Condenser design parameter optimization method, electronic device, and storage medium

Abstract

The application provides a design parameter optimization method of a condenser, an electronic device and a storage medium, belongs to the technical field of condenser design, and realizes rapid performance prediction of multiple parameter combinations by pre-establishing a prediction model of design parameter combinations and target responses, thereby changing the extensive mode of traditional condenser design relying on experience trial calculation and single-factor iterative debugging. The prediction model is used to obtain predicted target responses in batches, and parameter optimization screening is completed in combination with engineering constraints, thereby effectively solving the defects of strong blindness of traditional design parameter matching, large trial calculation amount, low research and development efficiency, difficulty in locking optimal parameters, and poor reproducibility of design results. The number of repeated modeling and simulation iterations is greatly reduced, the research and development cost is reduced, the target parameter combination considering engineering constraints and heat exchange and resistance performance can be quickly obtained, and the design efficiency and design accuracy of the condenser parameter optimization are effectively improved.

Description

Technical Field

[0001] This application belongs to the field of condenser design technology, and particularly relates to a method for optimizing condenser design parameters, electronic equipment, and storage medium. Background Technology

[0002] The condenser is the core heat exchange component of a refrigeration system, and its performance directly determines the system's energy efficiency, compressor power consumption, and operational stability. Performance improvements in air-cooled condensers are primarily limited by their air-side heat exchange capacity. Flat-tube finned condensers, with their advantages of one-piece molding, non-contact thermal resistance, and compact structure, are widely used in household refrigeration equipment. This type of condenser has numerous and interconnected fin structures and flow parameters; parameter selection directly determines the overall heat exchange and resistance performance of the heat exchanger.

[0003] Existing designs mostly rely on empirical calculations or single-factor simulation iterations, making it difficult to quickly predict the actual performance of different parameter combinations. Constrained by engineering requirements such as installation dimensions, manufacturing processes, and pressure drop limits, traditional designs require repeated modeling, calculations, and iterative adjustments, resulting in problems such as blind parameter matching, low optimization efficiency, and difficulty in quickly selecting the optimal parameter combination, thus failing to achieve efficient and accurate optimization of condenser parameters. Summary of the Invention

[0004] In view of this, embodiments of this application provide a method for optimizing the design parameters of a condenser, an electronic device, and a storage medium, which can achieve efficient and accurate optimization of condenser parameters.

[0005] In a first aspect, embodiments of this application provide a method for optimizing the design parameters of a condenser, including: Obtain the initial design parameters of the condenser and the corresponding target response; The initial design parameters are divided into multi-level gradients, and the parameter levels of each initial design parameter are set. An orthogonal array is constructed based on the parameter levels of each of the initial design parameters, wherein the orthogonal array includes: combinations of parameter levels of each of the initial design parameters; Simulation calculations are performed based on the orthogonal array to obtain the target response results corresponding to each parameter level combination; Target design parameters are selected from the initial design parameters based on the target response results corresponding to each combination of parameter levels. The prediction model is established based on the target design parameters and the target response; Obtain multiple sets of design parameter combinations for the condenser to be screened; The design parameter combinations are input into a pre-established prediction model to obtain the predicted target response corresponding to each design parameter combination. The prediction model includes a calculation relationship between the design parameter combinations and the target response, and the target response is used to characterize the performance of the condenser designed based on each design parameter combination. The design parameter combinations are filtered based on the engineering constraints of the condenser and the predicted target response to obtain the target design parameter combinations.

[0006] The method provided in this application, by pre-establishing a predictive model of design parameter combinations and target responses, enables rapid performance prediction of multiple parameter combinations, changing the traditional extensive mode of condenser design that relies on empirical calculations and single-factor iterative debugging. By obtaining predicted target responses in batches through the predictive model and combining them with engineering constraints to complete parameter optimization, it effectively solves the shortcomings of traditional design parameter matching, such as strong blindness, large amount of trial calculations, low R&D efficiency, difficulty in locking optimal parameters, and poor reproducibility of design results. It reduces the number of repeated modeling and simulation iterations, lowers R&D costs, and can quickly obtain target parameter combinations that take into account both engineering constraints and heat transfer and resistance performance, effectively improving the design efficiency and accuracy of condenser parameter optimization.

[0007] The method provided in this application, through parameter level gradient partitioning and orthogonal array experimental design, covers the coupling effects of multiple parameters with a small number of parameter level combinations, avoiding the massive computational pressure of full factorial simulation. Based on the orthogonal array simulation results, initial parameter screening is achieved, eliminating inefficient and weakly influential parameters, retaining only key design parameters for model fitting, effectively reducing the fitting dimensionality and redundant variables of the prediction model. This makes the constructed prediction model more targeted and the parameter mapping relationship more accurate, while ensuring the scientific validity and representativeness of the modeling data, providing a reliable model foundation for subsequent high-precision performance prediction and parameter optimization.

[0008] In some embodiments, the step of filtering target design parameters from the initial design parameters based on the target response results corresponding to each of the parameter level combinations includes: The signal-to-noise ratio corresponding to the target response is calculated based on the target response results corresponding to the combination of each parameter level. Calculate the average signal-to-noise ratio of each initial design parameter at each parameter level based on the signal-to-noise ratio corresponding to the target response; The trend of the average signal-to-noise ratio of each initial design parameter is determined based on the average signal-to-noise ratio of each initial design parameter at each parameter level. Target design parameters are selected from the initial design parameters based on the aforementioned trend.

[0009] The method provided in this application introduces a signal-to-noise ratio (SNR) analysis mechanism to normalize and quantify the responses of different performance targets, adapting to differentiated optimization objectives such as "the greater the heat exchange, the better" and "the lower the resistance, the better." By statistically analyzing the average SNR and its variation patterns at different levels of each parameter, the influence trends of each structural parameter and operating condition parameter on condenser performance are accurately characterized. This enables quantitative screening of target design parameters from initial design parameters, avoiding parameter omissions and misjudgments caused by subjective experience-based screening, and further improving the objectivity and scientific rigor of parameter selection.

[0010] In some embodiments, the step of filtering target design parameters from the initial design parameters based on the changing trend includes: Based on the aforementioned trend, calculate the difference between the maximum average signal-to-noise ratio and the minimum average signal-to-noise ratio for each initial design parameter at each parameter level; Based on the magnitude of the difference between each initial design parameter, the performance impact weights of each initial design parameter are ranked, and the ranking results are obtained. Based on the sorting results, target design parameters are selected from the initial design parameters.

[0011] The method provided in this application calculates the performance impact weight of each design parameter through signal-to-noise ratio range calculation, which can accurately identify the core and secondary parameters that play a dominant role in heat transfer performance, flow resistance, and overall performance. This guides subsequent model building to focus on high-contribution parameters and mitigate the interference of low-contribution parameters. It also provides a basis for parameter priority adjustment in engineering design, facilitating the prioritization of key sensitive parameters in scenarios with processing and assembly constraints, avoiding ineffective parameter optimization, and improving the overall optimization's targeting and engineering applicability.

[0012] In some embodiments, establishing the prediction model based on the target design parameters and the target response includes: The initial prediction model is obtained by fitting the target design parameters as independent variables and the target response as dependent variable. The initial prediction model was validated. If the initial prediction model passes verification, the initial prediction model is determined as the prediction model.

[0013] The method provided in this application obtains an initial prediction model based on the fitting of the selected target design parameters and the target response, and sets up a model verification step. It abandons the simple modeling method of traditional single linear fitting, and accurately characterizes the heat transfer resistance law of multi-parameter coupling and nonlinear changes. This ensures that the final prediction model has a good fitting effect, provides high-precision model support for batch parameter performance prediction and optimal parameter selection, and guarantees the authenticity and reliability of the optimization results.

[0014] In some embodiments, validating the initial prediction model includes: The parameters in the orthogonal array are combined horizontally and input into the initial prediction model to obtain the calculated target response; Calculate the prediction error between the calculated target response and the corresponding target response result; The initial prediction model is validated based on the prediction error, wherein if the prediction error is less than the error threshold, the initial prediction model is determined to have passed validation.

[0015] The method provided in this application quantifies the prediction error between the model's predicted values ​​and the simulated actual values, and uses an error threshold as the model admission criterion to achieve standardized and quantitative verification of the prediction model's accuracy. This effectively filters out prediction models with controllable errors and meeting accuracy standards, avoiding parameter optimization bias caused by low-precision models, ensuring that the model's prediction results closely approximate real heat transfer and resistance characteristics, and improving the accuracy and reliability of subsequent parameter optimization results.

[0016] In some embodiments, the process of filtering the design parameter combinations based on the engineering constraints of the condenser and the predicted target response to obtain a target design parameter combination includes: Based on the engineering constraints of the condenser, the combination of design parameters is screened to obtain a preliminary combination of design parameters. The engineering constraints include at least one of structural boundary constraints, operating condition constraints, and manufacturing constraints. The preliminary design parameters are filtered based on the predicted target response corresponding to each combination of preliminary design parameters to obtain the target design parameter combination.

