Phase-change heat exchange fin parameter optimization method and related device

Abstract

The invention belongs to the field of phase change heat storage, and discloses a phase change heat exchange fin parameter optimization method and a related device.According to the method, a three-dimensional parameterized model of a tree-shaped fractal fin containing the length ratio, the width fractal dimension and the fin number is established, representative parameter sample points are efficiently generated through Box-Behnken experimental design, and the representative parameter sample points are optimized; and a liquid phase rate and a total heat storage capacity response value are obtained by combining numerical simulation of phase change heat transfer, and multi-target parameter global optimization is realized based on a second-order polynomial regression model. By the adoption of the method, optimization efficiency and precision are remarkably improved, on the premise that a large number of repeated experiments or simulations are not needed, the optimal fin structure parameters capable of cooperatively strengthening heat conduction, promoting melting of the phase-change materials and maximizing the heat storage capacity are rapidly obtained, and a reliable design basis is provided for engineering application of the fractal fin strengthening phase-change heat storage technology.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of phase change heat storage, and particularly relates to a phase change heat transfer fin parameter optimization method and a related device. BACKGROUND

[0002] Under the dual driving of the continuous rise of global energy demand and the increasingly serious environmental problems caused by fossil energy, the development and efficient utilization of renewable energy (such as solar energy and wind energy) have become the core direction of global energy transformation, which is of great significance to alleviate energy crisis and improve ecological environment. However, the inherent intermittency and volatility of renewable energy make it difficult to directly achieve stable grid-connected power supply, which seriously restricts its large-scale application process. Under this background, energy storage technology as an "energy buffer" to balance energy supply and demand has become the key support to solve the above problems. Phase change heat storage technology has shown a broad application prospect in solar heat utilization, building heating, industrial waste heat recovery and other medium and low temperature heat energy storage and utilization scenarios due to its high energy density, stable phase change temperature and controllable heat storage and release process. Sodium acetate trihydrate (SAT) and other hydrated salt phase change materials have become the preferred materials in medium and low temperature heat storage scenarios due to their suitable phase change temperature of 57-59℃, high phase change latent heat and low preparation cost.

[0003] Although the hydrated salt phase change material has obvious advantages, it has the inherent defect of low thermal conductivity. The solid thermal conductivity of the material is about 0.60 W·m -1 ·K -1 , and the liquid thermal conductivity is only about 0.38 W·m -1 ·K -1 . This defect directly leads to low heat storage and release efficiency of the phase change heat transfer unit, which becomes the core bottleneck restricting the large-scale promotion of phase change heat storage technology. In order to strengthen the heat transfer performance of the hydrated salt phase change material, the existing technology often adds fins in the phase change unit to expand the heat conduction path. Common fin structures mainly include light pipe fins and rectangular fins. The rectangular fin has the problems of poor heat transfer uniformity and obvious inhibition effect on natural convection in the phase change process of the phase change material. The heat transfer strengthening effect of the light pipe fin is relatively limited, and both of them are difficult to meet the actual demand of efficient heat storage. The tree-shaped fractal fin is inspired by bionics, and optimizes the heat transfer path through multi-level branching structure, which shows unique potential in improving the uniformity of temperature field. However, there is a strong coupling relationship between the structure parameters of the fin, and the change of the parameters will simultaneously affect the heat transfer strengthening effect and the inhibition degree of natural convection. The traditional "single parameter trial and error" optimization method needs to rely on a large number of physical experiments or numerical simulations, which is not only low in efficiency and high in cost, but also difficult to find the global optimal structure parameters considering multiple performances.

[0004] Therefore, the low thermal conductivity of the hydrated salt phase change material and the deficiency of the existing fin heat transfer enhancement technology jointly cause the difficulty in improving the heat storage and release efficiency of the low-temperature phase change heat storage system. SUMMARY

[0005] The application aims to provide a phase change heat transfer fin parameter optimization method and related device, which can effectively solve the problem of low thermal conductivity of hydrated salt phase change material and the deficiency of the existing fin heat transfer enhancement technology, which jointly cause the difficulty in improving the heat storage and release efficiency of the low-temperature phase change heat storage system.

[0006] In order to achieve the above-mentioned purpose, the technical scheme adopted by the application is as follows: A phase change heat transfer fin parameter optimization method, comprising: Constructing a tree-shaped fractal fin parameterization model based on the geometric parameters of the double-pipe phase change heat transfer unit and the preselected core optimization parameters; wherein the core optimization parameters include length ratio, width fractal dimension and fin number; the length ratio is used to represent the length ratio of adjacent two levels of bifurcations of the bifurcated tree-shaped fractal fin; the width fractal dimension represents the fractal distribution coefficient of the bifurcated width; and the fin number represents the total number of fins uniformly distributed along the inner pipe circumference; Designing multiple groups of parameter sample points for the tree-shaped fractal fin parameterization model by using the Box-Behnken method; each group of parameter sample points corresponds to a group of parameter combinations of length ratio, width fractal dimension and fin number; Simulating the double-pipe phase change heat transfer unit geometric model in the tree-shaped fractal fin parameterization model at each group of parameter sample points to obtain the liquid phase rate and total heat storage as response values; Inputting each group of parameter sample points into the pre-constructed second-order polynomial regression model, taking the maximum liquid phase rate and total heat storage as the optimization target, solving the second-order polynomial regression model to obtain the optimal parameter sample point, and outputting the core optimization parameters corresponding to the optimal parameter sample point.

