Structural topology optimization design method for dual-fluid heat exchanger based on multi-material model

Abstract

The present invention relates to a method for topological optimization design of a dual-fluid heat exchanger structure based on a multi-material model, comprising: defining a topological optimization design domain and design variables of the heat exchanger including the flow areas of the hot and cold fluids; establishing an objective function and setting constraints, with the optimization objectives of maximizing the heat transfer within the design domain and minimizing the inlet and outlet pressure drops, and constructing a topological optimization model; solving the governing equations for the flow problems of the cold and hot fluids respectively, solving the corresponding topological optimization models, and obtaining the corresponding velocity fields; solving the heat transfer problem using the heat conduction equation based on the combined velocity field obtained by superimposing the two velocity fields; performing a sensitivity analysis based on the solution to the heat transfer problem, and updating the design variables using a topological optimization algorithm based on the results of the sensitivity analysis; and iterating and updating the design variables multiple times until the objective function converges and meets the predetermined design requirements. The present invention efficiently solves the problem of heat exchanger structure optimization in different scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of heat exchanger structure design, and 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. Due to the strict requirements on performance and space, finned plate type heat exchangers are the most common choice. By brazing fins, specific designs can be easily achieved and various characteristics can be flexibly adjusted according to the needs of the final application. Cross-flow 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 forming or bending metal sheets, and these geometries must be able to be easily connected to the final component. However, this manufacturing method limits the geometric shape combinations 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 shapes, but also significantly improves the performance of heat exchangers under certain design constraints, which is of great significance in high-demand applications such as aerospace.

[0004] Topology optimization is a structural optimization method that uses mathematical models and simulations to automatically generate the optimal material distribution or structural shape that meets specific performance requirements. It doesn't rely on traditional design solutions, but instead explores and designs the optimal structural layout by optimizing the objective function. Especially for complex engineering problems, topology optimization can help designers discover and implement innovative designs that are difficult to conceive using 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 optimizing the topology of the heat exchanger's internal flow channels and fin shapes, a more efficient heat exchange structure can be generated, achieving better thermal performance within a limited space. However, existing heat exchanger structural topology optimization methods, due to their single objective, neglect of multi-physics field coupling and actual manufacturing constraints, and low computational efficiency, are unable to effectively adapt to optimization needs under complex working conditions and are unable to effectively solve heat exchanger structural optimization problems in different scenarios. Summary of the Invention

[0006] In response to the shortcomings of the existing technology, 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 problems in different scenarios.

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

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

[0009] S1. Define the topology optimization design domain of the heat exchanger, which includes the hot and cold fluid flow areas, and define the topology optimization design variables γ1 and γ2. Where γ is a 0-1 binary function with respect to position x in the design domain. When γ(x) = 0, it indicates that the material property of position x in the design domain is solid, and γ(x) = 1, it indicates 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.

[0010] S2. Taking maximizing the heat transfer within the design domain and minimizing the inlet and outlet pressure drops as the optimization objectives, establish an objective function and set 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 the 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] Solve the control equations of the corresponding flow problems for the cold fluid flow problem and the hot fluid flow problem respectively, and use the topology optimization algorithm to solve the corresponding topology optimization model to obtain the 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. Solve the heat transfer problem using the heat conduction equation according to the combined velocity field;

[0026] S6. Performing a sensitivity analysis based on the solution to the heat transfer problem, and updating the design variables using a topology optimization algorithm based on the sensitivity analysis results;

[0027] S7. Repeat steps S3 to S6 to iteratively update the design variables multiple times 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 in system performance when the design variables are changed, thereby adjusting the permeability of the fluid region and optimizing 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 take the maximum or minimum value in the optimization problem, g i and h j where i and j are the numbers of the equality and inequality constraints, n and m are the total number of the equality 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 problem and the cold fluid flow problem are directly superimposed in space by an addition operation, thereby obtaining the combined velocity field.

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

[0060] By introducing a multi-objective weight adjustment strategy, multi-physics field coupling solution, and manufacturing constraint integration, this invention can generate efficient and practical optimization structures for different target requirements, working conditions, and manufacturing scenarios, thereby solving the problem that existing methods are difficult to efficiently adapt to complex scenarios. Specific advantages include:

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

[0062] This invention uses a multi-objective optimization method to achieve a balance between the flow 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. Furthermore, a reverse osmosis function is used to ensure that the two fluids do not mix.

