Genetic algorithm-based louver fin geometric parameter optimization method

Abstract

The invention discloses a louver fin geometric parameter optimization method based on a genetic algorithm, and the method comprises the steps: obtaining the louver region length, the louver region width, the fin thickness, the fin angle, the fin length and the fin number of louver fins, and carrying out the parametric modeling of the louver fins, obtaining a flow friction factor f and a heat transfer factor j of the fin structure of the shutter under variable working conditions; establishing space sample data points which are uniformly covered in the intervals to which the parameters belong, respectively solving the pressure drop and the outlet temperature corresponding to each sample point as output variables, and establishing a response surface function which is suitable for the meta-model and has a high prediction level; determining a weight factor of each input parameter to the output variable; and determining a mapping relation between an optimization variable and an output function in combination with a new input parameter, and performing optimization by applying a genetic algorithm to obtain an optimal structure of the louver fin.

Description

Technical Field

[0001] The invention relates to the technical field of optimization of louver fins for household air conditioners, and in particular to a method for optimizing geometric parameters of louver fins based on a genetic algorithm. Background Art

[0002] Household air conditioners usually use louvered fin heat exchangers for heat exchange. The characteristics of the heat exchanger are that the heat exchange area is large and the louvered fins at different angles will enhance the heat exchange effect. In the performance research of louvered fin heat exchangers, the geometric optimization of louvered fins is one of the focuses. However, due to the geometric complexity of louvered fin heat exchangers, there are few studies on the geometric optimization of louvered fins.

[0003] Facing the complex fins of the louvered fin heat exchanger, the traditional fin optimization design method has two unreasonable points: first, the traditional fin optimization design method is based on the calculation of some existing design examples, but due to the complexity of the louvered fin heat exchanger fins, there are few design examples, and it is difficult to obtain the optimal structure of the louvered fins through a large number of samples. Secondly, using the traditional fin optimization design, the geometric features of the fins are few and not easy to change with the progress of research, which greatly increases the workload of geometric modeling.

[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 method for optimizing the geometric parameters of louver fins based on a genetic algorithm. The genetic algorithm is used to obtain the optimal structure of the louver fins, which has a strong heat exchange effect and a small flow pressure drop. Through geometric parameterization, the geometric characteristics of the louver fins are studied more accurately.

[0006] A method for optimizing geometric parameters of louver fins based on genetic algorithm includes:

[0007] Step 1: Obtain the louver area length, louver area width, fin thickness, fin angle, fin length and fin number of the louver fin and perform parameterized modeling on the louver fin to obtain the flow friction factor f and heat transfer factor j of the louver fin structure under variable working conditions, 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;

[0008] Step 2, establishing spatial sample data points with uniform coverage within the intervals to which each parameter belongs, wherein the louver fin structure parameters are within the range of correct generation geometry, and using advanced Latin hypercube sampling to establish a meta-model of input parameters and output parameters, the meta-model including a response surface model of the fin structure input parameters to the output parameter pressure drop and the fin structure input parameters to the output parameter outlet temperature;

[0009] Step 3: Solve the pressure drop and outlet temperature corresponding to each sample point as output variables, and automatically select the response surface model with the largest prognostic coefficient based on the sensitivity module in the sensitivity analysis software Optislang;

[0010] Step 4: Use variance analysis to evaluate the contribution of each factor, quantify the impact of each input parameter on the output variable and output the contribution of each input parameter to the output variable;

[0011] Step 5: Determine the mapping relationship between the optimization variables and the output function in combination with the new input parameters, and use the response surface model and the optimization module to perform multi-objective optimization using the genetic algorithm.

[0012] In the above-mentioned method for optimizing the geometric parameters of louvered fins based on genetic algorithm, the mapping relationship between the input and output parameters of the louvered fin structure parameters is established within the restricted range to meet the constraint conditions. , where N is the number of fins. The number of fins copied in the 3D modeling software is n=N-2; L p is the fin spacing; P l is the length of the blind area.

[0013] In the genetic algorithm-based louver fin geometric parameter optimization method, the shift least squares approximation method is used to establish a response surface model of the fin structure input parameters to the output parameter pressure drop, and the linear regression method is used to establish a response surface model of the fin structure input parameters to the output parameter outlet temperature.

[0014] In the genetic algorithm-based method for optimizing the geometric parameters of louver fins, in step five, the genetic algorithm is used to perform optimization based on the established response surface, wherein the optimal sample point of the response surface model is used as the initial value of optimization.

[0015] In the above-mentioned method for optimizing the geometric parameters of louver fins based on genetic algorithm, in step 5, the genetic algorithm is used to find the optimal solution to satisfy the outlet temperature. , ;in, is the outlet temperature, is the outlet temperature corresponding to the initial fin geometry, The inlet and outlet pressure drops and is the inlet and outlet pressure drop corresponding to the initial fin geometry.

[0016] In the genetic algorithm-based method for optimizing the geometric parameters of louver fins, in step 2, a meta-model of input parameters and output parameters is established, and when the quality of the established meta-model is greater than 80%, multi-objective optimization is performed; if the model quality is less than 80%, the number of samples is expanded; based on the genetic algorithm optimization and output of the Pareto chart, the optimal solution that meets the settings is selected, the performance indicators of the optimized model and the original model are compared, and a suitable non-inferior solution is selected.

