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

Abstract

The invention relates to the technical field of phase change heat storage, in particular to a simulation method for enhancing a lung groove imitating blood vessel fin phase change heat storage system through nanofluid. The method comprises the following steps: firstly, establishing a three-dimensional physical model of the phase change heat storage enhanced heat transfer system simulating lung groove blood vessel fins, performing simulation analysis on the three-dimensional model, comparing a simulation result with an experimental result, and adjusting parameters of the simulation model to improve the accuracy of the simulation result; different nano-fluids are selected through the optimized simulation analysis platform to carry out enhanced heat transfer research on the TES, and the influence of the volume fraction of the nano-particles, the variety of the nano-particles, the temperature of the heat transfer fluid (HTF) and the initial speed on liquid phase distribution, temperature distribution, melting characteristics and kinetic energy consumption of the TES is analyzed. And finally, performing three-objective optimization on the kinetic energy, the TES heat storage amount and the melting time to obtain an objective function and an optimal value, and providing a certain reference basis for TES multi-objective optimization under the condition that the working medium is the nanofluid.

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 lung trough vascular fin enhanced by nanofluid. Background Art

[0002] With the intensification of climate change and the proposal of the "dual carbon" goal, the development and utilization of clean energy has become a top priority. As an inexhaustible clean energy, solar energy has received more and more attention. However, the intermittent nature of solar energy leads to its lack of stability, which has become one of the key factors limiting its large-scale application. Therefore, how to effectively store solar energy so that it can provide energy continuously and stably has become a research hotspot in this field.

[0003] Phase change energy storage technology uses the latent heat absorbed and released by phase change materials (PCM) during the phase change process to store and utilize energy, providing a new idea for solving the intermittent problem of solar energy. Phase change materials can absorb or release a large amount of heat when they melt or solidify, while the temperature change is very small, so they can store and release energy efficiently. Phase change energy storage technology has shown great application potential in various fields, such as 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. This system usually consists of two layers of pipes, with phase change materials filled in the middle and the outer pipe used for heat transfer. By controlling the flow of fluid in the pipe, the phase change material can be heated and cooled, thereby realizing energy storage and release.

[0004] Figure 1 The application scenario of the tube-in-tube phase change thermal storage system (TES) in the desalination system is demonstrated. The system uses daytime solar energy to store heat in phase change material (PCM) 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. The thermal accumulator consists of multiple vertical latent heat units.

[0005] In the casing phase change thermal storage system (TES), many researchers have borrowed 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 thermal energy storage efficiency. In addition, existing nanoparticles are widely used to enhance the thermal storage performance of PCM, but there is no systematic analysis of the combination of nanoparticles and HTF. Most of the research is to combine nanoparticles with PCM. In addition, few researchers have conducted multi-objective optimization design on the kinetic energy of nanofluids and the heat storage and heat storage time of TES. In summary, there is no systematic analysis of the mutual influence between the kinetic energy loss of nanofluids and the increase in thermal conductivity caused by the excessive volume fraction of nanoparticles. Moreover, when the heat exchange fluid uses nanofluids, there is a complex competitive game relationship between the initial velocity, temperature and the volume fraction of nanoparticles, which affects the initial kinetic energy and heat storage of TES flow. Therefore, the existing technology lacks a simulation method based on nanofluids to enhance the phase change thermal storage system of the simulated lung trough vascular fin, which provides 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 heat storage system of a lung trough vascular fin enhanced by nanofluids, so as to solve the problem that there is currently no research to systematically analyze the mutual influence between the kinetic energy loss of nanofluids and the increase in thermal conductivity caused by the excessive volume fraction of nanoparticles. Moreover, when the heat exchange fluid is nanofluid, there is a complex competitive game relationship between the initial velocity, temperature and volume fraction of nanoparticles, which affects the initial kinetic energy and heat storage capacity of TES flow. Therefore, the prior art lacks a simulation method based on nanofluids to enhance the phase change heat storage system of a lung trough vascular fin, which provides an accurate basis for the multi-objective optimization of TES rotation.

