Micro-channel deep groove structure optimization method based on parameter optimization, storage medium and system

Abstract

The invention discloses a micro-channel deep groove structure optimization method based on parameter optimization, a storage medium and a system, and the method comprises the steps: carrying out the optimization processing of collected initial design parameters of a micro-channel deep groove structure, constructing each micro-channel deep groove three-dimensional model corresponding to different optimization design parameters, simulating a flow heat exchange process to obtain flow heat exchange simulation distribution data, calculating corresponding wall surface heat exchange coefficients, and performing correlation analysis on the wall surface heat exchange coefficients and the corresponding optimization design parameters to obtain a shrinkage limit value range of each optimization design parameter; and processing the shrinkage limit value ranges of all the optimization design parameters by adopting an orthogonal test design method to obtain optimal design parameters so as to carry out optimization design on the micro-channel deep groove structure. According to the micro-channel deep groove structure optimization method based on parameter optimization, the storage medium and the system provided by the embodiment of the invention, comprehensive optimization of the deep groove structure of the micro-channel liquid cooling plate is realized.

Description

Technical Field

[0001] The present invention relates to the field of semiconductor integrated circuit technology, and in particular to a microchannel deep groove structure optimization method based on parameter optimization, a storage medium and a system. Background Art

[0002] Microchannel liquid cooling plate, also known as microchannel heat sink, uses micro-machining technology to form countless tiny flow channels on the heat sink to increase the heat dissipation area, improve the stability against overheated splashing liquid, and achieve heat dissipation of electronic equipment. The working principle of the microchannel liquid cooling plate is mainly to remove heat from the heat source and dissipate it to the external environment through the flow of coolant in the microchannel. With the continuous advancement of technology and the continuous improvement of application needs, higher requirements are placed on the heat exchange efficiency of the microchannel liquid cooling plate.

[0003] The deep groove structure refers to a groove with a certain depth and width formed in the coolant channel of the microchannel liquid cooling plate through precision machining or micromachining technology. The deep groove structure can not only enhance the heat exchange efficiency and optimize the fluid dynamics performance, but also improve the structural strength and stability. It is one of the key designs for the microchannel liquid cooling plate to achieve efficient heat dissipation.

[0004] However, slight changes in parameters such as the groove angle, groove bottom arc radius, groove wall roughness, and step width between grooves will cause changes in the flow state of the liquid near the wall, thereby affecting the heat transfer efficiency. In addition, the various parameters influence each other and jointly determine the liquid flow and heat transfer characteristics in the microchannel, making it difficult to achieve comprehensive optimization of the deep groove structure of the microchannel liquid cooling plate.

[0005] It can be seen that how to achieve comprehensive optimization of the deep groove structure of the microchannel liquid cooling plate has become a technical problem that needs to be urgently solved by those skilled in the art. Summary of the invention

[0006] The present invention provides a microchannel deep groove structure optimization method, storage medium and system based on parameter optimization to solve the technical problem that it is difficult to achieve comprehensive optimization of the deep groove structure of a microchannel liquid cooling plate in the prior art.

[0007] In order to solve the above technical problems, an embodiment of the present invention provides a microchannel deep groove structure optimization method based on parameter optimization.

[0008] The collected initial design parameters of the microchannel deep groove structure are optimized to construct three-dimensional models of each microchannel deep groove corresponding to different optimized design parameters; Based on each of the three-dimensional models of the microchannel deep grooves, the flow and heat transfer process is simulated with corresponding optimized design parameters to obtain respective flow and heat transfer simulation distribution data; Calculating the corresponding wall heat transfer coefficient based on each of the flow heat transfer simulation distribution data, performing correlation analysis on the wall heat transfer coefficient and the corresponding optimization design parameter, and obtaining the reduced value range of each optimization design parameter; An orthogonal test design method is used to process the reduced value range of all the optimization design parameters to obtain various optimization parameter value combinations, and each optimization parameter value combination is used for experimental analysis to obtain the wall heat transfer coefficient corresponding to each optimization parameter value combination; Based on the ranking results of all the wall heat transfer coefficients, the optimal design parameters are obtained to optimize the design of the microchannel deep groove structure.

[0009] As one of the preferred solutions, the initial design parameters of the collected microchannel deep groove structure are optimized to construct three-dimensional models of each microchannel deep groove corresponding to different optimized design parameters, including: Collecting initial design parameters of the microchannel deep groove structure; optimizing and adjusting the value range of the initial design parameters by using the wall heat transfer coefficient using an optimization algorithm to obtain various optimized design parameters; A three-dimensional modeling software is used to construct a three-dimensional model of the microchannel deep groove corresponding to each of the optimized design parameters.

