Simulation method for enhancing lung groove imitating blood vessel fin phase change heat storage system through rotating machine

Abstract

The invention relates to the technical field of phase change heat storage, in particular to a simulation method for enhancing a phase change heat storage system simulating lung groove blood vessel fins by adopting a rotating machine. The simulation method comprises the following steps: firstly, establishing a physical model of a solar phase change heat storage system based on lung groove blood vessel type fins, and then performing two-dimensional simulation analysis on the physical model by adopting a two-dimensional model; the method comprises the steps of simulating the melting and solidification process of PCM, analyzing the heat transfer performance and energy storage efficiency of a system, obtaining an experimental result by building a test device or based on the Internet, finally comparing a simulation result with the experimental result, adjusting parameters of a simulation model, improving the accuracy of the simulation result, and improving the simulation efficiency. Through the adjusted simulation model, the influence of the nano-particle volume fraction, the rotation speed, the fin material and the heat transfer fluid temperature on the liquid phase distribution, the temperature distribution and the melting characteristic of the latent heat system can be analyzed, and a certain reference basis is provided for latent heat system rotation multi-objective optimization.

Description

Technical Field

[0001] The invention relates to the technical field of phase-changing heat storage, and in particular to a simulation method of a phase-changing heat storage system of a simulated alveolar vascular fin enhanced by a rotating machine. Background Art

[0002] Phase change energy storage technology uses the latent heat absorbed and released by phase change materials (PCM) during the melting and solidification process to store and utilize the waste heat generated in human production and life. It is widely used in solar energy, building energy conservation, industrial waste heat recovery and battery thermal management systems. Among them, the casing latent heat system is one of the current research hotspots of phase change energy storage technology.

[0003] The latent heat exchanger (TES) consists of three main parts: inner tube, outer tube and fin. The hot fluid flows in the inner tube and transfers the heat to the fin, which acts as a carrier to transfer the heat to the PCM, thereby achieving enhanced heat exchange. Generally, the use of many phase change materials requires wrapping containers, such as sleeves, microcapsules, and porous media such as cement. The fin-type latent heat exchanger has broad application prospects in the fields of construction, thermal energy collection, thermal management of electronic devices, and efficient heat flux cooling of spacecraft. In the field of construction, it can be used to adjust the room temperature and improve energy utilization efficiency; in the field of solar energy, it can be used to store solar energy collected during the day for use at night. At present, in the field of medium and low temperature thermal storage, paraffin, as a low-cost, non-toxic and odorless organic phase change material, has become the mainstream material in phase change thermal storage technology and is widely used in thermal management and waste heat recovery. When selecting the paraffin model, it is necessary to comprehensively consider its specific heat capacity, latent heat value, phase change temperature, corrosiveness and other physical properties to ensure that it matches the application scenario.

[0004] In recent years, many researchers have drawn on the excellent heat transfer characteristics of biological structures in nature and applied them to the fin design of TES. Bionic structures have significant advantages in enhancing heat transfer and can effectively improve the efficiency of thermal energy storage. In addition, since nanoparticles have a high thermal conductivity, mixing nanoparticles with liquids can significantly increase the thermal conductivity of nanofluids, thereby enhancing heat transfer performance. Although nanoparticles can enhance heat transfer performance, as the volume fraction increases, the quality of PCM will also decrease. Therefore, multi-objective optimization is required to find the optimal nanoparticle volume fraction to achieve a balance between heat transfer performance and material quality. At the same time, rotation technology has also been applied to phase change energy storage systems, and its effect on enhancing heat transfer performance has been verified in many studies.

[0005] However, there is currently no research that systematically analyzes the mutual influence of the reduction in heat storage capacity caused by excessive volume fraction of nanoparticles under rotation. At the same time, there is a complex competitive game relationship between the volume fraction of nanoparticles in heat storage and PCM melting time. Therefore, the existing technology lacks a simulation method based on the use of rotating machinery to enhance the phase change thermal storage system of the simulated lung trough vascular fin, which can provide an accurate basis for the multi-objective optimization of TES rotation. Summary of the invention

[0006] The purpose of the present invention is to provide a simulation method for a phase change thermal storage system of a lung trough vascular fin enhanced by a rotating machine, aiming to solve the problem that there is currently no research that systematically analyzes the mutual influence between the reduction of heat storage capacity caused by excessive volume fraction of nanoparticles under rotating cases, and the complex competitive game relationship between the volume fraction of nanoparticles in heat storage capacity and PCM melting time. Therefore, the prior art lacks a simulation method based on the phase change thermal storage system of a lung trough vascular fin enhanced by a rotating machine, which provides an accurate basis for TES rotation multi-objective optimization.

[0007] To achieve the above object, the present invention provides a simulation method of a phase change thermal storage system of a simulated alveolar vascular fin enhanced by a rotating machine, comprising the following steps:

[0008] S1: Establish the physical model of solar phase change thermal storage system based on pulmonary trough vascular fins;

[0009] S2: Use a two-dimensional model to perform two-dimensional simulation analysis on the physical model, simulate the melting and solidification process of PCM, and analyze the heat transfer performance and energy storage efficiency of the system;

[0010] S3: Obtain experimental results by building experimental devices or based on the Internet;

[0011] S4: Compare the simulation results with the experimental results, adjust the parameters of the simulation model to improve the accuracy of the simulation results, analyze the effects of nanoparticle volume fraction, rotation speed, fin material and heat transfer fluid temperature on the liquid phase distribution, temperature distribution and melting characteristics of the latent heat system, and provide guidance for the optimal design of the system.