[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 nanofluid, comprising the following steps:

[0008] S1: Establish a physical model of the cross section of the phase-change heat storage and enhanced heat transfer system of the simulated lung trough vascular fin, and perform a three-dimensional simulation on the physical model to obtain a three-dimensional model of the phase-change heat storage and enhanced heat transfer system of the simulated lung trough vascular fin;

[0009] S2: Simulate and analyze the three-dimensional model of the cross section of the phase change heat storage and enhanced heat transfer system of the simulated lung trough vascular fin, 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, improve the accuracy of the simulation results, and obtain the simulation analysis platform of the optimized phase change heat storage and enhanced heat transfer system of the simulated lung trough vascular fin;

[0012] S5: Using the optimized simulation analysis platform of the lung trough vascular fin phase change heat storage enhanced heat transfer system, the effects of nanoparticle volume fraction, nanoparticle type, heat transfer fluid temperature and initial velocity on TES liquid phase distribution, temperature distribution, melting characteristics and kinetic energy consumption are analyzed to provide guidance for the optimal design of the latent heat system.

[0013] Among them, in step S1, the phase change heat storage and enhanced heat transfer system imitating the alveolar blood vessel fin 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, and the outer tube diameter Ф2 is 35 mm. The fin structure of the solar phase change heat storage system based on the alveolar blood vessel fin 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 decreasing, the branch width is L, and the bifurcation angles are θ and α.

[0014] 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:

[0015] Energy equation:

[0016]

[0017] Where ρ is the PCM density, H is the PCM material enthalpy, is the vector differential operator, k is the thermal conductivity;

[0018] Continuity equation:

[0019]

[0020] In the formula, is the velocity vector;

[0021] Momentum equation:

[0022]

[0023] 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:

[0024]

[0025] The liquid phase rate at different times is expressed as follows:

[0026]

[0027] Where T solid Solid phase temperature, T liquid is the PCM liquid temperature;

[0028] The total enthalpy consists of sensible enthalpy and latent enthalpy:

[0029]

[0030] ΔH=λL

[0031] H=λL+h

[0032] Among them, ΔH is the latent heat enthalpy, h is the sensible heat enthalpy;

[0033] Among them, in step S1, the physical model established involves 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:

[0034] The density and specific heat of nanofluids are shown in the following equations:

[0035] ρ nf =(1-φ ns )ρ f +φ ns ρ ns

[0036] (ρc p ) nf =(1-φ ns )(ρc p ) f +φ ns (ρc p ) ns

[0037] The effective dynamic viscosity of nanofluids is expressed by the Brinkman equation:

[0038]

[0039] The thermal conductivity of a nanofluid is given by the Brinkman equation:

[0040]

[0041] 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:

[0042] γ=8.4407(100φns ) -1.07304 (0.01<φ ns <0.1)

[0043] The empirical function f(T,φ) is as follows:

[0044]

[0045] The heat storage rate of the phase change material when melting while absorbing heat is expressed as:

[0046]

[0047] The heat storage density of the phase change material when it melts while absorbing heat is expressed as:

[0048]

[0049] Among them, in step S1, when performing two-dimensional simulation on the physical model, the temperature of the heat transfer fluid is simplified to the temperature of the pipe wall. 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:

[0050]

[0051] The fins and PCM are coupled boundary conditions that satisfy Fourier's heat conduction law, as shown below:

[0052]

[0053] Among them, in step S1, the specific contents of the two-dimensional simulation of 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.

[0054] Among them, in step S1, when the latent heat system is meshed by MESH software, the mesh adopts a mixed unstructured mesh of triangles and quadrilaterals. In the process of numerical calculation by 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.

[0055] Among them, in step S3, the specific steps of obtaining the experimental results by building the experimental device are:

[0056] An experimental device for the phase change heat storage and enhanced heat transfer system imitating the lung trough vascular fin was built, suitable PCM and nanoparticles were selected, nanofluids were prepared, and injected into the system, the parameters of the heat source and cooling system were adjusted, different working conditions were simulated, the melting and solidification process of PCM was recorded, and experimental results were obtained.

[0057] The present invention discloses a simulation method for a phase-change heat storage system of a simulated lung trough vascular fin enhanced by nanofluid. First, a physical model of a cross section of the phase-change heat storage enhanced heat transfer system of a simulated lung trough vascular fin is established, and a two-dimensional simulation is performed on the physical model to obtain a two-dimensional model of a cross section of the phase-change heat storage enhanced heat transfer system of a simulated lung trough vascular fin. The two-dimensional model is simulated and analyzed to simulate the melting and solidification process of PCM, analyze the heat transfer performance and energy storage efficiency of the system, and obtain experimental results. 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 to obtain an optimized simulation analysis platform. The optimized simulation analysis platform 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, melting characteristics and kinetic energy consumption of the latent heat system, thereby providing a certain reference basis for the multi-objective optimization of the rotation of the latent heat system. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] 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.

[0059] Figure 1 It is a schematic diagram of an application scenario of the existing sleeve phase change thermal storage system (TES) provided by the present invention in the heating of building maintenance structures.

