Temperature prediction method and system for solid-liquid phase change material
Through the combination of CFD and artificial neural network, the convenience and accuracy of temperature prediction of phase change materials are solved, efficient selection of phase change materials in electronic equipment, and the design efficiency and accuracy of the heat dissipation system are improved.
Patent Information
- Application Number
- CN202510393626.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-31
- Publication Date
- 2025-07-08
Smart Images
Figure CN120280057A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of heat dissipation of electronic devices, and particularly relates to a temperature prediction method and system for solid-liquid phase change materials. Background Art
[0002] The emergence of electronic devices has brought great convenience to people's daily life and work. In recent years, the field of electronic devices has also witnessed rapid development. With the development of information technology, the number of internal components of devices has increased, and the power demand has also grown accordingly. The internal heat-generating components are arranged compactly, and it is difficult to quickly conduct heat from the heat source to the outside of the device, resulting in the accumulation of internal heat and the generation of local high temperatures. Temperature has a great impact on the working performance of electronic devices, and heat dissipation is a key factor to ensure their normal operation and extend their service life. With the rapid development of electronic technology, the integration degree of devices is getting higher and higher, and the power consumption density is also getting larger and larger, making the heat dissipation problem particularly important. Currently, the heat dissipation of electronic devices is mainly divided into two categories, namely passive heat dissipation and active heat dissipation. Active heat dissipation refers to the way that electronic devices dissipate heat through forced convection, thermoelectric cooling and other ways that require additional energy consumption, which will generate additional energy consumption, increase the overall power of the device, and some cooling methods have relatively high noise, which may reduce the user experience. Passive heat dissipation refers to the way that the heat generated inside the electronic device is conducted out of the device through heat conduction, natural convection and radiation without additional power consumption. It is suitable for components that have requirements for the volume of the device and are difficult to set up forced convection heat dissipation channels inside, and transfer the heat to the environment.
[0003] Phase change energy storage heat dissipation is a type of passive heat dissipation, which uses the latent heat generated during the phase change of phase change materials to achieve the storage and utilization of thermal energy. Phase change materials require a large amount of latent heat during phase change, and have the advantages of large heat storage density, strong chemical stability, low cost, etc., so they are widely used in industrial waste heat recovery, power peak regulation, solar energy systems, green buildings and other fields. In addition, phase change materials also have a prominent feature that they do not change their own temperature during phase change by absorbing heat, and the absorbed heat is dissipated through natural convection, which can be applied to temperature control systems, especially the temperature control of electronic devices. Phase change energy storage is a current cutting-edge industry, and phase change energy storage cooling has very broad application prospects in electronic devices with high heat consumption, low heat flux density, and short working time. The heat dissipation method using phase change materials has broad application prospects. However, currently, the physical properties factors affecting the melting of phase change materials are relatively complex, such as latent heat, thermal conductivity and specific heat capacity before and after phase change, which brings great difficulties to the selection.
[0004] Since the prediction of temperature distribution involves the internal flow field information of the phase change material, it is difficult to directly conduct experimental research. When using the phase change material to dissipate heat from electronic devices, the difficulty in predicting the temperature of the phase change material lies in that when one end of the phase change material is a heat flux boundary condition and the other end is a convective boundary condition, its internal temperature changes with time. Therefore, currently in the selection of phase change materials, due to the incomplete consistency of the solid-liquid physical properties of the phase change material and the temperature interval during the two-phase transformation, and the relatively complex physical properties factors during the melting of the phase change material, the convenience and accuracy of predicting the temperature of the phase change material need to be further optimized. Summary of the Invention
[0005] The present invention provides a method and system for predicting the temperature of a solid-liquid phase change material, aiming to solve the problem that currently in the selection of phase change materials, due to the incomplete consistency of the solid-liquid physical properties of the phase change material and the temperature interval during the two-phase transformation, and the relatively complex physical properties factors during the melting of the phase change material, the convenience and accuracy of predicting the temperature of the phase change material need to be further optimized.
