Viscoelastic fluid heat flow coupling topological optimization method for micro-channel system design

Abstract

The invention discloses a viscoelastic fluid heat flow coupling topological optimization method for micro-channel system design. The method comprises the following steps: firstly, carrying out pretreatment, defining a viscoelastic fluid constitutive model, defining material attributes and carrying out finite element analysis; then constructing boundary conditions of the heat flow coupling topological optimization model; constructing a viscoelastic fluid heat flow coupling topological optimization mathematical description model, then constructing an optimization model, and finally verifying the topological optimization design result of the viscoelastic fluid system. According to the method, the heat flow coupling calculation of the viscoelastic fluid is carried out through the logarithmic tensor reconstruction method, and a relatively accurate analysis result can be obtained under a high Vesceng number; and in combination with a variable density topological optimization method with high design degree of freedom, a new configuration of a micro-channel system structure is obtained, and a new solution idea is provided for heat dissipation of a micro electronic component with high heat flux.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of micro-channel system structure optimization design, and particularly relates to a viscoelastic fluid heat flow coupling topology optimization method for micro-channel system design. TECHNICAL BACKGROUND

[0002] With the rapid development of electronic information technology, electronic components (such as CPU, GPU, and laser diode) are developing towards higher power density and smaller size. The heat flux density continues to rise, and the heat dissipation problem has become a key bottleneck restricting the performance, reliability and service life. Traditional air cooling, heat pipe, and vapor chamber heat dissipation methods gradually reach the performance limit in high heat flux density scenarios. Micro-channel systems, with their compact structure and high heat exchange capacity, have become a promising solution to solve the heat dissipation problem of the next generation of high-power electronic devices. However, Newtonian fluid heat dissipation working fluids, such as water, are limited by the scale effect of micro-channels and are in laminar flow in micro-channels. The internal layers of the fluid only rely on heat conduction to transfer heat, resulting in low heat exchange efficiency of the micro-channel system. How to enhance the heat exchange capacity of the micro-channel system has become a difficult problem.

[0003] In micro-channels, due to the compression and stretching of high molecular polymers in non-Newtonian viscoelastic fluids, viscoelastic fluids will produce elastic instability turbulence at low Reynolds numbers. This elastic turbulence will cause the mixing of internal layers of the fluid, providing great potential for improving heat exchange capacity. Compared with Newtonian fluids, the local heat transfer efficiency of viscoelastic fluids in elastic turbulent flow state can be improved by three times at the same Reynolds number.

[0004] Topology optimization methods establish mathematical models for heat flow coupling problems. Through the establishment of iterative formulas of optimal criteria conditions, the mathematical problems are solved by finite element method or finite volume method to obtain the optimal material layout. Topology optimization methods have been widely used in micro-channel system design. For example, the patent application entitled "Heat sink structure design method combining topology optimization and shape optimization" (publication number CN112966420A) is applied in micro-channel system design with Newtonian fluid as working fluid. However, there is still a lack of heat flow coupling topology optimization methods for viscoelastic fluid micro-channel system design.

[0005] In summary, there is an urgent need for a design method that can perform viscoelastic fluid micro-channel system topology optimization design. Topology optimization has high design freedom. The combination of viscoelastic fluid and micro-channel system topology optimization design can provide a new and effective solution for the optimization design of micro-channel system structure. SUMMARY

[0006] In order to overcome the above-mentioned prior art defects, the purpose of the present application is to provide a viscoelastic fluid heat flow coupling topology optimization method for micro-channel system design, establish a viscoelastic fluid heat flow coupling mathematical model, and apply a topology optimization method to optimize the structure of the viscoelastic fluid micro-channel system at the micro scale.

[0007] In order to achieve the above-mentioned purpose, the technical scheme adopted by the present application is:

[0008] A viscoelastic fluid heat flow coupling topology optimization method for micro-channel system design, first establishes the control equation of the heat flow coupling viscoelastic fluid, and solves it by using the logarithmic tensor reconstruction method; then establishes an interpolation model of the topology optimization problem based on the variable density method, and then uses the Helmholtz filter to perform density filtering, and finally optimizes the model by using the moving asymptote method.

