Topological optimization design method for double-fluid heat exchanger structure based on multi-material model

Abstract

The invention relates to a topological optimization design method for a two-fluid heat exchanger structure based on a multi-material model. The method comprises the following steps: defining a topological optimization design domain and design variables of a heat exchanger, wherein the topological optimization design domain comprises hot fluid and cold fluid flowing areas; with maximization of heat exchange amount in a design domain and minimization of inlet and outlet pressure drop as optimization objectives, an objective function is established, constraint conditions are set, and a topological optimization model is constructed; respectively solving control equations of flow problems of the cold fluid and the hot fluid, solving corresponding topological optimization models, and obtaining corresponding velocity fields; solving a heat transfer problem by using a heat conduction equation according to a combined velocity field obtained by superposing the two velocity fields; sensitivity analysis is carried out according to the solution of the heat transfer problem, and design variables are updated through a topological optimization algorithm according to a sensitivity analysis result; and iteratively updating the design variable for multiple times until the objective function is converged and the preset design requirement is met. The problem of heat exchanger structure optimization in different scenes is efficiently solved.

Description

Technical Field

[0001] The invention relates to the technical field of heat exchanger structure design, in particular to a dual-fluid heat exchanger structure topology optimization design method based on a multi-material model. Background Art

[0002] Compact heat exchangers are widely used in the aviation and automotive industries, especially in aviation and aerospace applications, where finned plate type heat exchangers become the most common choice due to the strict requirements on performance and space. Brazed fins can easily achieve specific designs and flexibly adjust various characteristics according to the needs of the final application. Crossflow plate heat exchangers are particularly popular in the aerospace industry due to their compactness, high performance and easy system integration. New design tools and manufacturing methods are still being explored to address the existing limitations of these heat exchangers and achieve extremely compact and lightweight components while improving thermal efficiency. The design details of many heat exchangers are often not driven by performance requirements, but by the limitations of the manufacturing process. Therefore, a deep understanding of the impact of parameters such as fin pitch, fin height and fin thickness is crucial to achieving high-performance, lightweight and repeatable heat exchangers.

[0003] The study of the correlation between fin shape and thermal efficiency provides a theoretical basis for the design of new heat exchangers. Traditionally, fins are usually manufactured by metal sheet forming or bending processes, and these geometries must be able to be easily connected to the final component. However, this manufacturing method limits the combination of geometries that can be used to develop new, efficient heat exchangers. With the advancement of modern industrial technology, more and more limitations have been overcome, especially the application of topology optimization in the design stage and the use of additive manufacturing technology in the manufacturing stage. The combination of these two emerging technologies not only expands the design space of heat exchangers and increases the possible geometric forms, but also significantly improves the performance of heat exchangers under certain design constraints, which is especially important in high-demand applications such as aerospace.

[0004] Topology optimization is a structural optimization method that automatically generates the optimal material distribution or structural shape that meets specific performance requirements through mathematical models and simulation calculations. It does not rely on traditional design solutions, but explores and designs the best structural layout through the optimization of the objective function. Especially in complex engineering problems, topology optimization can help designers discover and realize innovative designs that are difficult to think of with traditional methods.

[0005] In heat exchanger design, topology optimization is widely used to optimize fluid flow paths, improve heat exchange efficiency, and reduce structural weight. By topologically optimizing the internal flow channels and fin shapes of heat exchangers, a more efficient heat exchange structure can be generated to provide better thermal performance in a limited space. However, the existing heat exchanger structure topology optimization methods are difficult to efficiently adapt to the optimization needs under complex working conditions due to their single goals, ignoring multi-physical field coupling and actual manufacturing constraints, and low computational efficiency. They cannot efficiently solve the heat exchanger structure optimization problems in different scenarios. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention provides a dual-fluid heat exchanger structural topology optimization design method based on a multi-material model, aiming to efficiently solve the heat exchanger structure optimization problem in different scenarios.

