Integrated coupling topological structure design method based on phase change material and fluid cooling

Abstract

The invention discloses an integrated coupling topological structure design method based on a phase change material and fluid cooling, and relates to the technical field of thermal management. Inputting the non-uniform heat flux density distribution of the chip into a neural network prediction model to obtain initial topological cooling structure distribution of the phase change material and the fluid on the chip; the training sample set of the neural network prediction model is calculated according to a heat transfer control equation of phase change material and fluid coupling; the phase change material and fluid coupled heat transfer control equation is constructed by introducing a phase change energy storage function into a fluid momentum conservation equation, and is used for realizing embedded collaborative layout of a phase change material area and a fluid cooling channel; the phase change energy storage function represents the energy storage and release characteristics of the phase change material; and performing iterative optimization on the initial cooling structure to obtain target topological cooling structure distribution of the phase change material and the fluid on the chip. According to the method, the phase change material and fluid cooling integrated topological structure is optimized, so that the heat dissipation capability of the chip is improved.

Description

Technical Field

[0001] The present invention relates to the field of thermal management technology, and in particular to a design method for an integrated coupling topology structure based on phase change material and fluid cooling. Background Art

[0002] At present, with the rapid development of advanced science and technology in the fields of artificial intelligence and aerospace, such as high-performance computing chips, power modules, lasers, spacecraft thermal control systems, and high-temperature components of aircraft engines, the heat flux density per unit area continues to rise, usually reaching hundreds of W / cm2 or even higher. As the thermal load of the system continues to increase, if the accumulated heat cannot be transferred and released in a timely and efficient manner, it is very likely to cause performance degradation, device damage, and even systemic safety issues. Therefore, how to achieve efficient heat dissipation and controllable thermal management solutions within a confined structural space has become a key problem that urgently needs to be broken through in the current fields of energy, chemical engineering, and engineering thermophysics.

[0003] Existing technologies, such as chips, typically utilize phase change materials (PCMs) and fluids for cooling. PCMs are more suitable for instantaneous heat absorption and buffering, while fluid cooling systems play a key role in long-range heat transport and maintaining system thermal stability. The two complement each other and offer the potential for synergistic benefits in practical thermal management applications. Therefore, achieving structural integration and optimized coupling of these two within confined spaces has become a key technical direction for improving chip heat dissipation capabilities. Summary of the Invention

[0004] Based on this, it is necessary to provide a design method for an integrated coupling topology structure based on phase change material and fluid cooling to address the above technical problems. This method improves the heat dissipation capacity of the chip by optimizing the integrated topology structure of phase change material and fluid cooling.

[0005] The present invention adopts the following technical solutions: The present invention provides a design method for an integrated coupled topological structure based on phase change material and fluid cooling, comprising: The non-uniform heat flux density distribution of the chip is input into a neural network prediction model to obtain the initial topological cooling structure distribution of the phase change material and fluid on the chip. The training sample set of the neural network prediction model is calculated based on the heat transfer control equation for the coupled phase change material and fluid. The heat transfer control equation for the coupled phase change material and fluid is constructed by introducing the phase change energy storage function into the fluid momentum conservation equation to achieve an embedded coordinated layout of the phase change material area and the fluid cooling channel. The phase change energy storage function represents the energy storage and release characteristics of the phase change material. The initial topological cooling structure distribution is used as the initial population of the multi-objective genetic algorithm. Based on a pre-constructed multi-objective function, the algorithm iteratively optimizes the initial cooling structure to obtain the optimal solution. The multi-objective function is constructed by comprehensively considering thermal management performance, structural manufacturability, and fluid dynamics performance. The optimal solution is determined as the target topological cooling structure distribution of phase change material and fluid on the chip.

[0006] Optionally, the training sample set of the neural network prediction model includes a plurality of sample non-uniform heat flux density distributions of the sample chip and corresponding sample topological cooling structure distributions; the process of acquiring the training sample set of the neural network prediction model includes: For any sample with non-uniform heat flux density distribution, with the goal of the most uniform temperature distribution and the fastest thermal response, the heat transfer control equation of the phase change material and fluid coupling is iteratively solved through the sample's non-uniform heat flux density distribution to obtain the sample's topological cooling structure distribution.

