Collaborative optimization method for thermal performance of natural draft cooling tower

Abstract

The invention discloses a natural ventilation cooling tower thermal performance collaborative optimization method, which comprises the following steps of carrying out performance test on natural ventilation cooling tower filler to obtain performance parameters of a natural ventilation cooling tower, establishing a model based on the performance parameters of the natural ventilation cooling tower, and carrying out collaborative optimization on the thermal performance of the natural ventilation cooling tower. Extracting filler arrangement form parameters and water distribution structure parameters of the natural draft cooling tower, obtaining optimal uniformity sample points of the parameters by using an optimization algorithm and experimental design, obtaining the tower outlet water temperature of the natural draft cooling tower at the optimal uniformity sample points through CFD calculation, constructing a radial basis agent model based on data obtained through CFD calculation, and calculating the tower outlet water temperature of the natural draft cooling tower according to the radial basis agent model. And an optimization algorithm is used for carrying out optimization solution on the radial basis agent model, and filler arrangement form parameters and water distribution structure parameters corresponding to the lowest tower outlet water temperature of the natural ventilation cooling tower are obtained. By obtaining the optimal design of the filler arrangement and the water distribution structure, the outlet water temperature of the natural ventilation cooling tower is reduced to the maximum extent, and the purposes of energy conservation and consumption reduction of a thermal power generating unit are achieved.

Description

Technical Field

[0001] The invention relates to the technical field of medium and large-sized natural ventilation cooling towers, and is a method for collaboratively optimizing thermal performance of natural ventilation cooling towers. Background Art

[0002] The natural draft cooling tower is one of the main equipment in the cold end system of a thermal power plant. The decline in its cooling efficiency will cause the condenser vacuum to deteriorate, which will in turn increase the coal consumption of the thermal power unit, affecting the economy and safety of the thermal power unit. According to relevant literature data, for every 1°C increase in the outlet water temperature of the natural draft cooling tower of a 300MW thermal power unit, 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 the natural draft cooling tower is very important for energy saving and consumption reduction.

[0003] At present, the main measure to improve the thermal performance of natural ventilation cooling towers is to adjust the packing arrangement or water distribution structure. Most of the existing technologies optimize the cooling performance of cooling towers from the packing arrangement or water distribution structure alone. However, in actual projects, the influence of packing arrangement and water distribution structure on cooling tower performance is often interrelated. If the water distribution structure or packing arrangement is optimized alone, it is difficult to achieve the best optimization effect for the thermal performance of cooling towers. In addition, the cooling performance optimization technology of cooling towers now mostly adopts the method of repeated adjustment and selection, which has a huge workload and cannot accurately describe the relationship between packing arrangement parameters, water distribution structure parameters and cooling performance of natural ventilation cooling towers, making it difficult to achieve the optimal design of packing arrangement and water distribution structure of natural ventilation cooling towers. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method for collaborative optimization of the thermal performance of a natural ventilation cooling tower, which can quickly and effectively realize the collaborative optimization of the packing arrangement and water distribution structure of the natural ventilation cooling tower, obtain the optimal collaborative combination of the packing arrangement and the water distribution structure, and solve the problem of energy saving and consumption reduction of the cooling tower.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for collaboratively optimizing the thermal performance of a natural ventilation cooling tower, comprising the following steps: 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 in combination with the locust optimization algorithm to obtain the optimal sample points, and the maximum and minimum distance evaluation criteria are used to perform a uniformity test on the optimal sample points, 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 points are simulated and calculated using a CFD simulation model to obtain the natural ventilation cooling tower outlet water temperature at the optimal uniformity sample points; 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.

[0006] Further, 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 zis the air velocity in the vertical direction; g is the acceleration due to gravity.

[0007] Furthermore, the structural parameters of the natural ventilation cooling tower in step S1 are the geometric dimensions of the natural ventilation cooling tower, the elevation of the packing layer, the elevation 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 .

[0008] Further, 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.

[0009] Furthermore, 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.

[0010] Further, 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.

[0011] Furthermore, 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.

[0012] Furthermore, 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 the 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.

[0013] Further, 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 specifically 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; cmax 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.

[0014] Compared with the existing technology, the present invention has the following beneficial effects: (1) The present invention can achieve coordinated optimization of the packing arrangement form and the water distribution structure of a natural ventilation cooling tower, and obtain the packing arrangement form parameters and the water distribution structure parameters corresponding to the minimum outlet water temperature of the natural ventilation cooling tower, thereby overcoming the disadvantage that the prior art cannot achieve the optimal design of the packing arrangement form and the water distribution structure, and reducing the outlet water temperature of the natural ventilation cooling tower to the greatest extent, thereby achieving the purpose of energy saving and consumption reduction of thermal power units.

[0015] (2) Compared with the traditional natural ventilation cooling tower thermal performance optimization method, the natural ventilation cooling tower thermal performance collaborative optimization method proposed in the present invention has a greatly reduced amount of CFD simulation model calculation and is more convenient for practical engineering applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The present invention is a flow chart of a method for collaboratively optimizing the thermal performance of a natural ventilation cooling tower. DETAILED DESCRIPTION

[0017] like Figure 1 As shown, the present invention provides a technical solution: a method for collaboratively optimizing the thermal performance of a natural ventilation cooling tower, comprising the following steps: 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 maximum and minimum distance evaluation criteria are used to perform a uniformity test on the optimal sample points, and the optimal sample points that do not meet the evaluation criteria are regenerated until the optimal uniformity sample points are obtained. ; The best uniformity sample point is obtained through CFD simulation model Perform simulation calculations to obtain the optimal uniformity sample point natural ventilation cooling tower outlet water temperature ; represents the kth best uniformity sample point; represents the water temperature out of the natural ventilation cooling tower at the kth optimal uniformity sample point; Step S4: Water temperature out of natural ventilation cooling tower based on optimal uniformity sample point A radial basis function surrogate model is constructed, and the mapping relationship between the filler arrangement parameters, water distribution structure parameters and the outlet water temperature of the natural ventilation cooling tower is established 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.

[0018] Among them, the thermal performance of the filler in step S1 is , 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.

[0019] Among them, 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 .

[0020] The filler arrangement parameters in step S2 specifically include: the number of filler 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; It represents the outer radius of the nth partition, that is, the radial radius of the entire packing layer, which is determined by the cooling tower structure; Represents the partition fill thickness of the nth natural draft cooling tower.

[0021] 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.

[0022] Among them, the best uniformity sample point is obtained in step S3, and the specific process is: 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. . 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.

[0023] Among them, the prediction accuracy evaluation of the constructed radial basis proxy model in step S5 includes: the complex correlation coefficient , relative mean absolute error , relative maximum absolute error , multiple correlation coefficient R 2 The closer it is to 1, the higher the prediction accuracy of the radial basis proxy model, and the smaller the relative average absolute error RAAE and the relative maximum absolute error RMAE; the higher the prediction accuracy of the radial basis proxy model, the smaller the relative maximum absolute error RMAE, and the higher the local prediction accuracy of the radial basis proxy model; represents the actual value of the water temperature at the tower at the i-th optimal sample point; represents the predicted value of the water temperature out of the tower at the i-th optimal sample point; It represents the average value of the actual values ​​of the water temperature out of the tower at all the optimal sample points; Indicates the water temperature out of the tower at all optimal sample points The actual value of the water temperature at the optimal sample point The maximum absolute error between them.

[0024] Among them, 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 specifically 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;

[0025] 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;

[0026] 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:

[0027] (2);

[0028] 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;

[0029] 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.

[0030] 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 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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