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

[0064] The present invention adopts a sensitivity analysis method to accelerate 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 obvious 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 structural diagram of a pseudo three-dimensional heat exchanger model formed by stretching the flow channels of a two-dimensional topological heat exchanger designed for topological optimization according to an embodiment of the present invention.

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

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

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

[0072] S1. Define a topology optimization design domain for the heat exchanger, containing the hot and cold fluid flow areas, and define two topology optimization design variables, γ1 and γ2. γ is a 0-1 binary function at position x in the design domain, reflecting the fluid permeability. When γ(x) = 0, the material property at position x in the design domain is solid, and when γ(x) = 1, the material property at position x in the design domain is fluid. The subscripts 1 and 2 represent the cold and hot fluids, respectively.

[0073] Among them, 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 embodiment, 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 medium is water.

[0075] S2. Taking maximizing the heat transfer within 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 the 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 method, 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. At the same time, the topology optimization algorithm is used to solve the corresponding topology optimization model 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 region 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, 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 constraints 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 equality constraints and inequality constraints respectively, and s(γ) is the state variable function of 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] After independently solving the cold and hot fluid flow problems, the velocity fields of the two flows can be simply added together—that is, the velocity field solutions for the hot and cold fluids are spatially superimposed to obtain a combined velocity field. This combined velocity field reflects the flow of the cold and hot fluids in the overall system, providing the necessary foundation for subsequent heat transfer calculations.

[0104] S5. Solve the heat transfer problem using the heat conduction equation according to the combined velocity field.

[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 when each design variable changes are analyzed, including changes in heat transfer effect, flow resistance or other performance indicators.

[0116] As a specific implementation, when considering the flow and heat transfer problems comprehensively, 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 changes in the performance indicators when the design variables change, thereby adjusting the permeability of the fluid area and optimizing 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. Repeat steps S3 to S6 to iteratively update the design variables multiple times 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 topology heat exchanger obtained by topology optimization design in this embodiment can be stretched to form the following Figure 3 The pseudo three-dimensional heat exchanger model shown in the figure effectively enhances the fluid disturbance and heat transfer area, thereby strengthening 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 below.

[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 of 30℃ and inlet Reynolds number of 100.

[0133] A comprehensive comparison of heat transfer, inlet and outlet pressure difference, and temperature standard deviation was conducted, comparing the flow and heat transfer characteristics of the conventional heat exchanger and the topology optimized heat exchanger of this embodiment within the optimal constraint selection range obtained by two-dimensional numerical simulation. 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] As shown in Table 1, after optimizing the heat exchanger structure using the method of this embodiment, the flow capacity increased by 34.90%, the heat exchange capacity increased by 65.48%, and the temperature uniformity increased by 2.00%. This shows that the dual-fluid heat exchanger designed using the topology optimization method proposed in this embodiment is significantly superior to traditional parallel flow channel heat exchangers and serpentine flow channel heat exchangers in all aspects.

[0137] This embodiment also provides a dual-fluid heat exchanger structural 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 will understand that the foregoing descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art will be able to modify the technical solutions described in the foregoing embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A topology optimization design method for a dual-fluid heat exchanger structure based on a multi-material model, characterized in that: include: S1. Define the topology optimization design domain of the heat exchanger, which includes the hot and cold fluid flow areas, and define the topology optimization design variables γ1 and γ2. Where γ is a 0-1 binary function with respect to position x in the design domain. When γ(x) = 0, it indicates that the material property of position x in the design domain is solid, and γ(x) = 1, it indicates 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. S2. Taking maximizing the heat transfer within the design domain and minimizing the inlet and outlet pressure drops as the optimization objectives, establish an objective function and set 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 the 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: Solve the control equations of the corresponding flow problems for the cold fluid flow problem and the hot fluid flow problem respectively, and use the topology optimization algorithm to solve the corresponding topology optimization model to obtain the 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. Solve the heat transfer problem using the heat conduction equation according to the combined velocity field; S6. Performing a sensitivity analysis based on the solution to the heat transfer problem, and updating the design variables using a topology optimization algorithm based on the sensitivity analysis results; S7. Repeat steps S3 to S6 to iteratively update the design variables multiple times 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 in system performance when the design variables are changed, thereby adjusting the permeability of the fluid region and optimizing 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 take the maximum or minimum value in the optimization problem, g i and h j where i and j are the numbers of the equality and inequality constraints, n and m are the total number of the equality 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 problem and the cold fluid flow problem are directly superimposed in space by an addition operation, thereby obtaining the combined velocity field.

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