Heat exchanger performance evaluation method and system

Abstract

The invention provides a heat exchanger performance evaluation method and system.The method comprises the steps that a first parameter combination corresponding to a to-be-evaluated air-cooled heat exchanger is obtained through a processor, the first parameter combination comprises design parameters and environment parameters, and the design parameters are used for reflecting heat exchange design of the air-cooled heat exchanger; the environmental parameters are used for reflecting the operating environment of the air-cooled heat exchanger; the first parameter combination is input into a trained evaluation model, first evaluation information output by the evaluation model is obtained, and the first evaluation information comprises flow field distribution data and heat exchange performance; the evaluation model is used for analyzing the flow field distribution condition and the heat exchange performance of the air-cooled heat exchanger corresponding to the design parameters when the air-cooled heat exchanger operates in the operation environment corresponding to the environment parameters; and performing visualization processing on the first evaluation information, and displaying the first evaluation information after the visualization processing through a set user interaction interface, the visualization processing being at least used for performing color mapping on the flow field distribution data. The performance evaluation efficiency of the air-cooled heat exchanger can be improved.

Description

Technical Field

[0001] The present application relates to the field of heat exchange technology, and in particular to a heat exchanger performance evaluation method and system. Background Art

[0002] Air-cooled heat exchangers are devices that exchange heat through air flow. Due to their high efficiency, energy conservation, and environmental friendliness, they are widely used in applications such as air conditioning, industrial cooling systems, and energy storage systems. To reduce costs and improve performance, the heat transfer performance of air-cooled heat exchangers is often evaluated and optimized.

[0003] Currently, the heat transfer performance of air-cooled heat exchangers is usually evaluated through actual prototype testing or simulation to optimize the heat exchanger design. However, the production and debugging of prototypes takes a long time, while simulation requires high user expertise and computing resources, and also has the problem of a long time period, which limits the efficiency of heat transfer performance evaluation. Summary of the Invention

[0004] The present application provides a heat exchanger performance evaluation method and system to improve the efficiency of heat exchanger performance evaluation.

[0005] In a first aspect, some embodiments of the present application provide a heat exchanger performance evaluation method, comprising:

[0006] Acquiring, through a processor, a first parameter combination corresponding to the air-cooled heat exchanger to be evaluated, the first parameter combination including design parameters and environmental parameters, the design parameters being used to reflect the heat exchange design of the air-cooled heat exchanger, and the environmental parameters being used to reflect the operating environment of the air-cooled heat exchanger;

[0007] Inputting the first parameter combination into a trained evaluation model to obtain first evaluation information output by the evaluation model, wherein the first evaluation information includes flow field distribution data and heat exchange performance, and the evaluation model is used to analyze the flow field distribution and heat exchange performance of the air-cooled heat exchanger corresponding to the design parameters when operating under the operating environment corresponding to the environmental parameters;

[0008] Visualization processing is performed on the first evaluation information, and the first evaluation information after visualization processing is displayed through a set user interaction interface, and the visualization processing is at least used to perform color mapping on the flow field distribution data.

[0009] In the embodiment of the present application, since the trained evaluation model can learn the complex relationship between the heat exchange design, operating environment, flow field distribution, and heat exchange performance of an air-cooled heat exchanger during training, the trained evaluation model can quickly and accurately analyze the flow field distribution and heat exchange performance of the air-cooled heat exchanger to be evaluated under the operating environment corresponding to the environmental parameters in the input first parameter combination, thereby obtaining the required first evaluation information. This eliminates the need for professional simulation or prototype production for actual testing, thereby reducing the cost of air-cooled heat exchanger performance evaluation and the requirements for user expertise and computing resources, and effectively improving the efficiency of air-cooled heat exchanger performance evaluation. Furthermore, after obtaining the first evaluation information including flow field distribution data and heat exchange performance, it is visualized and then displayed through a predetermined user interaction interface. The visualization process at least performs color mapping on the flow field distribution data, so that changes in the flow field distribution can be represented by color differences, highlighting the flow field distribution characteristics, and thus helping users more intuitively understand the performance of the air-cooled heat exchanger corresponding to the first parameter combination and optimize its performance.

[0010] In a possible implementation of the first aspect, the environmental parameters are used to reflect a windy and sandy operating environment of the air-cooled heat exchanger, and inputting the first parameter combination into a trained evaluation model to obtain first evaluation information output by the evaluation model includes:

[0011] The evaluation model is used to analyze the flow field distribution and heat exchange performance of the air-cooled heat exchanger corresponding to the design parameters in the first parameter combination when operating in a windy and sandy operating environment corresponding to the environmental parameters.

[0012] In the above technical solution, the influence of sand and dust in the operating environment is fully taken into consideration, and the heat exchange performance is evaluated in combination with environmental parameters that can reflect the windy and sandy operating environment of the air-cooled heat exchanger. This enables the evaluation model to more accurately analyze the influence of the joint movement of sand and dust and cold air on the internal flow field and heat exchange when the air-cooled heat exchanger is operating in a windy and sandy operating environment, effectively improving the accuracy of the determined flow field distribution data and heat exchange performance, that is, improving the accuracy of the performance evaluation of the air-cooled heat exchanger.

[0013] In a possible implementation of the first aspect, before inputting the first parameter combination into the trained evaluation model to obtain first evaluation information output by the evaluation model, the method further includes:

[0014] Performing a simulation based on a heat exchanger simulation model and a set second parameter combination to obtain second evaluation information corresponding to the second parameter combination, wherein the heat exchanger simulation model is constructed based on the heat transfer characteristics of the air-cooled heat exchanger and is used to simulate the flow field distribution and heat transfer performance of the air-cooled heat exchanger corresponding to the design parameters when operating under the operating environment corresponding to the environmental parameters;

[0015] Constructing a training sample based on the second parameter combination and the corresponding second evaluation information;

[0016] The constructed evaluation model is trained according to the training samples to obtain a trained evaluation model.

[0017] In the above technical solution, since the heat exchanger simulation model is constructed based on the heat transfer characteristics of the air-cooled heat exchanger, it can accurately simulate the operating status of the corresponding air-cooled heat exchanger when operating in the operating environment based on the heat transfer principle and the input second parameter combination, and analyze to obtain more accurate second evaluation information. Therefore, simulation based on the heat exchanger simulation model and each second parameter combination can quickly and accurately obtain a large amount of high-quality data to construct training samples, and training samples covering various working conditions can be obtained by adjusting the second parameter combination, effectively improving the diversity and flexibility of the obtained training samples, and there is no need to collect actual operating data or obtain data in actual methods to construct training samples, effectively reducing the difficulty and cost of obtaining training samples.

[0018] In a possible implementation of the first aspect, before performing simulation based on the heat exchanger simulation model and the set second parameter combination to obtain second evaluation information corresponding to the second parameter combination, the method further includes:

[0019] Determining a multiphase flow model for simulating and analyzing the flow and heat transfer of air containing sand and dust inside an air-cooled heat exchanger in a sandstorm operating environment;

[0020] The heat exchanger simulation model is determined based on the multiphase flow model, a mathematical model corresponding to the multiphase flow model, and a set flow field grid.

[0021] In the above technical solution, the flow and heat transfer process of air containing wind and sand inside the air-cooled heat exchanger in a windy and sandy operating environment is simulated and analyzed by a multiphase flow model, and the multiphase flow inside the air-cooled heat exchanger in a dusty or windy and sandy environment is fully considered, which helps to improve the accuracy and reliability of the constructed heat exchanger simulation model.

[0022] In a possible implementation of the first aspect, determining the multiphase flow model includes:

[0023] The multiphase flow model is determined according to a continuous air phase and a continuous dust phase.

