Solar thermal utilization optimization method for collector, collector equipment and computer device

Abstract

The present invention discloses a method for optimizing solar thermal utilization in a heat collector, a heat collector device, and a computer device. The method comprises: using a boundary element method to predict the radiation characteristics of suspended nanoparticles based on an artificial neural network model; optimizing heat collector parameters and evaluating the radiation characteristics of the suspended nanoparticles using a genetic algorithm; constructing a heat collector structure optimized by the genetic algorithm within a finite volume method simulation framework and verifying the rationality of the heat collector structural parameters; preparing a ternary mixed nanosuspension and verifying the stability of the ternary mixed nanosuspension; preparing a heat collector device based on the ternary mixed nanosuspension; disposing a sensor network in the heat collector device to continuously collect data; determining the solar thermal utilization rate of the heat collector device based on the data; and uploading the data to a host computer for management and analysis. The present invention optimizes and improves the performance of the solar heat collector device, thereby enhancing the thermal performance of the heat collector device.

Description

Technical Field

[0001] The present invention relates to the technical field of renewable energy, and in particular to a solar thermal utilization optimization method of a heat collector, heat collector equipment and a computer device. Background Art

[0002] As a renewable energy source, solar energy has broad application prospects. Solar thermal technology converts solar radiation into heat through collectors, which can be used in areas such as hot water supply, space heating, and industrial process heating. Traditional solar collectors rely primarily on simple heat conduction and convection mechanisms, resulting in relatively low thermal efficiency. This is particularly true when dealing with high-concentration solar radiation, resulting in significant energy losses and low thermal efficiency.

[0003] Existing solar thermal collectors have some limitations in design and performance, which are 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. These materials have limited ability to absorb solar radiation, resulting in low heat absorption efficiency. During the operation of the collector, due to unreasonable design or improper material selection, there is a large heat loss, especially when the ambient temperature is low or the wind speed is high, the heat loss is more significant. Most existing collectors lack an intelligent control system and are unable to 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, traditional collectors have a complex structure, high maintenance costs, and are prone to failure during long-term operation, affecting the stability and reliability of the collector. Summary of the Invention

[0004] The present invention provides a solar thermal utilization optimization method for a heat collector, a heat 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 comprises:

[0006] S100: Predicting the radiation characteristics of suspended nanoparticles using the boundary element method based on an artificial neural network model;

[0007] S200: Optimizing the parameters of the collector by a genetic algorithm and evaluating the radiation characteristics of the suspended nanoparticles to maximize the solar energy weighted absorption coefficient and the heat collection efficiency of the collector;

[0008] S300: constructing a collector structure optimized by a genetic algorithm in a finite volume method simulation framework, and verifying the rationality of the collector structure parameters;

[0009] S400: preparing a ternary mixed nanosuspension, and performing dynamic light scattering, zeta potential analysis, and sedimentation tests on the prepared ternary mixed nanosuspension to verify the stability of the ternary mixed nanosuspension;

[0010] S500: Prepare a collector device based on the ternary mixed nanosuspension, arrange a sensor network in the collector device, continuously collect data on the temperature of the ternary mixed nanosuspension, solar irradiance, the flow rate of the ternary mixed nanosuspension and the thermal 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.

[0011] According to some embodiments of the present invention, step S100 includes:

[0012] S110: generating a data set using a boundary element method, wherein the data set is used to simulate the interaction between different suspended nanoparticles and solar radiation, the data set including data on characteristics of the suspended nanoparticles, properties of the medium in which the suspended nanoparticles are suspended, extinction efficiency, absorption efficiency, and scattering efficiency;

[0013] S120: Divide the data set into a training set, a validation set, and a test set according to a preset ratio;

[0014] S130: constructing a multi-layer neural network structure in the artificial neural network model, using the characteristics of the suspended nanoparticles in the training set and the medium properties of the suspended nanoparticles as input, using the extinction efficiency, the absorption efficiency, and the scattering efficiency in the training set as output, and using a Levenberg-Marquardt algorithm to train the artificial neural network model, and iteratively optimizing the artificial neural network model to minimize a mean square error;

[0015] S140: Evaluate the performance of the artificial neural network model during the training process using the data from the validation set, and adjust the hyperparameters of the artificial neural network model, wherein the hyperparameters include the number of hidden layers, the number of neurons, and the learning rate;

[0016] S150: Evaluate the performance of the artificial neural network model on independent data using the data of the test set to determine the generalization ability of the artificial neural network model.

[0017] According to some embodiments of the present invention, step S200 includes:

[0018] S210: Randomly generate a diverse candidate solution population within preset agreed conditions, wherein the preset agreed conditions include the length of the collector and the radius of the suspended nanoparticles;

[0019] S220: Calculating the fitness of each solution in the candidate solution population based on thermal efficiency, and evaluating the radiation characteristics of the suspended nanoparticles through CFD simulation;

[0020] S230: The genetic algorithm adopts an evolutionary strategy to select high-fitness solutions from the candidate solution population, mix the parameters of the collector for crossover processing, introduce random changes for mutation processing, and continuously iterate until the optimal solution of the candidate solution population is obtained;

[0021] S240: Maximizing the weighted solar energy absorption coefficient and the heat collection efficiency of the collector according to the optimal solution.

[0022] According to some embodiments of the present invention, step S300 includes:

[0023] S310: constructing a collector structure optimized by a genetic algorithm in a finite volume method simulation framework, and solving steady-state, incompressible Navier–Stokes equations and energy equations of the collector structure in the finite volume method simulation framework;

[0024] S320: Discretize the channel geometry into control volumes and use the SIMPLE algorithm to process the pressure and velocity coupling. Simulate the detailed distribution of temperature, velocity, and pressure to verify the rationality of the parameters selected in the collector structure after genetic algorithm optimization.

[0025] S330: varying key input parameters within the finite volume method simulation framework, the key input parameters including collector length and height, Reynolds number, glass transmittance, and suspended nanoparticle volume fraction, and quantifying the effects of the key input parameters on the outlet temperature rise and efficiency of the collector to identify key influencing factors;

[0026] S340: varying the flow rate of the ternary mixed nanosuspension, the solar irradiance, and the disturbance error fluctuation within the finite volume method simulation framework, and verifying the rationality and stability of the collector parameters optimized by the genetic algorithm.

[0027] According to some embodiments of the present invention, step S400 includes:

[0028] S410: Preparation of gold, copper, and platinum ternary mixed suspended nanoparticles by chemical reduction or laser ablation;

[0029] S420: treating the gold, copper, and platinum ternary mixed suspended nanoparticles in a base liquid by ultrasonic treatment, and treating the gold, copper, and platinum ternary mixed suspended nanoparticles by using a surfactant to obtain a gold, copper, and platinum ternary mixed nanosuspension;

[0030] S430: Performing dynamic light scattering, Zeta potential analysis, and sedimentation test on the gold, copper, and platinum ternary mixed nanosuspension to verify the long-term stability of the gold, copper, and platinum ternary mixed nanosuspension.

[0031] According to some embodiments of the present invention, the method further comprises:

[0032] S440: Construct a solar collector prototype based on the optimal size and concentration of suspended gold, copper, and platinum nanoparticles, including a low-iron transparent glass cover, a thermally insulating aluminum channel, and a connection interface.

[0033] S450: using a solar simulator to provide a constant irradiance, arranging thermocouples and ultrasonic flowmeters at the inlet and outlet of the collector prototype, measuring a temperature rise by the thermocouples, detecting a volume flow by the ultrasonic flowmeter, and determining a heat collection efficiency of the collector prototype based on the temperature rise and the volume flow;

[0034] S460: Comparing the heat collection efficiency of the heat collector preset by the genetic algorithm with the heat collection efficiency of the heat collector prototype for consistency, so as to verify the effectiveness of the artificial neural network model-genetic algorithm.