[0017] The method provided in this application adopts a two-layer screening mechanism of "initial selection based on engineering constraints + fine screening based on performance response". First, invalid parameter combinations that are unfeasible or exceed specifications are eliminated based on structural boundaries, operating conditions, and manufacturing constraints. Then, the optimal solution is selected based on the target response. This ensures that the optimized parameters meet the requirements of actual production assembly and overall machine operation, while achieving optimal matching of heat exchange performance and resistance performance. It solves the problems of poor manufacturability and weak engineering feasibility of traditional pure performance optimization solutions, taking into account both theoretical optimality and engineering practicality.

[0018] In some embodiments, the condenser is a flat-tube shovel-fin condenser, and the design parameters include at least one of: fin spacing, fin height, fin thickness, fin amplitude, fin wavelength, and air-side Reynolds number. The target response includes one of: heat transfer factor, friction factor, and comprehensive performance factor. The heat transfer factor reflects the structure's ability to enhance heat transfer, the friction factor characterizes the flow loss and friction resistance level of air flowing through the fin channels, and the comprehensive performance factor is a dimensionless index that is a coupling of the heat transfer factor and the friction factor.

[0019] Secondly, embodiments of this application provide an apparatus for optimizing the design parameters of a condenser, comprising: The acquisition module is used to acquire multiple sets of design parameter combinations to be screened for the condenser. The calculation module is used to input the design parameter combination into a pre-established prediction model to obtain the predicted target response corresponding to each design parameter combination. The prediction model includes a calculation relationship between the design parameter combination and the target response, and the target response is used to characterize the performance of the condenser designed based on each design parameter combination. The filtering module is used to filter the design parameter combination based on the engineering constraints of the condenser and the predicted target response to obtain the target design parameter combination.

[0020] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method as described in any one of the first aspects; Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in any of the preceding claims.

[0021] Fifthly, embodiments of this application provide a computer program product that, when run on an electronic device, causes the electronic device to execute any of the methods described above. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A schematic diagram illustrating the implementation process of a condenser design parameter optimization method provided in this application embodiment; Figure 2 A schematic diagram illustrating the implementation process of a condenser design parameter optimization method provided in this application embodiment; Figure 3 This application provides a schematic diagram of the main effect curve of factor j in an embodiment. Figure 4 This application provides a schematic diagram of the main effect curve of the f-factor. Figure 5A schematic diagram of the main effect curve of the JF factor provided in an embodiment of this application; Figure 6 A schematic diagram of a process for obtaining a prediction model is provided in an embodiment of this application; Figure 7 A schematic diagram of the error curve of a prediction model for factor j provided in an embodiment of this application; Figure 8 A schematic diagram of the error curve of a prediction model for the f-factor provided in an embodiment of this application; Figure 9 A schematic diagram of the error curve of a prediction model for the JF factor provided in an embodiment of this application; Figure 10 A schematic diagram of a device for optimizing the design parameters of a condenser provided in an embodiment of this application; Figure 11 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0024] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0025] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0026] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0027] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrases "if determined" or "if detected" may be interpreted, depending on the context, as "once determined," "in response to determination," "once detected," or "in response to detection."

[0028] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0029] References to "one embodiment" or "some embodiments" in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized.

[0030] Based on the problems in related technologies, embodiments of this application provide a method for optimizing the design parameters of a condenser that can be applied to electronic devices, such as mobile phones, computers, and servers. Figure 1 This is a schematic diagram illustrating the implementation process of a condenser design parameter optimization method provided in an embodiment of this application, as shown below. Figure 1 As shown, it includes: Step S1: Obtain the initial design parameters of the condenser and the corresponding target response.

[0031] In this embodiment, the initial design parameters refer to all adjustable structural and operating parameters that are predetermined based on the structural characteristics, operating conditions, and engineering experience of the flat tube shovel-fin condenser. These parameters include potential influencing parameters such as fin spacing, fin height, fin amplitude, fin wavelength, and air-side Reynolds number, and represent all candidate variables covering the air-side flow and heat transfer characteristics of the condenser.

[0032] In this embodiment, based on the structural characteristics, assembly constraints, processing limits, and operating condition range of the flat tube shovel-fin condenser, all adjustable initial structural parameters and operating condition parameters are determined; at the same time, a three-dimensional flow heat transfer numerical model of the condenser is established, and the initial performance response benchmark data corresponding to each parameter is obtained through simulation calibration, providing a data basis for subsequent parameter level division and experimental design.

[0033] For example, the structural boundaries and operating conditions of a flat-tube shovel-fin condenser are determined; design variables such as fin spacing Fp, fin height Fh, fin thickness Ft, fin amplitude A, total fin length La, fin wavelength L, and Reynolds number Re are selected; a three-dimensional flow heat transfer model is established; the target response includes the j-factor, f-factor, and JF-factor. The j-factor is used to evaluate heat transfer capacity, the f-factor is used to evaluate flow resistance, and the JF-factor is used to evaluate the comprehensive effect between heat transfer performance and flow resistance. Depending on actual design requirements, one target can be selected for single-objective optimization, or multiple targets can be selected for comprehensive screening.

[0034] Step S2: Perform multi-level gradient division on each of the initial design parameters, and set the parameter level of each of the initial design parameters.

[0035] In this embodiment of the application, the parameter level refers to the numerical level obtained by gradient discretization of each initial design parameter within its feasible engineering value range. It is usually divided into multiple gradient levels such as low, medium and high, which are used to characterize the variation range of the parameter within a reasonable range, so as to ensure that the subsequent experimental design can cover the complete working conditions and structural range.

[0036] In this embodiment, for each initial design parameter, considering actual processing size limits, installation space boundaries, and fan airflow operating range, a uniform gradient division is performed within a reasonable value range, setting multiple gradient parameter levels. The parameter level division balances engineering feasibility and parameter variation coverage, ensuring that the variation range of each parameter fully covers the product's conventional design range, guaranteeing the engineering universality of subsequent optimization results.

[0037] For example, initial design parameters affecting the air-side flow heat transfer performance of the flat-tube shovel-fin condenser are first selected as orthogonal design factors. These initial design parameters may include five parameters: fin spacing Fp, fin height Fh, amplitude A, air-side Reynolds number Re, and fin wavelength L. Each parameter is set to three levels, representing low, medium, and high levels, respectively. Taking a five-factor, three-level approach as an example, the parameters and levels can be set according to Table 1, as shown in Table 1.

[0038] Table 1 is a five-parameter, three-level design table provided in the embodiments of this application.

[0039] Step S3: Construct an orthogonal array based on the parameter levels of each of the initial design parameters, wherein the orthogonal array includes: combinations of the parameter levels of each of the initial design parameters.

[0040] In this embodiment, the orthogonal array is a parameter combination matrix constructed based on the orthogonal experiment principle. It has the characteristics of uniform dispersion and neat comparability. It can replace the full parameter traversal combination with a small number of typical parameter combinations, and significantly reduce the amount of simulation calculation while ensuring the representativeness of parameter analysis.

[0041] In this embodiment, an orthogonal array is generated using the standard orthogonal experimental design method based on the multi-level gradients of each initial design parameter. The orthogonal array can achieve uniform distribution of each parameter level and unbiased coupling coverage of parameter combinations, avoiding the massive computational pressure of full factorial experiments. It reflects the interaction and influence of multiple parameters with the fewest typical sample combinations, effectively reducing the scale of simulation calculations while ensuring the statistical validity of experimental data.

[0042] Continuing with the example above, if we were to perform a full factorial combination of the five parameters mentioned above, we would need to perform 3... 5 =243 sets of calculations. To reduce the computational load, this implementation uses an L27(35) orthogonal array to reduce the 243 sets of full factor combinations to 27 sets. This orthogonal array can ensure that each level of each parameter appears evenly in all combinations, and at the same time, it can ensure that the different level combinations of any two parameters have good balance. Therefore, without significantly reducing the representativeness of parameter analysis, the simulation or experimental workload in the design optimization process of flat tube shovel fin condenser can be greatly reduced.

[0043] Table 2 is a schematic table of parameter level combinations for the initial design parameters of a five-factor, three-level orthogonal array provided in an embodiment of this application. In the table, numbers 1, 2, and 3 represent the low, medium, and high levels of the corresponding parameters, respectively.

[0044] Table 2 is a schematic table of parameter level combinations for the initial design parameters of a five-factor, three-level orthogonal array provided in the embodiments of this application.

[0045] Based on the orthogonal array described above, 27 sets of calculation models for flat-tube shovel-fin condensers can be established sequentially. In each set of calculation models, only the horizontal combination of the corresponding parameters in the table is changed, while the remaining boundary conditions, material parameters, and calculation convergence criteria remain consistent.