[0007] Further, the geometric parameters of the double-pipe phase change heat transfer unit include the inner diameter and thickness of the inner pipe, the inner diameter and thickness of the outer pipe, and the material and bifurcation size of the bifurcated tree-shaped fractal fin.

[0008] Further, in the tree-shaped fractal fin parameterization model, the width of the bifurcated tree-shaped fractal fin satisfies the following relationship:

[0009] In the formula, W represents the width of the bifurcated tree-shaped fractal fin; W0 represents the root width; D represents the width fractal dimension, and the value is 1-3; k n represents the bifurcation order.

[0010] Further, the method for designing multiple sets of parameter sample points for the parameterized model of tree-shaped fractal fins by using the Box-Behnken method comprises the following steps: Taking the length ratio, the width fractal dimension, and the number of fins as independent variables, and taking the liquid phase rate and the total heat storage capacity at the preset time of heat storage as response values, an experimental matrix is constructed by using the Box-Behnken design; Each core optimization parameter in the experimental matrix is set at three levels to generate multiple sets of parameter sample points.

[0011] Further, the simulation of the geometric model of the pipe-in-pipe phase change heat transfer unit in the parameterized model of tree-shaped fractal fins at each set of parameter sample points to obtain the liquid phase rate and the total heat storage capacity as response values comprises the following steps: The geometric model of the pipe-in-pipe phase change heat transfer unit in the parameterized model of tree-shaped fractal fins is divided into a quadrilateral-dominated unstructured grid, and the grid in the bifurcation region of the fins is encrypted; The phase change material and the boundary conditions are set, and the setting process of the boundary conditions comprises the following steps: the inner tube wall is set as an isothermal boundary, the outer tube wall is set as an adiabatic boundary, and the solid-liquid interface is set as a coupled heat transfer boundary; the setting process of the phase change material comprises the following steps: the specific material, the solid density, the liquid density, and the phase change latent heat of the phase change material are set; The geometric model of the pipe-in-pipe phase change heat transfer unit after the unstructured grid division and the grid encryption is simulated at each set of parameter sample points; The enthalpy-porosity method is used to solve the solid-liquid phase change heat transfer equation to obtain the liquid phase rate and the total heat storage capacity, and the liquid phase rate and the total heat storage capacity are output as response values.

[0012] Further, the method for inputting each set of parameter sample points into the pre-constructed second-order polynomial regression model to maximize the liquid phase rate and the total heat storage capacity as the optimization target, solving the second-order polynomial regression model to obtain the optimal parameter sample point, and outputting the core optimization parameters corresponding to the optimal parameter sample point comprises the following steps: The parameter sample points are fitted to establish a second-order polynomial regression model of the response values and the independent variables, and the independent variables are the parameter combinations of the length ratio, the width fractal dimension, and the number of fins; the specific equation of the second-order polynomial regression model is as follows:

[0013] In the formula, β 0 is a constant term, β 1 β 3 is a linear term coefficient, β 11 β 33 is a quadratic term coefficient, β ​12 β 23 is an interaction term coefficient; is a response value; α is a length ratio, and Δ is a width fractal dimension, n is a fin number; According to the data of the simulation, a second-order polynomial regression model is solved to obtain a regression coefficient; The maximum liquid phase rate and the maximum total heat storage are set as optimization objectives, and the regression coefficient is combined to solve an optimal parameter sample point, and a core optimization parameter corresponding to the optimal parameter sample point is output.

[0014] Further, after the respective groups of parameter sample points are input into the pre-constructed second-order polynomial regression model to maximize the liquid phase rate and the total heat storage as optimization objectives, the second-order polynomial regression model is solved to obtain an optimal parameter sample point, and the core optimization parameter corresponding to the optimal parameter sample point is output, the method further includes: The core optimization parameter is simulated, the simulation result is compared with a regression model prediction value, and the core optimization parameter is evaluated according to a comparison result.

[0015] A phase change heat transfer fin parameter optimization system, comprising: A model construction module is configured to construct a tree-shaped fractal fin parameterized model based on geometric parameters of a double-pipe phase change heat transfer unit and preselected core optimization parameters; wherein the core optimization parameters include a length ratio, a width fractal dimension, and a fin number; the length ratio is used to represent a length ratio of adjacent two levels of bifurcations of the bifurcated tree-shaped fractal fin; the width fractal dimension represents a fractal distribution coefficient of bifurcated widths; and the fin number represents a total number of fins uniformly distributed along a circumference of an inner pipe; A sampling module is configured to design multiple groups of parameter sample points for the tree-shaped fractal fin parameterized model by using a Box-Behnken method; each group of parameter sample points corresponds to a group of parameter combinations of the length ratio, the width fractal dimension, and the fin number; A simulation module is configured to simulate a double-pipe phase change heat transfer unit geometric model in the tree-shaped fractal fin parameterized model under each group of parameter sample points, and solve to obtain a liquid phase rate and a total heat storage as response values; A solving module is configured to input the respective groups of parameter sample points into a pre-constructed second-order polynomial regression model, maximize the liquid phase rate and the total heat storage as optimization objectives, solve the second-order polynomial regression model, obtain an optimal parameter sample point, and output a core optimization parameter corresponding to the optimal parameter sample point.

