A high-efficiency heat exchanger optimization design method based on recirculation zone characteristics

Abstract

The present invention discloses a method for optimizing the design of a high-efficiency heat exchanger based on the characteristics of a recirculation zone, and belongs to the technical field of heat exchanger optimization. The present invention combines numerical simulation, mathematical models, and optimization algorithms, and establishes a parameterized model of the recirculation zone on the basis of a large amount of CFD simulation data results, performs two-step optimization on the heat exchanger as a whole and the recirculation zone, and solves the optimization problem through a genetic algorithm. The method can further reduce the size of the recirculation zone, improve the heat exchange efficiency, and reduce the flow resistance on the basis of ensuring that the heat exchanger as a whole has a high performance, and has theoretical guiding significance for the optimization of heat exchangers with recirculation zones.

Description

Technical Field

[0001] The invention belongs to the technical field of heat exchanger optimization, and in particular relates to a high-efficiency heat exchanger optimization design method based on recirculation zone characteristics. Background Art

[0002] With the spread of the global energy crisis, improving energy utilization is a major challenge facing the energy utilization industry. The performance of the energy utilization system mainly depends on the efficiency of the heat transfer equipment. Therefore, how to optimize the design of the heat exchanger to achieve the purpose of enhanced heat transfer has become a key issue in the development of the energy utilization system. The recirculation zone is a rotating side flow that often appears in the heat exchanger. It is often located at the edge of the mainstream and rotates in a closed state. The existence of the recirculation zone causes the fluid to stagnate and the mixing with the downstream incoming fluid is insufficient, resulting in low heat transfer efficiency in this area. Therefore, studying the optimization method of the heat exchanger for the recirculation zone is of great significance to the thermal efficiency and economy of the heat exchanger operation.

[0003] After testing the existing technologies, it is found that the existing technologies for heat exchanger optimization are as follows: (1) By adjusting the configuration of the heat exchanger components and optimizing the flow distribution method to improve the heat exchange efficiency of the heat exchanger. Taking Chinese patents CN106440861B and CN105160116B as examples, this method does not actually change the structure of the heat exchanger, and the phenomenon of the recirculation zone is not alleviated; (2) In the thermal calculation method, the optimal structural configuration is obtained by using optimization algorithms such as genetic algorithms and particle swarm algorithms. Taking Chinese patent CN106874661B as an example, this method ignores the influence of the recirculation zone on the heat exchanger and cannot minimize the recirculation zone on the basis of the high heat transfer efficiency of the heat exchanger; (3) Design a new vortex breaking structure and verify it through simulation technology. Taking Chinese patent CN107977482B as an example, this method has a large change in the structure of the heat exchanger and will increase the difficulty of processing and manufacturing. It cannot guarantee the optimal heat transfer flow efficiency of the heat exchanger structure within a given range, and is likely to affect other performances of the heat exchanger, such as increasing dust accumulation. Summary of the invention

[0004] In order to solve the above problems, the present invention provides a method for optimizing the design of a high-efficiency heat exchanger based on the characteristics of the recirculation zone. This method combines numerical simulation, mathematical models, and optimization algorithms. On the basis of a large amount of CFD simulation data results, a parameterized model of the recirculation zone is established, and two-step optimization is performed on the heat exchanger as a whole and the recirculation zone. The optimization problem is solved by a genetic algorithm. On the basis of ensuring that the heat exchanger as a whole has high performance, this method further reduces the size of the recirculation zone, improves heat exchange efficiency, and reduces flow resistance, which has theoretical guiding significance for the optimization of heat exchangers with recirculation zones.

