An intelligent optimization design method and system for a radiator

Abstract

The present invention provides an optimized design method, system and thermal management system for a radiator. Based on the structural parameters of the radiator, the heat transfer factor is used to evaluate the heat transfer performance of the radiator, and the friction factor is used to evaluate the resistance characteristics of the radiator; according to the heat transfer factor and the friction factor, a fitness function for evaluating the comprehensive performance of the radiator is determined; the firefly algorithm is introduced into the optimization process of the sparrow search algorithm, and the improved sparrow search algorithm is used to optimize the fitness function of the radiator to obtain the optimized structural parameters of the radiator. The present invention combines the sparrow search algorithm with the firefly algorithm, improves the problem that the sparrow search algorithm falls into local optimum, and optimizes the structural parameters of the radiator based on the correlation between the radiator parameters and the heat transfer coefficient and the resistance coefficient, so as to ensure that the heat dissipation effect of the radiator meets the thermal management requirements.

Description

Technical Field

[0001] The present invention belongs to the technical field of heat dissipation equipment design, and relates to an intelligent optimization design method and system for a radiator. Background Art

[0002] The statements in this part only provide background technical information related to the present invention, and do not necessarily constitute prior art.

[0003] During the operation of many large-scale devices, thermal management is required, and thermal management generally requires the participation and execution of a radiator. Taking an automobile as an example for illustration, during the driving of the automobile, a large amount of heat is generated, and all this heat is output to the external environment through thermal management. If the system cannot meet the heat dissipation requirements of the engine, the un-dissipated heat will affect the overall power performance of the automobile.

[0004] A common automobile thermal management system consists of components such as a radiator, an intercooler, a coolant pump, coolant pipes, and a thermostat. The coolant pipes connect the various components within the system and provide a flow channel for the coolant. Under the coordinated action of the pump and the radiator, the engine temperature is maintained within a suitable range to meet the normal operation of the automobile.

[0005] The radiator is the most important heat dissipation component in the large cycle of the thermal management system. Its main structure is an aluminum tube-and-fin radiator. In the tube-and-fin radiator, the high-temperature flowing coolant in the flat tubes flows through the radiator wall surface to conduct convective heat transfer with the air. The air passes through the radiator metal strips, while the coolant flows horizontally in the flat tubes. The geometric structure of the radiator has a great influence on the comprehensive performance of the radiator. For example, the opening angle of the radiator louvers, the fin pitch, the height of the heat dissipation strip, the thickness of the radiator, and the louver spacing all affect the heat transfer coefficient and pressure drop of the radiator. If not fully designed, the radiator has poor heat dissipation effect and large flow resistance, and cannot meet the requirements of thermal management.

[0006] As understood by the inventor, currently some technical personnel apply a certain intelligent algorithm for optimization. However, only applying a certain algorithm generally leads to getting stuck in a local optimum during optimization. When optimizing the structure of the radiator, it will always search for the optimum near the local optimum point, and it is difficult to find the true optimum parameter value. Summary of the Invention

[0007] In order to solve the above problems, the present invention proposes an intelligent optimization design method and system for a radiator. The present invention combines the sparrow search algorithm and the firefly algorithm, improves the problem that the sparrow search algorithm gets stuck in a local optimum, and optimizes the structural parameters of the radiator based on the correlation between the radiator parameters and the heat transfer coefficient and resistance coefficient, so as to ensure that the heat dissipation effect of the radiator meets the requirements of thermal management.

[0008] According to some embodiments, the present invention adopts the following technical solutions:

[0009] An intelligent optimization design method for a radiator, comprising the following steps:

[0010] Based on the radiator structure parameters, use the heat transfer factor to evaluate the heat transfer performance of the radiator, and use the friction factor to evaluate the resistance characteristics of the radiator;

[0011] According to the heat transfer factor and the friction factor, determine the fitness function for evaluating the comprehensive performance of the radiator;

[0012] Introduce the firefly algorithm into the optimization process of the sparrow search algorithm, and use the improved sparrow search algorithm to optimize the fitness function of the radiator to obtain the optimized radiator structure parameters.

[0013] As an alternative implementation, the radiator structure parameters include the key structures affecting the heat dissipation performance of the automotive radiator, such as the shutter opening angle, the heat dissipation strip wave pitch, the heat dissipation strip wave height, the radiator thickness, the height of the shutter, the fin thickness, and the shutter spacing.