[0006] To achieve the above object, the present invention adopts the following technical solutions: The present invention provides a method for predicting the temperature of a solid-liquid phase change material, including the following steps; S1. Determine the geometric parameters and physical property parameters of the candidate phase change material between the cold and heat sources; S2. Based on the geometric parameters and physical property parameters, calculate the temperature change data of the solid phase of the candidate phase change material through differential equations; S3. Calculate the phase change time of the candidate phase change material based on the phase change time neural network prediction model; S4. Use the curve parameter method to generate the temperature change data during the phase change process of the candidate phase change material; S5. Combine the temperature change data during the phase change process of the candidate phase change material with the temperature change data of the solid phase of the candidate phase change material to obtain the phase change completion time node and temperature of the phase change material; if the phase change completion time node of the phase change material does not meet the preset value, replace the candidate phase change material and re-predict the temperature; if it meets, enter S6; S6. Input the liquid property parameters of the candidate phase change material, and calculate the complete temperature change data of the candidate phase change material based on the phase change completion time node and temperature of the phase change material; preset the highest working temperature, if the complete temperature change data of the candidate phase change material does not meet the requirements of the preset highest working temperature, replace the candidate phase change material and re-predict the temperature; if it meets, complete the temperature prediction of the candidate phase change material.
[0007] In some embodiments, in S1, the hot end of the candidate phase change material is a heat flux boundary condition, and the cold end is a convective boundary condition; the basic parameters of the candidate phase change material include: the length of the candidate phase change material in the heat transfer direction, the initial temperature, the heat flux of the electronic device, the heat transfer coefficient and temperature of the environment, the candidate working fluid, and the physical properties of the solid phase.
[0008] In some embodiments, S2 specifically includes: calculating the temperature change of the candidate phase change material before phase change according to the one-dimensional unsteady heat conduction discrete control equation and boundary conditions, and obtaining the temperature-time change curve of the solid phase of the candidate phase change material and the moment data reaching the phase change point.
[0009] Further, in S1, the temperature change process of the solid phase of the candidate phase change material is represented by the one-dimensional unsteady heat conduction discrete control equation of the following formula (1): (1); The boundary condition is that when τ > 0 and x = 0, q = a, when τ > 0 and x = L, it is described as the following formula (2): (2); Wherein, is the heat flux density of the electronic device, is the environmental temperature, is the convective heat transfer coefficient between the phase change material and the environment, is the density of the solid-phase phase change material, c is the specific heat capacity of the solid-phase phase change material, V is the volume of the solid-phase phase change material, is the thermal conductivity of the solid-phase phase change material, is the source phase, is the temperature of the phase change material, is the length of the phase change material, and τ is the heat absorption time of the phase change material.
[0010] In some embodiments, in S3, a CFD calculation model of the phase change material is established. Using the thermal physical properties of each phase change material as design variables, a preset number of sample working conditions are designed by the LHS method, CFD calculations are performed on the sample working conditions to form sample data, and according to the sample data, a phase change time neural network prediction model is established for each phase change material.
[0011] In some embodiments, in S3, the input variables of the phase change time neural network prediction model are the thermal physical properties of the phase change material, and the output variable is the phase change time of the phase change material; wherein: The thermal physical properties of the phase change material include: the thermal conductivity, specific heat capacity, latent heat, density of the phase change material, and the heat flux density of the electronic device.
[0012] Further, in S3, the phase change time neural network prediction model adopts a BP neural network.
[0013] In some embodiments, in S4, the phase change time of the candidate phase change material is calculated according to the phase change time neural network prediction model. Points on the temperature change curve of the phase change section of the candidate phase change material are represented by one or more parameters to obtain the temperature change curve of the phase change section.
[0014] In some embodiments, in S6, calculating the complete temperature change data of the candidate phase change material includes: if the heat input of the liquid candidate phase change material is higher than the environmental heat loss, regarding the heat absorption process as a heat conduction process, and using a differential equation to calculate the temperature change of the liquid candidate phase change material until the phase change material reaches dynamic thermal equilibrium.