[0005] The technical solution of the present invention is as follows:

[0006] An optimization design method for a high-efficiency heat exchanger based on the characteristics of the recirculation zone includes three steps: numerical simulation, overall optimization, and recirculation zone optimization. The specific implementation method is as follows:

[0007] AS1. Execute numerical simulation steps: First, obtain n design variables selected for the target heat exchanger to form a design variable set X SJ =(X1,X2,X3,…,X n ) ; then in the design variable set X SJ In the constraint space of each design variable, the design samples with different design variable configurations are determined by sampling method; finally, for each design sample, a three-dimensional structural model is first constructed and meshed, numerical simulation is performed using computational fluid dynamics software CFD, and then the recirculation zone is parameterized. The numerical simulation results, recirculation zone parameterization results and corresponding design variable configurations of each design sample are stored in the database;

[0008] AS2, perform overall optimization step: obtain the parameters required for overall optimization from the database, take the n design variables in step AS1 as optimization variables, take maximizing the heat transfer flow comprehensive evaluation factor PEC of the heat exchanger as the optimization goal, use genetic algorithm to iteratively optimize all n design variables of the heat exchanger as a whole, and when the first convergence requirement is reached, stop the iteration and obtain the parameter set X composed of the overall optimized parameter values ZT ; If the iteration cannot meet the first convergence requirement, it is necessary to return to AS1 and re-grid;

[0009] AS3, perform the reflow zone optimization step: based on the reflow zone parameterization, perform sensitivity parameter screening, and SJ Perform sensitivity analysis and select the sensitive variable set X composed of design variables that are sensitive to the optimization target of the reflow zone from n design variables. MG , and then from the parameter set X ZT Extract the sensitive variable set X MG The parameter values ​​corresponding to the variables in the formula constitute the sensitivity parameter set X for reflow zone optimization. HL ; For sensitivity parameter set X HL The parameter values ​​of are scaled to form the parameter optimization interval, and the design variable parameter interval X′ for reflow zone optimization is obtained. HL =κ[X HL ], κ is the interval scaling factor; then the sensitive variable set X MG All sensitive variables in are used as optimization variables to minimize the area ratio A of the recirculation zone. w and heat transfer ratio Q w As the optimization target, in the design variable parameter interval X′ HLThe genetic algorithm is used to perform multi-objective optimization design of the recirculation zone. When the second convergence requirement is reached, the parameter set X consisting of the parameter values ​​of all sensitive variables after the recirculation zone optimization is obtained. * HL , and finally complete the optimization design of the heat exchanger. If the second convergence requirement cannot be met, it is necessary to rescale to form a new parameter optimization interval and then perform multi-objective optimization design of the recirculation zone.

[0010] Preferably, in step AS1, the method for parameterizing the reflow zone comprises the following steps:

[0011] BS1: Based on the turbulent kinetic energy TKE distribution results obtained by CFD numerical simulation, the working fluid of the heat exchanger is divided into the mainstream area and the recirculation area according to the size of the turbulent kinetic energy. The turbulent kinetic energy in the mainstream area is greater than TKE0, and the turbulent kinetic energy in the recirculation area is less than TKE0. TKE0 is the standard value of turbulent kinetic energy.

[0012] BS2: Extract the recirculation zone at the inlet from the recirculation zone separated from BS1, and derive the coordinate values ​​(x, y) and heat flux density value q of each calculation point in the recirculation zone in the flow direction from the CFD numerical simulation results. i (x,y);

[0013] BS3: Convert the coordinate data of the recirculation zone at the entrance obtained in BS2 into polar coordinates, where the boundary of the recirculation zone is selected as the pole of the polar coordinates, the edge close to the boundary of the calculation domain is selected as the polar axis of the polar coordinates, the distance from the point on the edge of the recirculation zone to the pole is defined as the radius r, and the deflection angle between each radius and the polar axis is θ. The shape of the recirculation zone is expressed as the function r(θ); finally, the area A of the recirculation zone is obtained. HL and the heat transfer Q in the recirculation zone HL , the calculation methods are:

[0014]

[0015] Q HL =A HL ∑q i (x,y).

