Optimization design method for heat exchange structure of concentrating photovoltaic photo-thermal assembly and related equipment

Abstract

The invention discloses a concentrating photovoltaic photo-thermal assembly heat exchange structure optimization design method and related equipment, and relates to the technical field of new energy, and the method comprises the steps: inputting a to-be-designed heat exchange area discrete model to a neural network operator agent model, so as to obtain corresponding evaluation indexes under different design parameters, the network operator agent model is obtained by performing preset training through a multi-physics field coupling finite element simulation result set, and the design parameters comprise shape characteristics of a flow area in the heat exchange area to be designed; performing multi-objective optimization and Pareto frontier solution set generation operation on the discrete model of the heat exchange area to be designed and the evaluation indexes corresponding to all the design parameters corresponding to the discrete model to obtain a Pareto frontier solution set; obtaining comprehensive weight information of the evaluation indexes; and according to the comprehensive weight information and the Pareto frontier solution set, carrying out comprehensive evaluation operation by adopting a TOPSIS and GRA combined method, and determining a target flow area.

Description

Technical Field

[0001] The present invention relates to the field of new energy technology, and more specifically, to a method for optimizing the design of heat exchange structures of concentrated photovoltaic and thermal assemblies and related equipment. Background Art

[0002] As a derivative of photovoltaic technology, solar photovoltaic thermal technology can reduce the temperature of solar cells while recycling the waste heat of solar cells through thermal management of solar cells, effectively improving the efficiency of solar energy utilization. Among solar photovoltaic thermal technologies, concentrated photovoltaic thermal technology has better economic benefits because it can reduce the overall cost while improving conversion efficiency and energy quality. However, the use of solar photovoltaic thermal technology under concentrated conditions is constrained by the concentration structure and installation level, and faces certain risks of over-temperature and uneven temperature distribution, which damages the photovoltaic efficiency and life. Optimizing the design of the heat exchange structure used for thermal management will effectively improve the performance of the components.

[0003] The current solar photovoltaic heat exchange structure design mainly relies on the designer's work experience, is highly dependent on professional technicians, and is subject to human subjectivity. At the same time, due to the large area of ​​optimization design search, the entire optimization process is often time-consuming and laborious. Summary of the invention

[0004] A series of simplified concepts are introduced in the Summary of the Invention section, which will be further described in detail in the Detailed Description of the Invention section. The Summary of the Invention section of this application does not mean to attempt to define the key features and essential technical features of the claimed technical solution, nor does it mean to attempt to determine the scope of protection of the claimed technical solution.

[0005] In the first aspect, the present application proposes a method for optimizing the heat exchange structure of a concentrated photovoltaic and thermal assembly, comprising:

[0006] Inputting the discrete model of the heat exchange zone to be designed into the neural network operator proxy model to obtain the corresponding evaluation index under different design parameters, wherein the network operator proxy model is obtained by pre-set training through a multi-physics field coupling finite element simulation result set, and the design parameters include the shape characteristics of the flow area in the heat exchange zone to be designed;

[0007] The discrete model of the heat exchange zone to be designed and the corresponding evaluation indicators under all the corresponding design parameters are subjected to multi-objective optimization and Pareto front solution set generation operations to obtain a Pareto front solution set;

[0008] Obtain comprehensive weight information of the above evaluation indicators;

[0009] According to the above comprehensive weight information and the above Pareto front solution set, a comprehensive evaluation operation is performed by combining TOPSIS and GRA to determine the target flow area.

[0010] In a feasible implementation, the specific steps of acquiring the neural network operator proxy model include:

[0011] Discretize the model of the heat exchange area to be designed to obtain a discrete model of the heat exchange area to be designed;

[0012] According to the discrete model of the heat exchange area to be designed, the corresponding geometric model is automatically generated according to the discretized design parameters;

[0013] Perform multi-physics field coupling finite element simulation according to the above geometric model to obtain simulation result data;

[0014] Performing data extraction and formatting output operations on the above simulation result data to obtain formatted data;

[0015] Performing feature extraction on the formatted data to obtain feature extraction data;

[0016] The neural network operator proxy model is trained according to the heat exchange area model to be designed and the feature extraction data to achieve a preset number of training times or a preset accuracy to obtain the neural network operator proxy model.

[0017] In a feasible implementation manner, the multi-physics field coupled finite element simulation includes heat conduction simulation, fluid flow simulation and current conduction simulation.

[0018] In a feasible implementation manner, the above-mentioned discrete model of the heat exchange zone to be designed and the corresponding evaluation indicators under all the corresponding design parameters are subjected to multi-objective optimization and Pareto front solution set generation operations to obtain the Pareto front solution set, including:

[0019] Generate an initial population according to the discrete model of the heat exchange zone to be designed and the corresponding evaluation indicators under all the corresponding design parameters;

[0020] Perform fitness evaluation operation on the above initial population to obtain the fitness of the individual population;

[0021] According to the individual fitness of the above population, the population evolution is guided by genetic operation and based on non-dominated sorting to obtain a new generation of population and its Pareto frontier solution set.

