Design method of louver fin micro-channel heat exchanger based on heat transfer unit optimization

Abstract

The invention discloses a shutter fin micro-channel heat exchanger design method based on heat transfer unit optimization, and the method comprises the steps: selecting a heat exchange unit according to a shutter fin micro-channel heat exchanger, carrying out the modeling of fins and micro-channels of the heat exchange unit, extracting the structural parameters of the heat exchange unit, and building sample space points; selecting a parameter subset with the maximum prognosis coefficient, and establishing a response surface model for each response variable; based on the response surface model, a genetic algorithm is used for optimizing in a limited interval and a constraint condition, and under the condition that the total volume of the heat exchanger is reduced, the heat exchange amount of the heat exchanger is increased, and the corresponding fan power is reduced; and taking the output parameter of the established response surface as a subset of an objective function optimized by the genetic algorithm, taking reduction of the fan power of the louver fin micro-channel heat exchanger and improvement of the heat exchange amount under a certain capacity as targets, obtaining a corresponding Pareto front, and giving an optimal solution in a geometric parameter dimension region.

Description

Technical Field

[0001] The invention relates to the technical field of louver fin microchannel heat exchanger optimization, and in particular to a louver fin microchannel heat exchanger design method based on heat transfer unit optimization. Background Art

[0002] Louvered fin microchannel heat exchangers are often used in household air conditioners for heat exchange, which have the advantages of good heat exchange effect and low flow resistance. However, the design optimization research of louvered fin microchannel heat exchangers is still very insufficient. The complexity of its structure is the main obstacle to the design of louvered fin microchannel heat exchangers, and it also greatly increases the workload of optimization design.

[0003] There are two unreasonable aspects in the traditional design method of louvered fin microchannel heat exchangers: First, the traditional design method of louvered fin microchannel heat exchangers uses the empirical formula in the existing heat exchanger design manual for design, but in fact, the heat exchange conditions of different louvered fin microchannels are different. The traditional design method of louvered fin microchannel heat exchangers cannot take this into account and is inconsistent with reality. Secondly, when using the traditional louvered fin microchannel heat exchanger design method to design a heat exchanger, the calculation is cumbersome and the design is difficult, which makes the workload of the louvered fin microchannel heat exchanger design very large and increases the design time.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the invention and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the invention

[0005] The present invention provides a design method for a louvered fin microchannel heat exchanger based on heat transfer unit optimization. The method uses a genetic algorithm based on the heat transfer unit to find the optimal design of the louvered fin microchannel heat exchanger. The method has an outstanding heat exchange effect and a small total volume of the heat exchanger, which is convenient for miniaturization of the heat exchanger. The workload of optimizing the louvered fin microchannel heat exchanger is greatly reduced.

[0006] A design method for a louvered fin microchannel heat exchanger based on heat transfer unit optimization includes: Step 1: Select a heat exchange unit according to the louvered fin microchannel heat exchanger, model the fins and microchannels of the heat exchange unit, and calculate the resistance factor and heat transfer factor under the structural parameters of the heat exchange unit to verify the effectiveness of the heat exchange unit; Step 2: extract the structural parameters of the heat exchange unit to establish sample space points, perform parameter sensitivity analysis, and calculate the heat exchange and flow conditions of the geometric model corresponding to each sample point; Step 3, select the parameter subset with the largest prognostic coefficient and build a response surface model for each response variable; Step 4: Based on the response surface model, a genetic algorithm is applied to find the optimum within the restricted range and the constraint conditions, so that the heat exchange of the heat exchanger is increased and the corresponding fan power is reduced while the total volume of the heat exchanger is reduced; Step five, the output parameters of the established response surface are used as a subset of the objective function of the genetic algorithm optimization, with the goal of reducing the fan power of the louvered fin microchannel heat exchanger and increasing the heat exchange capacity under a certain capacity, obtaining the corresponding Pareto frontier, and giving the optimal solution in the geometric parameter dimension region.