[0010] As one of the preferred solutions, the flow heat transfer simulation distribution data includes temperature distribution data, velocity vector distribution data and pressure distribution data. Based on each of the microchannel deep groove three-dimensional models, the flow heat transfer process is simulated with corresponding optimized design parameters to obtain each flow heat transfer simulation distribution data, including: Using an adaptive tetrahedron mesher to mesh each of the microchannel deep groove three-dimensional models to obtain each mesh unit and each unstructured mesh node; The fluid control equation of each grid unit is discretized by using a second-order upwind difference format, and the collected temperature value of each grid unit is processed to obtain temperature distribution data; The collected velocity vector and pressure values ​​of each unstructured grid node are processed to obtain velocity vector distribution data and pressure distribution data.

[0011] As one preferred solution, the calculation of the corresponding wall heat transfer coefficient based on each of the flow heat transfer simulation distribution data includes: The flow heat transfer simulation distribution data is subjected to turbulence simulation using the K-EPSILON turbulence model to obtain the turbulence simulation process data, the turbulence kinetic energy and the turbulence kinetic energy dissipation rate are calculated based on the turbulence simulation process data, and the wall heat transfer coefficient is calculated based on the turbulence kinetic energy and the turbulence kinetic energy dissipation rate.

[0012] As one of the preferred solutions, the wall heat transfer coefficient and the corresponding optimization design parameters are subjected to correlation analysis to obtain the reduced value range of each optimization design parameter, including: The average value of the Pearson correlation coefficient between each of the optimized design parameters and all the wall heat transfer coefficients is used as the parameter adjustment key factor of each of the optimized design parameters to evaluate the influence of the adjustment of each of the optimized design parameters on the wall heat transfer coefficient; The value range of each of the optimization design parameters is narrowed by using the parameter adjustment key factor to obtain a narrowed value range of each of the optimization design parameters.

[0013] As one of the preferred solutions, the orthogonal experimental design method is used to process the narrowed value ranges of all the optimization design parameters to obtain the value combinations of each optimization parameter, including: Dividing the reduced value range of each of the optimization design parameters into a plurality of discrete value intervals, and taking the median of each of the value intervals as the reduced median value of each of the optimization design parameters; Constructing an orthogonal test table for optimization design with the reduced median value of each optimization design parameter; Based on the orthogonal test table of the optimization design, each optimization parameter value combination is obtained.

[0014] As one of the preferred solutions, the test analysis is performed on each of the optimization parameter value combinations to obtain the wall heat transfer coefficient corresponding to each of the optimization parameter value combinations, including: Based on each of the optimization parameter value combinations, a corresponding heat exchange system test platform is built; A flow heat transfer test is carried out on the heat exchange system test platform with the optimized parameter value combination. When the flow heat transfer test reaches a preset state, corresponding flow heat transfer test distribution data is collected, and the corresponding wall heat transfer coefficient is calculated based on the flow heat transfer test distribution data.

[0015] As one of the preferred solutions, the optimal design parameters are obtained based on the sorting results of all the wall heat transfer coefficients, including: Constructing a heat transfer multi-objective function based on the combined results of all the wall heat transfer coefficients; Using each of the wall heat transfer coefficients to calculate the value of the heat transfer multi-objective function, and obtaining a comprehensive optimization result of the corresponding optimization parameter value combination; All the comprehensive optimization results are sorted, and the optimization parameter value combination corresponding to the largest comprehensive optimization result is taken as the optimal design parameter.

[0016] Yet another embodiment of the present invention provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, a microchannel deep groove structure optimization method based on parameter optimization as described above is implemented.

[0017] Another embodiment of the present invention provides a microchannel deep groove structure optimization system based on parameter optimization, the system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the microchannel deep groove structure optimization method based on parameter optimization as described above.