[0060] Figure 2 It is a flow chart of the steps of a simulation method of the present invention for using a nanofluid-enhanced phase-change heat storage system for pulmonary trough-like vascular fins.

[0061] Figure 3 It is a schematic diagram of the specific structure of the sleeve phase change thermal storage system (TES) provided by the present invention.

[0062] Figure 4 It is a verification diagram of four different grid numbers provided by the present invention.

[0063] Figure 5 It is a schematic diagram of the fitting results of the experiment and simulation of PCM melting and solidification provided by the present invention.

[0064] Figure 6It 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.

[0065] Figure 7 It is a data analysis diagram of the mixture of nanoparticles with different volume fractions provided by the present invention.

[0066] Figure 8 This is a temperature cloud diagram analysis diagram of TES with different nanoparticle volume fractions provided by the present invention.

[0067] Fig. 9 This is an analysis diagram of the melting characteristics of TES with different nanoparticle volume fractions provided by the present invention.

[0068] Fig.10 It is a melting characteristic analysis diagram of TES at different fluid velocities provided by the present invention.

[0069] Fig.11 3 is a melting characteristic analysis diagram of TES with different HTF initial temperatures provided by the present invention.

[0070] Fig.12 It is an analysis diagram of the influence of each response surface provided by the present invention on the melting time of TES.

[0071] Fig.13 It is an analysis diagram of the influence of various response surfaces provided by the present invention on the TES heat storage capacity.

[0072] Fig.14 It is an analysis diagram of the influence of each response surface provided by the present invention on the kinetic energy loss of TES.

[0073] Fig.15 It is a Pareto front curve diagram obtained by the multi-objective optimization provided by the present invention. DETAILED DESCRIPTION

[0074] 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.

[0075] See also Figures 1 to 15 The present invention provides a simulation method for a phase change heat storage system of a simulated lung trough vascular fin enhanced by nanofluid, comprising the following steps:

[0076] S1: Establish a physical model of the cross section of the phase-change heat storage and enhanced heat transfer system of the simulated lung trough vascular fin, and perform a three-dimensional simulation on the physical model to obtain a three-dimensional model of the phase-change heat storage and enhanced heat transfer system of the simulated lung trough vascular fin;

[0077] S2: Simulate and analyze the three-dimensional model of the cross section of the phase change heat storage and enhanced heat transfer system of the simulated lung trough vascular fin, simulate the melting and solidification process of PCM, and analyze the heat transfer performance and energy storage efficiency of the system;

[0078] S3: Obtain experimental results by building experimental devices or based on the Internet;

[0079] S4: Compare the simulation results with the experimental results, adjust the parameters of the simulation model, improve the accuracy of the simulation results, and obtain the simulation analysis platform of the optimized phase change heat storage and enhanced heat transfer system of the simulated lung trough vascular fin;

[0080] S5: Using the optimized simulation analysis platform of the lung trough vascular fin phase change thermal storage and enhanced heat transfer system, the effects of nanoparticle volume fraction, nanoparticle type, heat transfer fluid (HTF) temperature and initial velocity on TES liquid phase distribution, temperature distribution, melting characteristics and kinetic energy consumption are analyzed to provide guidance for the optimal design of the latent heat system.

[0081] In this embodiment, a physical model of the cross-section of the phase-change thermal storage and enhanced heat transfer system of the simulated lung trough vascular fin is first established, and the physical model is simulated in three dimensions to obtain a three-dimensional model of the phase-change thermal storage and enhanced heat transfer system of the simulated lung trough vascular fin. The three-dimensional model is simulated and analyzed to simulate the melting and solidification process of PCM, analyze the heat transfer performance and energy storage efficiency of the system, and obtain experimental results. 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. An optimized simulation analysis platform is obtained. The optimized simulation analysis platform can be used to analyze the influence of nanoparticle volume fraction, rotation speed, fin material and heat transfer fluid (HTF) temperature on the liquid phase distribution, temperature distribution, melting characteristics and kinetic energy consumption of the latent heat system, so as to provide a certain reference for the multi-objective optimization of the rotation of the latent heat system.