[0017] In the genetic algorithm-based method for optimizing the geometric parameters of louver fins, the crossflow fan blades are arranged in the air conditioner.

[0018] Compared with the prior art, the present invention has the following advantages: the method for optimizing the geometric parameters of louver fins based on genetic algorithm described in the present invention utilizes genetic algorithm to obtain the optimal structure of the louver fins, which has a strong heat exchange effect and a small flow pressure drop; through geometric parameterization, the geometric characteristics of the louver fins are obtained more accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] 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.

[0020] In the attached picture:

[0021] Figure 1 It is a flow chart for optimizing the geometric parameters of louver fins based on genetic algorithm;

[0022] Figure 2 It is a schematic diagram of the parameterized structure of the louver fin;

[0023] Figure 3 It is a Pareto diagram based on genetic algorithm multi-objective optimization;

[0024] Figure 4 It is a schematic diagram of the approximate model response fitness.

[0025] The present invention is further explained below in conjunction with the accompanying drawings and embodiments. DETAILED DESCRIPTION

[0026] 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.

[0027] 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.

[0028] 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.

[0029] like Figures 1 to 4 As shown, the louver fin geometric parameter optimization method based on genetic algorithm includes the following steps:

[0030] Step 1: Obtain the louver area length, louver area width, fin thickness, fin angle, fin length and fin number of the louver fin and perform parameterized modeling on the louver fin to obtain the flow friction factor f and heat transfer factor j of the louver fin structure under variable working conditions, 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;

[0031] Step 2, establishing spatial sample data points with uniform coverage within the intervals to which each parameter belongs, wherein the louver fin structure parameters are within the range of correct generation geometry, and using advanced Latin hypercube sampling to establish a meta-model of input parameters and output parameters, the meta-model including a response surface model of the fin structure input parameters to the output parameter pressure drop and the fin structure input parameters to the output parameter outlet temperature;

[0032] Step 3: Solve the pressure drop and outlet temperature corresponding to each sample point as output variables, and automatically select the response surface model with the largest prognostic coefficient based on the sensitivity module in the sensitivity analysis software Optislang;

[0033] Step 4: Use variance analysis to evaluate the contribution of each factor, quantify the impact of each input parameter on the output variable and output the contribution of each input parameter to the output variable;

[0034] Step 5: Determine the mapping relationship between the optimization variables and the output function in combination with the new input parameters, and use the response surface model and the optimization module to perform multi-objective optimization using the genetic algorithm.

[0035] In the preferred embodiment of the louver fin geometric parameter optimization method based on genetic algorithm, the louver fin structural parameters establish a mapping relationship between input and output parameters within the restricted range to meet the constraint conditions. , where N is the number of fins. The number of fins copied in the 3D modeling software is n=N-2; L p is the fin spacing; P l is the length of the blind area.

[0036] In a preferred embodiment of the louver fin geometric parameter optimization method based on genetic algorithm, the shift least squares approximation method is used to establish a response surface model of the fin structure input parameters to the output parameter pressure drop, and the linear regression method is used to establish a response surface model of the fin structure input parameters to the output parameter outlet temperature.

[0037] In a preferred implementation of the method for optimizing the geometric parameters of louver fins based on a genetic algorithm, in step five, a genetic algorithm is used to perform optimization based on an established response surface, wherein the optimal sample point of the response surface model is used as the initial value for optimization.

[0038] In the preferred implementation of the method for optimizing the geometric parameters of the louvered fin based on a genetic algorithm, in step 5, a genetic algorithm is used to optimize the outlet temperature. , ;in, are the outlet temperature and pressure drop corresponding to the initial fin geometry, respectively.

[0039] In a preferred implementation of the method for optimizing the geometric parameters of louver fins based on a genetic algorithm, in step 2, a meta-model of input parameters and output parameters is established, and when the quality of the established meta-model is greater than 80%, a multi-objective optimization is performed; if the quality of the model is less than 80%, the number of samples is expanded; based on the genetic algorithm optimization and output of the Pareto chart, the optimal solution that meets the settings is selected, the performance indicators of the optimized model and the original model are compared, and a suitable non-inferior solution is selected.

[0040] In a preferred implementation of the method for optimizing the geometric parameters of louver fins based on a genetic algorithm, the crossflow fan blades are arranged in an air conditioner.

