CN117709168B - Optimal design method and device for forced air cooling heat dissipation system - Google Patents
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Abstract
The invention provides an optimal design method and device for a forced air cooling heat dissipation system. The method comprises the following steps: acquiring a comprehensive thermal resistance model of the forced air cooling heat dissipation system; the forced air cooling heat dissipation system comprises a fin type radiator; the comprehensive thermal resistance model is a function of the size parameter of the fin radiator and the working point air quantity of the forced air cooling heat dissipation system; adopting a multi-objective optimization algorithm, taking the thermal resistance of the fin radiator and the volume of the forced air cooling heat dissipation system as optimization targets, taking the preset maximum value of the thermal resistance and the preset upper and lower limits of the size parameters as constraint conditions, optimizing the thermal resistance and the size parameters under the constraint conditions, and outputting various optimization schemes; and evaluating various optimization schemes by adopting an entropy weight TOPSIS method to determine the optimal scheme of the size parameters. The method can complete the optimal design of the forced air cooling heat dissipation system, ensure the optimal size and performance of the forced air cooling heat dissipation system, and greatly improve the optimization efficiency. The device can realize the method.
Description
Technical Field
The invention relates to the technical field of power electronic equipment heat dissipation, in particular to an optimal design method and device of a forced air cooling heat dissipation system.
Background
The temperature is one of the important factors influencing the reliability of the high-power-density switching power supply, and according to research, the reliability is reduced by 50% when the working temperature of the electronic equipment is increased by 10 ℃, so that on the basis of the structural design of the switching power supply, the heat dissipation system of the switching power supply is designed to control the temperature of main heating devices in the switching power supply, and the reliability of the equipment is very important to improve.
The current thermal design methods of power electronic devices are mainly divided into three types: (1) Thermal design is performed according to engineering practice experience, but an empirical formula is usually large in error and has no universality; (2) The thermal design is carried out on the basis of establishing a mathematical model according to the theory of heat transfer, and the accuracy of the method depends on the model precision and is generally complex; (3) Based on discrete mathematics and numerical calculation and based on a computer as a tool, the temperature distribution of the electronic equipment is solved through numerical simulation software such as FLOTHERM and ICEPAK on the basis of establishing a three-dimensional geometric model of the electronic equipment, so that a reference basis is provided for heat design and optimization.
The three methods can be used for primarily designing the heat dissipation system of the power electronic equipment, but with the development of the power electronic equipment in the directions of high power density and miniaturization, a radiator and a fan with smaller volumes are required to be selected as much as possible on the premise of meeting the heat dissipation requirement, so that the volume of the power electronic equipment is reduced, namely the size of the heat dissipation system is required to be optimized. However, the design method is based on an exhaustion method, namely, the size of a large number of heat dissipation systems is listed for calculation and analysis, and the optimal size is selected from the calculation, but on the one hand, the exhaustion method needs a large amount of calculation, so that the problem of low optimization efficiency exists; on the other hand, the exhaustive method may fall into a locally optimal solution, i.e. an optimal in the listed samples, rather than an actual optimal.
Accordingly, there is a need for improvements in light of the deficiencies of the prior art.
Disclosure of Invention
The invention mainly solves the technical problem of providing an optimal design method and device for a forced air cooling heat dissipation system, so as to complete the optimal design of the forced air cooling heat dissipation system, ensure the optimal size and performance of the forced air cooling heat dissipation system, and greatly improve the optimization efficiency.
According to a first aspect, an embodiment provides an optimal design method of a forced air cooling heat dissipation system. The method comprises the following steps:
acquiring a comprehensive thermal resistance model of the forced air cooling heat dissipation system;
the forced air cooling heat dissipation system comprises a fan, a system air channel and a fin type radiator;
the comprehensive thermal resistance model is a function of the size parameter of the fin type radiator and the working point air quantity of the forced air cooling heat dissipation system; the dimensional parameters include: the length of the fin type radiator, the width of the fin type radiator, the height of fins of the fin type radiator, the width of air channels in the fins and the number of the fins;
The expression of the comprehensive thermal resistance model is as follows: ；
wherein, R th,ha is the thermal resistance of the fin radiator; d is the thickness of the substrate of the fin radiator; x 1 and x 2 are the length of the finned radiator and the width of the finned radiator, respectively; the lambda hs is the thermal conductivity of the material adopted by the fin type radiator; the ρ air is the density of the air flowing through the fin radiator; the c air is the specific heat capacity of the air; the V 0 is the working point air quantity of the forced air cooling heat dissipation system; h is the average heat transfer coefficient of the fin radiator; the A eff is the effective convection surface area of the fin radiator;
adopting a multi-objective optimization algorithm, taking the volume of the thermal resistance and the forced air cooling heat dissipation system as optimization targets, taking a preset maximum value of the thermal resistance and a preset upper limit and a preset lower limit of the size parameter as constraint conditions, optimizing the size parameter of the fin type radiator under the constraint conditions, and outputting various optimization schemes of the size parameter;
And evaluating the multiple optimization schemes by adopting an entropy weight TOPSIS method to determine the optimal scheme of the size parameter under the constraint condition.
In one embodiment, the operating point air volume is obtained based on a fluid mechanical model of the fan and the fin radiator.
In one embodiment, the multi-objective optimization algorithm is a non-dominant fast ordering genetic algorithm;
The adoption of the multi-objective optimization algorithm takes the volumes of the thermal resistance and the forced air cooling heat dissipation system as optimization targets, takes the preset maximum value of the thermal resistance and the preset upper and lower limits of the dimension parameter as constraint conditions, optimizes the dimension parameter of the fin type heat radiator under the constraint conditions, and outputs various optimization schemes of the dimension parameter, wherein the optimization schemes comprise:
Taking the thermal resistance as a first objective function to be solved, taking the volume as a second objective function to be solved, and taking the size parameter as a variable to be optimized;
The expressions of the first objective function to be solved and the second objective function to be solved are respectively:
；
；
The F 1 is the first objective function to be solved; the F 2 is the second objective function to be solved; x 3 is the height of the fin; the H fan、Wfan and the L fan are respectively the height, the width and the length of the fan; the min represents an operation of obtaining a minimum value;
Determining the size N and the maximum iteration number of the population, and generating an initial population according to the constraint condition; wherein the initial iteration number of the population is 1;
Calculating an optimization target value of individuals in the population, and carrying out rapid non-dominant sorting and crowding calculation on the initial population according to the constraint condition and the optimization target value; wherein the optimized target value comprises a value of the first objective function to be solved and a value of the second objective function to be solved; the rapid non-dominant ranking is layering the population according to a non-inferior solution level of individuals in the population; the crowding degree calculation is to selectively sort individuals in the population at the same layer;
generating a new population: sequentially performing selection operation, cross operation, mutation operation and merging operation on the population to obtain the population after the merging operation, wherein the size of the population after the merging operation is 2N, calculating the first objective function to be solved and the second objective function to be solved (and constraint conditions), performing rapid non-dominant sorting and congestion degree calculation, and preferentially selecting N individuals in the population after the merging operation as the population of a new generation according to the dominant order obtained by the rapid non-dominant sorting, the congestion degree obtained by the congestion degree calculation and the constraint conditions;
Judging: judging whether the current iteration times reach the maximum iteration times or not;
If the current iteration times do not reach the maximum iteration times, adding 1 to the iteration times to obtain new iteration times, and returning to the step of generating a new population; if the current iteration times reach the maximum iteration times, turning to a step of outputting an optimization scheme;
the output optimization scheme comprises the following steps: taking the population of the last generation as the pareto optimal solution set; wherein the pareto optimal solution set comprises a plurality of optimization schemes of the size parameters;
In one embodiment, the expressions of h and a eff are respectively:
；
；
Wherein, ；
；
Wherein x 4 and x 5 are the width of the air duct in the fin and the number of the fins respectively; the lambda air is the thermal conductivity of the air; v air is the aerodynamic viscosity of the air.
