A method and apparatus for designing the structure of a micro-channel heat sink

Abstract

The application discloses a structure design method and device of a micro-channel radiator. The method comprises the following steps: establishing a topology optimization basic model according to the size of a chip heat source, the symmetry of the structure and the configuration of the flow inlet and outlet; dividing a high heat flux density optimization sub-region and a low heat flux density optimization sub-region according to the distribution characteristics of the chip heat flux density; constructing a micro-scale conjugate heat transfer topology mathematical model, introducing temperature uniformity as a third optimization target, constructing a multi-objective optimization model and setting differentiated constraints for each sub-region; independently iteratively solving the high heat flux density optimization sub-region and the low heat flux density optimization sub-region, and obtaining an optimal topology flow channel structure; and finally, through splicing fusion and three-dimensional heat flow coupling simulation verification, the micro-channel radiator flow channel structure is obtained. The application realizes the cooperation of local heat exchange intensification and global resistance reduction and efficiency improvement, significantly improves the chip heat dissipation efficiency and temperature uniformity, and reduces the system pump power loss and research and development cost.

Description

Technical Field

[0001] This application relates to the field of thermal management technology for electronic devices, and in particular to a structural design method and apparatus for a microchannel heat sink. Background Technology

[0002] With the continuous improvement of chip integration and the increasing diversification of functions, electronic chips integrate more and more modules of different power, forming multiple non-uniform heat sources with varying heat flux densities during operation. High temperature is the leading cause of electronic device failure; related studies show that it accounts for more than 55% of device failures. For every 1°C increase in chip temperature (in the 70~80°C range), reliability decreases by 5%, and under extreme conditions, it may even lead to thermal runaway or even fires and other safety accidents. Therefore, microchannel liquid cooling technology with high heat exchange efficiency has become a core means of thermal management for high-power chips.

[0003] Current microchannel heat sink design methods are mostly designed for uniform heat flux density scenarios, making it difficult to adapt to the complex thermal load characteristics of multi-module non-uniform heat sources. This easily leads to insufficient hot spot suppression in high heat flux density areas and excessive waste of cooling energy in low heat flux density areas, further exacerbating the non-uniformity of chip surface temperature. At the same time, it causes a sharp increase in flow resistance and excessive system pump power loss. In addition, traditional designs rely heavily on experience and intuition, resulting in high trial-and-error costs and long development cycles. Moreover, existing topology optimization methods mostly aim to enhance heat transfer and reduce flow resistance, ignoring temperature uniformity. They also employ a globally uniform optimization strategy, which cannot differentiate the design of local hot spots and cold areas.

[0004] Therefore, how to achieve targeted and precise cooling under non-uniform heat sources, while taking into account high heat transfer efficiency, low flow resistance and excellent temperature uniformity, and reducing the R&D cost of radiators, is an urgent problem to be solved. Summary of the Invention

[0005] In view of this, the microchannel heat sink structural design method and apparatus provided in this application embodiment can achieve targeted and precise cooling under non-uniform heat sources, while taking into account high heat transfer efficiency, low flow resistance and excellent temperature uniformity, and reducing heat sink R&D costs. The microchannel heat sink structural design method and apparatus provided in this application embodiment are implemented as follows: This application provides a structural design method for a microchannel heat sink, including: Geometric modeling and boundary condition setting are performed on the chip heat source size, structural symmetry, and flow inlet / outlet configuration to obtain a basic model for microchannel heat sink topology optimization. The basic model for microchannel heat sink topology optimization includes solid and fluid physical property parameters. The microchannel radiator topology optimization basic model and heat flux density distribution characteristics are divided into regions and design variables are assigned to obtain high heat flux density optimization sub-regions and low heat flux density optimization sub-regions. Mathematical modeling and field quantity transformation are performed on the high heat flux density optimization sub-region and the low heat flux density optimization sub-region to obtain a microscale conjugate heat transfer topology mathematical model. The heat exchange, flow loss, and temperature uniformity corresponding to the high heat flux density optimization sub-region and the low heat flux density optimization sub-region are respectively subject to multi-objective function construction and partitioned differential constraint processing to obtain a multi-objective optimization model. The microscale conjugate heat transfer topology mathematical model and the multi-objective optimization model are independently iterated and converged to obtain the optimal topology flow channel structure. The optimal topology flow channel structure is spliced ​​and fused and verified by three-dimensional thermal-fluid coupling simulation to obtain the flow channel structure of the microchannel heat sink.

[0006] In some embodiments, the geometric modeling and boundary condition setting process for the chip heat source size, structural symmetry, and flow inlet / outlet configuration yields a basic model for microchannel heat sink topology optimization. This basic model includes solid and fluid physical property parameters, including: The overall design domain is determined and symmetry simplification is performed on the chip heat source size and heat dissipation requirements to obtain a topology optimization calculation model; The topology optimization calculation model is processed by configuring the flow inlet and outlet and setting boundary conditions to obtain an initial model, which includes the flow boundary. The initial model is processed by defining solid and fluid physical property parameters to obtain the basic model for microchannel heat sink topology optimization.

[0007] In some embodiments, the process of dividing the microchannel heat sink topology optimization model and heat flux density distribution characteristics into regions and assigning design variables to obtain high heat flux density optimization sub-regions and low heat flux density optimization sub-regions includes: The microchannel radiator topology optimization model and the heat flux density distribution characteristics are subjected to regional boundary delineation to obtain preliminary partitions corresponding to different heat flux levels. The topology design space of the initial partition is processed by independent design variable allocation to obtain the topology optimization design variables for each partition; The topology optimization design variables of each partition are correlated with corresponding optimization objectives to obtain high heat flux density optimization sub-regions and low heat flux density optimization sub-regions.

[0008] In some embodiments, the step of performing mathematical modeling and field quantity transformation on the high heat flux density optimization sub-region and the low heat flux density optimization sub-region to obtain a microscale conjugate heat transfer topology mathematical model includes: Topological variables are defined and penalty mechanisms are constructed for the solid-liquid two-phase heat transfer and flow processes corresponding to the high heat flux density optimization sub-region and the low heat flux density optimization sub-region, respectively, to obtain the basic framework of topological mathematics. The solid-liquid two-phase distribution of the aforementioned topological mathematical framework is transformed into a continuous field quantity to obtain a permeability field with a unified description. The permeability field is processed by constructing conjugate heat transfer control equations to obtain a microscale conjugate heat transfer topological mathematical model.