[0016] A phase change heat transfer fin parameter optimization device, comprising: A memory is configured to store a computer program; ​A processor is configured to implement the steps of the phase change heat transfer fin parameter optimization method when the computer program is executed.

[0017] A computer readable storage medium stores a computer program, and the computer program is configured to implement the steps of the phase change heat transfer fin parameter optimization method when the computer program is executed by a processor.

[0018] Compared with the prior art, the present application has the beneficial effects as follows: The present application provides a phase change heat transfer fin parameter optimization method, which establishes a three-dimensional parameterized model of tree-shaped fractal fins containing length ratio, width fractal dimension and fin number, uses Box-Behnken experimental design to efficiently generate representative parameter sample points, obtains liquid phase rate and total heat storage response values in combination with numerical simulation of phase change heat transfer, and realizes multi-objective parameter global optimization based on a second-order polynomial regression model. The core parameters selected accurately respectively regulate the geometric proportion of the fractal structure, the distribution density of the heat conduction path and the heat exchange area; the Box-Behnken design systematically explores the multi-dimensional parameter space with the least simulation times; the regression model accurately quantifies the nonlinear coupling relationship between the parameters and the comprehensive influence on the heat storage performance through mathematical fitting, thereby breaking through the limitations of the traditional single-parameter trial-and-error method. The present application significantly improves the optimization efficiency and accuracy, and quickly obtains the optimal fin structure parameters that can synergistically strengthen heat conduction, promote the melting of phase change materials and maximize the heat storage capacity without the need for a large number of repeated experiments or simulations, thereby providing reliable design basis for the engineering application of the fractal fin enhanced phase change heat storage technology. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 The present application provides a whole structure schematic diagram of a double-pipe phase change heat transfer unit containing tree-shaped fractal fins; Figure 2 The present application provides a tree-shaped fin parameterization schematic diagram; Figure 3 The present application provides a width fractal dimension-fin number response surface three-dimensional graph; wherein (a) is the length ratio / width fractal dimension and liquid phase rate response relationship; (b) is the length ratio / fin number and liquid phase rate response relationship; (c) is the width fractal dimension / fin number and liquid phase rate response relationship; Figure 4 The present application provides a comparison diagram of liquid phase rate and heat storage during the heat storage process of the RSM optimized structure and the original structure; wherein (a) is the liquid phase rate change before and after optimization; (b) is the heat storage change before and after optimization; Figure 5 The present application provides a liquid phase rate cloud picture of the optimal parameter combination under the heat storage of 15000 s; Figure 6A flow chart of a phase change heat transfer fin parameter optimization method provided by the present application is provided. Figure 7 A structural schematic diagram of a phase change heat transfer fin parameter optimization system provided by the present application is provided. DETAILED DESCRIPTION

[0020] In order to facilitate a deep understanding of the technical solutions of the present application, the following technical terms are explained as follows: Box-Behnken: Box-Behnken Design (BBD) is an experimental design method for optimizing multivariable systems, which belongs to the category of response surface design.

[0021] As described in the background, there is a strong coupling relationship between the structural parameters (such as length ratio, width fractal dimension, and fin number), and the parameter changes will simultaneously affect the heat transfer enhancement effect and the natural convection suppression degree. The traditional "single parameter trial and error" optimization method needs to carry out a large number of physical experiments or numerical simulations, which has the disadvantages of low efficiency, high cost, and difficulty in finding the global optimal solution.

[0022] In order to achieve the above purpose, the present embodiment provides a phase change heat transfer fin parameter optimization method, which provides an RSM optimization scheme for a two-stage bifurcated tree-shaped fractal fin. In particular, the Box-Behnken design is combined to quantify the influence of fin parameters on the heat storage and release performance in the hydration salt phase change scenario, so as to fully exert the structural advantages of the tree-shaped fin, and further provide theoretical support for the engineering design of the phase change heat transfer unit.

[0023] As shown in Figure 6 The present embodiment provides a phase change heat transfer fin parameter optimization method, which comprises: A tree-shaped fractal fin parameterization model is constructed based on the geometric parameters of the double-pipe phase change heat transfer unit and the preselected core optimization parameters; wherein the core optimization parameters include length ratio, width fractal dimension, and fin number; the length ratio is used to represent the length ratio of the adjacent two stages of bifurcated tree-shaped fractal fins; the width fractal dimension represents the fractal distribution coefficient of the bifurcated width; and the fin number represents the total number of fins uniformly distributed along the inner tube circumference; A plurality of groups of parameter sample points are designed for the tree-shaped fractal fin parameterization model by using the Box-Behnken method; each group of parameter sample points corresponds to a group of parameter combinations of length ratio, width fractal dimension, and fin number; The double-pipe phase change heat transfer unit geometric model in the tree-shaped fractal fin parameterization model is simulated at each group of parameter sample points, and the liquid phase rate and total heat storage are solved to obtain the response values; The parameter sample points of each group are input into the pre-constructed second-order polynomial regression model to maximize the liquid phase rate and the total heat storage as the optimization target, the second-order polynomial regression model is solved to obtain the optimal parameter sample point, and the core optimization parameter corresponding to the optimal parameter sample point is output.

