Multi-objective optimization method and equipment for wing-shaped fin structure of printed circuit board heat exchanger with top gap

Abstract

The invention belongs to the related technical field of printed circuit board manufacturing, and discloses a multi-objective optimization method and device for a wing-shaped fin structure of a printed circuit board heat exchanger with a top gap, and the method comprises the steps: S1, obtaining the geometric parameters, including the gap distance, of a wing-shaped fin in the printed circuit board heat exchanger PCHE with the top gap; s2, carrying out nondimensionalization on the geometric parameters to obtain optimization design variables; s3, obtaining a sample set based on the optimization design variables; s4, constructing a first target agent model corresponding to the volume heat exchange coefficient hv and a second target agent model corresponding to the resistance coefficient f; s5, maximizing hv and minimizing f as optimization objectives, and optimizing the first target agent model and the second target agent model to obtain a Pareto optimal leading edge of flow and heat transfer performance; and S6, selecting variables of the compromise scheme from the Pareto optimal leading edge by using a multi-objective decision-making method so as to determine an optimal optimization scheme. The invention takes into account a top gap.

Description

Technical Field

[0001] The present invention belongs to the technical field related to the manufacture of printed circuit board type heat exchangers, and more specifically, relates to a multi-objective optimization method and equipment for an airfoil fin structure of a printed circuit board type heat exchanger with a top gap. Background Art

[0002] The growing global demand for energy has exacerbated global warming, air pollution and ozone depletion, so the demand for efficient green energy conversion systems has gradually increased. The supercritical carbon dioxide Brayton cycle shows a bright energy prospect. However, due to the high pressure and high temperature working conditions under supercritical conditions, traditional heat exchangers have been difficult to effectively meet the above requirements. A new type of heat exchanger called printed circuit heat exchanger (PCHE) can meet the needs of high pressure and high temperature systems and has the characteristics of high safety, high compactness and high efficiency. Compared with traditional mechanical processing and welding processes, PCHE mainly uses two technologies: chemical etching and diffusion bonding. Channels are formed by chemical etching on metal plates, and then these etched plates are stacked together and converted into a whole by diffusion welding. Therefore, PCHE can adapt to the extreme high temperature and high pressure working environment of the supercritical carbon dioxide Brayton cycle.

[0003] The channel types in PCHE are generally divided into two categories: continuous and discontinuous. Continuous channels mainly include straight channels, Z-shaped channels, and sin-shaped channels. The cross-sectional shapes of the channels mainly include semicircular, rectangular, triangular, and trapezoidal. Discontinuous channels mainly include S-shaped channels and wing-shaped channels. The fins mainly improve the flow and heat transfer characteristics of the heat exchanger by increasing the heat transfer area and enhancing the fluid disturbance. Among them, the wing-shaped fin PCHE exhibits excellent flow and heat transfer performance compared to fins of other shapes.

[0004] The airfoil fin PCHE has different channel forms. In order to obtain the optimal geometric parameters of the airfoil fin in the PCHE channel, the current existing research mainly focuses on the airfoil fin arrangement parameters (horizontal distance, vertical distance, staggered distance), and rarely studies the top gap. PCHE with different top gaps has different flow and heat transfer performance. The present invention changes the top gap of the fin without changing the channel height, and optimizes it and other three arrangement parameters at the same time. Due to limited computing resources, the more conventional method is to use numerical simulation and orthogonal experimental design methods to study the influence of different airfoil fin geometric parameters on heat transfer and pressure drop in the channel. However, the optimization under preset parameters through the traditional enumeration method cannot obtain the best results within a certain range of parameter changes, and the limitations are large, which makes it difficult to achieve the optimal design of the optimized structural configuration. Therefore, it is urgent to conduct in-depth research on the flow and heat transfer mechanism of the airfoil fin PCHE taking into account the top gap of the fin, and further optimize the airfoil fin structure to achieve the goal of high efficiency and low resistance. Summary of the invention

[0005] In view of the above defects or improvement needs of the prior art, the present invention provides a multi-objective optimization method and device for an airfoil fin structure of a printed circuit board heat exchanger with a top gap, aiming to find the geometric parameters of an airfoil fin PHCE with excellent comprehensive performance.