[0009] In some embodiments, the heat transfer, flow loss, and temperature uniformity corresponding to the high heat flux density optimization sub-region and the low heat flux density optimization sub-region are respectively subjected to multi-objective function construction and partitioned differential constraint processing to obtain a multi-objective optimization model, including: The heat exchange, flow loss, and temperature uniformity corresponding to the high heat flux density optimization sub-region and the low heat flux density optimization sub-region are normalized and weighted to obtain the basic form of the general multi-objective function. The general multi-objective function is subjected to partition-differentiated objective weight allocation processing to obtain partition objective functions adapted to different heat flux levels; The objective function of the partition is constrained by fluid volume fraction to obtain a multi-objective optimization model.

[0010] In some embodiments, the step of independently iteratively solving and converging the microscale conjugate heat transfer topology mathematical model and the multi-objective optimization model to obtain the optimal topology flow channel structure includes: The microscale conjugate heat transfer topology mathematical model and the multi-objective optimization model are solved by partitioned independent iterative solution to obtain the intermediate topology flow channel structure of each sub-region. The intermediate topological flow channel structure of each sub-region is subjected to iterative convergence judgment processing to obtain the partitioned topological flow channel structure that meets the preset convergence conditions; The partitioned topology flow channel structure that meets the preset convergence conditions is subjected to structural shaping processing to obtain the optimal topology flow channel structure.

[0011] In some embodiments, the process of splicing and fusing the optimal topological flow channel structure and performing three-dimensional thermal-fluid coupling simulation verification to obtain the flow channel structure of the microchannel heat sink includes: The optimal topology flow channel structure is partitioned, spliced, fused, and smoothed to obtain a two-dimensional microchannel flow channel structure. The two-dimensional microchannel flow channel structure is subjected to three-dimensional stretching and solid assembly to obtain a three-dimensional microchannel heat sink model. The three-dimensional microchannel heat sink model was subjected to three-dimensional thermal-fluid coupling simulation and performance verification to obtain the flow channel structure of the microchannel heat sink.

[0012] This application provides a structural design device for a microchannel heat sink, comprising: The processing module is used to perform geometric modeling and boundary condition setting on the chip heat source size, structural symmetry, and flow inlet / outlet configuration to obtain a basic model for microchannel heat sink topology optimization. The basic model for microchannel heat sink topology optimization includes solid and fluid physical property parameters. The partitioning module is used to perform regional partitioning and design variable allocation processing on the microchannel radiator topology optimization basic model and heat flux density distribution characteristics to obtain high heat flux density optimization sub-regions and low heat flux density optimization sub-regions. The processing module is also used to perform mathematical modeling and field quantity transformation processing on the high heat flux density optimization sub-region and the low heat flux density optimization sub-region to obtain a microscale conjugate heat transfer topology mathematical model. The processing module is also used to construct multi-objective functions and perform partitioned differential constraint processing on the heat exchange, flow loss and temperature uniformity corresponding to the high heat flux density optimization sub-region and the low heat flux density optimization sub-region, respectively, to obtain a multi-objective optimization model; The processing module is also used to perform independent iterative solution and convergence judgment on the microscale conjugate heat transfer topology mathematical model and the multi-objective optimization model to obtain the optimal topology flow channel structure. The simulation module is used to perform splicing and fusion and three-dimensional thermal-fluid coupling simulation verification on the optimal topology flow channel structure to obtain the flow channel structure of the microchannel heat sink.

[0013] The computer device provided in this application includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the program, it implements the method described in this application.

[0014] The computer-readable storage medium provided in this application embodiment stores a computer program thereon, which, when executed by a processor, implements the method described in this application embodiment.

[0015] This application provides a method and apparatus for designing a microchannel heat sink. The method establishes a topology optimization model based on the chip's heat source size, structural symmetry, and flow inlet / outlet configuration. It divides the chip into high heat flux density optimization sub-regions and low heat flux density optimization sub-regions based on the chip's heat flux density distribution characteristics. A microscale conjugate heat transfer topology mathematical model is constructed, introducing temperature uniformity as a third optimization objective. A multi-objective optimization model is built, and differentiated constraints are set for each sub-region. The high and low heat flux density optimization sub-regions are solved independently and iteratively to obtain the optimal topology flow channel structure. Finally, the flow channel structure of the microchannel heat sink is obtained through splicing, fusion, and three-dimensional heat-fluid coupling simulation verification. This enables targeted and precise cooling under non-uniform heat sources, while simultaneously achieving high heat transfer efficiency, low flow resistance, and excellent temperature uniformity, reducing heat sink development costs and solving the technical problems mentioned in the background art. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A schematic diagram illustrating the implementation process of a microchannel heat sink structural design method provided in this application embodiment; Figure 2 A schematic diagram illustrating the implementation process of obtaining a basic model for microchannel heat sink topology optimization, provided in an embodiment of this application; Figure 3 A schematic diagram of a structural design device for a microchannel heat sink provided in an embodiment of this application; Figure 4 This is a schematic diagram of the partition topology optimization technology provided in the embodiments of this application; Figure 5 This is a schematic diagram of a high heat flux density chip microchannel heat sink provided in an embodiment of this application; Figure 6 A temperature diagram of the heat source surface of a topology-optimized microchannel heat sink provided in an embodiment of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0019] The following description of some technologies involved in the embodiments of this application is provided to aid understanding and should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for clarity and brevity, some descriptions of well-known functions and structures are omitted in the following description.

[0020] Figure 1 This is a schematic flowchart illustrating the implementation of a microchannel heat sink structural design method provided in this application embodiment, including steps 101 to 106. Wherein, Figure 1 This is merely one execution order shown in the embodiments of this application and does not represent the only execution order of a microchannel heat sink structural design method. Where the final result can be achieved, Figure 1 The steps shown can be performed in parallel or in reverse order.

[0021] Step 101: Perform geometric modeling and boundary condition setting on the chip heat source size, structural symmetry, and flow inlet / outlet configuration to obtain the basic model for microchannel heat sink topology optimization.

