A parameter optimization design method for front and back partitioned louver fins

Abstract

The application discloses a parameter design method for front and rear partitioned louver fins, and belongs to the technical field of parameter design of front and rear partitioned louver fins. The method takes the front and rear partitioned louver fins as objects, under the fixed two-dimensional matrix and the periodic three-dimensional CFD calculation framework, first determines core design variables through sensitivity screening, then constructs parameter samples by adopting a Box-Behnken test design, combines CFD response results to establish an explicit proxy model of heat transfer factors and resistance factors, further carries out multi-objective optimization by adopting NSGA-II, and combines PEC indexes to carry out secondary screening on a Pareto solution set, and outputs representative parameter schemes suitable for different optimization targets, wherein the representative parameter schemes include a symmetric optimization group with priority of comprehensive thermal hydraulic performance and an asymmetric optimization group with priority of heat transfer performance. The method establishes a system parameter design path for the front and rear partitioned louver fins, and can realize collaborative optimization of air-side heat transfer performance and flow resistance performance.

Description

Technical Field

[0001] This invention relates to the field of front and rear partition louver fin parameter design technology, and in particular to a parameter design method for front and rear partition louver fins. Background Technology

[0002] Louvered fins are widely used on the air side of microchannel heat exchangers to improve airflow organization, disrupt the thermal boundary layer, and enhance heat transfer performance. Existing louvered fin heat exchangers are typically studied using symmetrical louver angles or a small number of asymmetrical samples for comparison. While increasing the overall louver angle enhances flow disturbance and heat transfer, it also significantly increases air-side drag and pressure drop. Conversely, decreasing the overall louver angle reduces drag, but insufficient downstream disturbance leads to a thicker thermal boundary layer along the flow path, resulting in a decline in overall heat transfer performance.

[0003] Most existing studies focus on single-factor comparisons or local parameter discussions, lacking a systematic optimization framework that addresses the bi-dimensional variable parameter space of front and rear partitions. On the one hand, full-space traversal computational fluid dynamics (CFD) requires a large computational workload and is inefficient; on the other hand, there are complex coupling relationships between design variables, making it difficult for traditional empirical calculations to simultaneously enhance air-side heat transfer and control drag, and to output representative parameter combinations for different engineering objectives. Summary of the Invention

[0004] The purpose of this invention is to provide a parameter optimization design method for front and rear section louver fins. This method uses the front and rear louver angles as independent design variables, combining sensitivity screening, experimental design, CFD response database, explicit surrogate model, multi-objective optimization, and PEC secondary decision-making to achieve synergistic optimization of air-side heat transfer performance and flow resistance performance. It outputs representative parameter schemes suitable for different optimization objectives, with the symmetrical optimization group showing better PEC and the asymmetrical optimization group showing better heat transfer factor.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a parameter optimization design method for front and rear partition louver fins, comprising the following steps: S1. Establish a periodic three-dimensional CFD numerical model with the front and rear partitioned louver fins as the research object, and verify the reliability of the CFD numerical model to obtain thermal-hydraulic performance data for parameter evaluation. S2. Perform sensitivity screening on the geometric parameters of the front and rear partition louver fins to determine the core design variables and their value ranges; S3. Construct experimental design sample points based on the core design variables, and calculate the heat transfer factor and drag factor corresponding to each sample point through the CFD numerical model to form a response database; S4. Based on the response database, establish an explicit proxy model for heat transfer factor and resistance factor, and verify the effectiveness of the explicit proxy model. S5. Based on the explicit surrogate model, perform multi-objective optimization with the goal of maximizing the heat transfer factor and minimizing the drag factor to obtain the Pareto optimal solution set; S6. Based on the comprehensive thermal-hydraulic performance evaluation index PEC, the optimal solution set of Pareto is screened a second time to output representative parameter schemes.

[0006] Preferably, in step S1, the CFD numerical model uses velocity inlet, pressure outlet, periodic boundary and heated wall boundary conditions to perform air-side heat-fluid coupling calculation, and the CFD numerical model is solved using the SST k-ω turbulence model.

[0007] Preferably, in step S1, the model reliability verification includes mesh independence verification and empirical correlation comparison verification, wherein the empirical correlation comparison verification adopts the Chang-Wang correlation.