[0035] According to some embodiments of the present invention, step S500 includes:

[0036] S510: Based on channel geometry and ternary hybrid nanosuspension, it uses anodized aluminum or stainless steel structural frames and tempered low-iron glass as the transparent cover of the collector equipment;

[0037] S520: Integrating an automatic flow control device into the collector device to maintain stable fluid circulation under different environmental conditions;

[0038] S530: deploying a sensor network supported by the Internet of Things, and continuously collecting data on the temperature of the ternary mixed nanosuspension, solar irradiance, flow rate of the ternary mixed nanosuspension, and heat collection efficiency of the collector device through the sensor network;

[0039] S540: Determine the solar thermal utilization rate of the collector device according to the data;

[0040] S550: Uploading the data and the solar thermal utilization rate of the collector equipment to a host computer for management and analysis.

[0041] According to some embodiments of the present invention, the method further comprises:

[0042] S511: preparing insulation walls and arranging the insulation walls on both sides of the heat collector device, wherein the insulation walls and the transparent cover plate are perpendicular to each other;

[0043] S512: Setting a heat collection channel in the heat collector device, and setting an inlet at a starting point of the heat collection channel, so that the ternary mixed nano-suspension enters the heat collection channel through the inlet and flows inside the heat collector device to absorb solar energy;

[0044] S513: An outlet is provided at the end of the heat collection channel, so that the ternary mixed nano suspension treated by solar heating can be discharged from the heat collection channel through the outlet.

[0045] An embodiment of the present invention further provides a heat collector device, comprising:

[0046] a transparent cover plate, arranged on the top of the collector device, for receiving solar radiation;

[0047] Insulation walls are provided on both sides of the collector device for thermal insulation to minimize heat conduction loss;

[0048] A ternary mixed nano suspension is provided inside the heat collector device for absorbing solar energy;

[0049] An inlet is provided at the starting point of the heat collection channel, and is used to provide an inlet for the ternary mixed nano suspension to enter the heat collection channel;

[0050] The outlet is arranged at the end of the heat collection channel and is used to provide an outlet for the ternary mixed nano suspension treated by solar heating to discharge from the heat collection channel.

[0051] The technical solution of the present invention also relates to a computer device, comprising a memory and a processor, wherein the processor implements the above method when executing a computer program stored in the memory.

[0052] The technical solution of the present invention further relates to a computer-readable storage medium having computer program instructions stored thereon, wherein the computer program instructions implement the above-mentioned method when executed by a processor.

[0053] The solar thermal utilization optimization method, collector device, and computer system provided by the embodiments of the present invention have at least one of the following advantages or beneficial effects: An artificial neural network model is developed and trained using the boundary element method. The boundary element method is used to simulate the radiation behavior of suspended nanoparticles under different boundary conditions. The artificial neural network quickly and accurately predicts the radiation characteristics of the suspended nanoparticles under different operating conditions. A genetic algorithm is used to optimize the structural parameters of the collector. The radiation characteristics of the suspended nanoparticles are used as part of the objective function of the genetic algorithm, combined with the collector performance indicators (such as heat collection efficiency and heat loss) for comprehensive evaluation. The collector parameters optimized by the genetic algorithm achieve higher absorption efficiency of the suspended nanoparticles in the collector while reducing heat loss, thereby improving the performance of the entire collector system. A simulation model of the collector structure is constructed using the finite volume method to simulate the flow and heat transfer process of the suspended nanoparticles in a suspended nanofluid within the collector structure. The finite volume method can accurately calculate parameters such as the temperature distribution and velocity field of the suspended nanoparticles in the nanofluid, as well as the thermal efficiency of the collector structure. The rationality of the collector structural parameters is then verified. A ternary hybrid nanosuspension was prepared and subjected to dynamic light scattering, zeta potential analysis, and sedimentation tests to verify its stability. A solar collector device was then fabricated based on the ternary hybrid nanosuspension. The ternary hybrid nanosuspension was then filled into the collector device through a heat collection channel, facilitating the flow and heat transfer of the ternary hybrid nanosuspension. A sensor network was deployed within the collector device to collect data on the ternary hybrid nanosuspension temperature, solar irradiance, flow rate, and the collector device's thermal efficiency. This data was used to determine the solar thermal utilization rate of the collector device and upload it to a host computer for management and analysis. The host computer stored, processed, and analyzed the data, generating various reports and charts for evaluating the collector device's performance and optimizing operating parameters. This data analysis revealed patterns in the collector device's performance under different operating conditions, enabling further optimization of the collector device's design and operating strategies, thereby improving the solar thermal utilization rate.

[0054] In addition, additional aspects and advantages of the present invention will be set forth in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a general flow chart of a method for optimizing solar thermal utilization of a collector provided by an embodiment of the present invention;

[0056] Figure 2 is a detailed flow chart of step S100 in the solar thermal utilization optimization method for a thermal collector provided by an embodiment of the present invention;

[0057] Figure 3 is a detailed flow chart of step S200 in the solar thermal utilization optimization method for a thermal collector provided by an embodiment of the present invention;

[0058] Figure 4 is a detailed flow chart of step S300 in the solar thermal utilization optimization method for a thermal collector provided by an embodiment of the present invention;

[0059] Figure 5 is a detailed flow chart of step S400 in the solar thermal utilization optimization method for a thermal collector provided by an embodiment of the present invention;

[0060] Figure 6 This is a detailed flow chart of the first method for optimizing solar thermal utilization of a collector provided by an embodiment of the present invention;

[0061] Figure 7 is a detailed flow chart of step S500 in the solar thermal utilization optimization method for a thermal collector provided by an embodiment of the present invention;

[0062] Figure 8 This is a second detailed flow chart of the method for optimizing solar thermal utilization of a collector provided by an embodiment of the present invention;

[0063] Figure 9 It is a structural schematic diagram of a heat collector device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The following will provide a clear and complete description of the concept, specific structure and technical effects of the present invention in conjunction with the embodiments and drawings to fully understand the purpose, scheme and effects of the present invention.

[0065] It should be noted that, unless otherwise specified, when a feature is referred to as being "fixed" or "connected" to another feature, it may be directly fixed or connected to the other feature, or it may be indirectly fixed or connected to the other feature. The singular forms "a", "said" and "the" 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 art. The terms used in this specification are only for describing specific embodiments and are not intended to limit the invention. The term "and / or" used herein includes any combination of one or more related listed items.

[0066] It should be understood that, although the present invention may adopt the terms first, second, third etc. to describe various elements, these elements should not be limited to these terms. These terms are only used to distinguish the 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 language ("for example", "such as" etc.) provided herein is only intended to better illustrate embodiments of the present invention, and unless otherwise required, will not impose limitations on the scope of the present invention.

[0067] In the related art, existing solar collectors have some limitations in design and performance, which are 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 a heat absorption medium. These materials have limited ability to absorb solar radiation, resulting in low heat absorption efficiency. During the operation of the collector, due to unreasonable design or improper material selection, there is a large heat loss, especially when the ambient temperature is low or the wind speed is high, the heat loss is more significant. Most of the existing collectors lack an intelligent control system and are unable to 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 they are prone to failure during long-term operation, affecting the stability and reliability of the collector.

[0068] Based on this, embodiments of the present invention provide a method for optimizing solar thermal utilization in a solar collector, a solar collector device, and a computer device. Using the boundary element method, an artificial neural network is developed and trained to quickly and accurately predict the radiation characteristics of ternary mixed suspended nanoparticles. A genetic algorithm system is used to optimize various collector parameters, including the collector's geometric dimensions (length and height), the particle size and concentration of the ternary mixed suspended nanoparticles, to maximize the weighted solar absorption coefficient and the overall thermal collection efficiency of the collector. A solar collector device is constructed using the ternary mixed nanosuspension, and data on various parameters of the collector device is obtained through a sensor network. Based on this data, the solar thermal utilization rate of the collector device is determined. Through the combined application of these technologies, optimized design and performance improvement of the solar collector device are achieved, thereby enhancing the thermal performance of the collector device.