[0007] The technical solution adopted by the present invention is as follows:

[0008] The present invention provides a dual-fluid heat exchanger structural topology optimization design method based on a multi-material model, comprising:

[0009] S1. Define the topology optimization design domain of the heat exchanger including the hot fluid and cold fluid flow areas, and define the topology optimization design variables γ1 and γ2; where γ is a 0-1 binary function about the position x in the design domain. When γ(x) = 0, it means that the material property of the position x in the design domain is solid, and γ(x) = 1, it means that the material property of the position x in the design domain is fluid; subscripts 1 and 2 represent the cold fluid and the hot fluid, respectively;

[0010] S2. Taking maximizing the heat exchange rate in the design domain and minimizing the inlet and outlet pressure drops as the optimization goals, establishing an objective function and setting constraints to construct a topology optimization model;

[0011] The objective function is obtained through dimensionless and weighted processing, and the expression is:

[0012]

[0013] Among them, J th is the heat transfer function value, For J th Reference value of J pr is the inlet and outlet pressure drop function value, For J pr The reference value of; w is the weight factor; Ω is the design domain;

[0014] The heat transfer function is:

[0015]

[0016] In the above formula, u and T are the velocity field and temperature field of the fluid in the design domain, n is the normal vector, ρ and C are p are the fluid density and specific heat capacity, respectively; Γ1 and Γ2 represent the inlet boundary and outlet boundary of the design domain, respectively;

[0017] The inlet and outlet pressure drop function is:

[0018]

[0019] In the above formula, p represents the pressure field of the fluid in the design domain;

[0020] S3. Establish the governing equations for the flow problem:

[0021]

[0022] For the cold fluid flow problem and the hot fluid flow problem, respectively, the control equations of the corresponding flow problems are solved, and at the same time, the corresponding topology optimization model is solved by using a topology optimization algorithm to obtain a corresponding velocity field;

[0023] Where u is the velocity field of the fluid in the design domain; ρ and μ are the density and kinematic viscosity of the fluid in the design domain; is the gradient operator; the body force term F = -α·u, α represents the reverse osmosis function;

[0024] S4, superimposing the velocity fields obtained by solving the two flow problems to obtain a combined velocity field;

[0025] S5. Solving the heat transfer problem using the heat conduction equation according to the combined velocity field;

[0026] S6. Perform sensitivity analysis according to the solution of the heat transfer problem, and update the design variables by a topology optimization algorithm according to the sensitivity analysis result;

[0027] S7, repeating steps S3 to S6 for multiple iterations to update the design variables until the objective function of the optimization target converges and meets the predetermined design requirements.

[0028] Further technical solutions are:

[0029] In step S3, the reverse osmosis function α is defined as follows:

[0030]

[0031] Among them, α1(γ1,γ2) and α2(γ1,γ2) represent the inverse of the permeability of the cold fluid and hot fluid respectively. max is the inverse of the maximum permeability, p1 and p2 are the penalty coefficients corresponding to γ1 and γ2 respectively.

[0032] In step S6, the sensitivity analysis includes:

[0033] Based on the solution of the heat transfer problem, the sensitivity of the objective function to the design variables is calculated, and the changes in system performance when each design variable changes are analyzed, including changes in heat transfer effect, flow resistance or other performance indicators.

[0034] When considering the flow and heat transfer problems, the sensitivity of the objective function to the design variables is the sum of multiple factors:

[0035]

[0036] When calculating the sensitivity of the objective function to the design variables, each term in the above formula is solved separately by the adjoint method, and then the design variable γ is iteratively updated.

[0037] In step S6, after the design variables are updated, the penalty coefficient of the reverse osmosis function is adjusted according to the change of system performance when the design variables change, so as to adjust the permeability of the fluid area and optimize the fluid flow.

[0038] The other performance indicators include temperature standard deviation T δ :

[0039]

[0040] Among them, T s is the temperature of a point on the surface of the design domain, T s,avg Refers to the average temperature of the design domain surface, A s Refers to the surface area of ​​the design domain.

[0041] In step S5, the heat conduction equation is used to solve the heat transfer problem, and a RAMP interpolation function defined on Ω is introduced in the solution process to distinguish between fluid and solid properties;

[0042] The heat conduction equation is:

[0043]

[0044] Among them, ρ(x), C p (x) represents the fluid density and specific heat capacity at position x in the design domain Ω, u is the superimposed velocity field, and T is the temperature field in the design domain; is the gradient operator, k(x) is the thermal conductivity at position x;

[0045] The RAMP interpolation function includes k(γ i ),ρ(γ i ) and c p (γ i ):

[0046]

[0047] Where i=1, 2 represents cold fluid and hot fluid respectively; k s , ρ s and c p，s are the thermal conductivity, density and specific heat capacity of the solid material respectively; k f , ρ f and c p，f are the thermal conductivity, density and specific heat capacity of the fluid material respectively; β k , β ρ , β cp are the corresponding penalty coefficients respectively.