[0014] As an alternative implementation, the heat transfer factor is:

[0015]

[0016] The friction factor is:

[0017]

[0018] In the formula, θ is the shutter opening angle, F p is the heat dissipation strip wave pitch, F h is the heat dissipation strip wave height, L d is the radiator thickness, L p is the shutter spacing, L h is the height of the shutter, δ is the fin thickness, ρ is the air density, u is the inlet air velocity, and μ is the dynamic viscosity of the air.

[0019] As an alternative implementation, the specific process of determining the fitness function for evaluating the comprehensive performance of the radiator according to the heat transfer factor and the friction factor includes using relevant factors to evaluate the comprehensive performance of the radiator. The relevant factors are obtained based on the heat transfer factor, the friction factor, and the correlation coefficient, and the relevant factors are used as the fitness value in the sparrow search algorithm.

[0020] Furthermore, use F jf factor to evaluate the comprehensive performance of the radiator, and its formula is:

[0021]

[0022] As an alternative implementation, the specific process of introducing the firefly algorithm into the optimization process of the sparrow search algorithm includes: in the sparrow search algorithm, the sparrow with a better fitness value is used as the discoverer, which is responsible for providing the foraging direction for the followers. During the update process of the discoverer's position, the Cartesian distance, light absorption coefficient, and maximum attraction considered in the position update of each firefly in the firefly algorithm are incorporated.

[0023] As a further improvement, in the improved sparrow search algorithm, the expression for updating the position of the discoverer is:

[0024]

[0025]

[0026] where, X i，j represents the position information of the i-th sparrow in the j-th dimension; t is the current iteration number; X a,j , X b,j are the j-th dimensional components of two randomly selected different individuals, and i ≠ a ≠ b; r ab is the distance between two different sparrows; ω is the adaptive parameter; β0 is the maximum attraction, γ is the light absorption coefficient; iter max is a constant representing the maximum number of iterations; R2 and ST represent the early warning value and the safety value respectively, Q is a random number following a normal distribution in [0,1], L represents a 1×d matrix where each element in the matrix is all 1, and α is a random number in the range (0, 1].

[0027] As an alternative implementation, the specific process of using the improved sparrow search algorithm to optimize the fitness function of the radiator includes:

[0028] Initialize the population, randomly generate multiple sparrow and firefly individuals, and calculate the key parameters of the radiator for the initial population;

[0029] Start the iteration, update the positions of the sparrows integrated with the firefly algorithm. After the update, calculate the fitness, and select the position of the optimal individual in the population, that is, obtain the parameters under the optimal fitness and record them until the iteration number is satisfied.

[0030] As an alternative implementation, the process of using the improved sparrow search algorithm to optimize the fitness function of the radiator is repeated multiple times, and the average value of the optimal solutions each time is used as the final value of the parameter optimization.

[0031] As an alternative implementation, the specific process of obtaining the optimized structural parameters of the radiator includes calculating the corresponding fitness using the optimal solution, that is, the optimal structural parameters, and taking the optimal structural parameters as the optimized structural parameters of the radiator.

[0032] An intelligent optimization design system for a radiator, comprising:

[0033] A performance description module, configured to evaluate the heat transfer performance of the radiator by using a heat transfer factor and evaluate the resistance characteristics of the radiator by using a friction factor based on the radiator structure parameters;

[0034] A fitness function construction module, configured to determine a fitness function for evaluating the comprehensive performance of the radiator according to the heat transfer factor and the friction factor;

[0035] An intelligent optimization module, configured to introduce a firefly algorithm into the optimization process of the sparrow search algorithm and optimize the fitness function of the radiator by using the improved sparrow search algorithm to obtain optimized radiator structure parameters.

[0036] A thermal management system, comprising a radiator designed by the above method or system.

[0037] Compared with the prior art, the beneficial effects of the present invention are:

[0038] The present invention combines the sparrow search algorithm with the firefly algorithm, improves the problem that the sparrow search algorithm falls into local optimum, and optimizes the structure parameters of the radiator based on the correlation between the radiator parameters and the heat transfer coefficient and the resistance coefficient.