[0041] In one embodiment, Figure 1 As shown in the figure, a genetic algorithm-based louver fin geometric parameter optimization process includes: parametric modeling of the heat exchange unit fins using Design Modeler, experimental design in the constraint space using optimal Latin hypercube sampling, parameter sensitivity analysis, and establishment of the most prognostic metamodel; when the quality of the established optimal prognostic original model is greater than 80%, multi-objective optimization is performed; if the model quality is less than 80%, the number of samples is increased; based on the genetic algorithm optimization and output of the Pareto chart, the optimal solution that meets the settings is selected, the performance indicators of the optimized model and the original model are compared, and a suitable non-inferior solution is selected;

[0042] In one embodiment, if Figure 2 As shown in the figure, the louver fins of the heat exchange unit are parametrically modeled, including: the louver area length P l , blind area width P d 、Fin thickness δ f , fin angle θ, fin spacing L p , fin length L and fin number N, a total of 7 control variables, as shown in Table 1 below, are the value ranges of the control variables. Then, the optimal Latin hypercube method is used to sample the decision space and solve the results of each sample point to establish a high-quality approximate estimation model for the control variables and output variables;

[0043] Table 1: Range of control variables

[0044] In one embodiment, if Figure 3 As shown, based on the established approximate estimation model, a genetic algorithm is used under certain constraints to perform multi-objective optimization of the outlet temperature and the pressure drop loss as small as possible, a Pareto chart is established, and the optimal value that meets the settings is selected. The following Table 2 is a comparison of the optimal solution and the initial value, and Table 3 is a comparison of the results. It can be seen that by changing the input variables, the output temperature can be reduced by 0.24%, while the pressure drop loss can be reduced by 18.6%, which proves that this optimization process can reduce the pressure drop loss while ensuring that the heat exchange amount remains almost unchanged.

[0045] Table 2: Comparison of structural parameters before and after optimization

[0046] Table 3: Comparison of results before and after optimization

[0047] In one embodiment, if Figure 4 As shown in the figure, the response surface model between the fitting input data and the sample point output results, the quality COP of the approximate model is 95% and 93% respectively, the mean absolute error (AMAE) is 0.05701 and 0.0585 respectively; the maximum absolute error (MAE) is 0.17641 and 0.15535 respectively; the root mean square error (RMSE) is 0.06754 and 0.06812 respectively; the determination coefficient R2 is 0.98154 and 0.94299 respectively, and the error indicators meet the requirements.

[0048] 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 method for optimizing geometric parameters of louver fins based on genetic algorithm, characterized in that: The steps include: Step 1: Obtain the louver area length, louver area width, fin thickness, fin angle, fin length and fin number of the louver fin and perform parameterized modeling on the louver fin to obtain the flow friction factor f and heat transfer factor j of the louver fin structure under variable working conditions, 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, establishing spatial sample data points with uniform coverage within the intervals to which each parameter belongs, wherein the louver fin structure parameters are within the range of correct generation geometry, and using advanced Latin hypercube sampling to establish a meta-model of input parameters and output parameters, the meta-model including a response surface model of the fin structure input parameters to the output parameter pressure drop and the fin structure input parameters to the output parameter outlet temperature; Step 3: Solve the pressure drop and outlet temperature corresponding to each sample point as output variables, and automatically select the response surface model with the largest prognostic coefficient based on the sensitivity module in the sensitivity analysis software Optislang; Step 4: Use variance analysis to evaluate the contribution of each factor, quantify the impact of each input parameter on the output variable and output the contribution of each input parameter to the output variable; Step 5: Determine the mapping relationship between the optimization variables and the output function in combination with the new input parameters, and use the response surface model and the optimization module to perform multi-objective optimization using the genetic algorithm.

2. The method for optimizing the geometric parameters of louver fins based on genetic algorithm according to claim 1, characterized in that: The louver fin structure parameters establish a mapping relationship between input and output parameters within the limit range to meet the constraints , where N is the number of fins. The number of fins copied in the 3D modeling software is n=N-2; L p is the fin spacing; P l is the length of the blind area.

3. The method for optimizing the geometric parameters of louver fins based on genetic algorithm according to claim 1, characterized in that: The shifting least squares approximation method was used to establish the response surface model of the fin structure input parameters to the output parameter pressure drop, and the linear regression method was used to establish the response surface model of the fin structure input parameters to the output parameter outlet temperature.

4. The method for optimizing the geometric parameters of louver fins based on genetic algorithm according to claim 1, characterized in that: In step five, a genetic algorithm is used to search for the best solution based on the established response surface, wherein the best sample point of the response surface model is used as the initial value for optimization.

5. The method for optimizing the geometric parameters of louver fins based on genetic algorithm according to claim 1, characterized in that: In step 5, a genetic algorithm is used to find the optimal solution to satisfy the outlet temperature. , ;in, is the outlet temperature, is the outlet temperature corresponding to the initial fin geometry, The inlet and outlet pressure drops and is the inlet and outlet pressure drop corresponding to the initial fin geometry.

6. The method for optimizing the geometric parameters of louver fins based on genetic algorithm according to claim 1, characterized in that: In step 2, a meta-model of input and output parameters is established. When the quality of the established meta-model is greater than 80%, multi-objective optimization is performed; if the quality of the model is less than 80%, the number of samples is increased; based on the genetic algorithm, optimization is performed and a Pareto chart is output, the optimal solution that meets the settings is selected, the performance indicators of the optimized model are compared with those of the original model, and a suitable non-inferior solution is selected.

7. The method for optimizing the geometric parameters of louver fins based on genetic algorithm according to claim 1, characterized in that: The crossflow fan blades are arranged in the air conditioner.

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