[0015] The present invention also provides a temperature prediction system for a solid-liquid phase change material. The system includes a solid-phase temperature calculation module for the phase change material, a melting temperature calculation module for the phase change material, a first determination module, a liquid-phase temperature calculation module for the phase change material, and a second determination module; wherein: The solid-phase temperature calculation module is configured to calculate the temperature change data of the solid phase of the candidate phase change material through a differential equation based on geometric parameters and physical property parameters; The phase change material melting temperature calculation module is configured to calculate the phase change time of the candidate phase change material based on the phase change time neural network prediction model; and generate the temperature change data of the phase change process of the candidate phase change material by using the curve parameter method; The first determination module is configured to determine that if the phase change completion time node of the phase change material does not meet the preset value, replace the candidate phase change material and re-perform temperature prediction; if it meets, continue with the subsequent steps; The liquid-phase temperature calculation module for the phase change material is configured to calculate the complete temperature change data of the candidate phase change material based on the liquid property parameters of the candidate phase change material, the phase change completion time node and temperature of the phase change material; The second determination module is configured to determine that if the complete temperature change data of the candidate phase change material does not meet the preset maximum operating temperature requirement, replace the candidate phase change material and re-perform temperature prediction; if it meets, complete the temperature prediction of the candidate phase change material.
[0016] Compared with the prior art, the temperature prediction method and system for a solid-liquid phase change material of the present invention have the following beneficial effects: A temperature prediction method for a solid-liquid phase change material according to the present invention realizes the prediction of the temperature of the phase change material by combining CFD, artificial neural network, curve parameter method, etc. Specifically, the CFD method provides a basis for determining the phase change time under the influence of the complex physical properties of the phase change material; the artificial neural network establishes a quantitative prediction model between the thermal conductivity, density, latent heat, specific heat capacity, heat flux density of the electronic device and the phase change time, fully utilizing the information of the sample data, and can effectively reflect the complex coupling relationship between different phase change materials and phase change times.
[0017] The present invention improves the problem that the current selection of phase change materials can only rely on simulation or experience simply. The simulation calculation has high requirements for computer configuration and long calculation time, and the empirical judgment has large errors and cannot predict the equilibrium temperature of the phase change material. The introduction of the CFD method and the artificial neural network makes up for the defects of the current solid-liquid phase change material model, can accurately and efficiently predict the temperature distribution of the phase change material under different heat fluxes and physical properties, and quickly evaluate the equilibrium temperature and phase change time when the electronic device uses the phase change material for heat dissipation, reduces the cost, improves the efficiency and accuracy, and thus provides technical support for the selection of engineering phase change materials, having better practical significance. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] The accompanying drawings in the specification are used to provide a further understanding of the present invention, and constitute a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention.
[0019] Figure 1 It is a schematic flow chart of a temperature prediction method for a solid-liquid phase change material according to the present invention; Figure 2 It is a schematic diagram of the phase change material and the electronic device in a temperature prediction method for a solid-liquid phase change material according to the present invention; Figure 3 It is a schematic diagram of the input and output of the BP neural network in a temperature prediction method for a solid-liquid phase change material according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and illustrated herein can be arranged and designed in various different configurations.
[0021] Accordingly, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without creative efforts shall fall within the scope of protection of the present invention.
[0022] It should be noted that like reference numerals and letters denote like items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.
[0023] In the description of the embodiments of the present invention, it should be noted that if terms such as "upper", "lower", "horizontal", "inner", etc. are used to indicate the orientation or positional relationship, it is based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the invention is usually placed during use. This is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention. In addition, terms such as "first", "second", etc. are only used for descriptive distinction and cannot be construed as indicating or implying relative importance.
[0024] In addition, if the term "horizontal" appears, it does not mean that the component is required to be absolutely horizontal, but it can be slightly inclined. For example, "horizontal" only means that its direction is more horizontal relative to "vertical", and does not mean that the structure must be completely horizontal, but it can be slightly inclined.
[0025] In the description of the embodiments of the present invention, it should also be noted that unless otherwise clearly specified and limited, if terms such as "set", "installed", "connected", "connected" are used, they should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.
[0026] How to provide a temperature prediction method for phase change materials so that, based on basic parameters and a prediction model, in the selection of phase change materials for heat dissipation of electronic devices, it can be relatively accurate and adaptable to ensure the heat dissipation performance of electronic devices.