[0022] In a feasible implementation, the above initial population is generated based on the Latin hypercube sampling method.

[0023] In a feasible implementation, the objective function corresponding to the above fitness evaluation operation includes maximizing thermal power output, minimizing temperature gradient, and maximizing electrical power output.

[0024] In a feasible implementation manner, the above-mentioned obtaining of the comprehensive weight information of the above-mentioned evaluation index includes:

[0025] Determine the subjective weight information corresponding to different evaluation indicators based on the best-worst method;

[0026] Calculate objective weight information based on the CRITIC method;

[0027] The weight values ​​of the subjective weight information and the objective weight information are solved according to the Nash equilibrium solution method of game theory to obtain comprehensive weight information.

[0028] In the second aspect, the present application proposes a device for optimizing the heat exchange structure of a concentrated photovoltaic and thermal assembly, comprising:

[0029] A first acquisition unit is used to input the discrete model of the heat exchange zone to be designed into the neural network operator proxy model to obtain the corresponding evaluation index under different design parameters, wherein the network operator proxy model is obtained by performing a preset number of trainings through a multi-physics field coupling finite element simulation result set, and the design parameters include the shape characteristics of the flow area in the heat exchange zone to be designed;

[0030] The second acquisition unit is used to perform multi-objective optimization and Pareto front solution set generation operations on the discrete model of the heat exchange zone to be designed and the corresponding evaluation indicators under all the corresponding design parameters, so as to obtain the Pareto front solution set;

[0031] A third acquisition unit is used to obtain comprehensive weight information of the above evaluation indicators;

[0032] The determination unit is used to perform a comprehensive evaluation operation by combining TOPSIS and GRA according to the comprehensive weight information and the Pareto front solution set to determine the target flow area.

[0033] In a third aspect, an electronic device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is used to implement the steps of the method for optimizing the heat exchange structure of a concentrated photovoltaic and thermal energy component as described in any one of the first aspects above when executing the computer program stored in the memory.

[0034] In a fourth aspect, the present application further proposes a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for optimizing the heat exchange structure of a concentrated photovoltaic thermal component of any one of the first aspects is implemented.

[0035] In summary, the traditional solar photovoltaic thermal heat exchange structure design relies on the designer's experience, and is designed and optimized through trial and error and manual adjustment. This method is not only time-consuming and laborious, but the optimization results are greatly affected by the designer's personal experience and subjectivity, and it is difficult to ensure the global optimality of the design. The present invention can quickly and accurately predict the performance indicators under different design parameters by introducing a neural network operator proxy model. The neural network operator proxy model is trained based on multi-physics field coupled finite element simulation data, replacing the complex numerical simulation process, greatly shortening the calculation time in the optimization process, reducing the dependence on high computing power resources, and making the optimization design more efficient. Rapid performance prediction is achieved through the proxy model, which can explore a larger design space in a short time, improve design efficiency and quality, and reduce the designer's subjective dependence. The existing photovoltaic thermal heat exchange structure design can often only be optimized on a single goal, such as maximizing thermal power or minimizing temperature, but ignores other important performance indicators, resulting in the system design may be biased towards a certain performance goal and lack of overall performance balance. The present invention can achieve a balance between multiple objective functions (such as maximizing thermal power output, minimizing temperature gradient, maximizing electrical power output, etc.) through multi-objective optimization and Pareto front solution set generation. A non-dominated sorting genetic algorithm (PB-NSGA-III) based on reference direction is used for multi-objective optimization to generate a Pareto front solution set. The Pareto front solution set contains a series of optimal design solutions, which achieve the best trade-off between different design objectives and provide designers with a variety of design options. Through multi-objective optimization, the overall performance of the system is improved, the local optimal problem caused by single-objective optimization is avoided, and the design solution is ensured to achieve the best balance between various objectives. In the existing design schemes, the selection of the final optimal solution usually depends on the designer's judgment or a simple weighted average method. This method lacks systematicity and objectivity and may not be able to effectively evaluate the advantages and disadvantages of each solution in multiple performance indicators. The present invention introduces a comprehensive evaluation method combining TOPSIS and GRA (grey correlation analysis). After generating the Pareto front solution set, the TOPSIS method is used to calculate the distance between each solution and the ideal solution and the negative ideal solution to evaluate the advantages and disadvantages of each solution. At the same time, the similarity between the scheme and the ideal solution is further evaluated by the grey correlation method combined with the GRA method. This combined method can comprehensively evaluate the relative advantages and disadvantages of each scheme from multiple angles. By combining TOPSIS and GRA, the advantages and disadvantages of the design scheme can be evaluated more comprehensively and objectively, ensuring that the optimal scheme finally selected is more scientific and reasonable. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present specification. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0037] Figure 1 A schematic diagram of a process for optimizing the design of a heat exchange structure of a concentrated photovoltaic and thermal assembly provided in an embodiment of the present application;