[0006] To achieve the above object, according to one aspect of the present invention, a multi-objective optimization method for an airfoil fin structure of a printed circuit board type heat exchanger with a top gap is provided, the method comprising the following steps:

[0007] S1, obtaining geometric parameters of the airfoil fins in the printed circuit board heat exchanger PCHE with top gap, wherein the geometric parameters include the horizontal distance L h , staggered distance L s , vertical distance L v And gap distance L g ;

[0008] S2, non-dimensionalizing the geometric parameters of the airfoil fin to obtain optimized design variables, wherein the optimized design variables include the horizontal number ζ h , interleaving number ζ s , vertical number ζ v and the number of gaps ζ g ;

[0009] S3, performing experimental design and numerical simulation on the optimized design variables to obtain a sample set;

[0010] S4, inputting the training set in the sample set into the neural network for training to obtain the PCHE volume heat transfer coefficient h v The corresponding first target proxy model and the second target proxy model corresponding to the PCHE drag coefficient f;

[0011] S5, with ζ h , s , v and g To optimize the variables, maximize h v and minimizing f as optimization objectives, and optimizing the first target proxy model and the second target proxy model using a multi-objective optimization algorithm to obtain the Pareto optimal frontier of flow performance and heat transfer performance;

[0012] S6, using a multi-objective decision-making method to select variables of the compromise solution from the Pareto optimal frontier to determine the best optimization solution.

[0013] Furthermore, using the airfoil fin chord length L c and channel height L z The dimensionless geometric parameters of the airfoil fin are defined; the horizontal number is: The interleaving number is: The vertical number is The number of gaps is

[0014] Furthermore, as the top clearance and arrangement of the airfoil fins change, the length, width, and height of the 3D model of the PCHE must meet the following constraints:

[0015]

[0016] Where, L x Indicates the channel length, L y Indicates the channel width, L z Indicates the channel height, L h Indicates the horizontal distance of the airfoil, L s Indicates the staggered distance of the airfoil, L v Indicates the vertical distance of the airfoil, L g Indicates the clearance distance of the airfoil, L c Indicates the chord length of the airfoil, L t Represents the airfoil thickness, i, j, k, and m are all positive integers.

[0017] Furthermore, an optimal Latin hypercube design is used to simulate and collect sample points of the optimization variables in the design space to obtain a training set and a test set; the training set is used to train the neural network, and the test set is used to test the accuracy of the neural network.

[0018] Furthermore, the first target proxy model and the second target proxy model are: PSO-BP neural network.

[0019] Furthermore, the input layer of the PSO-BP neural network contains 4 neurons, namely, the level number ζ h , interleaving number ζ s , vertical number ζ v and the gap number ζ g The output layer contains a neuron; the PSO-BP neural network is used to calculate the volume heat transfer coefficient h v And the drag coefficient f are trained separately.

[0020] Furthermore, the mean square error is used to quantitatively evaluate the prediction performance of the neural network. The process of improving the BP neural network using the improved particle swarm optimization algorithm includes: first, optimizing the weights and learning factors of the particle swarm algorithm to obtain the improved particle swarm algorithm; then, encoding the initial weights and thresholds of the proxy model into particles, and obtaining the optimal initial weights and thresholds corresponding to the minimum fitness value, and finally assigning them to the proxy model.

[0021] Furthermore, after step S5 and before step S6, the method further comprises the step of: v , minimize f, the single objective optimal solution corresponding to the maximum PF value scheme, and the two compromise schemes of VIKOR and TOPSIS are simulated and verified; if the h corresponding to the five schemes v If the relative error between the predicted value of the surrogate model and the simulation result is greater than the error threshold, the number of samples in the training set is supplemented, and steps S4-S6 are repeated until the relative error meets the accuracy requirement; the variable ζ corresponding to the VIKOR and TOPSIS compromise solution that meets the accuracy requirement is h , s , v and g As the optimal design parameters of the PCHE airfoil fin; wherein, there is a gap between the top of the fin and the channel wall of the airfoil fin structure of the optimized printed circuit board type heat exchanger with a top gap, and the gap number satisfies 0<ζ g <0.15.