[0009] A viscoelastic fluid heat flow coupling topology optimization method for micro-channel system design, comprising the following steps:

[0010] 1) Preprocessing:

[0011] 1.1) Define the viscoelastic fluid constitutive model: according to specific needs, use Oldroyd-B model, PPT model, power law model or Giesekus model;

[0012] 1.2) Define material properties: define density, solvent viscosity, solute viscosity, relaxation time, thermal conductivity, constant pressure heat capacity and specific heat rate according to material property definition;

[0013] 1.3) Finite element analysis: define the initial fluid design domain, and divide the initial fluid design domain into grids;

[0014] 2) Build the boundary conditions of the heat flow coupling topology optimization model:

[0015] 2.1) Define the thermal boundary conditions and the flow field boundary conditions: set the initial value and boundary conditions of the physical field according to the design requirements;

[0016] 2.2) Define the finite element analysis boundary conditions: set the maximum number of iterations N max , set the maximum allowed error ε0 for optimization termination, and set the upper limit of the material volume fraction V f ;

[0017] 3) Build the mathematical description model of viscoelastic fluid heat flow coupling topology optimization:

[0018] 3.1) Establish the control equation of the heat flow coupling viscoelastic fluid: for viscoelastic fluid flow, the control equation is defined as:

[0019]

[0020] where u, p, t, p, h s , T, k, c, Q are velocity, density, time, pressure, solvent viscosity, elastic stress, temperature, thermal conductivity, specific heat capacity, heat source; F is body force, which represents the resistance of porous media to fluid;

[0021] The elastic stress τ needs to be expressed according to the constitutive equation of viscoelastic fluid properties, and when the Oldroyd-B model is used, the elastic stress τ satisfies:

[0022]

[0023] where h p is the solute viscosity, is the stress tensor Oldroyd inverse differential, which is expressed as:

[0024]

[0025] and the constitutive equation of Oldroyd-B model is:

[0026]

[0027] where C is the high polymer molecular deformation rate tensor, I is the unit tensor, and l is the relaxation time of viscoelastic fluid;

[0028] The logarithmic tensor reconstruction method is used to solve the constitutive equation, and the natural logarithm Θ of the deformation rate tensor is defined as:

[0029] Θ = ln(C) = R ln(Λ) R T

[0030] where R is the matrix composed of the characteristic vectors of C, and A is the diagonal matrix composed of the eigenvalues of C;

[0031] The momentum equation is rewritten as:

[0032]

[0033] where u, p, t, p, h s , h p , l, are velocity, density, time, pressure, solvent viscosity, solute viscosity, relaxation time of viscoelastic fluid; C is the high polymer molecular deformation rate tensor; F is the body force, which represents the resistance of porous media to fluid;

[0034] The constitutive equation is rewritten as:

[0035]

[0036] where Ω is the anti-symmetric tensor, B is the diagonal tensor, Θ is the natural logarithm of the deformation rate tensor;

[0037]

[0038] where ω ij and m ij is expressed as:

[0039]

[0040] 3.2) Establish interpolation model of topology optimization problem based on variable density method: In variable density method, each element in fluid design domain is described by pseudo-density value, and pseudo-density value θ of each element takes any value in 0-1 interval, where 0 represents that the material of the element is solid, and 1 represents that the material of the element is fluid;

[0041] In the fluid design domain, the material is assumed to be porous medium material, and the volume force F in the domain is proportional to the velocity u, that is:

[0042] F = -αu

[0043] In the formula, α is the permeability, when θ = 0, α → ∞, F → ∞, which represents that the material is solid; when θ = 1, α → 0, F → 0, which represents that the material is liquid; α is interpolated through Darcy model:

[0044]

[0045] In the formula, q is a penalty factor, and α max is related to Darcy number Da and Reynolds number Re, and the relationship is as follows:

[0046]

[0047] Darcy number Da needs to be a very small number to ensure that α max is large enough;

[0048] The momentum equation is re-described as:

[0049]

[0050] 3.3) Filtering and projection: density filtering is performed using a Helmholtz filter, and the density filtering expression is:

[0051]

[0052] In the formula, r is the filtering radius, which needs to be greater than the grid edge size and is automatically controlled by the program, and θ c is the original control variable corrected by the optimization program, and θ fThese are the filtered variables; after Helmholtz filtering, the clear microchannel topology is obtained through the hyperbolic tangent projection function, and the projection expression is as follows:

[0053]

[0054] In the formula, θ is the projected design variable. β Let β be the projection point, and β be the projection slope.