[0008] Preferably, the front and rear partitioned louver fins include an outer frame body, and a front opening area and a rear opening area are spaced apart on the two opposite side walls of the outer frame body. Multiple louver fins are provided in the front opening area and the rear opening area, which are inclined relative to the direction of incoming flow.

[0009] Preferably, in step S2, the core design variable includes the fin spacing. , Venetian blind spacing Front section of louver corner and the rear corner of the louver The fin spacing The distance between two adjacent louver slats, the louver spacing The distance between the two opposite sidewalls of the outer frame body, and the angle of the front louver. The angle of inclination of the louver slats in the front window area, and the angle of the rear louver... The angle of inclination of the louvers in the rear window area.

[0010] Preferably, the value range of the core design variable is: 1.40 mm ≤ ≤ 2.00 mm; 1.10 mm ≤ ≤ 1.40 mm; 20.0° ≤ ≤ 32.0°; 20.0° ≤ ≤ 32.0°.

[0011] Preferably, in step S3, a four-factor, three-level Box-Behnken experimental design is used to construct parameter sample points.

[0012] Preferably, in step S5, the NSGA-II multi-objective optimization algorithm is used to optimize the parameters of the heat transfer factor and the drag factor.

[0013] Preferably, in step S6, the representative parameter scheme includes at least a symmetrical preferred group that prioritizes comprehensive thermal-hydraulic performance and an asymmetrical preferred group that prioritizes heat transfer performance.

[0014] Preferably, the parameter scheme corresponding to the symmetric preferred group is as follows: = 1.40 mm, = 1.40 mm, =20.0° = 20.0°; The parameter scheme corresponding to the asymmetric preferred group is: = 1.40 mm, = 1.40 mm, = 20.0°, = 24.0°.

[0015] Among them, the symmetric optimization group is superior in terms of the comprehensive thermal-hydraulic performance evaluation index PEC, while the asymmetric optimization group is superior in terms of the heat transfer factor j. Both representative parameter schemes were obtained by the same optimization method under different objective trade-offs, jointly demonstrating the effectiveness of the method of this invention.

[0016] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. This invention establishes a complete parametric design framework for front and rear partitioned louver fins. It establishes a parameter space around core variables, with clear objectives and well-defined boundaries, and the method exhibits high repeatability. It overcomes the limitation of existing technologies that only perform simple comparisons on a finite number of discrete samples.

[0017] 2. This invention employs sensitivity screening to eliminate variables that have a weak impact on overall performance, effectively reducing the trial-and-error costs and dimensionality disasters in subsequent optimization caused by invalid variables.

[0018] 3. This invention establishes a response database based on CFD calculations and constructs a quadratic explicit regression surrogate model. The high confidence of the model is verified through analysis of variance. The quadratic explicit regression surrogate model can replace large-scale repetitive CFD calculations, greatly improving design efficiency.

[0019] 4. This invention performs multi-objective optimization and combines the PEC evaluation index to perform secondary decision-making and screening of the solution set. This not only achieves synergistic optimization of heat transfer and resistance, but also outputs comprehensive energy efficiency priority scheme and heat exchange enhancement priority scheme respectively, which facilitates parameter decision-making for different engineering objectives. Attached Figure Description

[0020] Figure 1 This is a three-dimensional structural diagram of the front and rear partitioned louver fins of the present invention; Figure 2 This is a schematic diagram of the three-dimensional periodic computational domain and its boundary conditions of the present invention; Figure 3 This is a schematic diagram illustrating the mesh independence verification of the present invention; Figure 4 This is a schematic diagram comparing the CFD predictions of the baseline model of this invention with the correlation of the Chang-Wang experiment; Figure 5 This is the main effect signal-to-noise ratio analysis diagram based on the Taguchi method of this invention; Figure 6 The fin spacing of this invention Spacing with venetian blinds Schematic diagram of the response surface to the interactive regulation of heat transfer factors; Figure 7 The fin spacing of this invention With the front corner of the louver A schematic diagram of the response surface to the interaction of the drag factor; Figure 8 This is a schematic diagram of the Pareto optimal frontier solution set and decision point resistance factor calculated by the NSGA-II algorithm of this invention; Figure 9 This is a schematic diagram comparing the values ​​of heat transfer factor and drag factor under a wide range of operating conditions according to the present invention. Figure 10 This is a comparative diagram of the comprehensive performance evaluation index PEC under various operating conditions of the present invention.