[0048] In step S2, setting the constraint conditions includes: setting the volume fraction of the fluid in the entire design domain.

[0049] In step S3, the mathematical description of the topology optimization algorithm is:

[0050]

[0051] subject to:

[0052] g i (γ,s(γ))=0,i=1,...,n,

[0053] h j (γ,s(γ))≤0,j=1,...,m,

[0054]

[0055] In the formula, Minimize / Maximize F represents the objective function that needs to be maximized or minimized in the optimization problem, g i and h j where i and j are the numbers of the equality constraints and inequality constraints, n and m are the total numbers of the equality constraints and inequality constraints, and s(γ) is the state variable function of the design variable γ:

[0056]

[0057] The above formula represents the volume fraction of γ in the design domain Ω.

[0058] In step S4, the velocity fields obtained by solving the hot fluid flow channel problem and the cold fluid flow problem are directly superimposed in space through an addition operation, thereby obtaining the combined velocity field.

[0059] The beneficial effects of the present invention are as follows:

[0060] The present invention introduces a multi-objective weight adjustment strategy, a multi-physics field coupling solution, and a manufacturing constraint combination method to generate an efficient and practical optimization structure for different target requirements, working conditions, and manufacturing scenarios, thereby solving the problem that existing methods are difficult to efficiently adapt to complex scenarios. Specifically, it has the following advantages:

[0061] The present invention sets two design variables so that two fluids can be calculated in parallel, thereby improving the calculation efficiency.

[0062] The present invention adopts a multi-objective optimization method to achieve a balance between the flow performance and heat transfer performance of the heat exchanger. By adjusting the weight factor w, the flow channel morphology can be automatically adjusted according to different objective functions. At the same time, the reverse osmosis function is used to ensure that the two fluids will not mix.

[0063] The present invention sets volume fraction constraint conditions to avoid over-design, thereby achieving the purpose of lightweighting, saving costs and resources.

[0064] The present invention adopts a sensitivity analysis method to speed up the iteration speed.

[0065] Other features and advantages of the present invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 Schematic diagram of the process of the embodiment of the present invention.

[0067] Figure 2 Schematic diagram of the geometric structure of the topology optimization design domain according to an embodiment of the present invention.

[0068] Figure 3 This is a schematic diagram of the structure of a pseudo three-dimensional heat exchanger model formed after the flow channel of the two-dimensional topological heat exchanger designed by topological optimization according to an embodiment of the present invention is stretched.

[0069] In the figure: 1. Entrance; 2. Exit; 3. Design domain. DETAILED DESCRIPTION

[0070] The specific implementation of the present invention is described below with reference to the accompanying drawings.

[0071] See also Figure 1 , a dual-fluid heat exchanger structural topology optimization design method based on a multi-material model in this embodiment includes:

[0072] S1. Define a topology optimization design domain of the heat exchanger that contains the hot fluid and cold fluid flow areas, and define two design variables γ1 and γ2 for topology optimization; where γ is a 0-1 binary function of position x in the design domain, which is used to reflect the fluid permeability. When γ(x) = 0, it means that the material property of position x in the design domain is solid, and γ(x) = 1 means that the material property of position x in the design domain is fluid. The subscripts 1 and 2 represent the cold fluid and the hot fluid, respectively.

[0073] The topology optimization design domain geometry is as follows: Figure 2 As shown, flow channels for cold and hot fluids are designed in the design domain 3, and an inlet 1 and an outlet 2 are respectively provided at both ends of each flow channel.

[0074] As a specific implementation method, the length and width of the optimized design domain are 60 mm respectively, the cold and hot fluids are arranged in countercurrent, the inlet and outlet widths are 5 mm, the solid material is copper, and the fluid working medium is water.

[0075] S2. Taking maximizing the heat exchange in the design domain and minimizing the inlet and outlet pressure drops as the optimization goals, an objective function is established, constraints are set, and a topology optimization model is constructed.

[0076] The objective function is obtained through dimensionless and weighted processing, and the expression is:

[0077]

[0078] Among them, J th is the heat transfer function value, For J th Reference value of J pr is the inlet and outlet pressure drop function value, For J pr The reference value of; w is the weight factor; Ω is the design domain;

[0079] The heat transfer function is:

[0080]

[0081] In the above formula, u and T are the velocity field and temperature field of the fluid in the design domain, n is the normal vector, ρ and C are p are the fluid density and specific heat capacity, respectively; Γ1 and Γ2 represent the inlet boundary and outlet boundary of the design domain, respectively;

[0082] The inlet and outlet pressure drop function is:

[0083]

[0084] In the above formula, p represents the fluid pressure field in the design domain.