[0016] Preferably, the parameters obtained from the database in step AS2 include the design variables to be optimized, the heat transfer flow comprehensive evaluation factor PEC, the area ratio A of the recirculation zone, w and heat transfer ratio Q w ; The area of ​​the recirculation zone is A w and heat transfer Q w The area A of the recirculation zone HL and heat transfer Q HL The calculation method is:

[0017]

[0018]

[0019] Among them, A0 is the wall area of ​​the heat exchanger, and Q0 is the heat transfer capacity of the heat exchanger.

[0020] As a preferred method, after the heat exchanger optimization design is finally completed, the optimized parameter values ​​of each design variable need to be applied to the heat exchanger, and the recirculation zone performance before and after optimization should be compared in CFD. w and Q w , after verifying the optimization effect of the recirculation zone, the final optimization result is obtained.

[0021] Preferably, the sampling method adopts Latin hypercube sampling method.

[0022] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0023] 1. The present invention optimizes the recirculation zone in the second step based on the overall optimization in the first step, reduces the recirculation zone on the basis of ensuring the high heat transfer flow efficiency of the heat exchanger, further improves the heat exchange efficiency of the heat exchanger and reduces the flow resistance, and can be applied to various heat exchanger structures with recirculation zone phenomenon such as plate-fin type and plate type;

[0024] 2. The present invention optimizes the heat exchanger without making major changes to its structure, does not change other properties of the heat exchanger, and does not increase the difficulty of processing and manufacturing;

[0025] 3. The present invention establishes a parameterized optimization model of the recirculation zone that combines numerical simulation, mathematical model, and optimization algorithm. Based on a large amount of CFD simulation data results, a parameterized model of the recirculation zone is established, which can realize quantitative analysis and optimization of the recirculation zone. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic flow chart of the method for optimizing the design of a high-efficiency heat exchanger based on the recirculation zone characteristics of the present invention.

[0027] Figure 2 It is a schematic diagram of the parameterized model of the reflow zone of the present invention.

[0028] Figure 3 Schematic diagram of a pillow-shaped plate heat exchanger model in an embodiment of the present invention.

[0029] Figure 4 Results of sensitivity analysis of the reflow zone performance. DETAILED DESCRIPTION

[0030] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0031] In a preferred embodiment of the present invention, a high-efficiency heat exchanger optimization design method based on the recirculation zone characteristics is provided. The pillow plate heat exchanger is a typical heat exchanger with a large recirculation zone, so the structural optimization of the pillow plate heat exchanger (such as Figure 3 ) as the target heat exchanger of an embodiment of the present invention.

[0032] like Figure 1 As shown, in this embodiment, the high-efficiency heat exchanger optimization design method based on the recirculation zone characteristics includes three steps: numerical simulation, overall optimization, and recirculation zone optimization. The optimization method includes the following steps:

[0033] AS1: Execute numerical simulation steps:

[0034] First, obtain n design variables selected for the target heat exchanger to form a design variable set X SJ =(X1,X2,X3,…,X n ) ; then in the design variable set X SJ In the constraint space of each design variable, the Latin hypercube sampling method is used to determine the design samples with different design variable configurations; finally, for each design sample, a three-dimensional structural model is first constructed and meshed, and numerical simulation is performed using the computational fluid dynamics software CFD (Computational Fluid Dynamics). After the recirculation zone is parameterized, the numerical simulation results of each design sample, the recirculation zone parameterization results and the corresponding design variable configuration are stored in the database.

[0035] It should be noted that different target heat exchangers can be set with different design variables, and the constraint space of each design variable, i.e., the value range, can be determined based on experience or relevant experiments. When constructing a design sample, the Latin hypercube sampling method can be used to sample values ​​in the constraint space of each design variable, and then a set of values ​​of all n design variables constitutes a set of design variable configurations, thereby forming a design sample for CFD simulation.