[0022] In the embodiments of this application, such as Figure 5 As shown, Figure 5This is a schematic diagram of a high heat flux density chip microchannel heat sink provided in this application embodiment. First, based on the actual size and heat dissipation requirements of the circular high heat flux density chip, the overall design domain of the microchannel heat sink is determined. The overall diameter of the heat sink is set to 4.5 mm, consisting of a shell, a microchannel layer, and a substrate. The shell thickness is set to 0.5 mm, the microchannel layer thickness is set to 0.5 mm, and the substrate thickness is set to 0.3 mm. The overall thickness of the assembled heat sink is 1.3 mm. Considering the axisymmetric nature of the overall structure of the microchannel heat sink, to reduce the computational load of topology optimization and improve computational efficiency, a quarter region of the overall heat sink structure is selected as the computational model for topology optimization. Then, a flow boundary scheme is set, using a three-inlet, two-outlet flow boundary configuration. Three inlets and two outlets are set on the corresponding edges of the topology optimization model. The width of a single inlet is set to 0.14 mm, and the width of a single outlet is set to 0.12 mm. The velocity boundary conditions of the cooling fluid at the inlets and the pressure boundary conditions at the outlets are defined, with the outlet pressure set to standard atmospheric pressure. Finally, material properties were defined. The solid part of the microchannel radiator was set to be copper, and the cooling fluid was deionized water. The thermal physical parameters of copper, such as thermal conductivity, density, and specific heat capacity, as well as the fluid properties of deionized water, such as thermal conductivity, density, specific heat capacity, and dynamic viscosity, were determined. All the above parameters were imported into the topology optimization model to obtain the basic model of microchannel radiator topology optimization that includes solid and fluid properties.

[0023] Step 102: Perform region division and design variable allocation processing on the basic model of microchannel heat sink topology optimization and heat flux density distribution characteristics to obtain high heat flux density optimization sub-regions and low heat flux density optimization sub-regions.

[0024] In the embodiments of this application, such as Figure 4 As shown, Figure 4 This is a schematic diagram of the partitioned topology optimization technology provided in this application embodiment. Based on the heat flux density distribution characteristics of the chip under operating conditions, the optimization area is divided into two sub-regions: a circular area with a radius of 1.325 mm corresponding to the chip hotspot location is designated as the high heat flux density optimization area, and an annular area surrounding the high heat flux density optimization area with an inner diameter of 1.325 mm and an outer diameter of 2.25 mm is designated as the low heat flux density optimization area. In this embodiment, the fluid volume fraction in the high heat flux density optimization area is preset to 0.55, and the fluid volume fraction in the low heat flux density optimization area is 0.4. Independent topology optimization design variables, objective functions, and constraints are set for the two sub-regions respectively to achieve targeted optimization of different heat flux regions.

[0025] Step 103: Mathematical modeling and field quantity transformation are performed on the high heat flux density optimization sub-region and the low heat flux density optimization sub-region to obtain a microscale conjugate heat transfer topology mathematical model.

[0026] In this embodiment, a density-based topology optimization algorithm is employed, introducing a continuously varying material density variable γ as a design variable, γ∈[0,1]. The γ field is mapped to a permeability field using a porous media model, thereby achieving continuous characterization and transition of the solid and fluid two-phase distribution within the microchannel. A penalty coefficient is introduced to suppress intermediate density values, making γ approach 0 or 1, where γ=0 represents the solid region of the microchannel, which is dominated by thermal conduction; and γ=1 represents the liquid cooling medium region, which is dominated by convection.

[0027] Considering that the design domain is a porous medium containing both solid and liquid phases, it cannot be treated as a single heat conduction or heat convection problem. Therefore, a conjugate heat transfer control equation is established to uniformly describe the thermal behavior of the fluid and solid regions. The relevant physical quantities of the equation are defined as follows:

[0028] Where T is the temperature of the design domain. The heat generated in the solid domain, For fluid velocity vector, The thermal conductivity of the fluid, Let ρ be the thermal conductivity of the solid, ▽ be the Hamiltonian factor, and ρ be the fluid density. is the specific heat capacity of the fluid.

[0029] By solving the above equations simultaneously, a microscale conjugate heat transfer topological mathematical model is finally obtained.

[0030] Step 104: For the high heat flux density optimization sub-region and the low heat flux density optimization sub-region, a multi-objective function is constructed and a partitioned differential constraint is applied to the heat exchange, flow loss and temperature uniformity corresponding to them, respectively, to obtain a multi-objective optimization model.

[0031] In this embodiment, in addition to the traditional dual objectives of enhancing heat transfer and reducing flow resistance, temperature difference is introduced as a third optimization objective characterizing temperature uniformity. This embodiment defines three optimization indices: For heat exchange, For temperature difference (temperature uniformity), For flow loss, the physical quantities of each indicator are defined as follows:

[0032]

[0033]

[0034] in, Indicates heat exchange; Indicates temperature difference; Ω represents flow loss; γ represents the design domain; H represents the ideal heat source coefficient. The reference temperature is T; T represents the temperature of the design domain. The average temperature of the design domain; For fluid velocity vector; The fluid velocity is represented by the i-th coordinate. The j-coordinate represents the fluid velocity; x represents the spatial rectangular coordinate system. A spatial rectangular coordinate system representing the i-th coordinate; Let j represent a Cartesian coordinate system; α represent the reverse osmosis rate; and i and j represent different coordinates. First, the individual objectives are normalized, and then a weighted method is used to construct a general multi-objective optimization function:

[0035] in, , , These represent the initial values ​​of heat exchange, temperature difference, and flow loss, respectively. , , These represent the weights of heat exchange, temperature difference, and flow loss, respectively. Differential weights and fluid volume fraction constraints are set for the two sub-regions: High heat flux density optimization zone: the weight ratio of heat exchange, temperature difference, and flow loss is 0.7:0.15:0.15, and the fluid volume fraction is constrained to 0.55, so as to increase the coolant flow rate and flow disturbance and enhance local heat exchange; Low heat flux density optimization zone: The weight ratio of heat exchange, temperature difference and flow loss is 0.15:0.15:0.7, and the fluid volume fraction is constrained to 0.40, in order to optimize the flow channel morphology, reduce branches and dead zones, and reduce flow resistance.

[0036] Regardless of how the weights are allocated, the objective of temperature uniformity is always maintained to ensure uniform temperature distribution across the entire domain. Finally, the objective function and constraints are combined to obtain a multi-objective optimization model.

[0037] Step 105: Independent iterative solution and convergence judgment are performed on the microscale conjugate heat transfer topology mathematical model and the multi-objective optimization model to obtain the optimal topology flow channel structure.

[0038] In this embodiment, the finite element method combined with a variable density topology optimization algorithm is used to perform independent iterative calculations on the two optimization regions. In the high heat flux density region, a high-density, multi-branch complex flow channel structure is generated by optimizing the material density distribution to enhance heat transfer; in the low heat flux density region, a simple, low-resistance flow channel configuration is obtained.