[0046] Step S4: Perform simulation calculations based on the orthogonal array to obtain the target response results corresponding to the level combinations of each parameter.

[0047] In this embodiment of the application, the target response result refers to the actual performance result obtained after numerical simulation calculation of each group of parameter level combinations in the orthogonal array, including heat transfer factor, friction factor, comprehensive performance factor, etc., which provides basic sample data for subsequent parameter screening, weight analysis and model fitting.

[0048] In this embodiment of the application, based on the parameter level combination of each group in the orthogonal array, the corresponding flat tube shovel fin condenser simulation model is established in batches. Under the unified boundary conditions, material properties, convergence criteria and solution accuracy, steady-state flow heat transfer numerical calculation is carried out, and the target response results such as heat transfer factor, friction factor and comprehensive performance factor corresponding to each group of parameters are obtained to form a complete experimental sample dataset.

[0049] Step S5: Select target design parameters from the initial design parameters based on the target response results corresponding to each combination of parameter levels.

[0050] In this embodiment, the target design parameter refers to the core sensitive parameter that has a significant impact on the heat transfer performance, resistance performance and overall performance of the condenser, which is selected from all the initial design parameters. It is the key independent variable for subsequent modeling, and redundant parameters with weak influence and low performance contribution are eliminated.

[0051] In this embodiment, multiple sets of sample response data obtained from orthogonal experiments can be used as a basis, combined with statistical methods such as signal-to-noise ratio analysis and range analysis, to quantitatively analyze the influence and contribution of each initial design parameter on heat transfer performance, resistance performance, and overall performance. Redundant parameters with weak performance impact and low contribution rate can be eliminated, and core parameters that play a decisive role in condenser performance can be selected as target design parameters for subsequent modeling, thereby reducing model dimensionality and improving model fitting accuracy.

[0052] Step S6: Establish the prediction model based on the target design parameters and the target response.

[0053] In this embodiment, the high-precision, high-contribution target design parameters obtained through screening are used as independent variables, and the corresponding simulation target response is used as the dependent variable. A mapping relationship between parameters and performance is constructed through nonlinear regression fitting, forming a dedicated prediction model that can quickly predict condenser performance. This model can characterize the nonlinear coupling effect of multiple parameters, breaking through the limitations of traditional single-factor linear fitting, and providing high-precision model support for batch performance prediction and parameter optimization screening.

[0054] The method provided in this application, through parameter level gradient partitioning and orthogonal array experimental design, covers the coupling effects of multiple parameters with a small number of parameter level combinations, avoiding the massive computational pressure of full factorial simulation. Based on the orthogonal array simulation results, initial parameter screening is achieved, eliminating inefficient and weakly influential parameters, retaining only key design parameters for model fitting, effectively reducing the fitting dimensionality and redundant variables of the prediction model. This makes the constructed prediction model more targeted and the parameter mapping relationship more accurate, while ensuring the scientific validity and representativeness of the modeling data, providing a reliable model foundation for subsequent high-precision performance prediction and parameter optimization.

[0055] Step S7: Obtain multiple sets of design parameter combinations for the condenser to be screened.

[0056] In this embodiment, the condenser refers to an air-cooled flat-tube finned condenser in the refrigeration system. It is the core heat exchange component in the refrigeration circuit responsible for the heat release during refrigerant condensation. Convection heat transfer occurs on the air side within the finned channels, while the refrigerant releases heat as it flows inside the flat tube. Design parameters are controllable variables used to determine the condenser's fin structure and operating conditions. Examples include fin spacing, fin height, fin thickness, fin amplitude, fin wavelength, and Reynolds number on the air side. These design parameters are coupled and influence each other, jointly determining the condenser's heat exchange performance and flow resistance. A combination of design parameters is a set of parameters consisting of specific values ​​for multiple design parameters; different combinations represent a complete structural / operating condition design scheme for the condenser. Multiple sets of design parameter combinations to be screened constitute a batch of candidate design schemes.

[0057] In this embodiment, based on the upper and lower limits of the condenser's parameter values, experimental design methods, random sampling, grid sampling, and other methods are used to generate multiple sets of mutually independent candidate parameters within the feasible value range of each design parameter, thereby obtaining multiple sets of design parameter combinations to be screened.

[0058] Step S8: Input the design parameter combination into the pre-established prediction model to obtain the predicted target response corresponding to each design parameter combination. The prediction model includes a calculation relationship between the design parameter combination and the target response, and the target response is used to characterize the performance of the condenser designed based on each design parameter combination.

[0059] In this embodiment, the prediction model is a mathematical model pre-obtained through experimental design and simulation data fitting, establishing a mapping relationship between input parameters and output performance indicators. The model can be a response surface quadratic regression model, containing linear terms, quadratic terms, and parameter interaction terms, capable of characterizing the nonlinear coupling effect between multiple parameters. The target response is a dimensionless index used to quantify and evaluate condenser performance, exemplified by heat transfer factor, friction factor, and comprehensive performance factor. The heat transfer factor characterizes the air-side heat transfer enhancement capability, the friction factor characterizes the air-side flow resistance, and the comprehensive performance factor couples heat transfer and resistance to evaluate the overall performance of the condenser. The predicted target response is the calculated value of the target response obtained by inputting the design parameter combination into the prediction model, which is used to replace a large amount of direct CFD simulation and quickly predict the condenser performance corresponding to this set of parameters.

[0060] In this embodiment, each set of design parameter combinations to be screened is used as the input of the prediction model. The model's built-in mathematical relationships are invoked to batch calculate and output the predicted target response for each set of parameters. The prediction model is pre-trained offline and has undergone accuracy verification, eliminating the need for CFD simulations for each set of candidate parameters. The model utilizes the mathematical relationships between the parameters to quickly predict performance, significantly reducing computational costs. The predicted target response reflects the condenser's heat transfer capacity, flow resistance, or overall performance level under these parameters.

[0061] Step S9: Based on the engineering constraints of the condenser and the predicted target response, the design parameter combination is screened to obtain the target design parameter combination.

[0062] In this embodiment, the engineering constraints are hard boundary limitations that must be met in the condenser engineering design, including at least one of structural boundary constraints, operating condition constraints, and manufacturing constraints. Structural boundary constraints include, for example, the overall installation space and maximum permissible external dimensions. Operating condition constraints include, for example, the maximum permissible air-side pressure drop and the rated Reynolds number range. Manufacturing constraints include, for example, the minimum machinable fin spacing, fin strength, and tool machining limits. The target design parameter combination is the set of design parameters selected from all candidate schemes that simultaneously satisfies all engineering constraints and has the optimal target response performance; this is the finally determined preferred condenser design scheme.

[0063] In this embodiment, a two-layer screening logic can be adopted. The first layer iterates through all parameter combinations to be screened, eliminates parameter combinations that do not meet the engineering constraints, and obtains a preliminary set of feasible parameter combinations. The second layer, based on the numerical merits of the predicted target response corresponding to each preliminary combination, selects the optimal target design parameter combination from the feasible solution set according to the preset optimization objective (maximizing the heat transfer factor, minimizing the friction factor, or maximizing the comprehensive performance factor).

[0064] The method provided in this application, by pre-establishing a predictive model of design parameter combinations and target responses, enables rapid performance prediction of multiple parameter combinations, changing the traditional extensive mode of condenser design that relies on empirical calculations and single-factor iterative debugging. By obtaining predicted target responses in batches through the predictive model and combining them with engineering constraints to complete parameter optimization, it effectively solves the shortcomings of traditional design parameter matching, such as strong blindness, large amount of trial calculations, low R&D efficiency, difficulty in locking optimal parameters, and poor reproducibility of design results. It significantly reduces the number of repeated modeling and simulation iterations, lowers R&D costs, and can quickly obtain target parameter combinations that take into account both engineering constraints and heat transfer and resistance performance, effectively improving the design efficiency and accuracy of condenser parameter optimization.

[0065] In some embodiments, step S5 can be implemented through the following steps: Step S51: Calculate the signal-to-noise ratio corresponding to the target response based on the target response results corresponding to the combination of each parameter level.

[0066] In this embodiment, the signal-to-noise ratio is a dimensionless quantitative evaluation index in the Taguchi experimental analysis method. It is used to normalize the target responses of different types of condensers, distinguish the differentiated optimization attributes of heat transfer performance and resistance performance, eliminate data fluctuations caused by simulation calculation errors and random interference, and accurately reflect the true impact of parameter level changes on the target response.

[0067] In this embodiment, based on the target response results obtained from orthogonal array simulation, and combined with the optimization attributes of different performance indicators, the corresponding signal-to-noise ratio (SNR) values ​​are calculated using appropriate SNR calculation formulas. Specifically, for the heat transfer factor and comprehensive performance factor that maximize the optimization target, a higher SNR formula is used; for the friction factor and air-side pressure drop that minimize the optimization target, a lower SNR formula is used. Through SNR conversion, performance indicators with different dimensions and optimization directions are unified and normalized to a standardized evaluation standard, avoiding evaluation biases caused by differences in indicators and achieving standardized analysis of performance data.