[0022] The present invention also provides a multi-objective optimization system for the wing-shaped fin structure of a printed circuit board type heat exchanger with a top gap, the system comprising a memory and a processor, the memory storing a computer program, and the processor executing the multi-objective optimization method for the wing-shaped fin structure of a printed circuit board type heat exchanger with a top gap as described above when executing the computer program.

[0023] The present invention also provides a computer-readable storage medium, which stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions prompt the processor to implement the multi-objective optimization method for the airfoil fin structure of the printed circuit board type heat exchanger with a top gap as described above.

[0024] In general, compared with the prior art, the above technical solution conceived by the present invention, the multi-objective optimization method and device of the airfoil fin structure of the printed circuit board type heat exchanger with top gap provided by the present invention mainly has the following beneficial effects:

[0025] 1. Designing the airfoil fin PCHE with a top gap can significantly reduce the resistance in the flow process, while increasing the heat exchange area, and forming a vortex structure in the channel to accelerate the mixing of cold and hot fluids and further enhance heat exchange. When the structure has a small top gap, the PHCE has excellent thermal hydraulic performance.

[0026] 2. The present invention adjusts the top gap of the airfoil fin and makes the top gap dimensionless, combines the other three dimensionless arrangement parameters as optimization design variables, and performs parametric three-dimensional modeling of PCHE, thereby achieving as many airfoil geometric parameters as possible while ensuring the reasonable structure of the heat exchanger. A large amount of optimization is performed in the sample space using a proxy model, which reduces the time cost while meeting the requirements of universality and overcomes the limitations of pre-given fixed parameter values.

[0027] 3. The present invention adopts PSO-BP neural network as the proxy model, and uses PSO optimization algorithm to perform global optimization on the weights and thresholds of BP neural network, replacing the traditional random assignment method, thereby making up for the shortcomings of BP neural network algorithm, and has excellent prediction accuracy and generalization ability. It is feasible to use it as a proxy model to predict the flow and heat transfer performance of PCHE under different airfoil fin parameters.

[0028] 4. The present invention optimizes the optimization variables through a multi-objective genetic algorithm and obtains the Pareto optimal frontier. In the actual design process, decision makers can weigh the heat transfer performance and flow characteristics in combination with engineering requirements, and select a suitable decision method to select the corresponding optimization solution from the Pareto optimal solution set.

[0029] 5. The present invention optimizes the top clearance and geometric dimensions of the airfoil fins in PCHE, and performs multi-objective optimization based on DOE, PSO-BP neural network model, NSGA-II algorithm, VIKOR, and TOPSIS method. This optimization method can be extended to the optimization design of other types of heat exchangers. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a flow chart of a multi-objective optimization method for an airfoil fin structure of a printed circuit board type heat exchanger with a top gap provided by one embodiment of the present invention;

[0031] Figure 2 is a schematic diagram of a PCHE geometric model of an airfoil fin with a top gap in one embodiment of the present invention;

[0032] Figure 3 is a flow chart of a PSO-BP neural network in one embodiment of the present invention;

[0033] Figure 4 The heat transfer coefficient h is provided in one embodiment of the present invention. v and Pareto optimal frontier diagram of resistance coefficient f. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0035] The flow and heat transfer characteristics in PCHE are related to the arrangement of channels and the top clearance of fins. The present invention performs parametric three-dimensional modeling on the top clearance of airfoil fin PCHE combined with three arrangement parameters by combining driving dimensions and geometric constraints. Combined with CFD, proxy model and multi-objective optimization algorithm, optimization in a large range can be achieved to achieve the goal of high efficiency and low resistance.

[0036] See also Figure 1 and Figure 2 The present invention provides a multi-objective optimization method for the airfoil fin structure of a printed circuit board heat exchanger with a top gap. The optimization method can operate under extreme conditions and can be used for a supercritical CO2 Brayton cycle. First, the parameters are designed based on the experimental design method, and the parameters are dimensionless and then simulated to obtain a training set to train the PSO-BP neural network. Subsequently, a proxy model is used to predict the flow and heat transfer performance of the PCHE. Finally, the volume heat transfer coefficient h v and resistance coefficient f as the optimization objectives, the Pareto optimal frontier was obtained based on the NSGA-II algorithm, and the compromise solution with excellent thermal-hydraulic performance was selected by the TOPSIS method and the VIKOR method.