[0024] In the above technical solution, air and dust can be regarded as a whole (i.e., a single continuous medium), that is, a multiphase flow model is constructed through a continuous air phase and a continuous dust phase, so that the multiphase flow model can more accurately simulate the phase interaction and coupling relationship between air and dust, reduce errors caused by discretization, reduce the complexity of the multiphase flow model, and further improve the reliability and accuracy of the multiphase flow model.

[0025] In a possible implementation of the first aspect, before determining the heat exchanger simulation model based on the multiphase flow model, the mathematical model corresponding to the multiphase flow model, and the set flow field grid, the method further includes:

[0026] Meshing is performed on the flow fields of a first region and a second region set in the air-cooled heat exchanger to obtain the flow field mesh, wherein the density of the mesh corresponding to the first region and the density of the mesh corresponding to the second region are different.

[0027] In the above technical solution, since there are usually certain differences in the flow and heat transfer characteristics of different regions in the air-cooled heat exchanger, such as the physical characteristics of the high-gradient region are more complex, while the physical characteristics of the low-gradient region are usually simpler, therefore, in order to ensure the accuracy of the heat exchanger simulation model while reducing the calculation cost and complexity as much as possible, different regions in the air-cooled heat exchanger can be meshed with different densities, so that the regions can be meshed with corresponding densities according to the actual conditions of each region, and a balance between the calculation accuracy and efficiency of the heat exchanger simulation model can be achieved through targeted meshing.

[0028] In a possible implementation of the first aspect, constructing a training sample based on the second parameter combination and the corresponding second evaluation information includes:

[0029] performing feature dimensionality reduction processing on the flow field distribution data in the second evaluation information to obtain reduced-dimensional flow field distribution data;

[0030] The training sample is determined based on the second parameter combination, and the label of the training sample is determined based on the heat exchange performance in the second evaluation information and the flow field distribution data after dimensionality reduction.

[0031] In the above technical solution, the dimension of the flow field distribution data is reduced by feature dimensionality reduction processing, and at the same time, redundant information and noise can be effectively removed, and the dimension of the flow field distribution data after dimensionality reduction can be effectively reduced, thereby reducing the amount of calculation in the evaluation model training process and improving training efficiency.

[0032] In a possible implementation of the first aspect, after the processor acquires the first parameter combination corresponding to the air-cooled heat exchanger to be evaluated, the method further includes:

[0033] determining a target constraint condition based on the first parameter combination;

[0034] Based on the target constraints, the set optimization objectives, and the evaluation model, a multi-objective optimization algorithm is used to perform multi-objective optimization to obtain a Pareto optimal solution set, wherein the Pareto optimal solution set includes non-inferior solutions that satisfy the target constraints and the optimization objectives;

[0035] Show the Pareto optimal solution set.

[0036] In the above technical solution, after determining the target constraints and optimization objectives, multi-objective optimization is performed directly based on the trained evaluation model and multi-objective optimization algorithm. The evaluation model can quickly and accurately analyze the evaluation information corresponding to each solution, helping the multi-objective optimization algorithm to effectively compare and select solutions, thereby being able to quickly and accurately determine the required non-inferior solution. Compared with directly using a multi-objective optimization algorithm for multi-objective optimization, it can effectively guarantee the quality and reliability of the obtained solution, and can effectively reduce the amount of calculation and improve the optimization efficiency.

[0037] In a possible implementation of the first aspect, the environmental parameter includes sediment content, the target constraint includes a sediment content fluctuation range, and determining the target constraint based on the first parameter combination includes:

[0038] The sediment content fluctuation range is determined based on the sediment content in the first parameter combination and a target application scenario, where the target application scenario includes an application scenario corresponding to the air-cooled heat exchanger to be evaluated.

[0039] In the above technical solution, when setting the target constraints of the multi-objective optimization problem, the fluctuation range of the sand content is determined in combination with the sand content in the first parameter combination and the application scenario of the air-cooled heat exchanger to be evaluated, and it is used as a constraint condition to ensure that the change of sand content in the multi-objective optimization process will not exceed the sand content fluctuation range, thereby ensuring the reliability of the final optimization strategy in practical applications, and avoiding problems such as unstable performance of the air-cooled heat exchanger due to reasons such as fluctuations in sand content in the target application scenario.

[0040] In a second aspect, some embodiments of the present application further provide a heat exchanger performance evaluation system, the system comprising:

[0041] a parameter acquisition module, configured to acquire, through a processor, a first parameter combination corresponding to the air-cooled heat exchanger to be evaluated, wherein the first parameter combination includes design parameters and environmental parameters, wherein the design parameters are used to reflect the heat exchange design of the air-cooled heat exchanger, and the environmental parameters are used to reflect the operating environment of the air-cooled heat exchanger;

[0042] a model calling module, configured to input the first parameter combination into a trained evaluation model to obtain first evaluation information output by the evaluation model, wherein the first evaluation information includes flow field distribution data and heat exchange performance, and the evaluation model is configured to analyze the flow field distribution and heat exchange performance of the air-cooled heat exchanger corresponding to the design parameters when operating under an operating environment corresponding to the environmental parameters;

[0043] A visualization module is used to perform visualization processing on the first evaluation information and display the first evaluation information after visualization processing through a set user interaction interface, and the visualization processing is at least used to perform color mapping on the flow field distribution data.

[0044] In a third aspect, an embodiment of the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the heat exchanger performance evaluation method described in the first aspect are implemented.

[0045] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the heat exchanger performance evaluation method described in the first aspect are implemented.

[0046] In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product is run on a household appliance, the household appliance executes the heat exchanger performance evaluation method described in the first aspect above.

[0047] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0049] Figure 1 A timing interaction diagram of a heat exchanger performance evaluation method provided in some embodiments of the present application;

[0050] Figure 2 A schematic diagram of flow field distribution data after visualization provided in some embodiments of the present application;

[0051] Figure 3A schematic flow chart of a heat exchanger performance evaluation method provided in some embodiments of the present application;

[0052] Figure 4 A schematic flow chart of a heat exchanger performance evaluation method provided in some embodiments of the present application;

[0053] Figure 5 A schematic flow chart of a heat exchanger performance evaluation method provided in some embodiments of the present application;

[0054] Figure 6 A flowchart of a training evaluation model provided in some embodiments of the present application;

[0055] Figure 7 A flowchart of a training evaluation model provided in some embodiments of the present application;

[0056] Figure 8 A schematic flow chart of a heat exchanger performance evaluation method provided in some embodiments of the present application;

[0057] Figure 9 A schematic structural diagram of a heat exchanger performance evaluation system provided in some embodiments of the present application. DETAILED DESCRIPTION

[0058] The following embodiments are described in detail, with examples illustrated in the accompanying drawings. When the following description refers to the drawings, identical numbers in different figures represent identical or similar elements unless otherwise indicated. The embodiments described in the following embodiments are not intended to represent all possible implementations consistent with the present application. They are merely examples of systems and methods consistent with certain aspects of the present application, as detailed in the claims.

[0059] It should be noted that the brief descriptions of terms in this application are only for the purpose of facilitating the understanding of the embodiments described below, and are not intended to limit the embodiments of this application. Unless otherwise specified, these terms should be understood according to their ordinary and usual meanings.

[0060] In the specification and claims of this application and the accompanying drawings, the terms "first," "second," and the like are used to distinguish similar or similar objects or entities and are not necessarily intended to limit a particular order or precedence, unless otherwise noted. It should be understood that the terms used in this manner are interchangeable under appropriate circumstances.

[0061] The terms "comprise," "include," and "have," and any variations thereof, are intended to cover but not exclude inclusion; for example, a product or device comprising a list of components is not necessarily limited to all the components expressly listed but may include other components not expressly listed or inherent to such product or device.