[0007] In the design method of a louvered fin microchannel heat exchanger based on heat transfer unit optimization, step one is to model the louvered fin and the upper and lower microchannels adjacent thereto, and separate the fin from the middle, leaving only half of the fin and the microchannel structure on the same side thereof.

[0008] In the louvered fin microchannel heat exchanger design method based on heat transfer unit optimization, the resistance factor and heat transfer factor under the structural parameters include the fin spacing, flat tube outer height, louver area width, louver angle, flat tube inner width and number of flat tubes of the heat exchange unit.

[0009] In the design method of the louvered fin microchannel heat exchanger based on heat transfer unit optimization, in step 2, the sample space point satisfies: , where N t is the number of microchannels, B ti is the inner width of the flat tube and the floor function is the rounding function. The advanced Latin hypercube sampling method is used to establish sample points within the restricted interval to represent the nonlinear relationship between the input variables.

[0010] In the louvered fin microchannel heat exchanger design method based on heat transfer unit optimization, the heat exchange and flow conditions of the geometric model corresponding to each sample point include the heat exchange amount and pressure drop corresponding to each heat exchange unit as input parameters for the genetic algorithm optimization.

[0011] In the design method of the louvered fin microchannel heat exchanger based on heat transfer unit optimization, the prognosis coefficient is To evaluate the quality of the established response surface model; is the sum of squared prediction errors, which are estimated based on cross-validation; is the total change, ,in, is the actual output data of each sample, is the actual output data mean, and N is the number of samples.

[0012] In the design method of the louvered fin microchannel heat exchanger based on heat transfer unit optimization, in step 4, the optimization using the genetic algorithm is to meet the constraint conditions: , Under this condition, the optimal value of the minimum total volume of the heat exchanger, the maximum heat transfer, and the minimum fan power is achieved, where FW is the fan power, F p is the fin spacing, N c is the number of heat exchange unit columns, P d is the width of the blind area, H to is the outer height of the flat tube, N r is the number of rows of heat exchange units, is the inlet and outlet pressure drop of the fluid on the fin side, and q is the heat transfer capacity of the heat exchange unit.

[0013] In the louvered fin microchannel heat exchanger design method based on heat transfer unit optimization, in step five, the Pareto optimal frontier is obtained by changing the weight factors of three conflicting objective parameters during the optimization process using a genetic algorithm.

[0014] In the louvered fin microchannel heat exchanger design method based on heat transfer unit optimization, the louvered fin microchannel heat exchanger is arranged in an air conditioner.

[0015] Compared with the prior art, the present invention has the following advantages: the present invention uses a genetic algorithm based on a heat transfer unit to find the optimal design of a louvered fin microchannel heat exchanger, which has an outstanding heat exchange effect and a small total volume of the heat exchanger, and is convenient for miniaturization of the heat exchanger; the workload of optimizing the louvered fin microchannel heat exchanger is greatly reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] By reading the detailed description of the preferred specific embodiments below, various other advantages and benefits of the present invention will become clear to those of ordinary skill in the art. The drawings in the specification are only for the purpose of illustrating the preferred embodiments and are not considered to be limitations of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative work. Moreover, the same reference numerals are used to represent the same components throughout the drawings.

[0017] In the attached picture: Figure 1 It is a flow chart of louvered fin microchannel heat exchanger based on heat transfer unit optimization; Figure 2 It is a heat exchange unit diagram of a louvered fin microchannel heat exchanger; Figure 3 It is a Pareto frontier diagram based on genetic algorithm mixed variable multi-objective optimization; Figure 4 It is a geometric comparison diagram of the heat exchange unit before and after optimization.