[0018] Compared with the prior art, the embodiments of the present invention have the following advantages: (1) Optimizing the collected initial design parameters of the microchannel deep groove structure, constructing three-dimensional models of each microchannel deep groove corresponding to different optimized design parameters, simulating the flow and heat transfer process with the corresponding optimized design parameters based on each microchannel deep groove three-dimensional model, and obtaining the flow and heat transfer simulation distribution data of each flow and heat transfer. First, the value range of the initial design parameters is expanded on the basis of the initial design parameters, so as to facilitate the subsequent selection of the optimal design parameters within the expanded range, and at the same time, constructing a three-dimensional model of the microchannel deep groove, so as to facilitate the subsequent simulation of the flow and heat transfer of the microchannel deep groove structure under different design parameters; (2) In order to select the optimal design parameters, it is necessary to narrow the value range of the optimized design parameters within the expanded range. The corresponding wall heat transfer coefficient is calculated based on each flow heat transfer simulation distribution data. The wall heat transfer coefficient and the corresponding optimized design parameters are correlated and analyzed to obtain the reduced value range of each optimized design parameter. Before narrowing the value range of the optimized design parameters, the degree of influence of the adjustment of each optimized design parameter on the wall heat transfer coefficient is analyzed. The value range of the optimized design parameters with less influence on the wall heat transfer coefficient is narrowed to a greater extent to speed up the convergence speed of solving the optimal design parameters. The value range of the optimized design parameters with a greater influence on the wall heat transfer coefficient is narrowed to a smaller extent to ensure that the narrowed range can still cover the possible optimal design parameters. (3) The orthogonal experimental design method is used to process the narrowed value range of all optimization design parameters to obtain various optimization parameter value combinations. Experimental analysis is performed on each optimization parameter value combination to obtain the wall heat transfer coefficient corresponding to each optimization parameter value combination. The optimal design parameters are obtained based on the sorting results of all wall heat transfer coefficients to optimize the design of the microchannel deep groove structure. The values ​​of all optimization design parameters are further verified through experimental analysis to ensure the reliability of the obtained optimal design parameters. The deep groove structure of the microchannel liquid cooling plate is fully optimized through simulation and experimental analysis of the flow heat transfer process. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] FIG1 is a flow chart of a method for optimizing a microchannel deep trench structure based on parameter optimization in one embodiment of the present invention; FIG. 2 is a schematic diagram of obtaining optimal design parameters in one embodiment of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] In the description of this application, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the feature. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0022] In the description of the present application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. The terms "vertical", "horizontal", "left", "right", "upper", "lower" and similar expressions used herein are only for illustrative purposes, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0023] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by those skilled in the art. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood by specific circumstances.

[0024] An embodiment of the present invention provides a method for optimizing a microchannel deep groove structure based on parameter optimization. Specifically, please refer to FIG. 1 , which shows a flow chart of a method for optimizing a microchannel deep groove structure based on parameter optimization in one embodiment of the present invention.

[0025] Step S1: Optimizing the collected initial design parameters of the microchannel deep trench structure, and constructing three-dimensional models of each microchannel deep trench corresponding to different optimized design parameters.

[0026] Specifically, the initial design parameters of the microchannel deep groove structure are collected; the optimization algorithm is used to optimize the value range of the initial design parameters based on the wall heat transfer coefficient to obtain various optimized design parameters.

[0027] The 3D modeling software is used to construct the 3D model of the microchannel deep groove corresponding to each optimized design parameter.

[0028] Step S2: Based on each microchannel deep groove three-dimensional model, the flow and heat transfer process is simulated with corresponding optimized design parameters to obtain various flow and heat transfer simulation distribution data.

[0029] Specifically, an adaptive tetrahedron mesher is used to mesh each microchannel deep groove three-dimensional model to obtain each mesh unit and each unstructured mesh node.

[0030] The second-order upwind difference format is used to discretize the fluid control equation of each grid unit, and the collected temperature values ​​of each grid unit are processed to obtain temperature distribution data.

[0031] The collected velocity vector and pressure values ​​of each unstructured grid node are processed to obtain velocity vector distribution data and pressure distribution data.

[0032] It should be noted that the flow and heat transfer simulation distribution data includes temperature distribution data, velocity vector distribution data and pressure distribution data. The adaptive tetrahedral mesher is used to automatically generate tetrahedral meshes in three-dimensional space according to geometric features and calculation requirements. The adaptive tetrahedral mesher and the second-order upwind difference format are both well-known technologies and will not be elaborated in this embodiment.

[0033] Step S3: Calculate the corresponding wall heat transfer coefficient based on each flow heat transfer simulation distribution data, perform correlation analysis on the wall heat transfer coefficient and the corresponding optimization design parameter, and obtain the reduced value range of each optimization design parameter.