In an embodiment, the evaluating the multiple optimization schemes by using an entropy weight TOPSIS method to determine an optimal scheme of the size parameter under the constraint condition includes:
Determining an evaluation index; wherein the evaluation index includes the thermal resistance and the volume;
normalizing the evaluation index to obtain the normalized evaluation index;
calculating the information entropy of the evaluation index after normalization processing;
determining the weight of the evaluation index after normalization processing according to the information entropy;
calculating according to the weight to obtain the weighted evaluation index;
Determining a positive ideal solution and a negative ideal solution of the weighted evaluation index; wherein the positive ideal solution is the maximum value of the weighted evaluation index; the negative ideal solution is the minimum value of the weighted evaluation index;
determining Euclidean distances between the optimization scheme and the positive ideal solution and the negative ideal solution respectively;
And calculating the relative closeness of the optimization scheme to the positive ideal solution and the negative ideal solution respectively according to the Euclidean distance, and taking the optimization scheme with the maximum relative closeness as an optimal size scheme.
According to a second aspect, an embodiment provides an apparatus for optimizing a forced air cooling heat dissipation system. The device comprises:
The preprocessing module is configured to acquire a comprehensive thermal resistance model of the forced air cooling heat dissipation system;
The forced air cooling heat dissipation system comprises a fan, a system air channel and a fin type radiator; the comprehensive thermal resistance model is a function of the size parameter of the fin type radiator and the working point air quantity of the forced air cooling heat dissipation system; the dimensional parameters include: the length of the fin type radiator, the width of the fin type radiator, the height of fins of the fin type radiator, the width of air channels in the fins and the number of the fins;
the expression of the comprehensive thermal resistance model is as follows: ；
Wherein, R th,ha is the thermal resistance of the fin radiator; d is the thickness of the substrate of the fin radiator; x 1 and x 2 are the length and width of the fin radiator, respectively; the lambda hs is the thermal conductivity of the material adopted by the fin type radiator; the ρ air is the density of the air flowing through the fin radiator; the c air is the specific heat capacity of the air; the V 0 is the working point air quantity of the forced air cooling heat dissipation system; h is the average heat transfer coefficient of the fin radiator; the A eff is the effective convection surface area of the fin radiator;
The multi-objective optimization module is configured to adopt a multi-objective optimization algorithm, take the volume of the thermal resistance and the forced air cooling heat dissipation system as an optimization objective, take the preset maximum value of the thermal resistance and the preset upper and lower limits of the dimension parameter as constraint conditions, optimize the dimension parameter of the fin type radiator under the constraint conditions, and output various optimization schemes of the dimension parameter;
and the evaluation module is configured to evaluate the plurality of optimization schemes by adopting an entropy weight TOPSIS method so as to determine the optimal scheme of the size parameter under the constraint condition.
In one embodiment, the multi-objective optimization algorithm is a non-dominant fast ordering genetic algorithm;
The adoption of the multi-objective optimization algorithm takes the volumes of the thermal resistance and the forced air cooling heat dissipation system as optimization targets, takes the preset maximum value of the thermal resistance and the preset upper and lower limits of the dimension parameter as constraint conditions, optimizes the dimension parameter of the fin type heat radiator under the constraint conditions, and outputs various optimization schemes of the dimension parameter, wherein the optimization schemes comprise:
Taking the thermal resistance as a first objective function to be solved, taking the volume as a second objective function to be solved, and taking the size parameter as a variable to be optimized;
The expressions of the first objective function to be solved and the second objective function to be solved are respectively:
；
；
The F 1 is the first objective function to be solved; the F 2 is the second objective function to be solved; x 3 is the height of the fin; the H fan、Wfan and the L fan are respectively the height, the width and the length of the fan; the min represents an operation of obtaining a minimum value;
Determining the size N and the maximum iteration number of the population, and generating an initial population according to the constraint condition; wherein the initial iteration number of the population is 1;
Calculating an optimization target value of individuals in the population, and carrying out rapid non-dominant sorting and crowding calculation on the initial population according to the constraint condition and the optimization target value; wherein the optimized target value comprises a value of the first objective function to be solved and a value of the second objective function to be solved; the rapid non-dominant ranking is layering the population according to a non-inferior solution level of individuals in the population; the crowding degree calculation is to selectively sort individuals in the population at the same layer;
Generating a new population: sequentially performing selection operation, cross operation, mutation operation and merging operation on the population to obtain a population after the merging operation, wherein the size of the population after the merging operation is 2N, calculating the first objective function to be solved and the second objective function to be solved, performing rapid non-dominant sorting and congestion degree calculation, and preferentially selecting N individuals in the population after the merging operation as the population of a new generation according to the dominant order obtained by the rapid non-dominant sorting, the congestion degree obtained by the congestion degree calculation and the constraint condition;
Judging: judging whether the current iteration times reach the maximum iteration times or not; if the current iteration times do not reach the maximum iteration times, adding 1 to the iteration times to obtain new iteration times, and returning to the step of generating a new population; if the current iteration times reach the maximum iteration times, turning to a step of outputting an optimization scheme;
Outputting an optimization scheme: taking the population of the last generation as the pareto optimal solution set; wherein the pareto optimal solution set contains a plurality of optimization schemes of the size parameters.