[0012] Among them, in step S1, the solar phase change thermal storage system based on alveolar vascular fins is composed of a plurality of sleeve units, each unit includes an inner tube and an outer tube, the inner tube diameter Ф1 is 8 mm, the outer tube diameter Ф2 is 35 mm, and the fin structure of the solar phase change thermal storage system based on alveolar vascular fins imitates the structure of the alveolar artery. The fin adopts a fractal structure design, the length of the left branch adopts a 5-level fractal, and the right branch adopts a 4-level fractal, wherein the length of the left branch adopts the Fibonacci sequence of 5 mm, 3 mm, 2 mm and 1 mm to decrease, the branch width is L, and the bifurcation angles are θ and α.

[0013] Among them, in step S1, before establishing the physical model, the enthalpy porosity method is used to simulate the melting process of PCM in TES, and the control equation is as follows:

[0014] Energy equation:

[0015]

[0016] Where ρ is the PCM density, H is the PCM material enthalpy, is the vector differential operator, k is the thermal conductivity;

[0017] Continuity equation:

[0018]

[0019] In the formula, is the velocity vector;

[0020] Momentum equation:

[0021]

[0022] Where t is time; ρ is density; p is pressure; μ is dynamic viscosity; ξ is thermal expansion coefficient; T ref is the reference temperature, the source term The damping term of Darcy's law can be expressed as:

[0023]

[0024] The liquid phase rate at different times is expressed as follows:

[0025]

[0026] Where T solid Solid phase temperature, T liquid is the PCM liquid temperature;

[0027] The total enthalpy consists of sensible enthalpy and latent enthalpy:

[0028]

[0029] ΔH=λL

[0030] H=λL+h

[0031] Where ΔH is the latent heat enthalpy and h is the sensible heat enthalpy.

[0032] In step S1, the physical model established includes the physical property parameters of the nanofluid, and the physical property parameters of the nanofluid are calculated by the classical formula of nanoparticle mixture, and the physical property equation is as follows:

[0033] The density and specific heat of nanofluids are shown in the following equations:

[0034] ρ nf =(1-φ ns )ρ f +φ ns ρ ns

[0035] (ρc p ) nf =(1-φ ns )(ρc p ) f +φ ns (ρc p ) ns

[0036] The effective dynamic viscosity of nanofluids is expressed by the Brinkman equation:

[0037]

[0038] The thermal conductivity of a nanofluid is given by the Brinkman equation:

[0039]

[0040] Where B is the Boltzmann constant (B = 1.3807 × 10-23 J / K), and γ is a variable representing the volume fraction of nanoparticles expressed as:

[0041] γ=8.4407(100φ ns ) -1.07304 (0.01<φ ns <0.1)

[0042] The empirical function f(T,φ) is as follows:

[0043]

[0044] The heat storage rate of the phase change material when melting while absorbing heat is expressed as:

[0045]

[0046] The heat storage density of the phase change material when it melts while absorbing heat is expressed as:

[0047]

[0048] Among them, in step S2, when using the two-dimensional model to perform two-dimensional simulation analysis on the physical model, the temperature of the heat transfer fluid is simplified to the pipe wall temperature. At the initialization time t=0, the PCM temperature is 293K and is in a solidified state. When t>0, the HTF temperature is set to 363K to melt the PCM:

[0049]

[0050] There are coupled boundary conditions between the fin and PCm, which satisfy Fourier's law of heat conduction. The conditions are as follows:

[0051]

[0052] Among them, in step S2, the specific contents of using the two-dimensional model to perform two-dimensional simulation analysis on the physical model include: using MESH software to mesh the latent heat system, and using computational fluid dynamics software ANSYS Fluent to perform numerical calculations.

[0053] Among them, in step S2, when the latent heat system is meshed using MESH software, the mesh adopts a mixed unstructured mesh of triangles and quadrilaterals. In the process of numerical calculation using computational fluid dynamics software ANSYS Fluent, the Solidification / Melting model in Fluent software is selected for solution, and the PISO algorithm is used to deal with the coupling problem of pressure field and velocity field, and the PRESTO! algorithm is used for pressure correction.

[0054] Among them, in step S3, the specific steps of obtaining the experimental results by building the experimental device are:

[0055] An experimental device for the physical model of a solar phase change thermal storage system based on trough vascular fins was built. Appropriate PCM and nanoparticles were selected, nanofluids were prepared, and injected into the system. The parameters of the heat source and cooling system were adjusted to simulate different working conditions. The melting and solidification processes of PCM were recorded to obtain experimental results.