[0069] Reference Figure 1 As shown, Figure 1 This is a general flow chart of a solar thermal utilization optimization method for a heat collector provided by an embodiment of the present invention. The solar thermal utilization optimization method for a heat collector includes but is not limited to steps S100 to S500. Specifically,

[0070] S100: Predicting the radiation characteristics of suspended nanoparticles using the boundary element method based on an artificial neural network model;

[0071] S200: Optimize collector parameters and evaluate the radiation characteristics of suspended nanoparticles using genetic algorithms to maximize the solar weighted absorption coefficient and collector efficiency;

[0072] S300: Construct the collector structure optimized by genetic algorithm in the finite volume method simulation framework and verify the rationality of the collector structure parameters;

[0073] S400: preparing a ternary mixed nanosuspension, and performing dynamic light scattering, zeta potential analysis, and sedimentation tests on the prepared ternary mixed nanosuspension to verify the stability of the ternary mixed nanosuspension;

[0074] S500: Prepare a collector device based on the ternary mixed nanosuspension, arrange a sensor network in the collector device, and continuously collect data on the temperature of the ternary mixed nanosuspension, solar irradiance, flow rate of the ternary mixed nanosuspension and the thermal 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 the host computer for management and analysis.

[0075] In some embodiments of the present application, the radiation characteristics of suspended nanoparticles are predicted based on an artificial neural network model using the boundary element method. It is understandable that the boundary element method is a numerical calculation method for solving partial differential equations. It discretizes the boundaries of the problem and converts the problem into a boundary integral equation to solve it. In the prediction of nanoparticle radiation characteristics, the boundary element method is used to simulate the radiation behavior of nanoparticles under different boundary conditions, such as the thermal radiation exchange between nanoparticles and the surrounding medium. The boundary element method can accurately handle complex boundary conditions and is particularly suitable for situations such as nanoparticles with complex geometric shapes and multilayer structures.

[0076] Artificial neural networks (ANNs) are computational models that mimic the structure and function of neural networks in the human brain, possessing powerful nonlinear fitting and adaptive learning capabilities. When predicting the radiation characteristics of nanoparticles, ANNs can be trained using existing experimental data or numerical simulation data, thereby establishing a mapping relationship between inputs (such as the size, shape, and material of the nanoparticles) and outputs (radiation characteristics). For example, by inputting parameters such as the nanoparticle size distribution and material properties, ANNs can predict their radiation characteristics, such as the radiation absorption coefficient and scattering coefficient, under different operating conditions.

[0077] Therefore, an artificial neural network was developed and trained using the boundary element method to quickly and accurately predict the radiation properties (extinction coefficient) of suspended nanoparticles.

[0078] The collector parameters are optimized using a genetic algorithm. A genetic algorithm is a search and optimization algorithm based on the principles of natural selection and genetics. It simulates the biological evolution process, utilizing operations such as selection, crossover, and mutation to gradually optimize the objective function. In this embodiment of the present invention, the genetic algorithm is used to optimize the collector's structural parameters (such as its shape, dimensions, and nanofluid filling level) to maximize the weighted solar absorption coefficient and the collector's thermal efficiency.

[0079] The radiative properties of suspended nanoparticles are evaluated using a genetic algorithm. This radiative properties are then used as part of the objective function, combined with collector performance indicators such as thermal efficiency and heat loss, for a comprehensive evaluation. For example, collector parameters optimized using a genetic algorithm can achieve higher absorption efficiency for the suspended nanoparticles in the collector while reducing heat loss, thereby improving the performance of the entire collector system.

[0080] The collector structure optimized by genetic algorithm is constructed in the finite volume method simulation framework. The finite volume method is a numerical calculation method based on control volume, which is applied to solve fluid mechanics and heat conduction problems. It divides the calculation domain into multiple control volumes and then applies conservation laws to each control volume to establish discrete equations. In the present invention, the finite volume method is used to construct a simulation model of the collector and simulate the flow and heat transfer process of the nanosuspension in the collector. Through the finite volume method, parameters such as the temperature distribution and velocity field of the nanosuspension and the thermal efficiency of the collector can be accurately calculated.

[0081] Afterwards, the rationality of the collector's structural parameters is verified. Within the finite volume method simulation framework, the genetic algorithm-optimized collector structural parameters are input into the simulation model and compared with experimental data or theoretical results to verify their rationality. For example, the collector thermal efficiency calculated through simulation is compared with the experimental measurement value. If the error between the two is within a reasonable range, the optimized structural parameters are reasonable.

[0082] Prepare a ternary hybrid nanosuspension. This refers to a nanofluid formed by dispersing three different types of nanoparticles (such as metal nanoparticles, metal oxide nanoparticles, and carbon nanoparticles) in a base fluid. This hybrid nanosuspension can leverage the properties of different nanoparticles to improve the fluid's thermal conductivity and optical properties. For example, metal nanoparticles have high thermal conductivity, metal oxide nanoparticles have good chemical stability, and carbon nanoparticles have excellent optical absorption properties. The combination of these three can enhance the overall performance of the nanofluid. In an embodiment of the present invention, the ternary hybrid nanosuspension includes gold nanoparticles, copper nanoparticles, and platinum nanoparticles.

[0083] The prepared ternary hybrid nanosuspension was subjected to dynamic light scattering, zeta potential analysis, and sedimentation tests to verify the stability of the ternary hybrid nanosuspension. Specifically, dynamic light scattering was used to measure the particle size distribution and diffusion coefficient of the nanoparticles. Dynamic light scattering can be used to evaluate the dispersion state and stability of the nanoparticles in the fluid. If the nanoparticles are evenly dispersed in the fluid and the particle size distribution is stable, it indicates that the nanosuspension has good stability. Zeta potential analysis is used to measure the surface charge 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, thereby improving the stability of the ternary hybrid nanosuspension. The sedimentation test evaluates the stability of the ternary hybrid nanosuspension by observing the sedimentation rate and sedimentation time of the nanoparticles in the fluid. If the nanoparticles do not settle or settle very slowly over a long period of time, it indicates that the ternary hybrid nanosuspension has good stability.

[0084] A solar collector device is prepared based on a ternary mixed nanosuspension. The ternary mixed nanosuspension is filled into the solar collector through a heat collection channel or cavity, which is beneficial to the flow and heat transfer of the ternary mixed nanosuspension. A sensor network is arranged in the solar collector device, including a temperature sensor, a solar irradiance sensor, a flow sensor, etc. The temperature of the ternary mixed nanosuspension 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 mixed nanosuspension can be continuously collected through the flow sensor. The temperature sensor can monitor the temperature change of the ternary mixed nanosuspension in real time, the solar irradiance sensor can measure the intensity of solar radiation, and the flow sensor can measure the flow rate of the ternary mixed nanosuspension. These data can be used to calculate the solar thermal utilization rate of the solar collector device.

[0085] The collected data is uploaded to a host computer for management and analysis. The host computer can store, process, and analyze the data, generating various reports and charts for evaluating collector performance and optimizing operating parameters. Data analysis can reveal performance variations of collectors under different operating conditions, thereby further optimizing collector design and operating strategies and improving solar thermal utilization.

[0086] The proposed method for optimizing solar thermal utilization in a solar collector utilizes a boundary element method to develop and train an artificial neural network, enabling rapid and accurate prediction of the radiation characteristics of ternary mixed suspended nanoparticles. A genetic algorithm system is then used to optimize various collector parameters, including the collector's geometric dimensions (length and height), the particle size, and concentration of the ternary mixed suspended nanoparticles, to maximize the weighted solar absorption coefficient and overall collector efficiency. Furthermore, a collector device is constructed using the ternary mixed nanosuspension, and data on various parameters of the collector device is acquired through a sensor network. This data is then used to determine the collector device's solar thermal utilization rate. Through the combined application of these technologies, optimized design and performance improvements are achieved for the solar collector device, thereby enhancing its thermal performance.