[0055] 4) Constructing the optimization model: Optimization is performed using the moving asymptote method. Based on the relationship between the design variables, objective function, and constraint functions, the sensitivities of the objective function and constraint functions to the design variables are defined. The objective function, constraint functions, and their sensitivities are then fed into the moving asymptote optimizer to achieve optimization convergence and iterative updates of the design variables. When the number of iterations reaches the maximum number of iterations N... max When the objective function changes below the set maximum permissible error ε0, the optimization terminates and the optimized design results are output.

[0056] Find θ = [θ1, θ2, θ3, ..., θ N ]

[0057] MinΦ=Φ(θ)

[0058] stε(θ,s(θ))≤ε0,

[0059] G1(θ)=∫θdV-V f V0≤0,

[0060] G i (θ,s(θ))≤0

[0061] In the formula, s is the state variable of the corresponding physical problem, ε is the residual vector, ε0 is the maximum permissible error, G1 is the volume constraint of the material, and V f V0 is the upper limit of the set material volume fraction, and G is the set total volume of the design domain. i For other constraint functions;

[0062] 5) Verification of topology optimization design results of viscoelastic fluid system: The optimized design configuration obtained by the heat-fluid coupling topology optimization of viscoelastic fluid is simulated to verify its heat transfer performance.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] The present application realizes the topological optimization problem for viscoelastic fluid, and through the heat-flow coupling calculation of the tensor reconstruction method for viscoelastic fluid, relatively accurate analysis results can be obtained under high-dimensional Senbeger number; in combination with the variable-density topological optimization method with high design freedom, a new structure of the micro-channel system is obtained, which provides a new solution for the heat dissipation of high heat-flow-density micro electronic components, and provides a set of efficient and feasible scheme for the optimization design of the viscoelastic fluid micro-channel system. BRIEF DESCRIPTION OF DRAWINGS

[0065] Figure 1 The flow schematic diagram of the embodiment of the present application is shown.

[0066] Figure 2 The boundary condition schematic diagram of the embodiment of the present application is shown.

[0067] Figure 3 The optimization iteration schematic diagram of the embodiment of the present application is shown.

[0068] Figure 4 The finite element verification result of the embodiment of the present application is shown. DETAILED DESCRIPTION

[0069] The present application is further described below in combination with embodiments and drawings.

[0070] Reference Figure 1 A viscoelastic fluid heat-flow coupling topological optimization method for micro-channel system design, comprising the following steps:

[0071] 1) preprocessing:

[0072] 1.1) defining the viscoelastic fluid constitutive model: according to specific needs, Oldroyd-B model, PPT model, power law model or Giesekus model, etc. are adopted, and in the present embodiment, Oldroyd-B viscoelastic fluid model is selected;

[0073] 1.2) defining material properties: defining density, solvent viscosity, solute viscosity, relaxation time, thermal conductivity, constant pressure heat capacity, and specific heat rate according to material properties;

[0074] 1.3) finite element analysis: defining the initial fluid design domain, and dividing the grid for the initial fluid design domain;

[0075] In the present embodiment, the commercial software COMSOL is adopted to perform the topological optimization design of viscoelastic fluid;

[0076] 2) constructing the boundary conditions of the heat-flow coupling topological optimization model:

[0077] 2.1) defining the thermal boundary conditions and the flow field boundary conditions: setting the initial value and the boundary conditions of the physical field according to the design requirements;

[0078] 2.2) Define the finite element analysis boundary conditions: set the maximum number of iterations N max , set the maximum allowable error ε0 of optimization termination, set the upper limit of material volume fraction V f ;

[0079] The structure of the initial fluid design domain in this embodiment is a square fluid domain, as shown in Figure 2 , the size of the design domain is LxL, the size of the inlet and outlet and the flow development section is 0.2Lx0.2L, the velocity U in is 0.001L, L=1m; the inlet temperature T in =293.15K, the outlet pressure p out =0Pa, the heat source Q=1x10 6 W / m 2 , the wall is adiabatic; the unit adopts a square grid, and the number of grids is 10800; the material properties of the viscoelastic fluid are that the density ρ is 1x10 3 kg / m 3 , the constant pressure heat capacity c=4.2x10 3 J / kg·m 3 , the thermal conductivity k=0.59W / (m·K), the solvent viscosity η s =1.01mPa·s, the solute viscosity η p =12.10mPa·s, the relaxation time λ=0.25s, and the specific heat rate is 1;

