Design method of louver fin microchannel heat exchanger based on optimization of heat transfer unit

Abstract

The present disclosure discloses a louver fin micro-channel heat exchanger design method based on heat transfer unit optimization, wherein a heat transfer unit is selected according to the louver fin micro-channel heat exchanger, the fins and the micro-channel of the heat transfer unit are modeled, the structural parameters of the heat transfer unit are extracted to establish sample space points, a parameter subset with the largest prognosis coefficient is selected, and a response surface model is established for each response variable; based on the response surface model, a genetic algorithm is applied to optimization within the limit interval and the constraint condition, the heat exchanger volume is reduced, the heat exchanger heat transfer capacity is increased, and the corresponding fan power is reduced; the output parameters of the response surface are used as a subset of the objective function of the genetic algorithm optimization, so as to reduce the fan power of the louver fin micro-channel heat exchanger and improve the heat transfer capacity under a certain capacity, and the corresponding Pareto frontier is obtained, and the optimal solution in the geometric parameter dimension region is given.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of louver fin micro-channel heat exchanger optimization, and particularly relates to a louver fin micro-channel heat exchanger design method based on heat transfer unit optimization. BACKGROUND

[0002] The louver fin micro-channel heat exchanger is commonly used in household air conditioners for heat exchange, and has the advantages of good heat exchange effect and low flow resistance. The design and optimization of the louver fin micro-channel heat exchanger are currently insufficient, and the complexity of the structure of the louver fin micro-channel heat exchanger mainly causes the design obstacle of the louver fin micro-channel heat exchanger, and greatly increases the workload of the optimization design.

[0003] The traditional louver fin micro-channel heat exchanger design method has two unreasonable points: first, the traditional louver fin micro-channel heat exchanger design method is designed by using the empirical formula in the existing heat exchanger design manual, but in fact, the heat exchange conditions of different louver fin micro-channels are different, and the traditional louver fin micro-channel heat exchanger design method cannot consider this point, which is inconsistent with the actual situation. Secondly, when the heat exchanger is designed by using the traditional louver fin micro-channel heat exchanger design method, the calculation is complicated and the design is difficult, which greatly increases the workload of the louver fin micro-channel heat exchanger design and increases the design time.

[0004] The above information disclosed in the background section is only used to enhance the understanding of the background of the present application, and therefore can contain information that is not prior art known to those of ordinary skill in the art. SUMMARY

[0005] The present application provides a louver fin micro-channel heat exchanger design method based on heat transfer unit optimization, which uses a genetic algorithm based on heat transfer unit optimization to obtain the optimal design of the louver fin micro-channel heat exchanger, has outstanding heat exchange effect, and has a small overall volume of the heat exchanger, facilitating the miniaturization of the heat exchanger; and greatly reduces the workload of the optimization of the louver fin micro-channel heat exchanger.

[0006] A louver fin micro-channel heat exchanger design method based on heat transfer unit optimization, comprising the following steps:

[0007] Step one, the louver fin micro-channel heat exchanger is regarded as a combination of multiple heat transfer units, each heat transfer unit has the same heat exchange condition, one of the heat transfer units is selected, the fins and micro-channels of the heat transfer unit are modeled, and the fin side air resistance factor f and the heat transfer factor j of the selected heat transfer unit are calculated; wherein, , , A c is the inlet cross-sectional area, A s is the fin-air heat exchange area, is the air density, is the air inlet velocity, is the air constant pressure specific heat capacity, Pr is the air Prandtl number, is the air pressure drop between the inlet and the outlet, is the fin side air convection heat transfer coefficient;

[0008] Step two, extract the structural parameters of the heat exchange unit to establish a sample space point, calculate the heat exchange and flow condition data corresponding to the sample space point, use the Optislang module of Workbench to perform parameter sensitivity analysis, use the optimal prognosis meta-model MOP of the software to automatically screen out the parameters that have a significant impact on the model output, search for the best parameter subspace, realize the dimension reduction of the design space, take the best sample point as the initial value of optimization, and accelerate the optimization process;

[0009] Step three, automatically select the response surface model with the largest prognosis coefficient in the sensitivity module of the Optislang module;

[0010] Step four, based on the response surface model, use the optimization module to perform genetic algorithm multi-objective optimization, so as to increase the heat exchange capacity of the heat exchanger while reducing the overall volume of the heat exchanger, and correspondingly reduce the fan power;

[0011] Step five, take the output parameters of the response surface as a subset of the objective function of the genetic algorithm optimization, so as to reduce the fan power of the louver fin micro-channel heat exchanger and improve the heat exchange capacity under a certain capacity, and obtain the Pareto frontier; based on the weight analysis method, the overall volume V of the heat exchanger is assigned a weight of 0.4, the fan power FW is assigned a weight of 0.4, and the overall heat exchange capacity Q of the heat exchanger is assigned a weight of 0.2, to obtain the optimal solution.