[0056] The present invention adopts a simulation method of a phase change heat storage system of a lung trough vascular fin enhanced by a rotating machine. First, a physical model of a solar phase change heat storage system based on a lung trough vascular fin is established. Then, a two-dimensional model is used to perform a two-dimensional simulation analysis on the physical model, simulating the melting and solidification process of PCM, analyzing the heat transfer performance and energy storage efficiency of the system, and obtaining experimental results by building a test device or based on the Internet. Finally, the simulation results are compared with the experimental results, and the parameters of the simulation model are adjusted to improve the accuracy of the simulation results. The adjusted simulation model can be used to analyze the influence of the volume fraction of nanoparticles, the rotation speed, the fin material and the temperature of the heat transfer fluid (HTF) on the liquid phase distribution, temperature distribution and melting characteristics of the latent heat system. Finally, the heat storage and melting time of the latent heat system are multi-objective optimized to obtain the objective function and the optimal value, providing a certain reference basis for the rotation multi-objective optimization of the latent heat system. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0058] Figure 1 It is a flow chart of the steps of a simulation method of the present invention for enhancing a phase-change heat storage system of a simulated lung trough vascular fin by using a rotating machine.

[0059] Figure 2 It is a schematic diagram of a solar phase change thermal storage system based on trough vascular fins provided by the present invention.

[0060] Figure 3 It is a verification diagram of four different grid numbers provided by the present invention.

[0061] Figure 4 It is a schematic diagram of the fitting results of the experiment and simulation of PCM melting and solidification provided by the present invention.

[0062] Figure 5 It is a liquid phase cloud diagram and a temperature cloud diagram analysis diagram of a mixture of nanoparticles with different volume fractions provided by the present invention.

[0063] Figure 6 It is a data analysis diagram of the mixture of nanoparticles with different volume fractions provided by the present invention.

[0064] Figure 7 It is a temperature cloud diagram and speed cloud diagram analysis diagram of TES at different rotation speeds provided by the present invention.

[0065] Figure 8 It is a melting characteristic analysis diagram of TES at different rotation speeds provided by the present invention.

[0066] Fig. 9 It is a melting characteristic analysis diagram of TES under different fin materials provided by the present invention.

[0067] Fig.10 It is a melting characteristic analysis diagram of TES at different HTF temperatures provided by the present invention.

[0068] Fig.11 It is an influence analysis diagram of each response surface provided by the present invention.

[0069] Fig.12 It is a schematic diagram of the Pareto front obtained by the multi-objective optimization provided by the present invention. DETAILED DESCRIPTION

[0070] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0071] See also Figures 1 to 12 The present invention provides a simulation method for a phase change heat storage system of a lung trough vascular fin enhanced by a rotating machine, comprising the following steps:

[0072] S1: Establish the physical model of solar phase change thermal storage system based on pulmonary trough vascular fins;

[0073] S2: Use a two-dimensional model to perform two-dimensional simulation analysis on the physical model, simulate the melting and solidification process of PCM, and analyze the heat transfer performance and energy storage efficiency of the system;

[0074] S3: Obtain experimental results by building experimental devices or based on the Internet;

[0075] S4: Compare the simulation results with the experimental results, adjust the parameters of the simulation model to improve the accuracy of the simulation results, analyze the effects of nanoparticle volume fraction, rotation speed, fin material and heat transfer fluid temperature on the liquid phase distribution, temperature distribution and melting characteristics of the latent heat system, and provide guidance for the optimal design of the system.

[0076] In this embodiment, a physical model of a solar phase change thermal storage system based on a pulmonary trough vascular fin is first established, and then a two-dimensional model is used to perform a two-dimensional simulation analysis on the physical model, simulating the melting and solidification process of PCM, analyzing the heat transfer performance and energy storage efficiency of the system, and obtaining experimental results by building a test device or based on the Internet. Finally, the simulation results are compared with the experimental results, and the parameters of the simulation model are adjusted to improve the accuracy of the simulation results. The adjusted simulation model can be used to analyze the influence of the volume fraction of nanoparticles, rotation speed, fin material and heat transfer fluid (HTF) temperature on the liquid phase distribution, temperature distribution and melting characteristics of the latent heat system. Finally, the heat storage and melting time of the latent heat system are multi-objective optimized to obtain the objective function and optimal value, providing a certain reference for the multi-objective optimization of the latent heat system rotation.

[0077] in, Figure 2 (a) shows the application scenario of the tube-in-tube phase change thermal storage system (TES) in building maintenance structure heating. The system uses solar energy to store heat in phase change material (PCM) during the day and releases heat when needed to achieve efficient use of clean energy. The tube-in-tube system uses inner tube fluid heat exchange, and PCM stores heat between the inner and outer tubes, forming an efficient heat storage and release mechanism. In addition, there is a bearing in the middle of the TES for rotating heat storage. Figure 2 (b) shows the specific structure of TES. In step S1, the solar phase change thermal storage system based on alveolar vascular fins is composed of multiple sleeve units, each unit includes an inner tube and an outer tube, the inner tube diameter Φ1 is 8 mm, and the outer tube diameter Φ2 is 35 mm. The fin structure of the solar phase change thermal storage system based on alveolar vascular fins imitates the structure of the alveolar artery. The fin adopts a fractal structure design. The length of the left branch adopts a 5-level fractal, and the right branch adopts a 4-level fractal. The length of the left branch adopts the Fibonacci sequence. 5mm, 3mm, 2mm and 1mm decrease, the branch width is L, the bifurcation angles are θ and α, wherein, according to the research in the prior art, when θ is 31.6° and α is 43.4°, the bifurcation structure has the best energy loss characteristics and the best heat dissipation effect, so all bifurcation angles in this technical solution adopt this optimal angle. The phase change materials involved in the solar phase change thermal storage system based on the pulmonary trough vascular fin in this technical solution are paraffin and nanoparticles, and the physical properties of PCM and nanoparticles are shown in Table 1 below:

[0078] Table 1. Physical properties of PCM and nanoparticles

[0079]

[0080]

[0081] The structural parameters of the alveolar artery model fin are shown in Table 2 below:

[0082] Table 2. Structural parameters of the alveolar artery model fin

[0083] Grading Length H / mm Width L / mm Bifurcation angle θ Bifurcation angle α 1 2 1 31.6° 43.4° 2 5 0.8 31.6° 43.4° 3 3 0.6 31.6° 43.4° 4 2 0.4 31.6° 43.4° 5 1 0.4 31.6° 43.4°

[0084] Furthermore, since the phase change process of PCM is relatively complex, it will present a porous structure when melted. Therefore, in step S1, before establishing the physical model, the enthalpy-porosity method is used to calculate the phase change process of PCM in the TTES unit, and the following assumptions are made: (1) The liquid PCM is laminar and incompressible; (2) The thermal properties of PCM remain constant in the working temperature range, and the density adopts the Boussinesq assumption, which can be simplified to constant during cooling; (3) The heat loss between TTES and the external environment is ignored, and the thermal conductivity of the coupling wall is assumed to be the same as that of copper, and the thickness of the coupling wall is temporarily ignored; (4) The viscous dissipation coefficient is ignored. Based on the above assumptions, the enthalpy-porosity method simulates the melting process of PCM in TES. The control equation is as follows:

[0085] Energy equation:

[0086]

[0087] Where ρ is the PCM density, H is the PCM material enthalpy, is the vector differential operator, k is the thermal conductivity;

[0088] Continuity equation:

[0089]

[0090] In the formula, is the velocity vector;

[0091] Momentum equation:

[0092]

[0093] Where t is time; ρ is density; p is pressure; μ is dynamic viscosity; ξ is thermal expansion coefficient; T ref is the reference temperature, the source term The damping term of Darcy's law can be expressed as:

[0094]

[0095] The liquid phase rate at different times is expressed as follows:

[0096]

[0097] Where T solid Solid phase temperature, T liquid is the PCM liquid temperature;

[0098] The total enthalpy consists of sensible enthalpy and latent enthalpy:

[0099]

[0100] ΔH=λL

[0101] H=λL+h

[0102] Where ΔH is the latent heat enthalpy and h is the sensible heat enthalpy.

[0103] In step S1, the physical model established includes the physical property parameters of the nanofluid, and the physical property parameters of the nanofluid are calculated by the classical formula of nanoparticle mixture, and its physical property equation is as follows:

[0104] The density and specific heat of nanofluids are shown in the following equations:

[0105] ρ nf =(1-φ ns )ρ f +φ ns ρ ns

[0106] (ρc p ) nf =(1-φns )(ρc p ) f +φ ns (ρc p ) ns

[0107] The effective dynamic viscosity of nanofluids is expressed by the Brinkman equation:

[0108]

[0109] The thermal conductivity of a nanofluid is given by the Brinkman equation:

[0110]

[0111] Where B is the Boltzmann constant (B = 1.3807 × 10-23 J / K), and γ is a variable representing the volume fraction of nanoparticles expressed as:

[0112] γ=8.4407(100φ ns ) -1.07304 (0.01<φ ns <0.1)

[0113] The empirical function f(T,φ) is as follows:

[0114]

[0115] The heat storage rate of the phase change material when melting while absorbing heat is expressed as:

[0116]

[0117] The heat storage density of the phase change material when it melts while absorbing heat is expressed as:

[0118]

[0119] Furthermore, in step S2, the specific contents of using the two-dimensional model to perform two-dimensional simulation analysis on the physical model include: using MESH software to mesh the latent heat system, the mesh adopts a mixed unstructured mesh of triangles and quadrilaterals, and using computational fluid dynamics software ANSYS Fluent for numerical calculation. In the process of using computational fluid dynamics software ANSYS Fluent for numerical calculation, the Solidification / Melting model in Fluent software is selected for solution, and the PISO algorithm is used to deal with the coupling problem of pressure field and velocity field, and the PRESTO! algorithm is used for pressure correction. The relaxation factors of momentum, pressure term, energy term and liquid phase velocity are set to 0.7, 0.2, 1.0 and 0.9 respectively. The relative residuals of the energy equation and momentum equation are set to be less than 10-6 , the relative residual of the continuity equation is set to be less than 10 -4 .

[0120] Furthermore, when using MESH software to mesh the latent heat system, Figure 3 As shown, four different grid numbers of 18413, 23893, 48915 and 90511 were simulated. The results show that as the number of grids increases, the variation of the average temperature of the PCM gradually decreases. When the number of grids increases from 48915 to 90511, the average temperature of the PCM only decreases from 343.4283K to 342.9159K after 50 seconds, and the relative variation is only 0.0149%. This result shows that further increasing the number of grids has little effect on the results of heat transfer analysis. Therefore, this technical solution selects a grid number of 48915 as the final number of calculation grids. Figure 3 As shown in (b), the technical solution simulated four different time steps of 0.005s, 0.01s, 0.05s and 0.1s. The results show that the relative error of the average temperature of the PCM within 50 seconds is almost negligible when the time step is between 0.1s and 0.005s. Zooming in and observing, it can be found that the maximum step difference between the two is only 0.05K. In order to save computing resources, the technical solution selects a time step of 0.05s for subsequent calculations.