[0036] In this embodiment, the design variables selected are n=3, which are the horizontal and vertical pitch ratio S a 、Channel height h i , welding point diameter d sp , the set of design variables is represented by X SJ =(S a ,h i ,d sp ). The constraint space of the three design variables is shown in Table 1:

[0037] Table 1 Design variable constraint space

[0038] Design variables <![CDATA[S a ]]> <![CDATA[h i (mm)]]> <![CDATA[d sp (mm) <!-- 3 -->]]> Constraint Space 1-1.73 3-10 3-10

[0039] In the present invention, the purpose of parameterizing the reflow zone is to facilitate subsequent optimization of the reflow zone. Figure 2 As shown, the specific method for parameterizing the reflow zone in this embodiment is as follows:

[0040] BS1: Based on the turbulent kinetic energy TKE distribution results obtained by CFD numerical simulation, the working fluid of the heat exchanger is divided into the mainstream area and the recirculation area according to the size of the turbulent kinetic energy. The turbulent kinetic energy in the mainstream area is greater than TKE0, and the turbulent kinetic energy in the recirculation area is less than TKE0. TKE0 is the standard value of turbulent kinetic energy.

[0041] BS2: Extract the recirculation zone at the inlet from the recirculation zone separated from BS1, and derive the coordinate values ​​(x, y) and heat flux density value q of each calculation point in the recirculation zone in the flow direction from the CFD numerical simulation results. i (x,y);

[0042] BS3: Convert the coordinate data of the recirculation zone at the entrance obtained in BS2 into polar coordinates, where the boundary of the recirculation zone is selected as the pole of the polar coordinates, the edge close to the boundary of the calculation domain is selected as the polar axis of the polar coordinates, the distance from the point on the edge of the recirculation zone to the pole is defined as the radius r, and the deflection angle between each radius and the polar axis is θ. The shape of the recirculation zone is expressed as the function r(θ); finally, the area A of the recirculation zone is obtained. HL and the heat transfer Q in the recirculation zone HL , the calculation methods are:

[0043]

[0044] Q HL =A HL ∑q i (x,y).

[0045] AS2: Perform overall optimization steps:

[0046] The parameters required for overall optimization are obtained from the database. The n design variables in step AS1 are used as optimization variables. The PEC comprehensive evaluation factor of heat transfer flow of the heat exchanger is maximized as the optimization goal. The genetic algorithm is used to iteratively optimize all n design variables of the heat exchanger as a whole. The optimization problem can be expressed as:

[0047] Max F(X)=PEC

[0048] X=X SJ =[S a ,h i ,d sp ]

[0049] The above iterative optimization can pre-set the corresponding convergence requirement (referred to as the first convergence requirement). When the first convergence requirement is reached, the iteration is stopped and the parameter set X consisting of the overall optimized parameter values ​​is obtained. ZT If the first convergence requirement cannot be met when the iteration reaches the upper limit, it is necessary to return to AS1 to re-mesh, then update the database and use the genetic algorithm to perform iterative optimization again.

[0050] It should be noted that the parameters obtained from the database include the design variables to be optimized, the comprehensive evaluation factor of heat transfer flow PEC, the area ratio A of the recirculation zone, and the w and heat transfer ratio Q w Among them, PEC, A w and Q w It can be calculated based on the CFD numerical simulation results and the recirculation zone parameterization results in the database, as follows:

[0051] The area ratio of the recirculation zone is A w and heat transfer Q w The area A of the recirculation zone HL and heat transfer Q HL The calculation method is:

[0052]

[0053]

[0054] Among them, A0 is the wall area of ​​the heat exchanger, and Q0 is the heat transfer capacity of the heat exchanger.

[0055] The calculation method of PEC is:

[0056]

[0057] Wherein, Nu and f represent the Nusselt number and friction resistance coefficient of the heat exchanger respectively, and Nu0 and f0 represent the reference values ​​of the Nusselt number and friction resistance coefficient.