[0062] The term "module" refers to any known or later developed hardware, software, firmware, artificial intelligence, fuzzy logic, or combination of hardware and / or software code that is capable of performing the functionality associated with that element.

[0063] Figure 1 A timing interaction diagram of a heat exchanger performance evaluation method provided in some embodiments of the present application is shown. The heat exchanger performance evaluation method provided in the embodiments of the present application can be applied to systems such as a heat exchanger performance evaluation system or a heat exchanger design system, and can also be applied to electronic devices such as servers. The embodiments of the present application do not impose specific restrictions on this.

[0064] The above method is detailed as follows:

[0065] S101. Obtain, through a server, a first parameter combination corresponding to an air-cooled heat exchanger to be evaluated, where the first parameter combination includes design parameters and environmental parameters. The design parameters are used to reflect the heat exchange design of the air-cooled heat exchanger, and the environmental parameters are used to reflect the operating environment of the air-cooled heat exchanger.

[0066] It should be understood that the above-mentioned design parameters include but are not limited to parameters affecting heat exchange performance among heat exchanger design parameters such as fin angle, heat exchange surface angle, air inlet volume and refrigerant phase change temperature.

[0067] It should be understood that the above environmental parameters include but are not limited to environmental parameters such as ambient temperature, ambient humidity, air inlet temperature and sand content.

[0068] Optionally, the first parameter combination corresponding to the air-cooled heat exchanger to be evaluated may be input by a user through a user interface or a terminal device, or may be directly obtained from corresponding software such as a design tool through a data interface. Figure 1 The following description will be made by taking an example of obtaining a parameter combination input by a user as the first parameter combination corresponding to the air-cooled heat exchanger to be evaluated.

[0069] In some embodiments, the air-cooled heat exchanger to be evaluated can be an air-cooled heat exchanger in an air-conditioning system or an air-cooled heat exchanger in a temperature-controlled energy storage system. For example, during the development of an air-conditioning system using a heat exchanger design system, a processor can obtain a first parameter combination corresponding to the air-cooled heat exchanger of the air-conditioning system and perform a performance evaluation on the air-cooled heat exchanger to optimize the air-cooled heat exchanger and thereby improve the overall performance and efficiency of the air-conditioning system.

[0070] In the embodiment of the present application, since the design parameters can reflect the heat exchange design of the air-cooled heat exchanger, and the environmental parameters can reflect the operating environment of the air-cooled heat exchanger, the design parameters and environmental parameters corresponding to the air-cooled heat exchanger to be evaluated are obtained as a first parameter combination, and the heat exchange performance of the air-cooled heat exchanger to be evaluated can be analyzed more accurately based on the first parameter combination.

[0071] S102. Input the above-mentioned first parameter combination into the trained evaluation model to obtain the first evaluation information output by the above-mentioned evaluation model. The above-mentioned first evaluation information includes flow field distribution data and heat exchange performance. The above-mentioned evaluation model is used to analyze the flow field distribution status and heat exchange performance of the air-cooled heat exchanger corresponding to the above-mentioned design parameters when operating under the operating environment corresponding to the above-mentioned environmental parameters.

[0072] The flow field distribution data mentioned above generally refers to data that can reflect the flow field distribution status. It should be understood that the flow field distribution data may include but is not limited to distribution data of flow fields such as temperature field, velocity field, pressure field and turbulence field.

[0073] Heat exchange performance includes but is not limited to heat exchange amount, pressure drop (also known as inlet and outlet pressure drop), heat exchange efficiency and heat exchange rate, which are indicators or data that can reflect the heat exchange performance of the air-cooled heat exchanger.

[0074] It should be understood that the evaluation model can be constructed based on network structures such as convolutional neural networks, deep neural networks, residual networks or multi-layer perceptrons, and can be specifically set according to actual application requirements.

[0075] In some embodiments, the evaluation model may include a first network and a second network; the first network is used to analyze the flow field distribution of the air-cooled heat exchanger corresponding to the design parameters when it operates under an operating environment corresponding to the environmental parameters, and obtain flow field distribution data; the second network is used to analyze the heat exchange performance of the air-cooled heat exchanger corresponding to the design parameters when it operates under an operating environment corresponding to the environmental parameters.

[0076] In some embodiments, a first network can be constructed based on a convolutional neural network, and during the training process of the convolutional neural network, spatial features and other features in the flow field distribution data can be better learned. Therefore, the first network based on the convolutional neural network can quickly and accurately perform generative prediction of the flow field and obtain flow field distribution data with higher accuracy.

[0077] In some embodiments, a second network can be constructed based on a deep neural network, and a deep neural network usually has multiple hidden layers, which can better handle complex nonlinear relationships, that is, it can better learn the complex behavior of the air-cooled heat exchanger, so that the heat exchange performance of the air-cooled heat exchanger can be quickly and accurately analyzed based on the input first parameter combination. Optionally, a ReLU (Rectified Linear Unit) activation function can be used as the activation function of the hidden layer in the second network, and the ReLU activation function can better alleviate the gradient disappearance and reduce the amount of calculation by sparsely ...

[0078] ReLU(x)=max(0,x)

[0079] Where x represents the input data to the hidden layer, and ReLU(x) represents the output data of the hidden layer. In the above formula, if the input data to the hidden layer is greater than 0, the output of the hidden layer is the original value; if the input data to the hidden layer is less than or equal to 0, the output of the hidden layer is 0.

[0080] It should be understood that the first network and the second network can be connected in parallel or in series. In some embodiments, the first parameter combination and the flow field distribution data output by the first network can be input into the second network, so that the second network can analyze the heat exchange performance of the air-cooled heat exchanger to be evaluated under the operating environment corresponding to the environmental parameters, combining the flow field distribution within the air-cooled heat exchanger, and further improve the accuracy of the obtained heat exchange performance.

[0081] In an embodiment of the present application, the first parameter combination is input into a trained evaluation model, and the evaluation model can learn the complex relationship between the heat exchange design, operating environment, flow field distribution, and heat exchange performance of the air-cooled heat exchanger during training. Therefore, the evaluation model can quickly and accurately analyze the flow field distribution and heat exchange performance of the air-cooled heat exchanger to be evaluated when operating in the operating environment corresponding to the environmental parameters based on the design parameters and environmental parameters in the first parameter combination, and obtain the required flow field distribution data and heat exchange performance. There is no need to conduct professional simulation or prototype production for actual testing, which can effectively reduce the cost of air-cooled heat exchanger performance evaluation and reduce the requirements for user expertise and computing resources, while improving the efficiency of air-cooled heat exchanger performance evaluation.

[0082] S103 , performing visualization processing on the first evaluation information, and displaying the first evaluation information after visualization processing through a set user interaction interface, wherein the visualization processing is at least used for color mapping of the flow field distribution data.

[0083] It should be understood that color mapping generally refers to a technique that converts numerical or categorical data into different colors, for example, using gradients of different colors to represent data trends. In the practice of this application, color mapping of flow field distribution data can refer to representing different data in the flow field distribution data using different colors, thereby indicating changes in the flow field distribution through color differences and better highlighting the flow field distribution characteristics.

[0084] It should be noted that in the embodiment of the present application, the difference in color may refer to the difference in one or more color attributes such as hue, brightness, saturation, tone and concentration.

[0085] It should be understood that when color mapping is performed on the flow field distribution data, the data in the flow field distribution data can be directly mapped to corresponding colors and rendered through a set rendering engine to obtain the flow field distribution data after visualization; or, the data in the flow field distribution data can be mapped to corresponding colors according to a set color mapping table to obtain the flow field distribution data after visualization. The flow field distribution data can also be color mapped through methods such as the HSV color model, and the specific settings can be made according to actual application requirements.