[0024] The heat transfer fin parameter optimization method provided in the embodiment will be further described below with reference to the accompanying drawings: The embodiment provides a phase change heat transfer fin parameter optimization method, which is suitable for a low-temperature heat storage system taking sodium acetate trihydrate as a phase change material, and the specific implementation steps are as follows: Step S1, build a Spaceclaim-Fluent joint simulation environment: Step S11, start ANSYS Workbench2023R1, and add a "Geometry (Spaceclaim)" module and a "FluidFlow (Fluent)" module in the project flowchart in sequence, establish a data interaction link through a connection line between the modules, ensure that the geometric parameters modified in Spaceclaim can be updated to the Fluent simulation model in synchronization, and avoid repeated modeling errors.

[0025] Step S12, double-click the Fluent module to enter the setting interface, select a pressure-based transient solver, check the "Energy" option to open the energy equation, enable the "Solidification / Melting" solidification-melting model to simulate the phase change process, select a "Laminar" laminar flow model (because the movement speed of liquid sodium acetate trihydrate is lower than 0.1 m·s -1 , which meets the laminar flow condition); based on the Boussinesq assumption, set natural convection, and input 9.81 m·s -2 in the "Gravity" option, and the direction is along the negative direction of the global coordinate system y-axis.

[0026] Step S2, establish a tree-shaped fractal fin parameterized model: Step S21, construct a sleeve type phase change heat transfer unit geometric structure in Spaceclaim: the inner tube is made of 304 stainless steel, with an inner diameter of 36 mm, a wall thickness of 2 mm, and a length of 300 mm; the outer tube is made of the same material, with an inner diameter of 160 mm, a wall thickness of 5 mm, and a length of 300 mm; the inner tube and the outer tube are coaxially arranged, and the annular gap (the phase change material filling area) has a width of 57 mm. In the embodiment, a two-stage bifurcated tree-shaped fin is designed, the fin root is connected to the outer wall of the inner tube, and the end extends to the inner wall of the outer tube, and the material is 304 stainless steel (thermal conductivity 15 W·m -1 K -1The fin volume fraction is fixed at 9% by adjusting the bifurcation size (to avoid interference from material usage variations on performance comparison). This is based on a shell-and-tube phase change heat exchanger unit, specifically as follows: Figure 1 As shown, the heat exchange unit includes an inner tube, an outer tube, secondary branched tree-like fins, and a PCM filling area.

[0027] Step S22, as follows Figure 2 As shown, three core optimization parameters are defined in the Spaceclaim “Parameters” panel: Length ratio α: Sets the length of the first-order branch, and the length of the second-order branch = α × the length of the first-order branch. The value of α ranges from 0.6 to 1.4. width fractal dimension Delta Based on fractal theory, the fin width satisfies

[0028] in k For the bifurcation level, w 0 represents the root width, and Δ ranges from 1 to 3.

[0029] Number of fins n The total number of primary fins evenly distributed along the circumference of the inner tube, ranging from 6 to 10 (with intervals of 36º to 60º).

[0030] Step S23: Use Spaceclaim's "Interference Check" tool to check the geometric rationality and confirm that there is no crossover between the fins and that the phase change material filling area is a closed space.

[0031] Step S3: Parameter sampling based on Box-Behnken design: Step S31: Based on the core optimization parameters in the constructed tree-like fractal fin parameterization model, with length ratio... α (A) Width fractal dimension Δ (B) Number of fins n (C) is the independent variable, with the liquid phase fraction at 15000 s of heat storage ( Y 1) and total heat storage ( Y 2) To construct the experimental matrix for the response values ​​(liquid fraction reflects melting sufficiency, and total heat storage reflects energy storage capacity), the Box-Behnken design function of Design-Expert 13.0 software was used, as detailed below. Figure 3 As shown in (a), (b) and (c), with the liquid phase fraction as the Z-axis, the influence of the interaction between the two parameters on the heat storage performance can be clearly seen. It can be observed that the liquid phase fraction reaches the peak region when Δ=1 and n=10.

[0032] Step S32: Set the three levels for each parameter: α0.6 (-1), 1.0 (0), 1.4 (+1); Δ is 1 (-1), 2 (0), 3 (+1); n 6 (-1), 8 (0), 10 (+1), 13 groups of sample points are generated, each group of sample points corresponds to a unique parameter combination, and the specific parameter distribution covers the entire parameter space, ensuring the comprehensiveness of the model fitting.