[0058] The calculation method of Nusselt number Nu is:

[0059]

[0060]

[0061] Among them, h m Indicates the surface heat transfer coefficient of the heat exchanger, d h represents the hydraulic diameter of the computational domain, λ represents the thermal conductivity, represents the heat flux density of the wall, and LMTD represents the logarithmic mean temperature difference, which is calculated as follows:

[0062]

[0063] Wherein, Δt' represents the difference between the heat exchanger wall temperature and the working fluid inlet temperature, and Δt" represents the difference between the heat exchanger wall temperature and the working fluid outlet temperature.

[0064] The calculation method of friction resistance coefficient is:

[0065]

[0066] Among them, Δp represents the pressure loss per unit length, ρ represents the density of the working fluid, Indicates the average flow velocity of the working fluid.

[0067] It should be noted that the above parameter set X ZT In fact, it is a set of optimized parameter values ​​obtained by iterative optimization. ZT Corresponding to X SJ The n=3 design variables in have three optimized parameter values. * represents the parameter value after design variable optimization, then the optimized parameter set is X ZT =[S a * ,h i * ,d sp * ].

[0068] AS3: Perform the reflow zone optimization step:

[0069] First, based on the parameterization of the reflow zone, sensitivity parameters are screened. SJ Perform sensitivity analysis and select the sensitive variable set X composed of design variables that are sensitive to the optimization target of the reflow zone from n = 3 design variables. MG . Sensitive variable set X MG The design variables in can be called sensitive variables, where the number of sensitive variables can be expressed as m. ZT Extract the sensitive variable set X MG The parameter values ​​corresponding to the variables in the formula constitute the sensitivity parameter set X for reflow zone optimization. HL .

[0070] In this embodiment, the optimization target of the recirculation zone is the area ratio A of the recirculation zone. w and heat transfer ratio Q w , so the area ratio of the recirculation zone is A w and heat transfer ratio Q w A sensitivity analysis was performed (see Figure 4 ), we can see that the area of ​​the recirculation zone is larger than A wand heat transfer ratio Q w In h i and d sp The change is large when it changes, so there are a total of m = 2 sensitive variables, and the set of sensitive variables composed of them is X MG =(h i ,d sp ), so from the parameter set X ZT The sensitivity parameter set is extracted as X HL =[h i * ,d sp * ].

[0071] Then, the sensitivity parameters of the recirculation zone need to be further optimized to reduce the recirculation zone while ensuring a high heat transfer flow efficiency of the heat exchanger. However, since each parameter in the sensitivity parameter set is only a set of optimized values, it is necessary to first optimize the sensitivity parameter set X. HL The parameter values ​​of are scaled to form the parameter optimization interval, and the design variable parameter interval X′ for reflow zone optimization is obtained. HL =κ[X HL ], κ is the interval scaling factor. The initial value of the interval scaling factor can be selected based on experience or experiment. MG All m sensitive variables in are used as optimization variables to minimize the area ratio A of the recirculation zone w and heat transfer ratio Q w As the optimization target, in the design variable parameter interval X′ HL The genetic algorithm is used to perform multi-objective optimization design of the recirculation zone. The optimization problem at this time can be expressed as:

[0072] Min F(X)=[A w ,Q w ]

[0073] X=X MG =[h i ,d sp ]

[0074] st.X MG ∈X' HL

[0075] The above iterative optimization can pre-set the corresponding convergence requirements (referred to as the second convergence requirements). When the convergence requirements are met, the parameter set X consisting of the parameter values ​​of all sensitive variables after the reflow zone optimization is obtained. * HL, and finally complete the optimization design of the heat exchanger; however, if the second convergence requirement cannot be met when iterating to the upper limit of the number of iterations, it is necessary to change the interval scaling factor κ and re-scale it to form a new parameter optimization interval, and then use the genetic algorithm again to perform multi-objective optimization design of the recirculation zone.

[0076] In this embodiment, the optimized parameters of the reflow zone are: * HL =[h i ** ,d sp ** ].