[0012] In the louver fin micro-channel heat exchanger design method based on heat transfer unit optimization, step one, modeling includes louver fins and micro-channels adjacent to the upper and lower sides of the louver fins, the fins are divided in the middle, and only half of the fins and the micro-channels on the same side are retained.

[0013] In the louver fin micro-channel heat exchanger design method based on heat transfer unit optimization, the resistance factor and the heat transfer factor under the structural parameters include the fin spacing of the heat exchange unit, 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.

[0014] In the louver fin micro-channel heat exchanger design method based on heat transfer unit optimization, step two, the sample space point satisfies: , wherein N t is the number of micro-channels, B tiThe high-level Latin hypercube sampling method is used to establish sample points in the limited interval to represent the nonlinear relationship between the input variables, where the flat tube inner width is wide and the floor function is a down-rounding function. In the design method of the louver fin micro-channel heat exchanger based on the optimization of the heat transfer unit, the heat exchange and flow conditions of the geometric model corresponding to each sample point include the heat exchange amount and pressure drop of each heat transfer unit as input parameters of the genetic algorithm optimization.

[0015] In the design method of the louver fin micro-channel heat exchanger based on the optimization of the heat transfer unit, the prediction coefficient is to evaluate the quality of the established response surface model; wherein, is the sum of squared prediction errors, and the prediction error is estimated based on cross-validation; is the total variation, wherein y i is the actual output data of each sample, is the mean of the actual output data, and N is the sample number.

[0016] In the design method of the louver fin micro-channel heat exchanger based on the optimization of the heat transfer unit, in step four, the genetic algorithm optimization is used to meet the constraint condition: ,

[0017] Then, the optimal values of the minimum total volume, the maximum heat exchange amount and the minimum fan power of the heat exchanger are realized, wherein FW is the fan power, F p is the fin pitch, N c is the number of heat transfer unit columns, P d is the louver area width, H to is the outer height of the flat tube, N r is the number of heat transfer unit rows, is the pressure drop of the fluid inlet and outlet of the fin side, and q is the heat exchange amount of the heat transfer unit.

[0018] In the design method of the louver fin micro-channel heat exchanger based on the optimization of the heat transfer unit, in step five, the genetic algorithm optimization process is used to obtain the Pareto optimal front by changing the weight factors of the three conflicting target parameters.

[0019] In the design method of the louver fin micro-channel heat exchanger based on the optimization of the heat transfer unit, the louver fin micro-channel heat exchanger is arranged in an air conditioner.

[0020] Compared with the prior art, the present application has the following advantages: the present application uses the genetic algorithm optimization based on the heat transfer unit to obtain the optimal design of the louver fin micro-channel heat exchanger, the heat exchange effect is outstanding, the total volume of the heat exchanger is small, and the heat exchanger is small in size; and the workload of the optimization of the louver fin micro-channel heat exchanger is greatly reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Various other advantages and benefits of the present invention will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiments below. The accompanying drawings are intended only to illustrate preferred embodiments and are not to be construed as limiting the present invention. It should be understood that the drawings described below are merely examples of the present invention, and that those skilled in the art will be able to derive other drawings from these drawings without inventive effort. Throughout the drawings, identical reference numerals are used to denote identical components.

[0022] In the attached figure:

[0023] Figure 1 This is a flow chart of a louvered fin microchannel heat exchanger based on heat transfer unit optimization;

[0024] Figure 2 This is a diagram of the heat exchange unit of the louvered fin microchannel heat exchanger;

[0025] Figure 3 It is a Pareto frontier diagram based on genetic algorithm mixed variable multi-objective optimization;

[0026] Figure 4 This is a geometric comparison diagram of the heat exchange unit before and after optimization.

[0027] The present invention will be further explained below with reference to the accompanying drawings and embodiments. DETAILED DESCRIPTION

[0028] Specific embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although 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. Rather, these embodiments are provided 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.

[0029] 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 invention. The scope of protection of the present invention shall be as defined in the attached claims.

[0030] To facilitate understanding of the embodiments of the present invention, further explanation will be given below using specific embodiments as examples in conjunction with the accompanying drawings, and the accompanying drawings do not constitute a limitation on the embodiments of the present invention.