[0033] Step S4, constructing a numerical simulation model and carrying out heat storage and release performance simulation: Step S41, meshing and quality verification: import the Spaceclaim model into the Fluent-Mesh module, and for the geometric model of the tube-type phase change heat exchanger unit in the parameterized model of the tree-shaped fractal fin, use the "water-tight workflow" to divide the unstructured grid, and locally encrypt the key heat transfer areas such as the fin bifurcation and the outer wall of the inner tube (grid size 0.75 mm), and the grid size of the non-key area is set to 2 mm; through the "Mesh Quality" tool detection, the grid skewness (Skewness) ≤0.3, the aspect ratio (AspectRatio) ≤5, and the orthogonality (Orthogonality) ≥0.7, which meets the simulation accuracy requirements; through grid independence verification, when the grid number is 35034, the deviation of the liquid phase rate calculation value from the 38000 grid is <2%, and the final grid number is determined.

[0034] Step S42, material and boundary condition setting: in the "Materials" panel, define the phase change material: sodium acetate trihydrate (SAT) solid density 1450 kg·m -3 , liquid density 1280 kg·m -3 , solid thermal conductivity 0.60 W·m -1 ·K -1 , liquid thermal conductivity 0.38 W·m -1 ·K -1 , phase change temperature 58℃, phase change latent heat 264 kJ·kg -1 ; the inner wall of the inner tube is set as an isothermal boundary, the temperature is 353.15 K (80℃) in the heat storage stage and 300.15 K (27℃) in the heat release stage; the outer wall of the outer tube is set as an adiabatic boundary (heat flux density 0 W·m -2 ); the solid-liquid interface adopts the default coupled heat transfer setting.

[0035] Step S43, simulation solution and data recording: set the time step to 0.5 s (after time independence verification, the calculation result is stable when the step is ≤0.5 s), and set the convergence criteria to residual errors of continuity equation, momentum equation ≤10 -3 , and energy equation residual error ≤10 -6; Add liquid phase rate, total heat storage monitoring curve in "Monitors", set to record data every 100 steps; simulate 13 groups of sample points in turn, output liquid phase rate, total heat storage and middle cross-section temperature distribution data when heat storage is 15000 s, take liquid phase rate and total heat storage as response values.

[0036] Step S5, parameter optimization and optimal solution based on response surface method: Step S51, construct regression model: import simulation data of 13 groups of sample points into Design Expert software, select "Response Surface Regression" function, and construct quadratic polynomial regression equation. The regression equation is:

[0037]

[0038] wherein, A =α(length ratio), B =Δ(width fractal dimension), C = n (fin number). Step S52, model verification: correlation coefficient of liquid phase rate regression model R 2 =0.9528, which means that the model can explain 95.28% of the performance variation; the residual normal probability graph shows that the residual is randomly normally distributed, without systematic deviation, proving that the model fitting is reliable.

[0039] Step S53, parameter influence analysis: through variance analysis, it can be known that the P value of width fractal dimension Δ is less than 0.001 (extremely significant), the n value of fin number P is 0.012 (significant), and the P value of length ratio α is 0.385 (not significant); the response surface three-dimensional graph shows that when Δ=1, n increasing from 6 to 10 can make the liquid phase rate increase by 6.2%, while Delta =3, the increase is only 2.1%, which shows that Δ and n have strong interactive effect.

[0040] Step S54, solve optimal parameters: set Y 1 maximize, Y 2 maximize as optimization goal, constraint conditions α ∈[0.6,1.4], Δ∈[1,3], n ∈[6,10], get optimal parameter combination: α =1.4, Δ=1, n =10.

[0041] Step S55, verifying the optimal scheme: Fluent simulation verification is performed on the optimal parameter combination, as shown in (a), (b) and (c) in FIG. 6, the liquid phase rate is 0.92 when the heat storage is 15000 s, and the total heat storage is 366.7 kJ·kg Figure 4 Figure 5 -1 -1 α n Figure 5

[0042]

[0043] Figure 7 As shown in FIG. 6, the present embodiment further provides a parameter optimization system for phase change heat transfer fins, comprising: a model construction module, configured to construct a tree-shaped fractal fin parameterized model based on geometric parameters of a double-pipe phase change heat transfer unit and preselected core optimization parameters; wherein the core optimization parameters comprise a length ratio, a width fractal dimension and a fin number; the length ratio is used to represent a length ratio of two adjacent levels of the bifurcated tree-shaped fractal fins; the width fractal dimension represents a fractal distribution coefficient of the bifurcated width; and the fin number represents a total number of fins uniformly distributed along a circumference of an inner pipe; a sampling module, configured to design multiple groups of parameter sample points for the tree-shaped fractal fin parameterized model by using a Box-Behnken method; each group of parameter sample points corresponds to a group of parameter combinations of the length ratio, the width fractal dimension and the fin number; a simulation module, configured to simulate the double-pipe phase change heat transfer unit geometric model in the tree-shaped fractal fin parameterized model under each group of parameter sample points, and solve a liquid phase rate and a total heat storage to obtain response values; and a solving module, configured to input each group of parameter sample points into a pre-constructed second-order polynomial regression model, maximize the liquid phase rate and the total heat storage as optimization objectives, solve the second-order polynomial regression model, obtain optimal parameter sample points, and output core optimization parameters corresponding to the optimal parameter sample points.