[0039] This embodiment sets the iterative convergence condition as follows: First, the change in the objective function between adjacent iteration steps is less than... Secondly, the total number of iterations exceeds 300. When any one of the convergence conditions is met, the iteration stops, and the optimal topological flow channel structure for the high heat flux density region and the low heat flux density region is obtained.

[0040] Step 106: Perform splicing and fusion of the optimal topology flow channel structure and three-dimensional thermal-fluid coupling simulation verification to obtain the flow channel structure of the microchannel heat sink.

[0041] In the embodiments of this application, such as Figure 6 As shown, Figure 6 This is a temperature diagram of the heat source surface of the topology-optimized microchannel heat sink provided in this application embodiment. Using drawing software, the optimized high heat flux density region and low heat flux density region topological flow channel configurations are spliced ​​and merged to obtain a complete two-dimensional flow channel structure diagram of the microchannel heat sink. The combined flow channel is smoothed to eliminate sharp corners. Three-dimensional modeling software is used to 3D stretch the two-dimensional flow channel structure diagram to obtain a three-dimensional topology-optimized flow channel model. In the 3D modeling software, the microchannel heat sink is assembled into a stacked entity according to the packaging space scale. The assembly is imported into computational fluid dynamics simulation software, and the physical properties of the shell, the solid domain, the fluid domain, and the substrate are set respectively. Three-dimensional thermal-fluid coupling multiphysics simulation is performed to analyze the three-dimensional velocity vector field, inlet and outlet pressure drop loss, and chip junction temperature evolution characteristics of the microchannel heat sink.

[0042] The simulation results show the chip surface temperature gradients as follows: 336.21K, 336.04K, 335.87K, 335.70K, 335.54K, 335.37K, 335.20K, 335.03K, 334.86K, 334.69K, 334.52K; Maximum chip temperature =336.21K, average temperature =335.13K, maximum temperature difference at the heat source surface =1.69K; Total pressure loss at the inlet and outlet of the radiator .

[0043] Simulation results show that, under the condition of applying a non-uniform thermal load boundary on the chip surface, the microchannel flow structure obtained in this embodiment can ensure that the highest temperature in each region of the chip remains below 343.15 Kelvin during operation, verifying that the microchannel heat sink designed in this application has excellent thermo-hydraulic performance. Finally, a microchannel heat sink flow structure that meets the design requirements is obtained.

[0044] This application's embodiments integrate the entire process of geometric modeling, partitioning design, mathematical modeling, multi-objective optimization, iterative solution, and simulation verification. While improving the heat exchange capacity of the radiator and reducing flow losses, it also ensures chip surface temperature uniformity and effectively suppresses localized hot spots. The overall design logic is coherent and highly practical, capable of accurately adapting to chip heat sources with different power distributions, significantly improving the overall thermal-hydraulic performance of the microchannel radiator, and shortening the radiator's R&D and design cycle.

[0045] In the above Figure 1 Based on the above, this application embodiment also provides a schematic diagram of the implementation process for obtaining a basic model for microchannel heatsink topology optimization. For example... Figure 2 As shown, steps 201 to 203 are included: Step 201: Determine the overall design domain and simplify the symmetry of the chip heat source size and heat dissipation requirements to obtain the topology optimization calculation model.

[0046] In this embodiment, the overall design domain of the microchannel heat sink is first determined based on the actual size of the circular high heat flux density chip to be cooled and the preset heat dissipation requirements. In this embodiment, the overall diameter of the heat sink is set to 4.5 mm, and it is composed of three layers: a shell, a microchannel layer, and a substrate, stacked sequentially. The thickness of the shell is set to 0.5 mm, the thickness of the microchannel layer is set to 0.5 mm, and the thickness of the substrate is set to 0.3 mm. The overall thickness of the assembled heat sink is 1.3 mm. Considering that the overall structure of the circular microchannel heat sink has axisymmetric characteristics, in order to significantly reduce the amount of computation in the topology optimization process and improve computational efficiency, a quarter region of the overall structure of the heat sink is selected as the computational model for topology optimization.

[0047] Step 202: Configure the flow inlet and outlet and set the boundary conditions for the topology optimization calculation model to obtain the initial model, which includes the flow boundary.

[0048] In this embodiment, a three-inlet, two-outlet flow boundary configuration is adopted on the corresponding boundary of the aforementioned quarter-topology optimization calculation model. Three fluid inlets and two fluid outlets are set, with the width of each inlet set to 0.14 mm and the width of each outlet set to 0.12 mm. Simultaneously, flow boundary conditions are defined: a constant velocity boundary condition is set at the fluid inlets, and a standard atmospheric pressure boundary condition is set at the fluid outlets. The aforementioned flow boundary information is imported into the calculation model to obtain an initial model containing complete flow boundaries.

[0049] Step 203: Define the solid and fluid physical property parameters of the initial model to obtain the basic model for microchannel heat sink topology optimization.

[0050] In this embodiment, the solid portion of the microchannel heat sink is made of copper, and deionized water is used as the cooling fluid. Thermophysical parameters of copper, such as thermal conductivity, density, and specific heat capacity, are defined within the operating temperature range, as are fluid properties of deionized water, such as thermal conductivity, density, specific heat capacity, and dynamic viscosity. All these solid and fluid properties are imported into an initial model including flow boundaries, ultimately resulting in a basic topology optimization model for the microchannel heat sink containing complete geometric information, boundary conditions, and properties.

[0051] This application simplifies the design domain by combining chip size and structural symmetry, significantly reducing the computational scope and workload of topology optimization and effectively improving overall design efficiency. It standardizes the configuration of flow inlets and outlets and boundary conditions, ensuring that fluid flow conditions remain consistent with actual usage scenarios. It also uniformly defines the physical properties of solid materials and cooling fluids, guaranteeing the accuracy and consistency of the model's fundamental parameters from the outset. The basic model obtained through this step provides reliable geometric and physical premises for subsequent topology optimization and numerical calculations, avoiding distortion of optimization results due to model parameter deviations.

[0052] In some embodiments, the microchannel radiator topology optimization basic model and heat flux density distribution characteristics are divided into regions and design variables are assigned to obtain high heat flux density optimization sub-regions and low heat flux density optimization sub-regions. This includes: delineating the regional boundaries of the microchannel radiator topology optimization basic model and heat flux density distribution characteristics to obtain preliminary partitions corresponding to different heat flux levels.