[0007] Optionally, the heat transfer governing equation for the coupling of the phase change material and the fluid is: ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; in, is the Hamiltonian operator, is the velocity field vector, is the fluid density, is the spatial gradient of velocity, is the velocity field vector About time The derivative of For pressure The gradient, is the fluid dynamic viscosity, is the Darcy number of the fluid, is the thickness of the channel layer, is the design variable, represents the phase change material domain, represents the fluid domain, is the interpolation coefficient of α, is the temperature of the fluid, Temperature The time derivative, The spatial gradient of temperature, is the chip temperature, is the phase change interpolation function, is the heat flux density of the phase change material, is the phase change energy storage function, is the solid density of the phase change material, is the fluid density of the phase change material, is the melting point of the phase change material, is the specific heat capacity of the phase change material in solid state, is the constant pressure specific heat capacity when the phase change material changes to liquid state, is the solid density, is the specific heat capacity of the solid, is the solid thermal conductivity, is the thermal conductivity of the fluid, is the ratio of the thermal conductivity of the fluid to the thermal conductivity of the solid, is the convection heat transfer coefficient between the fluid and the chip layer in the Pseudo model, is the convection heat transfer coefficient between the solid and chip layers in the Pseudo model, , They are The interpolation coefficients, is the spatial gradient of chip layer temperature, is the chip layer temperature time change rate, is the heat flux density, is the thickness of the chip layer; The objective function of the heat transfer control equation for the coupling of phase change material and fluid is: ; in, and is the weight coefficient, max( T ) is the maximum temperature, Var( T ) is the temperature-time variation, X is the maximum integral value of the design variable.

[0008] Optionally, constraints in solving the heat transfer control equations for coupling the phase change material and the fluid include minimum radius constraints, structural boundary constraints, equivalent diameter constraints, connectivity constraints, phase change material encapsulation constraints, and thermal stress stability constraints; The minimum radius constraint is: ; in, is the minimum radius, After the minimum radius constraint field; The structural boundary constraints are: ; in, After projection field, is the threshold parameter, Control the steepness of the projection; The equivalent diameter constraint is: ; in, is the equivalent diameter of the local liquid area, is the local cross-sectional area, is the perimeter, is the minimum equivalent diameter, is the minimum diameter; The connectivity constraints are: ; in, is the connectivity function, is the export quantity, is the total volume flow rate at the inlet, For the The volume flow rate of each outlet; The encapsulation constraints of phase change materials are: If the region is a phase change material and the distance between the phase change material and the interface between the fluid channel is less than a boundary threshold, the region is determined to be a solid material; The thermal stress stability constraint is: ; when , build a thickening layer; in, is the thermal stress estimation function, is the Young's modulus of the phase change material, is the linear expansion coefficient of the phase change material, is the critical value of a given thickness.

[0009] Optionally, the loss function used in the neural network prediction model training process is: ; Wherein, is a loss value, represents the summation calculation of all points in a two-dimensional space, represents the position coordinates in the two-dimensional space, is a predicted topological cooling structure distribution, is a real topological cooling structure distribution, is a weight factor, is the gradient of the predicted topological cooling structure distribution.

[0010] Optionally, each optimization process of the initial cooling structure by the multi-objective genetic algorithm specifically includes: Perform fluid dynamics simulation on the initial cooling structure to obtain the temperature field and pressure field corresponding to the initial cooling structure; According to the temperature field and pressure field corresponding to the initial cooling structure, the multi-objective function value is calculated, and the fitness value is calculated according to the multi-objective function value; Update the initial cooling structure through the fitness value, and take the updated initial cooling structure as the initial cooling structure of the next iteration.

[0011] Optionally, the multi-objective function is: ; Wherein, , and represent the first objective function, the second objective function and the third objective function respectively, is a temperature field, is a temperature field gradient, is a pressure field gradient.

[0012] The application provides a design device for a coupling topological structure based on phase change material and fluid cooling integration, comprising: An initialization module is configured to input the non-uniform heat flux density distribution of a chip to a neural network prediction model to obtain an initial topological cooling structure distribution of the phase change material and the fluid on the chip; the training sample set of the neural network prediction model is calculated according to a heat transfer control equation of the phase change material and the fluid coupling; the heat transfer control equation of the phase change material and the fluid coupling is constructed by introducing a phase change energy storage function into a fluid momentum conservation equation, and is used to realize the embedded collaborative layout of the phase change material region and the fluid cooling channel; the phase change energy storage function represents the energy storage and release characteristics of the phase change material; An optimization module is configured to take the initial topology cooling structure distribution as an initial population of a multi-objective genetic algorithm, and perform iterative optimization on the initial cooling structure according to a pre-constructed multi-objective function by using the multi-objective genetic algorithm to obtain an optimal solution; the multi-objective function is constructed by comprehensively considering thermal management performance, structure manufacturability and fluid dynamics performance; A determination module is configured to determine the optimal solution as a target topology cooling structure distribution of the phase change material and the fluid on the chip.