[0087] Reference Figure 2 As shown, Figure 2 This is a detailed flow chart of step S100 in the solar thermal utilization optimization method of the collector provided by an embodiment of the present invention. Step S100 includes but is not limited to steps S110 to S150. Specifically,

[0088] 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 properties of the medium in which the suspended nanoparticles are suspended, extinction efficiency, absorption efficiency, and scattering efficiency.

[0089] S120: Divide the data set into a training set, a validation set, and a test set according to a preset ratio;

[0090] S130: Constructing a multi-layer neural network structure in an artificial neural network model, using the characteristics of the suspended nanoparticles and the properties of the medium in which the nanoparticles are suspended as input, and using the extinction efficiency, absorption efficiency, and scattering efficiency in the training set as output, and using the Levenberg-Marquardt algorithm to train the artificial neural network model, and iteratively optimizing the artificial neural network model to minimize the mean square error;

[0091] S140: Evaluate the performance of the artificial neural network model during training using validation set data and adjust the hyperparameters of the artificial neural network model, including the number of hidden layers, number of neurons, and learning rate;

[0092] S150: Evaluate the performance of the artificial neural network model on independent data using the data from the test set to determine the generalization ability of the artificial neural network model.

[0093] 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 is understood that the data in the data sets cover variations in the suspended nanoparticles' particle material, size, shape, medium refractive index, and solar spectrum wavelength. The generated data sets are divided into inputs and outputs, where the inputs include the characteristics of the suspended nanoparticles and the properties of the medium in which the suspended nanoparticles are suspended, and the outputs include extinction efficiency, absorption efficiency, and scattering efficiency. These data sets are then precisely divided into training, validation, and test sets in a ratio of 70%, 15%, and 15%. A multi-layered neural network structure was constructed within the artificial neural network model. The characteristics of the suspended nanoparticles and the properties of the medium in which they are suspended served as input, while the extinction efficiency, absorption efficiency, and scattering efficiency in the training set served as output. The artificial neural network model was trained using the Levenberg-Marquardt algorithm, a highly robust algorithm for nonlinear regression problems. The Levenberg-Marquardt algorithm is an efficient nonlinear least-squares optimization algorithm that quickly converges to a solution that minimizes the mean squared error (MSE). Therefore, the artificial neural network model can be iteratively optimized to minimize the MSE, ensuring high prediction accuracy. The performance of the artificial neural network model during training was evaluated using validation data, including metrics such as the MSE and correlation coefficient. Hyperparameters of the artificial neural network model, such as the number of hidden layers, number of neurons, and learning rate, were adjusted based on the evaluation results. The performance of the artificial neural network model on independent data was evaluated using test data to determine the model's generalization ability. Evaluation metrics included the MSE, correlation coefficient, and scatter plots of predicted and actual values.

[0094] The boundary element method was used to generate a data set, and an artificial neural network model was used to predict the radiation characteristics of suspended nanoparticles. Through a training, validation, and testing process, an accurate prediction model was established, providing theoretical support and guidance for the application of suspended nanoparticles in solar thermal collectors.

[0095] Reference Figure 3 As shown, Figure 3 This is a detailed flow chart of step S200 in the solar thermal utilization optimization method of the collector provided by an embodiment of the present invention. Step S200 includes but is not limited to steps S210 to S240. Specifically,

[0096] S210: randomly generating a diverse candidate solution population within preset agreed conditions, where the preset agreed conditions include the collector length and the radius of the suspended nanoparticles;

[0097] S220: The fitness of each solution in the candidate solution population is calculated based on thermal efficiency, and the radiation characteristics of the suspended nanoparticles are evaluated through CFD simulation;

[0098] S230: The genetic algorithm adopts an evolutionary strategy, which selects high-fitness solutions from the candidate solution population, performs crossover processing on the parameters of the hybrid collector, introduces random changes for mutation processing, and continuously iterates until the optimal solution of the candidate solution population is obtained;

[0099] S240: Maximizing the weighted solar absorption coefficient and the thermal efficiency of the collector according to the optimal solution.

[0100] In some embodiments of the present invention, the parameters of the collector are systematically optimized by a genetic algorithm, which includes specifying the optimization objective (maximizing the solar weighted absorption coefficient and the thermal collection efficiency of the collector) and setting the design variables (collector geometric parameters such as length and height, size and volume fraction of suspended nanoparticles).

[0101] First, a diverse population of candidate solutions is randomly generated within pre-defined conditions (collector length and suspended nanoparticle radius). Each solution represents a combination of collector and suspended nanoparticle parameters. The fitness of each solution in the candidate population is calculated based on thermal efficiency, which can be evaluated through CFD (computational fluid dynamics) simulations. CFD simulations are used to evaluate the radiation characteristics of the suspended nanoparticles, including extinction efficiency, absorption efficiency, and scattering efficiency. CFD simulations provide information about the flow and heat transfer of the suspended nanoparticles in the collector, allowing the thermal efficiency of the collector to be calculated. Next, a genetic algorithm employs an evolutionary strategy to select high-fitness solutions from the candidate population, i.e., those with high thermal efficiency. A crossover operation is performed on the collector parameters, exchanging some parameters between two high-fitness solutions to generate new solutions. Random variation is introduced through mutation, introducing random changes in certain parameters to increase the diversity of the population. This process is repeated until the optimal solution in the candidate population is obtained. This optimal solution maximizes the weighted solar absorption coefficient and improves the efficiency of the suspended nanoparticles in absorbing solar radiation. The optimal solution can also improve the thermal collection efficiency of the collector, that is, improve the efficiency of the collector in converting solar energy into thermal energy.

[0102] A genetic algorithm is used to optimize the parameters of the collector and suspended nanoparticles to maximize the weighted solar absorption coefficient and the collector's thermal efficiency. Evaluating the radiation characteristics of the suspended nanoparticles through CFD simulation provides theoretical support and guidance for collector design and optimization. Selecting the optimal solution from the candidate population allows for detailed simulation and sensitivity analysis to verify the reliability of the collector parameters, ensuring robustness to variations in the collector parameters during actual operation.

[0103] Reference Figure 4 As shown, Figure 4This is a detailed flow chart of step S300 in the solar thermal utilization optimization method of the collector provided by an embodiment of the present invention. Step S300 includes but is not limited to steps S310 to S340. Specifically,

[0104] S310: Construct a collector structure optimized by genetic algorithm in a finite volume method simulation framework, and solve the steady-state, incompressible Navier–Stokes equations and energy equations for the collector structure in the finite volume method simulation framework;

[0105] S320: Discretize the channel geometry into control volumes and use the SIMPLE algorithm to process the pressure and velocity coupling. Simulate the detailed distribution of temperature, velocity, and pressure to verify the rationality of the parameters selected in the collector structure after genetic algorithm optimization.

[0106] S330: Varying key input parameters within the finite volume method simulation framework, including collector structure length and height, Reynolds number, glass transmittance, and suspended nanoparticle volume fraction, and quantifying their impact on the collector structure's outlet temperature rise and efficiency to identify key influencing factors.

[0107] S340: Vary the flow rate, solar irradiance, and disturbance error fluctuations of the ternary mixed nanosuspension within the finite volume method simulation framework, and verify the rationality and stability of the collector structural parameters optimized by the genetic algorithm.