[0053] Specifically, based on the heat flux density distribution characteristics of the chip under actual operating conditions, high heat flux density hotspot regions and low heat flux density coldspot regions on the chip are identified, and boundaries are delineated within the design domain of the microchannel heat sink topology optimization basic model. In this embodiment, a circular region with a radius of 1.325 mm corresponding to the core hotspot location of the chip is designated as a preliminary high heat flux density partition; an annular region surrounding this circular region with an inner diameter of 1.325 mm and an outer diameter of 2.25 mm is designated as a preliminary low heat flux density partition. The two partitions together cover the complete topology optimization design domain.

[0054] Furthermore, the topology design space of the initial partition is processed by independent design variable allocation to obtain the topology optimization design variables for each partition.

[0055] Specifically, the topology design spaces for the initial high heat flux density and low heat flux density zones are set as two independent optimization domains, and each optimization domain is assigned a dedicated topology optimization design variable. The design variable for each zone only controls the solid-liquid two-phase distribution state within the corresponding region. The design variables for the two zones do not interfere with each other and are updated independently, ensuring that independent topology optimization design can be performed for different heat flux regions.

[0056] Furthermore, the topology optimization design variables of each partition are correlated with corresponding optimization objectives to obtain high heat flux density optimization sub-regions and low heat flux density optimization sub-regions.

[0057] Specifically, the design variables for the initial high heat flux density partition are correlated with the optimization objective primarily focused on enhancing heat transfer, ensuring that the optimization process in this region prioritizes meeting the needs of locally enhanced heat transfer and suppressing hotspot temperatures. Similarly, the design variables for the initial low heat flux density partition are correlated with the optimization objective primarily focused on reducing flow resistance, ensuring that the optimization process in this region prioritizes meeting the needs of reducing channel resistance and system pump power. Simultaneously, the temperature uniformity optimization objective is correlated with the design variables for both partitions, ensuring that both partitions can consider the temperature uniformity requirements of the chip surface during the optimization process. Ultimately, this results in high heat flux density and low heat flux density optimization sub-regions with independent optimization capabilities.

[0058] This application's embodiments divide the chip into regions based on heat flux density distribution, accurately matching the different heat dissipation requirements of high and low heat flux areas. Independent topology design variables are assigned to each region, achieving decoupled optimization between the two regions without interference. The region variables are associated with corresponding optimization objectives, enabling targeted design of high and low heat flux areas. This design approach abandons the traditional "one-size-fits-all" approach, enhancing heat dissipation capabilities in hotspot areas and optimizing flow channel morphology in regular areas, laying the foundation for achieving uniform temperature across the entire region and performance zone adaptation from a design perspective.

[0059] In some embodiments, mathematical modeling and field quantity transformation are performed on the high heat flux density optimization sub-region and the low heat flux density optimization sub-region to obtain a microscale conjugate heat transfer topological mathematical model, including: defining topological variables and constructing a penalty mechanism for the solid-liquid two-phase heat transfer and flow processes corresponding to the high heat flux density optimization sub-region and the low heat flux density optimization sub-region respectively, to obtain the basic framework of topological mathematics.

[0060] Specifically, a density-based topology optimization algorithm is employed, defining a unified material density variable for each of the two optimization sub-regions. This variable ranges from zero to one. When the material density variable is zero, it indicates a solid region within a microchannel, where heat transfer is primarily conductive. Conversely, when the material density variable is one, it indicates a liquid cooling medium region, where heat transfer is primarily convection. A penalty coefficient is introduced to construct a material penalty mechanism, penalizing intermediate values ​​of the material density variable. This aims to make the optimization result tend towards a clear solid-liquid two-phase distribution, avoiding ambiguous transition regions, thus establishing the mathematical framework for topology optimization.

[0061] Furthermore, the solid-liquid two-phase distribution within the topological mathematical framework is transformed into a continuous field quantity to obtain a permeability field with a unified description.

[0062] Specifically, based on the material density variable and penalty mechanism defined above, the solid-liquid two-phase distribution state within the two optimization sub-regions is transformed into a continuously varying permeability field. The magnitude of the permeability field is directly related to the value of the material variable; the larger the material variable, the higher the permeability at the corresponding location, and the easier it is for the cooling fluid to pass through; the smaller the material variable, the lower the permeability at the corresponding location, and the more difficult it is for the cooling fluid to pass through. Through this transformation, the solid-liquid distribution problem is converted into a continuous field distribution problem, laying the foundation for subsequently constructing a unified governing equation.

[0063] Furthermore, the permeability field is processed by constructing conjugate heat transfer control equations to obtain a microscale conjugate heat transfer topological mathematical model.

[0064] Specifically, considering that the design domains of both optimization sub-regions are porous media containing both solid and liquid phases, they cannot be separated into individual heat conduction or heat convection problems for separate solutions. Therefore, based on the aforementioned continuous permeability field, a conjugate heat transfer control equation is established that can simultaneously and uniformly describe the convective heat transfer behavior in the fluid region and the heat conduction behavior in the solid region. This control equation comprehensively considers key parameters such as the temperature distribution within the design domain, the heat generation in the solid region, the velocity vector of the cooling fluid, the thermal conductivity of the solid and fluid materials, the density of the fluid, and its specific heat capacity. It can accurately simulate the coupled heat transfer and flow process of the solid and liquid phases within the microchannel, ultimately yielding a complete microscale conjugate heat transfer topological mathematical model.

[0065] This application's embodiments establish a mature mathematical framework for variable-density topology optimization by defining topological variables and constructing a penalty mechanism. This framework can clearly distinguish between solid and fluid regions within microchannels. It transforms the solid-liquid two-phase distribution into a continuous permeability field, simplifying the solution of complex two-phase problems and improving the algorithm's operational stability. By combining the permeability field to establish conjugate heat transfer control equations, it can simultaneously and accurately simulate the coupling process of solid heat conduction and fluid convection, highly restoring the actual heat transfer and flow patterns inside microchannels. This effectively improves the computational accuracy of the mathematical model and ensures that the topology optimization results closely match actual working conditions.

[0066] In some embodiments, the heat exchange, flow loss, and temperature uniformity corresponding to the high heat flux density optimization sub-region and the low heat flux density optimization sub-region are respectively subjected to multi-objective function construction and partitioned differential constraint processing to obtain a multi-objective optimization model, including: normalizing and weighting the heat exchange, flow loss, and temperature uniformity corresponding to the high heat flux density optimization sub-region and the low heat flux density optimization sub-region to obtain a general multi-objective function basic form.