[0027] As Figure 1 shown, a temperature prediction method for a solid-liquid phase change material of the present invention includes the following steps; S1. Determine the geometric parameters and physical property parameters of the candidate phase change material between the cold and heat sources; S2. Calculate the temperature change data of the solid phase of the candidate phase change material through differential equations based on geometric parameters and physical property parameters; S3. Calculate the phase change time of the candidate phase change material based on the phase change time neural network prediction model; S4. Generate the temperature change data of the phase change process of the candidate phase change material using the curve parameter method; S5. Combine the temperature change data of the phase change process of the candidate phase change material with the temperature change data of the solid phase of the candidate phase change material to obtain the phase change completion time node and temperature of the phase change material; if the phase change completion time node of the phase change material does not meet the preset value, replace the candidate phase change material and re - conduct temperature prediction; if it meets, enter S6; S6. Input the liquid property parameters of the candidate phase change material, and calculate the complete temperature change data of the candidate phase change material based on the phase change completion time node and temperature of the phase change material; preset the maximum working temperature, if the complete temperature change data of the candidate phase change material does not meet the requirements of the preset maximum working temperature, replace the candidate phase change material and re - conduct temperature prediction; if it meets, complete the temperature prediction of the candidate phase change material.
[0028] The temperature prediction method of the present invention establishes a phase change time network model. Through the CFD method combined with an artificial neural network, predict the time consumed by the phase change process of different physical property working fluids per unit volume. Calculate the temperature changes before and after the phase change through discrete control equations and boundary conditions. Specifically, based on computational fluid dynamics (CFD), artificial neural networks, curve parameterization methods, and one - dimensional thermodynamic heat conduction models, for the heating process of the solid - liquid phase change material of any working fluid, it is divided into three parts: heat conduction before phase change, phase change, and heat conduction after phase change. The heat transfer process of the solid - liquid phase change material before phase change is a one - dimensional heat conduction process, and the temperature of the phase change material is calculated through the one - dimensional unsteady heat conduction discrete control equation and boundary conditions. Obtain the phase change time sample data of the same volume and the same material with different physical property parameters through the CFD method, and establish a phase change time prediction model using an artificial neural network. Make a linear fitting calculation of the temperature during the phase change process through the curve parameterization method. Finally, calculate the temperature change of the heat conduction process after the phase change through the control equation and boundary conditions to determine the temperature change of the entire heat absorption process of the phase change material. The present invention can accurately and efficiently predict the melting process of the phase change material under different heat flux densities, and thus can provide data reference for subsequent selection, and is expected to improve the heat dissipation performance of electronic devices.
[0029] The present invention establishes a CFD calculation model for phase change materials, uses the thermal conductivity, density, latent heat, specific heat capacity of each phase change material, and the heat flux density of the electronic device as design variables, designs a preset number of sample working conditions by the LHS method, performs CFD calculations on the sample working conditions to form sample data, and based on the sample data, establishes an artificial neural network model for each phase change material regarding the phase change time, and establishes a neural network prediction model for the phase change time for each phase change material.
[0030] The numerical simulation of Computational Fluid Dynamics (CFD) adopted by the present invention is an important means for predicting the temperature of the phase change materials of the present invention. By numerically solving the control equations of physical problems, complete information such as the flow field and temperature field can be determined. Moreover, the artificial neural network adopted by the present invention has significant advantages in dealing with complex quantitative relationships. After being trained based on the data set, it can achieve the approximation of complex non-linear functions and has strong adaptability to different types of complex quantitative relationships. Therefore, it is especially suitable for extracting the unknown non-linear relationship between input variables and output variables and for prediction.
[0031] When the phase change material is applied to the electronic device radiator, generally one side needs to absorb the heat of a fixed heat flux, and the other side dissipates the heat through natural convection. The hot end of the solid-liquid phase change material is the heat flux boundary condition, and the cold end is the convection boundary condition. The heat absorption process of the phase change material can be divided into three parts: the first part is the heat conduction of the solid phase, the second part is the phase change process, and the third part is the heat conduction process of the liquid phase. The present invention first needs to determine the length in the heat transfer direction of the phase change material, the initial temperature, the heat flux of the electronic device, the heat transfer coefficient and temperature of the environment, the selected working medium, and the physical properties of the solid phase.