[0082] in, Figure 3The specific structure of the sleeve phase change thermal storage system (TES) is shown. In step S1, the solar phase change thermal storage system based on the alveolar vascular fin is composed of multiple sleeve units, each unit includes an inner tube and an outer tube, the inner tube diameter Ф1 is 8mm, and the outer tube diameter Ф2 is 35mm. The fin structure of the solar phase change thermal storage system based on the alveolar vascular fin is modeled after 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 structure. The Nachi series decreases from 5mm, 3mm, 2mm and 1mm, the branch width is L, and the bifurcation angles are θ and α. 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. Therefore, 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. The physical properties of PCM and nanoparticles are shown in Table 1 below:

[0083] Table 1. Physical properties of PCM and nanoparticles

[0084] Physical properties Nanoparticles PCM Main Ingredients <![CDATA[Al2O3]]> RT55 <![CDATA[Density / kg / m 3 > 3970 770 Specific heat / J / (kg·K) 680 2000 Thermal conductivity / W / (m·K) 70.2 Thermal conductivity (K-1) 16.3 <![CDATA[4.5×10 -4 ]]> Latent heat / kJ / (kg) —— 170 Dynamic viscosity / kg / (m·s) —— <![CDATA[3.408×10 -4 ]]> Solid phase temperature / K —— 324 Liquidus temperature / K —— 330

[0085] The structural parameters of the alveolar artery model fin are shown in Table 2 below:

[0086] Table 2. Structural parameters of the alveolar artery model fin

[0087] 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°

[0088] 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 TES 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 TES 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:

[0089] Energy equation:

[0090]

[0091] Where ρ is the PCM density, H is the PCM material enthalpy, is the vector differential operator, k is the thermal conductivity;

[0092] Continuity equation:

[0093]

[0094] In the formula, is the velocity vector;

[0095] Momentum equation:

[0096]

[0097] 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:

[0098]

[0099] The liquid phase rate at different times is expressed as follows:

[0100]

[0101] Where T solid Solid phase temperature, T liquid is the PCM liquid temperature;

[0102] The total enthalpy consists of sensible enthalpy and latent enthalpy:

[0103]

[0104] ΔH=λL

[0105] H=λL+h

[0106] Where ΔH is the latent heat enthalpy and h is the sensible heat enthalpy.

[0107] The rotational kinetic energy is calculated using the classical kinetic energy formula:

[0108] The moment of inertia of the ring is:

[0109] I=m·(r1 2 +r2 2 ) / 2

[0110] The formula for calculating the kinetic energy of a ring rotating around its center is:

[0111] KE=(1 / 2)·I·ω 2

[0112] Among them, m is the mass of the annular sleeve, r1 is the inner radius, and r2 is the outer radius.

[0113] 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:

[0114] The density and specific heat of nanofluids are shown in the following equations:

[0115] ρ nf =(1-φ ns )ρ f +φ ns ρ ns

[0116] (ρc p ) nf =(1-φ ns )(ρc p ) f +φ ns (ρc p ) ns

[0117] The effective dynamic viscosity of nanofluids is expressed by the Brinkman equation:

[0118]

[0119] The thermal conductivity of a nanofluid is given by the Brinkman equation:

[0120]

[0121] 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:

[0122] γ=8.4407(100φ ns ) -1.07304 (0.01<φ ns <0.1)

[0123] The empirical function f(T,φ) is as follows:

[0124]

[0125] The heat storage rate of the phase change material when melting while absorbing heat is expressed as:

[0126]

[0127] The heat storage density of the phase change material when it melts while absorbing heat is expressed as:

[0128]

[0129] Furthermore, in step S1, the specific contents of the two-dimensional simulation of the physical model include: using MESH software to mesh the latent heat system, using computational fluid dynamics software ANSYS Fluent for numerical calculation, and when using MESH software to mesh the latent heat system, the mesh adopts a mixed unstructured mesh of triangles and quadrilaterals, and 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, wherein 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 .

[0130] Furthermore, when using MESH software to mesh the latent heat system, Figure 4 As shown,

[0131] Reasonable encryption was performed around the interface between the fin and the PCM, and the HTF fluid domain was divided using boundary layer meshes. In order to verify the mesh independence, Figure 4 (a) shows the simulation results using four different mesh numbers of 1512283, 2012954, 3217101, and 5256142. The results show that the PCM average temperature change gradually decreases with the increase of the mesh number. When the mesh number increases from 3217101 to 5256142, the PCM average temperature only increases from 327.3314 to 327.3426 after 300 seconds, and the relative change is only 0.0034%. Figure 4 The PCM average temperature in (a) shows that the curves with mesh numbers of 327101 and 5256142 are basically the same, with a very small difference. This shows that further increasing the number of meshes has little effect on the heat transfer analysis results. Therefore, this technical solution selects a mesh number of 3217101 as the final calculation mesh number. Figure 4 (b) shows the simulation results using 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 0.01s and 0.05s. Zooming in and observing, it can be found that the maximum temperature difference between 0.05s and 0.01s is only about 0.01K. In order to save computing resources, this paper chooses a time step of 0.05s for subsequent calculations.