[0037] The optimization method mainly comprises the following steps:

[0038] S1, obtaining geometric parameters of the airfoil fins in the printed circuit board heat exchanger PCHE with top gap, wherein the geometric parameters include the horizontal distance L h , staggered distance L s , vertical distance L v And gap distance L g .

[0039] S2, non-dimensionalizing the geometric parameters of the airfoil fin to obtain optimized design variables, wherein the optimized design variables include the horizontal number ζ h , interleaving number ζ s , vertical number ζ v and the number of gaps ζ g .

[0040] In this embodiment, the chord length L of the airfoil fin is used. c and channel height L zThe dimensionless geometric parameters of the airfoil fin are defined; the horizontal number is: The interleaving number is: The vertical number is The number of gaps is

[0041] In another embodiment, Figure 2 As shown in the figure, due to the periodicity and symmetry of the flow, in order to save computing resources, the model is simplified to a single-channel fluid domain as the numerical simulation calculation area, and 8 minimum periodic heat exchange units are considered along the flow direction. Parametric modeling is performed, and the airfoil fin used in the model is the NACA 0020 airfoil. c Take 4mm, L z Taking 1mm, the value range of the optimization design variables is shown in Table 1.

[0042] Table 1 Value range of optimization design variables

[0043] Design variables symbol Value range <![CDATA[Horizontal number ζ h > <![CDATA[ζ h ]]> [1.5,2.5] <![CDATA[Alternating number ζ s > <![CDATA[ζ s ]]> [0,1] <![CDATA[Vertical number ζ v > <![CDATA[ζ v ]]> [0.4,0.8] <![CDATA[Number of gaps ζ g > <![CDATA[ζ g ]]> [0.05,0.9]

[0044] S3, performing experimental design and numerical simulation on the optimized design variables in the design space to obtain a sample set.

[0045] In this embodiment, as the top gap and arrangement of the airfoil fins change, the length, width and height of the three-dimensional model of the PCHE must meet the following constraints:

[0046]

[0047] Where, L x Indicates the channel length, L y Indicates the channel width, L z Indicates the channel height, L h Indicates the horizontal distance of the airfoil, L s Indicates the staggered distance of the airfoil, L v Indicates the vertical distance of the airfoil, L g Indicates the clearance distance of the airfoil, L c Indicates the chord length of the airfoil, L t Represents the airfoil thickness, i, j, k, and m are all positive integers.

[0048] The optimal Latin hypercube design is adopted to collect sample points of the optimization variables in the design space for simulation to obtain a training set and a test set, wherein the training set is used to train the neural network, and the test set is used to test the accuracy of the neural network.

[0049] In one embodiment, based on the optimal Latin hypercube experimental design method with good space filling and uniformity, 200 design variable parameter combinations were collected in the design variable space, and the calculation results under the working condition of Re = 10000 were obtained through numerical simulation as the training set of the subsequent neural network model. The same method was used to collect 25 groups of sample points as the test set of the neural network model to verify the prediction accuracy of the neural network.

[0050] To ensure the structural integrity of the PCHE channel along the flow direction and perpendicular to the flow direction, the length, width and height of the PCHE three-dimensional model must meet the following constraints:

[0051]

[0052] Where, L x Indicates the channel length, L y Indicates the channel width, L z Indicates the channel height, L h Indicates the horizontal distance of the airfoil, L s Indicates the staggered distance of the airfoil, L v Indicates the vertical distance of the airfoil, L g Indicates the clearance distance of the airfoil, L c Indicates the chord length of the airfoil, L t Represents the airfoil thickness, i, j, k, and m are all positive integers.

[0053] In one embodiment, the length L of the PCHE channel x and width L y With the staggered distance L s , horizontal distance L h and vertical distance L v , and 8 minimum periodic structures are considered along the fluid flow direction. To ensure the structural integrity of the channel along the flow direction and perpendicular to the flow direction, the length, width and height of the three-dimensional model of PCHE must meet the following constraints:

[0054]

[0055] Where, L x Indicates the channel length, L y Indicates the channel width, L z Indicates the channel height, L h Indicates the horizontal distance of the airfoil, L s Indicates the staggered distance of the airfoil, L v Indicates the vertical distance of the airfoil, L g Indicates the clearance distance of the airfoil, L c Indicates the chord length of the airfoil, L tRepresents the airfoil thickness, i, j, k, and m are all positive integers.