[0068] In the embodiments of this application, for each combination, the heat transfer performance parameters and flow resistance parameters of the flat tube shovel-fin condenser are obtained, such as the j-factor, f-factor and comprehensive evaluation factor JF.

[0069] The signal-to-noise ratio (SNR) in numerical calculations is obtained using the Taguchi method; a higher SNR indicates a higher parameter level. In optimization studies, the goal is to achieve the highest heat transfer performance and overall performance, while minimizing flow resistance. Therefore, when dealing with heat transfer performance, a larger SNR value is better; when dealing with flow resistance, a smaller SNR value is better. The formulas for these are as follows: For targets such as the j-factor and JF-factor, where a larger value is better, a higher SNR value is used. ; For objectives such as the f-factor, where a smaller value is better, a signal-to-noise ratio (SNR) that is optimally smaller is adopted. By averaging the SNR at different parameter levels, the optimal combination of levels for each objective can be obtained.

[0070] ; Where Yi is the target response obtained in the i-th calculation or experiment, and n is the number of repetitions.

[0071] Step S52: Calculate the average signal-to-noise ratio of each initial design parameter at each parameter level based on the signal-to-noise ratio corresponding to the target response.

[0072] In this embodiment, the average signal-to-noise ratio (SNR) refers to the arithmetic mean of the SNRs corresponding to all parameter combinations under the same initial design parameter and at the same parameter level. It is used to quantitatively characterize the overall performance under a single parameter and a single gradient level, and can intuitively reflect the correlation between parameter level changes and condenser performance. The average SNR change trend refers to the continuous change pattern of the average SNR corresponding to the gradient change of a single initial design parameter from low level, medium level to high level, including characteristics such as increasing, decreasing, increasing then decreasing, and decreasing then increasing. This is used to determine the regulatory effect and correlation of the parameter on condenser performance.

[0073] In this embodiment, for each initial design parameter, group statistics are performed according to different parameter levels. The signal-to-noise ratio (SNR) values ​​corresponding to all parameter combinations at the same level are extracted, and the average SNR of each parameter at different levels is calculated by averaging. This step can eliminate random errors in single-group experimental data, refine the stability performance characteristics of a single parameter under different gradients, and provide standardized statistical data for subsequent analysis of parameter influence patterns.

[0074] Step S53: Determine the trend of the average signal-to-noise ratio of each initial design parameter based on the average signal-to-noise ratio of each initial design parameter at each parameter level.

[0075] In this embodiment, the multi-gradient average signal-to-noise ratio (SNR) data of each initial design parameter are traversed, and the differences in average SNR values ​​at low, medium, and high parameter levels are compared to identify the performance change trends of each parameter as its value changes. This trend allows for precise determination of the changes in the condenser's heat exchange performance, resistance performance, and overall performance as parameter values ​​increase. It also clarifies the positive, negative, and critical control characteristics of each parameter on condenser performance, and uncovers the intrinsic correlation between parameters and performance.

[0076] Step S54: Select target design parameters from the initial design parameters based on the changing trend.

[0077] In this embodiment, based on the average signal-to-noise ratio variation trend of each initial design parameter, parameters with significant performance change trends and high sensitivity to the target response are selected as target design parameters. Redundant parameters with flat trends, minimal impact on performance from parameter gradient changes, and extremely low contribution rates are eliminated, retaining only the core sensitive parameters that can effectively regulate the heat exchange and resistance performance of the condenser. This significantly simplifies the dimensions of subsequent modeling parameters and improves the fitting accuracy and computational efficiency of the prediction model.

[0078] The method provided in this application introduces a signal-to-noise ratio (SNR) analysis mechanism to normalize and quantify the responses of different performance targets, adapting to differentiated optimization objectives such as "the greater the heat exchange, the better" and "the lower the resistance, the better." By statistically analyzing the average SNR and its variation patterns at different levels of each parameter, the influence trends of each structural parameter and operating condition parameter on condenser performance are accurately characterized. This enables quantitative screening of target design parameters from initial design parameters, avoiding parameter omissions and misjudgments caused by subjective experience-based screening, and further improving the objectivity and scientific rigor of parameter selection.

[0079] In some embodiments, step S54 can be implemented through the following steps: Step S541: Calculate the difference between the maximum average signal-to-noise ratio and the minimum average signal-to-noise ratio of each initial design parameter at each parameter level based on the changing trend.

[0080] In this embodiment, the difference is the signal-to-noise ratio range, which refers to the difference between the maximum and minimum average signal-to-noise ratios among the average signal-to-noise ratios corresponding to all parameter levels for a single initial design parameter. It is a core indicator for quantifying the impact of parameter level fluctuations on condenser performance. The larger the range value, the stronger the ability of the parameter value to regulate condenser performance and the higher the performance sensitivity; the smaller the range value, the weaker the impact of the parameter on performance.

[0081] In this embodiment, based on the obtained multi-level average signal-to-noise ratios and trends of each parameter, for each initial design parameter, the average signal-to-noise ratio values ​​corresponding to all gradient parameter levels are iterated, and the maximum and minimum average signal-to-noise ratios of the parameter are selected. The difference between the two is the signal-to-noise ratio range. This range can accurately quantify the maximum impact of a single parameter on the target response performance of the condenser within the engineering feasible value range, transforming the originally qualitative trend change into a comparable and quantifiable numerical indicator, eliminating subjective judgment errors.

[0082] Step S542: Based on the magnitude of the difference between each initial design parameter, sort the performance impact weights of each initial design parameter to obtain the sorting result.

[0083] In this embodiment, the performance impact weight ranking is based on the priority ranking of the signal-to-noise ratio range of each initial design parameter. This ranking is used to accurately distinguish the contribution levels of different design parameters to the condenser's heat transfer performance, resistance performance, and overall performance, and to intuitively divide core dominant parameters into secondary auxiliary parameters, providing a quantitative basis for parameter selection and parameter priority control. The ranking result refers to a list of all initial design parameters arranged in descending or ascending order of performance impact, clearly reflecting the differences in the performance impact weight of each parameter. This serves as a direct criterion for accurately selecting high-contribution target design parameters.

[0084] In this embodiment, the signal-to-noise ratio range data of all initial design parameters are aggregated, and all initial design parameters are uniformly weighted and sorted based on the magnitude of the range. A larger range value indicates a more significant change in condenser performance caused by the horizontal fluctuation of that parameter, and a higher weight in the parameter's performance impact; a smaller range value indicates a weaker regulatory effect and stronger redundancy of that parameter on overall performance. This sorting method allows for precise differentiation of the performance contribution levels of each parameter, enabling a quantitative comparison of the influence capabilities of multiple parameters.

[0085] Step S543: Based on the sorting results, select target design parameters from the initial design parameters.

[0086] In this embodiment, based on the ranking of parameter performance impact weights, a reasonable screening threshold is set, prioritizing highly sensitive parameters with large ranges, high weight rankings, and dominant effects on condenser heat transfer and resistance performance as target design parameters; redundant parameters with extremely small ranges, low weights, weak performance contributions, and ineffective parameter adjustments are eliminated. This screening method uses quantitative data as the sole criterion, avoiding the subjectivity and ambiguity of traditional qualitative screening, accurately identifying core parameters that have a substantial regulatory effect on the overall performance of the condenser, further simplifying modeling variables, and ensuring the relevance and accuracy of subsequent prediction models.

[0087] The method provided in this application calculates the performance impact weight of each design parameter through signal-to-noise ratio range calculation, which can accurately identify the core and secondary parameters that play a dominant role in heat transfer performance, flow resistance, and overall performance. This guides subsequent model building to focus on high-contribution parameters and mitigate the interference of low-contribution parameters. It also provides a basis for parameter priority adjustment in engineering design, facilitating the prioritization of key sensitive parameters in scenarios with processing and assembly constraints, avoiding ineffective parameter optimization, and improving the overall optimization's targeting and engineering applicability.

[0088] In some embodiments, step S6 can be implemented through the following steps: Step S61: Fit the target design parameters as independent variables and the target response as the dependent variable to obtain the initial prediction model.

[0089] In this embodiment of the application, model fitting refers to the process of fitting and constructing a mathematical mapping relationship between parameters and performance based on mathematical statistical regression methods, using the core target design parameters obtained by screening as input variables and the target response obtained by simulation as output samples. This process can accurately characterize the nonlinear change law of condenser performance under the coupling effect of multiple parameters.

