natural ventilation cooling tower, a three-dimensional model, a grid model and a CFD simulation model of the natural ventilation cooling tower are parameterized and established; the filler of the natural ventilation cooling tower is extracted through the three-dimensional model, the grid model and the CFD simulation model of the natural ventilation cooling tower, and the filler arrangement form parameters and water distribution structure parameters of the natural ventilation cooling tower are obtained; Step S3: Based on the filler arrangement parameters and water distribution structure parameters of the natural ventilation cooling tower, a sampling space is constructed under given constraints using a multidimensional Latin hypercube experimental design, sample points are generated from the sampling space, and then the sample points are iteratively optimized using the locust optimization algorithm to obtain the optimal sample points. The optimal sample points are tested for uniformity using the maximum and minimum distance evaluation criteria, and the optimal sample points that do not meet the evaluation criteria are regenerated until the optimal uniformity sample points are obtained; The optimal uniformity sample point is simulated and calculated by the CFD simulation model to obtain the outlet water temperature of the natural ventilation cooling tower at the optimal uniformity sample point; Step S4: constructing a radial basis function surrogate model based on the outlet water temperature of the natural ventilation cooling tower at the optimal uniformity sample point, and establishing a mapping relationship between the packing arrangement parameters and the water distribution structure parameters of the natural ventilation cooling tower and the outlet water temperature of the natural ventilation cooling tower through the radial basis function surrogate model; Step S5: Evaluate the prediction accuracy of the constructed radial basis proxy model. When the prediction accuracy evaluation meets the requirements, go to step S6; when the prediction accuracy evaluation does not meet the requirements, go to step S3, resample the optimal uniformity sample points, and reconstruct the radial basis proxy model until the radial basis proxy model meets the prediction accuracy evaluation requirements; Step S6: Use the locust optimization algorithm to optimize and solve the mapping relationship between the natural ventilation cooling tower packing layout parameters, water distribution structure parameters and the natural ventilation cooling tower outlet water temperature in the radial basis proxy model after the prediction accuracy evaluation, and obtain the packing layout form parameters and water distribution structure parameters corresponding to the minimum outlet water temperature of the natural ventilation cooling tower.

2. A natural ventilation cooling tower thermal performance collaborative optimization method according to claim 1, characterized in that: The thermal properties of the filler in step S1 are , the resistance performance is , represents the mass transfer coefficient per unit volume of packing; Δp represents the packing pressure drop; α represents the constant coefficient of thermal performance; β is the ventilation density g in thermal performance a The index of γ represents the water density q in thermal performance. q The index of resistance performance; A0 is the constant coefficient of resistance performance; M represents the v z The index of g a represents ventilation density; q q is the water density; a is the air density; v z is the air velocity in the vertical direction; g is the acceleration due to gravity.

3. A natural ventilation cooling tower thermal performance collaborative optimization method according to claim 2, characterized in that: The structural parameters of the natural ventilation cooling tower in step S1 are the geometric dimensions of the natural ventilation cooling tower, the height of the packing layer, the height of the spray device, and the thickness of the packing layer; the working parameters are the ambient air humidity A, the dry bulb temperature T g , circulating cooling water flow Q w , circulating cooling water inlet temperature T in , atmospheric pressure P a , water density q w .

4. A natural ventilation cooling tower thermal performance collaborative optimization method according to claim 3, characterized in that: The packing arrangement parameters in step S2 specifically include: the number of packing partitions n of the natural ventilation cooling tower f , the outer radius of the partition filler of the natural ventilation cooling tower , Partition fill thickness for natural draft cooling towers , the constraints of the filler arrangement parameters of the natural draft cooling tower are: , , ; Indicates the outer circle radius of the first partition; Indicates the outer circle radius of the second partition; Indicates the outer circle radius of the third partition; Indicates the outer circle radius of the n-1th partition; Indicates the outer circle radius of the nth partition; Represents the partition fill thickness of the nth natural draft cooling tower.

5. A natural ventilation cooling tower thermal performance collaborative optimization method according to claim 4, characterized in that: The water distribution structure parameters in step S2 specifically include: the number of partitions n of the water distribution surface of the natural ventilation cooling tower w , water density of zoned water distribution in natural ventilation cooling tower ;Number of partitions n of the water distribution surface of the natural draft cooling tower w The outer radius of the partition fill of the natural ventilation cooling tower and the number of fill partitions n of the natural ventilation cooling tower f Keeping the same, the water density constraint of the zoned water distribution of the natural ventilation cooling tower is: ; Respectively The area of ​​the water distribution surface in each zone; It represents the watering density of the zoned water distribution of the nth natural ventilation cooling tower.