[0039] The present invention incorporates the firefly algorithm position update into the position update process of the discoverer in the sparrow search algorithm to improve the problem that the sparrow algorithm falls into local optimum.

[0040] The present invention determines the search range of the sparrow search algorithm through an early warning value and a safety value, and can increase the local search ability or strengthen the global search ability according to the search times and situations.

[0041] To make the above objects, features and advantages of the present invention more obvious and understandable, the following specific preferred embodiments are given and described in detail in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The specification drawings forming a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention.

[0043] Figure 1 It is a schematic flow chart of an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] The present invention will be further described below in conjunction with the drawings and embodiments.

[0045] It should be noted that the following detailed description is illustrative and aims to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention pertains.

[0046] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular forms are also intended to include the plural forms. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they specify the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0047] Embodiment 1

[0048] In this embodiment, the sparrow search algorithm is combined with the firefly algorithm to improve the problem that the sparrow search algorithm falls into local optimum. Based on the correlation between the radiator parameters and the heat transfer coefficient and resistance coefficient, the structural parameters of the radiator are optimized.

[0049] The specific solution is introduced as follows. As Figure 1 shown, an intelligent optimization design method for a radiator includes the following steps:

[0050] Introduce the firefly algorithm into the optimization process of the sparrow search algorithm to obtain an improved sparrow search algorithm;

[0051] Based on the structural parameters of the radiator, use the heat transfer factor to evaluate the heat transfer performance of the radiator, and use the friction factor to evaluate the resistance characteristics of the radiator;

[0052] According to the heat transfer factor and the friction factor, determine the fitness function for evaluating the comprehensive performance of the radiator;

[0053] Use the improved sparrow search algorithm to optimize the fitness function of the radiator to obtain the optimized structural parameters of the radiator.

[0054] Specifically, in this embodiment, the key structures affecting the heat dissipation performance of the automotive radiator are the louver opening angle, the fin pitch, the fin height, the radiator thickness, the louver height, the fin thickness, and the louver spacing. The heat transfer performance and resistance characteristics of the radiator are mainly evaluated by the heat transfer factor j and the friction factor f. The specific calculation formulas are as follows:

[0055]

[0056]

[0057] In the formula, θ is the louver opening angle, F p is the fin pitch, Fh is the wave height of the heat dissipation belt, L d is the thickness of the radiator, L p is the louver pitch, L h is the height of the louver, δ is the fin thickness, ρ is the air density, u is the inlet air velocity, and μ is the dynamic viscosity of the air.

[0058] The parameters in this embodiment are only exemplary. In other embodiments, the above parameters may be further refined or adjusted precisely, which is easy for those skilled in the art to think of and should reasonably fall within the protection scope of the present invention.

[0059] Use F jf factor to evaluate the comprehensive performance of the radiator, and its formula is:

[0060]

[0061] Sparrows search for food, and a population composed of n sparrows can be expressed in the following form:

[0062]

[0063] In the formula, n is the number of sparrows, i is the current iteration number, and d is the variable dimension. Then, the fitness values of all sparrows can be expressed in the following form:

[0064]

[0065] Among them, f represents the fitness value.

[0066] In the sparrow search algorithm, the discoverer with a better fitness value will preferentially obtain food during the search process. The sparrow with a better fitness value is used as the discoverer, which is responsible for providing the foraging direction for the followers. The position update method of the discoverer is as follows:

[0067]

[0068] where t represents the current iteration number, j = 1, 2, 3,..., d. iter max is a constant representing the maximum number of iterations. X i , jIt represents the position information of the i-th sparrow in the j-th dimension. α ∈ (0, 1] is a random number. R2 (R2 ∈ [0, 1]) and ST (ST ∈ [0.5, 1]) represent the warning value and the safety value respectively. Q is a random number obeying the normal distribution of [0, 1]. L represents a 1×d matrix, where each element in the matrix is all 1. When R2 < ST, it means that there are no predators around the foraging environment at this time, and the discoverer can perform extensive search operations. If R2 ≥ ST, it indicates that some sparrows in the population have discovered the predator and sent out an alarm to other sparrows in the population. At this time, all sparrows need to quickly fly to other safe places to forage.