[0132] Furthermore, in order to verify the accuracy of numerical simulation and improve work efficiency, the present technical solution directly uses the experimental data in the prior art for comparative analysis with the numerical simulation results (the experimental data in the prior art here use the experimental data of the research by Soltanl et al.), and uses its experimental data to verify that the algorithm model and relaxation factor and other parameters of the present technical solution are reasonable. Figure 5 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 technical solution has high accuracy and can provide reliable theoretical support for subsequent research work.

[0133] Furthermore, the mutual verification between simulation results and experimental results shows that:

[0134] 1. Analysis of different nanoparticle modification studies;

[0135] Among them, in order to enhance the heat transfer of PCM, many researchers adopt the method of mixing nanoparticles with PCM, but few people have studied the mixing of nanoparticles with HTF to achieve the purpose of enhancing heat transfer. This section takes four types of nanoparticles, Al2O3, CuO, TiO2 and Cu at a volume fraction of 5%, as the research objects. They have the advantages of high thermal conductivity, non-toxicity and non-corrosiveness, and are widely used in academic and industrial fields. The study selected the simulated lung trough vascular fin latent heat system with pure water as the heat transfer medium as the control group. Figure 6The liquid phase cloud diagram of TES is shown. From 100s to 550s, it is obvious that the heat transfer effect of HTF containing nanoparticles is significantly higher than that of HTF with pure water working fluid, indicating that nanoparticles have obvious advantages in improving the thermal conductivity of HTF. In addition, a horizontal comparison of different nanoparticles found that Cu has the best heat transfer effect, which is also proportional to the thermal conductivity of nanoparticles. In terms of thermal conductivity, Cu>CuO>Al2O3>TiO2, so the melting of PCM also follows this order to melt faster or slower. In addition, the cloud diagram also shows that the upper half of the PCM melts faster and the lower half melts slower. This is because of two reasons. First, the flow direction of HTF is from top to bottom, so the HTF temperature in the upper area is higher and the HTF in the lower area is lower. According to Fourier's law of heat conduction, the decrease in temperature difference leads to a decrease in heat transfer; second, natural convection will occur during the melting process of PCM, the heat flow will rise, and the upper half will melt faster. Finally, we found that in general, the cross-sectional melting of PCM is the heat transfer of HTF to all directions through the simulated alveolar vascular fins. This type of fin can transfer heat to various dead corners very well. In addition, there are not too many branch fins occupying the volume of PCM. Therefore, this fin shape is a good heat transfer shape, which is not only beneficial to the heat dissipation of the human body, but also beneficial to the heat storage of TES.

[0136] in, Figure 7 (a) and Figure 7 (b) shows the effect of different nanoparticles on the melting process of PCM. The results show that adding 5% volume fraction of Cu has the best heat transfer effect on PCM, and its melting rate and average temperature are significantly improved. At 500 seconds, the average temperature of 5% Cu is about 12K 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 HTF also increases, thereby accelerating heat transfer and improving heat transfer effects. In addition, Figure 7 (a) It can be found that the average temperature of PCM has almost no significant change between 50s and 300s, showing a straight line, which indicates that during this period PCM is storing latent heat and a typical phase change effect occurs. Figure 7 (c) shows that Al2O3 and TiO2 are almost similar in terms of heat storage rate and heat storage density, which is also because the thermal conductivity of the two is relatively similar. The data once again proves that the heat transfer effect of TES is positively correlated with the thermal conductivity of nanoparticles. Compared with pure PCM, the heat storage density of the group with a volume fraction of 5% nanoparticles increased by 58.14kJ / kg and the heat storage rate increased by 0.04kJ / s, reflecting a certain heat exchange enhancement effect. However, it should be noted that the addition of nanoparticles will increase the mass of HTF, resulting in an increase in the required kinetic energy, which will be optimized later.