[0056] S4, inputting the training set in the sample set into the neural network for training to obtain the PCHE volume heat transfer coefficient h v The corresponding first target proxy model and the PCHE drag coefficient f corresponding to the second target proxy model.

[0057] The first target proxy model and the second target proxy model are: PSO-BP neural network.

[0058] In one embodiment, 200 and 25 samples are taken as the training set and test set of the neural network respectively. For this embodiment, the BP neural network improved by the particle swarm optimization algorithm (PSO-BP) is used as the proxy model. The use of PSO to optimize the BP neural network makes up for the shortcomings of the BP neural network algorithm, so that the BP neural network can jump out of the local extreme value, which helps to accelerate the convergence process and enhance the accuracy and generalization ability of the BP neural network. The input layer contains 4 neurons, which are the level number ζ h , interleaving number ζ s , vertical number ζ v and the gap number ζ g , due to the small number of input variables, a single hidden layer is selected, and the output layer contains one neuron. The PSO-BP neural network is used to calculate the volume heat transfer coefficient h v And the drag coefficient f are trained separately.

[0059] In order to quantitatively evaluate the prediction performance of the neural network, the following mean square error (MSE) is introduced to measure the prediction accuracy and generalization ability of the model.

[0060] Through the method of trial and comparison, 10 BP neural networks with different network structures, with the number of hidden layer neuron nodes ranging from 3 to 12, were trained in turn. When the number of hidden layer neuron nodes was 10, the corresponding mean square error (MSE) reached the minimum value. Therefore, the 4-10-1 neural network structure was adopted.

[0061] The activation function is an important part of the BP neural network. The activation functions of the hidden layer and the output layer are selected as tansig function and purelin function respectively. purelin(x)=x.

[0062] In one embodiment, the process of improving the BP neural network using the improved particle swarm optimization algorithm (PSO) includes the following steps: first, by optimizing the weights and learning factors of the particle swarm algorithm, an improved particle swarm algorithm is obtained; then, the initial weights and thresholds of the proxy model are encoded into particles, and the optimal initial weights and thresholds corresponding to the minimum fitness value are obtained, and finally assigned to the proxy model.

[0063] Specifically, the iterative formulas for particle velocity and position are as follows:

[0064]

[0065] Where w is the inertia weight; c1 and c2 are individual learning factors and social learning factors respectively; r1 and r2 are random numbers between (0,1); k is the number of iterations; v id is the velocity of the ith particle; P id is the local optimal position of the ith particle, P gd is the global optimal position of the particle swarm, x id is the position of the ith particle.

[0066] In order to balance the global search capability and the local improvement capability, this implementation adopts nonlinear inertia weight:

[0067]

[0068] Among them, w max is the upper bound of inertia weight, w min is the lower bound of the inertia weight, t is the current number of iterations, and t max is the maximum number of iterations.

[0069] This implementation adopts an asynchronous learning factor, and the corresponding expression is:

[0070]

[0071] Where: c 1s , c 2s are the initial values ​​of the learning factors c1 and c2, respectively. 1e , c 2e They are the termination values ​​of the learning factors c1 and c2 respectively.

[0072] In one embodiment, the inertia weight w max =0.9, w min =0.3, c 1s =c 2e =2.5, c 1e =c 2s =0.5.

[0073] The PSO-BP neural network algorithm process is as follows Figure 3 As shown, the specific implementation steps are as follows:

[0074] (1) Data preprocessing: Normalize the sample data and divide it into training set and test set.

[0075] (2) Construct BP neural network structure: Select the appropriate network structure, including the number of nodes in the input layer, hidden layer and output layer.

[0076] (3) Initialize the particle swarm.

[0077] (4) Calculate fitness: Calculate fitness based on mean square error (MSE).

[0078] (5) Update individual extreme values ​​and global extreme values: Compare the individual fitness values ​​of the current generation of particles in the population with the individual fitness values ​​of the previous generation and update them.