[0032] For the temperature change process of the first part of the phase change material, the present invention calculates the temperature change before the phase change according to the one-dimensional unsteady heat conduction discrete control equation and boundary conditions of the following formula, and obtains the temperature change curve with time and the moment reaching the phase change point: (1); The boundary condition is that when τ > 0 and x = 0, q = a, when τ > 0 and x = L, it is described by the following formula (2): (2); Where, is the heat flux density of the electronic device, is the environmental temperature, is the convective heat transfer coefficient between the phase change material and the environment, is the density of the solid-phase phase change material, c is the specific heat capacity of the solid-phase phase change material, V is the volume of the solid-phase phase change material, is the thermal conductivity of the solid-state phase change material, is the source phase, is the temperature of the phase change material, is the length of the phase change material, and τ is the heat absorption time of the phase change material.
[0033] The heat conduction process of the first part requires differentiating the phase change material in time and space. Set the space step and the time step . Determine the number of space units according to the length x of the phase change material in the heat transfer direction, and determine the number of time units according to the time span to be calculated.
[0034] (3); (4); Among them, t 0 is the calculation time of the heat absorption process of the phase change material, is the number of time units of the phase change material, is the number of space units of the phase change material.
[0035] The initial temperature of the phase change material , and is represented by the vector : (5); Among them, T 1 is the initial temperature of the phase change material, When there is heat flux input on one side, the differential equation of the heat conduction equation for the phase change material is: (6); Among them, is the space step, and are the temperatures at the th and th time steps at the th space unit respectively, is the time step, is the heat flux density.
[0036] When the third kind of boundary condition is used, discretize the boundary. When the boundary point = 0: (7); While calculating the heat conduction process of the first part, judge the temperature, When it is greater than the melting point, end the calculation and record the time at this moment.
[0037] In some embodiments, the geometric parameters and physical property parameters of the phase change material of the present invention include: the length determining the heat transfer direction of the phase change material, the initial temperature, the heat flux of the electronic device, the heat transfer coefficient and temperature of the environment, the selected working fluid, and the physical property parameters of the solid phase.
[0038] Furthermore, the present invention establishes a CFD calculation model for the phase change material, uses various thermal conductivities, densities, latent heats, specific heat capacities, and the heat flux density of the electronic device as design variables, designs a certain number of sample working conditions by the LHS method, performs CFD calculations on each sample working condition, determines the phase change time of the phase change material, and establishes a phase change time neural network prediction model for the thermal conductivity, density, solid-liquid phase change latent heat, specific heat capacity, and heat flux density with respect to the phase change melting time based on the sample data. Among them, the input variables of the phase change time neural network prediction model are the thermal conductivity, specific heat capacity, latent heat, density of the phase change material, and the heat flux density of the electronic device, and the output variable is the phase change time of the phase change material.
[0039] The present invention calculates the phase change time of each phase change material according to the established artificial neural network model; among them, the artificial neural network uses a BP (backpropagation) neural network.
[0040] The temperatures of the actual phase change material before and after melting are inconsistent. The temperature interval from the start of phase change to the completion of phase change of the phase change material is called the phase change temperature interval. According to the parameters of the solid-phase heat conduction end time, phase change time, and phase change temperature interval of the phase change material, the temperature change curve of the phase change section is obtained by the curve parameter method, and the temperature and time after phase change are obtained. If the phase change completion time node of the phase change material does not meet the requirement of the preset working time of the phase change material for the heat dissipation of the electronic device, then return to recalculate the temperature change data of the solid phase of the phase change material after replacing the candidate phase change material; If it is satisfied, input the physical properties of the liquid phase of the phase change material, calculate the temperature change data of the solid phase of the phase change material through differential equations, and combine it with the temperature changes of the solid phase and the phase change process of the phase change material to obtain the complete temperature change data of the phase change material. Specifically: if the heat input after the phase change material completely changes phase is higher than the heat loss of the environment, since the heat transfer coefficient of the natural convection process inside the liquid phase change material is very small, the heat absorption process can still be regarded as a heat conduction process. Use the one-dimensional unsteady heat conduction discrete control equation and boundary conditions to calculate the temperature change of the liquid phase change material until the phase change material reaches dynamic thermal equilibrium.