[0108] In some embodiments of the present invention, in order to ensure that the parameters of the collector optimized by the genetic algorithm can achieve the expected thermal performance under actual conditions, the geometric model of the collector structure optimized by the genetic algorithm is first constructed in the finite volume method simulation framework, and the channel geometry of the collector is discretized into multiple control bodies. The conservation law is applied to each control body to establish a discrete equation. The steady-state, incompressible Navier-Stokes equations and energy equations in the collector structure are solved to describe the flow behavior of the ternary mixed nanosuspension. The Navier-Stokes equations include momentum equations and continuity equations, which are used to calculate the velocity field and pressure field of the ternary mixed nanosuspension. The energy equation is solved to describe the heat conduction and convection behavior of the ternary mixed nanosuspension. The energy equation is used to calculate the temperature field of the ternary mixed nanosuspension, taking into account factors such as solar radiation absorption, heat conduction and convection. The energy equation incorporates the extinction coefficient from the radiation characteristics of suspended nanoparticles predicted by an artificial neural network model, accounting for both convective and radiative heat transfer. The SIMPLE algorithm (Semi-Implicit Method for Pressure-Linked Equations) is a numerical method for addressing pressure-velocity coupling. It iteratively solves the momentum and continuity equations, gradually updating the velocity and pressure fields until convergence. The simulation yields detailed temperature, velocity, and pressure distributions, including velocity vector diagrams, temperature contour plots, and pressure profiles for the suspended nanosuspension of the ternary mixed nanofluid. These results validate the parameters selected for the collector structure optimized using the genetic algorithm.

[0109] After verifying the baseline performance, a sensitivity analysis was conducted. Key parameters, such as the collector structure length and height, Reynolds number, glass transmittance, and suspended nanoparticle volume fraction, were varied within the finite volume method (FVM) simulation framework within the actual operating range. The outlet temperature rise and efficiency of the collector structure were calculated through simulation. By comparing simulation results for different parameter combinations, the impact of key input parameters on the collector structure's performance was quantified and key influencing factors identified. The flow rate of the ternary mixed nanosuspension, solar irradiance, and disturbance error fluctuations (such as ambient temperature fluctuations) were varied within the FVM simulation framework. Simulations verified the rationality and stability of the genetically optimized collector structure parameters. For example, the collector's performance under different operating conditions was evaluated by simulating the thermal efficiency and outlet temperature rise of the collector under different flow rates and solar irradiances. By introducing disturbance error fluctuations, the collector's anti-interference capability and stability in actual operation were evaluated.

[0110] The finite volume method simulation framework enables detailed simulation of fluid flow and heat transfer within a collector structure optimized using a genetic algorithm. By varying key input parameters and perturbation error fluctuations, the impact of these parameters on the collector's structural performance can be quantified, verifying the rationality and stability of the optimized collector parameters. This approach provides a scientific basis for collector design and optimization, helping to improve collector performance and reliability.

[0111] Reference Figure 5 As shown, Figure 5 This is a detailed flow chart of step S400 in the solar thermal utilization optimization method of the collector provided by an embodiment of the present invention. Step S400 includes but is not limited to steps S410 to S430. Specifically,

[0112] S410: Preparation of gold, copper, and platinum ternary mixed suspended nanoparticles by chemical reduction or laser ablation;

[0113] S420: treating the gold, copper, and platinum ternary mixed suspended nanoparticles in a base liquid by ultrasonic treatment, and treating the gold, copper, and platinum ternary mixed suspended nanoparticles by using a surfactant to obtain a gold, copper, and platinum ternary mixed nanosuspension;

[0114] S430: Dynamic light scattering, zeta potential analysis, and sedimentation tests were performed on the gold, copper, and platinum ternary mixed nanosuspension to verify the long-term stability of the gold, copper, and platinum ternary mixed nanosuspension.

[0115] In some embodiments of the present invention, the experimental stage first prepares a ternary mixed nanosuspension. The ternary mixed suspended nanoparticles are synthesized by chemical reduction or laser ablation, and then uniformly dispersed in a base liquid such as Therminol VP-1 or water by ultrasonic treatment to obtain a ternary mixed nanosuspension of gold, copper, and platinum. Appropriate surfactants are added to enhance stability and prevent agglomeration. The optimal volume fraction is usually 10⁻ 5 The order of magnitude is determined by an integrated artificial neural network model-genetic algorithm method to achieve the optimal balance between solar energy absorption and fluid viscosity. The prepared ternary hybrid nanosuspension 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 nanosuspension.

[0116] Specifically, chemical reduction is a method for preparing nanoparticles in which metal ions are reduced to metal nanoparticles using a chemical reducing agent. For example, gold nanoparticles can be prepared using sodium citrate as a reducing agent and stabilizer. A chloroauric acid (HAuCl4) solution is mixed with a sodium citrate solution and reacted under heating to produce gold nanoparticles. Copper sulfate (CuSO4) solution is mixed with a sodium borohydride (NaBH4) solution to produce copper nanoparticles. Chloroplatinic acid (H2PtCl6) solution is mixed with an ascorbic acid (vitamin C) solution to produce platinum nanoparticles. The prepared gold, copper, and platinum nanoparticles are then mixed to form a ternary mixed nanosuspension.

[0117] Laser ablation is a method that uses a high-energy laser beam to ablate a metal target to generate metal nanoparticles. During laser ablation, the metal target material is instantly vaporized by the laser, forming metal vapor, which then cools to form nanoparticles. A gold target is placed in deionized water and laser ablated to generate gold nanoparticles. A copper target is placed in deionized water and laser ablated to generate copper nanoparticles. A platinum target is placed in deionized water and laser ablated to generate platinum nanoparticles. The prepared gold, copper, and platinum nanoparticles are then mixed to form a ternary mixed suspension of nanoparticles.

[0118] Ultrasonic treatment can enhance the dispersibility of ternary mixed suspended nanoparticles in a base liquid. Through the cavitation effect of ultrasound, the ternary mixed suspended nanoparticles are evenly dispersed in the base liquid. A ternary mixed suspended nanoparticle of gold, copper, and platinum is added to a base liquid (such as deionized water or ethylene glycol). The mixture is treated using an ultrasonic processor, typically for 30 minutes to 1 hour at a power of 200-400W. Surfactants can adsorb onto the surface of the ternary mixed suspended nanoparticles, forming a protective film that prevents agglomeration of the ternary mixed suspended nanoparticles and thus improves the stability of the ternary mixed suspension. For example, a surfactant such as polyvinylpyrrolidone (PVP) or cetyltrimethylammonium bromide (CTAB) can be added to the ultrasonically treated ternary mixed nanoparticle suspension and stirred until the surfactant is evenly adsorbed on the surface of the ternary mixed suspended nanoparticles.

[0119] Dynamic light scattering (DLS) is used to measure the size distribution and diffusion coefficient of suspended nanoparticles in ternary hybrid nanosuspensions. By analyzing the intensity fluctuations of scattered light, the size distribution and diffusion coefficient of the suspended nanoparticles in the ternary hybrid nanosuspension can be determined. The treated ternary hybrid nanosuspension is placed in a dynamic light scattering instrument for measurement. The size distribution and diffusion coefficient of the suspended nanoparticles in the ternary hybrid nanosuspension are recorded to evaluate the dispersibility and stability of the ternary hybrid nanosuspension.

[0120] Zeta potential analysis is used to measure the surface charge of suspended nanoparticles in a ternary hybrid nanosuspension. The higher the absolute value of the zeta potential, the greater the electrostatic repulsion between the suspended nanoparticles in the ternary hybrid nanosuspension, and the less likely they are to agglomerate. The treated ternary hybrid nanosuspension is placed in a zeta potential analyzer for measurement, and the zeta potential value is recorded to assess the surface charge and stability of the suspended nanoparticles in the ternary hybrid nanosuspension.