In one embodiment, the expressions of h and a eff are respectively:
；
；
Wherein, ；
；
Wherein x 4 and x 5 are the width of the air duct in the fin and the number of the fins respectively; the lambda air is the thermal conductivity of the air; v air is the aerodynamic viscosity of the air.
In an embodiment, the evaluating the multiple optimization schemes by using an entropy weight TOPSIS method to determine an optimal scheme of the size parameter under the constraint condition includes:
Determining an evaluation index; wherein the evaluation index includes the thermal resistance and the volume;
normalizing the evaluation index to obtain the normalized evaluation index;
calculating the information entropy of the evaluation index after normalization processing;
determining the weight of the evaluation index after normalization processing according to the information entropy;
calculating according to the weight to obtain the weighted evaluation index;
Determining a positive ideal solution and a negative ideal solution of the weighted evaluation index; wherein the positive ideal solution is the maximum value of the weighted evaluation index; the negative ideal solution is the minimum value of the weighted evaluation index;
determining Euclidean distances between the optimization scheme and the positive ideal solution and the negative ideal solution respectively;
And calculating the relative closeness of the optimization scheme to the positive ideal solution and the negative ideal solution respectively according to the Euclidean distance, and taking the optimization scheme with the maximum relative closeness as an optimal size scheme.
According to a third aspect, a computer-readable storage medium is provided in one embodiment. The computer-readable storage medium includes a program. The program is capable of being executed by a processor to carry out the method as described in any of the embodiments herein.
The beneficial effects of the application are as follows:
The optimal design method of the forced air cooling heat dissipation system comprises the following steps: acquiring a comprehensive thermal resistance model of the forced air cooling heat dissipation system; the forced air cooling heat dissipation system comprises a fin type radiator; the comprehensive thermal resistance model is a function of the size parameter of the fin radiator and the working point air quantity of the forced air cooling heat dissipation system; adopting a multi-objective optimization algorithm, taking the thermal resistance of the fin radiator and the volume of the forced air cooling heat dissipation system as optimization targets, taking the preset maximum value of the thermal resistance and the preset upper and lower limits of the size parameters as constraint conditions, optimizing the thermal resistance and the size parameters under the constraint conditions, and outputting various optimization schemes of the size parameters; and evaluating various optimization schemes by adopting an entropy weight TOPSIS method to determine the optimal scheme of the size parameters under the constraint condition. The method can complete the optimal design of the forced air cooling heat dissipation system, ensure the optimal size and performance of the forced air cooling heat dissipation system, and greatly improve the optimization efficiency. The device can realize the method.
Drawings
FIG. 1 is a flow chart of an optimization design method of a forced air cooling heat dissipation system according to an embodiment;
FIG. 2 is a schematic diagram of a forced air cooling system according to an embodiment;
FIG. 3 is a flow diagram of various optimization schemes employing a multi-objective optimization algorithm to output dimensional parameters according to one embodiment;
FIG. 4 is a flow chart of an embodiment of determining the optimal solution of the size parameter under constraint conditions;
FIG. 5 is a finite element temperature simulation comparison of a finned radiator before and after optimization of an embodiment;
Fig. 6 is a schematic block diagram of an apparatus for optimizing a design of a forced air cooling system according to an embodiment.
Detailed Description
The application will be described in further detail below with reference to the drawings by means of specific embodiments. Wherein like elements in different embodiments are numbered alike in association. In the following embodiments, numerous specific details are set forth in order to provide a better understanding of the present application. However, one skilled in the art will readily recognize that some of the features may be omitted, or replaced by other elements, materials, or methods in different situations. In some instances, related operations of the present application have not been shown or described in the specification in order to avoid obscuring the core portions of the present application, and may be unnecessary to persons skilled in the art from a detailed description of the related operations, which may be presented in the description and general knowledge of one skilled in the art.
Furthermore, the described features, operations, or characteristics of the description may be combined in any suitable manner in various embodiments. Also, various steps or acts in the method descriptions may be interchanged or modified in a manner apparent to those of ordinary skill in the art. Thus, the various orders in the description and drawings are for clarity of description of only certain embodiments, and are not meant to be required orders unless otherwise indicated.
The numbering of the components itself, e.g. "first", "second", etc., is used herein merely to distinguish between the described objects and does not have any sequential or technical meaning. The term "coupled" as used herein includes both direct and indirect coupling (coupling), unless otherwise indicated.
The technical scheme of the present application will be described in detail with reference to examples.
Referring to fig. 1, the present application provides an optimized design method of a forced air cooling system. The optimization design method comprises the following steps:
Step S100: acquiring a comprehensive thermal resistance model of the forced air cooling heat dissipation system;
the comprehensive thermal resistance model is a function of the size parameter of the fin radiator and the working point air quantity of the forced air cooling heat dissipation system; the dimensional parameters include: the length of the fin type radiator, the width of the fin type radiator, the height of fins of the fin type radiator, the width of air channels in the fins and the number of the fins;
the expression of the comprehensive thermal resistance model is as follows: ；
Wherein R th,ha is the thermal resistance of the fin radiator; d is the thickness of the base plate of the fin radiator; x 1 and x 2 are the length of the finned radiator and the width of the finned radiator, respectively; lambda hs is the thermal conductivity of the material used for the fin radiator; ρ air is the density of the air flowing through the fin radiator; c air is the specific heat capacity of air flowing through the fin radiator; v 0 is the working point air quantity of the forced air cooling heat dissipation system; h is the average heat transfer coefficient of the fin radiator; a eff is the effective convection surface area of the fin radiator;
Step S200: adopting a multi-objective optimization algorithm, taking the volume of the thermal resistance and the forced air cooling heat dissipation system as an optimization target, taking the preset maximum value of the thermal resistance and the preset upper and lower limits of the size parameter as constraint conditions, optimizing the size parameter of the fin type radiator under the constraint conditions, and outputting various optimization schemes of the size parameter;
Step S300: and evaluating various optimization schemes by adopting an entropy weight TOPSIS method to determine the optimal scheme of the size parameters under the constraint condition.
In the step S100, the forced air cooling and heat dissipation system may be a forced air cooling and heat dissipation system of a high-power-density switching power supply, a heat dissipation system of a high-power-density power electronic device, or the like. Referring to fig. 2, the forced air cooling heat dissipation system includes a fan, a system air duct, and a fin type heat sink. The system air duct refers to a passage for connecting the fan to the inlet of the fin type radiator. No shielding exists in the system air duct. Among them, the above-mentioned fin type radiator is one of the most widely used heat exchange devices among gas and liquid heat exchangers. The fin radiator achieves the purpose of enhancing heat transfer by additionally arranging fins on a common base pipe. The base pipe may be steel pipe, stainless steel pipe, copper pipe, etc. The fins can also be made of steel strips, stainless steel strips, copper strips, aluminum strips and the like. The fin type radiator can be divided into a winding type radiator, a serial type radiator and the like in the fin structure form. The fin radiator is mainly composed of a plurality of rows of parallel spiral fin tube bundles between air flow directions.