[0069] The position update formula for followers is as follows:

[0070]

[0071] Among them, X P is the optimal position currently occupied by the discoverer, and X worst represents the current global worst position. A represents a 1×d matrix, where each element is randomly assigned 1 or -1, and A + = A T (AA T ) -1 . When i > n / 2, it indicates that the i-th joiner with a lower fitness value has not obtained food and is in a very hungry state. At this time, it needs to fly to other places to forage to obtain more energy.

[0072] When sparrows forage, they need to be on guard against natural enemies. Randomly select SD (20% is taken in this article) sparrows from each generation as early warning birds. When the warning value judges that the natural enemy is approaching, the sparrows in the population will abandon the current target and fly to another safe position. Its position update formula is:

[0073]

[0074] Among them, where X best is the current global optimal position. β, as a step size control parameter, is a random number obeying the normal distribution with a mean of 0 and a variance of 1. K ∈ [-1, 1] is a random number, and f i is the fitness value of the current sparrow individual. f g and f w are the current global best and worst fitness values respectively. ε is a very small constant, which is set to 10 -6 to avoid a zero denominator.

[0075] When f i > f g it means that the sparrow is at the edge of the population at this time and is extremely vulnerable to attacks by predators. Xbest It means that the sparrow at this position is the best position in the population and is also very safe. f i = f g When, this indicates that the sparrows in the middle of the population are aware of the danger and need to get closer to other sparrows to minimize their risk of being preyed upon. K represents the direction of sparrow movement and is also the step size control parameter.

[0076] Integrate the firefly algorithm into the sparrow search algorithm. The relative brightness of each firefly i to another firefly j within its field of view:

[0077]

[0078] where I i is the absolute brightness of firefly i; γ is the light absorption coefficient and can be set as a constant; r ij is the Cartesian distance from firefly i to firefly j

[0079] The updated position of each firefly is:

[0080]

[0081] where t is the number of iterations of the algorithm, β0 is the maximum attraction, that is, the attraction of the firefly at the light source (r = 0), ξ i is a random number obtained from a Gaussian distribution, uniform distribution, etc., and λ is the random term coefficient. To improve the problem that the sparrow algorithm falls into local optimum, the firefly algorithm is introduced into the sparrow algorithm. The expression of the improved discoverer is:

[0082]

[0083]

[0084] where, X a,j , X b,j are the j - dimensional components of two randomly selected different individuals, and i ≠ a ≠ b; r ab is the distance between two different sparrows; ω is the adaptive parameter. In the initial stage of algorithm iteration, the adaptive parameter is small, which is conducive to the exploration of the algorithm; in the later stage of iteration, the adaptive parameter becomes larger, which is conducive to the exploitation of the algorithm.

[0085] R2 (R2 ∈ [0, 1]) and ST (ST ∈ [0.5, 1]) represent the warning value and the safety value respectively. The search range of the sparrow search algorithm is changed through the warning value and the safety value. When , taking ST ∈ [0.5, 1], the formula R2 = 0.5 + 0.5cos(γr ab) Change the search range of the sparrow algorithm. After every 10 calculations, increase the local search ability and take R2 ∈ [0, 1], ST = 0.6 + 0.4|cos(γr ab )|; when , enhance the global search ability, take ST ∈ [0.5, 1], R2 = 0.2 + 0.8|cos(γr ab )|.

[0086] Use the sparrow search algorithm integrated with the firefly algorithm to optimize the F jf factor, determine the optimal value, and then obtain the optimal louver opening angle, fin pitch, heat dissipation belt wave height, radiator thickness, and louver spacing.

[0087] In this embodiment, the specific optimization process includes:

[0088] Initialize the population, randomly generate 1000 sparrow and firefly individuals, and calculate the key parameters of the radiator for the initial population.

[0089] Start iteration, update the sparrow positions of the integrated firefly algorithm, calculate the fitness after the update, and select the optimal individual position in the population, that is, obtain and record the parameters under the optimal fitness. The number of iterations is 200 times.

[0090] To avoid the contingency brought by a single optimization, in this embodiment, it is executed 10 times repeatedly, and the average value of the optimal solutions each time is taken as the final value of the parameter optimization, and the corresponding fitness is calculated using the optimal solution, that is, the optimal structural parameters.