[0086] For example, assuming that the flow field distribution data includes the distribution data of the temperature field and the velocity field; when color mapping is performed on the temperature field distribution data and the velocity field distribution data, different values ​​in the temperature field distribution data and the velocity field distribution data are mapped to grays of different shades (i.e., different brightness) through the set rendering engine, and the following is obtained: Figure 2 The visual cloud diagram shown.

[0087] It should be understood that when visualizing the first evaluation information, the flow field distribution data and heat transfer performance in the first evaluation information may be visualized differently. For example, the flow field distribution data may be converted into cloud maps, streamline maps, or arrow maps for display through color mapping, and the heat transfer performance may be converted into charts, graphs, or text for display through data statistics or other methods.

[0088] In some embodiments, when analyzing the flow field distribution of an air-cooled heat exchanger to be evaluated under an operating environment corresponding to the environmental parameters, the evaluation model can output flow field distribution data in the form of a numerical matrix. During visualization of the flow field distribution data, a predetermined rendering engine maps different values ​​in the numerical matrix to corresponding colors within a predetermined color range. Bilinear interpolation can be used to match the physical grid, thereby representing changes in the flow field distribution through color differences. Bilinear interpolation can also be used to achieve smooth color transitions, resulting in a smooth visualization cloud map.

[0089] It should be understood that when the first evaluation information after visualization is displayed through a set user interaction interface, the set user interaction interface can be the user interaction interface of the performance evaluation system or the user interaction interface of the terminal device corresponding to the user. The embodiment of the present application does not impose any specific restrictions on this.

[0090] In an embodiment of the present application, the first evaluation information is converted into intuitive and specific data through visualization processing and then displayed, which can improve the information transmission effect of the first evaluation information, so that the user can better understand the flow field distribution and heat exchange performance of the corresponding air-cooled heat exchanger based on the displayed first evaluation information after visualization processing, helping the user to better optimize the design of the air-cooled heat exchanger.

[0091] In the embodiment of the present application, since the trained evaluation model can learn the complex relationship between the heat exchange design, operating environment, flow field distribution, and heat exchange performance of the air-cooled heat exchanger during training, the trained evaluation model can quickly and accurately analyze the flow field distribution and heat exchange performance of the air-cooled heat exchanger to be evaluated when operating in the operating environment corresponding to the environmental parameters based on the input design parameters and environmental parameters, and obtain the required first evaluation information without the need for professional simulation or prototype production for actual testing, thereby reducing the cost of air-cooled heat exchanger performance evaluation and reducing the requirements for user expertise and computing resources, and can effectively improve the efficiency of performance evaluation of air-cooled heat exchangers. At the same time, after the first evaluation information obtained is converted into intuitive and specific data through visualization processing, the first evaluation information after visualization processing is displayed through a set user interaction interface. In the visualization processing, at least the flow field distribution data will be color mapped, so that the changes in flow field distribution can be represented by color differences, the flow field distribution characteristics can be highlighted, and the information transmission effect of the first evaluation information can be effectively improved, so that the user can better understand the flow field distribution and heat exchange performance of the corresponding air-cooled heat exchanger based on the displayed first evaluation information after visualization processing, helping the user to better perform design optimization of the air-cooled heat exchanger.

[0092] It should be noted that Figure 1 Taking the example of the same processor inputting the first parameter combination into the trained evaluation model and visualizing the first evaluation information, in other embodiments, other processors or modules may also input the first parameter combination obtained by the processor into the trained evaluation model, and other processors or modules may visualize the first evaluation information output by the evaluation model. The specific settings can be made according to actual application requirements, and the embodiments of the present application do not impose specific restrictions on this.

[0093] For example, in a distributed performance evaluation system, the processor can obtain the first parameter combination corresponding to the air-cooled heat exchanger to be evaluated, and then the calling module inputs the first parameter combination into the trained evaluation model. After obtaining the first evaluation information output by the evaluation model, the processing module visualizes the first evaluation information, and finally displays the visualized first evaluation information through the user interaction interface.

[0094] In some embodiments, the above environmental parameters are used to reflect the wind and sand operating environment of the air-cooled heat exchanger. Figure 3 The flow chart of a heat exchanger performance evaluation method shown in FIG. 1 includes the following steps: inputting the first parameter combination into a trained evaluation model to obtain first evaluation information output by the evaluation model:

[0095] The evaluation model is used to analyze the flow field distribution and heat exchange performance of the air-cooled heat exchanger corresponding to the design parameters in the first parameter combination when operating in a windy and sandy operating environment corresponding to the environmental parameters.

[0096] It should be understood that the wind and sand operating environment may refer to an operating environment containing sand or wind and sand; environmental parameters may include one or more sand and dust related parameters such as sand content, sand transport rate and sand flux to reflect the wind and sand operating environment of the air-cooled heat exchanger.

[0097] Since the air-cooled heat exchanger needs to remove heat through the flow of air, and in an environment containing dust, the dust will flow with the air during the heat exchange process of the air-cooled heat exchanger, which will change the flow characteristics and thermal resistance of the air, etc., and may have a great impact on the heat exchange performance. Therefore, in order to improve the accuracy of the heat exchange performance evaluation, environmental parameters that can reflect the wind and sand operating environment are obtained, so that the evaluation model can better analyze the impact of the wind and sand operating environment corresponding to the environmental parameters on the heat exchange process of the air-cooled heat exchanger during the evaluation process, thereby more accurately analyzing the distribution of the flow field and the heat exchange performance of the air-cooled heat exchanger, which helps to improve the accuracy of the obtained flow field distribution data and heat exchange performance.

[0098] In the embodiment of the present application, the influence of sand and dust in the operating environment is fully considered, and the heat exchange performance is evaluated in combination with environmental parameters that can reflect the windy and sandy operating environment of the air-cooled heat exchanger, so that the evaluation model can more accurately analyze the influence of the joint movement of sand and dust and cold air on the internal flow field and heat exchange when the air-cooled heat exchanger is operating in a windy and sandy operating environment, effectively improving the accuracy of the determined flow field distribution data and heat exchange performance, that is, improving the accuracy of the performance evaluation of the air-cooled heat exchanger.

[0099] Figure 4 A schematic diagram of a heat exchanger performance evaluation method provided by some embodiments is shown. Figure 4 Before inputting the first parameter combination into the trained evaluation model to obtain the first evaluation information output by the evaluation model, the following steps are also included:

[0100] Simulation is performed based on the heat exchanger simulation model and the set second parameter combination to obtain second evaluation information corresponding to the above-mentioned second parameter combination. The above-mentioned heat exchanger simulation model is constructed based on the heat exchange characteristics of the air-cooled heat exchanger, and is used to simulate the flow field distribution and heat exchange performance of the air-cooled heat exchanger corresponding to the above-mentioned design parameters when operating under the operating environment corresponding to the above-mentioned environmental parameters.

[0101] A training sample is constructed based on the second parameter combination and the corresponding second evaluation information.

[0102] The constructed evaluation model is trained according to the above training samples to obtain a trained evaluation model.

[0103] It should be understood that the second parameter combination generally also includes design parameters for reflecting the heat exchange design of the air-cooled heat exchanger and environmental parameters for reflecting the operating environment of the air-cooled heat exchanger.

[0104] Optionally, the heat exchanger simulation model includes but is not limited to a CFD model (Computational Fluid Dynamics, computational fluid dynamics model), a partition model, a dynamic simulation model and other simulation models, which can be specifically set according to actual needs.

