Solar heat utilization optimization method of heat collector, heat collector equipment and computer device

Abstract

The invention discloses a solar heat utilization optimization method of a heat collector, heat collector equipment and a computer device. The method comprises the following steps: predicting radiation characteristics of suspended nanoparticles based on an artificial neural network model by utilizing a boundary element method; parameters of the heat collector are optimized through a genetic algorithm, and the radiation characteristics of the suspended nanoparticles are evaluated; constructing a heat collector structure optimized by the genetic algorithm in the finite volume method simulation framework, and verifying the rationality of parameters of the heat collector structure; preparing a ternary mixed nano suspension, and verifying the stability of the ternary mixed nano suspension; a heat collector device is prepared based on the ternary mixed nano suspension, a sensor network is arranged in the heat collector device to continuously collect data, the solar heat utilization rate of the heat collector device is determined according to the data, and the data is uploaded to an upper computer to be managed and analyzed. Optimization and performance improvement of the solar heat collector equipment are achieved, and the thermal performance of the heat collector equipment is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of renewable energy, and particularly relates to a method for optimizing solar thermal utilization of a collector, a collector device, and a computer device. Background Art

[0002] As a renewable energy source, solar energy has broad application prospects. Solar thermal utilization technology converts solar radiant energy into heat energy through a collector, and is used in fields such as hot water supply, space heating, and industrial process heating. Traditional solar collectors mainly rely on simple heat conduction and convection mechanisms, and their thermal efficiency is relatively low. Especially when dealing with high-concentration solar radiation, there are problems of large energy losses and low thermal utilization efficiency.

[0003] Existing solar collectors have some limitations in design and performance, mainly reflected in low heat absorption efficiency, large heat loss, and lack of intelligent control. Traditional collectors usually use a single material or a simple fluid as the heat absorption medium, and the absorption ability of these materials for solar radiation is limited, resulting in low heat absorption efficiency. During the operation of the collector, due to unreasonable design or improper material selection, there are large heat losses, especially in the case of low ambient temperature or high wind speed, the heat losses are more significant. Most existing collectors lack an intelligent control system and cannot automatically adjust operating parameters according to environmental conditions (such as solar irradiance, ambient temperature, etc.), resulting in unstable performance under different working conditions. In addition, the structure of traditional collectors is complex, the maintenance cost is high, and it is prone to failures during long-term operation, affecting the stability and reliability of the collector. Summary of the Invention

[0004] The present invention provides a method for optimizing solar thermal utilization of a collector, a collector device, and a computer device, aiming to solve at least one of the technical problems existing in the prior art.

[0005] The technical solution of the present invention is a method for optimizing solar thermal utilization of a collector, which includes: S100: Predict the radiation characteristics of suspended nanoparticles based on an artificial neural network model using the boundary element method; S200: Optimize the parameters of the collector through a genetic algorithm and evaluate the radiation characteristics of the suspended nanoparticles to maximize the solar weighted absorption coefficient and the heat collection efficiency of the collector; S300: Construct the collector structure optimized by the genetic algorithm in a finite volume method simulation framework and verify the rationality of the collector structure parameters; S400: Prepare a ternary hybrid nanofluid and perform dynamic light scattering, Zeta potential analysis, and sedimentation tests on the prepared ternary hybrid nanofluid to verify the stability of the ternary hybrid nanofluid; S500: Prepare a collector device based on the ternary hybrid nanofluid suspension. Arrange a sensor network in the collector device, continuously collect data on the temperature of the ternary hybrid nanofluid suspension, solar irradiance, the flow rate of the ternary hybrid nanofluid suspension, and the heat collection efficiency of the collector device through the sensor network, determine the solar thermal utilization rate of the collector device based on the data, and upload the data to a host computer for management and analysis.

[0006] According to some embodiments of the present invention, the step S100 includes: S110: Generate a data set using the boundary element method. The data set is used to simulate the interaction between different suspended nanoparticles and solar radiation. The data set includes data on the characteristics of the suspended nanoparticles, the medium properties of the suspended nanoparticles, extinction efficiency, absorption efficiency, and scattering efficiency. S120: Divide the data set into a training set, a validation set, and a test set according to a preset ratio. S130: Construct a multi-layer neural network structure in the artificial neural network model. Use the characteristics of the suspended nanoparticles and the medium properties of the suspended nanoparticles in the training set as inputs, and use the extinction efficiency, absorption efficiency, and scattering efficiency in the training set as outputs. And use the Levenberg-Marquardt algorithm to train the artificial neural network model, and iteratively optimize the artificial neural network model to minimize the mean square error. S140: Evaluate the performance of the artificial neural network model during the training process through the data in the validation set, and adjust the hyperparameters of the artificial neural network model. The hyperparameters include the number of hidden layers, the number of neurons, and the learning rate. S150: Evaluate the performance of the artificial neural network model on independent data through the data in the test set to determine the generalization ability of the artificial neural network model.

[0007] According to some embodiments of the present invention, the step S200 includes: S210: Randomly generate a population of diverse candidate solutions within preset agreed conditions. The preset agreed conditions include the length of the collector and the radius of the suspended nanoparticles. S220: The fitness of each solution in the candidate solution population is calculated based on the thermal efficiency, and the radiation characteristics of the suspended nanoparticles are evaluated through CFD simulation. S230: The genetic algorithm adopts an evolutionary strategy. By selecting the high-fitness solutions of the candidate solution population, cross-processing the parameters of the collector, and introducing random variations for mutation processing, continuous iteration is carried out until the optimal solution of the candidate solution population is obtained. S240: Maximize the solar weighted absorption coefficient and the heat collection efficiency of the collector according to the optimal solution.

[0008] According to some embodiments of the present invention, the step S300 includes: S310: Construct a collector structure optimized by a genetic algorithm in a finite volume method simulation framework, and solve the steady-state and incompressible Navier–Stokes equations and energy equations of the collector structure in the finite volume method simulation framework; S320: Discretize the channel geometry into control volumes, and use the SIMPLE algorithm to handle the pressure-velocity coupling, and simulate to obtain the detailed distribution of temperature, velocity and pressure, and verify the rationality of the selected parameters in the collector structure optimized by the genetic algorithm; S330: Vary the key input parameters within the finite volume method simulation framework, where the key input parameters include the collector length and height, Reynolds number, glass transmittance, and suspended nanoparticle volume fraction, and quantify the influence of the key input parameters on the outlet temperature rise and efficiency of the collector to identify the key influencing factors; S340: Vary the flow rate of the ternary hybrid nanofluid, solar irradiance and disturbance error fluctuations within the finite volume method simulation framework, and verify the rationality and stability of the collector parameters optimized by the genetic algorithm.

[0009] According to some embodiments of the present invention, the step S400 includes: S410: Prepare gold, copper, platinum ternary hybrid suspended nanoparticles by chemical reduction or laser ablation methods; S420: Treat the gold, copper, platinum ternary hybrid suspended nanoparticles by ultrasonic waves in a base liquid, and use a surfactant to treat the gold, copper, platinum ternary hybrid suspended nanoparticles to obtain a gold, copper, platinum ternary hybrid nanofluid; S430: Perform dynamic light scattering, Zeta potential analysis and sedimentation tests on the gold, copper, platinum ternary hybrid nanofluid to verify the long-term stability of the gold, copper, platinum ternary hybrid nanofluid.

[0010] According to some embodiments of the present invention, the method further includes: S440: Construct a collector prototype based on the optimal size and the concentration of gold, copper, platinum ternary hybrid suspended nanoparticles, where the collector prototype includes a low-iron transparent glass cover plate, a thermally insulated aluminum channel and a connection interface; S450: Use a solar simulator to provide a constant irradiance. Arrange thermocouples and ultrasonic flowmeters at the inlet and outlet of the collector prototype. Measure the temperature rise through the thermocouples and detect the volumetric flow rate through the ultrasonic flowmeters. Determine the heat collection efficiency of the collector prototype based on the temperature rise and the volumetric flow rate; S460: Compare the consistency between the heat collection efficiency of the collector preset by the genetic algorithm and the heat collection efficiency of the collector prototype to verify the effectiveness of the artificial neural network model - genetic algorithm.