[0137] 2. Effect of different nanoparticle volume fractions on TES heat transfer;

[0138] Among them, the difference in the volume fraction of nanoparticles will directly affect the thermal conductivity of HTF, thereby affecting the melting of PCM. The initial working conditions of this part are that the HTF temperature and speed are 263K and 0.1m / s respectively. Figure 8 The figure shows the TES temperature cloud diagram analysis of different nanoparticle volume fractions. It can be seen intuitively from the figure that the mixture of nanoparticles and HTF at 5% volume has the best heat transfer effect. At 550s, most of the PCM has melted. From the side, the volume of the solid PCM in the blue area decreases with the increase of volume fraction. The addition of nanoparticle volume fraction will increase the mass of HTF while improving the thermal conductivity, resulting in an increase in the required kinetic energy. In addition, from Fig. 9 From the data, we can see more intuitively that the average temperature and liquid fraction of PCM increase fastest at 5% volume fraction. And we can find that the difference is not very large before 300 seconds, and after 300 seconds, the temperature does not change significantly. This is mainly because 300 seconds ago, the PCM was undergoing a phase change process, and the temperature itself changed very little, but the liquid fraction changed greatly, because the liquid fraction itself is more sensitive than temperature, especially during the PCM melting process. Fig. 9 (c) It can be found that the heat storage temperature of 5% Cu nanoparticles is increased by 10K, the heat storage amount is increased by 19.94%, the melting time is reduced by 11.74%, the heat storage density is increased by 19.84%, and the heat storage rate is increased by 20.14%. The heat storage amount, heat storage density and heat storage rate here are calculated within the range of 500s. This time period is selected because most of the PCM is melted in all cases at 500s, so it is more scientific to choose this time period. In summary, the increase in the volume fraction of nanoparticles can significantly improve the thermal storage performance of TES.

[0139] 3. Effect of different nanofluid velocities on TES heat transfer;

[0140] In the phase change energy storage system, the initial velocity of the nanofluid has a great influence on the thermal storage performance. From the Newton cooling formula, it can be found that the greater the flow rate, the greater the convective heat transfer coefficient will be, and the more obvious the heat transfer effect will be. Fig.10 (a) and (b) show the heat transfer of PCM average temperature and liquid phase ratio at different initial velocities. It can be seen that with every increase of 0.05m / s in the initial velocity, the PCM average temperature and liquid phase ratio increase successively, but the increase is getting smaller and smaller. This shows that the flow rate should not be increased blindly, otherwise the TES thermal storage effect will not be improved much while sacrificing too much kinetic energy, which will result in extremely low cost performance. Therefore, further analysis will be done in combination with kinetic energy later. Fig.10 (c) It can be found that the average temperature of PCM increases by 17K, the heat storage increases by 28.34%, the melting time decreases by 7.73%, the heat storage density increases by 28.35%, and the heat storage rate increases by 28.26% when the initial speed is 0.25m / s compared with 0.05m / s. In addition, it is interesting that when the speed exceeds 0.15m / s, the curves of heat storage density and heat storage rate are close to flat, which means that when the speed exceeds 0.15m / s, the heat transfer performance is basically not improved much, but it will consume too much kinetic energy.

[0141] 4. Effect of HTF temperature on TES heat transfer;

[0142] 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 result in 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. Fig.11 (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.11 (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 90.58%, the average PCM temperature increased by 12.76%, the complete melting time decreased by 56.43%, the heat storage density increased by 90.65%, and the heat storage rate increased by 90.56%. Therefore, the HTF temperature has the greatest impact on the performance of TES. Therefore, in order to achieve the best heat storage effect, higher requirements are placed on the solar collector. The heat transfer fluid needs to be heated to 373K, which is the vapor-liquid phase transition point of water, as much as possible. In this way, heat can be used as much as possible for seawater desalination operation, and more energy-saving and efficient energy utilization can be achieved.

[0143] 5. Multi-objective optimization of nanocirculation of TES;

[0144] 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 impact of multiple factors on one or more response variables. Specifically, this technical solution uses Box-Behnken design (BBD) for variable design, and its influencing factor parameters are shown in Table 4;

[0145] Table 4. Design parameters of BBD impact factors

[0146]

[0147] The response surface diagram output by RSM was used for analysis, and the response surface regression equation and the optimal solution were fitted.

[0148] in, Fig.12 (a), (b) and (c) represent the analysis of the influence of each response surface on the TES melting time. Fig.12 (d) Comparison analysis between the RSM predicted values ​​and the actual values ​​obtained by the numerical model. Fig.12 (d) It can be found that the RSM predicted value and the actual value fit perfectly, so it can be shown that this response surface method design is successful. Fig.11 The red area represents a long melting time, and the blue area represents a short melting time. It can be clearly seen that the blue area is mainly concentrated in the range of initial temperature of 368K to 373K, initial velocity of 0.15m / s to 0.25m / s, and nanofluid volume fraction of 4% to 5%. In addition, the study found that in terms of melting time, the effect of initial temperature on TES is significantly greater than the initial velocity and nanoparticle volume fraction. This is mainly because according to Fourier's law of heat conduction, the temperature difference has a direct effect on heat transfer, while the HTF velocity and nanoparticles only improve the heat transfer coefficient, so the HTF temperature has a greater impact. The target equation corresponding to the specific melting time is shown in the following formula:

[0149] Complete melting time=163368+8625.5A-868.07B-268.2C-26AB+16.25AC+0.7125BC+1825A 2 +1.1575B 2 -0.5C 2

[0150] Fig.13 (a), (b) and (c) represent the analysis of the impact of each response surface on the TES heat storage capacity. Fig.13 (d) Comparison analysis between the RSM predicted values ​​and the actual values ​​obtained by the numerical model. Fig.12 (d) It can be found that the RSM predicted value and the actual value fit perfectly, so it can be shown that this response surface method design is successful. Fig.13 The red area represents a large amount of heat storage, and the blue area represents a small amount of heat storage. It can be clearly seen that the red area is mainly concentrated in the range of initial temperature of 368K to 373K, initial velocity of 0.15m / s to 0.25m / s, and nanofluid volume fraction of 4% to 5%. The fitted heat storage target equation is shown in the following formula:

[0151] Heat storage capacity=-419.60046+71.68017A+2.08374B+1.90938C-0.111669AB-1.09696AC-0.003356BC-61.73895A 2 -0.002525B 2 -0.064964C 2

[0152] Fig.14 (a), (b) and (c) represent the analysis of the impact of each response surface on the kinetic energy consumption of TES. Fig.13 (d) Comparison analysis between the RSM predicted values ​​and the actual values ​​obtained by the numerical model. Fig.14 (d) It can be found that the RSM predicted value and the actual value fit perfectly, so it can be shown that this response surface method design is successful. Fig.14 The red area represents a large kinetic energy consumption, and the blue area represents a small kinetic energy consumption. It can be clearly seen that the blue area is mainly concentrated in the range where the initial velocity is smaller and the volume fraction of the nanoparticles is smaller. This is mainly because from the kinetic energy formula, it can be known that the kinetic energy is positively correlated with the HTF velocity and density, so these two need to be as small as possible. This is in a competitive relationship with the heat storage and melting time. In addition, it can be found that the kinetic energy has almost no relationship with the initial temperature of the HTF, so when considering the kinetic energy consumption, the initial temperature of the HTF does not need to be considered too much. The fitted kinetic energy consumption target equation is shown in the following formula:

[0153] Kinetic energy=0.485836-5.50375A-0.000908B-0.107062C+0.000000000000000276335AB+1.83625AC-0.00000000000000000543489BC+95.1375A 2 +0.00000125B 2 -0.000031C 2

[0154] Furthermore, this scheme uses the non-dominated sorting genetic algorithm (NSGA-II) for multi-objective optimization. Using the objective equation of RSM for optimization, we get Fig.15The Pareto frontier shown in Figure 1 is shown in Figure 2. According to the given three decision variables: initial velocity (0.05-0.25 m / s), initial temperature (353-373 K) and volume fraction (1-5%), the Pareto curve with heat storage, melting time and kinetic energy as the objective function is obtained. Since the goal is to obtain the maximum heat storage, the minimum melting time and the minimum kinetic energy, there is an obvious competitive relationship between the three objective functions. When the melting time increases, the value of kinetic energy decreases. When the heat storage increases, the value of kinetic energy increases. Therefore, point O is found on the Pareto curve, and the value of the objective function corresponding to this point is taken as the optimal solution of multi-objective optimization. The values ​​of heat storage, melting time and kinetic energy at point O are 10.33 kJ, 492.19 K and 2.25 mJ respectively, and the corresponding values ​​of initial velocity, initial temperature and volume fraction are 0.15 m / s, 373.00 K and 4.41% respectively. Table 4 lists the basic parameter settings of the optimization algorithm. The fitting data was brought into Fluent software for calculation verification, and it was found that the error of heat storage was only 0.031%, and the melting time was 0.019%. The optimization results were reasonable.

[0155] The conclusion is as follows:

[0156] (1) By comparing different nanoparticles for heat transfer analysis, it was found that in terms of heat transfer effect, Cu>CuO>Al2O3>TiO2. In addition, when comparing nanoparticles with different volume fractions, 5% Cu nanoparticles increased the heat storage temperature by 10K, the heat storage capacity by 19.94%, the melting time by 11.74%, the heat storage density by 19.84%, and the heat storage rate by 20.14% compared with 1% nanoparticles. The higher the volume fraction, the more conducive to heat transfer, but it also increases the HTF density, which is not conducive to flow.