[0105] In some embodiments, the heat exchanger simulation model can be a CFD model. When constructing a heat exchanger simulation model, a physical model can be established based on the heat transfer characteristics of the air-cooled heat exchanger. The physical model can be described as forced convection of air in the air-cooled heat exchanger, exchanging heat with a heat transfer medium (such as a refrigerant, etc.); based on the complexity of the flow field of the air-cooled heat exchanger, the flow field is meshed using a corresponding grid type (such as a structured grid and / or an unstructured grid). Then, a mathematical model is constructed based on control equations such as the conservation equations of mass, momentum, and energy, and boundary conditions corresponding to each boundary and wall are set.

[0106] Optionally, when setting the boundary conditions corresponding to the boundary and wall, the velocity inlet can be set as the boundary inlet condition, and the pressure outlet can be set as the pressure outlet; the surface of the air-cooled heat exchanger is assumed to be a constant temperature wall condition (that is, the internal refrigerant gas-liquid phase change heat transfer temperature remains stable), and the remaining wall surfaces are set to adiabatic no-slip conditions.

[0107] Optionally, when constructing the mathematical model for the CFD model, the standard k-ε model can be used as the turbulence model to better simulate forced convection of air. Furthermore, an enhanced wall function can be used to capture the boundary layer flow state, thereby improving the simulation accuracy of air convection near the wall.

[0108] In some embodiments, after constructing the heat exchanger simulation model, the heat exchanger simulation model can be subjected to an error test to verify the accuracy of the evaluation information obtained by the heat exchanger simulation model based on the parameter combination simulation. For example, the expected evaluation information corresponding to the test parameter combination (including design parameters and environmental parameters) based on typical working conditions (such as standard working conditions or extreme working conditions, etc.) can be obtained through laboratory experiments, and the corresponding simulation evaluation information can be obtained by simulating the test parameter combination through the constructed heat exchanger simulation model. The accuracy of the heat exchanger simulation model is measured based on the error between the expected evaluation information and the simulation evaluation information, so that when the error is large (i.e., the accuracy does not meet the requirements), the heat exchanger simulation model can be corrected to ensure the accuracy and reliability of the final heat exchanger simulation model.

[0109] Optionally, when the heat exchanger simulation model needs to be corrected, the heat exchanger simulation model may be corrected by adjusting parameters such as parameters of the turbulence model or parameters of the wall function in the mathematical model.

[0110] Optionally, different multiple (e.g., N, where N is greater than 1) second parameter combinations can be obtained by adjusting the parameter values, that is, by adjusting the values ​​of the design parameters and the environmental parameters, different multiple second parameter combinations can be obtained to construct sufficient training samples to train the evaluation model, thereby ensuring the accuracy of the obtained evaluation model.

[0111] It should be understood that a training sample generally includes a second parameter combination and second evaluation information corresponding to the second parameter combination.

[0112] It should be understood that in the process of training the constructed evaluation model based on the training samples, the second parameter combination in the training samples is usually input into the evaluation model to obtain the predicted evaluation information output by the evaluation model; the second evaluation information in the training samples is used as a label value to measure the error of the predicted evaluation information output by the evaluation model, and then the parameters of the evaluation model are adjusted according to the error to obtain the adjusted evaluation model, that is, the trained evaluation model.

[0113] For example, the loss value (i.e., error) can be calculated using a set loss function (such as a cross-entropy loss function), predicted evaluation information, and the corresponding second evaluation information, and then the evaluation model can be adjusted based on the loss value and the set optimization algorithm (such as a gradient descent algorithm) to obtain an adjusted evaluation model.

[0114] In some embodiments, the set loss function can be a hybrid loss function based on mean square error and structural similarity. Optionally, the hybrid loss function can be expressed as follows:

[0115] L mix =α·L MSE +β·L SSIM

[0116] Among them, L mix represents the mixed loss function; L MSE represents the mean square error loss function, α represents the first weight corresponding to the mean square error loss function; L SSIM Represents the structural similarity loss function, and β represents the second weight corresponding to the mean square error loss function. It should be understood that the first weight and the second weight can be determined according to user settings or inputs, or can be calculated by an intelligent algorithm.

[0117] Optionally, if the adjusted evaluation model does not meet the training requirements (such as the accuracy reaches the accuracy threshold, such as 0.98; or the number of iterations reaches the number threshold, such as 100), training can be continued based on the adjusted evaluation model until the adjusted evaluation model meets the training requirements to obtain a trained evaluation model.

[0118] In some embodiments, before training the constructed evaluation model based on the training samples, each data in the training samples can be normalized first, and the data can be mapped to a set numerical range (such as the [0, 1] range) through normalization.

[0119] As an example, each data can be normalized by Min-Max normalization. The normalized data can be expressed as follows:

[0120]

[0121] Among them, X before represents the data before normalization X, X norm Represents the normalized data X, X max Indicates the maximum value of data X in each training sample, X min Indicates the minimum value of the data X in each training sample.

[0122] In the embodiment of the present application, since the heat exchanger simulation model is constructed based on the heat exchange characteristics of the air-cooled heat exchanger, it can accurately simulate the operating status of the corresponding air-cooled heat exchanger when operating in the operating environment based on the heat exchange principle and the input second parameter combination, and analyze to obtain more accurate second evaluation information. Therefore, simulation based on the heat exchanger simulation model and each second parameter combination can quickly and accurately obtain a large amount of high-quality data to construct training samples, and training samples covering various working conditions can be obtained by adjusting the second parameter combination, effectively improving the diversity and flexibility of the obtained training samples, and there is no need to collect actual operating data or obtain data in actual methods to construct training samples, effectively reducing the difficulty and cost of obtaining training samples.

[0123] Figure 5 A schematic diagram of a heat exchanger performance evaluation method provided by some embodiments is shown. Figure 5 Before performing simulation based on the heat exchanger simulation model and the set second parameter combination to obtain the second evaluation information corresponding to the second parameter combination, the following steps are also included:

[0124] A multiphase flow model is determined. The multiphase flow model is used to simulate and analyze the flow and heat transfer process of air containing sand and dust inside an air-cooled heat exchanger in a windy and sandy operating environment.

[0125] The heat exchanger simulation model is determined based on the multiphase flow model, the mathematical model corresponding to the multiphase flow model and the set flow field grid.

[0126] It should be understood that the multiphase flow model in the embodiment of the present application may include an air phase model and a dust phase model. In other embodiments, the multiphase flow model may also include models of other phases such as a liquid phase model.

[0127] Optionally, the multiphase flow model may be a VOF model (Volume of Fluid), an Eulerian model, a mixture model, or an interface tracking model. The multiphase flow model to be used may be determined based on the application scenario of the air-cooled heat exchanger.

[0128] In the embodiment of the present application, since the air-cooled heat exchanger is operating in an environment containing dust or windy sand, the internal air flow usually carries dust with it for joint movement, and the joint movement of air and dust and the individual movement of air usually have certain differences in flow behavior and physical properties. Therefore, when constructing a heat exchanger simulation model, the flow and heat transfer process of the air containing sand inside the air-cooled heat exchanger in the windy sand operating environment can be simulated and analyzed through a multiphase flow model, fully considering the multiphase flow inside the air-cooled heat exchanger in the dusty or windy sand environment, which helps to improve the accuracy and reliability of the constructed heat exchanger simulation model.

[0129] In some embodiments, determining the multiphase flow model includes:

[0130] The multiphase flow model is determined based on a continuous air phase and a continuous dust phase.