[0121] Furthermore, in order to verify the accuracy of numerical simulation and improve work efficiency, the present technical scheme directly uses the experimental data in the prior art to compare and analyze with the numerical simulation results (the experimental data in the prior art here uses the experimental data collected from the prior art based on the Internet), and uses its experimental data to verify that the algorithm model and relaxation factor and other parameters of this study are reasonable. Figure 4 The comparative analysis between the numerical simulation results and the experimental data is presented. From the curve trend of the PCM liquid phase ratio and the PCM average temperature, the numerical simulation results are highly consistent with the experimental data. The maximum deviation of the PCm liquid phase ratio is only 2.92%, and the maximum deviation of the PCM average temperature is only 0.91%. This shows that the numerical model of this study has high accuracy and can provide reliable theoretical support for subsequent research work.

[0122] Furthermore, the mutual verification between simulation results and experimental results shows that:

[0123] 1. Analysis of nanoparticle modification research;

[0124] Among them, in order to improve the thermal conductivity of phase change energy storage materials (PCM), many researchers have adopted the method of mixing nanoparticles with PCM. This technical solution uses Al2O3 nanoparticles as the research object, which has the advantages of high thermal conductivity, non-toxicity and non-corrosiveness, and is widely used in academic and industrial fields. The study selected a simulated lung trough vascular fin latent heat system without nanoparticles as the control group, and added Al2O3 nanoparticles with a volume fraction of 2.5% and 5% for comparative studies. Figure 5 As shown in the figure, the liquid phase cloud map and temperature cloud map show that in different time periods, with the increase of the volume fraction of nanoparticles, the melting rate of the latent heat system has increased, but the effect is not significant. It is worth noting that at 40s and 60s, the melting of PCM shows that the melting rate of the upper half is faster than that of the lower half. This phenomenon is mainly attributed to the thermal lift effect generated during the melting of the latent heat system, which causes the heat to transfer upward, thereby forming natural convection and accelerating the melting rate of the upper half. In addition, the cloud map shows that the alveolar vascular fin can effectively improve the heat transfer efficiency with its unique fractal appearance, transfer heat to the dead corner area of ​​PCM, and use its internal hollow area to store more PCM for heat storage. It can be seen that it is reasonable to select the alveolar artery vascular fin to enhance the heat exchange design of the latent heat system.

[0125] in, Figure 6 (a) shows the effect of different volume fractions of nanoparticles on the melting process of PCm. The results show that adding 5% volume fraction of nanoparticles has the best heat transfer effect on PCM, and its melting rate and average temperature are significantly improved. At 100 seconds, the average temperature of the 5% nanoparticle group is 1.2K higher than that of the pure PCM group. This phenomenon is mainly attributed to the fact that the addition of nanoparticles increases the thermal conductivity of PCM. When the volume fraction of nanoparticles increases, the thermal conductivity of the mixed PCM also increases, thereby accelerating heat transfer and improving heat transfer effects. However, Figure 6 (c) shows that the addition of nanoparticles will lead to a decrease in the heat storage capacity of PCM. This is because although the nanoparticles improve the thermal conductivity, they also reduce the volume fraction of PCM, thereby reducing the mass of PCM, and ultimately resulting in a decrease in the total heat storage capacity. Therefore, it is necessary to optimize the volume fraction of nanoparticles to achieve optimal thermal performance. Subsequent studies will focus on this issue. Nevertheless, in terms of heat storage rate and heat storage density, the addition of 5% volume fraction of nanoparticles still has advantages. Compared with pure PCM, the heat storage density of this group increased by 4.35 kJ / kg, and the heat storage rate increased by 0.07 kJ / s, reflecting a certain heat exchange enhancement effect. However, it should be noted that the addition of nanoparticles did not increase the total heat storage capacity. Therefore, it is necessary to balance the effects of these two aspects by optimizing the volume fraction of nanoparticles to achieve the best energy storage performance.

[0126] 2. The influence of different rotation speeds on TES heat transfer;

[0127] Among them, the natural convection generated by PCM during the melting process will cause uneven temperature distribution of TES, thereby slowing down the melting rate of PCM. However, rotation can effectively break the natural convection and significantly improve the heat exchange efficiency of TES. This study adopts the method of rotating the entire TES and designs a bearing in the center. All rotations move counterclockwise around the center of the circle. Due to the unique pulmonary trough vascular design, the fins on the left are higher than those on the right. Therefore, during the counterclockwise rotation, the fins on the left will sweep more PCM, while the fins on the right will contact the hollow area of ​​​​the TES, thereby realizing the comprehensive synergistic heat transfer of the TES. Figure 7 (a) shows that the higher the rotation speed, the faster the melting of PCM. In addition, it can be observed that the melting of TES is not significantly improved in the rotation speed range of 0.15rpm to 0.25rpm, indicating that there is an optimal rotation speed in this area to be optimized. Figure 7 (b) shows that at 20 seconds, there are significantly more red areas at 0.25 rpm, indicating that a higher rotation speed will also lead to a faster speed in the initial flow field area of ​​the PCM and significantly break the original natural convection velocity field.