[0018] The present invention is further explained below in conjunction with the accompanying drawings and embodiments. DETAILED DESCRIPTION

[0019] The specific embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the specific embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0020] It should be noted that certain words are used in the specification and claims to refer to specific components. Those skilled in the art should understand that technicians may use different nouns to refer to the same component. This specification and claims do not use the difference in nouns as a way to distinguish components, but use the difference in the functions of the components as the criterion for distinction. As mentioned throughout the specification and claims, "including" or "comprising" is an open term, so it should be interpreted as "including but not limited to". The subsequent description of the specification is a preferred embodiment of the present invention, but the description is based on the general principles of the specification and is not intended to limit the scope of the present invention. The scope of protection of the present invention shall be determined by the attached claims.

[0021] To facilitate understanding of the embodiments of the present invention, further explanation will be given below by taking specific embodiments as examples in conjunction with the accompanying drawings, and each of the accompanying drawings does not constitute a limitation on the embodiments of the present invention.

[0022] like Figures 1 to 4 As shown, the design method of the louvered fin microchannel heat exchanger based on heat transfer unit optimization includes the following steps: Step 1: Select a heat exchange unit according to the louvered fin microchannel heat exchanger, model the fins and microchannels of the heat exchange unit, and calculate the resistance factor and heat transfer factor under the structural parameters of the heat exchange unit to verify the effectiveness of the heat exchange unit; Step 2: extract the structural parameters of the heat exchange unit to establish sample space points, perform parameter sensitivity analysis, and calculate the heat exchange and flow conditions of the geometric model corresponding to each sample point; Step 3, select the parameter subset with the largest prognostic coefficient and build a response surface model for each response variable; Step 4: Based on the response surface model, a genetic algorithm is applied to find the optimum within the restricted range and the constraint conditions, so that the heat exchange of the heat exchanger is increased and the corresponding fan power is reduced while the total volume of the heat exchanger is reduced; Step five, the output parameters of the established response surface are used as a subset of the objective function of the genetic algorithm optimization, with the goal of reducing the fan power of the louvered fin microchannel heat exchanger and increasing the heat exchange capacity under a certain capacity, obtaining the corresponding Pareto frontier, and giving the optimal solution in the geometric parameter dimension region.

[0023] In a preferred embodiment of the louvered fin microchannel heat exchanger design method based on heat transfer unit optimization, step one is to model the louvered fin and the upper and lower microchannels adjacent thereto, and separate the fin from the middle, leaving only half of the fin and the microchannel structure on the same side thereof.

[0024] In a preferred embodiment of the louvered fin microchannel heat exchanger design method based on heat transfer unit optimization, the resistance factor and heat transfer factor under the structural parameters include the fin spacing, flat tube outer height, louver area width, louver angle, flat tube inner width and number of flat tubes of the heat exchange unit.

[0025] In a preferred embodiment of the design method of a louvered fin microchannel heat exchanger based on heat transfer unit optimization, in step 2, the sample space point satisfies: , where N t is the number of microchannels, B ti is the inner width of the flat tube and the floor function is the rounding function. The advanced Latin hypercube sampling method is used to establish sample points within the restricted interval to represent the nonlinear relationship between the input variables.

[0026] In a preferred embodiment of the louvered fin microchannel heat exchanger design method based on heat transfer unit optimization, the heat exchange and flow conditions of the geometric model corresponding to each sample point include the heat exchange and pressure drop corresponding to each heat exchange unit as input parameters for the genetic algorithm optimization.

[0027] In a preferred embodiment of the design method of a louvered fin microchannel heat exchanger based on heat transfer unit optimization, the prognosis coefficient is To evaluate the quality of the established response surface model; is the sum of squared prediction errors, which are estimated based on cross-validation; is the total change, ,in, is the actual output data of each sample, is the actual output data mean, and N is the number of samples.