[0131] Since heat exchange is usually achieved inside an air-cooled heat exchanger through high-speed flow of air, the flow of air usually drives the dust to flow at high speed. Therefore, in the embodiment of the present application, in order to better simulate and analyze the flow and heat transfer of multiphase fluids, the air and dust can be regarded as a whole (i.e., a single continuous medium), that is, a multiphase flow model is constructed through a continuous air phase and a continuous dust phase, so that the multiphase flow model can more accurately simulate the phase interaction and coupling relationship between air and dust, reduce errors caused by discretization, reduce the complexity of the multiphase flow model, and further improve the reliability and accuracy of the multiphase flow model.

[0132] It should be understood that the multiphase flow model includes but is not limited to an N-phase mixed flow model, an Eulerian-Eulerian model, a mixture model, or an interface tracking model.

[0133] In some embodiments, the multiphase flow model can describe the relative motion of dust particles and gas by slip velocity, without having to track each dust particle or solve multiple sets of equations, and can better balance the accuracy and efficiency of the calculation. And / or, the multiphase flow model can assume that the dust particles and the fluid (air in the embodiment of the present application) are in local equilibrium on a spatial scale, but allows the capture and simulation of behaviors such as sedimentation or diffusion due to inertia or gravity by calculating the relative velocity. And / or, the multiphase flow model can define the dust properties of the dust phase based on dust data (such as dust density or the diameter of dust particles, etc.), and can also be combined with a turbulence model to simulate the impact of dust particles on the boundary layer, thereby further improving the simulation accuracy of the heat exchanger simulation model based on the wind and sand environment.

[0134] As an example, the multiphase flow model can be a Mixture model, with a continuous air phase as the primary phase and a continuous dust phase as the secondary phase. Optionally, the volume fraction of the dust phase can be determined based on the dust conditions of the air-cooled heat exchanger's application scenario. For example, assuming the dust phase volume fraction ranges from 0 to 0.01‰, and assuming the air-cooled heat exchanger is used in a desert, Gobi desert, or wasteland with severe dust, a larger volume fraction (e.g., 0.008‰) can be determined based on the set value range as the dust phase volume fraction. Because the Mixture model not only describes the relative motion of dust and gas through slip velocity but also allows for local spatial equilibrium between dust and air, it captures and simulates dust settling or diffusion due to inertia or gravity by calculating relative velocity, and defines the volume fraction of the dust phase. Therefore, compared to other models such as the Euler model, it can more accurately simulate wind-blown sand movement and improve the simulation accuracy of the heat exchanger simulation model.

[0135] In some embodiments, the volume fraction of the dust phase can be calculated based on the mass concentration of air and the density of the dust.

[0136] Figure 6 A schematic diagram of a process for training an evaluation model in a heat exchanger performance evaluation method provided in some embodiments is shown. Figure 6 Before determining the heat exchanger simulation model based on the multiphase flow model, the mathematical model corresponding to the multiphase flow model, and the set flow field grid, the following steps are also included:

[0137] The flow fields of the first region and the second region set in the air-cooled heat exchanger are respectively meshed to obtain the flow field mesh, wherein the density of the mesh corresponding to the first region and the mesh corresponding to the second region in the flow field mesh are different.

[0138] Optionally, the first area and the second area can be determined based on user settings or input, or can be obtained by analyzing the heat transfer characteristics and structural characteristics of the air-cooled heat exchanger through intelligent algorithms such as large models. The specific settings can be made according to actual application requirements.

[0139] In other embodiments, a third region may be set, and the flow fields of the first region, the second region, and the third region set in the air-cooled heat exchanger may be gridded to obtain flow field grids, wherein the density of the grids corresponding to the respective regions is different.

[0140] For example, a high gradient area such as the area corresponding to the near wall surface can be set as the first area, a medium gradient area such as the area corresponding to the fin gap can be set as the second area, and a low gradient area such as the area corresponding to the mainstream channel can be set as the third area; correspondingly, the density of the grids corresponding to the first area, the second area and the third area decreases as the gradient level of the area (i.e., high gradient, medium gradient or low gradient) decreases.

[0141] In the embodiment of the present application, since there are usually certain differences in the flow and heat transfer characteristics of different regions in the air-cooled heat exchanger, such as the physical characteristics of the high-gradient region are more complex, while the physical characteristics of the low-gradient region are usually simpler, therefore, in order to ensure the accuracy of the heat exchanger simulation model while reducing the calculation cost and complexity as much as possible, different regions in the air-cooled heat exchanger can be meshed with different densities, so that the regions can be meshed with corresponding densities according to the actual conditions of each region, and a balance between the calculation accuracy and efficiency of the heat exchanger simulation model can be achieved through targeted meshing.

[0142] In some embodiments, the first region may be the region corresponding to the near-wall flow boundary layer in an air-cooled heat exchanger, and the second region may be the region outside the first region in the air-cooled heat exchanger. The density of the mesh corresponding to the first region in the flow field grid is greater than the density of the mesh corresponding to the second region, i.e., the meshing process is equivalent to performing mesh encryption on the first region during meshing. This meshing process enables the constructed heat exchanger simulation model to more accurately analyze the nonlinear distribution of velocity and temperature in the near-wall flow boundary layer, thereby improving the simulation accuracy of the heat exchanger simulation model.

[0143] Figure 7 A schematic diagram of a process for training an evaluation model in a heat exchanger performance evaluation method provided in some embodiments is shown. Figure 7 The above-mentioned step of constructing a training sample based on the second parameter combination and the corresponding second evaluation information includes the following steps:

[0144] Perform feature dimensionality reduction processing on the flow field distribution data in the second evaluation information to obtain the flow field distribution data after dimensionality reduction.

[0145] The training samples are determined based on the second parameter combination, and labels of the training samples are determined based on the heat exchange performance in the second evaluation information and the dimensionality-reduced flow field distribution data.

[0146] Feature dimensionality reduction usually refers to converting high-dimensional data into low-dimensional representation through mathematical transformation or feature selection, while retaining the key information of the original data as much as possible.

[0147] It should be understood that feature dimensionality reduction processing includes but is not limited to principal component analysis, linear discriminant analysis, t-distributed stochastic neighbor embedding (t-SNE) or autoencoder processing, which can be specifically set according to actual application requirements.

[0148] In some embodiments, when performing feature dimensionality reduction processing on the flow field distribution data in the second evaluation information, principal component analysis can be performed on the flow field distribution data, and then the principal component features in the flow field distribution data whose cumulative contribution rate is greater than a set threshold (such as 95%) are retained to obtain the required flow field distribution data after dimensionality reduction. Subsequently, the flow field distribution data after dimensionality reduction is used as a label for training samples for training, which can significantly reduce data redundancy and computational complexity.

[0149] In the embodiment of the present application, considering that the flow field distribution data is usually high-dimensional data containing a large number of features, the amount of computation required to directly use the high-dimensional data for calculations is usually high, which makes the computational cost of the model training process high and the training time long. Therefore, the flow field distribution data in the second evaluation information is first subjected to feature dimensionality reduction processing. The dimension of the flow field distribution data is reduced by feature dimensionality reduction processing, and redundant information and noise can be effectively removed at the same time, and the dimension of the flow field distribution data after dimensionality reduction is effectively reduced, thereby reducing the amount of computation required in the evaluation model training process and improving training efficiency.

[0150] It should be understood that in other embodiments, a training sample may be constructed directly based on the second parameter combination and the corresponding second evaluation information, wherein the second evaluation information serves as a label for the training sample. Then, before measuring the error of the predicted evaluation information of the evaluation model based on the label of the training sample (i.e., the second evaluation information), feature dimensionality reduction processing is performed on the flow field distribution data in the label to obtain the reduced-dimensional flow field distribution data; and then, the error of the flow field distribution data is calculated based on the reduced-dimensional flow field distribution data in the label and the flow field distribution data in the predicted evaluation information.