[0021] 1. Outer frame; 2. Front window area; 3. Rear window area; 4. Venetian blinds. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and preferred embodiments. However, it should be noted that many details listed in the specification are merely to provide the reader with a thorough understanding of one or more aspects of the invention, and these aspects of the invention can be achieved even without these specific details.

[0023] Please see Figures 1 to 10This invention provides a parameter design method for the fins of front and rear partitioned louvers, the technical solution of which is as follows: like Figure 1 As shown, the front and rear partitioned louver fins include an outer frame body 1 and louver fins 4 with a front opening area 2 and a rear opening area 3 on the outer frame body along the incoming flow direction. The front and rear sections are symmetrical in length, the number of openings in the front and rear sections is consistent, and only the louver angles of the front and rear sections are different.

[0024] A parameter design method for front and rear partitioned louver fins includes the following steps: S1. Establish a periodic three-dimensional CFD numerical model with the front and rear partitioned louver fins as the research object, and verify the reliability of the CFD numerical model to obtain thermal-hydraulic performance data for parameter evaluation.

[0025] Specifically, a periodic three-dimensional CFD numerical model is established, and air-side heat-fluid coupling calculations are performed using this model. The computational domain is set as the velocity inlet, pressure outlet, periodic boundary, and heated wall boundary, and a steady-state turbulence model is used for solution. In this embodiment, the SST k-ω turbulence model is employed. The Reynolds number Re and heat transfer factor are used. Resistance factor The evaluation index of comprehensive thermal-hydraulic performance, PEC, is used to evaluate different parameters of louver fins.

[0026] The Reynolds number Re is expressed by the following formula: The convective heat transfer coefficient is expressed by the following formula: The logarithmic mean temperature difference is expressed as follows: heat transfer factor It can be expressed by the following formula: Resistance factor It can be expressed by the following formula: The comprehensive thermal-hydraulic performance evaluation index PEC is expressed by the following formula: in, and This is the value corresponding to the baseline model.

[0027] To ensure the reliability of the numerical results, the CFD model was verified for mesh independence. The specific steps are as follows: By setting multiple grid sizes, the changing trends of pressure drop ΔP and heat transfer coefficient h were compared. A standard grid was selected that balances computational cost and result stability while meeting computational accuracy requirements. The CFD results of the benchmark model were compared and verified with literature correlations; in this embodiment, the Chang-Wang correlation was used.

[0028] The comparison results show that, Factors and The factors showed a consistent trend, and the error was within an acceptable range, indicating that the established numerical model can be used for subsequent parameter design and optimization.

[0029] To verify the sensitivity of the selected turbulence model to the comparison results, after completing the grid independence verification and empirical correlation comparison verification, Re was selected. Lp For three representative operating conditions (=110, 200, and 288), supplementary laminar flow model calculations were performed on the baseline structure, the symmetrical optimization group (comprehensive energy efficiency optimization structure), and the asymmetrical optimization group (heat transfer enhancement optimization structure), and compared with SSTk. The results of the ω model were compared and analyzed. The results are shown in Table 1: Table 1 Comparison of simulation results from SST turbulence model and laminar flow model Table 1 shows that, under the three representative operating conditions mentioned above, the laminar flow model and SSTk... The trends in the thermal-hydraulic performance of each structure obtained from the ω model are consistent. Specifically, the asymmetric enhancement scheme, specifically the asymmetric optimization group, exhibits higher heat transfer factors under the selected operating conditions. This indicates that their advantages in enhancing heat transfer are consistent. Meanwhile, the drag factor varies across models. Although the predictions differed in numerical value, they did not change the ranking of the different structural schemes.

[0030] S2. Perform sensitivity screening on the geometric parameters of the front and rear partition louver fins to determine the core design variables and their value ranges.

[0031] Specifically, before optimization, sensitivity analysis of the geometric parameters was first performed using the Taguchi method. Main effect signal-to-noise ratio analysis was then conducted to assess the impact of different variables on the heat transfer factor. With resistance factor The degree of influence. Analysis results show that the fin spacing... For heat transfer factor The most significant impact; Venetian blind spacing Resistance factor The most significant impact is on fin thickness. The impact on overall thermal-hydraulic performance is relatively weak, therefore it was fixed at 0.12 mm in subsequent optimizations. This invention does not perform indiscriminate optimization of all geometric parameters, but first eliminates weakly sensitive variables through sensitivity screening, and then focuses on... , , and Four core variables construct the parameter space. The scope of the design variables is: 1.40 mm ≤ ≤ 2.00 mm 1.10 mm ≤ ≤ 1.40 mm 20.0° ≤ ≤ 32.0° 20.0° ≤ ≤ 32.0° S3. Construct experimental design sample points based on the core design variables, and calculate the heat transfer factor and drag factor corresponding to each sample point through the CFD numerical model to form a response database.