[0028] In a preferred embodiment of the design method of the louvered fin microchannel heat exchanger based on heat transfer unit optimization, in step 4, the optimization using the genetic algorithm is performed under the condition that the constraint conditions are met: , Under this condition, the optimal value of the minimum total volume of the heat exchanger, the maximum heat transfer, and the minimum fan power is achieved, where FW is the fan power, F p is the fin spacing, N c is the number of heat exchange unit columns, P d is the width of the blind area, H to is the outer height of the flat tube, N r is the number of rows of heat exchange units, is the inlet and outlet pressure drop of the fluid on the fin side, and q is the heat transfer capacity of the heat exchange unit.

[0029] In a preferred implementation of the louvered fin microchannel heat exchanger design method based on heat transfer unit optimization, in step five, the Pareto optimal frontier is obtained by changing the weight factors of three conflicting objective parameters during the genetic algorithm optimization process.

[0030] In a preferred embodiment of the louvered fin microchannel heat exchanger design method based on heat transfer unit optimization, the louvered fin microchannel heat exchanger is arranged in an air conditioner.

[0031] In one embodiment, a design method for a louvered fin microchannel heat exchanger based on heat transfer unit optimization includes: Step 1: According to the existing microchannel heat exchanger, an effective heat exchange unit is selected, the fins and microchannels of the heat exchange unit are modeled, and the resistance factor and heat transfer factor under the structural parameters of the heat exchange unit are calculated to verify the effectiveness of the heat exchange unit; Step 2: extract the structural parameters of the heat exchange unit, establish sample space points, perform parameter sensitivity analysis, and calculate the heat exchange and flow conditions of the geometric model corresponding to each sample point; Step 3, select the parameter subset with the largest prognostic coefficient and build the most predictive meta-model for each response variable; Step 4: Based on the established response surface model, a genetic algorithm is used to find the optimal solution within the restricted range and constraints. While the total volume of the heat exchanger is reduced, the heat transfer capacity of the heat exchanger is increased and the corresponding fan power is reduced.

[0032] Step five, the output parameters of the established response surface are used as a subset of the objective function of the genetic algorithm optimization, with the goal of reducing the fan power of the microchannel heat exchanger and increasing the heat transfer under a certain capacity, and the corresponding Pareto front is obtained, giving the optimal solution in the geometric parameter dimension region.

[0033] The optimal design of the louvered fin microchannel heat exchanger was obtained by using a genetic algorithm based on the heat transfer unit. The heat transfer effect is outstanding and the total volume of the heat exchanger is small, which facilitates the miniaturization of the heat exchanger and greatly reduces the workload of optimizing the louvered fin microchannel heat exchanger.

[0034] Preferably, in the method for designing a louvered fin microchannel heat exchanger based on optimization of a heat transfer unit, the effective heat exchange unit selected in step one is based on the premise that the heat exchange flow conditions of the heat exchanger are uniform, and a representative heat exchange unit is selected to simplify the solution.

[0035] Preferably, the method for designing a louvered fin microchannel heat exchanger based on heat transfer unit optimization, wherein the fins and microchannels of the heat exchange unit are modeled in step one, a louvered fin and the upper and lower microchannels adjacent to it are selected, and since the fins are symmetrical, in order to reduce the solution time, the fins are separated from the middle, and only half of the fins and the microchannel structure on the same side are retained.

[0036] In one embodiment, Figure 1 As shown in the figure, the design method of the louvered fin microchannel heat exchanger based on the optimization of the heat transfer unit includes: using the energy efficiency-unit number method to design a three-dimensional model of the louvered microchannel heat exchanger that meets the heat exchange requirements, selecting the heat exchange unit, and using the empirical formula to compare the simulated value and experimental value of the heat transfer factor and friction factor on the louvered fin side of the heat exchanger; using Design Modeler to perform parameterized modeling on the heat exchange unit, using the optimal Latin hypercube sampling to perform experimental design in the constraint space, performing parameter sensitivity analysis, and establishing a proxy model between the input parameters and the output parameters; when the quality of the proxy model is greater than 85%, a mixed variable multi-objective optimization is performed; if the quality of the proxy model is less than 85%, the number of samples is increased; outputting a three-dimensional Pareto chart of the total heat transfer of the heat exchanger, the total volume of the heat exchanger, and the required power, comparing the performance indicators of the optimized model with the original model, and selecting a suitable non-inferior solution.