[0011] According to some embodiments of the present invention, the step S500 includes: S510: Based on the channel geometry and ternary hybrid nanofluid, use a structural frame made of anodized aluminum or stainless steel and tempered low-iron glass as the transparent cover plate of the collector device; S520: Integrate an automatic flow control device in the collector device to maintain a stable fluid circulation under different environmental conditions; S530: Deploy a sensor network supported by the Internet of Things, and continuously collect data on the temperature of the ternary hybrid nanofluid, solar irradiance, the flow rate of the ternary hybrid nanofluid, and the heat collection efficiency of the collector device through the sensor network; S540: Determine the solar thermal utilization rate of the collector device based on the data; S550: Upload the data and the solar thermal utilization rate of the collector device to the host computer for management and analysis.

[0012] According to some embodiments of the present invention, the method further includes: S511: Prepare a heat insulation wall and set the heat insulation wall on both sides of the collector device, and the heat insulation wall and the transparent cover plate are perpendicular to each other; S512: Set a heat collection channel in the collector device and set an inlet at the starting point of the heat collection channel to enable the ternary hybrid nanofluid to enter the heat collection channel through the inlet and flow inside the collector device to absorb solar energy; S513: Set an outlet at the end point of the heat collection channel to enable the ternary hybrid nanofluid heated by solar energy to be discharged from the heat collection channel through the outlet.

[0013] An embodiment of the present invention also provides a collector device, including: A transparent cover plate, arranged on the top of the collector device, for receiving solar irradiation; A heat insulation wall, arranged on both sides of the collector device, for thermal insulation to minimize heat conduction loss; A ternary hybrid nanofluid, disposed inside the collector device, for absorbing solar energy; An inlet, disposed at the starting point of the heat collection channel, for providing an entrance for the ternary hybrid nanofluid to enter the heat collection channel; An outlet, disposed at the end point of the heat collection channel, for providing an outlet for the ternary hybrid nanofluid heated by solar energy to discharge from the heat collection channel.

[0014] The technical solution of the present invention further relates to a computer device, including a memory and a processor. When the processor executes a computer program stored in the memory, the above method is implemented.

[0015] The technical solution of the present invention further relates to a computer-readable storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the above method is implemented.

[0016] The solar thermal utilization optimization method, collector device, and computer device provided by the embodiments of the present invention at least have one of the following advantages or beneficial effects: Develop and train an artificial neural network model using the boundary element method. Simulate the radiation behavior of suspended nanoparticles under different boundary conditions through the boundary element method. The artificial neural network quickly and accurately predicts the radiation characteristics of suspended nanoparticles under different working conditions. Use the genetic algorithm to optimize the structural parameters of the collector, and use the radiation characteristics of suspended nanoparticles as part of the objective function in the genetic algorithm. Combine the performance indicators of the collector (such as heat collection efficiency, heat loss, etc.) for comprehensive evaluation. The collector parameters optimized by the genetic algorithm enable the suspended nanoparticles to have a higher absorption efficiency in the collector, while reducing heat loss, thereby improving the performance of the entire heat collection system. Construct a simulation model of the collector structure using the finite volume method to simulate the flow and heat transfer processes of the suspended nanoparticles in the nanofluid in the collector structure. Through the finite volume method, parameters such as the temperature distribution, velocity field of the suspended nanoparticles in the nanofluid, and the thermal efficiency of the collector structure can be accurately calculated; then, verify the rationality of the collector structure parameters. Prepare a ternary hybrid nanofluid suspension, and perform dynamic light scattering, Zeta potential analysis, and sedimentation tests on the prepared ternary hybrid nanofluid suspension to verify the stability of the ternary hybrid nanofluid suspension. Then, based on the ternary hybrid nanofluid suspension, prepare a collector device. The ternary hybrid nanofluid suspension is filled into the collector device through the heat collection channel, which is beneficial to the flow and heat transfer of the ternary hybrid nanofluid suspension. Arrange a sensor network in the collector device to collect data on the temperature of the ternary hybrid nanofluid suspension, solar irradiance, the flow rate of the ternary hybrid nanofluid suspension, and the heat collection efficiency of the collector device. Determine the solar thermal utilization rate of the collector device based on the data, and upload the data to the host computer for management and analysis. The host computer can store, process, and analyze the data, generate various reports and charts, and be used to evaluate the performance of the collector device and optimize the operating parameters. Through data analysis, the performance change law of the collector device under different working conditions can be found, thereby further optimizing the design and operating strategy of the collector device and improving the solar thermal utilization rate.

[0017] In addition, some of the additional aspects and advantages of the present invention will be given in the following description, some will become apparent from the following description, or will be understood through the practice of the present invention. Brief Description of the Drawings

[0018] Figure 1 is the overall flowchart of a solar thermal utilization optimization method for a collector provided by an embodiment of the present invention; Figure 2 is the detailed flowchart of step S100 in the solar thermal utilization optimization method for a collector provided by an embodiment of the present invention; Figure 3It is a detailed flowchart of step S200 in the solar thermal utilization optimization method of the collector provided by an embodiment of the present invention; Figure 4 It is a detailed flowchart of step S300 in the solar thermal utilization optimization method of the collector provided by an embodiment of the present invention; Figure 5 It is a detailed flowchart of step S400 in the solar thermal utilization optimization method of the collector provided by an embodiment of the present invention; Figure 6 It is the first detailed flowchart of the solar thermal utilization optimization method of the collector provided by an embodiment of the present invention; Figure 7 It is a detailed flowchart of step S500 in the solar thermal utilization optimization method of the collector provided by an embodiment of the present invention; Figure 8 It is the second detailed flowchart of the solar thermal utilization optimization method of the collector provided by an embodiment of the present invention; Figure 9 It is a schematic structural diagram of a collector device provided by an embodiment of the present invention. Detailed implementation manners

[0019] The following will clearly and completely describe the concept, specific structure and technical effects generated by the present invention in combination with embodiments and drawings, so as to fully understand the purpose, solution and effects of the present invention.

[0020] It should be noted that, unless otherwise specified, when a certain feature is referred to as "fixed" or "connected" to another feature, it can be directly fixed or connected to the other feature, or indirectly fixed or connected to the other feature. The singular forms "a", "the" and "said" used herein are also intended to include the plural forms, unless the context clearly indicates otherwise. In addition, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field of the present invention. The terms used in the description of the present invention herein are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0021] It should be understood that although terms such as first, second, and third may be used in the present invention to describe various elements, these elements should not be limited to these terms. These terms are only used to distinguish elements of the same type from each other. For example, without departing from the scope of the present invention, the first element may also be referred to as the second element, and similarly, the second element may also be referred to as the first element. The use of any and all examples or exemplary languages (such as "for example", "such as", etc.) provided herein is only intended to better illustrate the embodiments of the present invention, and unless otherwise required, will not impose limitations on the scope of the present invention.

[0022] In the related art, there are some limitations in the design and performance of existing solar collectors, mainly reflected in the low heat absorption efficiency, large heat loss, and lack of intelligent control. Traditional collectors usually use a single material or a simple fluid as the heat absorption medium. The absorption capacity of these materials for solar radiation is limited, resulting in low heat absorption efficiency. During the operation of the collector, due to unreasonable design or improper material selection, there is a large amount of heat loss, especially in the case of low ambient temperature or high wind speed, and the heat loss is more significant. Most of the existing collectors lack an intelligent control system and cannot automatically adjust the operating parameters according to environmental conditions (such as solar irradiance, ambient temperature, etc.), resulting in unstable performance under different working conditions. In addition, the structure of traditional collectors is complex, the maintenance cost is high, and it is prone to failure during long-term operation, affecting the stability and reliability of the collector.

[0023] Based on this, the embodiments of the present invention provide a solar thermal utilization optimization method for a collector, a collector device, and a computer device. The artificial neural network is developed and trained using the boundary element method, which can quickly and accurately predict the radiative properties of ternary hybrid suspended nanoparticles; the genetic algorithm system is applied to optimize the parameters of the collector, including the geometric dimensions (length and height) of the collector, the particle size and concentration of the ternary hybrid suspended nanoparticles, to maximize the solar weighted absorption coefficient and the overall heat collection efficiency of the collector; a collector device is constructed using the ternary hybrid nanofluid, and various parameter data of the collector device are obtained through a sensor network, and the solar thermal utilization rate of the collector device is determined based on these data. Through the comprehensive application of these technologies, the optimized design and performance improvement of the solar collector device are realized, thereby improving the thermal performance of the collector device.