[0041] Preset the maximum working temperature. If the temperature at which the phase change material reaches equilibrium does not meet the requirement of the preset maximum working temperature during the operation of the electronic device, then return to recalculate the temperature change data of the solid phase of the phase change material after replacing the candidate phase change material; if it is satisfied, the temperature prediction of the phase change material is completed. Furthermore, based on the predicted temperature data, a phase change material that meets the working conditions can be selected relatively accurately.
[0042] The present invention also provides a temperature prediction system for a solid-liquid phase change material. The system includes a solid-phase temperature calculation module for the phase change material, a melting temperature calculation module for the phase change material, a first determination module, a liquid-phase temperature calculation module for the phase change material, and a second determination module. Specifically: The solid-phase temperature calculation module is configured to calculate, based on geometric parameters and physical property parameters, the temperature change data of the solid phase of the candidate phase change material through a differential equation. The melting temperature calculation module for the phase change material is configured to calculate the phase change time of the candidate phase change material based on a phase change time neural network prediction model; and generate the temperature change data during the phase change process of the candidate phase change material by using the curve parameter method. The first determination module is configured to determine that if the phase change completion time node of the phase change material does not meet the preset value, the candidate phase change material is replaced and the temperature prediction is performed again; if it meets, the subsequent steps are continued. The liquid-phase temperature calculation module for the phase change material is configured to calculate the complete temperature change data of the candidate phase change material based on the liquid-state property parameters of the candidate phase change material, the phase change completion time node of the phase change material, and the temperature. The second determination module is configured to determine that if the complete temperature change data of the candidate phase change material does not meet the preset maximum operating temperature requirement, the candidate phase change material is replaced and the temperature prediction is performed again; if it meets, the temperature prediction of the candidate phase change material is completed.
[0043] The above system provides a carrier for the temperature prediction method of a solid-liquid phase change material of the present invention, thereby realizing the precise selection of the phase change material.
[0044] The temperature prediction method and system of a solid-liquid phase change material of the present invention are further described in detail below through specific embodiments.
[0045] As Figure 1 shown, the temperature prediction method of a solid-liquid phase change material of the present invention includes the following steps: Determine the physical properties of the phase change material as Figure 1 shown, and determine the geometric parameters. As Figure 2 shown, use three-dimensional modeling software to establish a geometric model of the phase change material as Figure 2 shown, use Ansys Mesh to divide the mesh, use Ansys Fluent to establish a CFD calculation model, use the thermal conductivity, density, latent heat, specific heat capacity, and heat flux density of the electronic device as variables, use the LHS method to design 120 samples as the training set, and then design 30 samples as the test set. The inlet is the heat flux boundary condition, and the outlet is the convection boundary condition. Perform CFD calculations on the sample points according to the designed sample points to determine the total cumulative heat dissipation rate of each phase change material, and determine the phase change time of the phase change material. Using the one-dimensional unsteady heat conduction discrete control equation and boundary conditions, the differential equation of the solid-phase heat conduction section of the phase change material is calculated to obtain the end time of the solid-phase heat conduction of the phase change material and the phase change time.
[0046] As Figure 3 shown, using Matlab software, according to the sample data, a BP neural network is used to establish a neural network prediction model of the phase change time with respect to the phase change melting time for the thermal conductivity, density, latent heat, specific heat capacity, and input heat flux density respectively. Figure 3 It is a schematic diagram of the input and output parameters of the BP neural network in the neural network prediction model of the phase change time.
[0047] Among them, after establishing the BP neural network for predicting the phase change time of each phase change material based on 120 sample data in the training set, 30 samples in the test set are used to test the prediction performance of the BP neural network.
[0048] According to the end time of the solid-phase heat conduction of the phase change material, the phase change time, and the phase change temperature interval parameter, the temperature change curve of the phase change section is obtained by the curve parameter method, and the temperature and time after the phase change are obtained.