[0034] Specifically, the K-EPSILON turbulence model is used to perform turbulence simulation on the flow heat transfer simulation distribution data to obtain turbulence simulation process data, the turbulent kinetic energy and the turbulent kinetic energy dissipation rate are calculated based on the turbulent kinetic energy and the turbulent kinetic energy dissipation rate, and the wall heat transfer coefficient is calculated based on the turbulent kinetic energy and the turbulent kinetic energy dissipation rate.

[0035] It should be noted that the K-EPSILON turbulence model is a well-known technology and will not be elaborated in detail in this application.

[0036] Furthermore, the average value of the Pearson correlation coefficient between each optimized design parameter and all wall heat transfer coefficients is used as the key factor for parameter adjustment of each optimized design parameter to evaluate the degree of influence of the adjustment of each optimized design parameter on the wall heat transfer coefficient; The value range of each optimization design parameter is narrowed by adjusting the key factors of the parameters, and the narrowed value range of each optimization design parameter is obtained.

[0037] Step S4: using the orthogonal experimental design method to process the reduced value range of all optimization design parameters, obtaining various optimization parameter value combinations, and performing experimental analysis on each optimization parameter value combination to obtain the wall heat transfer coefficient corresponding to each optimization parameter value combination.

[0038] Specifically, the reduced value range of each optimization design parameter is divided into several discrete value intervals, the median of each value interval is used as the reduced median value of each optimization design parameter, an optimization design orthogonal test table is constructed with the reduced median value of each optimization design parameter, and each optimization parameter value combination is obtained based on the optimization design orthogonal test table.

[0039] Furthermore, based on each optimized parameter value combination, a corresponding heat exchange system test platform is built, and a flow heat transfer test is carried out on the heat exchange system test platform with the optimized parameter value combination. When the flow heat transfer test reaches a preset state, the corresponding flow heat transfer test distribution data is collected, and the corresponding wall heat transfer coefficient is calculated based on the flow heat transfer test distribution data.

[0040] Step S5: Obtaining optimal design parameters based on the ranking results of all wall heat transfer coefficients to optimize the design of the microchannel deep groove structure.

[0041] Specifically, a heat transfer multi-objective function is constructed based on the combination results of all wall heat transfer coefficients, and the value of the heat transfer multi-objective function is calculated using each wall heat transfer coefficient to obtain a comprehensive optimization result of the corresponding combination of optimization parameter values.

[0042] All comprehensive optimization results are sorted, and the combination of optimization parameter values ​​corresponding to the largest comprehensive optimization result is taken as the optimal design parameters.

[0043] Specifically, please refer to FIG. 2 , which is a schematic diagram showing how to obtain the optimal design parameters in one embodiment of the present invention.

[0044] Yet another embodiment of the present invention provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, a microchannel deep groove structure optimization method based on parameter optimization as described above is implemented.

[0045] Another embodiment of the present invention provides a microchannel deep groove structure optimization system based on parameter optimization, the system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the microchannel deep groove structure optimization method based on parameter optimization as described above.

[0046] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for optimizing a microchannel deep groove structure based on parameter optimization, characterized in that: The method comprises: The collected initial design parameters of the microchannel deep groove structure are optimized to construct three-dimensional models of each microchannel deep groove corresponding to different optimized design parameters; Based on each of the three-dimensional models of the microchannel deep grooves, the flow and heat transfer process is simulated with corresponding optimized design parameters to obtain respective flow and heat transfer simulation distribution data; Calculating the corresponding wall heat transfer coefficient based on each of the flow heat transfer simulation distribution data, performing correlation analysis on the wall heat transfer coefficient and the corresponding optimization design parameter, and obtaining the reduced value range of each optimization design parameter; An orthogonal test design method is used to process the reduced value range of all the optimization design parameters to obtain various optimization parameter value combinations, and each optimization parameter value combination is used for experimental analysis to obtain the wall heat transfer coefficient corresponding to each optimization parameter value combination; Based on the ranking results of all the wall heat transfer coefficients, the optimal design parameters are obtained to optimize the design of the microchannel deep groove structure.

2. The method for optimizing a microchannel deep groove structure based on parameter optimization according to claim 1, characterized in that: The method of optimizing the collected initial design parameters of the microchannel deep groove structure and constructing three-dimensional models of each microchannel deep groove corresponding to different optimized design parameters includes: Collecting initial design parameters of the microchannel deep groove structure; optimizing and adjusting the value range of the initial design parameters by using the wall heat transfer coefficient using an optimization algorithm to obtain various optimized design parameters; A three-dimensional modeling software is used to construct a three-dimensional model of the microchannel deep groove corresponding to each of the optimized design parameters.