[0090] In this embodiment, the target design parameters with high contribution and high sensitivity obtained through the aforementioned screening are used as independent variables in the model input; the corresponding target responses such as heat transfer factor, friction factor, and comprehensive performance factor obtained from orthogonal experimental simulation are used as dependent variables in the model output. A second-order quadratic regression fitting method is used to fit the data, constructing an initial prediction model that includes linear effects, quadratic nonlinear effects, and multi-parameter interactions. This overcomes the limitations of traditional linear models in representing the coupling effects of parameters, accurately adapts to the characteristics of multi-parameter linkage constraints and nonlinear performance changes in flat-tube shovel-fin condensers, and initially establishes a quantitative mapping relationship between design parameters and condenser performance.

[0091] Step S62: Validate the initial prediction model.

[0092] In this embodiment of the application, verification refers to the process of checking the fitting accuracy, generalization ability and reliability of the initial prediction model by quantifying the error index. This is used to determine whether the model can truly fit the actual flow heat transfer law of the condenser and to eliminate unqualified models with fitting failure or excessive deviation.

[0093] In this embodiment, to avoid problems such as poor fitting, local distortion, and insufficient generalization ability in the initial prediction model, a comprehensive accuracy verification of the initially fitted prediction model is required. Instead of single-sample verification, a cross-validation method using multiple sets of orthogonal experimental samples and random verification samples is employed. This compares the deviation between the model's prediction results and the simulated real results, verifying model performance from multiple dimensions including fitting accuracy, stability, and generalization ability. This process identifies model fitting defects and effectively avoids subsequent parameter optimization deviations caused by insufficient model accuracy.

[0094] Step S63: If the initial prediction model passes the verification, the initial prediction model is determined as the prediction model.

[0095] In this embodiment, a preset model accuracy judgment standard is established. When the prediction error of the initial prediction model meets the preset engineering error threshold, the goodness of fit is up to standard, and there are no obvious local distortion problems, the initial prediction model is deemed to have passed the verification and is officially designated as the prediction model for this application. If the model accuracy does not meet the standard, the process is repeated to re-optimize the fitting parameters, supplement the experimental samples, and correct the model coefficients until the model verification is successful. The finalized prediction model can be directly used for rapid performance prediction of subsequent large-scale parameter combinations, providing reliable model support for batch screening of condenser design parameters and optimization of optimal solutions.

[0096] In this embodiment, the prediction model is based on the response surface methodology, which is commonly used as a holistic method for fitting models and analyzing the interaction between parameters and the objective function. Fitting the flat tube shovel-shaped fins using the response surface methodology involves the correlations of five parameters with respect to heat transfer and flow. Furthermore, the interaction between the five parameters is obtained through modified quadratic regression. The prediction module can be represented as follows: ; In the formula: y is the target response; τ i and τ j The coded or dimensionless value of the design variable; n is the number of parameters; For constant terms, The coefficients are linear. For interaction coefficients, The coefficient of the quadratic term, This represents statistical error.

[0097] The method provided in this application obtains an initial prediction model based on the fitting of the selected target design parameters and the target response, and sets up a model verification step. It abandons the simple modeling method of traditional single linear fitting, and accurately characterizes the heat transfer resistance law of multi-parameter coupling and nonlinear changes. This ensures that the final prediction model has a good fitting effect, provides high-precision model support for batch parameter performance prediction and optimal parameter selection, and guarantees the authenticity and reliability of the optimization results.

[0098] In some embodiments, step S62 can be implemented through the following steps: Step S621: Input the parameter level combination in the orthogonal array into the initial prediction model to obtain the calculated target response.

[0099] In this embodiment, the parameter level combinations in the orthogonal array are standardized parameter sample combinations obtained through orthogonal experimental design and which have undergone simulation calculations. These samples are characterized by uniform dispersion and neat comparability, covering different gradient levels and parameter coupling conditions of each design parameter. They can serve as standard benchmark samples for model verification, ensuring the representativeness and comprehensiveness of the verification samples. The target response is calculated by inputting the parameter combinations of the standard verification samples into the initial prediction model and then using the model fitting formula to calculate the output performance prediction value. This value characterizes the model's performance prediction results for known sample conditions and is a direct reflection of the model's predictive capability.

[0100] In this embodiment, all parameter levels of the orthogonal array used to construct the initial prediction model are selected as standard verification samples. The core target design parameters of each group are sequentially input into the initial prediction model to be verified. Through the model's built-in fitting calculation relationships, the calculated target responses corresponding to each sample are obtained one by one, namely the predicted heat transfer factor, friction factor, and comprehensive performance factor values. Using orthogonal samples as the verification dataset ensures that the verification conditions cover different levels and coupling combinations of parameters, avoiding the bias caused by local sample verification, and comprehensively evaluating the overall fitting performance of the model.

[0101] Step S622: Calculate the prediction error between the calculated target response and the corresponding target response result.

[0102] In this embodiment, the target response result refers to the simulated real performance result corresponding to the orthogonal array parameter combination. It is the real benchmark data obtained through numerical simulation in the early stage and can be used as the true value basis for evaluating the prediction accuracy of the model. The prediction error is a quantitative index of the deviation between the model's calculated target response and the simulated real target response result. It is used to accurately reflect the degree of fitting deviation of the initial prediction model and can intuitively determine the model's prediction accuracy.

[0103] In this embodiment, for each set of verification samples, the calculated target response output by the model is compared one by one with the simulated real target response results corresponding to that set of parameters, and the prediction error between the two is quantitatively calculated. Through error calculation, the degree of deviation between the model's predicted value and the real simulation value can be accurately quantified, intuitively reflecting the model's fitting accuracy for different parameter combinations and different performance indicators, realizing the quantitative analysis of model deviation, and abandoning the ambiguity of traditional qualitative judgment.

[0104] Step S623: Validate the initial prediction model based on the prediction error, wherein if the prediction error is less than the error threshold, the initial prediction model is determined to have passed the validation.

[0105] In this embodiment, the error threshold is a pre-set maximum allowable error limit that meets the accuracy requirements of engineering design. It is a standardized quantitative threshold for determining whether a model is qualified or not. It can be uniformly set according to the accuracy requirements of condenser design to achieve standardized and quantitative determination of model verification.

[0106] In this embodiment, an error threshold for the optimized design of the condenser engineering is pre-set, and the prediction error of each group of samples is compared with the preset error threshold. If the prediction error of all or most of the validation samples is less than the error threshold, it indicates that the initial prediction model has high overall fitting accuracy, controllable bias, and good generalization performance, and can truly reflect the mapping law between design parameters and performance response. In this case, the initial prediction model is deemed to have passed validation and can be used for subsequent batch prediction and parameter optimization. If a large number of samples have prediction errors exceeding the threshold, the model is deemed to have insufficient fitting accuracy, and it is necessary to return to re-optimize the fitting coefficients, adjust the model order, or supplement the experimental samples until the model validation is qualified.

[0107] The method provided in this application quantifies the prediction error between the model's predicted values ​​and the simulated actual values, and uses an error threshold as the model admission criterion to achieve standardized and quantitative verification of the prediction model's accuracy. This effectively filters out prediction models with controllable errors and meeting accuracy standards, avoiding parameter optimization bias caused by low-precision models, ensuring that the model's prediction results closely approximate real heat transfer and resistance characteristics, and significantly improving the accuracy and reliability of subsequent parameter optimization results.

[0108] In some embodiments, step S9 can be implemented through the following steps: Step S91: Based on the engineering constraints of the condenser, the combination of design parameters is screened to obtain a preliminary combination of design parameters. The engineering constraints include at least one of structural boundary constraints, operating condition constraints, and manufacturing constraints.

[0109] In this embodiment, engineering constraints refer to the rigid boundary restrictions that the flat-tube shovel-fin condenser must meet during design, processing, assembly, and actual operation. These constraints are the core basis for determining the engineering feasibility of parameter combinations and specifically include three types of core constraints: structural boundary constraints, operating condition constraints, and processing constraints. These constraints work together to define the effective feasible range of the parameters. Structural boundary constraints refer to dimensional constraints formed by the limitations of the overall installation space and equipment structural layout. These mainly include boundary limits such as the overall dimensions of the condenser, the maximum fin layout range, and the pipe installation spacing. These constraints are used to eliminate parameter combinations that exceed the equipment installation space or are incompatible with the structural layout. Operating condition constraints refer to the performance limits of the condenser during actual operation, including the maximum allowable pressure drop on the air side, the rated wind speed range, the operating condition Reynolds number range, and the system's rated heat exchange requirements. These constraints are used to avoid parameter combinations that lead to excessive power consumption, excessive operating pressure drop, or poor operating condition adaptability. Machining constraints refer to process constraints formed by the limitations of fin cutting technology, tool precision, material properties, and batch production conditions. These constraints include minimum machinable fin spacing, fin forming limit amplitude, fin structural strength limit, and machining tolerance range. They are used to eliminate parameter combinations that cannot be achieved by the process, have poor forming quality, or cannot guarantee batch consistency.