[0024] Refer to Figure 1 as shown Figure 1 FIG. is the overall flowchart of a solar thermal utilization optimization method for a collector provided by an embodiment of the present invention. The solar thermal utilization optimization method for a collector includes but is not limited to steps S100 to S500. Specifically, S100: Predict the radiative properties of suspended nanoparticles based on an artificial neural network model using the boundary element method; S200: Optimize the parameters of the collector through the genetic algorithm and evaluate the radiative properties of the suspended nanoparticles to maximize the solar weighted absorption coefficient and the heat collection efficiency of the collector; S300: Construct the collector structure optimized by the genetic algorithm in the finite volume method simulation framework and verify the rationality of the collector structure parameters; S400: Prepare a ternary hybrid nanofluid suspension, and perform dynamic light scattering, Zeta potential analysis, and sedimentation tests on the prepared ternary hybrid nanofluid suspension to verify the stability of the ternary hybrid nanofluid suspension; S500: Based on the ternary hybrid nanofluid suspension, prepare a collector device. Arrange a sensor network in the collector device, continuously collect data on the temperature of the ternary hybrid nanofluid suspension, solar irradiance, the flow rate of the ternary hybrid nanofluid suspension, and the heat collection efficiency of the collector device through the sensor network. Determine the solar thermal utilization rate of the collector device according to the data, and upload the data to the host computer for management and analysis.

[0025] In some embodiments of the present application, the boundary element method is used to predict the radiative properties of suspended nanoparticles based on an artificial neural network model. It can be understood that the boundary element method is a numerical calculation method for solving partial differential equations. It transforms the problem into a boundary integral equation by discretizing the boundary of the problem. In the prediction of the radiative properties of nanoparticles, the radiative behavior of nanoparticles under different boundary conditions, such as the thermal radiative exchange between nanoparticles and the surrounding medium, is simulated through the boundary element method. The boundary element method can accurately handle complex boundary conditions, especially suitable for the case of nanoparticles with complex geometries and multi-layer structures.

[0026] An artificial neural network is a computational model that simulates the structure and function of the human brain neuron network, with powerful non-linear fitting ability and self-adaptive learning ability. When predicting the radiative properties of nanoparticles, the artificial neural network can be trained using existing experimental data or numerical simulation data to establish a mapping relationship between the input (such as the size, shape, material, etc. of the nanoparticles) and the output (radiative properties). For example, by inputting parameters such as the size distribution and material properties of the nanoparticles, the artificial neural network can predict its radiative properties under different working conditions, such as the radiative absorption coefficient, scattering coefficient, etc.

[0027] Therefore, by using the boundary element method to develop and train an artificial neural network, the radiative properties (extinction coefficient) of suspended nanoparticles can be predicted quickly and accurately.

[0028] Optimize the parameters of the collector through a genetic algorithm. The genetic algorithm is a search optimization algorithm based on the principles of natural selection and genetics. It gradually optimizes the objective function by simulating the biological evolution process and using operations such as selection, crossover, and mutation. In the embodiments of the present invention, the structural parameters of the collector (such as the shape, size, filling amount of the nanofluid, etc.) are optimized through the genetic algorithm to maximize the solar weighted absorption coefficient and the heat collection efficiency of the collector.

[0029] Evaluate the radiative properties of suspended nanoparticles through a genetic algorithm. By taking the radiative properties of suspended nanoparticles as part of the objective function and combining the performance indicators of the collector (such as the heat collection efficiency, heat loss, etc.), a comprehensive evaluation is carried out. For example, the collector parameters optimized by the genetic algorithm can enable the suspended nanoparticles to have a higher absorption efficiency in the collector, while reducing heat loss, thereby improving the performance of the entire heat collection system.

[0030] Construct the collector structure optimized by the genetic algorithm in the finite volume method simulation framework. The finite volume method is a numerical calculation method based on control volumes and is applied to the solution of fluid mechanics and heat conduction problems. It establishes discrete equations by dividing the computational domain into multiple control volumes and then applying the conservation laws to each control volume. In the present invention, the finite volume method is used to build a simulation model of the collector to simulate the flow and heat transfer processes of the nanofluid in the collector. Through the finite volume method, parameters such as the temperature distribution, velocity field of the nanofluid, and the heat efficiency of the collector can be accurately calculated.

[0031] After that, verify the rationality of the collector structure parameters. In the finite volume method simulation framework, input the collector structure parameters optimized by the genetic algorithm into the simulation model, and verify whether the optimized structure parameters are reasonable by comparing with experimental data or theoretical results. For example, compare the heat efficiency of the collector obtained by simulation calculation with the experimental measurement value. If the error between the two is within a reasonable range, it indicates that the optimized structure parameters are reasonable.

[0032] Prepare a ternary hybrid nanofluid. A ternary hybrid nanofluid refers to a nanofluid formed by dispersing three different types of nanoparticles (such as metal nanoparticles, metal oxide nanoparticles, and carbon nanoparticles, etc.) in a base fluid. This hybrid nanofluid can comprehensively utilize the characteristics of different nanoparticles to improve the thermal conductivity and optical properties of the fluid. For example, metal nanoparticles have a high thermal conductivity, metal oxide nanoparticles have good chemical stability, and carbon nanoparticles have excellent optical absorption properties. The mixture of the three can improve the comprehensive performance of the nanofluid. In the embodiment of the present invention, the ternary hybrid nanofluid includes gold nanoparticles, copper nanoparticles, and platinum nanoparticles.

[0033] Dynamic light scattering, Zeta potential analysis and sedimentation tests were carried out on the prepared ternary hybrid nanofluid to verify the stability of the ternary hybrid nanofluid. Specifically, dynamic light scattering was used to measure the particle size distribution and diffusion coefficient of the nanoparticles. The dispersion state and stability of the nanoparticles in the fluid can be evaluated by dynamic light scattering. If the nanoparticles are uniformly dispersed in the fluid and the particle size distribution is stable, it indicates that the nanofluid has good stability. Zeta potential analysis was used to measure the charge situation on the surface of the nanoparticles. The higher the absolute value of the Zeta potential, the greater the electrostatic repulsion between the nanoparticles, and the less likely they are to agglomerate, thus improving the stability of the ternary hybrid nanofluid. Sedimentation tests were used to evaluate the stability of the ternary hybrid nanofluid by observing the sedimentation rate and sedimentation time of the nanoparticles in the fluid. If the nanoparticles do not sediment or sediment very slowly over a long period of time, it indicates that the ternary hybrid nanofluid has good stability.

[0034] Based on the ternary hybrid nanofluid, a collector device was prepared. The ternary hybrid nanofluid was filled into the collector through a heat collection channel or cavity, which is beneficial to the flow and heat transfer of the ternary hybrid nanofluid. A sensor network was arranged in the collector device, including temperature sensors, solar irradiance sensors, flow sensors, etc. The temperature of the ternary hybrid nanofluid can be continuously collected through the temperature sensor, the solar irradiance can be continuously collected through the solar irradiance sensor, and the flow rate of the ternary hybrid nanofluid can be continuously collected through the flow sensor. The temperature change of the ternary hybrid nanofluid can be monitored in real time through the temperature sensor, the solar radiation intensity can be measured by the solar irradiance sensor, and the flow rate of the ternary hybrid nanofluid can be measured by the flow sensor. The solar thermal utilization rate of the collector device can be calculated through these data.

[0035] The collected data was uploaded to the host computer for management and analysis. The host computer can store, process and analyze the data, generate various reports and charts, and be used to evaluate the performance of the collector device and optimize the operating parameters. Through data analysis, the performance change law of the collector device under different working conditions can be found, so as to further optimize the design and operation strategy of the collector device and improve the solar thermal utilization rate.

[0036] The solar thermal utilization optimization method of the collector provided by the present invention uses the boundary element method to develop and train an artificial neural network, which can quickly and accurately predict the radiative properties of ternary hybrid suspended nanoparticles; applies a genetic algorithm system to optimize various parameters of the collector, including the geometric dimensions (length and height) of the collector, the particle size and concentration of the ternary hybrid suspended nanoparticles, so as to maximize the solar weighted absorption coefficient and the overall heat collection efficiency of the collector; constructs a collector device using the ternary nano-suspension, and obtains various parameter data of the collector device through a sensor network, and determines the solar thermal utilization rate of the collector device according to these data. Through the comprehensive application of these technologies, the optimized design and performance improvement of the solar collector device are realized, thereby improving the thermal performance of the collector device.