[0080] 3) Construct the mathematical description model of viscoelastic fluid heat flow coupling topology optimization:

[0081] 3.1) Establish the control equation of heat flow coupling viscoelastic fluid: for incompressible viscoelastic fluid flow, the control equation is defined as:

[0082]

[0083] In the formula, u, ρ, t, p, η s , τ, T, k, c, Q are velocity, density, time, pressure, solvent viscosity, elastic stress, temperature, thermal conductivity, specific heat capacity, heat source; F is the volume force, which represents the resistance of the porous medium to the fluid;

[0084] The elastic stress τ needs to be expressed according to the properties of the viscoelastic fluid by selecting the corresponding constitutive equation, when the Oldroyd-B model is adopted, the elastic stress τ satisfies:

[0085]

[0086] Where η p is the solute viscosity, The inverse differential of the stress tensor Oldroyd type is expressed as:

[0087]

[0088] The constitutive equation of Oldroyd-B model is:

[0089]

[0090] Where C is the deformation rate tensor of polymer molecules, I is the unit tensor, and λ is the relaxation time of viscoelastic fluid.

[0091] To solve the problem of high-dimensional Seiberg number, the logarithmic tensor reconstruction method is used to solve the constitutive equation. The natural logarithm Θ of the deformation rate tensor is defined as:

[0092] Θ = ln(C) = R ln(Λ)R T

[0093] Where R is the matrix composed of the characteristic vectors of C, and Λ is the diagonal matrix composed of the characteristic values of C.

[0094] The momentum equation is rewritten as:

[0095]

[0096] In the formula, u, ρ, t, p, η s , η p , λ are velocity, density, time, pressure, solvent viscosity, solute viscosity, and relaxation time of viscoelastic fluid; C is the deformation rate tensor of polymer molecules, and F is the volume force, indicating the resistance of porous media to fluid.

[0097] The constitutive equation is rewritten as:

[0098]

[0099] Where Ω is the skew-symmetric tensor, B is the diagonal tensor, Θ is the natural logarithm of the deformation rate tensor, and Λ is the diagonal matrix composed of the characteristic values of C.

[0100]

[0101] Where ω ij and m ij are expressed as:

[0102]

[0103] Step 3.1) In the viscoelastic thermal flow coupling topology optimization problem, the logarithmic tensor reconstruction method is introduced to solve the elastic stress, which effectively suppresses the numerical divergence under high-dimensional Seiberg number, improves the numerical stability in the viscoelastic fluid calculation and topology optimization process, and ensures that the optimization target has a solution;

[0104] 3.2) An interpolation model of the topology optimization problem based on the variable density method is established: in the variable density method, each element in the fluid design domain is described by a pseudo-density value, and the pseudo-density value θ of each element takes any value in the interval 0-1, where 0 represents that the element material is solid, and 1 represents that the element material is fluid;

[0105] In the fluid design domain, the material is assumed to be a porous medium material, and the body force F in the domain is proportional to the velocity u, that is:

[0106] F=-αu

[0107] In the formula, α is the permeability, when θ=0, α→∞, F→∞, which represents that the material is solid; when θ=1, α→0, F→0, which represents that the material is liquid; α is interpolated through the Darcy model:

[0108]

[0109] In the formula, q is a penalty factor, and α max is related to the Darcy number Da and the Reynolds number Re, and the relationship is as follows:

[0110]

[0111] The Darcy number Da needs to be a very small number to ensure that α max is large enough; the Reynolds number Re of this embodiment is 15, and the Darcy number Da is 1e-8, so that the resistance of the porous medium to the fluid is large enough; the initial value of the optimization variable pseudo-density θ0 is 1; the penalty factor q is 0.01;

[0112] The momentum equation is rewritten as:

[0113]

[0114] In the formula, u, ρ, t, p, η s , η p , λ are velocity, density, time, pressure, solvent viscosity, solute viscosity, and viscoelastic fluid relaxation time; C is the deformation rate tensor of the polymer molecule;

[0115] Step 3.2) A topology optimization model based on the variable density method is established in the viscoelastic thermal flow coupling topology optimization problem, and the relationship between the solid region and the fluid region and the resistance to the fluid are established through the Darcy interpolation model, so as to establish a material optimization model for topology optimization;