[0079] (6) Update particle velocity and position.

[0080] (7) Output the optimal solution of the particle: When the fitness value is less than the preset value or reaches the maximum evolutionary generation, the optimal initial weight and threshold are obtained.

[0081] (8) The algorithm terminates and outputs the optimal network: When the prediction error is less than the preset value or reaches the maximum number of training times, the optimized network is obtained.

[0082] The relevant parameter settings in the algorithm are described as follows: the neural network learning rate is 0.01, the maximum number of iterations is 1000, and the error accuracy is 1×10 -6 .

[0083] S5, with ζ h , s , v and g To optimize the variables, maximize h v The optimization objectives are to minimize f and to use a multi-objective optimization algorithm to optimize the first target proxy model and the second target proxy model to obtain the Pareto optimal frontier of flow performance and heat transfer performance.

[0084] The multi-objective optimization algorithm is: NSGA-Ⅱ multi-objective genetic algorithm; the multi-objective decision-making method is: multi-criteria compromise ranking method, superior and inferior solution distance method.

[0085] In one embodiment, the multi-objective optimization problem usually includes three elements: decision variables, objective functions, and constraints. Generally speaking, blindly pursuing the improvement of heat transfer performance will inevitably increase pressure loss; conversely, only pursuing low flow resistance will inevitably lead to the deterioration of heat transfer performance, and there is a contradictory relationship between the objectives. Therefore, in the design process of PCHE, the two aspects of flow characteristics and heat transfer performance should be comprehensively considered, and the h of the airfoil fin PCHE should be comprehensively considered. v and f are two optimization objectives, aiming to maximize h v And minimize f, the objective function is selected with h v Or f are the two neural network prediction models for output, and the detailed parameter settings are listed in Table 2.

[0086] Table 2, parameter settings

[0087] parameter Population size Evolutionary Algebra Crossover probability Mutation probability Value 100 500 0.9 0.1

[0088] In another embodiment, VIKOR (Multi-Criteria Compromise Ranking) is a common multi-objective decision-making method that ranks and weighs different decision criteria to determine the best compromise solution. In VIKOR, it is first necessary to clarify the importance and priority of each criterion, then evaluate the alternative solutions, and finally determine the most suitable solution; the TOPSIS (Topology Distance Method) is a decision-making method for multi-objective optimization problems, and its basic idea is to evaluate and rank by calculating the distance between each alternative solution and the ideal solution. In this method, it is first necessary to determine the ideal solution, then calculate the distance between each alternative solution and the ideal solution, and finally select the solution with the smallest distance as the best solution.

[0089] Supercritical CO2 is used as the working fluid for calculation, and its physical properties are obtained through the Real Gas Model in the National Institute Standard and Technology to accurately reflect the changes in the physical properties of supercritical CO2 during the flow and heat transfer process; when setting the solver, the solution type is set to 3D compressible fluid steady-state flow, pressure-based implicit solver, the turbulence model selects the SST k-ω model, the inlet Reynolds number (Re) is set to 10000, the boundary condition is set to mass flow inlet, the mass flow rate is determined by Re, and the inlet temperature is 380K; the outlet is set to a pressure outlet, and the outlet pressure is 8.0MPa; symmetric boundary conditions are used on the walls on both sides of the channel, and constant wall temperature conditions are used on the upper and lower walls of the heat exchange section and the airfoil fin surface. The wall temperature is 430K, and the upper and lower walls of the inlet and outlet sections are adiabatic boundary conditions.

[0090] S6, using a multi-objective decision-making method to select variables of the compromise solution from the Pareto optimal frontier to determine the best optimization solution.

[0091] In another embodiment, after step S5 and before step S6, the method further comprises the step of: v , minimizing f, the single objective optimal solution corresponding to the maximum PF value solution, and the two compromise solutions of VIKOR and TOPSIS are simulated and verified;

[0092] If the h corresponding to the five schemes v If the relative error between the predicted value of the proxy model and the simulation result is greater than the error threshold, the number of samples in the training set is supplemented, and steps S4-S6 are repeated until the relative error meets the accuracy requirement; the variable ζ corresponding to the compromise solution that meets the accuracy requirement is h , s , v and g As the optimal design parameters of the PCHE airfoil fin; wherein, there is a gap between the top of the fin and the channel wall of the airfoil fin structure of the optimized printed circuit board type heat exchanger with a top gap, and the gap number satisfies 0<ζ g <0.15.