In the step S100, when the dimensional parameter and the operating point V 0 of the fin radiator are known, the thermal resistance of the fin radiator can be calculated according to the integrated thermal resistance model.
In some embodiments, the operating point air volume is based on a hydrodynamic model of the fan and the fin radiator. For example, in some embodiments, an expression of static pressure impedance and air volume of the fan may be obtained by polynomial fitting according to a characteristic curve of the fan; then, according to the hydrodynamic theory, an expression between the static pressure impedance and the air quantity of the fin type radiator is obtained; then, the two expressions are combined to obtain the air quantity when the static pressure drop of the fan and the static pressure impedance of the fin radiator are equal, namely, the air quantity at the working point.
In the step S100, the average heat transfer coefficient is defined as the amount of heat transferred per second by 1 x 1m of wall area when the temperature difference between the fluid and the solid surface is 1K. The magnitude of the average heat transfer coefficient h can reflect the strength of convective heat transfer. The larger the effective convection surface area a eff, the higher the heat dissipation efficiency of the fin type heat sink.
In some embodiments, the expressions of h and a eff above are:
；
；
Wherein, ；
；
Wherein x 4 and x 5 are the width of the air channel in the fin and the number of the fins respectively; lambda air is the thermal conductivity of the air; v air is the aerodynamic viscosity of the air. Lambada air =0.027w/(m·k) at normal temperature and pressure, v air=1.86×10-5 pa·s at normal temperature and pressure.
In some embodiments, in step S200 described above, other existing multi-objective optimization algorithms other than the dominant fast ordering genetic algorithm may be employed. The multi-objective optimization algorithm is an algorithm that solves for the optimal solution of multiple objective functions.
In the step S200, a person skilled in the art may determine the preset maximum value of the thermal resistance and the preset upper and lower limits of the size parameter according to the actual application scenario, and the preset maximum value of the thermal resistance and the preset upper and lower limits of the size parameter are not limited. For example, a person skilled in the art determines the upper and lower limits of the size of the fin radiator (i.e., the preset upper and lower limits of the size parameter) according to the PCB (printed circuit board) layout and mounting position of the switching power supply component.
In some embodiments, the multi-objective optimization algorithm is a non-dominant fast ordering genetic algorithm. Referring to fig. 3, in the step S200, a multi-objective optimization algorithm is adopted, the volume of the heat resistance and forced air cooling heat dissipation system is used as an optimization objective, the preset maximum value of the heat resistance and the preset upper and lower limits of the dimension parameter are used as constraint conditions, the heat resistance and the dimension parameter of the fin type heat sink are optimized under the constraint conditions, and a plurality of optimization schemes of the dimension parameter are output, including:
step S210: taking the thermal resistance as a first objective function to be solved, taking the volume as a second objective function to be solved, and taking the size parameter as a variable to be optimized;
the expressions of the first objective function to be solved and the second objective function to be solved are respectively:
；
；
Wherein F 1 is a first objective function to be solved; f 2 is a second objective function to be solved; x 3 is the height of the fin; h fan、Wfan and L fan are the height, width and length of the fan respectively; min represents an operation of obtaining a minimum value;
Step S220: determining the size N and the maximum iteration number of the population, and generating an initial population according to constraint conditions; wherein, the iteration number of the population initiation is 1;
step S230: calculating an optimized target value of individuals in the population, and carrying out rapid non-dominant sorting and crowding degree calculation on the initial population according to constraint conditions and the optimized target value; the optimization target value comprises a numerical value of a first objective function to be solved and a numerical value of a second objective function to be solved; the rapid non-dominant ranking is to stratify the population according to the non-inferior solution level of individuals in the population; the crowding degree is calculated by selectively sequencing individuals in the population at the same layer;
Step S240 of generating a new population: sequentially performing selection operation, cross operation, mutation operation and merging operation on the population to obtain a merged population, wherein the size of the merged population is 2N, calculating a first objective function to be solved and a second objective function to be solved, performing rapid non-dominant sorting and congestion degree calculation, and preferentially selecting N individuals as a new generation population in the merged population according to the dominant order obtained by the rapid non-dominant sorting, the congestion degree obtained by the congestion degree calculation and constraint conditions;
step S250 of judgment: judging whether the current iteration number reaches the maximum iteration number or not;
if the current iteration number does not reach the maximum iteration number, adding 1 to the iteration number to obtain a new iteration number, and returning to the step S240 of generating a new population; if the current iteration number reaches the maximum iteration number, turning to a step S260 of outputting an optimization scheme;
Step S260 of outputting the optimization scheme: taking the population of the last generation as the pareto optimal solution set; wherein, the pareto optimal solution set contains a plurality of optimization schemes of the size parameters.
In the step S210, the volume of the forced air cooling system is equal to the sum of the volume of the fin radiator and the volume of the fan.
In the step S220, a person skilled in the art can determine the size N and the maximum iteration number of the population according to the actual application scene requirement, and the size N and the maximum iteration number are not limited herein.
In step S230, the effect of the fast non-dominant ranking is to direct the search to the Pareto (Pareto) optimal solution set direction. The target values, i.e. the values of the first objective function to be solved and the values of the second objective function to be solved, are optimized. The constraint value refers to thermal resistance. The constraint condition means that the thermal resistance value does not exceed a preset maximum value of the thermal resistance.
It should be noted that, in the step S230, the specific process of "calculating the optimization target value of the individuals in the population and performing the rapid non-dominant sorting and the congestion degree calculation on the initial population according to the constraint condition and the optimization target value" all belong to the prior art in the field, so that a detailed description thereof is omitted herein.
In the step S240 of generating a new population, the probability of the crossover operation and the mutation operation may be 10%. The probability of the crossover operation and the mutation operation can be determined by a person skilled in the art according to the actual scene requirement. The selection operation is performed, two individuals can be randomly selected from the current population (namely the parent population), then the better individuals are selected according to the layering condition and the crowding distance of the two individuals, and the operation is repeated until N individuals are selected from the current population. Crossover and mutation operations are performed on the selected N individuals, thereby generating a population of offspring. And combining the parent population and the offspring population to form a new population.