[0121] The sedimentation test evaluates the stability of a ternary hybrid nanosuspension by observing the sedimentation rate and sedimentation time of the suspended nanoparticles in the suspension. If the suspended nanoparticles do not settle or settle very slowly over a long period of time, the ternary hybrid nanosuspension has good stability. The treated ternary hybrid nanosuspension is placed in a transparent container and allowed to stand for 24 hours. The sedimentation of the suspended nanoparticles is observed, and the sedimentation rate and sedimentation time are recorded.

[0122] Ternary mixed suspensions of gold, copper, and platinum nanoparticles were prepared by chemical reduction or laser ablation. Ultrasonication and surfactant treatment in the base liquid enhanced the dispersibility and stability of the mixed suspensions. The long-term stability of the mixed suspensions was verified by dynamic light scattering, zeta potential analysis, and sedimentation tests. These steps ensured the stability and performance of the mixed nanosuspensions in practical applications.

[0123] Reference Figure 6 As shown, Figure 6 This is a first detailed flow chart of the method for optimizing solar thermal utilization of a heat collector provided by an embodiment of the present invention. The method for optimizing solar thermal utilization of a heat collector further includes but is not limited to steps S440 to S460. Specifically,

[0124] S440: Build a solar collector prototype based on the optimal size and concentration of suspended gold, copper, and platinum nanoparticles. The prototype includes a low-iron transparent glass cover, a thermally insulating aluminum channel, and a connection interface.

[0125] S450: A solar simulator is used to provide constant irradiance. Thermocouples and ultrasonic flow meters are placed at the inlet and outlet of the collector prototype. The temperature rise is measured by the thermocouples, and the volume flow rate is detected by the ultrasonic flow meter. The heat collection efficiency of the collector prototype is determined based on the temperature rise and volume flow rate.

[0126] S460: Compare the consistency of the thermal collection efficiency of the collector preset by the genetic algorithm and the thermal collection efficiency of the collector prototype to verify the effectiveness of the artificial neural network model-genetic algorithm.

[0127] In some embodiments of the present invention, a laboratory-scale solar collector prototype with optimized dimensions and concentration is constructed based on a validated collector structure and ternary mixed suspension nanoparticle concentration. The prototype collector structure includes a low-iron transparent glass cover, a thermally insulating aluminum channel, and easily accessible connection interfaces. It is understood that the low-iron transparent glass has high light transmittance, minimizing solar radiation loss through reflection and absorption, ensuring that more solar radiation energy enters the collector prototype. The low-iron transparent glass is selected with an appropriate thickness to ensure mechanical strength and optical properties. The aluminum channel has excellent thermal conductivity, enabling rapid transfer of absorbed heat to the gold, copper, and platinum mixed nanosuspension. The inner wall of the channel can be specially treated (such as coating or texturing) to enhance its ability to absorb solar radiation. The thermally insulating aluminum channel is wrapped with a thermally insulating material to reduce heat loss to the surrounding environment. Connection interfaces are provided at the inlet and outlet of the collector prototype for connecting piping and measuring equipment. These interfaces should be well sealed to ensure smooth and leak-free flow of the gold, copper, and platinum mixed nanosuspension within the collector prototype.

[0128] A solar simulator was used to provide constant solar irradiance to simulate actual solar radiation conditions. The irradiance of the solar simulator was adjusted to match the solar irradiance in the actual application. Thermocouples were installed 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. Ultrasonic flow meters were installed at the inlet and outlet of the collector prototype to detect the volumetric flow rate of the ternary hybrid nanosuspension. The ultrasonic flow meters should have high precision and non-contact measurement characteristics to ensure the accuracy of the measurement results.

[0129] In one embodiment, a solar simulator is started to provide a constant solar irradiance; a thermal collector prototype is started to allow the ternary hybrid nanosuspension to flow in the thermal collection channel; the fluid temperature at the inlet and outlet of the thermal collector prototype is measured by thermocouples, and the temperature rise is recorded; the volume flow rate of the ternary hybrid suspension is measured by an ultrasonic flowmeter; and the thermal collection efficiency of the thermal collector prototype is calculated based on the temperature rise and the volume flow rate of the ternary hybrid nanosuspension.

[0130] The thermal efficiency of the collector prototype is calculated using the following formula:

[0131]

[0132] in, η is the thermal efficiency, T out is the temperature of the ternary mixed nanosuspension at the outlet, T in is the temperature of the ternary mixed nanosuspension at the inlet, is the mass flow rate of the ternary mixed nanosuspension, cpis the specific heat capacity of the ternary mixed nanosuspension, I is the solar irradiance, A is the effective area of ​​the collector prototype.

[0133] The experimentally measured thermal collection efficiency of the prototype collector is compared with the efficiency predicted by the genetic algorithm. The differences between the two are analyzed to evaluate the effectiveness of the artificial neural network model and the genetic algorithm. If the experimental results are consistent or close to the predicted results, the artificial neural network model and the genetic algorithm are effective in optimizing the collector design.

[0134] In the embodiment of the present invention, by constructing a collector prototype based on the optimal size and concentration of the ternary mixed nanofluid of gold, copper and platinum, and measuring its heat collection efficiency under experimental conditions, the effectiveness of the artificial neural network model and genetic algorithm in optimizing the collector design can be verified. This method provides a scientific basis for the design and optimization of the collector and helps to improve the performance and reliability of the collector.

[0135] Reference Figure 7 As shown, Figure 7 This is a detailed flow chart 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,

[0136] S510: Based on channel geometry and ternary hybrid nanosuspension, it uses anodized aluminum or stainless steel structural frames and tempered low-iron glass as the transparent cover of the collector equipment;

[0137] S520: Integrate automatic flow control devices into collector equipment to maintain stable fluid circulation under different environmental conditions;

[0138] S530: Deploy a sensor network supported by the Internet of Things to continuously collect data on the temperature of the ternary hybrid nanosuspension, solar irradiance, the flow rate of the ternary hybrid nanosuspension, and the thermal efficiency of the collector equipment.

[0139] S540: Determine the solar thermal utilization rate of the collector device based on the data;

[0140] S550: Upload the data and solar thermal utilization rate of the collector equipment to the host computer for management and analysis.

[0141] In some embodiments of the present invention, after successful validation of a laboratory collector prototype, a pilot-scale solar collector device based on industrial-grade components will be developed. Based on the channel geometry and ternary hybrid nanosuspension, an anodized aluminum or stainless steel structural frame and tempered low-iron glass will be used as the transparent cover of the collector device. The channel geometry of the collector will be designed based on the optimization results to ensure smooth flow of the ternary hybrid nanosuspension and good heat exchange performance. The heat collection channel can be made of anodized aluminum or stainless steel, which have good thermal conductivity and corrosion resistance. Tempered low-iron glass is used as the transparent cover of the collector device. Tempered glass has high strength and good impact resistance, while low-iron glass has high light transmittance, which can minimize reflection and absorption losses of solar radiation. An automatic flow control device is integrated into the collector device to maintain stable fluid circulation under different environmental conditions. The automatic flow control device can automatically adjust the flow of the ternary hybrid nanosuspension based on factors such as ambient temperature and solar irradiance, ensuring efficient operation of the collector device. The environmental parameters and the state of the ternary mixed nanosuspension are monitored in real time through a sensor network and fed back to the controller. The controller automatically adjusts the flow control device according to the preset control strategy to maintain the stability of the ternary mixed nanosuspension circulation.

[0142] A sensor network powered by the Internet of Things (IoT) is deployed in the collector. The sensor network includes temperature sensors, solar irradiance sensors, flow sensors, and efficiency sensors. The temperature sensor continuously collects the temperature of the ternary hybrid nanosuspension, while the solar irradiance sensor measures solar radiation intensity. The flow sensor detects the flow rate of the ternary hybrid nanosuspension. The efficiency sensor evaluates the collector efficiency. The sensor network transmits collected data, including parameters such as temperature, solar irradiance, flow rate of the ternary hybrid nanosuspension, and thermal efficiency, to a host computer in real time using IoT technology. The data is used to calculate the solar thermal efficiency of the collector. The data and the solar thermal efficiency of the collector are then uploaded to the host computer for management and analysis. The host computer can store, process, and analyze the data, generating reports and charts for evaluating the performance of the collector and optimizing operating parameters.