[0085] As a specific implementation, the constraints of the topology optimization model are set as follows: the volume fraction of the fluid in the entire heat exchanger design domain

[0086] S3. Establish the governing equations for the flow problem:

[0087]

[0088] For the cold fluid flow problem and the hot fluid flow problem respectively, the physical field is initialized and solved based on the finite element analysis according to the control equation of each flow problem, and the corresponding topology optimization model is solved using the topology optimization algorithm to obtain the corresponding velocity field.

[0089] Where u is the velocity field of the fluid in the design domain; ρ and μ are the density and kinematic viscosity of the fluid in the design domain; is the gradient operator; F is the volume force term, which is used to prevent fluid mixing and prevent the fluid from penetrating the solid, F = -α·u, α represents the reverse osmosis function, which is used to control the fluid permeability of each area to ensure that the hot fluid has an independent flow path.

[0090] As a specific implementation, the reverse osmosis function is defined as follows:

[0091]

[0092] Among them, α1(γ1,γ2) and α2(γ1,γ2) represent the inverse of the permeability of the cold fluid and hot fluid respectively. max is the inverse of the maximum permeability, and p1 and p2 are the penalty coefficients corresponding to γ1 and γ2 respectively.

[0093] As a specific implementation, the mathematical description of the topology optimization algorithm is:

[0094]

[0095] subject to:

[0096] g i (γ,s(γ))=0,i=1,...,n,

[0097] h j (γ,s(γ))≤0,j=1,...,m,

[0098]

[0099] Where Minimize / Maximize F represents the objective function that needs to take the maximum or minimum value in the optimization problem, that is, the objective function established in step S2, g i and h jThe equality constraints and inequality constraints in the constraint conditions set in step S2 are respectively, i and j are the numbers of the equality constraints and inequality constraints respectively, n and m are the total number of the equality constraints and inequality constraints respectively, and s(γ) is the state variable function about the design variable γ:

[0100]

[0101] It represents the volume fraction of γ within the design domain Ω.

[0102] S4. Superimpose the velocity fields obtained by solving the two flow problems to obtain a combined velocity field.

[0103] Among them, after solving the cold fluid flow problem and the hot fluid flow problem independently, the velocity fields of the two flows can be obtained by simple addition operation, that is, directly superimposing the velocity field solutions of the hot fluid and the cold fluid in space to obtain a combined velocity field. The combined velocity field reflects the flow of cold and hot fluids in the overall system, providing the necessary basis for subsequent heat transfer calculations.

[0104] S5. According to the combined velocity field, the heat conduction equation is used to solve the heat transfer problem.

[0105] The heat conduction equation is:

[0106]

[0107] Among them, ρ(x), C p (x) represents the fluid density and specific heat capacity at position x in the design domain Ω, u is the superimposed velocity field, and T is the temperature field in the design domain; is the gradient operator, k(x) is the thermal conductivity at position x.

[0108] As a specific implementation, since the density, specific heat and thermal conductivity of fluid and solid materials are different, a RAMP interpolation function defined on Ω is introduced in the process of solving the heat transfer problem to distinguish the fluid and solid properties.

[0109] The RAMP interpolation function includes k(γ i ),ρ(γ i ) and c p (γ i ):

[0110]

[0111]

[0112] Where i=1, 2 represents cold fluid and hot fluid respectively; k s , ρ s and c p，sare the thermal conductivity, density and specific heat capacity of the solid material respectively; k f , ρ f and c p，f are the thermal conductivity, density and specific heat capacity of the fluid material respectively; β k , β ρ , β cp are the corresponding penalty coefficients respectively.

[0113] S6. Perform sensitivity analysis based on the solution to the heat transfer problem, and update the design variables using a topology optimization algorithm based on the sensitivity analysis results.

[0114] The sensitivity analysis includes:

[0115] Based on the solution of the heat transfer problem, the sensitivity of the objective function to the design variables is calculated, that is, the changes in system performance, including changes in heat transfer effect, flow resistance or other performance indicators, are analyzed when each design variable changes.