[0032] Specifically, after sensitivity screening, a four-factor, three-level Box-Behnken design method was used to construct parameter sample points. The design variable was the fin spacing. , Venetian blind spacing Front section of louver corner and the rear corner of the louver A total of 27 parameter combinations were constructed. The corresponding heat transfer factor for each parameter combination was obtained through CFD numerical calculations. and resistance factor This forms a response database.

[0033] The encoding method for the four variables is as follows: A corresponds to the fin spacing. B corresponds to the venetian blind spacing. C corresponds to the front louver corner. D corresponds to the rear section of the louvered corner. As shown in Table 2 below: Table 2 Optimization Design Variables and Horizontal Range Settings The results of 27 Box-Behnken samples are shown in the table below: Table 3. 27 Box-Behnken design matrices and corresponding CFD response results S4. Based on the response database, establish an explicit proxy model for the heat transfer factor and the resistance factor, and verify the effectiveness of the explicit proxy model.

[0034] Specifically, based on the results of 27 Box-Behnken samples, a quadratic explicit regression model of heat transfer factor j and drag factor f was established.

[0035] The heat transfer factor regression model is as follows: The resistance factor regression model is as follows: Where A, B, C, and D are the coded values ​​of the design variables.

[0036] To verify the effectiveness of the established quadratic explicit regression model, the following was performed: and The model underwent analysis of variance (ANOVA). The results are shown in the table below: Table 4. Analysis of variance and statistical index verification of the quadratic regression model. Table 5. Comparison and error analysis of the thermo-hydraulic performance of the baseline, RSM prediction, and CFD verification configurations. The results show that the model is statistically significant overall, with good fitting and predictive abilities, and can replace a large number of repetitive CFD calculations for subsequent optimization. Based on the established regression model, response surface methodology was further conducted on the main variables and their interactions. The results show that: and right and The effects were all significant; and The matching relationship has a significant impact on the overall thermal-hydraulic performance; the difference in angle between the front and rear sections is not necessarily better the larger it is, but needs to be in harmony with... and Collaborative matching.

[0037] S5. Based on the explicit surrogate model, perform multi-objective optimization with the goal of maximizing the heat transfer factor and minimizing the drag factor to obtain the Pareto optimal solution set.

[0038] Specifically, based on the established quadratic explicit proxy model, the NSGA-II multi-objective optimization algorithm is used for parameter optimization.

[0039] The optimization objective is: Objective 1: Maximize the heat transfer factor ; Objective 2: Minimize the resistance factor .

[0040] The optimization constraints are: 1.40 mm ≤ ≤ 2.00 mm; 1.10 mm ≤ ≤ 1.40 mm; 20.0° ≤ ≤ 32.0°; 20.0° ≤ ≤ 32.0° = 0.12 mm.

[0041] The Pareto optimal front solution set is obtained by solving using NSGA-II.

[0042] S6. Based on the comprehensive thermal-hydraulic performance evaluation index PEC, the optimal solution set of Pareto is screened a second time to output representative parameter schemes.

[0043] Specifically, the Pareto solution set is further screened using the PEC index to obtain a representative parameter scheme.

[0044] The preferred output is the following representative parameter scheme: The preferred outputs are the following two representative parameter schemes: Symmetrical optimization group (overall energy efficiency priority scheme): = 1.40 mm, = 1.40 mm, = 20.0°, = 20.0°; Asymmetric preferred group (preferred scheme for heat transfer enhancement): = 1.40 mm, = 1.40 mm, = 20.0°, = 24.0°.

[0045] This invention revolves around , , and Four core variables establish the parameter space with clear variable boundaries, resulting in high reproducibility of the method. Sensitivity screening eliminates weakly sensitive variables, reducing the trial-and-error costs caused by invalid variables. A rapid prediction tool is established through Box-Behnken design and a quadratic explicit surrogate model, significantly reducing the computational workload required for full-space CFD traversal. By combining NSGA-II and PEC, the collaborative design of heat transfer and resistance performance is achieved, and comprehensive energy efficiency priority schemes and heat transfer enhancement priority schemes are output, enabling parameter decisions for different engineering objectives.