[0038] Figure 2 A schematic diagram of a discrete model of a heat exchange zone to be designed provided in an embodiment of the present application;

[0039] Figure 3 A schematic diagram of a Pareto front solution set obtained for solving provided in an embodiment of the present application;

[0040] Figure 4 A schematic diagram of a device for optimizing the heat exchange structure of a concentrated photovoltaic and thermal assembly provided in an embodiment of the present application;

[0041] Figure 5 A schematic diagram of the structure of an electronic device for optimizing the heat exchange structure of a concentrated photovoltaic thermal assembly provided in an embodiment of the present application. DETAILED DESCRIPTION

[0042] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0043] See also Figure 1 , which is a schematic diagram of a process for optimizing the design of a heat exchange structure of a concentrated photovoltaic and thermal assembly provided in an embodiment of the present application, and may specifically include:

[0044] S110, inputting the discrete model of the heat exchange zone to be designed into the neural network operator proxy model to obtain the corresponding evaluation index under different design parameters, wherein the network operator proxy model is obtained by performing a preset number of training on a multi-physics field coupling finite element simulation result set, and the design parameters include the shape characteristics of the flow area in the heat exchange zone to be designed;

[0045] For example, Figure 2 The figure shows a schematic diagram of a discrete model of a heat exchange zone to be designed. The discrete model of the heat exchange zone to be designed is input into a neural network operator proxy model for performance evaluation. The neural network operator proxy model is obtained by training a set of multi-physics field coupled finite element simulation results for a preset number of times. The proxy model can quickly predict performance indicators under different design parameters. Design parameters include but are not limited to shape characteristics of the flow area in the heat exchange zone to be designed, such as the geometry, size, degree of curvature of the flow channel and other characteristics.

[0046] S120, performing multi-objective optimization and Pareto front solution set generation operations on the discrete model of the heat exchange zone to be designed and the corresponding evaluation indicators under all the corresponding design parameters, so as to obtain a Pareto front solution set;

[0047] Exemplarily, the discrete model of the heat exchange zone to be designed and the evaluation indicators under all corresponding design parameters are subjected to multi-objective optimization operations, and a Pareto front solution set is generated. The Pareto front solution set contains multiple optimal solutions that balance different design objectives. This step can be performed by an optimization algorithm (such as PB-NSGA-III), so that the solution set can effectively approximate the Pareto front in the multi-objective space.

[0048] Through multi-objective optimization, multiple objective functions can be considered simultaneously, such as maximizing thermal power output, minimizing temperature gradient, maximizing electrical power output, etc. Through iterative optimization by genetic algorithm, a series of Pareto front solution sets representing multi-objective optimization are gradually obtained. These solution sets represent the optimal balance solution between different objectives, providing a basis for subsequent comprehensive evaluation.

[0049] S130, obtaining comprehensive weight information of the above evaluation indicators;

[0050] For example, after generating the Pareto front solution set, the next step is to obtain the comprehensive weight information of each evaluation indicator. This weight information is determined by comprehensively considering the importance of each evaluation indicator to ensure that different indicators can be reasonably reflected in the final evaluation.

[0051] S140. Based on the comprehensive weight information and the Pareto front solution set, a comprehensive evaluation operation is performed using a combination of TOPSIS and GRA to determine the target flow area.

[0052] For example, based on the comprehensive weight information and Pareto front solution set obtained above, a method combining TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and GRA (Grey Relational Analysis) is used to conduct a comprehensive evaluation operation on the design scheme, and finally determine the target flow area. Through this comprehensive evaluation method, the design scheme with the best performance in various performance indicators can be selected from the Pareto front solution set.

[0053] The TOPSIS method evaluates the solution closest to the ideal solution by calculating the distance between each design solution in the solution set and the ideal solution and the negative ideal solution. The TOPSIS method evaluates the pros and cons of the solutions by calculating the distance between each solution and the ideal solution and the negative ideal solution. The ideal solution represents the optimal solution for all objectives, while the negative ideal solution represents the worst solution for all objectives. By calculating the relative distance between each solution and the ideal solution and the negative ideal solution, the comprehensive approximation of each solution can be obtained. The solution with higher approximation, that is, the solution closer to the ideal solution, is considered to be a better choice. The GRA method evaluates the similarity between each solution and the ideal solution through the grey correlation degree, providing a supplementary evaluation of the pros and cons of the solution. The TOPSIS-GRA combined method can make full use of the advantages of the two methods, evaluate the relative pros and cons of each solution from multiple angles, and ensure that the selection of the optimal solution is more scientific and reasonable.