It should be noted that, in the step S240 of generating a new population, "the population is sequentially subjected to the selection operation, the cross operation, the mutation operation, and the merging operation to obtain a population after the merging operation, the size of the population after the merging operation is 2N, the first objective function to be solved and the second objective function to be solved are calculated, the rapid non-dominant sorting and the congestion degree calculation are performed, and the specific process of selecting N individuals as a new generation population in the population after the merging operation according to the dominant order obtained by the rapid non-dominant sorting, the congestion degree calculated by the congestion degree, and the constraint condition all belong to the prior art in the field, so that the description is not repeated here.
In step S260 of the above-mentioned output optimization scheme, the pareto optimal solution set includes a plurality of pareto optimal solutions. For the multi-objective optimization problem, the pareto optimal solution is only one acceptable solution of the problem, and a plurality of pareto optimal solutions generally exist. Wherein the term "pareto optimal solution set" is common general knowledge in the art.
It should be noted that, in the step S260 of the above-mentioned output optimization scheme, the specific process of "taking the population of the last generation as the pareto optimal solution set" belongs to the prior art/common general knowledge in the art, so that a detailed description thereof will not be provided here.
In some embodiments, the least-ordered solution may be selected from the pareto optimal solution set as the optimal size parameter, and the optimal thermal resistance and the optimal volume may be obtained.
In step S260 of the output optimization scheme, the optimization degree of each optimization scheme for the volume and the thermal resistance is different. So far, the multi-objective optimization solution of the fin radiator is completed.
In the step S300, the entropy weight TOPSIS method is composed of the entropy weight method and the TOPSIS method. The entropy weight method is used for calculating the objective weight of the evaluation index, and the evaluation index comprises the thermal resistance and the volume. The TOPSIS rule is used for calculating the relative closeness between the weighted evaluation index and the ideal index of each optimization scheme, sequencing according to the relative closeness and selecting the scheme with the maximum relative closeness as the optimal scheme of the size parameter.
In some embodiments, please refer to fig. 4, in the step S300, the method of evaluating the multiple optimization schemes by using the entropy weight TOPSIS method to determine the optimal scheme of the size parameter under the constraint condition includes:
step S310: determining an evaluation index; wherein the evaluation index comprises thermal resistance and volume;
step S320: normalizing the evaluation index to obtain a normalized evaluation index;
step S330: calculating the information entropy of the normalized evaluation index;
Step S340: determining the weight of the normalized evaluation index according to the information entropy;
Step S350: obtaining a weighted evaluation index according to weight calculation;
step S360: determining a positive ideal solution and a negative ideal solution of the weighted evaluation index; wherein the positive ideal is the maximum value of the weighted evaluation index; the negative ideal solution is the minimum value of the weighted evaluation index;
step S370: determining Euclidean distances between the optimization scheme and the positive ideal solution and the negative ideal solution respectively;
Step S380: and calculating the relative closeness of the optimal scheme to the positive ideal solution and the negative ideal solution respectively according to the Euclidean distance, and taking the optimal scheme with the maximum relative closeness as the optimal size scheme.
In step S320, the purpose of the normalization process is to facilitate comparison of the evaluation indexes of different optimizations. The specific mode of normalization processing is the conventional normalization mode.
In the step S350, the weighted evaluation index is obtained according to the weight calculation, and an existing conventional weighting formula may be adopted: weighted evaluation index = original evaluation index.
It should be noted that the specific processes of the steps S310 to S380 are all related to the prior art (i.e. entropy weight TOPSIS method), so that the description thereof will not be repeated here.
For a further understanding of the present application, please refer to fig. 5, fig. 5 shows a finite element temperature simulation comparison of the finned radiator before and after optimization in accordance with an embodiment of the present application; wherein (a) in fig. 5 shows the result of the finite element temperature simulation of the fin type radiator before optimization by using the optimization design method of the present application, and (b) in fig. 5 shows the result of the finite element temperature simulation of the fin type radiator after optimization by using the optimization design method of the present application; the components in fig. 5 (such as component 1, component 2, component 3, and component 4) are components inside the fin-type radiator; the thermal interface material of fig. 5 is positioned between the component and the heat sink to enhance thermal conduction therebetween. It can be seen that the operating temperature of components inside the fin radiator can be effectively reduced after the optimization design method is used for optimization. For example, the component in fig. 5 may be a metal-oxide semiconductor field effect transistor (i.e., MOSFET, a heat generating device) that is directly mounted on the fin heat sink.
In one embodiment, the thermal resistance of the fin type radiator and the volume of the heat dissipation system before the optimization (i.e. during the initial design) according to the optimization design method of the present application are respectively: 0.0712 The thermal resistance and the volume of a radiating system of the fin radiator optimized by the optimized design method are respectively 0.0661 (DEG C/W), 0.000785m 3, and the thermal resistance and the volume are optimized to different degrees, so that the feasibility of the optimized design method is proved.
It can be seen that the optimal design method of the forced air cooling heat dissipation system provided by the application combines the non-dominant rapid ordering genetic algorithm, the entropy weight method and the TOPSIS method, avoids the problems of large calculated amount, low optimization efficiency and the like caused by exhausting all dimension parameters in the dimension optimization process of the forced air cooling heat dissipation system by the traditional method, further can rapidly realize the thermal resistance optimization and the volume optimization of the forced air cooling heat dissipation system, effectively reduces the working temperature of internal components of the switching power supply, reduces the volume of the switching power supply and greatly improves the comprehensive performance of the switching power supply.
The above is some descriptions of an optimized design method of a forced air cooling heat dissipation system. The application also discloses an optimal design device of the forced air cooling heat dissipation system in some embodiments. Referring to fig. 6, the apparatus includes:
A preprocessing module 100 configured to obtain a comprehensive thermal resistance model of the forced air cooling heat dissipation system;
the forced air cooling heat dissipation system comprises a fan, a system air duct and a fin type radiator; the comprehensive thermal resistance model is a function of the size parameter of the fin radiator and the working point air quantity of the forced air cooling heat dissipation system; the dimensional parameters include: the length of the fins in the fin type radiator, the width of the fin type radiator, the height of the fins of the fin type radiator, the width of the air channels in the fins and the number of the fins;
The expression of the comprehensive thermal resistance model is as follows: ；
Wherein R th,ha is the thermal resistance of the fin radiator; d is the thickness of the base plate of the fin radiator; x 1 and x 2 are the length and width, respectively, of the fin radiator; lambda hs is the thermal conductivity of the material used for the fin radiator; ρ air is the density of the air flowing through the fin radiator; c air is the specific heat capacity of air flowing through the fin radiator; v 0 is the working point air quantity of the forced air cooling heat dissipation system; h is the average heat transfer coefficient of the fin radiator; a eff is the effective convection surface area of the fin radiator;
The multi-objective optimization module 200 is configured to adopt a multi-objective optimization algorithm, take the volume of the thermal resistance and the forced air cooling heat dissipation system as an optimization objective, take the preset maximum value of the thermal resistance and the preset upper and lower limits of the dimension parameter as constraint conditions, optimize the dimension parameter of the fin type radiator under the constraint conditions, and output various optimization schemes of the dimension parameter;
The evaluation module 300 is configured to evaluate the multiple optimization schemes by adopting an entropy weight TOPSIS method so as to determine the optimal scheme of the size parameters under the constraint condition.