[0143] By designing a solar collector based on channel geometry and ternary hybrid nanosuspensions, and integrating automated flow control devices with IoT-enabled sensor networks, efficient operation and real-time monitoring of the collector can be achieved. Data analysis and management can further optimize collector design and operation strategies, improving solar thermal utilization efficiency.

[0144] Reference Figure 8 As shown, Figure 8This is a second detailed flow chart of the method for optimizing solar thermal utilization of a heat collector provided by an embodiment of the present invention. The method for optimizing solar thermal utilization of a heat collector further includes but is not limited to steps S511 to S513. Specifically,

[0145] S511: Prepare insulation walls and place them on both sides of the collector device, with the insulation walls and the transparent cover plate being perpendicular to each other;

[0146] S512: Setting a heat collection channel in the heat collector device, and setting an inlet at the starting point of the heat collection channel, so that the ternary mixed nano suspension enters the heat collection channel through the inlet and flows inside the heat collector device to absorb solar energy;

[0147] S513: An outlet is provided at the end of the heat collection channel so that the ternary mixed nano suspension treated by solar heating can be discharged from the heat collection channel through the outlet.

[0148] In some embodiments of the present invention, after the laboratory collector prototype is successfully verified, a pilot-scale solar collector device will be developed based on industrial-grade components. The preparation method of the collector device includes: first, preparing an insulation wall. The insulation wall adopts a high-efficiency thermal insulation material, such as polyurethane foam, rock wool or glass fiber, etc. These materials have low thermal conductivity and can effectively reduce heat loss. The insulation wall is set on both sides of the collector device to ensure that the insulation wall and the transparent cover are perpendicular to each other. The insulation wall fits tightly to the side of the collector to reduce heat loss to the surrounding environment; the transparent cover 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 adhesives, screws or clips to fix the insulation wall to the collector device to ensure that it will not loosen during operation.

[0149] The heat collection channels can be made of anodized aluminum or stainless steel, which have excellent thermal conductivity and corrosion resistance, effectively absorbing and transmitting solar energy. These channels ensure uniform flow of the ternary hybrid nanosuspension, and can be either parallel or serpentine. The cross-sectional area of ​​the channels should be optimized based on the flow rate and heat exchange requirements of the ternary hybrid nanosuspension, ensuring smooth flow and effective heat exchange.

[0150] An inlet is provided at the starting point of the heat collection channel so that the ternary mixed nano-suspension enters the heat collection channel through the inlet. The inlet should be designed to be easily connected to an external pipeline to ensure that the ternary mixed nano-suspension can smoothly enter the heat collection channel. An outlet is provided at the end of the heat collection channel so that the ternary mixed nano-suspension, which has been heated by solar energy, can be discharged from the heat collection channel through the outlet. The outlet should also be designed to be easily connected to an external pipeline to ensure that the ternary mixed nano-suspension, which has been heated by solar energy, can smoothly discharge from the heat collection channel.

[0151] By preparing and placing insulation walls on both sides of the collector, as well as designing heat collection channels within the collector with a rationally arranged inlet and outlet, the thermal efficiency and operational stability of the collector can be effectively improved. The insulation walls reduce heat loss, while the rational channel design and inlet and outlet placement ensure that the ternary mixed nanosuspension can fully absorb solar energy and flow smoothly, improving the overall performance of the collector.

[0152] Reference Figure 9 As shown, Figure 9 It is a structural schematic diagram of a collector device provided by an embodiment of the present invention, the collector device includes a transparent cover 1, an insulation wall 2, a ternary mixed nanosuspension, an inlet 3 and an outlet 5; the collector device also includes a support frame 6, which is used to provide support for the collector device body and its parts. The transparent cover 1 is arranged on the top of the collector device for solar radiation penetration and prevention of fluid evaporation; the insulation wall 2 is arranged on both sides of the collector device for thermal insulation to minimize heat conduction loss; the ternary mixed nanosuspension is arranged inside the collector device for absorbing solar energy; the inlet 3 is arranged at the starting point of the heat collection channel 4 for providing an entrance for the ternary mixed nanosuspension to enter the heat collection channel 4, and the outlet 5 is arranged at the end point of the heat collection channel 4 for providing an outlet for the ternary mixed nanosuspension treated by solar energy to discharge from the heat collection channel 4.

[0153] The collector equipment improves the solar energy absorption efficiency and heat utilization efficiency by optimizing the structural design and material selection. The transparent cover 1 is used to allow solar radiation to penetrate directly, transfer the solar radiation energy to the inside of the collector equipment, and reduce heat loss. The transparent cover 1 is installed on the top of the collector equipment and fits tightly with other components of the collector equipment to ensure sealing and prevent heat loss. The insulation wall 2 is used to provide thermal insulation, minimize the loss of heat inside the collector equipment to the surrounding environment through heat conduction, and improve the thermal efficiency of the collector equipment. Commonly used insulation materials for the insulation wall 2 include polyurethane foam, rock wool, glass fiber, etc. These materials have low thermal conductivity and can effectively insulate. The insulation wall 2 is arranged on both sides of the collector equipment, perpendicular to the transparent cover 1, and tightly fits the side of the collector to ensure good insulation effect.

[0154] The main function of the ternary mixed nanosuspension 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 and thermal conductivity properties, and can effectively absorb solar radiation and convert it into thermal energy.

[0155] The inlet 3 is provided at the starting point of the heat collection channel 4 and is usually equipped with a connection interface for connecting to an external pipeline to ensure that the ternary mixed nanosuspension can smoothly enter the heat collection channel 4. The outlet 5 is provided at the end point of the heat collection channel 4 and is also equipped with a connection interface for connecting to an external pipeline to ensure that the ternary mixed nanosuspension can smoothly discharge from the heat collection channel 4.

[0156] It is understood that the working principle of the thermal collector device is as follows: the transparent cover plate 1 receives solar radiation and transfers the solar radiation energy into the interior of the thermal collector device; the ternary hybrid nanosuspension absorbs the solar radiation energy and converts it into heat energy. The ternary hybrid nanosuspension circulates within the thermal collection channel 4, entering the thermal collection channel 4 through the inlet 3, absorbing the solar radiation energy within the thermal collection channel 4, raising its own temperature, and then exiting the thermal collection channel 4 through the outlet 5. The discharged high-temperature ternary hybrid nanosuspension can be used in various heat utilization scenarios, such as hot water supply and space heating.

[0157] It should be appreciated that the method steps in the embodiments of the present invention can be implemented or executed by computer hardware, a combination of hardware and software, or by 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 desired, 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 be run on a programmed application-specific integrated circuit.

[0158] Furthermore, the operations of the processes described herein may be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described herein (or variations and / or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) that collectively executes on one or more processors, by hardware, or a combination thereof. The computer program includes a plurality of instructions that can be executed by one or more processors.

[0159] Further, the methods can be implemented in any type of computing platform that is operably connected to a suitable computer, including but not limited to a personal computer, a minicomputer, a mainframe, a workstation, a network or distributed computing environment, a separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, etc. Various aspects of the present invention can be implemented as machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, an optical read and / or write storage medium, RAM, ROM, etc., so that it can be read by a programmable computer, and when the storage medium or device is read by the computer, it can be used to configure and operate the computer to perform the processes described herein. In addition, the machine-readable code, or portions thereof, can be transmitted over a wired or wireless network. When such media includes instructions or programs that implement the steps described above 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 can also include the computer itself.

[0160] The computer program can be applied to input data to perform the functions described herein, thereby converting the input data to generate output data that is stored in a 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 converted data represents a physical and tangible object, including a specific visual depiction of the physical and tangible object produced on the display.