[0054]

[0055]

[0056] sw ij =s ij wg j (12)

[0057] TG i is the comprehensive relative approximation value of the i-th Pareto solution; C i + and C i - is the comprehensive approximation value, C i + It reflects the closeness between the evaluation object and the ideal solution. The larger the value, the better the Pareto solution is. i - It reflects the closeness between the evaluation object and the ideal solution. The larger its value, the worse the Pareto solution. α and β are the approximation coefficients. D i + , D i -, R i + and R i - is the grey correlation between the Pareto solution and the ideal solution; d i + d i - 、r i + and r i - The grey correlation between the Pareto solution and the ideal solution after dimensionless processing; R ij + and R ij - is the grey correlation coefficient of the Pareto solution and the positive and negative ideal solutions for the jth evaluation index; sw j best is the optimal value of the jth weighted evaluation index, i.e., the positive ideal solution; sw j worst is the worst value of the jth weighted evaluation index, i.e., the negative ideal solution; ξ is the resolution coefficient; sw ij is the weighted index value of the jth evaluation index of the i-th Pareto solution; s ij is the index value of the jth evaluation index of the i-th Pareto solution; wg j is the combined weight of the jth evaluation index.

[0058] In summary, the traditional solar photovoltaic thermal heat exchange structure design relies on the designer's experience, and is designed and optimized through trial and error and manual adjustment. This method is not only time-consuming and laborious, but the optimization results are greatly affected by the designer's personal experience and subjectivity, and it is difficult to ensure the global optimality of the design. The present invention introduces a neural network operator proxy model to quickly and accurately predict performance indicators under different design parameters. The neural network operator proxy model is trained based on multi-physics field coupling finite element simulation data, replacing the complex numerical simulation process, greatly shortening the calculation time in the optimization process, reducing the dependence on high computing power resources, and making the optimization design more efficient. Through the proxy model to achieve rapid performance prediction, it is possible to explore a larger design space in a short time, improve design efficiency and quality, and reduce the designer's subjective dependence. The existing photovoltaic thermal heat exchange structure design can often only be optimized on a single goal, such as maximizing thermal power or minimizing temperature, but ignores other important performance indicators, resulting in the system design may be biased towards a certain performance goal, lacking overall performance balance. The present invention can achieve a balance between multiple objective functions (such as maximizing thermal power output, minimizing temperature gradient, maximizing electrical power output, etc.) through multi-objective optimization and Pareto front solution set generation. A non-dominated sorting genetic algorithm (PB-NSGA-III) based on reference direction is used for multi-objective optimization to generate a Pareto front solution set. The Pareto front solution set contains a series of optimal design solutions, which achieve the best trade-off between different design objectives and provide designers with a variety of design options. Through multi-objective optimization, the overall performance of the system is improved, the local optimal problem caused by single-objective optimization is avoided, and the design solution is ensured to achieve the best balance between various objectives. In the existing design schemes, the selection of the final optimal solution usually depends on the designer's judgment or a simple weighted average method. This method lacks systematicity and objectivity and may not be able to effectively evaluate the advantages and disadvantages of each solution in multiple performance indicators. The present invention introduces a comprehensive evaluation method combining TOPSIS and GRA (grey correlation analysis). After generating the Pareto front solution set, the TOPSIS method is used to calculate the distance between each solution and the ideal solution and the negative ideal solution to evaluate the advantages and disadvantages of each solution. At the same time, the similarity between the scheme and the ideal solution is further evaluated by the grey correlation method combined with the GRA method. This combined method can comprehensively evaluate the relative advantages and disadvantages of each scheme from multiple angles. By combining TOPSIS and GRA, the advantages and disadvantages of the design scheme can be evaluated more comprehensively and objectively, ensuring that the optimal scheme finally selected is more scientific and reasonable.

[0059] In some examples, the specific steps of obtaining the neural network operator proxy model include:

[0060] Discretize the model of the heat exchange area to be designed to obtain a discrete model of the heat exchange area to be designed;

[0061] According to the discrete model of the heat exchange area to be designed, the corresponding geometric model is automatically generated according to the discretized design parameters;

[0062] Perform multi-physics field coupling finite element simulation according to the above geometric model to obtain simulation result data;

[0063] Performing data extraction and formatting output operations on the above simulation result data to obtain formatted data;

[0064] Performing feature extraction on the formatted data to obtain feature extraction data;

[0065] The neural network operator proxy model is trained according to the heat exchange area model to be designed and the feature extraction data to achieve a preset number of training times or a preset accuracy to obtain the neural network operator proxy model.

[0066] For example, first, the heat exchange area to be designed is discretized, with the goal of discretizing the continuous design space into a set of parameterized grids or units. This process enables the complex design area to be described with discrete points, thus providing a basis for subsequent geometric modeling and numerical simulation. The heat exchange area is divided into several small units, each of which can define its boundary conditions, material properties, etc., so that effective numerical processing can be performed through mathematical modeling methods.