[0116] 3.3) Filtering and projection: In the topology optimization problem, in order to avoid grid dependence and checkerboard phenomenon, it is necessary to add density filtering, and in the flow-thermal coupled topology optimization problem, density filtering can effectively avoid the ill-posedness of the optimization problem; using Helmholtz filter for density filtering, the density filtering expression is:

[0117]

[0118] In the formula, r is the filtering radius, which needs to be greater than the grid edge size, which is automatically controlled by the program, θ c is the original control variable after optimization program correction, θ f is the variable after filtering; after Helmholtz filtering, the design domain will produce obvious gray scale, which is not conducive to obtaining clear boundary, through the hyperbolic tangent projection function, clear microchannel topology morphology is obtained, and the projection expression is as follows:

[0119]

[0120] In the formula, θ is the design variable after projection, θ β is the projection point, θ β is 0.5, and β is the projection slope, which is 6;

[0121] Step 3.3) In the viscoelastic thermal flow coupled topology optimization problem, Helmholtz filter and hyperbolic tangent projection function are introduced, which effectively avoids the appearance of grid dependence and checkerboard phenomenon in variable density method topology optimization, and clear microchannel topology optimization morphology is obtained;

[0122] 4) Construction of optimization model: the moving asymptote method is used as the optimization method, according to the relationship expression of design variable and objective function, constraint function, the sensitivity of objective function and constraint function to design variable is defined, the objective function, constraint function and its sensitivity are brought into the moving asymptote optimizer, the optimization convergence and iterative update of design variable are realized, when the iteration step number reaches the maximum iteration number N max , or the change of objective function is less than the maximum allowed error ε0, the optimization is terminated and the optimization design result is output;

[0123] Find θ = [θ1, θ2, θ3,..., θ N ]

[0124] Min Φ = Φ(θ)

[0125] s.t. ε(θ, s(θ)) ≤ ε0,

[0126] G1(θ) = ∫θ dV - V f V0≤0,

[0127] G i(θ, s(θ)) < 0

[0128] where s is the state variable of the corresponding physical problem, ε is the residual vector, ε0 is the maximum allowable error, G1 is the volume constraint of the material, V f is the upper limit of the set material volume fraction, V0 is the set design domain volume, G i is the other constraint function;

[0129] The maximum number of iterations N max is taken as 100, and the maximum allowable error ε0 for optimization termination is initialized as 1e -9 -4; the movement limit is set as 0.1; the optimization objective function is the minimization of the average temperature of the design domain, and the constraint is set as the upper limit of the material volume fraction V f between 0.5-0.6, and the pressure drop constraint Δp = 1 Pa; the optimization process is as shown in Figure 3 ;

[0130] 5) Verification of the topological optimization design results of the viscoelastic fluid system: using OpenFOAM, the microchannel system obtained by the topological optimization design of the viscoelastic fluid heat flow coupling is scaled according to the same Reynolds number Re and the flow conditions of the Visenbeerg number Wi, and the corresponding boundary conditions are set for simulation analysis to verify the flow performance and heat dissipation performance; the simulation results are as shown in Figure 4 ; the results show that when the inlet velocity is set as 1 m / s and the heat source is set as Q = 1 × 10 6 W / m 2 , the maximum temperature of the microchannel system is 293.96 K, which is only less than 1 K higher than the room temperature of 293 K, indicating that the design results have good heat dissipation performance.

Claims

1. A viscoelastic fluid heat flow coupling topology optimization method for microchannel system design, characterized in that: First, the governing equations of the heat-fluid coupled viscoelastic fluid are established, and the logarithmic tensor reconstruction method is used for solving; then, the interpolation model of the topology optimization problem based on the variable density method is established, and the density filtering is performed using the Helmholtz filter, and finally the model is optimized by the moving asymptote method.