[0128] Furthermore, Figure 8 The melting characteristics of TES at different rotation speeds are shown. Figure 8 (a) and Figure 8 (b) It can be seen that with the increase of rotation speed, the average temperature and liquid phase rate of the phase change material (PCM) both show an upward trend. In addition, with the passage of time, the temperature growth rate and liquid phase growth rate decrease significantly, which is mainly attributed to the gradual decrease in the temperature difference between the PCM and the heat transfer fluid (HTF) after the PCM temperature increases, resulting in a weak temperature growth in the later period. It is worth noting that most of the PCM in all TES has been melted at about 70 seconds, so the subsequent heat storage research will be carried out at about 70 seconds. Figure 8 (c) shows that at a rotation speed of 0.25rpm, the heat storage and average temperature of TES reach the maximum, while the complete melting time is the shortest. Compared with 0rpm, its heat storage increases by 15kJ, the average temperature increases by 7K, and the complete melting time is shortened by 47.83%. The data show that rotation has a significant effect on improving the heat transfer performance of TES. Figure 8(d) shows that at around 0.15rpm, the heat storage density and heat storage rate of TES tend to rise slowly. From 0.15rpm to 0.2rpm, the heat storage density and heat storage rate increased by 1.19% and 1.16% respectively, but due to the increase in rotation speed. Obviously, when the rotation speed of TES exceeds 0.2rpm, its cost performance gradually decreases. Therefore, there is an optimal rotation speed value between 0.15rpm and 0.2rpm, which can strike a balance between optimal heat transfer effect and minimum kinetic energy consumption.

[0129] 3. The influence of different fin materials on TTES heat transfer;

[0130] Among them, in the phase change energy storage system, the fin acts as a bridge between PCM and HTF, and its material selection is crucial. The ideal fin material should have both high thermal conductivity and low density to achieve efficient heat transfer and low power consumption operation. This technical solution analyzes several common metal materials as rotating fins, including steel, copper, aluminum and nickel, and evaluates their impact on the thermal performance of PCM through numerical simulation. The physical properties of the fin material are shown in Table 4:

[0131] Table 3. Physical properties of various fin materials

[0132] Physical properties Copper fins Aluminum fin Steel fins Main Ingredients Copper Aluminum Steel <![CDATA[Density / Kg / m 3 > 8978 2719 8030 Specific heat t / J / (Kg·K) 381 871 502.48 Thermal conductivity / W / (m·K) 387.6 202.4 16.27

[0133] like Fig. 9 As shown, the results show that the thermal conductivity of the fin is positively correlated with the melting rate and average temperature of the PCM. The higher the thermal conductivity of the fin, the higher the average PCM temperature and the faster the melting rate. Specifically, the copper fin exhibits the best heat transfer effect, and its PCM average temperature and heat storage are significantly higher than other materials, and the melting time is also significantly shortened. Taking copper fins and steel fins as examples, in the same period of time, the heat storage of copper fins increased by 169%, the average PCM temperature increased by 11.14%, and the melting time decreased by 79.51%. However, the higher density of copper fins also causes the TES to be too large, resulting in certain costs for transportation and installation and maintenance. Although aluminum fins are superior to copper in density, they are slightly inferior in thermal conductivity. Taking into account the heat transfer effect and power consumption factors, this technical solution selects copper fins with excellent thermal conductivity as the material for further research.

[0134] 4. Effect of HTF temperature on TES heat transfer;

[0135] HTF temperature has a significant effect on the thermal storage performance of TES. According to Fourier's law of heat conduction, the greater the temperature difference between HTF and PCM, the higher the heat flux density and the higher the heat transfer efficiency. However, HTF temperature is affected by solar radiation. Too high HTF temperature will place higher requirements on solar energy utilization efficiency and weather conditions, while too low HTF temperature will lead to slow PCM heat storage. Therefore, it is very important to study the effect of HTF temperature on TES heat transfer. This study selected the HTF temperature range of 353K to 373K, and simplified the HTF temperature to the tube wall temperature for analysis. Fig.10 (a) and (b) show that as the HTF temperature increases, the PCM liquid fraction and average temperature both increase significantly. This is because the higher temperature difference promotes the heat transfer efficiency, allowing the fin temperature and wall temperature to be quickly transferred to the PCM, thereby achieving a temperature rise of the phase change material in a shorter time. Fig.10 (c) shows that when the HTF temperature is 373K, all heat transfer indicators of TES reach the highest value. Compared with the HTF temperature of 353K, the heat storage capacity increased by 20%, the average PCM temperature increased by 6.39%, the complete melting time decreased by 20.32%, the heat storage density increased by 13.31%, and the heat storage rate increased by 50.19%. Therefore, the HTF temperature has a significant effect on the performance of TES. However, from Fig.10 (b) It can be found that when the TES liquid phase rate exceeds 363K, the liquid phase rate growth rate gradually decreases with the increase of temperature. This means that when the HTF temperature exceeds 363K, the performance improvement brought by further increasing the temperature will no longer be significant. In summary, in order to reduce auxiliary power consumption while ensuring thermal storage efficiency, it is recommended to control the HTF temperature below 363K. This will help optimize the application of TES in HVAC heating systems and achieve more energy-saving and efficient energy utilization.