[0037] In one embodiment, if Figure 2 As shown, the details of the heat exchange unit of the shutter heat exchanger include: the width of the shutter area P d , flat tube inner width B ti , shutter angle θ, flat tube outer height H to , fin spacing F p The heat exchanger is composed of heat exchange units. To optimize the volume of the entire heat exchanger, the assembly structure of the heat exchange unit must be considered. Therefore, the number of rows N of the heat exchange unit should be considered. r and the number of columns N c ; Use optimal Latin hypercube sampling to establish 200 sample points for these parameters and calculate the results. Table 1 below shows some of the results: Table 1: Result data of some sample points

[0038] In one embodiment, if Figure 3As shown in the figure, after the high-quality response surface model is established, a genetic algorithm (NSGA-II) is used to perform an optimization exploration analysis on the heat exchange unit of the louvered fin heat exchanger in the constraint space of 8 input variables, with the total heat exchange Q of the heat exchanger changing within a certain range as a constraint, the total volume V of the heat exchanger being the minimum, the required heating (cooling) power P of the heat exchanger being the minimum as the goal, and the number of microchannels not exceeding the outer width of the flat tube as a restriction condition. The inspection rate is set to 0.5, the mutation probability is set to 0.33, the initial population size is set to 10, the parent population size is set to 10, the archive population size is set to 10,000, the maximum number of iterations is set to 1,000, and the Pareto frontier is obtained after 1,000 cycles. The 7624th sample point is selected. Table 2 below is a comparison between the selected optimal solution and the initial value. It can be seen that the heat exchange is reduced by 1.58%, the required power is reduced by 29.8%, and the total volume is reduced by 32.3%. It is proved that this method can greatly reduce the required power and the total volume of the heat exchanger while losing very little heat transfer, which provides great value for the lightweight application of heat exchanger models.

[0039] Table 2: Pareto optimal solution set

[0040] In one embodiment, if Figure 4 As shown, the optimal solution model is established and compared with the original model. It can be seen intuitively from Table 3 below that by increasing the width of the louver area, reducing the louver angle and the fin spacing, the flow heat transfer can be significantly improved. While ensuring that the heat transfer of the unit is slightly reduced, the flow resistance pressure drop is significantly reduced, thereby reducing the power required for heating (cooling) of the heat exchanger.

[0041] Table 3: Parameter comparison

[0042] Although the embodiments of the present invention are described above in conjunction with the accompanying drawings, the present invention is not limited to the above specific embodiments and application fields, and the above specific embodiments are only illustrative and instructive, rather than restrictive. A person of ordinary skill in the art can also make many forms under the guidance of this specification and without departing from the scope of protection of the claims of the present invention, all of which belong to the protection of the present invention.