[0037] Refer to Figure 2 as shown Figure 2 is the detailed flowchart of step S100 in the solar thermal utilization optimization method of the collector provided by the embodiment of the present invention. Step S100 includes but is not limited to steps S110 to S150. Specifically, S110: Generate a data set using the boundary element method. The data set is used to simulate the interaction between different suspended nanoparticles and solar radiation. The data set includes data on the properties of the suspended nanoparticles, the medium properties of the suspended nanoparticles, extinction efficiency, absorption efficiency, and scattering efficiency. S120: Divide the data set into a training set, a validation set, and a test set according to a preset ratio. S130: Construct a multi-layer neural network structure in the artificial neural network model. Use the properties of the suspended nanoparticles and the medium properties of the suspended nanoparticles in the training set as inputs, and use the extinction efficiency, absorption efficiency, and scattering efficiency in the training set as outputs. And use the Levenberg-Marquardt algorithm to train the artificial neural network model, and iteratively optimize the artificial neural network model to minimize the mean square error. S140: Evaluate the performance of the artificial neural network model during the training process through the data of the validation set, and adjust the hyperparameters of the artificial neural network model. The hyperparameters include the number of hidden layers, the number of neurons, and the learning rate. S150: Evaluate the performance of the artificial neural network model on independent data through the data of the test set to determine the generalization ability of the artificial neural network model.

[0038] In some embodiments of the present invention, first, a large number of data sets are generated using the boundary element method. These data sets are used to simulate the interaction between different suspended nanoparticles and solar radiation. It can be understood that the data in the data sets cover changes such as the particle material, size, shape, refractive index of the medium, and solar spectral wavelength of the suspended nanoparticles. The generated data sets are divided into input and output. The input includes the characteristics of the suspended nanoparticles and the medium properties of the suspended nanoparticles, and the output includes the extinction efficiency, absorption efficiency, and scattering efficiency. These data sets are accurately divided into a training set, a validation set, and a test set according to the ratio of 70%, 15%, and 15%. A multi-layer neural network structure is constructed in the artificial neural network model. The characteristics of the suspended nanoparticles and the medium properties of the suspended nanoparticles in the training set are used as the input, and the extinction efficiency, absorption efficiency, and scattering efficiency in the training set are used as the output. The Levenberg-Marquardt algorithm is used to train the artificial neural network model. The Levenberg-Marquardt algorithm has strong robustness in dealing with nonlinear regression problems. The Levenberg-Marquardt algorithm is an efficient nonlinear least squares optimization algorithm that can quickly converge to the solution that minimizes the mean square error. Therefore, it can iteratively optimize the artificial neural network model to achieve the minimization of the mean square error, thereby ensuring high accuracy of prediction. The performance of the artificial neural network model during the training process is evaluated through the data in the validation set, including indicators such as the mean square error and the correlation coefficient. The hyperparameters of the artificial neural network model, such as the number of hidden layers, the number of neurons, and the learning rate, are adjusted according to the evaluation results. The performance of the artificial neural network model on independent data is evaluated through the data in the test set to determine the generalization ability of the model. The evaluation indicators can include the mean square error, the correlation coefficient, and the scatter plot of the predicted value and the actual value.

[0039] Generate data sets using the boundary element method and predict the radiation characteristics of suspended nanoparticles through an artificial neural network model. Through the training, validation, and testing processes, an accurate prediction model can be established to provide theoretical support and guidance for the application of suspended nanoparticles in solar collectors.

[0040] Refer to Figure 3 as shown Figure 3 is the detailed flowchart of step S200 in the solar thermal utilization optimization method of the solar collector provided by the embodiment of the present invention. Step S200 includes but is not limited to steps S210 to S240. Specifically, S210: Randomly generate a population of diverse candidate solutions within preset agreed conditions. The preset agreed conditions include the length of the solar collector and the radius of the suspended nanoparticles; S220: The fitness of each solution in the population of candidate solutions is calculated based on the thermal efficiency, and the radiation characteristics of the suspended nanoparticles are evaluated through CFD simulation; S230: The genetic algorithm adopts an evolutionary strategy. By selecting high-fitness solutions from the candidate solution population, performing crossover processing on the parameters of the hybrid collector, and introducing random variations for mutation processing, it continuously iterates until the optimal solution of the candidate solution population is obtained. S240: Maximize the solar weighted absorption coefficient and the heat collection efficiency of the collector according to the optimal solution.

[0041] In some embodiments of the present invention, the parameters of the collector are systematically optimized by a genetic algorithm, including clarifying the optimization objectives (maximizing the solar weighted absorption coefficient and the heat collection efficiency of the collector) and setting design variables (collector geometric parameters, such as length and height, the size and volume fraction of suspended nanoparticles).

[0042] First, within a preset convention condition (collector length and the radius of suspended nanoparticles), a diverse candidate solution population is randomly generated. Each solution represents a set of parameter combinations of the collector and suspended nanoparticles. The fitness of each solution in the candidate solution population is calculated based on the thermal efficiency, and the thermal efficiency can be evaluated through CFD (Computational Fluid Dynamics) simulation. The radiation characteristics of suspended nanoparticles, including extinction efficiency, absorption efficiency, and scattering efficiency, are evaluated using CFD simulation. The CFD simulation can provide the flow and heat transfer conditions of suspended nanoparticles in the collector, thereby calculating the thermal efficiency of the collector. Next, the genetic algorithm adopts an evolutionary strategy, selects high-fitness solutions from the candidate solution population, that is, selects solutions with high thermal efficiency, performs crossover processing on the parameters of the collector, that is, exchanges some parameters between two high-fitness solutions to generate new solutions, and introduces random variations for mutation processing, that is, introduces random variations in some parameters to increase the diversity of the population. The above process is continuously iterated until the optimal solution in the candidate solution population is obtained. According to the optimal solution, the solar weighted absorption coefficient can be maximized, and the absorption efficiency of suspended nanoparticles for solar radiation can be improved. The optimal solution can also improve the heat collection efficiency of the collector, that is, improve the efficiency of the collector in converting solar energy into heat energy.

[0043] The parameters of the collector and suspended nanoparticles are optimized using a genetic algorithm to maximize the solar weighted absorption coefficient and the heat collection efficiency of the collector. Evaluating the radiation characteristics of suspended nanoparticles through CFD simulation can provide theoretical support and guidance for the design and optimization of the collector. Selecting the optimal solution from the candidate solution population can simulate and verify the reliability of the collector's parameters in detail through sensitivity analysis, ensuring that the changes in the collector's parameters during actual operation have good robustness.

[0044] Refer to Figure 4 as shown Figure 4 is the detailed flowchart of step S300 in the solar thermal utilization optimization method of the collector provided by the embodiment of the present invention. Step S300 includes but is not limited to steps S310 to S340. Specifically, S310: Construct the collector structure optimized by the genetic algorithm in the finite volume method simulation framework, and solve the steady-state and incompressible Navier–Stokes equations and energy equations of the collector structure in the finite volume method simulation framework; S320: Discretize the channel geometry into control volumes, and use the SIMPLE algorithm to handle the pressure-velocity coupling, and simulate to obtain the detailed distribution of temperature, velocity and pressure, and verify the rationality of the selected parameters in the collector structure optimized by the genetic algorithm; S330: Vary the key input parameters within the finite volume method simulation framework. The key input parameters include the length and height of the collector structure, the Reynolds number, the glass transmittance, and the volume fraction of suspended nanoparticles, and quantify the influence of the key input parameters on the outlet temperature rise and efficiency of the collector structure to identify the key influencing factors; S340: Vary the flow rate of the ternary hybrid nanofluid, the solar irradiance, and the disturbance error fluctuation within the finite volume method simulation framework, and verify the rationality and stability of the collector structure parameters optimized by the genetic algorithm.