[0091] The parameter settings of the above embodiments can all be replaced or transformed in other embodiments.

[0092] Embodiment 2

[0093] The difference from Embodiment 1 is that in this embodiment, first, based on the radiator structure parameters, the heat transfer factor is used to evaluate the heat transfer performance of the radiator, and the friction factor is used to evaluate the resistance characteristics of the radiator;

[0094] According to the heat transfer factor and the friction factor, determine the fitness function for evaluating the comprehensive performance of the radiator.

[0095] Then introduce the firefly algorithm into the optimization process of the sparrow search algorithm, and use the improved sparrow search algorithm to optimize the fitness function of the radiator to obtain the optimized radiator structure parameters.

[0096] Other processes have no substantial difference from Embodiment 1. In the setting of some parameters, according to the specific situation and requirements, there are slight adjustments and changes compared with the parameter settings of Embodiment 1.

[0097] Embodiment 3

[0098] An intelligent optimization design system for a radiator, comprising:

[0099] A performance description module, configured to evaluate the heat transfer performance of the radiator by using a heat transfer factor and evaluate the resistance characteristics of the radiator by using a friction factor based on the radiator structure parameters;

[0100] A fitness function construction module, configured to determine a fitness function for evaluating the comprehensive performance of the radiator according to the heat transfer factor and the friction factor;

[0101] An intelligent optimization module, configured to introduce the firefly algorithm into the optimization process of the sparrow search algorithm, and use the improved sparrow search algorithm to optimize the fitness function of the radiator to obtain optimized radiator structure parameters.

[0102] Example 4

[0103] A thermal management system, comprising a radiator designed by the method or system provided in the above example. The radiator can be a water radiator, an intercooler, an oil cooler, an evaporator of an automotive air conditioner, a condenser, etc.

[0104] Of course, the application scope of the present invention is not limited to automobiles, and can also be robots, electronic devices, mechanical devices, etc.

[0105] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0106] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0107] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to work in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the functions specified in one or more of the flows Figure 1 and / or boxes. Figure 1 The functions specified in one or more boxes.

[0108] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, such that a series of operational steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one or more of the flows Figure 1 and / or boxes. Figure 1 The functions specified in one or more boxes.

[0109] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0110] Although the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, it is not a limitation on the protection scope of the present invention. Those skilled in the art should understand that, based on the technical solutions of the present invention, various modifications or deformations that can be made without creative efforts by those skilled in the art are still within the protection scope of the present invention.

Claims

1. An optimization design method for a radiator, characterized in that, It includes the following steps: Based on the radiator structure parameters, use the heat transfer factor to evaluate the heat transfer performance of the radiator, and use the friction factor to evaluate the resistance characteristics of the radiator; According to the heat transfer factor and the friction factor, determine the fitness function for evaluating the comprehensive performance of the radiator; Introduce the firefly algorithm into the optimization process of the sparrow search algorithm, and use the improved sparrow search algorithm to optimize the fitness function of the radiator to obtain the optimized radiator structure parameters; The specific process of introducing the firefly algorithm into the optimization process of the sparrow search algorithm includes: in the sparrow search algorithm, the sparrow with a better fitness value is used as the discoverer, which is responsible for providing the foraging direction for the followers. In the process of updating the position of the discoverer, incorporate the Cartesian distance, light absorption coefficient, and maximum attraction considered when updating the position of each firefly in the firefly algorithm; In the improved sparrow search algorithm, the expression for updating the position of the discoverer is: Among them, X i，j represents the position information of the i-th sparrow in the j-th dimension; t is the current iteration number; X a,j , X b,j are the j-th dimensional components of two randomly selected different individuals, and i≠a≠b; r ab is the distance between two different sparrows; ω is the adaptive parameter; β0 is the maximum attraction, γ is the light absorption coefficient; iter max is a constant representing the maximum number of iterations; R2 and ST represent the early warning value and the safety value respectively, Q is a random number obeying the normal distribution of [0,1], L represents a 1×d matrix, where each element in the matrix is all 1, and α is a random number within the range of (0,1]; The specific process of using the improved sparrow search algorithm to optimize the fitness function of the radiator includes: Initialize the population, randomly generate multiple sparrow and firefly individuals, and calculate the key parameters of the radiator for the initial population; Start iteration, update the positions of the sparrows integrating the firefly algorithm, calculate the fitness after the update, and select the optimal individual position in the population to obtain the parameters under the optimal fitness and record them until the iteration times are met; Repeat the process of using the improved sparrow search algorithm to optimize the fitness function of the radiator multiple times, and use the average value of the optimal solutions each time as the final value of the parameter optimization; The specific process of obtaining the optimized radiator structure parameters includes calculating the corresponding fitness using the optimal solution, that is, the optimal structure parameters, and taking the optimal structure parameters as the optimized radiator structure parameters.