6. A natural ventilation cooling tower thermal performance collaborative optimization method according to claim 5, characterized in that: In step S3, the optimal uniformity sample point is obtained, and the specific process is as follows: Step S31: Based on the filler arrangement parameters and water distribution structure parameters of the natural ventilation cooling tower, a sampling space is constructed under given constraints using a multidimensional Latin hypercube experimental design, sample points are generated from the sampling space, and the generated sample points are marked as an initial population; Step S32: using the locust optimization algorithm to iteratively optimize the initial population to obtain optimal sample points; Step S33: Checking sample points on the boundary of the multidimensional Latin hypercube experimental design interval to prevent the generated sample points from crossing the boundary; Step S34: Perform a uniformity test on the optimal sample points by using the maximum and minimum distance evaluation criteria, and regenerate the optimal sample points that do not meet the evaluation criteria until the optimal uniformity sample points are obtained. ; represents the kth best uniformity sample point.

7. A natural ventilation cooling tower thermal performance collaborative optimization method according to claim 6, characterized in that: The objective function of the radial basis proxy model in step S4 is expressed as: （1）； In the formula, represents the water temperature out of the tower corresponding to the optimal uniformity sample point x; i represents the i-th optimal uniformity sample point; represents the weight coefficient; represents basis functions; Represents the Euclidean distance between two points; Represents the center point of the i-th basis function.

8. A natural ventilation cooling tower thermal performance collaborative optimization method according to claim 7, characterized in that: In step S5, the prediction accuracy evaluation of the constructed radial basis proxy model includes: complex correlation coefficient, relative mean absolute error, relative maximum absolute error. The closer the complex correlation coefficient is to 1, the higher the prediction accuracy of the radial basis proxy model, and the smaller the relative mean absolute error and relative maximum absolute error are; the higher the prediction accuracy of the radial basis proxy model, the smaller the relative maximum absolute error is, and the higher the local prediction accuracy of the radial basis proxy model is.

9. A natural ventilation cooling tower thermal performance collaborative optimization method according to claim 8, characterized in that: The packing arrangement parameters and water distribution structure parameters corresponding to the lowest outlet water temperature of the natural ventilation cooling tower in step S6 are as follows: Step S61: Initialization population position of locust optimization algorithm , maximum number of iterations and linearly decreasing parameter c; is the dth dimension of the i-th locust, and D is the dimension of the search space; Step S62: Calculate the fitness of each individual in the initial population position of the locust optimization algorithm, and obtain the position of the individual with the best current fitness; Step S63: By formula Update the linear reduction parameter c; c max The maximum value of the linearly decreasing parameter c, c min is the minimum value of the linearly decreasing parameter c, N iter is the current iteration number; Step S64: Update the position of the current best fitness individual, and calculate the updated position of the current best fitness individual, and compare it with the position of the historical best fitness individual. When the calculated updated position of the current best fitness individual is better than the position of the historical best fitness individual, update it. When the calculated updated position of the current best fitness individual is not better than the position of the historical best fitness individual, do not update it. The update formula of the position of the current best fitness individual is as follows: （2）； In the formula, To iterate to the Nth iter The dth dimension of the ith locust is, To iterate to the Nth iter The dth dimension of the jth locust is N, which is the population size of the locust. ,ub d is the upper boundary of the locust position in the dth dimension, lb d is the lower boundary of the locust position in the dth dimension, d ij (N iter ) is the locust population at the Nth iter The distance between the i-th locust and the j-th locust at the iteration, is the dth dimension of the optimal position of the locust; represents the activation function; , f is the attraction strength between locusts; l is the attraction range between locusts; Step S65: Calculate the current iteration number N in the position of the individual with the best fitness after updating iter Determine whether the maximum number of iterations N has been reached iter,max , when the maximum number of iterations N is not reached iter,max , then return to step S62, re-acquire the position of the current best fitness individual, and when the maximum number of iterations N is reached iter,max , the optimization ends, and the position of the individual with the best fitness after calculation and update is the filler arrangement form parameters and water distribution structure parameters corresponding to the lowest outlet water temperature of the natural ventilation cooling tower.

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