[0044] The present application further provides a parameter optimization device for phase change heat transfer fins, comprising: a memory, configured to store a computer program; and a processor, configured to implement steps of the parameter optimization method for phase change heat transfer fins when the computer program is executed.

[0045] ​​​​​​​​​The processor executes the computer program to realize the steps of optimizing the parameters of the phase change heat transfer fin, for example: constructing a tree-shaped fractal fin parameterized model based on the geometric parameters of the double-pipe phase change heat transfer unit and preselected core optimization parameters; wherein the core optimization parameters include a length ratio, a width fractal dimension, and a fin number; the length ratio is used to represent the length ratio of adjacent two levels of bifurcations of the bifurcated tree-shaped fractal fin; the width fractal dimension represents a fractal distribution coefficient of the bifurcated width; and the fin number represents the total number of fins uniformly distributed along the inner pipe circumference; using a Box-Behnken method to design multiple groups of parameter sample points for the tree-shaped fractal fin parameterized model; each group of parameter sample points corresponds to a group of parameter combinations of the length ratio, the width fractal dimension, and the fin number; simulating the double-pipe phase change heat transfer unit geometric model in the tree-shaped fractal fin parameterized model at each group of parameter sample points to obtain a liquid phase rate and a total heat storage as response values; inputting each group of parameter sample points into a pre-constructed second-order polynomial regression model to maximize the liquid phase rate and the total heat storage as optimization objectives, and solving the second-order polynomial regression model to obtain an optimal parameter sample point, and outputting the core optimization parameters corresponding to the optimal parameter sample point.

[0046] Alternatively, the processor executes the computer program to realize the functions of the modules in the above system, for example: a model construction module, configured to construct a tree-shaped fractal fin parameterized model based on the geometric parameters of the double-pipe phase change heat transfer unit and preselected core optimization parameters; wherein the core optimization parameters include a length ratio, a width fractal dimension, and a fin number; the length ratio is used to represent the length ratio of adjacent two levels of bifurcations of the bifurcated tree-shaped fractal fin; the width fractal dimension represents a fractal distribution coefficient of the bifurcated width; and the fin number represents the total number of fins uniformly distributed along the inner pipe circumference; a sampling module, configured to use a Box-Behnken method to design multiple groups of parameter sample points for the tree-shaped fractal fin parameterized model; each group of parameter sample points corresponds to a group of parameter combinations of the length ratio, the width fractal dimension, and the fin number; a simulation module, configured to simulate the double-pipe phase change heat transfer unit geometric model in the tree-shaped fractal fin parameterized model at each group of parameter sample points to obtain a liquid phase rate and a total heat storage as response values; and a solving module, configured to input each group of parameter sample points into a pre-constructed second-order polynomial regression model to maximize the liquid phase rate and the total heat storage as optimization objectives, and solve the second-order polynomial regression model to obtain an optimal parameter sample point, and output the core optimization parameters corresponding to the optimal parameter sample point.

[0047] Exemplarily, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of completing preset functions, which are used to describe the execution process of the computer program in the phase change heat transfer fin parameter optimization device. For example, the computer program can be divided into a model construction module, a sampling module, a simulation module and a solving module; the specific functions of each module are as follows: the model construction module is used to construct a tree-shaped fractal fin parameterized model based on the geometric parameters of the double-pipe phase change heat transfer unit and the preselected core optimization parameters; wherein the core optimization parameters include a length ratio, a width fractal dimension and a fin number; the length ratio is used to represent the length ratio of two adjacent levels of the bifurcated tree-shaped fractal fin; the width fractal dimension represents the fractal distribution coefficient of the bifurcated width; and the fin number represents the total number of fins uniformly distributed along the inner tube circumference; the sampling module is used to design multiple groups of parameter sample points for the tree-shaped fractal fin parameterized model by using the Box-Behnken method; each group of parameter sample points corresponds to a group of parameter combinations of the length ratio, the width fractal dimension and the fin number; the simulation module is used to simulate the double-pipe phase change heat transfer unit geometric model in the tree-shaped fractal fin parameterized model at each group of parameter sample points, and solve the liquid phase rate and the total heat storage capacity as response values; and the solving module is used to input each group of parameter sample points into the pre-constructed second-order polynomial regression model, maximize the liquid phase rate and the total heat storage capacity as the optimization target, solve the second-order polynomial regression model to obtain the optimal parameter sample point, and output the core optimization parameters corresponding to the optimal parameter sample point.

[0048] The phase change heat transfer fin parameter optimization device can be a desktop computer, a notebook, a palm computer and a cloud server, etc. The phase change heat transfer fin parameter optimization device can include, but is not limited to, a processor, a memory. Those skilled in the art can understand that the above is an example of the phase change heat transfer fin parameter optimization device, and does not constitute a limitation on the phase change heat transfer fin parameter optimization device, and can include more components than the above, or combine certain components, or different components, for example, the phase change heat transfer fin parameter optimization device can also include an input / output device, a network access device, a bus, etc.