[0031] 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:

[0032] Step 1: Select a heat exchange unit based on 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;

[0033] Step 2: extracting the structural parameters of the heat exchange unit to establish sample space points, performing parameter sensitivity analysis, and calculating the heat exchange and flow conditions of the geometric model corresponding to each sample point;

[0034] Step 3: Select the parameter subset with the largest prognostic coefficient and build a response surface model for each response variable;

[0035] Step 4: Based on the response surface model, a genetic algorithm is applied to find the optimal solution within the restricted range and the constraints, so as to increase the heat exchange capacity of the heat exchanger and reduce the corresponding fan power while ensuring that the total volume of the heat exchanger is reduced;

[0036] In 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 transfer capacity under a certain capacity, and the corresponding Pareto front is obtained, giving the optimal solution in the geometric parameter dimension region.

[0037] 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 to separate the fin from the middle, retaining only half of the fin and the microchannel structure on the same side thereof.

[0038] 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.

[0039] 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 points satisfy: , where N t is the number of microchannels, B tiThe flat tube inner width and the floor function are a down-rounding function, and the advanced Latin hypercube sampling method is used to establish sample points in the limited interval to represent the nonlinear relationship between the input variables.

[0040] In the preferred embodiment of the louver fin micro-channel 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 transfer unit as input parameters for the genetic algorithm optimization.

[0041] In the preferred embodiment of the louver fin micro-channel heat exchanger design method based on heat transfer unit optimization, the prediction coefficient is to evaluate the quality of the established response surface model; wherein, is the sum of squared prediction errors, and the prediction error is estimated based on cross-validation; is the total variation, wherein y i is the actual output data of each sample, is the mean of the actual output data, and N is the number of samples.

[0042] In the louver fin micro-channel heat exchanger design method based on heat transfer unit optimization, in step four, the genetic algorithm optimization is used to meet the constraint conditions: ,

[0043] Next, the optimal values of the total volume of the heat exchanger, the heat exchange amount, and the fan power are realized, wherein FW is the fan power, F p is the fin pitch, N c is the number of heat transfer unit columns, P d is the louver area width, H to is the outer height of the flat tube, N r is the number of heat transfer unit rows, is the pressure drop of the fluid inlet and outlet of the fin, and q is the heat transfer amount of the heat transfer unit. In the preferred embodiment of the louver fin micro-channel heat exchanger design method based on heat transfer unit optimization, in step five, the genetic algorithm optimization process is used to obtain the Pareto optimal frontier by changing the weight factors of the three conflicting target parameters.

[0044] In the preferred embodiment of the louver fin micro-channel heat exchanger design method based on heat transfer unit optimization, the louver fin micro-channel heat exchanger is provided in an air conditioner.

[0045] In one embodiment, the louver fin micro-channel heat exchanger design method based on heat transfer unit optimization includes:

[0046] Step 1: Based on 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;

[0047] Step 2: Extract the structural parameters of the heat exchange unit, establish sample space points, perform parameter sensitivity analysis, and calculate the heat transfer and flow conditions of the geometric model corresponding to each sample point;

[0048] Step 3: Select the parameter subset with the largest prognostic coefficient to build the most predictive meta-model for each response variable;

[0049] Step 4: Based on the established response surface model, a genetic algorithm is applied to find the optimal solution within the restricted range and constraints. While ensuring that the total volume of the heat exchanger is reduced, the heat exchange capacity of the heat exchanger is increased and the corresponding fan power is reduced.

[0050] In step five, the output parameters of the established response surface are used as a subset of the objective function of the genetic algorithm optimization. The goal is to reduce the fan power of the microchannel heat exchanger and increase the heat transfer capacity under a certain capacity, obtain the corresponding Pareto front, and give the optimal solution in the geometric parameter dimension region.

[0051] The optimal design of the louvered fin microchannel heat exchanger was obtained by using a genetic algorithm based on heat transfer units. The heat transfer effect is outstanding and the total volume of the heat exchanger is relatively small, which facilitates the miniaturization of the heat exchanger and greatly reduces the workload of optimizing the louvered fin microchannel heat exchanger.

[0052] Preferably, in the design method of a louvered fin microchannel heat exchanger based on heat transfer unit optimization, the effective heat exchange unit selected in step one is based on the premise that the heat exchange flow condition of the heat exchanger is uniform, and representative heat exchange units are selected to simplify the solution.

[0053] Preferably, the method for designing a louvered fin microchannel heat exchanger based on heat transfer unit optimization is as follows: in step one, the fins and microchannels of the heat exchange unit are modeled, a louvered fin and the upper and lower microchannels adjacent to it are selected, and since the fin is symmetrical, in order to reduce the solution time, the fin is separated from the middle, retaining only half of the fin and the microchannel structure on the same side.