[0157] (2) The study found that when the initial speed of 0.25 m / s was compared with 0.05 m / s, the average PCM temperature increased by 17 K, the heat storage increased by 28.34%, the melting time decreased by 7.73%, the heat storage density increased by 28.35%, and the heat storage rate increased by 28.26%. In addition, it is interesting that when the speed exceeds 0.15 m / s, the curves of heat storage density and heat storage rate are close to flat, which means that when the speed exceeds 0.15 m / s, the heat transfer performance is basically not improved much, but it will consume too much kinetic energy. In addition, the HTF temperature is heated to 373 K as much as possible. The higher the temperature, the more conducive to heat transfer.

[0158] (3) TES was optimized according to the response surface methodology to obtain the target equations for heat storage, melting time, and kinetic energy consumption. The study found that in terms of heat storage and melting time, the effect of HTF initial temperature on TES was significantly greater than that of HTF initial velocity and nanoparticle volume fraction. However, in terms of kinetic energy consumption, the effect of HTF initial velocity was greater than that of nanoparticle volume fraction and HTF initial velocity.

[0159] (4) The optimal parameter combination was determined by multi-objective optimization using the non-dominated sorting genetic algorithm II (NSGA-II): the values ​​of melting time and kinetic energy were 10.33 kJ, 492.19 K and 2.25 mJ, respectively, and the corresponding values ​​of initial velocity, initial temperature and volume fraction were 0.15 m / s, 373.00 K and 4.41%, respectively.

[0160] In summary, the verification results provide an important theoretical reference for the simulation and optimization design of a phase change thermal storage system using nanofluid-enhanced lung trough vascular fins.

[0161] 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 simulated lung trough vascular fin enhanced by nanofluid, characterized in that: The steps include: S1: Establish a physical model of the cross section of the phase-change heat storage and enhanced heat transfer system of the simulated lung trough vascular fin, and perform a three-dimensional simulation on the physical model to obtain a three-dimensional model of the phase-change heat storage and enhanced heat transfer system of the simulated lung trough vascular fin; S2: Simulate and analyze the three-dimensional model of the cross section of the phase change heat storage and enhanced heat transfer system of the simulated lung trough vascular fin, 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, improve the accuracy of the simulation results, and obtain the simulation analysis platform of the optimized phase change heat storage and enhanced heat transfer system of the simulated lung trough vascular fin; S5: Using the optimized simulation analysis platform of the lung trough vascular fin phase change heat storage enhanced heat transfer system, the effects of nanoparticle volume fraction, nanoparticle type, heat transfer fluid temperature and initial velocity on TES liquid phase distribution, temperature distribution, melting characteristics and kinetic energy consumption are analyzed to provide guidance for the optimal design of the latent heat system.

2. The simulation method of the nanofluid enhanced alveolar vascular fin phase change thermal storage system according to claim 1, characterized in that: In step S1, the phase change thermal storage and enhanced heat transfer system imitating the alveolar blood vessel fin 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, and the outer tube diameter Ф2 is 35 mm. The fin structure of the solar phase change thermal storage system based on the alveolar blood vessel fin is imitated from 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 decreasing, the branch width is L, and the bifurcation angles are θ and α.

3. The simulation method of the nanofluid enhanced alveolar vascular fin phase change thermal storage system as claimed in claim 2, 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 nanofluid enhanced alveolar vascular fin phase change thermal storage system as claimed in claim 3, 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 nanofluid enhanced alveolar vascular fin phase change thermal storage system as claimed in claim 4, characterized in that: In step S1, when performing a two-dimensional simulation 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: The fins and PCM are coupled boundary conditions that satisfy Fourier's heat conduction law, as shown below:

6. The simulation method of the nanofluid enhanced alveolar vascular fin phase change thermal storage system as claimed in claim 5, characterized in that: In step S1, the specific contents of the two-dimensional simulation of 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 nanofluid enhanced alveolar vascular fin phase change thermal storage system according to claim 6, characterized in that: In step S1, 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 nanofluid enhanced alveolar vascular fin phase change thermal storage system as claimed in claim 7, 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 phase change heat storage and enhanced heat transfer system imitating the lung trough vascular fin was built, suitable PCM and nanoparticles were selected, nanofluids were prepared, and injected into the system, the parameters of the heat source and cooling system were adjusted, different working conditions were simulated, the melting and solidification process of PCM was recorded, and experimental results were obtained.

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