[0045] In some embodiments of the present invention, to ensure that the parameters of the collector optimized by the genetic algorithm can achieve the expected thermal performance under actual conditions, first, a geometric model of the collector structure optimized by the genetic algorithm will be constructed in the finite volume method simulation framework. The channel geometry of the collector is discretized into multiple control volumes, and the conservation law is applied to each control volume to establish the discrete equations. Solve the steady-state and incompressible Navier–Stokes equations and energy equations in the collector structure to describe the flow behavior of the ternary hybrid nanofluid. The Navier–Stokes equations include the momentum equation and the continuity equation, which are used to calculate the velocity field and pressure field of the ternary hybrid nanofluid. Solve the energy equation to describe the heat conduction and convection behavior of the ternary hybrid nanofluid. The energy equation is used to calculate the temperature field of the ternary hybrid nanofluid, considering factors such as solar radiation absorption, heat conduction, and convection. Among them, the energy equation combines the extinction coefficient in the radiation characteristics of suspended nanoparticles predicted by the artificial neural network model, considers convective and radiative heat transfer, and uses the SIMPLE algorithm to handle the pressure-velocity coupling. The SIMPLE (Semi-Implicit Method for Pressure-Linked Equations) algorithm is a numerical method used to handle the pressure-velocity coupling problem. By iteratively solving the momentum equation and the continuity equation, the velocity field and pressure field are gradually updated until convergence. The simulation can obtain the detailed distribution of temperature, velocity, and pressure, including the velocity vector diagram, temperature contour diagram, and pressure distribution diagram of the suspended nanofluid in the ternary hybrid nanofluid. Through these results, the rationality of the selected parameters in the collector structure optimized by the genetic algorithm can be verified.

[0046] After verifying the benchmark performance, further sensitivity analysis is carried out: within the finite volume method simulation framework in the actual operating range, key parameters such as the collector structure length and height, Reynolds number, glass transmittance, and volume fraction of suspended nanoparticles are changed, and the outlet temperature rise value and efficiency of the collector structure are calculated through simulation. By comparing the simulation results under different parameter combinations, the influence of key input parameters on the performance of the collector structure is quantified, and key influencing factors are identified. Within the finite volume method simulation framework, the flow rate of the ternary hybrid nanofluid, solar irradiance, and disturbance error fluctuations (such as fluctuations in ambient temperature) are changed. By changing these parameters within the finite volume method simulation framework, the rationality and stability of the collector structure parameters optimized by the genetic algorithm are verified through simulation. For example, by calculating the heat collection efficiency and outlet temperature rise of the collector under different flow rates and solar irradiances through simulation, the performance of the collector structure under different working conditions is evaluated. By introducing disturbance error fluctuations, the anti-interference ability and stability of the collector during actual operation are evaluated.

[0047] Through the finite volume method simulation framework, the fluid flow and heat conduction processes in the collector structure optimized by the genetic algorithm can be detailedly simulated. By changing the key input parameters and disturbance error fluctuations, the influence of these parameters on the performance of the collector structure can be quantified, and the rationality and stability of the optimized collector structure parameters can be verified. This method provides a scientific basis for the design and optimization of the collector, which helps to improve the performance and reliability of the collector.

[0048] Refer to Figure 5 As shown in Figure 5 is the detailed flowchart of step S400 in the solar thermal utilization optimization method of the collector provided by the embodiment of the present invention. Step S400 includes but is not limited to steps S410 to S430. Specifically, S410: Prepare gold, copper, and platinum ternary hybrid suspended nanoparticles by chemical reduction or laser ablation methods; S420: Process the gold, copper, and platinum ternary hybrid suspended nanoparticles by ultrasonic waves in the base liquid, and process the gold, copper, and platinum ternary hybrid suspended nanoparticles with surfactants to obtain a gold, copper, and platinum ternary hybrid nanofluid; S430: Perform dynamic light scattering, Zeta potential analysis, and sedimentation tests on the gold, copper, and platinum ternary hybrid nanofluid to verify the long-term stability of the gold, copper, and platinum ternary hybrid nanofluid.

[0049] In some embodiments of the present invention, in the experimental stage, the preparation of the ternary hybrid nanofluid suspension is carried out first. The ternary hybrid nanofluid nanoparticles are synthesized by chemical reduction or laser ablation methods, and then uniformly dispersed in a base fluid such as Therminol VP-1 or water by ultrasonic treatment to obtain a ternary hybrid nanofluid suspension of gold, copper, and platinum, supplemented with an appropriate surfactant to enhance stability and prevent agglomeration. The optimal volume fraction is usually on the order of 10⁻ 5 order of magnitude, determined by the integrated artificial neural network model-genetic algorithm method to achieve the best balance between solar absorption and fluid viscosity. The prepared ternary hybrid nanofluid suspension will be subjected to characterization experiments such as dynamic light scattering, Zeta potential analysis, and sedimentation tests to verify the long-term stability of the ternary hybrid nanofluid suspension.

[0050] Specifically, the chemical reduction method is a method for preparing nanoparticles, in which metal ions are reduced to metal nanoparticles by chemical reducing agents. For example, sodium citrate can be used as a reducing agent and stabilizer to prepare gold nanoparticles. Mix the chloroauric acid (HAuCl4) solution with the sodium citrate solution and react under heating conditions to generate gold nanoparticles; mix the copper sulfate (CuSO4) solution with the sodium borohydride (NaBH4) solution to generate copper nanoparticles; mix the chloroplatinic acid (H2PtCl6) solution with the ascorbic acid (vitamin C) solution to generate platinum nanoparticles; then, mix the prepared gold nanoparticles, copper nanoparticles, and platinum nanoparticles to form a ternary hybrid nanofluid suspension.

[0051] The laser ablation method is a method that uses a high-energy laser beam to ablate a metal target to generate metal nanoparticles. During the laser ablation process, the metal target is instantly vaporized under the action of the laser to form a metal vapor, which forms nanoparticles after cooling. Place the gold target in deionized water and ablate the gold target with a laser to generate gold nanoparticles. Place the copper target in deionized water and ablate the copper target with a laser to generate copper nanoparticles. Place the platinum target in deionized water and ablate the platinum target with a laser to generate platinum nanoparticles. Then, mix the prepared gold nanoparticles, copper nanoparticles, and platinum nanoparticles to form a ternary hybrid nanofluid suspension.

[0052] Ultrasonic treatment can enhance the dispersion of ternary hybrid suspended nanoparticles in the base liquid. Through the cavitation effect of ultrasonic waves, the ternary hybrid suspended nanoparticles are uniformly dispersed in the base liquid. The ternary hybrid suspended nanoparticles of gold, copper, and platinum are added to the base liquid (such as deionized water or ethylene glycol). An ultrasonic processor is used to treat the mixture, usually with a treatment time of 30 minutes to 1 hour and a power of 200 - 400 W. Surfactants can be adsorbed on the surface of the ternary hybrid suspended nanoparticles to form a protective film, preventing the ternary hybrid suspended nanoparticles from agglomerating, thereby improving the stability of the ternary hybrid suspension. For example, surfactants such as polyvinylpyrrolidone PVP and cetyltrimethylammonium bromide CTAB are selected and added to the ternary hybrid nano - suspension after ultrasonic treatment, and stirred evenly to make the surfactants uniformly adsorbed on the surface of the ternary hybrid suspended nanoparticles.

[0053] Dynamic light scattering technology is used to measure the particle size distribution and diffusion coefficient of the suspended nanoparticles in the ternary hybrid nano - suspension. By analyzing the intensity fluctuations of the scattered light, the particle size distribution and diffusion coefficient of the suspended nanoparticles in the ternary hybrid nano - suspension can be obtained. The treated ternary hybrid nano - suspension is placed in a dynamic light scattering instrument for measurement, and the particle size distribution and diffusion coefficient of the suspended nanoparticles in the ternary hybrid nano - suspension are recorded to evaluate the dispersion and stability of the ternary hybrid nano - suspension.

[0054] Zeta potential analysis is used to measure the charge condition on the surface of the suspended nanoparticles in the ternary hybrid nano - suspension. The higher the absolute value of the Zeta potential, the greater the electrostatic repulsion between the suspended nanoparticles in the ternary hybrid nano - suspension, and the less likely they are to agglomerate. The treated ternary hybrid nano - suspension is placed in a Zeta potential analyzer for measurement, and the Zeta potential value is recorded to evaluate the surface charge condition and stability of the suspended nanoparticles in the ternary hybrid nano - suspension.

[0055] The sedimentation test evaluates the stability of the ternary hybrid nano - suspension by observing the sedimentation rate and sedimentation time of the suspended nanoparticles in the ternary hybrid nano - suspension. If the suspended nanoparticles do not sediment or sediment very slowly within a long time, it indicates that the ternary hybrid nano - suspension has good stability. The treated ternary hybrid nano - suspension is placed in a transparent container and left to stand for 24 hours, and the sedimentation of the suspended nanoparticles is observed, and the sedimentation rate and sedimentation time are recorded.