[0013] The application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the phase change material and fluid cooling integrated coupling topology structure design method.

[0014] The application provides a computer device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the phase change material and fluid cooling integrated coupling topology structure design method when executing the program.

[0015] The above at least one technical scheme adopted by the application can achieve the following beneficial effects: In the application, the phase change energy storage function is embedded into the fluid momentum conservation equation, so that the constructed heat transfer control equation can accurately depict the coupling heat dissipation mechanism of "phase change material heat absorption / heat release + fluid flow transport", compared with a traditional pure fluid model, the synergistic effect of "phase change material storing heat through phase change latent heat" and "fluid flow quickly conducting heat" can be described at the same time, so that the heat transfer control equation is closer to the real heat dissipation physical process, thereby ensuring the prediction accuracy of the neural network prediction model, then the initial topology cooling structure distribution is predicted through the neural network model, and the non-uniform heat flux density distribution is converted into a reasonable initial topology structure output; then, the multi-objective function constructed by comprehensively considering thermal management performance, structure manufacturability and fluid dynamics performance can avoid the situation that a single pursuit of heat dissipation performance leads to a structure that cannot be manufactured or fluid flow failure, that is, the initial topology structure output by the neural network prediction model is taken as a population starting point, and iterative optimization is performed on the initial topology structure by using the multi-objective genetic algorithm, so that topologies with poor heat dissipation, difficult manufacturing and large flow resistance can be eliminated, and a more adaptive target topology cooling structure distribution is obtained, thereby improving the heat dissipation capacity of the chip. BRIEF DESCRIPTION OF DRAWINGS

[0016] The accompanying drawings, which are included to provide a further understanding of the application and constitute a part of this application, illustrate certain illustrative embodiments of the application and together with the description serve to explain the application. In the drawings:

[0017] Figure 1 A phase change material and fluid cooling integrated coupling topology structure design method provided by the application is shown in the flowchart. Figure 2 A schematic diagram of a chip structure provided by the present application is shown in FIG. 1. Figure 3 A schematic diagram of a topology cooling structure distribution after topology optimization by a heat transfer control equation is provided by the present application is shown in FIG. 2. Figure 4 A schematic diagram of a topology cooling structure distribution after optimization of manufacturing and assembly constraints is provided by the present application is shown in FIG. 3. Figure 5 Another schematic diagram of a design method process of an integrated coupling topology structure based on phase change material and fluid cooling provided by the present application is shown in FIG. 4. Figure 6 A schematic diagram of a computer device for implementing the design method of an integrated coupling topology structure based on phase change material and fluid cooling provided by the present application is shown in FIG. 5. Explanation of reference signs: 1, chip layer; 2, channel layer; 3, fluid channel after topology optimization; 4, solid layer after topology optimization; 5, phase change material after topology optimization; 6, fluid channel after optimization of manufacturing and assembly constraints; 7, solid layer after optimization of manufacturing and assembly constraints; 8, phase change material after optimization of manufacturing and assembly constraints. DETAILED DESCRIPTION

[0018] To make the purpose, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described clearly and completely below in combination with specific embodiments of the present application and corresponding drawings. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0019] In the prior art, traditional cooling systems mostly rely on a single mechanism, such as forced convection liquid cooling, natural convection, heat pipe heat dissipation, etc. These methods have good heat dissipation performance under medium heat flux density, but when facing complex structures, space limitations or high transient thermal shock scenarios, they have certain limitations in response speed, thermal control accuracy and local adaptability. Therefore, in recent years, the field of thermal management has introduced phase change materials and topology optimization design methods, in order to achieve better cooling performance through multi-physical mechanism coupling and system-level integrated design.