[0093] Simulation data such as Figure 4 As shown in the figure, the Pareto optimal frontier obtained by combining neural network and optimization algorithm and the geometric model diagram of the compromise solution and the single-objective optimal solution when Reynolds number Re = 10000. It can be observed that h v The two objectives of and f constrain each other, each point on the curve represents a non-dominated solution, and the set of all solutions constitutes the Pareto optimal solution set.

[0094] Table 3 lists the optimization design variables (ζ h , s , v and g ) and the objective function (h v , f), where the comprehensive performance evaluation factor (PF) is defined as:

[0095] h v0 and f0 represent the structural parameters ζ h =2,ζ s =0.5,ζ v =0.6,ζ g = h under 0 v and f.

[0096] As supercritical carbon dioxide flows in the PCHE, the working fluid is heated by the high-temperature wall surface and the temperature continues to rise. The present invention increases the heat exchange area by adding a top gap, thereby enhancing the heat exchange. In addition, due to the appearance of the gap, a vortex is formed in the channel, which accelerates the mixing of cold and hot fluids and further enhances the heat exchange.

[0097] The analysis shows that the relative errors of the five schemes are all within 5%, indicating that the prediction accuracy of the neural network is feasible and meets the needs of industrial design. min Compared with the TOPSIS and VIKOR schemes, although f increased by about 3 / 4, h v The improvement is more than two times. This shows that the TOPSIS scheme has a higher comprehensive performance. vmax Compared with the TOPSIS scheme and the VIKOR scheme, the h v It is only reduced by about 1 / 4, while f is significantly reduced by half, PF max Scheme and h vmax The results of the schemes are similar. The TOPSIS scheme and the VIKOR scheme show similar flow and heat transfer characteristics, which can significantly reduce the flow resistance while meeting the high heat transfer performance requirements. Through the above analysis, it can be found that f min The scheme has a large number of gaps. Although the flow resistance is small, the heat exchange capacity is also significantly reduced. Therefore, when optimizing the geometric parameters of PCHE, the comprehensive performance of PCHE with a large gap structure is not ideal, and it is impossible to take into account both enhanced heat exchange and reduced flow resistance. Choosing a structure with a smaller gap is beneficial to improving the thermal hydraulic performance.

[0098] Table 3. Optimization design variables and corresponding values ​​of objective functions for five different types of schemes

[0099]

[0100] The present invention also provides a multi-objective optimization system for the wing-shaped fin structure of a printed circuit board type heat exchanger with a top gap, the system comprising a memory and a processor, the memory storing a computer program, and the processor executing the multi-objective optimization method for the wing-shaped fin structure of a printed circuit board type heat exchanger with a top gap as described above when executing the computer program.

[0101] The present invention also provides a computer-readable storage medium, which stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions prompt the processor to implement the multi-objective optimization method for the airfoil fin structure of the printed circuit board type heat exchanger with a top gap as described above.

[0102] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-objective optimization method for an airfoil fin structure of a printed circuit board heat exchanger with a top gap, characterized in that: The method comprises the following steps: S1, obtaining geometric parameters of the airfoil fins in the printed circuit board heat exchanger PCHE with top gap, wherein the geometric parameters include the horizontal distance L h , staggered distance L s , vertical distance L v And gap distance L g ; S2, non-dimensionalizing the geometric parameters of the airfoil fin to obtain optimized design variables, wherein the optimized design variables include the horizontal number ζ h , interleaving number ζ s , vertical number ζ v and the number of gaps ζ g ; S3, performing experimental design and numerical simulation on the optimized design variables to obtain a sample set; S4, inputting the training set in the sample set into the neural network for training to obtain the PCHE volume heat transfer coefficient h v The corresponding first target proxy model and the second target proxy model corresponding to the PCHE drag coefficient f; S5, with ζ h , s , v and g To optimize the variables, maximize h v and minimizing f as optimization objectives, and optimizing the first target proxy model and the second target proxy model using a multi-objective optimization algorithm to obtain the Pareto optimal frontier of flow performance and heat transfer performance; S6, using a multi-objective decision-making method to select variables of the compromise solution from the Pareto optimal frontier to determine the best optimization solution.