In some embodiments, the multi-objective optimization algorithm is a non-dominant fast ordering genetic algorithm. The above-mentioned adoption multi-objective optimization algorithm, regard the volume of thermal resistance and forced air cooling heat dissipation system as the optimization target, regard preset maximum value of thermal resistance and preset upper and lower limit of size parameter as the constraint condition, optimize thermal resistance and size parameter of the fin type radiator under the constraint condition, output multiple optimization scheme of size parameter, include:
taking the thermal resistance as a first objective function to be solved, taking the volume as a second objective function to be solved, and taking the size parameter as a variable to be optimized; the expressions of the first objective function to be solved and the second objective function to be solved are respectively:
；/>
；
Wherein F 1 is a first objective function to be solved; f 2 is a second objective function to be solved; x 3 is the height of the fin; h fan、Wfan and L fan are the height, width and length of the fan respectively; min represents an operation of obtaining a minimum value; determining the size N and the maximum iteration number of the population, and generating an initial population according to constraint conditions; wherein, the iteration number of the population initiation is 1;
Calculating an optimized target value of individuals in the population, and carrying out rapid non-dominant sorting and crowding degree calculation on the initial population according to constraint conditions and the optimized target value; the optimization target value comprises a numerical value of a first objective function to be solved and a numerical value of a second objective function to be solved; the rapid non-dominant ranking is to stratify the population according to the non-inferior solution level of individuals in the population; the crowding degree is calculated by selectively sequencing individuals in the population at the same layer;
generating a new population: sequentially performing selection operation, cross operation, mutation operation and merging operation on the population to obtain a merged population, wherein the size of the merged population is 2N, calculating a first objective function to be solved and a second objective function to be solved, performing rapid non-dominant sorting and congestion degree calculation, and preferentially selecting N individuals as new populations in the merged population according to the dominant order obtained by the rapid non-dominant sorting, the congestion degree obtained by the congestion degree calculation and constraint conditions;
judging: judging whether the current iteration number reaches the maximum iteration number or not; if the current iteration number does not reach the maximum iteration number, adding 1 to the iteration number to obtain a new iteration number, and returning to the step of generating a new population;
If the current iteration number reaches the maximum iteration number, turning to a step of outputting an optimization scheme;
Outputting an optimization scheme: taking the population of the last generation as the pareto optimal solution set; wherein, the pareto optimal solution set contains a plurality of optimization schemes of the size parameters.
In some embodiments, the least-ordered solution may be selected from the pareto optimal solution set as the optimal size parameter, and the optimal thermal resistance and the optimal volume may be obtained.
In some embodiments, the expressions for h and a eff are:
；
；
Wherein, ；
；/>
Wherein x 4 and x 5 are the width of the air channel in the fin and the number of the fins respectively; lambda air is the thermal conductivity of the air; v air is the aerodynamic viscosity of the air. Lambda air =0.027w/(m·k) at normal temperature and pressure, v air=1.86×10-5 pa·s at normal temperature and pressure
In some embodiments, the evaluating the multiple optimization schemes by using the entropy weight TOPSIS method to determine the optimal scheme of the size parameter under the constraint condition includes:
determining an evaluation index; wherein the evaluation index comprises thermal resistance and volume;
normalizing the evaluation index to obtain a normalized evaluation index;
calculating the information entropy of the normalized evaluation index;
determining the weight of the normalized evaluation index according to the information entropy;
Obtaining a weighted evaluation index according to weight calculation;
Determining a positive ideal solution and a negative ideal solution of the weighted evaluation index; wherein the positive ideal is the maximum value of the weighted evaluation index; the negative ideal solution is the minimum value of the weighted evaluation index;
determining Euclidean distances between the optimization scheme and the positive ideal solution and the negative ideal solution respectively;
and calculating the relative closeness of the optimal scheme to the positive ideal solution and the negative ideal solution respectively according to the Euclidean distance, and taking the optimal scheme with the maximum relative closeness as the optimal size scheme.
It should be noted that, the specific workflow and technical effects of the device for optimizing the forced air cooling heat dissipation system are substantially the same as those of the method for optimizing the forced air cooling heat dissipation system, so that the description thereof is omitted herein.
The above is some descriptions of an optimally designed device for a forced air cooling heat dissipation system. Also disclosed in some embodiments of the application is a computer-readable storage medium including a program executable by a processor to implement the method of optimizing design as in any of the embodiments herein.
Reference is made to various exemplary embodiments herein. However, those skilled in the art will recognize that changes and modifications may be made to the exemplary embodiments without departing from the scope herein. For example, the various operational steps and components used to perform the operational steps may be implemented in different ways (e.g., one or more steps may be deleted, modified, or combined into other steps) depending on the particular application or taking into account any number of cost functions associated with the operation of the system.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. Additionally, as will be appreciated by one of skill in the art, the principles herein may be reflected in a computer program product on a computer readable storage medium preloaded with computer readable program code. Any tangible, non-transitory computer readable storage medium may be used, including magnetic storage devices (hard disks, floppy disks, etc.), optical storage devices (CD-to-ROM, DVD, blu-Ray disks, etc.), flash memory, and/or the like. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including means which implement the function specified. The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified.
While the principles herein have been shown in various embodiments, many modifications of structure, arrangement, proportions, elements, materials, and components, which are particularly adapted to specific environments and operative requirements, may be used without departing from the principles and scope of the present disclosure. The above modifications and other changes or modifications are intended to be included within the scope of this document.
The foregoing detailed description has been described with reference to various embodiments. However, those skilled in the art will recognize that various modifications and changes may be made without departing from the scope of the present disclosure. Accordingly, the present disclosure is to be considered as illustrative and not restrictive in character, and all such modifications are intended to be included within the scope thereof. Also, advantages, other advantages, and solutions to problems have been described above with regard to various embodiments. The benefits, advantages, solutions to problems, and any element(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, system, article, or apparatus. Furthermore, the term "couple" and any other variants thereof are used herein to refer to physical connections, electrical connections, magnetic connections, optical connections, communication connections, functional connections, and/or any other connection.