[0067] Specifically, this application uses heat transfer, flow loss, and temperature difference as three major optimization indicators, corresponding to heat dissipation capacity, fluid flow resistance, and temperature uniformity, respectively. First, the values ​​of each indicator in the initial model are collected as baseline values. Then, the heat transfer, flow loss, and temperature difference indicators are normalized one by one to eliminate dimensional differences between different physical quantities. Next, a unified multi-objective function is constructed based on the normalized indicators. This basic form can simultaneously consider heat transfer performance, flow characteristics, and temperature distribution characteristics, and is applicable to two different optimization sub-regions.

[0068] Furthermore, the basic form of the general multi-objective function is subjected to partition-differentiated objective weight allocation processing to obtain partition objective functions adapted to different heat flux levels.

[0069] Specifically, for the high heat flux density optimization sub-region, enhancing heat transfer is the core requirement. Appropriate weights are assigned to heat transfer, temperature difference, and flow loss, focusing on improving local heat dissipation capacity while also considering temperature uniformity and flow conditions. For the low heat flux density optimization sub-region, reducing flow resistance is the core requirement. A higher weight is assigned to flow loss, and the weights for heat transfer and temperature difference are reasonably set. Both sub-regions retain weighted terms for temperature uniformity to ensure a consistent overall temperature distribution. This yields the partitioned objective functions adapted to the high and low heat flux regions, respectively.

[0070] Furthermore, the objective function of the partition is constrained by fluid volume fraction to obtain a multi-objective optimization model.

[0071] Specifically, fluid volume fraction constraints are set for the two sub-regions based on their optimization requirements. A higher fluid volume fraction is set for the high heat flux density optimization sub-region to increase coolant flow rate and flow disturbance, thereby enhancing heat transfer. A lower fluid volume fraction is set for the low heat flux density optimization sub-region to simplify the flow channel structure, reduce redundant branches and flow dead zones, and thus reduce overall flow resistance. The objective functions of each sub-region are combined with the corresponding fluid volume fraction constraints to form a complete multi-objective optimization model.

[0072] This application's embodiments eliminate dimensional differences between different optimization indices through normalized weighting processing, ensuring the rationality of the multi-objective function calculation logic. Differentiated objective weights are assigned to different zones, prioritizing heat transfer enhancement in high heat flux regions and reducing flow resistance in low heat flux regions, achieving zone-specific performance optimization. Combined with zone-specific fluid volume fraction constraints, the fluid ratio and flow channel layout of each region are further precisely controlled. The combination of these three elements achieves synergistic optimization of the three objectives of heat transfer capacity, flow loss, and temperature uniformity, making the multi-objective optimization model more adaptable to non-uniform heat flux conditions and providing stronger optimization guidance.

[0073] In some embodiments, the microscale conjugate heat transfer topology mathematical model and the multi-objective optimization model are independently iteratively solved and converged to obtain the optimal topology flow channel structure, including: performing partitioned independent iterative solution processing on the microscale conjugate heat transfer topology mathematical model and the multi-objective optimization model to obtain the intermediate topology flow channel structure of each sub-region.

[0074] Specifically, the finite element method combined with a variable density topology optimization algorithm is used to substitute the high heat flux density optimization sub-region and the low heat flux density optimization sub-region into the microscale conjugate heat transfer topology mathematical model and the corresponding multi-objective optimization model for calculation. The two sub-regions complete iterative calculations independently, and the topological variables within the region are continuously updated in each iteration, adjusting the solid-liquid distribution morphology based on the model calculation results. The high heat flux density optimization sub-region gradually evolves into a complex multi-branch flow channel morphology to enhance heat transfer, while the low heat flux density optimization sub-region gradually forms a simplified configuration to reduce flow resistance. After each round of calculation, the intermediate topology flow channel structure of the corresponding partition is output.

[0075] Furthermore, the intermediate topological flow channel structure of each sub-region is subjected to iterative convergence judgment processing to obtain the partitioned topological flow channel structure that meets the preset convergence conditions.

[0076] Specifically, this application sets two iterative convergence criteria: first, the change in the objective function value between two adjacent iterations is less than one part per million; second, the total number of iterations reaches three hundred. After generating each round of intermediate topological flow channel structure, the objective function value of the corresponding sub-region is extracted and compared with the result of the previous iteration. If the numerical change meets the convergence threshold requirement, or the number of iterations reaches the set upper limit, the iteration of that sub-region is determined to be complete; if the convergence condition is not met, the next round of iteration is continued until the convergence requirement is met, ultimately obtaining the partitioned topological flow channel structures corresponding to the two sub-regions that meet the convergence criteria.

[0077] Furthermore, the partitioned topology flow channel structure that meets the preset convergence conditions is subjected to structural shaping processing to obtain the optimal topology flow channel structure.

[0078] Specifically, the high heat flux density zone topology and low heat flux density zone topology and channel structures, after convergence, are uniformly regularized. This involves streamlining the channel direction, correcting local contours, and removing fragmented and invalid structures generated during the iteration process to ensure continuous and reasonable channel morphology. After regularization, the topology of the two zones is solidified, integrating the zone channel structures into a unified whole, ultimately yielding the complete optimal topology and channel structure.

[0079] This application's embodiments employ a partitioned independent iterative solution method, which has a clear computational logic and allows for targeted optimization of two sub-regions, improving iterative computation efficiency. Clear iterative convergence criteria are set to effectively control the optimization termination node, ensuring stable and reliable output results and avoiding problems such as infinite iteration or insufficient optimization. The converged partitioned structure undergoes a shaping process, eliminating invalid and fragmented structures generated during iteration, and standardizing the overall flow channel morphology to ensure continuity and rationality of the flow channel structure, ultimately obtaining a topological flow channel structure with optimal morphology and satisfactory performance.

[0080] In some embodiments, the optimal topological flow channel structure is spliced ​​and fused and verified by three-dimensional thermal-fluid coupling simulation to obtain the flow channel structure of the microchannel heat sink, including: performing partition splicing and fusion and smoothing post-processing on the optimal topological flow channel structure to obtain a two-dimensional microchannel flow channel structure.

[0081] Specifically, the optimal topological flow channel structure in the high heat flux density region and the optimal topological flow channel structure in the low heat flux density region, obtained after iterative convergence, are precisely aligned and fused to eliminate structural gaps and contour deviations at the junction of the two regions. Simultaneously, the overall flow channel structure undergoes smoothing optimization to remove fine burrs, sharp corners, and invalid fragmented structures generated during the topology optimization iteration process, thus standardizing the overall flow channel direction and ensuring continuous, smooth, and uniform transitions throughout the entire flow channel. This ultimately yields a complete and regularly shaped two-dimensional microchannel flow channel structure.