[0056] Prepare gold, copper, and platinum ternary hybrid suspended nanoparticles by chemical reduction or laser ablation methods, and improve the dispersibility and stability of the ternary hybrid suspension in the base liquid through ultrasonic treatment and surfactant treatment. Verify the long-term stability of the ternary hybrid suspension through dynamic light scattering, Zeta potential analysis, and sedimentation tests. These steps ensure the stability and performance of the ternary hybrid nanosuspension in practical applications.

[0057] Refer to Figure 6 as shown Figure 6 is the first detailed flowchart of the solar thermal utilization optimization method for the solar collector provided by the embodiment of the present invention. The solar thermal utilization optimization method for the solar collector further includes, but is not limited to, steps S440 to S460. Specifically, S440: Construct a solar collector prototype based on the optimal size and the concentration of gold, copper, and platinum ternary hybrid suspended nanoparticles. The solar collector prototype includes a low-iron transparent glass cover plate, a thermally insulated aluminum channel, and a connection interface; S450: Use a solar simulator to provide a constant irradiance. Arrange thermocouples and ultrasonic flow meters at the inlet and outlet of the solar collector prototype. Measure the temperature rise through the thermocouple, detect the volumetric flow rate through the ultrasonic flow meter, and determine the heat collection efficiency of the solar collector prototype based on the temperature rise and the volumetric flow rate; S460: Compare the consistency of the heat collection efficiency of the solar collector preset by the genetic algorithm and the heat collection efficiency of the solar collector prototype to verify the effectiveness of the artificial neural network model - genetic algorithm.

[0058] In some embodiments of the present invention, based on the verified solar collector structure and the concentration of the ternary hybrid suspended nanoparticles, construct a laboratory-scale solar collector prototype based on the optimal size and concentration. The structure of the solar collector prototype includes a low-iron transparent glass cover plate, a thermally insulated aluminum channel, and a connection interface for easy operation. It can be understood that the low-iron transparent glass has a high light transmittance, which can minimize the reflection and absorption losses of solar radiation and ensure that more solar radiation energy enters the interior of the solar collector prototype. Select low-iron transparent glass with an appropriate thickness to ensure its mechanical strength and optical performance. The aluminum channel has good thermal conductivity and can quickly transfer the absorbed heat to the gold, copper, and platinum ternary hybrid nanosuspension. The inner wall of the channel can be specially treated (such as coating or texturing) to improve its ability to absorb solar radiation. The outside of the thermally insulated aluminum channel is wrapped with a thermal insulation material to reduce the heat dissipation to the surrounding environment. Connection interfaces are provided at the inlet and outlet of the solar collector prototype for connecting pipes and measuring devices. The interfaces should be well sealed to ensure the smooth flow of the gold, copper, and platinum ternary hybrid nanosuspension in the solar collector prototype without leakage.

[0059] Use a solar simulator to provide a constant solar irradiance and simulate actual solar radiation conditions. Adjust the irradiance of the solar simulator to be consistent with the solar irradiance in actual applications. Install thermocouples at the inlet and outlet of the collector to measure the temperature change of the fluid. The thermocouples should have high precision and fast response characteristics to accurately measure the temperature rise. Install an ultrasonic flowmeter at the inlet and outlet of the collector prototype to detect the volume flow rate of the ternary hybrid nanofluid suspension. The ultrasonic flowmeter should have high precision and non-contact measurement characteristics to ensure the accuracy of the measurement results.

[0060] In one embodiment, start the solar simulator to provide a constant solar irradiance; start the collector prototype to make the ternary hybrid nanofluid suspension flow in the heat collection channel; measure the fluid temperature at the inlet and outlet of the collector prototype through the thermocouple and record the temperature rise; measure the volume flow rate of the ternary hybrid suspension through the ultrasonic flowmeter; calculate the heat collection efficiency of the collector prototype according to the temperature rise and the volume flow rate of the ternary hybrid nanofluid suspension.

[0061] The heat collection efficiency of the collector prototype is calculated by the following formula:

[0062] Where, η is the heat collection efficiency, T out is the temperature of the ternary hybrid nanofluid suspension at the outlet, T in is the temperature of the ternary hybrid nanofluid suspension at the inlet, is the mass flow rate of the ternary hybrid nanofluid suspension, cp is the specific heat capacity of the ternary hybrid nanofluid suspension, I is the solar irradiance, A is the effective area of the collector prototype.

[0063] Compare the heat collection efficiency of the collector prototype measured experimentally with the heat collection efficiency preset by the genetic algorithm. Analyze the difference between the two and evaluate the effectiveness of the artificial neural network model and the genetic algorithm. If the experimental results are consistent with or close to the preset results, it indicates that the artificial neural network model and the genetic algorithm are effective in optimizing the collector design.

[0064] In the embodiment of the present invention, by constructing a collector prototype based on the optimal size and the concentration of the ternary hybrid nanofluid of gold, copper, and platinum, and measuring its heat collection efficiency under experimental conditions, the effectiveness of the artificial neural network model and the genetic algorithm in optimizing the collector design can be verified. This method provides a scientific basis for the design and optimization of the collector, which helps to improve the performance and reliability of the collector.

[0065] Refer to Figure 7 as shown, Figure 7It is a detailed flowchart of step S500 in the solar thermal utilization optimization method of the collector provided by an embodiment of the present invention. Step S500 includes but is not limited to steps S510 to S550. Specifically, S510: Based on channel geometry and ternary hybrid nanofluids, use an anodized aluminum or stainless-steel structural frame and tempered low-iron glass as the transparent cover of the collector device; S520: Integrate an automatic flow control device in the collector device to maintain a stable fluid circulation under different environmental conditions; S530: Deploy a sensor network supported by the Internet of Things, and continuously collect data on the temperature of the ternary hybrid nanofluids, solar irradiance, the flow rate of the ternary hybrid nanofluids, and the heat collection efficiency of the collector device through the sensor network; S540: Determine the solar thermal utilization rate of the collector device based on the data; S550: Upload the data and the solar thermal utilization rate of the collector device to the host computer for management and analysis.

[0066] In some embodiments of the present invention, after the successful verification of the laboratory collector prototype, a pilot-scale solar collector device will be developed based on industrial-grade components. Based on channel geometry and ternary hybrid nanofluids, use an anodized aluminum or stainless-steel structural frame and tempered low-iron glass as the transparent cover of the collector device; design the channel geometry of the collector according to the optimization results to ensure smooth flow of the ternary hybrid nanofluids and good heat exchange performance. The heat collection channels can be made of anodized aluminum or stainless steel, which have good thermal conductivity and corrosion resistance. Use tempered low-iron glass as the transparent cover of the collector device. Tempered glass has high strength and good impact resistance, and low-iron glass has high light transmittance, which can minimize the reflection and absorption losses of solar radiation. Integrate an automatic flow control device in the collector device to maintain a stable fluid circulation under different environmental conditions. The automatic flow control device can automatically adjust the flow rate of the ternary hybrid nanofluids according to factors such as environmental temperature and solar irradiance to ensure the efficient operation of the collector device. Monitor the environmental parameters and the state of the ternary hybrid nanofluids in real time through the sensor network and feedback them to the controller. The controller automatically adjusts the flow control device according to the preset control strategy to maintain the stability of the ternary hybrid nanofluids circulation.

[0067] Deploy an Internet of Things (IoT)-enabled sensor network in the collector device. The sensor network includes a temperature sensor, a solar irradiance sensor, a flow sensor, and an efficiency sensor. The temperature sensor is used to continuously collect the temperature of the ternary hybrid nanofluid. The solar irradiance sensor is used to measure the solar radiation intensity. The flow sensor is used to detect the flow rate of the ternary hybrid nanofluid. The efficiency sensor is used to evaluate the heat collection efficiency of the collector device. The sensor network transmits the collected data in real time to the host computer through IoT technology. The data includes parameters such as temperature, solar irradiance, the flow rate of the ternary hybrid nanofluid, and the heat collection efficiency. Based on the collected data, calculate the solar thermal utilization rate of the collector device. Upload the data and the solar thermal utilization rate of the collector device to the host computer for management and analysis. The host computer can store, process, and analyze the data, generating various reports and charts for evaluating the performance of the collector device and optimizing the operating parameters.

[0068] By designing the collector device based on the channel geometry and the ternary hybrid nanofluid, and integrating an automatic flow control device and an IoT-enabled sensor network, efficient operation and real-time monitoring of the collector device can be achieved. Through data analysis and management, the design and operation strategy of the collector can be further optimized to improve the solar thermal utilization rate.