[0046] Among them, within the wide range of operating conditions investigated, the symmetrical preferred group (preferred scheme for heat transfer enhancement) maintained a high heat transfer factor. The results show that the front and rear partitioned angular structure has a good and sustained advantage in enhancing heat transfer; the symmetrical preferred group (comprehensive energy efficiency priority scheme) maintains a better comprehensive thermal-hydraulic performance evaluation index (PEC), indicating that it is more suitable as the comprehensive energy efficiency priority structure. The above results show that the representative scheme output by this invention is not only effective for a single design condition, but also maintains a stable performance ranking relationship within the wide range of conditions investigated, and has good engineering applicability and method robustness.

[0047] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A parameter design method for front and rear partitioned louver fins, characterized in that, Includes the following steps: S1. Establish a periodic three-dimensional CFD numerical model with the front and rear partitioned louver fins as the research object, and verify the reliability of the CFD numerical model to obtain thermal-hydraulic performance data for parameter evaluation. S2. Perform sensitivity screening on the geometric parameters of the front and rear partition louver fins to determine the core design variables and their value ranges; S3. Construct experimental design sample points based on the core design variables, and calculate the heat transfer factor and drag factor corresponding to each sample point through the CFD numerical model to form a response database; S4. Based on the response database, establish an explicit proxy model for heat transfer factor and resistance factor, and verify the effectiveness of the explicit proxy model. S5. Based on the explicit surrogate model, perform multi-objective optimization with the goal of maximizing the heat transfer factor and minimizing the drag factor to obtain the Pareto optimal solution set; S6. Based on the comprehensive thermal-hydraulic performance evaluation index PEC, the Pareto optimal solution set is screened a second time to output representative parameter schemes.

2. The parameter design method according to claim 1, characterized in that: In step S1, the CFD numerical model uses velocity inlet, pressure outlet, periodic boundary and heated wall boundary conditions to perform air-side heat-fluid coupling calculation, and the CFD numerical model is solved using the SST k-ω turbulence model.

3. The parameter design method according to claim 1 or 2, characterized in that: In step S1, the model reliability verification includes mesh independence verification and empirical correlation comparison verification, wherein the empirical correlation comparison verification adopts the Chang-Wang correlation.

4. The parameter design method according to claim 1, characterized in that: The front and rear partitioned louver fins include an outer frame body. A front opening area and a rear opening area are spaced apart on two opposite side walls of the outer frame body. Multiple louver fins are provided in the front opening area and the rear opening area, which are inclined relative to the direction of incoming flow.

5. The parameter design method according to claim 4, characterized in that: In step S2, the core design variables include fin spacing. , Venetian blind spacing Front section of louver corner and the rear corner of the louver The fin spacing The distance between two adjacent louver slats, the louver spacing The distance between the two opposite sidewalls of the outer frame body, and the angle of the front louver. The angle of inclination of the louver slats in the front window area, and the angle of the rear louver slats. The angle of inclination of the louvers in the rear window area.

6. The parameter design method according to claim 5, characterized in that: The value range of the core design variables is as follows: 1.40 mm ≤ ≤ 2.00 mm;1.10 mm ≤ ≤ 1.40 mm;20.0° ≤ ≤ 32.0°;20.0° ≤ ≤ 32.0°。 7. The parameter design method according to claim 1, characterized in that: In step S3, a four-factor, three-level Box-Behnken experimental design is used to construct parameter sample points.

8. The parameter design method according to claim 1, characterized in that: In step S5, the NSGA-II multi-objective optimization algorithm is used to optimize the parameters of the heat transfer factor and the drag factor.

9. The parameter design method according to claim 6, characterized in that: In step S6, the representative parameter scheme includes at least a symmetrical preferred group that prioritizes comprehensive thermal-hydraulic performance and an asymmetrical preferred group that prioritizes heat transfer performance.

10. The parameter design method according to claim 9, characterized in that: The parameter scheme corresponding to the symmetric preferred group is as follows: = 1.40 mm, = 1.40 mm, = 20.0°, = 20.0°; The parameter scheme corresponding to the asymmetric preferred group is: = 1.40 mm, = 1.40 mm, =20.0° = 24.0°.

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