[0110] In this embodiment, among the batch of design parameter combinations to be screened, the first round of constraint screening is conducted based on the actual engineering application requirements of the condenser. Each parameter combination is verified to ensure it meets the preset structural boundaries, operating conditions, and manufacturing constraints. Invalid parameter combinations are eliminated if their external dimensions exceed the installation space, operating pressure drop exceeds system limits, structural parameters exceed manufacturing process limits, or fin strength does not meet usage requirements. All parameter combinations that meet engineering implementation requirements, have reasonable structures, are suitable for operating conditions, and can be mass-produced constitute the initial set of design parameter combinations. This step prioritizes eliminating all impractical solutions to avoid the disconnect between theoretical optimization results and engineering reality, ensuring that subsequent selection is based on feasible solutions.

[0111] Step S92: Based on the predicted target response corresponding to each combination of initial design parameters, the initial design parameters are filtered to obtain the target design parameter combination.

[0112] In this embodiment, the preliminary design parameter combination refers to the set of feasible parameter combinations that meet the structural, operating, and manufacturing constraints after initial screening based on engineering constraints. These parameter combinations all have a basis for engineering feasibility and no inherent flaws in implementation, providing an effective sample pool for subsequent performance optimization screening. The target design parameter combination refers to the optimal performance parameter scheme obtained from all preliminary feasible parameter combinations based on the predicted target response, simultaneously meeting the dual requirements of engineering feasibility and optimal overall performance. Based on the engineering-feasible preliminary parameter combinations, a second layer of performance optimization screening is conducted. The predicted target response corresponding to each preliminary parameter combination is retrieved, i.e., quantitative performance indicators such as heat transfer factor, friction factor, and comprehensive performance factor. Optimization screening is completed according to preset optimization objectives: when the objective is to enhance heat transfer, the parameter combination with the largest heat transfer factor is selected; when the objective is to reduce resistance, the parameter combination with the smallest friction factor is selected; and when the objective is to improve the overall energy efficiency of the machine, the parameter combination with the largest comprehensive performance factor is selected. Finally, the target design parameter combination that balances engineering feasibility with optimal heat transfer performance and resistance performance is selected, completing the precise optimization of the condenser design parameters.

[0113] The method provided in this application adopts a two-layer screening mechanism of "initial selection based on engineering constraints + fine screening based on performance response". First, invalid parameter combinations that are unfeasible or exceed specifications are eliminated based on structural boundaries, operating conditions, and manufacturing constraints. Then, the optimal solution is selected based on the target response. This ensures that the optimized parameters meet the requirements of actual production assembly and overall machine operation, while achieving optimal matching of heat exchange performance and resistance performance. It solves the problems of poor manufacturability and weak engineering feasibility of traditional pure performance optimization solutions, taking into account both theoretical optimality and engineering practicality.

[0114] In some embodiments, the condenser is a flat-tube shovel-fin condenser, and the design parameters include at least one of: fin spacing, fin height, fin thickness, fin amplitude, fin wavelength, and air-side Reynolds number. The target response includes one of: heat transfer factor, friction factor, and comprehensive performance factor. The heat transfer factor reflects the structure's ability to enhance heat transfer, the friction factor characterizes the flow loss and friction resistance level of air flowing through the fin channels, and the comprehensive performance factor is a dimensionless index that is a coupling of the heat transfer factor and the friction factor.

[0115] In some embodiments, the method further includes: substituting the selected target design parameter combination into a preset condenser numerical simulation model to obtain the simulation verification heat transfer factor and the simulation verification friction factor; comparing the simulation verification results with the model's predicted target response and calculating the secondary verification error; if the secondary verification error is less than a preset accuracy threshold, locking the current target design parameter combination as the final optimization scheme; if the error exceeds the threshold, returning to the weight iteration step to re-optimize and select until the accuracy requirements are met.

[0116] In some embodiments, the design parameters include fin spacing, fin height, fin amplitude, fin wavelength, and air-side Reynolds number. The value ranges for each design parameter are limited to: fin spacing 2mm to 4mm, fin height 3mm to 5mm, fin amplitude 0.4mm to 0.8mm, fin wavelength 5.23mm to 7.85mm, and air-side Reynolds number 500 to 1000.

[0117] Table 3 is a design parameter table provided in an embodiment of this application, as shown in Table 3:

[0118] In some embodiments, a Pareto non-dominated sorting multi-objective iterative optimization algorithm with a constraint penalty mechanism is used to find the global optimum of the initially selected design parameter combination. This includes: constructing a multi-objective optimization mathematical model with three-dimensional optimization objectives of maximizing the heat transfer factor, minimizing the friction factor, and maximizing the comprehensive performance factor; constructing a nonlinear penalty function based on structural boundary, processing limit, and maximum allowable pressure drop constraints; applying performance penalty attenuation to parameter combinations that exceed engineering constraints; eliminating infeasible solutions; performing fast non-dominated sorting and crowding distance calculation on all parameter combinations; hierarchically dividing the Pareto optimal solution set; retaining the optimal parameter individuals with uniform distribution and significant non-dominated advantage within the solution set; iteratively updating the parameter population through crossover mutation; and after convergence, adaptively selecting the unique optimal parameter combination from the Pareto front solution set based on the operating condition weight as the target design parameter combination.

[0119] In this embodiment, when the average performance fluctuation of the Pareto optimal solution set within a consecutive preset number of iterations is less than a preset fluctuation threshold, and the prediction error of the best individual is continuously less than a preset accuracy threshold, it is determined that the iterative optimization has converged, and the final target design parameter combination is output; if the convergence condition is not met, the population individuals are updated, and the non-dominated sorting and penalty calculation are re-executed until the dual convergence threshold is met.

[0120] Based on the foregoing embodiments, this application provides a method for optimizing the design parameters of a condenser. Figure 2 This is a schematic diagram illustrating the implementation process of a condenser design parameter optimization method provided in an embodiment of this application, as shown below. Figure 2 As shown, it includes: Step S11: Determine the design variables (same as the initial design parameters in the above embodiments) and target response of the flat tube shovel-fin condenser.

[0121] In this embodiment, the structural form of the flat-tube shovel-fin condenser to be optimized is first determined. This flat-tube shovel-fin condenser includes a refrigerant channel component and multiple flat-tube shovel-shaped fins. The refrigerant channel component can be a flat tube, a microchannel flat tube, or a heat exchange substrate. The flat-tube shovel-shaped fins are disposed on the outer surface of the refrigerant channel component and are arranged at intervals along a direction perpendicular to the airflow direction, with air flowing through the channels between the flat-tube shovel-shaped fins. After determining the structural form, the main design variables of the flat-tube shovel-fin condenser are selected (same as the initial design parameters in the above embodiment). Preferably, the design variables include fin spacing Fp, fin height Fh, fin amplitude A, fin wavelength L, and air-side Reynolds number Re. Fin spacing Fp affects the flow area and hydraulic diameter between adjacent fins; fin height Fh affects the heat exchange area and air channel height; amplitude A affects local acceleration and disturbance at wave crests and troughs; wavelength L affects the heat exchange area, flow path, and pressure drop along the flow path; Re reflects the air-side flow rate level.

[0122] In this embodiment, the target response is determined based on the optimization objective. If the optimization objective is to improve heat transfer capacity, factor j or heat transfer capacity Q can be selected; if the optimization objective is to reduce air-side resistance, factor f or air-side pressure drop ΔP can be selected; if the optimization objective is to balance heat transfer and resistance, factor JF can be selected, or both factor j and factor f can be retained for comprehensive screening.

[0123] In this embodiment, the j-factor, f-factor, and JF-factor are used as the basic target response. A larger j-factor indicates better air-side heat transfer performance; a smaller f-factor indicates lower air-side flow resistance; and a larger JF-factor indicates better overall performance after considering the resistance cost. For actual products, both the j-factor and f-factor can be output simultaneously for easier alignment with overall system design specifications.

[0124] Table 4 is a schematic table illustrating the main impacts of different design variables on performance provided in the embodiments of this application. As shown in Table 4,

[0125] Step S12: Orthogonal array design based on Taguchi method.

[0126] In this embodiment, the Taguchi method is used to orthogonally array the design variables of the flat-tube shovel-fin condenser. The advantage of the Taguchi method is that it covers the main parameter combinations with fewer calculations or experiments, and quickly determines the impact of each parameter level on the target response through signal-to-noise ratio analysis.

[0127] When five design variables—Fp, Fh, A, L, and Re—are selected, and each design variable is set with three levels, an L27(35) orthogonal array can be used. If the number of design variables is small, an L9 or L16 orthogonal array can be used; if the number of design variables is large or the number of levels is different, an L25, L32, L36, or L64 orthogonal array can be used. The selection of the orthogonal array should ensure that the levels of each design variable appear in a balanced manner and that the main parameter combinations are effectively covered. The levels of each parameter should be determined according to engineering constraints. For example, the low level of Fp should not be less than the minimum fin spacing allowed for processing, the high level of A should not exceed the allowable amplitude of fin forming, the high level of Fh should not exceed the installation space height, the low level of L should meet the requirements of fin strength and processing stability, and the high level of Re should not exceed the maximum flow rate that the fan can provide in the applicable scenario. If the parameter level settings are outside the processing range, the optimal combination obtained by the Taguchi method may not be feasible for actual manufacturing.