[0069] Refer to Figure 8 as shown in Figure 8 is the second detailed flowchart of the solar thermal utilization optimization method for the collector provided by the embodiment of the present invention. The solar thermal utilization optimization method for the collector further includes but is not limited to steps S511 to S513. Specifically, S511: Prepare a thermal insulation wall and set the thermal insulation wall on both sides of the collector device. The thermal insulation wall and the transparent cover plate are perpendicular to each other. S512: Set a heat collection channel in the collector device and set an inlet at the starting point of the heat collection channel to enable the ternary hybrid nanofluid to enter the heat collection channel through the inlet and flow inside the collector device to absorb solar energy. S513: Set an outlet at the end point of the heat collection channel to enable the ternary hybrid nanofluid that has been heated by solar energy to be discharged from the heat collection channel through the outlet.

[0070] In some embodiments of the present invention, after the successful verification of the laboratory collector prototype, a pilot-scale solar collector device will be developed based on industrial-grade components. The preparation method of the collector device includes: First, prepare the thermal insulation wall. The thermal insulation wall uses efficient thermal insulation materials such as polyurethane foam, rock wool, or fiberglass, etc. These materials have a low thermal conductivity and can effectively reduce heat dissipation. Set the thermal insulation wall on both sides of the collector device, ensure that the thermal insulation wall is perpendicular to the transparent cover plate, and the thermal insulation wall closely fits the side of the collector to reduce heat dissipation to the surrounding environment; the transparent cover plate should be installed on the top of the collector to ensure that solar radiation can enter the heat collection channel to the maximum extent. Use fixing methods such as adhesives, screws, or buckles to fix the thermal insulation wall on the collector device to ensure that it will not loosen during operation.

[0071] The heat collection channel can be made of anodized aluminum or stainless steel. These materials have good thermal conductivity and corrosion resistance and can effectively absorb and transfer solar energy. The heat collection channel is used to make the ternary hybrid nanofluid flow uniformly inside it, and the heat collection channel adopts a parallel channel or a serpentine channel. The cross-sectional area of the heat collection channel should be optimized according to the flow rate of the ternary hybrid nanofluid and the heat exchange requirements to ensure that the ternary hybrid nanofluid flows smoothly in the heat collection channel and has good heat exchange effects.

[0072] Set an inlet at the starting point of the heat collection channel to allow the ternary hybrid nanofluid to enter the heat collection channel through the inlet. The inlet should be designed in a structure that can conveniently connect to the external pipeline to ensure that the ternary hybrid nanofluid can smoothly enter the heat collection channel. Set an outlet at the end point of the heat collection channel to allow the solar-heated ternary hybrid nanofluid to be discharged from the heat collection channel through the outlet. The outlet should also be designed in a structure that can conveniently connect to the external pipeline to ensure that the solar-heated ternary hybrid nanofluid can be smoothly discharged from the heat collection channel.

[0073] By preparing the thermal insulation wall and setting it on both sides of the collector device, and setting the heat collection channel in the collector device and reasonably arranging the inlet and outlet, the thermal efficiency and operation stability of the collector device can be effectively improved. The thermal insulation wall can reduce heat dissipation, while the reasonable channel design and inlet and outlet settings ensure that the ternary hybrid nanofluid can fully absorb solar energy and flow smoothly, improving the overall performance of the collector device.

[0074] Refer to Figure 9 as shown Figure 9It is a schematic structural diagram of a collector device provided by an embodiment of the present invention. The collector device includes a transparent cover plate 1, a thermal insulation wall 2, a ternary hybrid nanofluid, an inlet 3, and an outlet 5. The collector device further includes a support frame 6, which is used to provide support force for the collector device body and its parts. The transparent cover plate 1 is arranged on the top of the collector device, used for solar radiation to penetrate and prevent fluid evaporation. The thermal insulation wall 2 is arranged on both sides of the collector device, used for thermal insulation to minimize heat conduction loss. The ternary hybrid nanofluid is arranged inside the collector device, used for absorbing solar energy. The inlet 3 is arranged at the starting point of the heat collection channel 4, used to provide an inlet for the ternary hybrid nanofluid to enter the heat collection channel 4. The outlet 5 is arranged at the end point of the heat collection channel 4, used to provide an outlet for the ternary hybrid nanofluid heated by solar energy to discharge from the heat collection channel 4.

[0075] Through optimizing the structural design and material selection, the collector device improves the absorption efficiency of solar energy and the thermal utilization efficiency. The transparent cover plate 1 is used to allow solar radiation to directly penetrate, transfer the solar radiation energy to the inside of the collector device, and at the same time reduce heat dissipation. The transparent cover plate 1 is installed on the top of the collector device, and closely cooperates with other components of the collector device to ensure tightness and prevent heat dissipation. The thermal insulation wall 2 is used to provide thermal insulation, minimizing the loss of heat inside the collector device to the surrounding environment by heat conduction and improving the thermal efficiency of the collector device. Commonly used thermal insulation materials for the thermal insulation wall 2 include polyurethane foam, rock wool, glass fiber, etc. These materials have low thermal conductivity and can effectively insulate heat. The thermal insulation wall 2 is arranged on both sides of the collector device, perpendicular to the transparent cover plate 1, and closely fits the side of the collector to ensure good heat insulation effect.

[0076] The main function of the ternary hybrid nanofluid is to absorb solar energy. It is composed of three metal nanoparticles of gold, copper, and platinum and a base liquid. These nanoparticles have good optical absorption performance and heat conduction performance, and can effectively absorb solar radiation and convert it into heat energy.

[0077] The inlet 3 is arranged at the starting point of the heat collection channel 4 and is usually equipped with connection interfaces for connecting external pipelines to ensure that the ternary hybrid nanofluid can smoothly enter the heat collection channel 4. The outlet 5 is arranged at the end point of the heat collection channel 4 and is also equipped with connection interfaces for connecting external pipelines to ensure that the ternary hybrid nanofluid can smoothly discharge from the heat collection channel 4.

[0078] It can be understood that the working principle of the collector device is as follows: the transparent cover plate 1 receives solar irradiation and transfers the solar radiation energy to the inside of the collector device; the ternary hybrid nanofluid absorbs the solar radiation energy and converts it into heat energy. The ternary hybrid nanofluid circulates in the heat collection channel 4. The ternary hybrid nanofluid enters the heat collection channel 4 from the inlet 3, absorbs the solar radiation energy in the heat collection channel 4, raises its own temperature, and is discharged from the heat collection channel 4 through the outlet 5. The discharged high-temperature ternary hybrid nanofluid can be used in various heat utilization scenarios, such as hot water supply, space heating, etc.

[0079] It should be recognized that the method steps in the embodiments of the present invention can be implemented or carried out by computer hardware, a combination of hardware and software, or computer instructions stored in a non-transitory computer-readable memory. The method can use standard programming techniques. Each program can be implemented in a high-level procedural or object-oriented programming language to communicate with the computer system. However, if necessary, the program can be implemented in assembly or machine language. In any case, the language can be a compiled or interpreted language. In addition, for this purpose, the program can run on a dedicated integrated circuit programmed for this purpose.

[0080] In addition, the operations of the processes described herein can be performed in any suitable order, unless otherwise indicated herein or otherwise clearly contradicted by the context. The processes described herein (or variations and / or combinations thereof) can be executed under the control of one or more computer systems configured with executable instructions, and can be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) that is executed commonly on one or more processors, by hardware, or a combination thereof. The computer program includes a plurality of instructions executable by one or more processors.

[0081] Further, the method may be implemented in any type of computing platform operatively connected to a suitable one, including but not limited to personal computers, minicomputers, mainframes, workstations, network or distributed computing environments, separate or integrated computer platforms, or communicating with charged particle tools or other imaging devices, etc. Aspects of the present invention may be implemented in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into the computing platform, such as a hard disk, optical read and / or write storage medium, RAM, ROM, etc., such that it is readable by a programmable computer and, when read by the storage medium or device, can be used to configure and operate the computer to perform the processes described herein. In addition, the machine-readable code, or portions thereof, may be transmitted via wired or wireless networks. When such media includes instructions or programs that implement the above-described steps in conjunction with a microprocessor or other data processor, the invention described herein includes these and other different types of non-transitory computer-readable storage media. When programmed according to the methods and techniques of the present invention, the present invention may also include the computer itself.