[0049] According to the one-dimensional thermodynamic model, the temperature change before the phase change of each phase change material is calculated. According to the established artificial neural network model, the phase change time of each phase change material is calculated, and the phase change curve is fitted by the curve parameter method. If the phase change completion time node of the phase change material does not meet the requirement of the preset working time of the phase change material for the heat dissipation of the electronic device, then return to recalculate the end time of the solid-phase heat conduction of the phase change material and the phase change time based on the geometric parameters and physical properties through the differential equation; If it is satisfied, input the liquid property parameters of the phase change material. Based on the end time of the solid-phase heat conduction of the phase change material and the phase change time, according to the one-dimensional unsteady heat conduction discrete control equation and boundary conditions, calculate the temperature change after the phase change of the phase change material to obtain the complete temperature change curve of the phase change material, that is, the complete temperature change data of the phase change material. If the complete temperature change data of the phase change material does not meet the requirement of the preset maximum working temperature during the operation of the electronic device, then return to recalculate the end time of the solid-phase heat conduction of the phase change material and the phase change time based on the geometric parameters and physical properties through the differential equation; if it is satisfied, it means that the phase change material meets the temperature conditions of this working condition, providing reliable technical data for the selection of the phase change material in this working condition.
[0050] In summary, the temperature prediction method and system for a solid-liquid phase change material of the present invention effectively improve the problem that in the process of selecting a phase change material, due to the incomplete consistency of the solid-liquid physical properties of the phase change material, the temperature interval during the two-phase transition, and the complex physical properties of the phase change material during melting, the convenience and accuracy of predicting the temperature of the phase change material are not high. The present invention can quickly evaluate the equilibrium temperature and phase change time when an electronic device uses a phase change material for heat dissipation, provides reliable technical support for the selection of engineering phase change materials, and helps to select the most suitable phase change material for a specific application scenario. By accurately predicting the temperature distribution and phase change time of the phase change material, the heat dissipation system of the electronic device can be designed more effectively. At the same time, by adopting the prediction method of the present invention, the R & D cost can be reduced to a certain extent, and the R & D efficiency can be improved.
[0051] Finally, it should be noted that the above are only the preferred embodiments of the present invention and do not impose any form of limitation on the present invention; any person skilled in the art can smoothly implement the present invention according to the description in the specification and the above; however, any minor changes, modifications, and equivalent variations made by those skilled in the art within the scope of the technical solution of the present invention using the technical content disclosed above are equivalent embodiments of the present invention; at the same time, any equivalent changes, modifications, and variations made to the above embodiments based on the essence of the present invention still fall within the protection scope of the technical solution of the present invention.
Claims
1. A temperature prediction method for a solid-liquid phase change material, characterized in that, It includes the following steps; S1. Determine the geometric parameters and physical property parameters of the candidate phase change material between the cold and heat sources; S2. Based on the geometric parameters and physical property parameters, calculate the temperature change data of the solid phase of the candidate phase change material through differential equations; S3. Calculate the phase change time of the candidate phase change material based on the phase change time neural network prediction model; S4. Use the curve parameter method to generate the temperature change data of the phase change process of the candidate phase change material; S5. Combine the temperature change data of the phase change process of the candidate phase change material with the temperature change data of the solid phase of the candidate phase change material to obtain the phase change completion time node and temperature of the phase change material; if the phase change completion time node of the phase change material does not meet the preset value, replace the candidate phase change material and re - conduct temperature prediction; if it meets, enter S6; S6. Input the liquid property parameters of the candidate phase change material, and calculate the complete temperature change data of the candidate phase change material based on the phase change completion time node and temperature of the phase change material; preset the maximum working temperature, if the complete temperature change data of the candidate phase change material does not meet the requirements of the preset maximum working temperature, replace the candidate phase change material and re - conduct temperature prediction; if it meets, complete the temperature prediction of the candidate phase change material.
2. The temperature prediction method of the solid-liquid phase change material according to claim 1, characterized in that In S1, the hot end of the candidate phase change material is the heat flux boundary condition, and the cold end is the convective boundary condition; the basic parameters of the candidate phase change material include: the length in the heat transfer direction of the candidate phase change material, the initial temperature, the heat flux of the electronic device, the heat transfer coefficient and temperature of the environment, the candidate working fluid and the physical property parameters of the solid phase.