[0151] Figure 8 A schematic diagram of a heat exchanger performance evaluation method provided by some embodiments is shown. Figure 8After obtaining the first parameter combination corresponding to the air-cooled heat exchanger to be evaluated, the following steps are also included:

[0152] The target constraint condition is determined based on the first parameter combination.

[0153] Based on the above-mentioned objective constraints, the set optimization objectives and the above-mentioned evaluation model, a multi-objective optimization algorithm is used to perform multi-objective optimization to obtain a Pareto optimal solution set. The above-mentioned Pareto optimal solution set includes non-inferior solutions that meet the above-mentioned objective constraints and the above-mentioned optimization objectives.

[0154] Show the above Pareto optimal solution set.

[0155] Constraints are typically used to limit the feasible range of data to ensure that the optimized structure meets requirements such as physical feasibility and safety standards. It should be understood that constraints include, but are not limited to, parameters such as voltage drop thresholds, temperature thresholds, and humidity thresholds, and can be set based on actual application requirements.

[0156] In the embodiments of the present application, the target constraint conditions set can be used to limit the feasible range of variables (such as the heat exchange surface angle and sand content). When determining the target constraint conditions, the variables whose feasible ranges need to be limited can be determined based on the parameters included in the first parameter combination. The feasible ranges of the individual variables can then be determined in combination with the values ​​of the parameters in the first parameter combination to obtain the target constraint conditions.

[0157] Optimization objectives are typically used to guide the optimization process. It should be understood that optimization objectives can be set or input by the user, or automatically calculated based on the heat exchanger's design objectives using intelligent algorithms such as large models. In some embodiments, optimization objectives can include maximizing heat transfer and minimizing pressure drop.

[0158] Multi-objective optimization algorithms are typically used to solve optimization problems with multiple conflicting objectives. They aim to find a set of optimal solutions (called a Pareto optimal solution set) rather than a single solution, as it is often impossible to achieve optimality across multiple objectives simultaneously.

[0159] It should be understood that multi-objective optimization algorithms include, but are not limited to, particle swarm optimization, ant colony optimization, or genetic algorithms. In some embodiments, the NSGA-II (Non-dominated Sorting Genetic Algorithm II) genetic algorithm or the NSGA-III (Non-dominated Sorting Genetic Algorithm III) genetic algorithm can be used as the multi-objective optimization algorithm for multi-objective optimization.

[0160] Among them, the Pareto optimal solution set generally refers to the set of all non-inferior solutions in a multi-objective optimization problem, and these solutions form a boundary called the Pareto Front in the objective space. A non-inferior solution generally refers to a solution in the Pareto optimal solution set that is not dominated by other feasible solutions on all objectives (i.e., a solution is better than other solutions on at least one objective and is not worse on other objectives). In an embodiment of the present application, the obtained Pareto optimal solution set includes non-inferior solutions that simultaneously meet the objective constraints and the optimization objectives.

[0161] In order to improve the R&D and design efficiency of heat exchangers and reduce the difficulty of R&D and design, after obtaining the first parameter combination corresponding to the heat exchanger to be evaluated, after determining the appropriate target constraints based on the first parameter combination, the multi-objective optimization algorithm is used to directly perform multi-objective optimization in combination with the target constraints, optimization objectives and evaluation model to obtain a Pareto optimal solution set that includes non-inferior solutions that satisfy the target constraints and optimization objectives.

[0162] It should be understood that when using a multi-objective optimization algorithm for multi-objective optimization, the evaluation model can be used to determine corresponding evaluation information based on the parameter combinations corresponding to each solution, thereby determining whether each solution meets the optimization objective based on the evaluation information. Furthermore, by using a trained evaluation model to quickly and accurately analyze the evaluation information corresponding to each solution, the multi-objective optimization algorithm can effectively compare and select solutions, effectively improving the accuracy and efficiency of multi-objective optimization.

[0163] Since the Pareto optimal solution set usually includes multiple non-inferior solutions that meet the target constraints and optimization objectives, these non-inferior solutions reflect the balance and trade-offs between different objectives. After determining the Pareto optimal solution set, it is usually necessary to combine the corresponding decision-making strategy to determine the final solution from it, thereby determining the optimization strategy. Therefore, in order to improve the accuracy of the final optimization strategy, after determining the Pareto optimal solution set, the Pareto optimal solution set can be displayed, allowing users to select appropriate solutions from it according to the actual application requirements of the heat exchanger to determine the final optimization strategy, thereby improving the flexibility of the heat exchanger optimization design and reducing limitations.

[0164] In some embodiments, upon receiving an optimization request (for requesting to obtain a Pareto optimal solution set) input by a user through a terminal device or a user interaction interface, the target constraint conditions can be determined based on the first parameter combination and subsequent multi-objective optimization can be performed to display the Pareto optimal solution set required by the user.

[0165] In an embodiment of the present application, after determining the target constraints and optimization objectives, multi-objective optimization is performed directly based on the trained evaluation model and multi-objective optimization algorithm. The evaluation model can quickly and accurately analyze the evaluation information corresponding to each solution, and help the multi-objective optimization algorithm to effectively compare and select solutions, so that the required non-inferior solution can be determined quickly and accurately. Compared with directly using a multi-objective optimization algorithm for multi-objective optimization, it can effectively guarantee the quality and reliability of the obtained solution, and can effectively reduce the amount of calculation and improve the optimization efficiency.

[0166] In some embodiments, the environmental parameter includes sediment content, the target constraint includes a sediment content fluctuation range, and the determining the target constraint based on the first parameter combination includes the following steps:

[0167] The sand content fluctuation range is determined based on the sand content in the first parameter combination and a target application scenario, where the target application scenario includes an application scenario corresponding to the air-cooled heat exchanger to be evaluated.

[0168] It should be understood that the sediment content fluctuation threshold may include an upper sediment content limit and / or a lower sediment content limit, which may be set according to actual application requirements.

[0169] Optionally, when determining the sand content fluctuation range based on the sand content and the target application scenario in the first parameter combination, the degree of fluctuation can be determined in combination with the type of the target application scenario (such as desert, city or Gobi, etc.), and then the sand content fluctuation range is calculated based on the sand content and the degree of fluctuation.

[0170] Alternatively, the fluctuation value corresponding to each application scenario may be pre-set. When the sediment content fluctuation range needs to be determined, the sediment content fluctuation range is directly calculated based on the sediment content in the first parameter combination and the fluctuation value corresponding to the target application scenario.

[0171] In some embodiments, two boundary values ​​of the sediment content fluctuation range can be calculated based on the fluctuation value corresponding to the target application scenario and the sediment content in the first parameter combination, and then each boundary value can be weighted calculated according to the weighting coefficient corresponding to the type of the target application scenario, and the required sediment content fluctuation range can be determined based on the weighted boundary values.

[0172] In the embodiment of the present application, since there are usually certain differences in the sand content in different application scenarios, and the sand content in the same application scenario usually also has certain fluctuations, therefore, when setting the target constraint conditions of the multi-objective optimization problem, the fluctuation range of the sand content is determined in combination with the sand content in the first parameter combination and the application scenario of the air-cooled heat exchanger to be evaluated, and it is used as a constraint condition to ensure that the change in sand content during the multi-objective optimization process will not exceed the sand content fluctuation range, thereby ensuring the reliability of the final optimization strategy in actual application, and avoiding problems such as unstable performance of the air-cooled heat exchanger due to reasons such as fluctuations in sand content in the target application scenario.