[0049] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The processor is the control center of the parameter optimization of the phase change heat dissipation fin, and is connected with various parts of the parameter optimization of the phase change heat dissipation fin through various interfaces and lines.

[0050] The memory can be used to store the computer program and / or modules, and the processor realizes various functions of the parameter optimization of the phase change heat dissipation fin by running or executing the computer program and / or modules stored in the memory and calling the data stored in the memory.

[0051] The memory can mainly include a program storage area and a data storage area. The program storage area can store an operating system, at least one application program required by a function (such as a sound playing function, an image playing function, etc.), etc. The data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory devices.

[0052] The application further provides a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to realize the steps of the parameter optimization method of the phase change heat dissipation fin.

[0053] The modules / units of the parameter optimization system of the phase change heat dissipation fin are stored in a computer readable storage medium if they are realized in the form of software function units and sold or used as independent products.

[0054] Based on such understanding, the application implements all or part of the processes in the phase change heat transfer fin parameter optimization method described above, which can also be completed by instructing related hardware through a computer program. The computer program can be stored in a computer readable storage medium, and the computer program can implement the steps of the phase change heat transfer fin parameter optimization method described above when executed by a processor. The computer program includes computer program codes, which can be in the form of source code, object code, executable files or preset intermediate forms, etc.

[0055] The computer readable storage medium can include any entity or device, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc. that can carry the computer program code.

[0056] It should be noted that the content contained in the computer readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable storage medium does not include electrical carrier signals and telecommunication signals.

[0057] Therefore, the application provides a phase change heat transfer fin parameter optimization method, which has the following advantages compared with the existing optimization method. First, the optimization efficiency is significantly improved: the Box-Behnken design only needs 13 sample points to cover the interaction effect of 3 parameters and 3 levels, which reduces the simulation amount by 51.9% compared with the full factor design (27 groups), and greatly reduces the optimization cost. Second, the parameter relationship is quantified accurately: the nonlinear relationship between fin parameters and heat storage performance is visualized through a second-order response surface model, and it is clear that the width fractal dimension is the key control parameter (the influence weight ratio is ≥40%), which solves the problem of fuzzy parameter influence in traditional methods. Third, the heat storage performance is comprehensively optimized: the optimal parameter combination makes the liquid phase rate of the tree-shaped fin in the middle of the heat storage period increase by 3.3% compared with the rectangular fin, and the total heat release in the heat release stage increases by 25%, which coordinates the contradiction between heat conduction strengthening and natural convection suppression. Fourth, strong engineering applicability: the optimization results can directly guide the processing and manufacturing of phase change heat transfer units, and the method can be extended to tree-shaped fins of other bifurcation orders, providing a theoretical basis for the design of phase change heat storage systems for solar heating, industrial waste heat recovery and other scenes.

[0058] The above embodiment is only one of the implementation manners of the technical scheme of the present application, and the scope of the present application is not limited to the above embodiment, but also includes any changes, substitutions and other implementation manners that are easily thought of by those skilled in the art within the technical scope disclosed by the present application.

Claims

1. A method for parameter optimization of a phase change heat transfer fin, characterized in that, The application relates to a method for optimizing the geometric parameters of a double-pipe phase change heat transfer unit. The method comprises the following steps: constructing a tree-shaped fractal fin parameterized model based on the geometric parameters of the double-pipe phase change heat transfer unit and preselected core optimization parameters; wherein the core optimization parameters include a length ratio, a width fractal dimension and a fin number; the length ratio is used to represent the length ratio of two adjacent levels of the bifurcated tree-shaped fractal fin; the width fractal dimension represents the fractal distribution coefficient of the bifurcated width; and the fin number represents the total number of fins uniformly distributed along the inner pipe circumference; designing multiple groups of parameter sample points for the tree-shaped fractal fin parameterized model by using a Box-Behnken method; each group of parameter sample points corresponds to a group of parameter combinations of the length ratio, the width fractal dimension and the fin number; simulating the double-pipe phase change heat transfer unit geometric model in the tree-shaped fractal fin parameterized model under each group of parameter sample points, and solving the liquid phase rate and the total heat storage capacity as response values; inputting each group of parameter sample points into a pre-constructed second-order polynomial regression model, taking the maximum liquid phase rate and the maximum total heat storage capacity as optimization objectives, solving the second-order polynomial regression model, obtaining the optimal parameter sample point, and outputting the core optimization parameters corresponding to the optimal parameter sample point. The geometric parameters of the double-pipe phase change heat transfer unit include the inner diameter and thickness of the inner pipe, the inner diameter and thickness of the outer pipe, and the material and bifurcation size of the bifurcated tree-shaped fractal fin. In the tree-shaped fractal fin parameterized model, the width of the bifurcated tree-shaped fractal fin satisfies the following relationship: The method for designing multiple groups of parameter sample points for the tree-shaped fractal fin parameterized model by using the Box-Behnken method comprises the following steps: taking the length ratio, the width fractal dimension and the fin number as independent variables, and taking the liquid phase rate and the total heat storage capacity at the preset heat storage time as response values, an experimental matrix is constructed by using the Box-Behnken design; the three levels of each core optimization parameter in the experimental matrix are set to generate multiple groups of parameter sample points.