3. The method for optimizing a microchannel deep groove structure based on parameter optimization according to claim 1, characterized in that: The flow heat transfer simulation distribution data includes temperature distribution data, velocity vector distribution data and pressure distribution data. Based on each of the microchannel deep groove three-dimensional models, the flow heat transfer process is simulated with corresponding optimized design parameters to obtain each flow heat transfer simulation distribution data, including: Using an adaptive tetrahedron mesher to mesh each of the microchannel deep groove three-dimensional models to obtain each mesh unit and each unstructured mesh node; The fluid control equation of each grid unit is discretized by using a second-order upwind difference format, and the collected temperature value of each grid unit is processed to obtain temperature distribution data; The collected velocity vector and pressure values ​​of each unstructured grid node are processed to obtain velocity vector distribution data and pressure distribution data.

4. The method for optimizing a microchannel deep groove structure based on parameter optimization according to claim 3, characterized in that: The calculating the corresponding wall heat transfer coefficient based on each of the flow heat transfer simulation distribution data comprises: The flow heat transfer simulation distribution data is subjected to turbulence simulation using the K-EPSILON turbulence model to obtain the turbulence simulation process data, the turbulence kinetic energy and the turbulence kinetic energy dissipation rate are calculated based on the turbulence simulation process data, and the wall heat transfer coefficient is calculated based on the turbulence kinetic energy and the turbulence kinetic energy dissipation rate.

5. The method for optimizing a microchannel deep groove structure based on parameter optimization according to claim 1, characterized in that: The correlation analysis is performed on the wall heat transfer coefficient and the corresponding optimization design parameters to obtain the reduced value range of each optimization design parameter, including: The average value of the Pearson correlation coefficient between each of the optimized design parameters and all the wall heat transfer coefficients is used as the parameter adjustment key factor of each of the optimized design parameters to evaluate the influence of the adjustment of each of the optimized design parameters on the wall heat transfer coefficient; The value range of each of the optimization design parameters is narrowed by using the parameter adjustment key factor to obtain a narrowed value range of each of the optimization design parameters.

6. The method for optimizing a microchannel deep groove structure based on parameter optimization according to claim 1, characterized in that: The orthogonal test design method is used to process the narrowed value ranges of all the optimization design parameters to obtain the value combinations of each optimization parameter, including: Dividing the reduced value range of each of the optimization design parameters into a plurality of discrete value intervals, and taking the median of each of the value intervals as the reduced median value of each of the optimization design parameters; Constructing an orthogonal test table for optimization design with the reduced median value of each optimization design parameter; Based on the orthogonal test table of the optimization design, each optimization parameter value combination is obtained.

7. The method for optimizing a microchannel deep groove structure based on parameter optimization according to claim 1, characterized in that: The test analysis is performed on each of the optimization parameter value combinations to obtain the wall heat transfer coefficient corresponding to each of the optimization parameter value combinations, including: Based on each of the optimization parameter value combinations, a corresponding heat exchange system test platform is built; A flow heat transfer test is carried out on the heat exchange system test platform with the optimized parameter value combination. When the flow heat transfer test reaches a preset state, corresponding flow heat transfer test distribution data is collected, and the corresponding wall heat transfer coefficient is calculated based on the flow heat transfer test distribution data.

8. The method for optimizing a microchannel deep groove structure based on parameter optimization according to claim 1, characterized in that: The optimal design parameters are obtained based on the sorting results of all the wall heat transfer coefficients, including: Constructing a heat transfer multi-objective function based on the combined results of all the wall heat transfer coefficients; Using each of the wall heat transfer coefficients to calculate the value of the heat transfer multi-objective function, and obtaining a comprehensive optimization result of the corresponding optimization parameter value combination; All the comprehensive optimization results are sorted, and the optimization parameter value combination corresponding to the largest comprehensive optimization result is taken as the optimal design parameter.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, a microchannel deep groove structure optimization method based on parameter optimization as described in any one of claims 1 to 8 is implemented.

10. A microchannel deep groove structure optimization system based on parameter optimization, characterized in that: The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, a microchannel deep groove structure optimization method based on parameter optimization as described in any one of claims 1 to 8 is implemented.

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