[0173] Combined with the above Figures 1 to 8 , describes in detail the heat exchanger performance evaluation method of the embodiment of the present application, and will be combined with Figure 9 Describe the system embodiment of the present application. It should be understood that the heat exchanger performance evaluation system in the embodiment of the present application can execute the various heat exchanger performance evaluation methods of the aforementioned embodiments of the present application, that is, the specific working processes of the following various products can refer to the corresponding processes in the aforementioned method embodiments.

[0174] Figure 9 Schematic diagram of the structure of a heat exchanger performance evaluation device provided in some embodiments of the present application is shown. Figure 9 The device includes a parameter acquisition module 910, a model calling module 920 and a visualization module 930.

[0175] A parameter acquisition module is used to obtain a first parameter combination corresponding to the air-cooled heat exchanger to be evaluated. The first parameter combination includes design parameters and environmental parameters. The design parameters are used to reflect the heat exchange design of the air-cooled heat exchanger, and the environmental parameters are used to reflect the operating environment of the air-cooled heat exchanger.

[0176] The model calling module is used to input the above-mentioned first parameter combination into the trained evaluation model to obtain the first evaluation information output by the above-mentioned evaluation model. The above-mentioned first evaluation information includes flow field distribution data and heat exchange performance. The above-mentioned evaluation model is used to analyze the flow field distribution status and heat exchange performance of the air-cooled heat exchanger corresponding to the above-mentioned design parameters when operating in the operating environment corresponding to the above-mentioned environmental parameters.

[0177] The visualization module is used to perform visualization processing on the first evaluation information and display the first evaluation information after visualization processing.

[0178] Each unit module of the heat exchanger performance evaluation system can respectively execute the corresponding steps in the above method embodiment, so each unit module will not be described in detail here. Please refer to the description of the corresponding steps above for details.

[0179] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a controller, the steps in the above-mentioned various method embodiments can be implemented.

[0180] An embodiment of the present application provides a computer program product. When the computer program product runs on a controller, the controller can implement the steps of the control method described in each of the above embodiments when the computer program product is executed.

[0181] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process in the above-mentioned embodiment method by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / electronic device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0182] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0183] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0184] In the embodiments provided in this application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0185] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0186] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A heat exchanger performance evaluation method, characterized in that: include: Acquiring, through a processor, a first parameter combination corresponding to the air-cooled heat exchanger to be evaluated, the first parameter combination including design parameters and environmental parameters, the design parameters being used to reflect the heat exchange design of the air-cooled heat exchanger, and the environmental parameters being used to reflect the operating environment of the air-cooled heat exchanger; Inputting the first parameter combination into a trained evaluation model to obtain first evaluation information output by the evaluation model, wherein the first evaluation information includes flow field distribution data and heat exchange performance, and the evaluation model is used to analyze the flow field distribution and heat exchange performance of the air-cooled heat exchanger corresponding to the design parameters when operating under the operating environment corresponding to the environmental parameters; Visualization processing is performed on the first evaluation information, and the first evaluation information after visualization processing is displayed through a set user interaction interface, and the visualization processing is at least used to perform color mapping on the flow field distribution data.

2. The heat exchanger performance evaluation method according to claim 1, characterized in that: The environmental parameters are used to reflect the wind and sand operating environment of the air-cooled heat exchanger. The first parameter combination is input into the trained evaluation model to obtain first evaluation information output by the evaluation model, including: The evaluation model is used to analyze the flow field distribution and heat exchange performance of the air-cooled heat exchanger corresponding to the design parameters in the first parameter combination when operating in a windy and sandy operating environment corresponding to the environmental parameters.

3. The heat exchanger performance evaluation method according to claim 1, wherein: Before inputting the first parameter combination into the trained evaluation model to obtain first evaluation information output by the evaluation model, the method further includes: Performing a simulation based on a heat exchanger simulation model and a set second parameter combination to obtain second evaluation information corresponding to the second parameter combination, wherein the heat exchanger simulation model is constructed based on the heat transfer characteristics of the air-cooled heat exchanger and is used to simulate the flow field distribution and heat transfer performance of the air-cooled heat exchanger corresponding to the design parameters when operating under the operating environment corresponding to the environmental parameters; Constructing a training sample based on the second parameter combination and the corresponding second evaluation information; The constructed evaluation model is trained according to the training samples to obtain a trained evaluation model.

4. The heat exchanger performance evaluation method according to claim 3, wherein: Before performing simulation based on the heat exchanger simulation model and the set second parameter combination to obtain second evaluation information corresponding to the second parameter combination, the method further includes: Determining a multiphase flow model for simulating and analyzing the flow and heat transfer of air containing sand and dust inside an air-cooled heat exchanger in a sandstorm operating environment; The heat exchanger simulation model is determined based on the multiphase flow model, a mathematical model corresponding to the multiphase flow model, and a set flow field grid.

5. The heat exchanger performance evaluation method according to claim 4, characterized in that: Determining the multiphase flow model includes: The multiphase flow model is determined according to a continuous air phase and a continuous dust phase.

6. The heat exchanger performance evaluation method according to claim 4, wherein: Before determining the heat exchanger simulation model based on the multiphase flow model, the mathematical model corresponding to the multiphase flow model, and the set flow field grid, the method further includes: Meshing is performed on the flow fields of a first region and a second region set in the air-cooled heat exchanger to obtain the flow field mesh, wherein the density of the mesh corresponding to the first region and the density of the mesh corresponding to the second region are different.

7. The heat exchanger performance evaluation method according to claim 3, wherein: The constructing of a training sample based on the second parameter combination and the corresponding second evaluation information includes: performing feature dimensionality reduction processing on the flow field distribution data in the second evaluation information to obtain reduced-dimensional flow field distribution data; The training sample is determined based on the second parameter combination, and the label of the training sample is determined based on the heat exchange performance in the second evaluation information and the flow field distribution data after dimensionality reduction.

8. The heat exchanger performance evaluation method according to any one of claims 1 to 7, characterized in that: After the processor acquires the first parameter combination corresponding to the air-cooled heat exchanger to be evaluated, the method further includes: determining a target constraint condition based on the first parameter combination; Based on the target constraints, the set optimization objectives, and the evaluation model, a multi-objective optimization algorithm is used to perform multi-objective optimization to obtain a Pareto optimal solution set, wherein the Pareto optimal solution set includes non-inferior solutions that satisfy the target constraints and the optimization objectives; Show the Pareto optimal solution set.

9. The heat exchanger performance evaluation method according to claim 8, wherein: The environmental parameter includes sediment content, the target constraint condition includes a sediment content fluctuation range, and determining the target constraint condition based on the first parameter combination includes: The sediment content fluctuation range is determined based on the sediment content in the first parameter combination and a target application scenario, where the target application scenario includes an application scenario corresponding to the air-cooled heat exchanger to be evaluated.

10. A heat exchanger performance evaluation system, characterized in that: include: a parameter acquisition module, configured to acquire, through a processor, a first parameter combination corresponding to the air-cooled heat exchanger to be evaluated, wherein the first parameter combination includes design parameters and environmental parameters, wherein the design parameters are used to reflect the heat exchange design of the air-cooled heat exchanger, and the environmental parameters are used to reflect the operating environment of the air-cooled heat exchanger; a model calling module, configured to input the first parameter combination into a trained evaluation model to obtain first evaluation information output by the evaluation model, wherein the first evaluation information includes flow field distribution data and heat exchange performance, and the evaluation model is configured to analyze the flow field distribution and heat exchange performance of the air-cooled heat exchanger corresponding to the design parameters when operating under an operating environment corresponding to the environmental parameters; A visualization module is used to perform visualization processing on the first evaluation information and display the first evaluation information after visualization processing through a set user interaction interface, and the visualization processing is at least used to perform color mapping on the flow field distribution data.

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