2. The method according to claim 1, wherein The simulation of the double-pipe phase change heat transfer unit geometric model in the tree-shaped fractal fin parameterized model under each group of parameter sample points to obtain the liquid phase rate and the total heat storage capacity as response values comprises the following steps: performing quadrilateral-dominated unstructured grid division on the double-pipe phase change heat transfer unit geometric model in the tree-shaped fractal fin parameterized model, and performing grid encryption on the fin bifurcation region; setting the phase change material and the boundary conditions; the setting process of the boundary conditions comprises the following steps: the inner pipe wall is set as an isothermal boundary, the outer pipe wall is set as an adiabatic boundary, and the solid-liquid interface is set as a coupled heat transfer boundary; the setting process of the phase change material comprises the following steps: the specific material, the solid density, the liquid density and the phase change latent heat of the phase change material are set; simulating the double-pipe phase change heat transfer unit geometric model after the unstructured grid division and the grid encryption under each group of parameter sample points; solving the solid-liquid phase change heat transfer equation based on the enthalpy-porosity method to obtain the liquid phase rate and the total heat storage capacity, and outputting the liquid phase rate and the total heat storage capacity as response values.

3. The method according to claim 1, wherein The method for optimizing the geometric parameters of a double-pipe phase change heat transfer unit. In the formula, represents the width of the bifurcated tree-shaped fractal fin; represents the root width; represents the width fractal dimension, and takes a value of 1-3; k represents the bifurcation number.

4. The method according to claim 1, wherein ​ ​ ​ 5. The method according to claim 1, wherein ​ ​ ​ ​ ​ 6. The method according to claim 1, wherein The parameter sample points are input into the pre-constructed second-order polynomial regression model respectively, and the second-order polynomial regression model is solved to obtain the optimal parameter sample points, and the core optimization parameters corresponding to the optimal parameter sample points are output, including: The parameter sample points are fitted, and a second-order polynomial regression model of the response value and the independent variable is established, the independent variable being a parameter combination of the length ratio, the width fractal dimension and the fin number; the specific equation of the second-order polynomial regression model is as follows: wherein, β 0 is a constant term, β 1 β 3 is a coefficient of a linear term, β 11 β 33 is a coefficient of a quadratic term, β 12 β 23 is a coefficient of an interaction term; is a response value; α is a length ratio, Δ is a width fractal dimension, n is a number of fins;​​ The second-order polynomial regression model is solved according to the simulation data to obtain the regression coefficients; The liquid phase rate maximization and the total heat storage maximization are set as optimization objectives, and the optimal parameter sample points are solved by combining the regression coefficients, and the core optimization parameters corresponding to the optimal parameter sample points are output.

7. The method according to claim 1, wherein After the parameter sample points are input into the pre-constructed second-order polynomial regression model respectively, and the second-order polynomial regression model is solved to obtain the optimal parameter sample points, and the core optimization parameters corresponding to the optimal parameter sample points are output, the method further includes: The core optimization parameters are simulated, the simulation results are compared with the regression model prediction values, and the core optimization parameters are evaluated according to the comparison results.

8. A phase change heat fin parameter optimization system, characterized in that, Including: The model construction module is used to construct a tree-shaped fractal fin parameterized model based on the geometric parameters of the double-pipe phase change heat transfer unit and the preselected core optimization parameters; wherein the core optimization parameters include a length ratio, a width fractal dimension and a fin number; the length ratio is used to represent the length ratio of adjacent two levels of bifurcations of the bifurcated tree-shaped fractal fin; the width fractal dimension represents a fractal distribution coefficient of the bifurcated width; and the fin number represents the total number of fins uniformly distributed along the inner tube circumference; The sampling module is used to design multiple groups of parameter sample points for the tree-shaped fractal fin parameterized model by using the Box-Behnken method; each group of parameter sample points corresponds to a group of parameter combinations of the length ratio, the width fractal dimension and the fin number; The simulation module is used to simulate the double-pipe phase change heat transfer unit geometric model in the tree-shaped fractal fin parameterized model under each group of parameter sample points, and solve to obtain the liquid phase rate and the total heat storage as response values; The solving module is used to input each group of parameter sample points into the pre-constructed second-order polynomial regression model, maximize the liquid phase rate and the total heat storage as optimization objectives, solve the second-order polynomial regression model, obtain the optimal parameter sample points, and output the core optimization parameters corresponding to the optimal parameter sample points.

9. A phase change heat exchanger fin parameter optimization device, characterized in that, Including: The memory is used to store a computer program; The processor is used to execute the computer program to realize the steps of the phase change heat transfer fin parameter optimization method in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1-9. The computer program is executed by the processor to realize the steps of the phase change heat transfer fin parameter optimization method in any one of claims 1-7.

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