[0082] Furthermore, the two-dimensional microchannel flow channel structure is subjected to three-dimensional stretching and solid assembly to obtain a three-dimensional microchannel heat sink model.

[0083] Specifically, a 3D modeling approach is used to stretch the processed 2D microchannel flow structure to a uniform thickness, matching the preset microchannel layer thickness dimensions to generate a 3D microchannel fluid and solid base structure. Based on this, the heat sink substrate, microchannel layer, and outer shell are sequentially assembled, strictly matching the previously set thickness parameters of each structural layer and the overall assembly dimensions, thus recreating the complete solid assembly structure of the microchannel heat sink and obtaining a standardized 3D microchannel heat sink simulation model.

[0084] Furthermore, a three-dimensional thermal-fluid coupling simulation and performance verification process was performed on the three-dimensional microchannel heat sink model to obtain the flow channel structure of the microchannel heat sink.

[0085] Specifically, the completed 3D microchannel heat sink model was imported into fluid simulation software to re-verify the physical properties of the solid material and cooling fluid, and to set boundary conditions for the non-uniform heat flux load on the chip, the fluid inlet velocity boundary, and the outlet standard atmospheric pressure boundary. Three-dimensional thermal-fluid coupled multiphysics simulation calculations were conducted to solve and monitor core thermo-hydraulic performance parameters in real time, such as the highest chip surface temperature, the maximum temperature difference at the heat source surface, and the pressure drop at the inlet and outlet of the heat sink. The simulation results were used to verify whether the heat dissipation and flow performance of the topology-optimized channel met the design standards. After all performance indicators met the standards, the final microchannel heat sink channel structure was determined.

[0086] This application's embodiments eliminate joint gaps and sharp structures by splicing and smoothing the partitioned flow channels, avoiding local eddies and additional flow resistance, and optimizing fluid flow. The two-dimensional flow channels are expanded into a three-dimensional solid model and assembled, fully restoring the actual assembly structure of the radiator, ensuring that the designed structure can directly correspond to physical processing. Performance verification is conducted using three-dimensional thermal-fluid coupling simulation, allowing for comprehensive early detection of key indicators such as radiator heat dissipation capacity, pressure drop loss, and temperature distribution. Performance verification is completed during the design phase, significantly reducing the cost and risk of physical prototyping and repeated modifications, ensuring the design scheme has practical application value.

[0087] While this application provides the method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive labor. The order of steps listed in this embodiment is merely one possible execution order among many and does not represent the only execution order. In actual device or client product execution, the methods shown in this embodiment or the accompanying drawings can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment).

[0088] like Figure 3 As shown in the illustration, this application also provides a structural design device 300 for a microchannel heat sink. The device includes: The processing module 301 is used to perform geometric modeling and boundary condition setting on the chip heat source size, structural symmetry and flow inlet / outlet configuration to obtain the microchannel heat sink topology optimization basic model, which includes solid and fluid physical property parameters.

[0089] The partitioning module 302 is used to partition the microchannel radiator topology optimization basic model and heat flux density distribution characteristics into regions and assign design variables to obtain high heat flux density optimization sub-regions and low heat flux density optimization sub-regions.

[0090] The processing module 301 is also used to perform mathematical modeling and field quantity transformation processing on the high heat flux density optimization sub-region and the low heat flux density optimization sub-region to obtain a microscale conjugate heat transfer topology mathematical model.

[0091] The processing module 301 is also used to construct multi-objective functions and perform partitioned differential constraint processing for the heat exchange, flow loss and temperature uniformity corresponding to the high heat flux density optimization sub-region and the low heat flux density optimization sub-region, respectively, to obtain a multi-objective optimization model.

[0092] The processing module 301 is also used to independently iterate and converge to the microscale conjugate heat transfer topology mathematical model and the multi-objective optimization model to obtain the optimal topology flow channel structure.

[0093] Simulation module 303 is used to perform splicing and fusion of the optimal topology flow channel structure and three-dimensional thermal-fluid coupling simulation verification to obtain the flow channel structure of the microchannel heat sink.

[0094] Some modules in the apparatus described in this application can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, classes, etc., that perform a specific task or implement a specific abstract data type. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0095] The apparatus or module described in the above embodiments can be implemented by a computer chip or physical entity, or by a product with a certain function. For ease of description, the above apparatus is described by dividing it into various modules according to their functions. When implementing the embodiments of this application, the functions of each module can be implemented in one or more software and / or hardware. Of course, a module that implements a certain function can also be implemented by combining multiple sub-modules or sub-units.

[0096] The methods, apparatus, or modules described in this application can be implemented in a computer-readable program code manner. The controller can be implemented in any suitable manner, such as a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of a memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code manner, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included within it for implementing various functions can also be considered as structures within the hardware component. Alternatively, the device used to implement various functions can be viewed as either a software module that implements the method or a structure within a hardware component.

[0097] This application also provides an apparatus, the apparatus comprising: a processor; a memory for storing processor-executable instructions; wherein, when the processor executes the executable instructions, it implements the method described in this application.

[0098] This application also provides a non-volatile computer-readable storage medium storing a computer program or instructions thereon, which, when executed, enables the method described in this application embodiment to be implemented.

[0099] Furthermore, in the various embodiments of the present invention, each functional module can be integrated into a processing module, or each module can exist independently, or two or more modules can be integrated into a single module.

[0100] The aforementioned storage media include, but are not limited to, Random Access Memory (RAM), Read-Only Memory (ROM), Cache, Hard Disk Drive (HDD), or Memory Card. The memory can be used to store computer program instructions.

[0101] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary hardware. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, or it can be embodied in the process of data migration. The computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, mobile terminal, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0102] The various embodiments described in this specification are presented in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. All or part of this application can be used in numerous general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, mobile communication terminals, multiprocessor systems, microprocessor-based systems, programmable electronic devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices, etc.

[0103] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of this application.