[0067] Based on the discretized design parameters, the corresponding geometric models are automatically generated. The generation of geometric models is usually done with the help of computer-aided design (CAD) software, using parametric design tools or script programming to quickly and massively generate geometric models that meet the discretized parameters. The geometric models may include heat exchange structures with different flow channel shapes and sizes. By embedding the discretized parameters into the CAD design through scripts, these geometric models can be automatically generated in batches.

[0068] After generating the geometric model, the finite element method (FEM) is used to simulate the multi-physics coupling. The multi-physics here refers to the coupling effect of multiple physical phenomena such as heat transfer, fluid mechanics, and electricity. By solving the control equations such as the energy equation, momentum equation, and continuity equation, the physical field distribution of the system under different design parameters (such as temperature field, flow velocity field, etc.) is simulated. For a certain heat exchange structure, the internal heat flow distribution, current flow path, etc. are simulated to obtain the corresponding temperature distribution, fluid velocity and other physical quantities.

[0069] Extract valid data from the simulation results, including temperature, pressure, flow rate and other physical quantities. After extraction, these data need to be standardized, denoised, and output in a unified format to ensure that subsequent data analysis and model training can proceed smoothly. Extract the maximum temperature, average temperature, pressure loss, etc. of the system under different design conditions from the simulation results, and then format these data to ensure consistency and comparability between data sets.

[0070] The purpose of feature extraction of formatted data is to identify key physical quantities or design parameters from a large amount of simulation data. These feature data can simplify the data dimension while retaining the main factors affecting system performance and provide training data for subsequent neural network proxy models. For example: extract core indicators such as temperature gradient, fluid pressure loss, and electric power from simulation data and use these indicators as feature data.

[0071] The neural network operator proxy model is trained using feature extracted data. By learning the complex relationship between input (such as design parameters) and output (such as system performance) in multi-physics simulation data, the neural network operator proxy model can quickly predict performance under different design parameters. The goal of the training process is to achieve a preset number of training times or to achieve specific accuracy requirements. By continuously adjusting the weights and biases of the neural network, it is able to predict the heat transfer performance under given design parameters with high accuracy, and ultimately generate an efficient proxy model to replace the complex numerical simulation process.

[0072] Finally, through discretization, geometric modeling, multi-physics simulation, data extraction and feature extraction, a neural network proxy model is used to learn the input-output relationship in the simulation data, and a proxy model that can quickly predict performance is obtained. This proxy model significantly reduces the computational cost of complex numerical simulation and speeds up the efficiency of design optimization.

[0073] In some examples, the multi-physics coupled finite element simulation includes heat conduction simulation, fluid flow simulation, and current conduction simulation.

[0074] For example, heat conduction simulation aims to predict the temperature distribution at different locations inside and on the surface of a component. It simulates how heat is transferred in a solid structure, especially heat conduction through the component material. Fluid flow simulation is used to simulate the flow behavior of fluids (such as air or coolant) inside or around a component, mainly solving fluid dynamics problems. It analyzes how the coolant flows in the flow channel and takes heat away from the component. Current conduction simulation analyzes the current distribution inside the component, especially the power conduction process in photovoltaic systems. It describes how the current is conducted through the material and analyzes the impact of different design parameters on the current distribution. These simulations are not isolated, but interact with each other. For example, heat conduction and current conduction will affect each other, because changes in temperature will change the resistivity of the material, and the Joule heating generated by the current will affect the temperature distribution; at the same time, the cooling effect of fluid flow will also affect the overall thermal conductivity characteristics of the component. Therefore, these three physical fields need to be coupled together for simulation to accurately capture the mutual influence between different physical phenomena.

[0075] For example, multi-physics field coupling finite element simulation can be performed through but not limited to the following formulas:

[0076] 1) Continuity equation

[0077]

[0078] 2) Momentum equation

[0079]

[0080] 3) Energy equation

[0081]

[0082] In the above formula, u, v, w are the velocity components in the x, y, and z directions respectively; ρ, v w , c p , k m are density, dynamic viscosity, specific heat capacity and thermal conductivity respectively; T is the temperature term; S h is the internal heat source term.

[0083] 4) Circuit equation

[0084] I=I ph -I D -I sh (18)

[0085]

[0086] I ph,ref =I sc,ref (27)

[0087]

[0088] In the above formula, I is the output current; I ph is the photocurrent; I D is the dark current; I0 ​​is the reverse saturation current; I sh is the current through the internal parallel resistor; q is the basic charge; U is the output voltage; R s is the internal series resistance; n is the ideal factor; k is the Boltzmann constant; T is the battery temperature; R sh is the internal parallel resistance; E g is the bandgap width of the solar cell material; α ISC is the temperature coefficient of short-circuit current; α Rs is the temperature coefficient of the internal series resistance; I SC is the short-circuit current; V OC The subscript ref in the above formula indicates that the parameter value is the parameter value under the standard test conditions of solar cells.