[0136] 5. Multi-objective optimization of rotating TES;

[0137] This technical solution uses the response surface method (RSM) to perform multi-objective optimization analysis on TES, aiming to determine the best combination of factors to optimize TES performance. RSM is a statistical method used to study the effects of multiple factors on one or more response variables. Specifically, this study uses the central composite design (CCD) for variable design, and its influencing factor parameters are shown in Table 4;

[0138] Table 4. CCD impact factor design parameters

[0139]

[0140] The response surface diagram output by RSM was used for analysis, and the response surface regression equation and the optimal solution were fitted.

[0141] in, Fig.11Various response surface analysis diagrams obtained by RSM optimization are shown. Fig.11 (a) and (b) show the maximum heat storage area (red area), which is located in the range of rotation speed 0.15-0.25rpm and nanoparticle volume fraction 1%-2%. This result shows that increasing the rotation speed to enhance heat transfer and reducing the nanoparticle volume fraction to improve the quality of the phase change material (PCM) can effectively increase the heat storage. Fig.11 (c) and (d) show the minimum melting time region (blue area), which is located in the range of nanoparticle volume fraction 4%-5% and TES rotation speed 0.2-0.25rpm. The addition of nanoparticles increases the thermal conductivity of the composite phase change material, thereby enhancing heat transfer and shortening the melting time. However, from Fig.11 The slope of (d) shows that the effect of rotation speed on melting time is much greater than that of nanoparticle volume fraction. In summary, there is a competitive relationship between heat storage, melting time and kinetic energy consumption. In order to achieve the best performance, further multi-objective optimization is needed to balance the contradictions between various performance indicators and find the best combination of nanoparticle volume fraction and rotation speed to maximize heat storage, minimize melting time and minimize kinetic energy consumption.

[0142] Furthermore, the technical solution uses a non-dominated sorting genetic algorithm (NSGA-II) to perform multi-objective optimization on the battery in order to find the optimal rotation speed and volume fraction under the objectives of maximum heat storage, minimum melting time and minimum kinetic energy loss. The objective function of the optimization process includes heat storage, melting time and kinetic energy loss, and the decision variables are selected as rotation speed and volume fraction, which vary in the range of 0.05-0.25rad / s and 1-5%, respectively. Fig.12 The Pareto frontier obtained by multi-objective optimization is shown. Fig.12 As shown in the figure, there is an obvious competitive relationship between the three objective functions: when the heat storage increases, the kinetic energy loss increases; when the melting time increases, the heat storage decreases and the kinetic energy loss decreases. Therefore, it is necessary to find a balance point on the Pareto frontier, that is, point O, as the optimal solution for multi-objective optimization. At point O, the heat storage is 182.18 kJ, the melting time is 90.84 s, the kinetic energy loss is 56.49 nJ, and the corresponding rotation speed and volume fraction are 0.16 rad / s and 1.02%, respectively. Table 5 lists the specific parameter settings of the optimization algorithm. Substituting the optimization results into the simulation for calculation, it is found that the heat storage error is only 0.021%, which is extremely small, indicating that the optimization results are significantly effective.

[0143] The conclusion is as follows:

[0144] (1) The fractal fins based on alveolar vessels have good heat transfer effects. Compared with pure PCM, the heat storage density of PCM mixed with 5% nanoparticle volume fraction is increased by 4.35 kJ / kg and the heat storage rate is increased by 0.07 kJ / s, but the heat storage capacity is reduced by 6 kJ.

[0145] (2) Rotation significantly improves the heat transfer performance of TES. The heat storage at 0.25rpm increases by 15kJ compared to 0rpm, the average temperature increases by 7K, and the complete melting time decreases by 47.83%. In addition, from 0.15rpm to 0.2rpm, the heat storage density and heat storage rate increase by 1.19% and 1.16% respectively. However, due to the increase in rotation speed, the kinetic energy consumption of PCM increases by 77.76%. When the TES rotates at a speed exceeding 0.2rpm, the cost performance gradually decreases.

[0146] (3) In the comparison between copper fins and steel fins, the heat storage capacity increased by 169% within 70 seconds, the average PCM temperature at 70 seconds increased by 11.14%, and the complete melting time decreased by 79.51%. Although the aluminum fins have a lower density, resulting in the least kinetic energy consumption, the copper fins have a better heat transfer advantage.

[0147] (4) The optimal values ​​obtained by multi-objective optimization using RSM and NSGA-II are heat storage, melting time, and kinetic energy loss, which are 182.18 kJ, 90.84 s, and 56.49 nJ, respectively, and the values ​​of rotation speed and volume fraction are 0.16 rad / s and 1.02%, respectively.

[0148] In summary, the verification results provide an important theoretical reference for the optimal design of the horizontal phase change thermal storage system based on the alveolar vascular contour design.

[0149] What is disclosed above is only a preferred embodiment of the present invention, and it certainly cannot be used to limit the scope of rights of the present invention. Ordinary technicians in this field can understand that all or part of the processes of the above embodiment and equivalent changes made according to the claims of the present invention still fall within the scope of the invention.

Claims

1. A simulation method for a phase change thermal storage system of a lung trough vascular fin enhanced by a rotating machine, characterized in that: The steps include: S1: Establish the physical model of solar phase change thermal storage system based on pulmonary trough vascular fins; S2: Use a two-dimensional model to perform two-dimensional simulation analysis on the physical model, simulate the melting and solidification process of PCM, and analyze the heat transfer performance and energy storage efficiency of the system; S3: Obtain experimental results by building experimental devices or based on the Internet; S4: Compare the simulation results with the experimental results, adjust the parameters of the simulation model to improve the accuracy of the simulation results, analyze the effects of nanoparticle volume fraction, rotation speed, fin material and heat transfer fluid temperature on the liquid phase distribution, temperature distribution and melting characteristics of the latent heat system, and provide guidance for the optimal design of the system.