[0077] Therefore, after the heat exchanger optimization design is finally completed, the final optimization value of the sensitive variable among all n design variables is selected as X * HL The parameter values ​​in , and the final optimized values ​​of the remaining non-sensitive variables are selected as X ZT Parameter values ​​in .

[0078] In this embodiment, the final optimization results of the three design variables are [S a * ,h i ** ,d sp ** ].

[0079] After the heat exchanger optimization design is finally completed, the optimized parameter values ​​of each design variable need to be applied to the heat exchanger, and the recirculation zone performance before and after optimization should be compared in CFD. w and Q w After verifying the optimization effect of the recirculation zone, if the optimization purpose is met, the final optimization result can be obtained. In this embodiment, the structural parameters before and after optimization are shown in Table 2, the performance comparison results of the heat exchanger under various working conditions before and after optimization are shown in Table 3, and the average performance optimization of the heat exchanger after optimization is shown in Table 4.

[0080] According to Table 3, under various working conditions, the PEC factor of the heat exchanger after optimization is higher than that before optimization, and the optimization effect increases with the increase of Reynolds number, and the PEC increases by 29.3% on average. After optimization, the area ratio and heat transfer ratio of the recirculation zone have decreased significantly, and they have decreased by an average of 25.3% and 20.0% respectively within the calculated Reynolds number range, especially when the Reynolds number is 8000, the area ratio of the recirculation zone decreases from 0.333 to 0.236, and the decrease reaches 29.1%. It can be proved that the proposed two-step optimization method of the heat exchanger with recirculation zone parameterization has achieved effective improvement of various target parameters, the heat transfer performance of the heat exchanger has been significantly improved, and the area ratio and heat transfer ratio of the recirculation zone have been effectively reduced.

[0081] Table 2 Comparison of structural parameters before and after optimization

[0082] <![CDATA[S a ]]> <![CDATA[h i (mm)]]> <![CDATA[d sp (mm)]]> Before optimization 1.71 3 10 After optimization 1.62 6 4

[0083] Table 3 Comparison results of heat exchanger performance under multiple conditions before and after optimization

[0084]

[0085] Table 4 Average performance improvement of heat exchanger after optimization

[0086] Performance Indicators PEC <![CDATA[A w ]]> <![CDATA[Q w <!-- 6 -->]]> +29.3％ -25.3％ -20.0％

[0087] It can be seen that this method optimizes the whole body first and then the recirculation zone, and on the basis of ensuring that the heat exchanger has a high performance as a whole, further reduces the size of the recirculation zone, improves the heat transfer efficiency and reduces the flow resistance, which has theoretical guiding significance for the optimization of heat exchangers with recirculation zones.