[0089] In some examples, the above-mentioned discrete model of the heat exchange zone to be designed and the corresponding evaluation indicators under all the corresponding design parameters are subjected to multi-objective optimization and Pareto front solution set generation operations to obtain the Pareto front solution set, including:

[0090] Generate an initial population according to the discrete model of the heat exchange zone to be designed and the corresponding evaluation indicators under all the corresponding design parameters;

[0091] Perform fitness evaluation operation on the above initial population to obtain the fitness of the individual population;

[0092] According to the individual fitness of the above population, the population evolution is guided by genetic operation and based on non-dominated sorting to obtain a new generation of population and its Pareto frontier solution set.

[0093] In some examples, the initial population is generated based on a Latin hypercube sampling method.

[0094] In some examples, the objective functions corresponding to the above-mentioned fitness evaluation operations include maximizing thermal power output, minimizing temperature gradient, and maximizing electrical power output power.

[0095] For example, Figure 4The figure shows a schematic diagram of the Pareto front solution set obtained by solving. In the first step of multi-objective optimization, it is crucial to generate the initial population. Each individual in the population represents a possible design scheme, and its characteristics are determined by the discrete model of the heat exchange zone to be designed and the corresponding design parameters. One of the methods for generating the initial population is Latin Hypercube Sampling (LHS), which can sample evenly in the multidimensional design space, thereby ensuring the diversity of the population and covering the possible range of design parameters. For example: based on the geometric and physical characteristics of the discrete model of the heat exchange zone to be designed, the LHS method is used to generate the initial population. Each individual may represent a flow channel design scheme, with specific parameters such as flow channel width, length, curvature, etc.

[0096] After the initial population is generated, the fitness of each individual population is evaluated. The basis for fitness evaluation is a predefined objective function, which reflects the specific design requirements of multi-objective optimization. In the heat exchange structure design of photovoltaic thermal components, common objective functions include:

[0097] Maximize thermal power output: Ensure the design performs best in terms of thermal conversion efficiency.

[0098] Minimize temperature gradients: Reduce uneven temperature distribution in the system and prevent local overheating.

[0099] Maximize electrical power output: Improve the electrical power output performance of components.

[0100] In each design, the neural network operator proxy model is used to quickly predict the values ​​of these objective functions. A heat exchange zone design may perform better in some aspects but worse in others, so multi-objective optimization can find a balance point that suits different design requirements.

[0101] After completing the fitness evaluation, the population is evolved using three operations in the genetic algorithm:

[0102] Selection operation: Through methods such as roulette selection or tournament selection, better individuals are selected to enter the next generation based on fitness values.

[0103] Crossover operation: Through genetic recombination, the design parameters of two individuals are combined to generate new individuals and increase population diversity.

[0104] Mutation operation: By randomly changing the design parameters of some individuals, the population can explore new design spaces and avoid falling into local optimal solutions.

[0105] After each iteration, the population evolution is guided based on non-dominated sorting, and the Pareto front solution set of the population is updated. Non-dominated sorting evaluates the performance of each individual in the multi-objective space and selects individuals that are not dominated by other individuals to enter the Pareto front solution set. These solution sets represent the best trade-off solutions between various objective functions.

[0106] Through genetic operations, the design schemes are continuously optimized. One scheme may have outstanding performance in thermal power output, but low electrical power output; while another scheme has obvious advantages in temperature gradient control. Through non-dominated sorting, it is determined which individuals can enter the Pareto front solution set and gradually approach the global optimal solution.

[0107] Multi-objective optimization generates new populations through multiple iterations and continuously updates the Pareto frontier solution set. The termination condition of the iterative process is usually set as: reaching the preset maximum number of iterations. Or, the change in the optimization index of each generation of population is less than the set convergence threshold.

[0108] Through the above multi-objective optimization process, especially in the non-dominated sorting genetic algorithm based on reference direction (PB-NSGA-III), the optimization process can find the best trade-off between different design objectives (such as maximizing thermal power output, minimizing temperature gradient and maximizing electrical power output) through genetic operations and non-dominated sorting, and generate Pareto front solution sets. These solution sets provide designers with a variety of design options, and ultimately they can choose the appropriate design scheme according to actual needs. In this optimization method, the generation of the initial population (such as based on Latin hypercube sampling), the efficient implementation of fitness evaluation, and the evolutionary operation through the genetic algorithm jointly ensure the effectiveness and comprehensiveness of the optimization process.

[0109] In some examples, the obtaining of the comprehensive weight information of the evaluation indicators includes:

[0110] Determine the subjective weight information corresponding to different evaluation indicators based on the best-worst method;

[0111] Calculate objective weight information based on the CRITIC method;

[0112] The weight values ​​of the subjective weight information and the objective weight information are solved according to the Nash equilibrium solution method of game theory to obtain comprehensive weight information.