[0161] The above description is merely a preferred embodiment of the present invention. The present invention is not limited to the aforementioned embodiments. As long as the technical effects of the present invention are achieved by the same means, any modifications, equivalent substitutions, 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 modifications and variations of its technical solutions and / or implementation methods are possible.

Claims

1. A method for optimizing solar thermal utilization of a collector, characterized in that: The method comprises: S100: Predicting the radiation characteristics of suspended nanoparticles using the boundary element method based on an artificial neural network model; S200: Optimizing the parameters of the collector by a genetic algorithm and evaluating the radiation characteristics of the suspended nanoparticles to maximize the solar energy weighted absorption coefficient and the heat collection efficiency of the collector; S300: constructing a collector structure optimized by a genetic algorithm in a finite volume method simulation framework, and verifying the rationality of the collector structure parameters; S400: preparing a ternary mixed nanosuspension, and performing dynamic light scattering, Zeta potential analysis, and sedimentation tests on the prepared ternary mixed nanosuspension to verify the stability of the ternary mixed nanosuspension; S500: preparing a heat collector device based on the ternary hybrid nanosuspension, disposing a sensor network in the heat collector device, continuously collecting data on the temperature of the ternary hybrid nanosuspension, solar irradiance, flow rate of the ternary hybrid nanosuspension, and heat collection efficiency of the heat collector device through the sensor network, determining the solar thermal utilization rate of the heat collector device based on the data, and uploading the data to a host computer for management and analysis; Wherein, the step S300 includes: S310: constructing a collector structure optimized by a genetic algorithm in a finite volume method simulation framework, and solving steady-state, 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 process the pressure and velocity coupling. Simulate the detailed distribution of temperature, velocity, and pressure to verify the rationality of the parameters selected in the collector structure after genetic algorithm optimization. S330: varying key input parameters within the finite volume method simulation framework, the key input parameters including collector length and height, Reynolds number, glass transmittance, and suspended nanoparticle volume fraction, and quantifying the effects of the key input parameters on the outlet temperature rise and efficiency of the collector to identify key influencing factors; S340: varying the flow rate of the ternary mixed nanosuspension, the solar irradiance, and the disturbance error fluctuation within the finite volume method simulation framework, and verifying the rationality and stability of the collector parameters optimized by the genetic algorithm.

2. The solar thermal utilization optimization method of a heat collector according to claim 1, characterized in that: The step S100 includes: S110: generating a data set using a boundary element method, wherein the data set is used to simulate the interaction between different suspended nanoparticles and solar radiation, the data set including data on characteristics of the suspended nanoparticles, properties of the medium in which the suspended nanoparticles are suspended, 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: constructing a multi-layer neural network structure in the artificial neural network model, using the characteristics of the suspended nanoparticles in the training set and the medium properties of the suspended nanoparticles as input, using the extinction efficiency, the absorption efficiency, and the scattering efficiency in the training set as output, and using a Levenberg-Marquardt algorithm to train the artificial neural network model, and iteratively optimizing the artificial neural network model to minimize a mean square error; S140: Evaluate the performance of the artificial neural network model during the training process using the data from the validation set, and adjust the hyperparameters of the artificial neural network model, wherein 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 using the data of the test set to determine the generalization ability of the artificial neural network model.

3. The solar thermal utilization optimization method of a heat collector according to claim 1, characterized in that: The step S200 includes: S210: Randomly generate a diverse candidate solution population within preset agreed conditions, wherein the preset agreed conditions include the length of the collector and the radius of the suspended nanoparticles; S220: Calculating the fitness of each solution in the candidate solution population based on thermal efficiency, and evaluating the radiation characteristics of the suspended nanoparticles through CFD simulation; S230: The genetic algorithm adopts an evolutionary strategy to select high-fitness solutions from the candidate solution population, mix the parameters of the collector for crossover processing, introduce random changes for mutation processing, and continuously iterate until the optimal solution of the candidate solution population is obtained; S240: Maximizing the weighted solar energy absorption coefficient and the heat collection efficiency of the collector according to the optimal solution.

4. The solar thermal utilization optimization method of a collector according to claim 1, characterized in that: The step S400 includes: S410: Preparation of gold, copper, and platinum ternary mixed suspended nanoparticles by chemical reduction or laser ablation; S420: treating the gold, copper, and platinum ternary mixed suspended nanoparticles in a base liquid by ultrasonic treatment, and treating the gold, copper, and platinum ternary mixed suspended nanoparticles by using a surfactant to obtain a gold, copper, and platinum ternary mixed nanosuspension; S430: Performing dynamic light scattering, Zeta potential analysis, and sedimentation test on the gold, copper, and platinum ternary mixed nanosuspension to verify the long-term stability of the gold, copper, and platinum ternary mixed nanosuspension.

5. The solar thermal utilization optimization method of a heat collector according to claim 4, characterized in that: The method further comprises: S440: Construct a solar collector prototype based on the optimal size and concentration of suspended gold, copper, and platinum nanoparticles, including a low-iron transparent glass cover, a thermally insulating aluminum channel, and a connection interface. S450: using a solar simulator to provide a constant irradiance, arranging thermocouples and ultrasonic flowmeters at the inlet and outlet of the collector prototype, measuring a temperature rise by the thermocouples, detecting a volume flow by the ultrasonic flowmeter, and determining a heat collection efficiency of the collector prototype based on the temperature rise and the volume flow; S460: Comparing the heat collection efficiency of the heat collector preset by the genetic algorithm with the heat collection efficiency of the heat collector prototype for consistency, so as to verify the effectiveness of the artificial neural network model-genetic algorithm.

6. The solar thermal utilization optimization method of a heat collector according to claim 1, characterized in that: The step S500 includes: S510: Based on channel geometry and ternary hybrid nanosuspension, it uses anodized aluminum or stainless steel structural frames and tempered low-iron glass as the transparent cover of the collector equipment; S520: Integrating an automatic flow control device into the collector device to maintain stable fluid circulation under different environmental conditions; S530: deploying a sensor network supported by the Internet of Things, and continuously collecting data on the temperature of the ternary mixed nanofluid suspension, solar irradiance, the flow rate of the ternary mixed 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 according to the data; S550: Uploading the data and the solar thermal utilization rate of the collector equipment to a host computer for management and analysis.

7. The solar thermal utilization optimization method of a heat collector according to claim 6, characterized in that: The method further comprises: S511: preparing insulation walls and arranging the insulation walls on both sides of the heat collector device, wherein the insulation walls and the transparent cover plate are perpendicular to each other; S512: Setting a heat collection channel in the heat collector device, and setting an inlet at a starting point of the heat collection channel, so that the ternary mixed nano-suspension enters the heat collection channel through the inlet and flows inside the heat collector device to absorb solar energy; S513: An outlet is provided at the end of the heat collection channel, so that the ternary mixed nano suspension treated by solar heating can be discharged from the heat collection channel through the outlet.

8. A collector device for implementing the solar thermal utilization optimization method of the collector according to claim 7, characterized in that: include: A transparent cover plate (1) is arranged on the top of the heat collector device and is used to receive solar radiation; Insulation walls (2) are provided on both sides of the heat collector device and are used for thermal insulation to minimize heat conduction loss; A ternary mixed nano suspension is provided inside the heat collector device for absorbing solar energy; An inlet (3) is provided at the starting point of the heat collection channel and is used to provide an inlet for the ternary mixed nano suspension to enter the heat collection channel (4); The outlet (5) is arranged at the end of the heat collection channel (4) and is used to provide an outlet for the ternary mixed nano suspension treated by solar heating to be discharged from the heat collection channel (4).

9. A computer device comprising a memory and a processor, characterized in that: The method according to any one of claims 1 to 7 is implemented when the processor executes the computer program stored in the memory.

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