[0088] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can still modify the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or perform equivalent replacements on some of the technical features thereof; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. They should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A high-efficiency heat exchanger optimization design method based on recirculation zone characteristics, characterized in that: It includes three steps: numerical simulation, overall optimization, and reflow zone optimization. The specific implementation methods are as follows: AS1. Execute numerical simulation steps: First, obtain n design variables selected for the target heat exchanger to form a design variable set X SJ =(X1,X2,X3,…,X n ) ; then in the design variable set X SJ In the constraint space of each design variable, the design samples with different design variable configurations are determined by sampling method; finally, for each design sample, a three-dimensional structural model is first constructed and meshed, numerical simulation is performed using computational fluid dynamics software CFD, and then the recirculation zone is parameterized. The numerical simulation results, recirculation zone parameterization results and corresponding design variable configurations of each design sample are stored in the database; AS2, perform overall optimization step: obtain the parameters required for overall optimization from the database, take the n design variables in step AS1 as optimization variables, take maximizing the heat transfer flow comprehensive evaluation factor PEC of the heat exchanger as the optimization goal, use genetic algorithm to iteratively optimize all n design variables of the heat exchanger as a whole, and when the first convergence requirement is reached, stop the iteration and obtain the parameter set X composed of the overall optimized parameter values ZT ; If the iteration cannot meet the first convergence requirement, it is necessary to return to AS1 and re-grid; AS3, perform the reflow zone optimization step: based on the reflow zone parameterization, perform sensitivity parameter screening, and SJ Perform sensitivity analysis and select the sensitive variable set X composed of design variables that are sensitive to the optimization target of the reflow zone from n design variables. MG , and then from the parameter set X ZT Extract the sensitive variable set X MG The parameter values ​​corresponding to the variables in the formula constitute the sensitivity parameter set X for reflow zone optimization. HL ; For sensitivity parameter set X HL The parameter values ​​of are scaled to form the parameter optimization interval, and the design variable parameter interval X′ for reflow zone optimization is obtained. HL =κ[X HL ], κ is the interval scaling factor; then the sensitive variable set X MG All sensitive variables in are used as optimization variables to minimize the area ratio A of the recirculation zone. w and heat transfer ratio Q w As the optimization target, in the design variable parameter interval X′ HL The genetic algorithm is used to perform multi-objective optimization design of the recirculation zone. When the second convergence requirement is met, the parameter set X consisting of the parameter values ​​of all sensitive variables after the recirculation zone optimization is obtained. * HL , and finally complete the optimization design of the heat exchanger. If the second convergence requirement cannot be met, it is necessary to rescale to form a new parameter optimization interval and then perform multi-objective optimization design of the recirculation zone; In step AS1, the method for parameterizing the reflow zone includes the following steps: BS1: Based on the turbulent kinetic energy TKE distribution results obtained by CFD numerical simulation, the working fluid of the heat exchanger is divided into the mainstream area and the recirculation area according to the size of the turbulent kinetic energy. The turbulent kinetic energy in the mainstream area is greater than TKE0, and the turbulent kinetic energy in the recirculation area is less than TKE0. TKE0 is the standard value of turbulent kinetic energy. BS2: Extract the recirculation zone at the inlet from the recirculation zone separated from BS1, and derive the coordinate values ​​(x, y) and heat flux density value q of each calculation point in the recirculation zone in the flow direction from the CFD numerical simulation results. i (x,y); BS3: Convert the coordinate data of the recirculation zone at the entrance obtained in BS2 into polar coordinates, where the boundary of the recirculation zone is selected as the pole of the polar coordinates, the edge close to the boundary of the calculation domain is selected as the polar axis of the polar coordinates, the distance from the point on the edge of the recirculation zone to the pole is defined as the radius r, and the deflection angle between each radius and the polar axis is θ. The shape of the recirculation zone is expressed as the function r(θ); finally, the area A of the recirculation zone is obtained. HL and the heat transfer Q in the recirculation zone HL , the calculation methods are: Q HL =A HL ∑q i (x,y)。 2. The high-efficiency heat exchanger optimization design method based on recirculation zone characteristics according to claim 1 is characterized in that: The parameters obtained from the database in step AS2 include the design variables to be optimized, the heat transfer flow comprehensive evaluation factor PEC, the area ratio A of the recirculation zone, w and heat transfer ratio Q w ; The area of ​​the recirculation zone is A w and heat transfer Q w The area A of the recirculation zone HL and heat transfer Q HL The calculation method is: Among them, A0 is the wall area of ​​the heat exchanger, and Q0 is the heat transfer capacity of the heat exchanger.

3. The method for optimizing the design of a high-efficiency heat exchanger based on the recirculation zone characteristics according to claim 1, characterized in that: When the heat exchanger optimization design is finally completed, the optimized parameter values ​​of each design variable need to be applied to the heat exchanger, and the recirculation zone performance before and after optimization should be compared in CFD. w and Q w , after verifying the optimization effect of the recirculation zone, the final optimization result is obtained.

4. The method for optimizing the design of a high-efficiency heat exchanger based on the recirculation zone characteristics according to claim 1, characterized in that: The sampling method adopts Latin hypercube sampling method.

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