[0116] As a specific implementation, when comprehensively considering the flow and heat transfer problems, the sensitivity of the objective function to the design variables is the sum of multiple factors:

[0117]

[0118] When calculating the sensitivity of the objective function to the design variables, each term in the above formula is solved separately by the adjoint method, and then the design variable γ is iteratively updated.

[0119] As a specific implementation, the heat transfer effect, flow resistance or other performance indicators include:

[0120] Heat transfer capacity (i.e. heat transfer effect index):

[0121]

[0122] Inlet and outlet pressure difference (i.e. flow resistance index):

[0123] Δp=p in -p out

[0124] Temperature standard deviation (i.e. other performance indicators):

[0125]

[0126] Among them, T s is the temperature of a point on the surface of the design domain, T s,avg Refers to the average temperature of the design domain surface, A s Refers to the surface area of ​​the design domain.

[0127] As a specific implementation, after the design variables are updated in step S6, the penalty coefficients p1 and p2 of the reverse osmosis function can be adjusted according to the change of the performance index when the design variables change, so as to adjust the permeability of the fluid area and optimize the fluid flow channel, thereby improving the performance of the overall system.

[0128] As a specific implementation, adjusting the reverse osmosis function specifically includes: adjusting a penalty coefficient in the reverse osmosis function.

[0129] S7, repeating steps S3 to S6 for multiple iterations to update the design variables until the objective function of the optimization target converges and meets the predetermined design requirements.

[0130] In specific applications, the flow channel of the two-dimensional topological heat exchanger obtained by the topological optimization design of this embodiment can be stretched to form a Figure 3 The pseudo three-dimensional heat exchanger model shown in the figure effectively enhances the fluid disturbance and heat transfer area and strengthens the heat transfer effect.

[0131] The flow and heat dissipation performance of the heat exchanger designed with topology optimization in this embodiment is compared and verified as follows.

[0132] Design the fluid volume fraction of the traditional channel heat exchanger and the heat exchanger with the topology optimization design of this embodiment The numerical simulation model was used to carry out flow and heat transfer simulation under the same working conditions. The specific working conditions were set as: temperature difference 30°C and inlet Reynolds number 100.

[0133] The heat transfer, inlet and outlet pressure difference, and temperature standard deviation indicators are comprehensively compared, and the flow heat transfer characteristics of the traditional heat exchanger and the topology optimized heat exchanger of this embodiment within the optimal constraint condition selection range obtained by two-dimensional numerical simulation are compared. The results are shown in Table 1:

[0134] Table 1 Comparison between the traditional channel heat exchanger and the topology optimized heat exchanger of this embodiment

[0135]

[0136] It can be seen from Table 1 that after the heat exchanger structure is optimized by the method of this embodiment, the flow capacity is improved by 34.90%, the heat exchange capacity is improved by 65.48%, and the temperature uniformity is improved by 2.00%, indicating that the dual-fluid heat exchanger designed by the topology optimization method proposed in this embodiment is significantly better than the traditional parallel flow channel heat exchanger and serpentine flow channel heat exchanger in all aspects.

[0137] This embodiment also provides a dual-fluid heat exchanger structure topology optimization design system based on a multi-material model, and the system is used to execute the topology optimization design method.

[0138] Those skilled in the art can understand that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention is described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions recorded in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for structural topology optimization design of a dual-fluid heat exchanger based on a multi-material model, characterized in that: include: S1. Define the topology optimization design domain of the heat exchanger including the hot fluid and cold fluid flow areas, and define the topology optimization design variables γ1 and γ2; where γ is a 0-1 binary function about the position x in the design domain. When γ(x) = 0, it means that the material property of the position x in the design domain is solid, and γ(x) = 1, it means that the material property of the position x in the design domain is fluid; subscripts 1 and 2 represent the cold fluid and the hot fluid, respectively; S2. Taking maximizing the heat exchange rate in the design domain and minimizing the inlet and outlet pressure drops as the optimization goals, establishing an objective function and setting constraints to construct a topology optimization model; The objective function is obtained through dimensionless and weighted processing, and the expression is: Among them, J th is the heat transfer function value, For J th Reference value of J pr is the inlet and outlet pressure drop function value, For J pr The reference value of; w is the weight factor; Ω is the design domain; The heat transfer function is: In the above formula, u and T are the velocity field and temperature field of the fluid in the design domain, n is the normal vector, ρ and C are p are the fluid density and specific heat capacity, respectively; Γ1 and Γ2 represent the inlet boundary and outlet boundary of the design domain, respectively; The inlet and outlet pressure drop function is: In the above formula, p represents the pressure field of the fluid in the design domain; S3. Establish the governing equations for the flow problem: For the cold fluid flow problem and the hot fluid flow problem, respectively, the control equations of the corresponding flow problems are solved, and at the same time, the corresponding topology optimization model is solved by using a topology optimization algorithm to obtain a corresponding velocity field; Where u is the velocity field of the fluid in the design domain; ρ and μ are the density and kinematic viscosity of the fluid in the design domain; is the gradient operator; the body force term F = -α·u, α represents the reverse osmosis function; S4, superimposing the velocity fields obtained by solving the two flow problems to obtain a combined velocity field; S5. Solving the heat transfer problem using the heat conduction equation according to the combined velocity field; S6. Perform sensitivity analysis according to the solution of the heat transfer problem, and update the design variables by a topology optimization algorithm according to the sensitivity analysis result; S7, repeating steps S3 to S6 for multiple iterations to update the design variables until the objective function of the optimization target converges and meets the predetermined design requirements.