[0113] Exemplarily, the best-worst method (BWM) is a method for calculating the subjective weight of each evaluation indicator. BWM calculates the subjective weight of each evaluation indicator by constructing a one-to-many comparison matrix to compare the relative importance of each evaluation indicator with respect to the most important and least important indicators. This method improves the efficiency and consistency of weight calculation by reducing the number of pairwise comparisons. The specific steps include: first, determining the most important and least important evaluation indicators; then, evaluating the relative importance of other indicators and these two extreme indicators to form a comparison matrix; finally, calculating the weight of each indicator by solving the optimization model. The advantage of the BWM method lies in its high efficiency and good stability, and it can quickly obtain the subjective weight of the evaluation indicator without losing accuracy.

[0114] V BO =(v B1 ,v B2 ,…,v Bn ) (29)

[0115] V OW =(v 1W ,v 2W ,…,v nW ) (30)

[0116]

[0117] In the above formula, V BO is the importance vector of evaluation index; V OW is the unimportance vector of the evaluation index; ξ is the consistency index; w j 、w B and w W are the weight of the jth evaluation index, the weight of the most important evaluation index, and the weight of the least important evaluation index; v Bj and v jW They are respectively the preference degree of the most important evaluation index relative to the jth evaluation index and the preference degree of the jth evaluation index relative to the least important evaluation index.

[0118] CRITIC method calculates objective weights: The CRITIC (Criteria Importance Through Intercriteria Correlation) method is used to calculate the objective weights of each evaluation indicator. It quantifies the importance of the indicator through the variability and correlation of the evaluation indicators. Specifically, the CRITIC method measures the information content and independence of each indicator in the overall evaluation system by calculating the standard deviation of each indicator and the correlation coefficient with other indicators. The standard deviation reflects the variability of the indicator, and the correlation coefficient reflects the redundancy between indicators. By considering these two factors, the CRITIC method can effectively identify those evaluation indicators that have both high variability and low correlation with other indicators, and assign them higher weights. This method focuses on the objective attributes of the data and can effectively reduce subjective bias in multi-objective comprehensive evaluation.

[0119]

[0120]

[0121] S is the normalized decision matrix; m is the number of Pareto solutions; n is the number of evaluation indicators; Z is the original decision matrix composed of Pareto solutions; and are the best and worst values ​​of the jth evaluation index respectively; w j is the weight of the jth evaluation index; σ j is the standard deviation of the jth criterion; r jj’ is the correlation coefficient between the two evaluation indicators.

[0122] Game theory combination weighting: After obtaining the subjective and objective weights, they need to be combined to form the final weight set. The subjective and objective weights are solved according to the game theory Nash equilibrium solution method. 。

[0123]

[0124] wg is the final combined weight; w i are the weight values ​​obtained from the subjective and objective perspectives respectively; a i is the weighting coefficient of the weight obtained from the subjective and objective perspectives respectively; a i * is the normalized weighting coefficient.

[0125] like Figure 4 As shown, this application proposes a device for optimizing the heat exchange structure of a concentrated photovoltaic and thermal assembly, comprising:

[0126] A first acquisition unit 21 is used to input the discrete model of the heat exchange zone to be designed into the neural network operator proxy model to obtain the corresponding evaluation index under different design parameters, wherein the network operator proxy model is obtained by performing a preset number of training on a multi-physics field coupling finite element simulation result set, and the design parameters include the shape characteristics of the flow area in the heat exchange zone to be designed;

[0127] The second acquisition unit 22 is used to perform multi-objective optimization and Pareto front solution set generation operations on the discrete model of the heat exchange zone to be designed and the corresponding evaluation indicators under all the corresponding design parameters, so as to obtain the Pareto front solution set;

[0128] The third acquisition unit 23 is used to obtain the comprehensive weight information of the above evaluation indicators;

[0129] The determination unit 24 is used to perform a comprehensive evaluation operation by combining TOPSIS and GRA according to the comprehensive weight information and the Pareto front solution set to determine the target flow area.

[0130] like Figure 5 As shown, an embodiment of the present application also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor, and when the processor 320 executes the computer program 311, the steps of any method for optimizing the heat exchange structure of the above-mentioned concentrated photovoltaic thermal component are implemented.

[0131] Since the electronic device introduced in this embodiment is a device used to implement a concentrated photovoltaic and thermal component heat exchange structure optimization design device in the embodiment of the present application, based on the method introduced in the embodiment of the present application, technical personnel in this field can understand the specific implementation mode of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present application will not be introduced in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of the present application is within the scope of protection of this application.

[0132] In the specific implementation process, when the computer program 311 is executed by the processor, it can achieve Figure 1 Any implementation manner in the corresponding embodiments.

[0133] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0134] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0135] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0136] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0137] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0138] An embodiment of the present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device executes the process of optimizing the heat exchange structure of a concentrated photovoltaic thermal component in the corresponding embodiment.

[0139] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or a data center that includes one or more available media integration. The available medium can be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state drive (SSD)), etc.