[0128] The geometric model of the flat-tube shovel-fin condenser is established group by group based on the orthogonal array, and numerical calculations or experimental tests are completed under the same boundary conditions and solution settings. Each set of parameters outputs at least one target response among the j-factor, f-factor, JF-factor, heat transfer Q, and air-side pressure drop ΔP.

[0129] After completing the orthogonal array calculations, the target responses for each group are compiled into an orthogonal experimental results table. This table is used for both subsequent signal-to-noise ratio analysis and for calculating the contribution rate using analysis of variance. If the calculations for certain parameter combinations do not converge, the geometry should be checked for excessively small flow channels and whether the boundary conditions are reasonable. If necessary, the parameter ranges should be corrected.

[0130] Table 5 is a schematic table of a three-level orthogonal design provided in the embodiments of this application.

[0131] Table 5 provides a five-factor, three-level orthogonal array combination method from Table 2, as shown in the following table. In the table, numbers 1, 2, and 3 represent the low, medium, and high levels of the corresponding parameters, respectively.

[0132] Based on the above orthogonal array, 27 sets of calculation models for flat tube shovel-fin condensers can be established in sequence.

[0133] Step S13: Taguchi signal-to-noise ratio analysis and optimization level determination.

[0134] In this embodiment, Taguchi signal-to-noise ratio (SNR) analysis is performed on the orthogonal array calculation results. SNR is used to transform the target response into a comparable evaluation metric and reflects the stability and quality of the target response to changes in parameter levels.

[0135] For targets such as the j-factor, JF-factor, and heat exchange rate Q, larger values ​​are more advantageous; therefore, a signal-to-noise ratio with larger values ​​being preferred is adopted. ; For targets such as the f-factor and air side pressure drop ΔP, smaller values ​​are more advantageous; therefore, a signal-to-noise ratio that prioritizes smaller values ​​is adopted. ; Where Yi is the target response obtained from the i-th calculation or experiment, and n is the number of repetitions. When each set of parameters is simulated only once, n=1.

[0136] Based on the signal-to-noise ratio (SNR) results of each orthogonal array, the average SNR of each design variable at each level is calculated. For example, the average SNR of all corresponding experimental groups when Fp is at level 1, level 2, and level 3 is calculated, and the three are compared. The higher the average SNR, the more favorable that level is for the current target response.

[0137] By plotting the main effect curve of signal-to-noise ratio (SNR), the changing trend of each design variable can be intuitively determined. If the average SNR of a certain parameter changes significantly with the level, it indicates that the parameter is more sensitive to the target response; if the curve is relatively flat, it indicates that the parameter has a smaller impact within the set range.

[0138] Figure 3 This is a schematic diagram of the main effect curve of factor j provided in an embodiment of this application. Figure 4 This is a schematic diagram of the main effect curve of factor f provided in an embodiment of this application. Figure 5 A schematic diagram of the main effect curve of the JF factor provided in this application embodiment is shown below. Figures 3 to 5 As shown, where, Figures 3 to 5In the figure, A, B, C, D, and E represent the average signal-to-noise ratio (SNR) at each parameter level. The figure shows that the SNR of the heat transfer factor increases with increasing fin spacing, initially increases and then decreases with increasing fin height and amplitude, and decreases with increasing Re and fin wavelength, making it sensitive to changes in Re and fin spacing. The SNR of the friction factor increases with increasing fin spacing and fin wavelength, initially increases and then decreases with increasing Re, initially decreases and then increases with increasing fin height, and decreases with increasing amplitude, making it sensitive to changes in amplitude and fin length. The SNR of the comprehensive heat transfer performance factor increases with increasing fin spacing, fin height, and fin wavelength, and decreases with increasing amplitude and Re, making it sensitive to changes in fin spacing, amplitude, and Re. The Taguchi method can also provide a ranking of the influence of each parameter. This ranking is mainly based on the range of the average SNR at different levels, i.e., the difference between the maximum and minimum average SNR.

[0139] In this embodiment, a larger range indicates a more significant impact of parameter level changes on the target response. The ranges of structural parameters for factor j are calculated as follows: D>A>C>B>E; the ranges for structural parameters for factor f are calculated as follows: C>E>A>D>B; and the ranges for structural parameters for factor JF are calculated as follows: A>D>C>E>B. The magnitude of the range reflects, to some extent, the order of influence of each parameter on the objective function. A ranking of key design variables is formed based on the range magnitude. This ranking guides the priority adjustment of the structural dimensions and the control of processing tolerances for the flat-tube finned condenser.

[0140] For high heat transfer targets, the parameter level combination that maximizes the signal-to-noise ratio of the j-factor or the heat transfer Q can be selected; for low resistance targets, the parameter level combination that maximizes the signal-to-noise ratio of the f-factor or the pressure drop ΔP can be selected; for comprehensive performance targets, the parameter level combination that maximizes the signal-to-noise ratio of the JF-factor can be selected. This combination may not appear directly in the original orthogonal array, therefore further verification is needed through response surface methodology, confirmation simulation, or confirmation experiments.

[0141] Step S14: Establish a prediction model based on the response surface methodology.

[0142] In this embodiment, after determining the range of the main parameters (the same as the target design parameters in the above embodiment) using the Taguchi method, a predictive model between the target response and the combination of design parameters is established using the response surface methodology. The response surface methodology can describe the nonlinear effects and interactions of the parameters, making it suitable for further optimization within a continuous parameter range.

[0143] Response surface methodology (RSM) sample points can be obtained through Box-Behnken design, central composite design, or full factorial design. If there are many design variables, parameters with higher contribution rates can be selected for inclusion in the RSM design based on Taguchi analysis results; if there are few design variables, all major parameters can be included in the RSM model.

[0144] The preferred response surface prediction model is a quadratic regression model. ; Where y is the target response, which can be the j-factor, f-factor, or JF-factor; τ i and τ j The coded or dimensionless value of the design variable; n is the number of parameters; For constant terms, The coefficients are linear. For interaction coefficients, The coefficient of the quadratic term, This represents statistical error.

[0145] In response surface methodology (RSM) modeling, Fp, Fh, A, L, and Re can be used directly as independent variables, or dimensionless combined variables can be employed. For example, Fp / Fh reflects the relative relationship between fin spacing and height, Fh / A reflects the relative relationship between fin height and amplitude, and L / Fp reflects the ratio between wavelength and fin spacing. Dimensionless processing improves the model's applicability to flat-tube finned condensers of different sizes.

[0146] After the response surface model is established, its accuracy needs to be verified. Predicted values ​​can be compared with simulated or experimental values ​​to calculate the maximum relative error, average relative error, coefficient of determination R², and adjusted coefficient of determination. If the prediction error is less than a preset threshold, for example, the prediction error for factor j, factor f, or factor JF is less than 10%, then the model is considered suitable for rapid prediction and parameter optimization of flat-tube shovel-fin condensers.

[0147] Please refer to Table 3, which shows the range of variation for five parameters of the flat tube shovel-shaped fins. Figure 6 This is a schematic diagram of a process for obtaining a prediction model provided in an embodiment of this application, such as... Figure 6 As shown, firstly, structural parameters describing the flat tube shovel-shaped fins need to be selected as independent variables. If the influence of flow variations is considered, the Reynolds number Re can also be added as an independent variable. After selecting the independent variables, the variation range of each parameter is selected according to actual needs. Then, the independent variables and the objective response function matrix (EDM matrix) are designed using the Box-Behnken design method (BBD). After completing the experimental design, the design table is expanded and calculated according to the numerical model to process the data and obtain the objective response function. The objective function in this section includes three factors (j, f factors, and JF factors). After calculating the objective response function corresponding to the EDM matrix, it is expressed in logarithmic form. Convert the parameters and the target response function into an IOM matrix, then establish a nonlinear regression model for the objective function using the response surface methodology, and verify the accuracy of the mathematical model. Finally, the correlation formula of the objective function, that is, the prediction model, is obtained. Table 6 shows a schematic table of a total of 41 sets of experimental data designed by the Box-Behnken design method, including different structural parameters and their corresponding objective functions.