2. The method according to claim 1, characterized in that In step S3, the reverse osmosis function α is defined as follows: Among them, α1(γ1,γ2) and α2(γ1,γ2) represent the inverse of the permeability of the cold fluid and hot fluid respectively. max is the inverse of the maximum permeability, p1 and p2 are the penalty coefficients corresponding to γ1 and γ2 respectively.

3. The method according to claim 2, characterized in that In step S6, the sensitivity analysis includes: Based on the solution of the heat transfer problem, the sensitivity of the objective function to the design variables is calculated, and the changes in system performance when each design variable changes are analyzed, including changes in heat transfer effect, flow resistance or other performance indicators.

4. The method according to claim 3, characterized in that When considering the flow and heat transfer problems, the sensitivity of the objective function to the design variables is the sum of multiple factors: When calculating the sensitivity of the objective function to the design variables, each term in the above formula is solved separately by the adjoint method, and then the design variable γ is iteratively updated.

5. The method according to claim 3, characterized in that: In step S6, after the design variables are updated, the penalty coefficient of the reverse osmosis function is adjusted according to the change of system performance when the design variables change, so as to adjust the permeability of the fluid area and optimize the fluid flow.

6. The method according to claim 3, characterized in that The other performance indicators include temperature standard deviation T δ : Among them, T s is the temperature of a point on the surface of the design domain, T s,avg Refers to the average temperature of the design domain surface, A s Refers to the surface area of ​​the design domain.

7. The method according to claim 1, characterized in that In step S5, the heat conduction equation is used to solve the heat transfer problem, and a RAMP interpolation function defined on Ω is introduced in the solution process to distinguish between fluid and solid properties; The heat conduction equation is: Among them, ρ(x), C p (x) represents the fluid density and specific heat capacity at position x in the design domain Ω, u is the superimposed velocity field, and T is the temperature field in the design domain; is the gradient operator, k(x) is the thermal conductivity at position x; The RAMP interpolation function includes k(γ i ),ρ(γ i ) and c p (γ i ): Where i=1, 2 represents cold fluid and hot fluid respectively; k s , ρ s and c p，s are the thermal conductivity, density and specific heat capacity of the solid material respectively; k f , ρ f and c p，f are the thermal conductivity, density and specific heat capacity of the fluid material respectively; β k , β ρ , β cp are the corresponding penalty coefficients respectively.

8. The method according to claim 1, characterized in that In step S2, setting the constraint conditions includes: setting the volume fraction of the fluid in the entire design domain.

9. The method according to claim 1, characterized in that: In step S3, the mathematical description of the topology optimization algorithm is: Minimize / Maximize F(γ,s(γ)), γ subject to: g i (γ,s(γ))=0,i=1,...,n, h j (γ,s(γ))≤0,j=1,...,m, In the formula, Minimize / Maximize F represents the objective function that needs to be maximized or minimized in the optimization problem, g i and h j where i and j are the numbers of the equality constraints and inequality constraints, n and m are the total numbers of the equality constraints and inequality constraints, and s(γ) is the state variable function of the design variable γ: The above formula represents the volume fraction of γ in the design domain Ω.

10. The method according to claim 1, characterized in that In step S4, the velocity fields obtained by solving the hot fluid flow channel problem and the cold fluid flow problem are directly superimposed in space through addition operation, so as to obtain the combined velocity field.

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