2. The simulation method of the phase change thermal storage system of the alveolar vascular fin enhanced by a rotary machine as claimed in claim 1 is characterized in that: In step S1, the solar phase change thermal storage system based on alveolar vascular fins is composed of a plurality of sleeve units, each unit includes an inner tube and an outer tube, the inner tube diameter Ф1 is 8 mm, the outer tube diameter Ф2 is 35 mm, and the fin structure of the solar phase change thermal storage system based on alveolar vascular fins imitates the structure of the alveolar artery. The fin adopts a fractal structure design, the length of the left branch adopts a 5-level fractal, and the right branch adopts a 4-level fractal, wherein the length of the left branch decreases by 5 mm, 3 mm, 2 mm and 1 mm using the Fibonacci sequence, the branch width is L, and the bifurcation angles are θ and α.

3. The simulation method of the phase change thermal storage system using a rotary machine to enhance the simulated alveolar vascular fin as claimed in claim 2 is characterized in that: In step S1, before establishing the physical model, the enthalpy porosity method is used to simulate the melting process of PCM in TES, and the control equation is as follows: Energy equation: Where ρ is the PCM density, H is the PCM material enthalpy, is the vector differential operator, k is the thermal conductivity; Continuity equation: In the formula, is the velocity vector; Momentum equation: Where, t is time; ρ is the density; p is the pressure; μ is the dynamic viscosity; ξ is the thermal expansion coefficient; T ref is the reference temperature, the source term The damping term of Darcy's law can be expressed as: The liquid phase rate at different times is expressed as follows: Where T solid Solid phase temperature, T liquid is the PCM liquid temperature; The total enthalpy consists of sensible enthalpy and latent enthalpy: ΔH=λL H=λL+h Where ΔH is the latent heat enthalpy and h is the sensible heat enthalpy.

4. The simulation method of the phase change thermal storage system using a rotary machine to enhance the simulated alveolar vascular fin as claimed in claim 3 is characterized in that: In step S1, the physical model established involves the physical parameters of the nanofluid, and the physical parameters of the nanofluid are calculated using the classical formula of nanoparticle mixtures, and the physical property equation is as follows: The density and specific heat of nanofluids are shown in the following equations: r nf =(1-φ ns )r f +φ ns r ns (ρc p ) nf =(1-φ ns )(ρc p ) f +φ ns (ρc p ) ns The effective dynamic viscosity of nanofluids is expressed by the Brinkman equation: The thermal conductivity of a nanofluid is given by the Brinkman equation: Where B is the Boltzmann constant (B = 1.3807 × 10-23 J / K), and γ is a variable representing the volume fraction of nanoparticles expressed as: γ=8.4407(100φ ns ) -1.07304 (0.01<φ ns <0.1) The empirical function f(T,φ) is as follows: The heat storage rate of the phase change material when melting while absorbing heat is expressed as: The heat storage density of the phase change material when it melts while absorbing heat is expressed as:

5. The simulation method of the phase change thermal storage system of the alveolar vascular fin enhanced by a rotary machine as claimed in claim 4 is characterized in that: In step S2, when the two-dimensional model is used to perform two-dimensional simulation analysis on the physical model, the temperature of the heat transfer fluid is simplified to the pipe wall temperature. At the initialization time t=0, the PCM temperature is 293K and is in a solidified state. When t>0, the HTF temperature is set to 363K to melt the PCM: There is a coupled boundary condition between the fin and PCm, which satisfies Fourier's heat conduction law as shown below:

6. The simulation method of the phase change thermal storage system of the alveolar vascular fin enhanced by a rotary machine as claimed in claim 5, characterized in that: In step S2, the specific contents of using the two-dimensional model to perform two-dimensional simulation analysis on the physical model include: using MESH software to mesh the latent heat system, and using computational fluid dynamics software ANSYS Fluent to perform numerical calculations.

7. The simulation method of the phase change thermal storage system of the alveolar vascular fin enhanced by a rotary machine as claimed in claim 6, characterized in that: In step S2, when meshing the latent heat system using MESH software, the mesh adopts a mixed unstructured mesh of triangles and quadrilaterals. In the process of numerical calculation using computational fluid dynamics software ANSYS Fluent, the Solidification / Melting model in Fluent software is selected for solution, and the PISO algorithm is used to deal with the coupling problem of pressure field and velocity field, and the PRESTO! algorithm is used for pressure correction.

8. The simulation method of the phase change thermal storage system using a rotary machine to enhance the simulated alveolar vascular fin as claimed in claim 7 is characterized in that: In step S3, the specific steps of obtaining the experimental results by building the experimental device are: An experimental device for the physical model of a solar phase change thermal storage system based on trough vascular fins was built. Appropriate PCM and nanoparticles were selected, nanofluids were prepared, and injected into the system. The parameters of the heat source and cooling system were adjusted to simulate different working conditions. The melting and solidification processes of PCM were recorded to obtain experimental results.

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