2. The method of claim 1, wherein, The method comprises the following steps: 3) constructing a mathematical description model of heat-fluid coupled topology optimization of viscoelastic fluid: 3.1) establishing the governing equations of the heat-fluid coupled viscoelastic fluid; 3.2) establishing the interpolation model of the topology optimization problem based on the variable density method: in the variable density method, each element in the fluid design domain is described by a pseudo-density value, and the pseudo-density value θ of each element takes any value in the interval of 0-1, wherein 0 represents that the material of the element is solid, and 1 represents that the material of the element is fluid; In the fluid design domain, the material is assumed to be a porous medium material, and the volume force F in the domain is proportional to the velocity u, that is: F=-αu In the formula, α is the permeability, when θ=0, α→∞, F→∞, which indicates that the material is solid; when θ=1, α→0, F→0, which indicates that the material is liquid; α is interpolated through the Darcy model: where q is a penalty factor, a max In relation to the Darcy number Da and the Reynolds number Re, the following applies: The Darcy number Da must be a small number to ensure that a max is sufficiently large; The momentum equation is re-described as: 3.3) filtering and projection: the density filtering is performed using the Helmholtz filter, and the density filtering expression is: where r is the filter radius, which needs to be larger than the mesh edge size, and θ c is the original control variable, which is modified by the optimization program f is the variable after filtering; after the Helmholtz filter, the clear microchannel topological morphology is obtained by the hyperbolic tangent projection function, and the projection expression is as follows: In the formula, θ is a design variable after projection, θ β is a projection point, and β is a projection slope. 4) Constructing optimization model: through moving asymptote method for optimization, according to the relationship of design variables and objective function, constraint function, defining the sensitivity of objective function, constraint function to design variables, bringing objective function, constraint function and their sensitivity into moving asymptote optimizer, realizing optimization convergence and iterative update of design variables, when the iteration step reaches the maximum iteration number N max , or the change of objective function is lower than the maximum allowed error ε0, the optimization is terminated and the optimization design result is output. Find θ = [θ1, θ2, θ3,..., θ N ] MinΦ=Φ(θ) s.t.ε(θ,s(θ))≤ε0, G1(θ) = ∫θ dV - V f V0≤ 0, G i (θ, s(θ)) ≤ 0 where s is the state variable of the corresponding physical problem, ε is the residual vector, ε0 is the maximum allowed error, G1 is the volume constraint of the material, V f is the set upper limit of the material volume fraction, V0 is the set volume of the design domain, G i is the other constraint function; 5) verification of the topology optimization design result of the viscoelastic fluid system: the optimized design configuration obtained by the heat-fluid coupled topology optimization of the viscoelastic fluid is simulated to verify the heat exchange performance.

3. The method of claim 2, wherein, Step 3.1) establishes the governing equations of the heat-fluid coupled viscoelastic fluid, specifically: for the viscoelastic fluid flow, the governing equation is defined as: where u, p, t, p, h s , T, k, c, Q are velocity, density, time, pressure, solvent viscosity, elastic stress, temperature, thermal conductivity, specific heat capacity, heat source; F is the volume force, indicating the resistance of the porous medium to the fluid; The elastic stress τ needs to be expressed according to the properties of the viscoelastic fluid by selecting the corresponding constitutive equation, when the Oldroyd-B model is adopted, the elastic stress τ satisfies: where η p is the solute viscosity, is the stress tensor Oldroyd type inverse derivative, expressed as: And the constitutive equation of the Oldroyd-B model is: Wherein C is the deformation rate tensor of the polymer molecule, I is the unit tensor, and λ is the relaxation time of the viscoelastic fluid; The logarithmic tensor reconstruction method is used for solving the constitutive equation, and the natural logarithm Θ of the deformation rate tensor is defined as: Θ = ln(C) = R ln(A) R T Wherein R is a matrix composed of the characteristic vectors of C, and Λ is a diagonal matrix composed of the characteristic values of C; The momentum equation is re-described as: wherein η p , λ is the solute viscosity, the viscoelastic fluid relaxation time; The constitutive equation is re-described as: Wherein Ω is an antisymmetric tensor, B is a diagonal tensor, and Θ is the natural logarithm of the deformation rate tensor; where ω ij and m ij is expressed as:

4. The method of claim 2, wherein, It comprises: 1) preprocessing: 1.1) defining the viscoelastic fluid constitutive model: according to specific needs, the Oldroyd-B model, PPT model, power law model or Giesekus model is adopted; 1.2) defining the material properties: the density, solvent viscosity, solute viscosity, relaxation time, thermal conductivity, constant pressure heat capacity and specific heat rate are defined according to the material properties; 1.3) finite element analysis: defining the initial fluid design domain and dividing the grid; 2) constructing the boundary conditions of the heat-fluid coupled topology optimization model: 2.1) defining the thermal boundary conditions and the flow field boundary conditions: setting the initial value and the boundary conditions of the physical field according to the design requirements; 2.2) Define the boundary conditions for the finite element analysis: set the maximum number of iterations N max , set the maximum allowed error ε0for the termination of the optimization, set the upper limit for the material volume fraction V f .

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