[0148] Table 6 is a schematic table of a total of 41 sets of experimental data designed by the Box-Behnken design method

[0149] Through the above method and calculation results, the correlation formulas for the j factor, f factor and JF factor are obtained as follows:

[0150]

[0151]

[0152] The applicable range of the above correlation formulas is: 2mm<Fp<4mm, 3mm<Fh<5mm, 0.4mm<A<0.8mm, 500<Re<1000, 5.23mm<L<7.85mm. In addition, in order to test the accuracy of the correlation formulas, it is necessary to verify the prediction results obtained by the correlation formulas, Figure 7 is a schematic diagram of the error curve of a prediction model for the j factor provided by the embodiment of the present application, Figure 8 is a schematic diagram of the error curve of a prediction model for the f factor provided by the embodiment of the present application, Figure 9 is a schematic diagram of the error curve of a prediction model for the JF factor provided by the embodiment of the present application, as shown in Figures 7 to 9 , it can be seen that the errors between the prediction results of the j factor, f factor and JF factor obtained from the correlation formula model and the simulation data are all within 10%, which indicates that the prediction model has certain reliability and can be used to predict the performance of the flat tube skived fin.

[0153] High heat transfer type combinations aim to maximize the j-factor or heat transfer capacity Q. This combination is suitable for flat-tube finned condensers with limited installation space that require high heat transfer capacity within a small volume. During screening, the f-factor or air-side pressure drop ΔP should also be checked to avoid significant deviations in the fan operating point due to excessive disturbance. Low resistance type combinations aim to minimize the f-factor or air-side pressure drop ΔP. This combination is suitable for products sensitive to noise, fan power consumption, or airflow. During screening, the j-factor or heat transfer capacity Q should also be checked to avoid insufficient heat transfer capacity due to excessive reduction in resistance. Comprehensive performance type combinations aim to maximize the JF-factor, or are selected based on the constraint that heat transfer performance meets requirements and flow resistance does not exceed limits. This combination is more suitable for conventional household refrigeration equipment because it considers both heat transfer capacity and air-side resistance.

[0154] Step S15, Application of the prediction model.

[0155] When a predictive model is available, a large number of candidate parameter combinations can be generated in a continuous parameter space, and the target response of each combination can be quickly predicted using a response surface model. Combinations that do not meet processing constraints, size constraints, or pressure drop constraints are then eliminated, and several combinations with the optimal target response are selected as candidate optimization combinations from the remaining combinations. When screening candidate combinations, the order of contribution should also be considered. For parameters with high contribution rates, levels near processing limits should be avoided to prevent performance fluctuations caused by manufacturing deviations; for parameters with low contribution rates, standard dimensions that are easy to manufacture and assemble can be prioritized.

[0156] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0157] According to the foregoing embodiments, this application provides a device for optimizing the design parameters of a condenser. The various modules and units included in the device can be implemented by a processor in a computer device; of course, they can also be implemented by specific logic circuits. In the implementation process, the processor can be a central processing unit (CPU), a microprocessor (MPU), a digital signal processor (DSP), or a field programmable gate array (FPGA), etc.

[0158] This application provides an apparatus for optimizing the design parameters of a condenser. Figure 10A schematic diagram of a device for optimizing the design parameters of a condenser provided in this application embodiment is shown below. Figure 10 As shown, the condenser design parameter optimization device 1000 includes: The acquisition module 1001 is used to acquire multiple sets of design parameter combinations to be screened for the condenser; The calculation module 1002 is used to input the design parameter combination into a pre-established prediction model to obtain the predicted target response corresponding to each design parameter combination. The prediction model includes a calculation relationship between the design parameter combination and the target response, and the target response is used to characterize the performance of the condenser designed based on each design parameter combination. The screening module 1003 is used to screen the design parameter combination based on the engineering constraints of the condenser and the predicted target response to obtain the target design parameter combination.

[0159] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0160] In addition, the device described above can be a software unit, a hardware unit, or a combination of software and hardware. It can also be integrated into an electronic device as a separate component, or it can exist as a standalone electronic device.

[0161] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0162] Figure 11 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 11 As shown, the electronic device 600 of this embodiment may include: at least one processor 60 ( Figure 11Only one processor 60, memory 61, and computer program 62 stored in memory 61 and executable on at least one processor 60 are shown. When the processor 60 executes the computer program 62, it implements the steps in any of the above method embodiments, or the processor 60 executes the computer program 62 to implement the functions of each module / unit in the above device or system embodiments.

[0163] For example, computer program 62 may be divided into one or more modules / units, one or more of which are stored in memory 61 and executed by processor 60 to complete this application. One or more modules / units may be a series of computer program 62 instruction segments capable of performing a specific function, which describe the execution process of computer program 62 in electronic device 600.

[0164] This application also provides a computer-readable storage medium storing a computer program 62, which, when executed by a processor 60, implements the steps in the above-described method embodiments.

[0165] This application provides a computer program product that, when run on an electronic device, enables the electronic device to perform the steps described in the various method embodiments above.

[0166] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program 62 instructing related hardware. The computer program 62 can be stored in a computer-readable storage medium, and when executed by the processor 60, it can implement the steps of the various method embodiments described above. The computer program 62 includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate form. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a terminal, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include a USB flash drive, a portable hard drive, a magnetic disk, or an optical disk.

[0167] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0168] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0169] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0170] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0171] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for optimizing the design parameters of a condenser, characterized in that, include: Obtain the initial design parameters of the condenser and the corresponding target response; The initial design parameters are divided into multi-level gradients, and the parameter levels of each initial design parameter are set. An orthogonal array is constructed based on the parameter levels of each of the initial design parameters, wherein the orthogonal array includes: combinations of parameter levels of each of the initial design parameters; Simulation calculations are performed based on the orthogonal array to obtain the target response results corresponding to each parameter level combination; Target design parameters are selected from the initial design parameters based on the target response results corresponding to each combination of parameter levels. The prediction model is established based on the target design parameters and the target response; Obtain multiple sets of design parameter combinations for the condenser to be screened; The design parameter combinations are input into a pre-established prediction model to obtain the predicted target response corresponding to each design parameter combination. The prediction model includes a calculation relationship between the design parameter combinations and the target response, and the target response is used to characterize the performance of the condenser designed based on each design parameter combination. The design parameter combinations are filtered based on the engineering constraints of the condenser and the predicted target response to obtain the target design parameter combinations.

2. The method according to claim 1, characterized in that, The process of selecting target design parameters from the initial design parameters based on the target response results corresponding to each of the parameter level combinations includes: The signal-to-noise ratio corresponding to the target response is calculated based on the target response results corresponding to the combination of each parameter level. Calculate the average signal-to-noise ratio of each initial design parameter at each parameter level based on the signal-to-noise ratio corresponding to the target response; The trend of the average signal-to-noise ratio of each initial design parameter is determined based on the average signal-to-noise ratio of each initial design parameter at each parameter level. Target design parameters are selected from the initial design parameters based on the aforementioned trend.

3. The method according to claim 2, characterized in that, The step of selecting target design parameters from the initial design parameters based on the changing trend includes: Based on the aforementioned trend, calculate the difference between the maximum average signal-to-noise ratio and the minimum average signal-to-noise ratio for each initial design parameter at each parameter level; Based on the magnitude of the difference between each initial design parameter, the performance impact weights of each initial design parameter are ranked, and the ranking results are obtained. Based on the sorting results, target design parameters are selected from the initial design parameters.

4. The method according to claim 1, characterized in that, The step of establishing the prediction model based on the target design parameters and the target response includes: The initial prediction model is obtained by fitting the target design parameters as independent variables and the target response as dependent variable. The initial prediction model was validated. If the initial prediction model passes verification, the initial prediction model is determined as the prediction model.

5. The method according to claim 4, characterized in that, The verification of the initial prediction model includes: The parameters in the orthogonal array are combined horizontally and input into the initial prediction model to obtain the calculated target response; Calculate the prediction error between the calculated target response and the corresponding target response result; The initial prediction model is validated based on the prediction error, wherein if the prediction error is less than the error threshold, the initial prediction model is determined to have passed the validation.

6. The method according to any one of claims 1 to 5, characterized in that, The design parameter combination is filtered based on the engineering constraints of the condenser and the predicted target response to obtain the target design parameter combination, including: Based on the engineering constraints of the condenser, the combination of design parameters is screened to obtain a preliminary combination of design parameters. The engineering constraints include at least one of structural boundary constraints, operating condition constraints, and manufacturing constraints. The preliminary design parameters are filtered based on the predicted target response corresponding to each combination of preliminary design parameters to obtain the target design parameter combination.

7. The method according to claim 6, characterized in that, The condenser is a flat-tube shovel-fin condenser. The design parameters include at least one of the following: fin spacing, fin height, fin thickness, fin amplitude, fin wavelength, and air-side Reynolds number. The target response includes one of the following: heat transfer factor, friction factor, and comprehensive performance factor. The heat transfer factor reflects the structure's ability to enhance heat transfer. The friction factor characterizes the flow loss and friction resistance level of air flowing through the fin channels. The comprehensive performance factor is a dimensionless index that is a coupling of the heat transfer factor and the friction factor.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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