Those skilled in the art will recognize that many changes may be made to the details of the above-described embodiments without departing from the underlying principles of the invention. Accordingly, the scope of the invention should be determined only by the following claims.
Claims (8)
1. An optimization design method of a forced air cooling heat dissipation system is characterized by comprising the following steps:
Acquiring a comprehensive thermal resistance model of the forced air cooling heat dissipation system; the forced air cooling heat dissipation system comprises a fan, a system air channel and a fin type radiator; the comprehensive thermal resistance model is a function of the size parameter of the fin type radiator and the working point air quantity of the forced air cooling heat dissipation system; the dimensional parameters include: the length of the fin type radiator, the width of the fin type radiator, the height of fins of the fin type radiator, the width of air channels in the fins and the number of the fins; the expression of the comprehensive thermal resistance model is as follows:
；
wherein, R th,ha is the thermal resistance of the fin radiator; d is the thickness of the substrate of the fin radiator; x 1 and x 2 are the length of the finned radiator and the width of the finned radiator, respectively; the lambda hs is the thermal conductivity of the material adopted by the fin type radiator; the ρ air is the density of the air flowing through the fin radiator; the c air is the specific heat capacity of the air; the V 0 is the working point air quantity of the forced air cooling heat dissipation system; h is the average heat transfer coefficient of the fin radiator; the A eff is the effective convection surface area of the fin radiator;
Adopting a multi-objective optimization algorithm, taking the volume of the thermal resistance and the forced air cooling heat dissipation system as optimization targets, taking a preset maximum value of the thermal resistance and a preset upper limit and a preset lower limit of the size parameter as constraint conditions, optimizing the size parameter of the fin type radiator under the constraint conditions, and outputting various optimization schemes of the size parameter;
Evaluating the multiple optimization schemes by adopting an entropy weight TOPSIS method to determine an optimal scheme of the size parameter under the constraint condition;
wherein the multi-objective optimization algorithm is a non-dominant fast ordering genetic algorithm;
The adoption of the multi-objective optimization algorithm takes the volumes of the thermal resistance and the forced air cooling heat dissipation system as optimization targets, takes the preset maximum value of the thermal resistance and the preset upper and lower limits of the dimension parameter as constraint conditions, optimizes the dimension parameter of the fin type heat radiator under the constraint conditions, and outputs various optimization schemes of the dimension parameter, wherein the optimization schemes comprise:
taking the thermal resistance as a first objective function to be solved, taking the volume as a second objective function to be solved, and taking the size parameter as a variable to be optimized; the expressions of the first objective function to be solved and the second objective function to be solved are respectively:
；
；
The F 1 is the first objective function to be solved; the F 2 is the second objective function to be solved; x 3 is the height of the fin; the H fan、Wfan and the L fan are respectively the height, the width and the length of the fan; the min represents an operation of obtaining a minimum value;
Determining the size N and the maximum iteration number of the population, and generating an initial population according to the constraint condition; wherein the initial iteration number of the population is 1;
Calculating an optimization target value of individuals in the population, and carrying out rapid non-dominant sorting and crowding calculation on the initial population according to the constraint condition and the optimization target value; wherein the optimized target value comprises a value of the first objective function to be solved and a value of the second objective function to be solved; the rapid non-dominant ranking is layering the population according to a non-inferior solution level of individuals in the population; the crowding degree calculation is to selectively sort individuals in the population at the same layer;
Generating a new population: sequentially performing selection operation, cross operation, mutation operation and merging operation on the population to obtain a population after the merging operation, wherein the size of the population after the merging operation is 2N, calculating the first objective function to be solved and the second objective function to be solved, performing rapid non-dominant sorting and congestion degree calculation, and preferentially selecting N individuals from the population after the merging operation as the population of a new generation according to the dominant order obtained by the rapid non-dominant sorting, the congestion degree obtained by the congestion degree calculation and the constraint condition;
Judging: judging whether the current iteration times reach the maximum iteration times or not;
If the current iteration times do not reach the maximum iteration times, adding 1 to the iteration times to obtain new iteration times, and returning to the step of generating a new population; if the current iteration times reach the maximum iteration times, turning to a step of outputting an optimization scheme;
The output optimization scheme comprises the following steps: taking the population of the last generation as the pareto optimal solution set; wherein the pareto optimal solution set contains a plurality of optimization schemes of the size parameters.
2. The optimization design method according to claim 1, wherein the operating point air volume is obtained based on a fluid mechanics model of the fan and the fin radiator.
3. The optimization design method according to claim 1, wherein the expressions of h and a eff are respectively:
；
；
Wherein, ；
；
Wherein x 4 and x 5 are the width of the air duct in the fin and the number of the fins respectively; the lambda air is the thermal conductivity of the air; v air is the aerodynamic viscosity of the air.
4. The optimization design method as set forth in claim 1, wherein the evaluating the plurality of optimization schemes by using an entropy weight TOPSIS method to determine an optimal scheme of the size parameter under the constraint condition includes:
Determining an evaluation index; wherein the evaluation index includes the thermal resistance and the volume;
normalizing the evaluation index to obtain the normalized evaluation index;
calculating the information entropy of the evaluation index after normalization processing;
determining the weight of the evaluation index after normalization processing according to the information entropy;
calculating according to the weight to obtain the weighted evaluation index;
Determining a positive ideal solution and a negative ideal solution of the weighted evaluation index; wherein the positive ideal solution is the maximum value of the weighted evaluation index; the negative ideal solution is the minimum value of the weighted evaluation index;
determining Euclidean distances between the optimization scheme and the positive ideal solution and the negative ideal solution respectively;
And calculating the relative closeness of the optimization scheme to the positive ideal solution and the negative ideal solution respectively according to the Euclidean distance, and taking the optimization scheme with the maximum relative closeness as an optimal size scheme.