[0054] In one embodiment, Figure 1As shown in the figure, the design method of the louver fin microchannel heat exchanger based on heat transfer unit optimization includes: using the energy efficiency-unit number method to design a three-dimensional model of the louver microchannel heat exchanger that meets the heat exchange requirements, selecting the heat exchange unit, and using empirical formulas to compare the simulated and experimental values ​​of the heat transfer factor and friction factor on the louver fin side of the heat exchanger; using Design Modeler to parametrically model the heat exchange unit, using optimal Latin hypercube sampling to perform experimental design in the constraint space, performing parameter sensitivity analysis, and establishing a surrogate model between input parameters and output parameters; when the quality of the surrogate model is greater than 85%, a mixed variable multi-objective optimization is performed; if the quality of the surrogate model is less than 85%, the number of samples is increased; outputting a three-dimensional Pareto chart of the total heat transfer capacity, total volume of the heat exchanger, and required power of the heat exchanger, comparing the performance indicators of the optimized model with the original model, and selecting a suitable non-inferior solution.

[0055] In one embodiment, Figure 2 As shown, the details of the heat exchange unit of the louver heat exchanger include: the width of the louver 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 assembled from 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 must be considered. r and the number of columns N c ; Optimal Latin Hypercube sampling was used to establish 200 sample points for these parameters and the results were calculated. Table 1 below shows some of the results:

[0056] Table 1: Result data of some sample points

[0057]

[0058] In one embodiment, Figure 3As shown, after the high-quality response surface model is established, a genetic algorithm (NSGA-II) is used to change the total heat exchange Q of the heat exchanger in a certain range as a constraint, the total volume V of the heat exchanger is minimized, the heating (cooling) power P required by the heat exchanger is minimized as an objective, and the number of microchannels is limited by the outer width of the flat tube. The optimization exploration analysis of the louver fin heat exchange unit is performed in the constraint space of 8 input variables. The checking rate is set to 0.5, the mutation probability is 0.33, the initial population size is 10, the parent population size is 10, the archive population size is 10000, and the maximum number of iterations is 1000. After 1000 cycles, the Pareto frontier is obtained. The 7624th sample point is selected, and Table 2 below is the comparison between the selected optimal solution and the initial value. It can be seen that the heat exchange capacity decreases by 1.58%, the required power decreases by 29.8%, and the total volume decreases 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 a small amount of heat exchange capacity, which provides great value for the lightweight application of the heat exchanger model.

[0059] Table 2: Pareto optimal solution set

[0060]

[0061] In one embodiment, as shown in Figure 4 , the model of the optimal solution is established and compared with the original model. As can be seen directly from Table 3 below, by increasing the louver area width, reducing the louver angle and fin pitch, the flow and heat exchange can be significantly improved. While ensuring that the heat exchange capacity of the unit decreases slightly, the flow resistance pressure drop is significantly reduced, thereby reducing the heating (cooling) power required by the heat exchanger.

[0062] Table 3: Parameter comparison

[0063]

[0064] Although the embodiments of the present application are described above in combination with the drawings, the present application is not limited to the above specific embodiments and application fields, and the above specific embodiments are only illustrative and guiding, but not limiting. Those skilled in the art can make many forms under the guidance of the present specification and without departing from the scope protected by the claims of the present application, which are all within the protection scope of the present application.

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. , , 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 capacity of air at constant pressure, Pr is the Prandtl number of air, 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 Workbench's Optislang module to perform parameter sensitivity analysis, use the software's optimal prognosis metamodel (MOP) to automatically screen out parameters that have a significant impact on model output, search for the optimal parameter subspace, achieve design space dimensionality reduction, and use the optimal sample points as optimization initial values ​​to accelerate the optimization process. Step 3: Automatically select 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, a genetic algorithm multi-objective optimization is performed using the optimization module to achieve an increase in the heat exchanger heat transfer capacity and a corresponding decrease in the fan power while ensuring that the total volume of the heat exchanger is reduced; In step 5, 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 and increasing the heat transfer of the louvered fin microchannel heat exchanger under a certain capacity, to obtain the Pareto front; 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 transfer Q of the heat exchanger to obtain the optimal solution.

2. The design method of 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 adjacent upper and lower microchannels, separate the fin from the middle, and retain only 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 of the heat exchange unit, 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, which serve 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, , where y i 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 the optimal conditions, the total volume of the heat exchanger is minimized, the heat transfer capacity is maximized, and the fan power is minimized. Among them, 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 heat exchange unit rows, 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 during the optimization process using the genetic algorithm.

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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