3. The temperature prediction method of the solid-liquid phase change material according to claim 1, wherein In S2, it specifically includes: calculating the temperature change of the candidate phase change material before phase change according to the one - dimensional unsteady heat conduction discrete control equation and boundary conditions, to obtain the temperature - time change curve of the solid phase of the candidate phase change material and the moment data when reaching the phase change point.
4. The temperature prediction method of the solid-liquid phase change material according to claim 3, characterized in that, In S1, the temperature change process of the solid phase of the candidate phase change material is expressed by the one - dimensional unsteady heat conduction discrete control equation of formula (1) as follows: （1）； The boundary conditions are τ > 0, when x = 0, q = a, when τ > 0 and x = L, which is described by the following formula (2): （2）； Among them, is the heat flux density of the electronic device, is the ambient temperature, is the convective heat transfer coefficient between the phase change material and the environment, is the density of the solid phase change material, c is the specific heat capacity of the solid phase change material, V is the volume of the solid phase change material, is the thermal conductivity of the solid phase change material, is the source phase, is the temperature of the phase change material, is the length of the phase change material, and τ is the heat absorption time of the phase change material.
5. The temperature prediction method of the solid-liquid phase change material according to claim 1, wherein In S3, establish a CFD calculation model for the phase change material, use the thermal physical property parameters of each phase change material as design variables, design a preset number of sample working conditions by the LHS method, conduct CFD calculations on the sample working conditions to form sample data, and establish a phase change time neural network prediction model for each phase change material according to the sample data.
6. The temperature prediction method of the solid-liquid phase change material according to claim 5, characterized in that In S3, the input variable of the phase change time neural network prediction model is the thermal physical property parameters of the phase change material, and the output variable is the phase change time of the phase change material; where: The thermal physical property parameters of the phase change material include: the thermal conductivity, specific heat capacity, latent heat, density of the phase change material, and the heat flux density of the electronic device.
7. The temperature prediction method of the solid-liquid phase change material according to claim 5, characterized in that In S3, the phase change time neural network prediction model uses the BP neural network.
8. The temperature prediction method of the solid-liquid phase change material according to claim 1, wherein, In S4, calculate the phase change time of the candidate phase change material according to the phase change time neural network prediction model, represent the points on the temperature - time change curve of the phase change section of the candidate phase change material through one or more parameters, and obtain the temperature change curve of the phase change section.
9. The temperature prediction method for the solid-liquid phase change material according to claim 1, wherein In S6, calculating the complete temperature change data of the candidate phase change material includes: if the heat input of the liquid candidate phase change material is higher than the ambient heat loss, regarding the endothermic process as a heat conduction process and using a differential equation to calculate the temperature change of the liquid candidate phase change material until the phase change material reaches dynamic thermal equilibrium.
10. A system on which the temperature prediction method of the solid-liquid phase change material according to any one of claims 1-9 is based, characterized in that, The system includes a solid-phase temperature calculation module for the phase change material, a melting temperature calculation module for the phase change material, a first determination module, a liquid-phase temperature calculation module for the phase change material, and a second determination module; where: The solid-phase temperature calculation module is configured to calculate, based on geometric parameters and physical property parameters, the temperature change data of the solid phase of the candidate phase change material through a differential equation; The melting temperature calculation module for the phase change material is configured to calculate the phase change time of the candidate phase change material based on a phase change time neural network prediction model; and generate the temperature change data of the phase change process of the candidate phase change material using the curve parameter method; The first determination module is configured to determine that if the phase change completion time node of the phase change material does not meet the preset value, replace the candidate phase change material and re-perform temperature prediction; if it meets, continue with the subsequent steps; The liquid-phase temperature calculation module for the phase change material is configured to calculate the complete temperature change data of the candidate phase change material based on the liquid property parameters of the candidate phase change material, the phase change completion time node and temperature of the phase change material; The second determination module is configured to determine that if the complete temperature change data of the candidate phase change material does not meet the preset maximum operating temperature requirement, replace the candidate phase change material and re-perform temperature prediction; if it meets, complete the temperature prediction of the candidate phase change material.