[0082] A computer program can be applied to input data to perform the functions described herein, thereby transforming the input data to generate output data stored in non-volatile memory. The output information can also be applied to one or more output devices, such as a display. In a preferred embodiment of the present invention, the transformed data represents physical and tangible objects, including a specific visual depiction of the physical and tangible objects generated on a display.

[0083] As described above, only the preferred embodiments of the present invention are given, and the present invention is not limited to the above-described embodiments. As long as the same means are used to achieve the technical effects of the present invention, any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention. Within the scope of protection of the present invention, various different modifications and variations may be made to its technical solutions and / or implementation manners.

Claims

1. Optimization method for solar thermal utilization of a collector, characterized in that, The method includes: S100: Predict the radiative properties of suspended nanoparticles based on an artificial neural network model using the boundary element method; S200: Optimize the parameters of the collector by a genetic algorithm and evaluate the radiative properties of the suspended nanoparticles to maximize the solar weighted absorption coefficient and the heat collection efficiency of the collector; S300: Construct the collector structure optimized by the genetic algorithm in the finite volume method simulation framework and verify the rationality of the collector structure parameters; S400: Prepare a ternary hybrid nanofluid and conduct dynamic light scattering, Zeta potential analysis and sedimentation tests on the prepared ternary hybrid nanofluid to verify the stability of the ternary hybrid nanofluid; S500: Prepare a collector device based on the ternary hybrid nanofluid, arrange a sensor network in the collector device, continuously collect data on the temperature of the ternary hybrid nanofluid, solar irradiance, the flow rate of the ternary hybrid nanofluid and the heat collection efficiency of the collector device through the sensor network, determine the solar thermal utilization rate of the collector device according to the data, and upload the data to a host computer for management and analysis.

2. The solar thermal utilization optimization method of the collector according to claim 1, characterized in that The step S100 includes: S110: Generate a data set using the boundary element method, where the data set is used to simulate the interaction between different suspended nanoparticles and solar radiation, and the data set includes data on the properties of the suspended nanoparticles, the medium properties of the suspended nanoparticles, extinction efficiency, absorption efficiency and scattering efficiency; S120: Divide the data set into a training set, a validation set and a test set according to a preset ratio; S130: Construct a multi-layer neural network structure in the artificial neural network model, use the properties of the suspended nanoparticles and the medium properties of the suspended nanoparticles in the training set as inputs, use the extinction efficiency, the absorption efficiency and the scattering efficiency in the training set as outputs, and use the Levenberg-Marquardt algorithm to train the artificial neural network model, iteratively optimizing the artificial neural network model to minimize the mean square error; S140: Evaluate the performance of the artificial neural network model during the training process through the data in the validation set and adjust the hyperparameters of the artificial neural network model, where the hyperparameters include the number of hidden layers, the number of neurons and the learning rate; S150: Evaluate the performance of the artificial neural network model on independent data through the data in the test set to determine the generalization ability of the artificial neural network model.

3. The solar thermal utilization optimization method of the collector according to claim 1, characterized in that, The step S200 includes: S210: Randomly generate a diverse population of candidate solutions within preset agreed conditions, where the preset agreed conditions include the length of the collector and the radius of the suspended nanoparticles; S220: Calculate the fitness of each solution in the candidate solution population based on the thermal efficiency and evaluate the radiative properties of the suspended nanoparticles through CFD simulation; S230: The genetic algorithm adopts an evolutionary strategy. By selecting high-fitness solutions from the candidate solution population, cross-processing the parameters of the collector, and introducing random variations for mutation processing, it continuously iterates until the optimal solution of the candidate solution population is obtained; S240: Maximize the solar weighted absorption coefficient and the heat collection efficiency of the collector according to the optimal solution.

4. The solar thermal utilization optimization method of the collector according to claim 1, characterized in that The step S300 includes: S310: Construct a collector structure optimized by the genetic algorithm in the finite volume method simulation framework, and solve the steady-state, incompressible Navier–Stokes equation and energy equation of the collector structure in the finite volume method simulation framework; S320: Discretize the channel geometry into control volumes, and use the SIMPLE algorithm to handle the pressure-velocity coupling. The detailed distributions of temperature, velocity, and pressure are obtained through simulation to verify the rationality of the selected parameters in the collector structure optimized by the genetic algorithm; S330: Vary the key input parameters within the finite volume method simulation framework. The key input parameters include the length and height of the collector, the Reynolds number, the glass transmittance, and the volume fraction of suspended nanoparticles, and quantify the influence of the key input parameters on the outlet temperature rise and efficiency of the collector to identify the key influencing factors; S340: Vary the flow rate of the ternary hybrid nanofluid, the solar irradiance, and the disturbance error fluctuation within the finite volume method simulation framework, and verify the rationality and stability of the collector parameters optimized by the genetic algorithm.

5. The solar thermal utilization optimization method of the collector according to claim 1, characterized in that, The step S400 includes: S410: Prepare gold, copper, platinum ternary hybrid suspended nanoparticles by chemical reduction or laser ablation methods; S420: Treat the gold, copper, platinum ternary hybrid suspended nanoparticles by ultrasonic waves in the base liquid, and use surfactants to treat the gold, copper, platinum ternary hybrid suspended nanoparticles to obtain a gold, copper, platinum ternary hybrid nanofluid; S430: Conduct dynamic light scattering, Zeta potential analysis, and sedimentation tests on the gold, copper, platinum ternary hybrid nanofluid to verify the long-term stability of the gold, copper, platinum ternary hybrid nanofluid.

6. The solar thermal utilization optimization method of the collector according to claim 5, characterized in that The method further includes: S440: Construct a collector prototype based on the optimal size and the concentration of the gold, copper, platinum ternary hybrid nanofluid. The collector prototype includes a low-iron transparent glass cover plate, a thermally insulated aluminum channel, and connection interfaces; S450: Use a solar simulator to provide a constant irradiance. Arrange thermocouples and ultrasonic flow meters at the inlet and outlet of the collector prototype. Measure the temperature rise through the thermocouples and detect the volume flow through the ultrasonic flow meters. Determine the heat collection efficiency of the collector prototype according to the temperature rise and the volume flow; S460: Compare the consistency of the heat collection efficiency of the collector preset by the genetic algorithm and the heat collection efficiency of the collector prototype to verify the effectiveness of the artificial neural network model - genetic algorithm.

7. The solar thermal utilization optimization method of the collector according to claim 1, characterized in that The step S500 includes: S510: Based on the channel geometry and the ternary hybrid nanofluid, use an anodized aluminum or stainless steel structural frame and tempered low-iron glass as the transparent cover plate of the collector device; S520: Integrate an automatic flow control device in the collector device to maintain a stable fluid circulation under different environmental conditions; S530: Deploy a sensor network supported by the Internet of Things to continuously collect data on the temperature of the ternary hybrid nanofluid suspension, solar irradiance, the flow rate of the ternary hybrid nanofluid suspension, and the heat collection efficiency of the collector device through the sensor network; S540: Determine the solar thermal utilization rate of the collector device based on the data; S550: Upload the data and the solar thermal utilization rate of the collector device to the host computer for management and analysis.

8. The solar thermal utilization optimization method of the collector according to claim 7, characterized in that, The method further includes: S511: Prepare a thermal insulation wall and set the thermal insulation wall on both sides of the collector device, and the thermal insulation wall and the transparent cover plate are perpendicular to each other; S512: Set a heat collection channel in the collector device and set an inlet at the starting point of the heat collection channel to enable the ternary hybrid nanofluid suspension to enter the heat collection channel through the inlet and flow inside the collector device to absorb solar energy; S513: Set an outlet at the end point of the heat collection channel to enable the ternary hybrid nanofluid suspension heated by solar energy to be discharged from the heat collection channel through the outlet.

9. A collector device, characterized in that, It includes: A transparent cover plate (1), arranged on the top of the collector device for receiving solar irradiation; A thermal insulation wall (2), arranged on both sides of the collector device for thermal insulation to minimize heat conduction loss; A ternary hybrid nanofluid suspension, arranged inside the collector device for absorbing solar energy; An inlet (3), arranged at the starting point of the heat collection channel for providing an entrance for the ternary hybrid nanofluid suspension to enter the heat collection channel (4); An outlet (5), arranged at the end point of the heat collection channel (4) for providing a discharge port for the ternary hybrid nanofluid suspension heated by solar energy to be discharged from the heat collection channel (4).

10. A computer device, comprising a memory and a processor, characterized in that, When the processor executes the computer program stored in the memory, it implements the method according to any one of claims 1 to 8.

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