5. An optimal design device of a forced air cooling heat dissipation system is characterized by comprising:
The preprocessing module is configured to acquire a comprehensive thermal resistance model of the forced air cooling heat dissipation system; the forced air cooling heat dissipation system comprises a fan, a system air channel and a fin type radiator; the comprehensive thermal resistance model is a function of the size parameter of the fin type radiator and the working point air quantity of the forced air cooling heat dissipation system; the dimensional parameters include: the length of the fin type radiator, the width of the fin type radiator, the height of fins of the fin type radiator, the width of air channels in the fins and the number of the fins;
The expression of the comprehensive thermal resistance model is as follows:
；
Wherein, R th,ha is the thermal resistance of the fin radiator; d is the thickness of the substrate of the fin radiator; x 1 and x 2 are the length of the fin radiator and the width of the fin radiator, respectively; the lambda hs is the thermal conductivity of the material adopted by the fin type radiator; the ρ air is the density of the air flowing through the fin radiator; the c air is the specific heat capacity of the air; the V 0 is the working point air quantity of the forced air cooling heat dissipation system; h is the average heat transfer coefficient of the fin radiator; the A eff is the effective convection surface area of the fin radiator;
The multi-objective optimization module is configured to adopt a multi-objective optimization algorithm, take the volume of the thermal resistance and the forced air cooling heat dissipation system as an optimization objective, take the preset maximum value of the thermal resistance and the preset upper and lower limits of the dimension parameter as constraint conditions, optimize the dimension parameter of the fin type radiator under the constraint conditions, and output various optimization schemes of the dimension parameter;
The evaluation module is configured to evaluate the multiple optimization schemes by adopting an entropy weight TOPSIS method so as to determine the optimal scheme of the size parameter under the constraint condition;
wherein the multi-objective optimization algorithm is a non-dominant fast ordering genetic algorithm;
The adoption of the multi-objective optimization algorithm takes the volumes of the thermal resistance and the forced air cooling heat dissipation system as optimization targets, takes the preset maximum value of the thermal resistance and the preset upper and lower limits of the dimension parameter as constraint conditions, optimizes the dimension parameter of the fin type heat radiator under the constraint conditions, and outputs various optimization schemes of the dimension parameter, wherein the optimization schemes comprise:
taking the thermal resistance as a first objective function to be solved, taking the volume as a second objective function to be solved, and taking the size parameter as a variable to be optimized; the expressions of the first objective function to be solved and the second objective function to be solved are respectively:
；
；
The F 1 is the first objective function to be solved; the F 2 is the second objective function to be solved; x 3 is the height of the fin; the H fan、Wfan and the L fan are respectively the height, the width and the length of the fan; the min represents an operation of obtaining a minimum value;
Determining the size N and the maximum iteration number of the population, and generating an initial population according to the constraint condition; wherein the initial iteration number of the population is 1;
Calculating an optimization target value of individuals in the population, and carrying out rapid non-dominant sorting and crowding calculation on the initial population according to the constraint condition and the optimization target value; wherein the optimized target value comprises a value of the first objective function to be solved and a value of the second objective function to be solved; the rapid non-dominant ranking is layering the population according to a non-inferior solution level of individuals in the population; the crowding degree calculation is to selectively sort individuals in the population at the same layer;
Generating a new population: sequentially performing selection operation, cross operation, mutation operation and merging operation on the population to obtain a population after the merging operation, wherein the size of the population after the merging operation is 2N, calculating the first objective function to be solved and the second objective function to be solved, performing rapid non-dominant sorting and congestion degree calculation, and preferentially selecting N individuals in the population after the merging operation as the population of a new generation according to the dominant order obtained by the rapid non-dominant sorting, the congestion degree obtained by the congestion degree calculation and the constraint condition;
Judging: judging whether the current iteration times reach the maximum iteration times or not; if the current iteration times do not reach the maximum iteration times, adding 1 to the iteration times to obtain new iteration times, and returning to the step of generating a new population; if the current iteration times reach the maximum iteration times, turning to a step of outputting an optimization scheme;
The output optimization scheme comprises the following steps: taking the population of the last generation as the pareto optimal solution set; wherein the pareto optimal solution set contains a plurality of optimization schemes of the size parameters.
6. The optimum design apparatus according to claim 5, wherein the expressions of h and a eff are respectively:
；
；
Wherein, ；
；
Wherein x 4 and x 5 are the width of the air duct in the fin and the number of the fins respectively; the lambda air is the thermal conductivity of the air; v air is the aerodynamic viscosity of the air.
7. The optimizing design apparatus as set forth in claim 5, wherein said evaluating the plurality of optimizing schemes using entropy weight TOPSIS method to determine the optimal scheme of the size parameter under the constraint condition comprises:
Determining an evaluation index; wherein the evaluation index includes the thermal resistance and the volume;
normalizing the evaluation index to obtain the normalized evaluation index;
calculating the information entropy of the evaluation index after normalization processing;
determining the weight of the evaluation index after normalization processing according to the information entropy;
calculating according to the weight to obtain the weighted evaluation index;
Determining a positive ideal solution and a negative ideal solution of the weighted evaluation index; wherein the positive ideal solution is the maximum value of the weighted evaluation index; the negative ideal solution is the minimum value of the weighted evaluation index;
determining Euclidean distances between the optimization scheme and the positive ideal solution and the negative ideal solution respectively;
And calculating the relative closeness of the optimization scheme to the positive ideal solution and the negative ideal solution respectively according to the Euclidean distance, and taking the optimization scheme with the maximum relative closeness as an optimal size scheme.
8. A computer-readable storage medium, characterized by comprising a program executable by a processor to implement the optimum design method according to any one of claims 1 to 4.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108717488A (en) * | 2018-05-17 | 2018-10-30 | 温州大学 | A kind of multi-objective optimization design of power method of forced air cooling cooling system heat structure |
| CN113343380A (en) * | 2021-05-31 | 2021-09-03 | 温州大学 | Forced air cooling radiator optimization method and system based on multi-objective particle swarm algorithm |
| CN115048861A (en) * | 2022-06-06 | 2022-09-13 | 中国人民解放军海军工程大学 | Intelligent distribution method and system for 3D chip embedded liquid cooling channel flow |
| CN115130749A (en) * | 2022-06-29 | 2022-09-30 | 重庆大学 | NSGA-III and TOPSIS fused data-driven multi-objective optimization method |
| CN116776773A (en) * | 2023-06-26 | 2023-09-19 | 淮阴工学院 | Tube type optimization system and method for straight fin tube type heat exchanger |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108717488A (en) * | 2018-05-17 | 2018-10-30 | 温州大学 | A kind of multi-objective optimization design of power method of forced air cooling cooling system heat structure |
| CN113343380A (en) * | 2021-05-31 | 2021-09-03 | 温州大学 | Forced air cooling radiator optimization method and system based on multi-objective particle swarm algorithm |
| CN115048861A (en) * | 2022-06-06 | 2022-09-13 | 中国人民解放军海军工程大学 | Intelligent distribution method and system for 3D chip embedded liquid cooling channel flow |
| CN115130749A (en) * | 2022-06-29 | 2022-09-30 | 重庆大学 | NSGA-III and TOPSIS fused data-driven multi-objective optimization method |
| CN116776773A (en) * | 2023-06-26 | 2023-09-19 | 淮阴工学院 | Tube type optimization system and method for straight fin tube type heat exchanger |
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