2. The multi-objective optimization method for the airfoil fin structure of a printed circuit board type heat exchanger with a top gap according to claim 1, characterized in that: Based on the airfoil fin chord length L c and channel height L z The dimensionless geometric parameters of the airfoil fin are defined; the horizontal number is: The interleaving number is: The vertical number is The number of gaps is 3. The multi-objective optimization method for the airfoil fin structure of a printed circuit board type heat exchanger with a top gap according to claim 1, characterized in that: The length, width, and height of the PCHE 3D model must meet the following constraints: Where, L x Indicates the channel length, L y Indicates the channel width, L z Indicates the channel height, L h Indicates the horizontal distance of the airfoil, L s Indicates the staggered distance of the airfoil, L v Indicates the vertical distance of the airfoil, L g Indicates the clearance distance of the airfoil, L c Indicates the chord length of the airfoil, L t Represents the airfoil thickness, i, j, k, and m are all positive integers.

4. The multi-objective optimization method for the airfoil fin structure of a printed circuit board type heat exchanger with a top gap according to claim 1, characterized in that: The optimal Latin hypercube design is adopted to collect sample points of the optimization variables in the design space for simulation to obtain a training set and a test set; the training set is used to train the neural network, and the test set is used to test the accuracy of the neural network.

5. The multi-objective optimization method for the airfoil fin structure of a printed circuit board type heat exchanger with a top gap according to any one of claims 1 to 4, characterized in that: The first target proxy model and the second target proxy model are: PSO-BP neural network.

6. The multi-objective optimization method for the airfoil fin structure of a printed circuit board type heat exchanger with a top gap according to claim 5, characterized in that: The input layer of the PSO-BP neural network contains four neurons, namely, the level number ζ h , interleaving number ζ s , vertical number ζ v and the gap number ζ g The output layer contains a neuron; the PSO-BP neural network is used to calculate the volume heat transfer coefficient h v And the drag coefficient f are trained separately.

7. The multi-objective optimization method for the airfoil fin structure of a printed circuit board type heat exchanger with a top gap according to claim 6, characterized in that: The prediction performance of the neural network was quantitatively evaluated using mean square error; The process of improving the BP neural network by using the improved particle swarm optimization algorithm includes: firstly, optimizing the weights and learning factors of the particle swarm algorithm to obtain the improved particle swarm algorithm; Then, the initial weights and thresholds of the proxy model are encoded into particles, and the optimal initial weights and thresholds corresponding to the minimum fitness value are obtained, and finally assigned to the proxy model.

8. The multi-objective optimization method for the airfoil fin structure of a printed circuit board type heat exchanger with a top gap according to any one of claims 1 to 4, characterized in that: After step S5 and before step S6, the method further comprises the following steps: v , minimize f, the single objective optimal solution corresponding to the maximum PF value scheme, and the two compromise schemes of VIKOR and TOPSIS are simulated and verified; if the h corresponding to the five schemes v If the relative error between the predicted value of the surrogate model and the simulation result is greater than the error threshold, the number of samples in the training set is supplemented, and steps S4-S6 are repeated until the relative error meets the accuracy requirement; the variable ζ corresponding to the VIKOR and TOPSIS compromise solution that meets the accuracy requirement is h , s , v and g As the optimal design parameters of the PCHE airfoil fin; wherein, there is a gap between the top of the fin and the channel wall of the airfoil fin structure of the optimized printed circuit board type heat exchanger with a top gap, and the gap number satisfies 0<ζ g <0.

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9. A multi-objective optimization system for the airfoil fin structure of a printed circuit board heat exchanger with a top gap, characterized in that: The system includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the multi-objective optimization method for the airfoil fin structure of a printed circuit board type heat exchanger with a top gap as described in any one of claims 1 to 8 is executed.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores machine-executable instructions, and when the machine-executable instructions are called and executed by the processor, the machine-executable instructions prompt the processor to implement the multi-objective optimization method for the airfoil fin structure of a printed circuit board type heat exchanger with a top gap as described in any one of claims 1-8.

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