Claims

1. A design method for a louvered fin microchannel heat exchanger based on heat transfer unit optimization, characterized in that: The steps include: Step 1: Consider the louvered fin microchannel heat exchanger as a combination of multiple heat exchange units, each heat exchange unit has the same heat exchange conditions, select one of the heat exchange units, model the fins and microchannels of the heat exchange unit, and calculate the fin side air resistance factor f and heat transfer factor j of the selected heat exchange unit; where: , , A c is the inlet cross-sectional area, A s is the heat exchange area between the fin and the air, is the air density, is the air inlet velocity, is the specific heat of air at constant pressure, is the air Prandtl number, Air inlet and outlet pressure drop, is the fin side air convection heat transfer coefficient; Step 2: extract the structural parameters of the heat exchange unit to establish sample space points, calculate the heat exchange and flow condition data corresponding to the sample space points, use the Optislang module of Workbench to perform parameter sensitivity analysis, use the software's optimal prognosis metamodel (MOP) to automatically screen out parameters that have a significant impact on the model output, search for the best parameter subspace, achieve design space dimensionality reduction, use the best sample point as the optimization initial value, and accelerate the optimization process; Step 3, automatically selecting the response surface model with the largest prognostic coefficient based on the sensitivity module in the Optislang module; Step 4: Based on the response surface model, use the optimization module to perform genetic algorithm multi-objective optimization, so as to increase the heat exchange rate of the heat exchanger and reduce the corresponding fan power while ensuring that the total volume of the heat exchanger is reduced; Step 5: Use the output parameters of the established response surface as a subset of the objective function of the genetic algorithm optimization, with the goal of reducing the fan power and increasing the heat exchange rate of the louvered fin microchannel heat exchanger under a certain capacity, and obtain the Pareto frontier; Based on the weight analysis method, a weight of 0.4 is assigned to the total volume V of the heat exchanger, a weight of 0.4 is assigned to the fan power FW, and a weight of 0.2 is assigned to the total heat exchange rate Q of the heat exchanger, so as to obtain the optimal solution.

2. A design method for a louvered fin microchannel heat exchanger based on heat transfer unit optimization according to claim 1, characterized in that: Step 1: Model the louver fin and the upper and lower microchannels adjacent to it, separate the fin from the middle, and only retain half of the fin and the microchannel structure on the same side.

3. The design method of a louvered fin microchannel heat exchanger based on heat transfer unit optimization according to claim 1, characterized in that: The resistance factor and heat transfer factor under the structural parameters include the fin spacing, the outer height of the flat tube, the width of the louver area, the louver angle, the inner width of the flat tube and the number of flat tubes of the heat exchange unit.

4. The design method of a louvered fin microchannel heat exchanger based on heat transfer unit optimization according to claim 1, characterized in that: Step 2: The sample space points satisfy: , where N t is the number of microchannels, B ti is the inner width of the flat tube and the floor function is the rounding function. The advanced Latin hypercube sampling method is used to establish sample points within the restricted interval to represent the nonlinear relationship between the input variables.

5. The design method of a louvered fin microchannel heat exchanger based on heat transfer unit optimization according to claim 1, characterized in that: The heat exchange and flow conditions of the geometric model corresponding to each sample point include the heat exchange amount and pressure drop corresponding to each heat exchange unit as input parameters for the genetic algorithm optimization.

6. The design method of a louvered fin microchannel heat exchanger based on heat transfer unit optimization according to claim 1, characterized in that: The prognostic coefficient is To evaluate the quality of the established response surface model; is the sum of squared prediction errors, which are estimated based on cross-validation; is the total change, ,in, is the actual output data of each sample, is the actual output data mean, and N is the number of samples.

7. The design method of a louvered fin microchannel heat exchanger based on heat transfer unit optimization according to claim 1, characterized in that: In step 4, the optimization using genetic algorithm is to meet the constraints: , Under this condition, the optimal value of the minimum total volume of the heat exchanger, the maximum heat transfer, and the minimum fan power is achieved, where FW is the fan power, F p is the fin spacing, N c is the number of heat exchange unit columns, P d is the width of the blind area, H to is the outer height of the flat tube, N r is the number of rows of heat exchange units, is the inlet and outlet pressure drop of the fluid on the fin side, and q is the heat transfer capacity of the heat exchange unit.

8. The design method of a louvered fin microchannel heat exchanger based on heat transfer unit optimization according to claim 1, characterized in that: In step five, the Pareto optimal frontier is obtained by changing the weight factors of the three conflicting objective parameters in the genetic algorithm optimization process.

9. The design method of a louvered fin microchannel heat exchanger based on heat transfer unit optimization according to claim 1, characterized in that: The louvered fin microchannel heat exchanger is installed in the air conditioner.

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