2. The optimized design method of a radiator as claimed in claim 1, wherein The radiator structure parameters include the key structures affecting the heat dissipation performance of the automotive radiator, such as the shutter opening angle, fin pitch, fin height, radiator thickness, shutter height, fin thickness, and shutter spacing.

3. The optimization design method of a radiator according to claim 1, characterized in that, The heat transfer factor is: The friction factor is: where θ is the opening angle of the louver, F p is the pitch of the heat dissipation fin, F h is the height of the heat dissipation fin, L d is the thickness of the radiator, L p is the louver spacing, L h is the height of the louver, δ is the fin thickness, ρ is the air density, u is the inlet air velocity, and μ is the dynamic viscosity of air.

4. The optimized design method of a radiator as claimed in claim 1 or 3, characterized in that, The specific process of determining the fitness function for evaluating the comprehensive performance of the radiator according to the heat transfer factor and the friction factor includes evaluating the comprehensive performance of the radiator using relevant factors. The relevant factors are obtained based on the heat transfer factor, the friction factor, and a correlation coefficient, and the relevant factors are used as the fitness value in the sparrow search algorithm.

5. An optimized design system for a radiator, characterized in that It includes: A performance description module configured to evaluate the heat transfer performance of the radiator using the heat transfer factor and evaluate the resistance characteristics of the radiator using the friction factor based on the radiator structure parameters; A fitness function construction module configured to determine the fitness function for evaluating the comprehensive performance of the radiator according to the heat transfer factor and the friction factor; An intelligent optimization module configured to introduce the firefly algorithm into the optimization process of the sparrow search algorithm and use the improved sparrow search algorithm to optimize the fitness function of the radiator to obtain the optimized radiator structure parameters; The specific process of introducing the firefly algorithm into the optimization process of the sparrow search algorithm includes: in the sparrow search algorithm, the sparrow with a better fitness value is used as the discoverer, which is responsible for providing the foraging direction for the followers. During the update process of the discoverer's position, the Cartesian distance, light absorption coefficient, and maximum attraction considered in the position update of each firefly in the firefly algorithm are incorporated. In the improved sparrow search algorithm, the update expression for the discoverer's position is: Among them, X i，j represents the position information of the i-th sparrow in the j-th dimension; t is the current iteration number; X a,j , X b,j are the j-th dimensional components of two randomly selected different individuals, and i≠a≠b; r ab is the distance between two different sparrows; ω is the adaptive parameter; β0 is the maximum attraction, γ is the light absorption coefficient; iter max is a constant representing the maximum number of iterations; R2 and ST represent the early warning value and the safety value respectively, Q is a random number obeying the normal distribution of [0,1], L represents a 1×d matrix where each element in the matrix is all 1, and α is a random number within the range of (0,1]; The specific process of using the improved sparrow search algorithm to optimize the fitness function of the radiator includes: Initialize the population, randomly generate multiple sparrow and firefly individuals, and calculate the key parameters of the radiator for the initial population. Start iteration, update the positions of the sparrows integrated with the firefly algorithm. After the update, calculate the fitness, and select the position of the optimal individual in the population to obtain the parameters under the optimal fitness and record them until the iteration times are met. Repeat the process of using the improved sparrow search algorithm to optimize the fitness function of the radiator multiple times, and take the average value of the optimal solutions each time as the final value of the parameter optimization. The specific process of obtaining the optimized structural parameters of the radiator includes calculating the corresponding fitness, that is, the optimal structural parameters, using the optimal solution, and taking the optimal structural parameters as the optimized structural parameters of the radiator.

6. A thermal management system, characterized in that, A radiator obtained by the method according to any one of claims 1-4 or the system design according to claim 5.

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