[0140] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0141] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, 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 an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0142] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0143] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0144] 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 technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0145] The above 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. However, 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 embodiments of the present application.

Claims

1. A method for optimizing the heat exchange structure of a concentrated photovoltaic thermal assembly, characterized in that: include: Inputting the discrete model of the heat exchange zone to be designed into the neural network operator proxy model to obtain the corresponding evaluation index under different design parameters, wherein the network operator proxy model is obtained by performing a preset number of trainings through a multi-physics field coupled finite element simulation result set, and the design parameters include the shape characteristics of the flow area in the heat exchange zone to be designed; Perform multi-objective optimization and Pareto front solution set generation operations on the discrete model of the heat exchange zone to be designed and the corresponding evaluation indicators under all the corresponding design parameters to obtain the Pareto front solution set; Obtaining comprehensive weight information of the evaluation index; A comprehensive evaluation operation is performed by combining TOPSIS and GRA according to the comprehensive weight information and the Pareto front solution set to determine the target flow area.

2. The method for optimizing the heat exchange structure of a concentrated photovoltaic and thermal assembly according to claim 1, characterized in that: The specific steps of obtaining the neural network operator proxy model include: Discretize the model of the heat exchange area to be designed to obtain a discrete model of the heat exchange area to be designed; Automatically generate a corresponding geometric model according to the discrete model of the heat exchange area to be designed and the discretized design parameters; Perform multi-physics field coupling finite element simulation according to the geometric model to obtain simulation result data; Performing data extraction and formatting output operations on the simulation result data to obtain formatted data; Performing feature extraction on the formatted data to obtain feature extraction data; The neural network operator proxy model is trained according to the heat exchange area model to be designed and the feature extraction data to achieve a preset number of training times or a preset accuracy to obtain the neural network operator proxy model.

3. The method for optimizing the heat exchange structure of a concentrated photovoltaic and thermal assembly according to claim 1, characterized in that: The multi-physics field coupled finite element simulation includes heat conduction simulation, fluid flow simulation and current conduction simulation.

4. The method for optimizing the heat exchange structure of a concentrated photovoltaic thermal assembly according to claim 1, characterized in that: The step of performing multi-objective optimization and Pareto front solution set generation operations on the discrete model of the heat exchange zone to be designed and the corresponding evaluation indicators under all the corresponding design parameters to obtain the Pareto front solution set includes: Generate an initial population according to the discrete model of the heat exchange zone to be designed and the corresponding evaluation indicators under all the corresponding design parameters; Performing a fitness evaluation operation on the initial population to obtain individual fitness of the population; According to the fitness of the individuals in the population, the population is guided to evolve through genetic operations based on non-dominated sorting to obtain a new generation of population and its Pareto frontier solution set.

5. The method for optimizing the heat exchange structure of a concentrated photovoltaic and thermal assembly according to claim 4, characterized in that: The initial population is generated based on the Latin hypercube sampling method.

6. The method for optimizing the heat exchange structure of a concentrated photovoltaic and thermal assembly according to claim 4, characterized in that: The objective functions corresponding to the fitness evaluation operation include maximizing thermal power output, minimizing temperature gradient, and maximizing electrical power output.

7. The method for optimizing the heat exchange structure of a concentrated photovoltaic and thermal assembly according to claim 1, characterized in that: The obtaining of the comprehensive weight information of the evaluation index includes: Determine the subjective weight information corresponding to different evaluation indicators based on the best-worst method; Calculate objective weight information based on the CRITIC method; The subjective weight information and the objective weight information are solved for weight values ​​according to the Nash equilibrium solution method of game theory to obtain comprehensive weight information.

8. A device for optimizing the heat exchange structure of a concentrated photovoltaic and thermal assembly, characterized in that: include: A first acquisition unit is used to input the discrete model of the heat exchange zone to be designed into the neural network operator proxy model to obtain the corresponding evaluation index under different design parameters, wherein the network operator proxy model is obtained by performing a preset number of trainings through a multi-physics field coupling finite element simulation result set, and the design parameters include shape characteristics of the flow area in the heat exchange zone to be designed; The second acquisition unit is used to perform multi-objective optimization and Pareto front solution set generation operations on the discrete model of the heat exchange zone to be designed and the corresponding evaluation indicators under all the corresponding design parameters, so as to obtain the Pareto front solution set; A third acquisition unit, used to acquire comprehensive weight information of the evaluation index; A determination unit is used to perform a comprehensive evaluation operation by combining TOPSIS and GRA according to the comprehensive weight information and the Pareto front solution set to determine the target flow area.

9. An electronic device, comprising: A memory and a processor, wherein the processor is used to implement the steps of the method for optimizing the heat exchange structure of a concentrated photovoltaic thermal component as described in any one of claims 1 to 7 when executing a computer program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for optimizing the heat exchange structure of a concentrated photovoltaic thermal assembly as described in any one of claims 1 to 7 are implemented.

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