Claims

1. A structural design method for a microchannel heat sink, characterized in that, include: Geometric modeling and boundary condition setting are performed on the chip heat source size, structural symmetry, and flow inlet / outlet configuration to obtain a basic model for microchannel heat sink topology optimization. The basic model for microchannel heat sink topology optimization includes solid and fluid physical property parameters. The microchannel radiator topology optimization basic model and heat flux density distribution characteristics are divided into regions and design variables are assigned to obtain high heat flux density optimization sub-regions and low heat flux density optimization sub-regions. Mathematical modeling and field quantity transformation are performed on the high heat flux density optimization sub-region and the low heat flux density optimization sub-region to obtain a microscale conjugate heat transfer topology mathematical model. The heat exchange, flow loss, and temperature uniformity corresponding to the high heat flux density optimization sub-region and the low heat flux density optimization sub-region are respectively subject to multi-objective function construction and partitioned differential constraint processing to obtain a multi-objective optimization model. The microscale conjugate heat transfer topology mathematical model and the multi-objective optimization model are independently iterated and converged to obtain the optimal topology flow channel structure. The optimal topology flow channel structure is spliced ​​and fused and verified by three-dimensional thermal-fluid coupling simulation to obtain the flow channel structure of the microchannel heat sink.

2. The method according to claim 1, characterized in that, The process of geometric modeling and boundary condition setting for the chip heat source size, structural symmetry, and flow inlet / outlet configuration yields a basic model for microchannel heat sink topology optimization. This basic model includes solid and fluid physical property parameters, including: The overall design domain is determined and symmetry simplification is performed on the chip heat source size and heat dissipation requirements to obtain a topology optimization calculation model; The topology optimization calculation model is processed by configuring the flow inlet and outlet and setting boundary conditions to obtain an initial model, which includes the flow boundary. The initial model is processed by defining solid and fluid physical property parameters to obtain the basic model for microchannel heat sink topology optimization.

3. The method according to claim 1, characterized in that, The process of dividing the microchannel radiator topology optimization model and heat flux density distribution characteristics into regions and assigning design variables yields high heat flux density optimization sub-regions and low heat flux density optimization sub-regions, including: The microchannel radiator topology optimization model and the heat flux density distribution characteristics are subjected to regional boundary delineation to obtain preliminary partitions corresponding to different heat flux levels. The topology design space of the initial partition is processed by independent design variable allocation to obtain the topology optimization design variables for each partition; The topology optimization design variables of each partition are correlated with corresponding optimization objectives to obtain high heat flux density optimization sub-regions and low heat flux density optimization sub-regions.

4. The method according to claim 1, characterized in that, The mathematical modeling and field transformation processing of the high heat flux density optimization sub-region and the low heat flux density optimization sub-region yields a microscale conjugate heat transfer topology mathematical model, including: Topological variables are defined and penalty mechanisms are constructed for the solid-liquid two-phase heat transfer and flow processes corresponding to the high heat flux density optimization sub-region and the low heat flux density optimization sub-region, respectively, to obtain the basic framework of topological mathematics. The solid-liquid two-phase distribution of the aforementioned topological mathematical framework is transformed into a continuous field quantity to obtain a permeability field with a unified description. The permeability field is processed by constructing conjugate heat transfer control equations to obtain a microscale conjugate heat transfer topological mathematical model.

5. The method according to claim 1, characterized in that, The heat transfer, flow loss, and temperature uniformity corresponding to the high heat flux density optimization sub-region and the low heat flux density optimization sub-region are respectively subjected to multi-objective function construction and partitioned differential constraint processing to obtain a multi-objective optimization model, including: The heat exchange, flow loss, and temperature uniformity corresponding to the high heat flux density optimization sub-region and the low heat flux density optimization sub-region are normalized and weighted to obtain the basic form of the general multi-objective function. The general multi-objective function is subjected to partition-differentiated objective weight allocation processing to obtain partition objective functions adapted to different heat flux levels; The objective function of the partition is constrained by fluid volume fraction to obtain a multi-objective optimization model.

6. The method according to claim 1, characterized in that, The process of independently iterating and convergent-juxtaposing the microscale conjugate heat transfer topology mathematical model and the multi-objective optimization model to obtain the optimal topology flow channel structure includes: The microscale conjugate heat transfer topology mathematical model and the multi-objective optimization model are solved by partitioned independent iterative solution to obtain the intermediate topology flow channel structure of each sub-region. The intermediate topological flow channel structure of each sub-region is subjected to iterative convergence judgment processing to obtain the partitioned topological flow channel structure that meets the preset convergence conditions; The partitioned topology flow channel structure that meets the preset convergence conditions is subjected to structural shaping processing to obtain the optimal topology flow channel structure.

7. The method according to claim 1, characterized in that, The optimal topological flow channel structure is spliced ​​and fused, and then subjected to three-dimensional thermal-fluid coupling simulation verification to obtain the flow channel structure of the microchannel heat sink, including: The optimal topology flow channel structure is partitioned, spliced, fused, and smoothed to obtain a two-dimensional microchannel flow channel structure. The two-dimensional microchannel flow channel structure is subjected to three-dimensional stretching and solid assembly to obtain a three-dimensional microchannel heat sink model. The three-dimensional microchannel heat sink model was subjected to three-dimensional thermal-fluid coupling simulation and performance verification to obtain the flow channel structure of the microchannel heat sink.

8. A structural design device for a microchannel heat sink, characterized in that, include: The processing module is used to perform geometric modeling and boundary condition setting on the chip heat source size, structural symmetry, and flow inlet / outlet configuration to obtain a basic model for microchannel heat sink topology optimization. The basic model for microchannel heat sink topology optimization includes solid and fluid physical property parameters. The partitioning module is used to perform regional partitioning and design variable allocation processing on the microchannel radiator topology optimization basic model and heat flux density distribution characteristics to obtain high heat flux density optimization sub-regions and low heat flux density optimization sub-regions. The processing module is also used to perform mathematical modeling and field quantity transformation processing on the high heat flux density optimization sub-region and the low heat flux density optimization sub-region to obtain a microscale conjugate heat transfer topology mathematical model. The processing module is also used to construct multi-objective functions and perform partitioned differential constraint processing on the heat exchange, flow loss and temperature uniformity corresponding to the high heat flux density optimization sub-region and the low heat flux density optimization sub-region, respectively, to obtain a multi-objective optimization model; The processing module is also used to perform independent iterative solution and convergence judgment on the microscale conjugate heat transfer topology mathematical model and the multi-objective optimization model to obtain the optimal topology flow channel structure. The simulation module is used to perform splicing and fusion and three-dimensional thermal-fluid coupling simulation verification on the optimal topology flow channel structure to obtain the flow channel structure of the microchannel heat sink.

9. A computer device comprising a memory and a processor, the memory storing a computer program executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 7.

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