[0020] Phase change materials (PCMs) are a class of functional materials that can achieve thermal buffering and temperature regulation by absorbing and releasing latent heat. They have excellent transient thermal response characteristics. During the heat absorption process, a large amount of heat can be stored at almost constant temperature, effectively slowing down the temperature rise of the system and delaying the conduction of heat to the key devices. Therefore, PCMs are widely used in heat storage units and passive cooling structures. Although PCMs have unique advantages in transient thermal management, they are essentially a means of temporarily storing heat. When the heat source continues to work or the ambient temperature rises for a long time, the heat storage capacity of PCMs will gradually saturate, and if there is no subsequent heat release channel, the system will still face the problem of heat accumulation. In contrast, fluid cooling is an active cooling method that can continuously remove heat from the heat source area by flowing cooling medium during device operation, ensuring that the system is in a relatively stable thermal equilibrium state. For example, by optimizing the flow channel structure, adjusting the flow rate, and adding turbulence structures, the heat transfer efficiency can be effectively improved. Especially in micro-channel cooling schemes, with the high specific surface area characteristics, higher convective heat transfer performance can be achieved in limited space. In general, PCMs are more suitable for transient heat absorption and buffering regulation functions, while fluid cooling systems play a key role in long-distance heat transport and system thermal stability maintenance. Both functions complement each other and have the potential for synergistic effect in actual thermal management applications. Therefore, how to realize the structural integration and coupling optimization of the two in limited space has become an important technical direction to improve the heat dissipation capacity of high heat load systems.

[0021] In one way, a battery thermal management device coupling composite phase change material plates with liquid cooling systems is proposed. The device adopts an interlaced structure design of liquid cooling plates and composite phase change material plates, and each composite phase change material plate is tightly attached to the two sides of the battery monomer. The material plate is embedded with high thermal conductivity cooling fins, and part of the structure extends into the adjacent liquid cooling channel and directly contacts with the cooling liquid, significantly improving the overall heat dissipation capacity of the system and improving the temperature consistency of each battery monomer.

[0022] In another way, a battery thermal management device coupling liquid cooling and composite phase change materials is proposed. The device uses silicon carbide high thermal conductivity material to build a heat conduction channel, effectively cutting off the direct heat transfer path between the high temperature heat source and the battery under extreme working conditions to achieve heat source isolation, improving the operation safety of the battery in high temperature environment. At the same time, combining the continuous heat dissipation capacity of the liquid cooling system and the temperature buffering characteristics of the phase change material, the device effectively overcomes the problems of complex structure, large mass, large space occupation and strong dependence on pipeline and pump of traditional liquid cooling systems, while meeting the thermal management performance, improving the integration and adaptability of the system, especially suitable for weight and volume sensitive application scenarios.

[0023] However, the existing phase change material thermal management structure is mostly filled or attached, the structure is relatively simple, there is a lack of effective deep coupling between the liquid cooling system, and the overall integration is low. At the same time, the phase change material and the liquid cooling channel are often designed independently, and it is difficult to realize the cooperative optimization of the heat path at the structure level, which limits the overall performance of the thermal management system. In order to improve the comprehensive performance of the thermal management system, in recent years, researchers have begun to explore the liquid cooling structure design method based on topology optimization, and through numerical optimization means, the fluid channel and the heat exchange interface are reasonably arranged in the specified design space, so as to improve the heat flow regulation ability of the system. In the prior art, an optimization method and system of a liquid cooling plate-phase change material coupled battery thermal management system based on topology optimization are provided. The method first establishes a two-dimensional topology optimization model of the liquid cooling plate, and obtains the optimal structure thereof through a mathematical optimization algorithm; then, combined with the composite battery thermal management system model, multi-objective cooperative optimization is carried out, and finally the integrated design of the liquid cooling plate structure and the phase change material region is realized, thereby improving the overall heat dissipation performance and temperature uniformity of the system. However, the topology optimization method in the research still takes the fluid-solid heat exchange structure in the liquid cooling plate itself as the research object, and lacks integrated consideration of the introduction of the phase change material, which is difficult to fully play the advantage of the phase change material in dynamic heat buffering, and also fails to solve the problem of efficient cooperation between the phase change material and the fluid cooling.

[0024] In the current high heat flux density application scenario, the traditional PCM and fluid cooling combined structure mostly adopts a filling or juxtaposition layout, the structure integration degree is low, the space utilization efficiency is poor, and it is difficult to realize compact design and continuous efficient heat control. Due to the limited thermal conductivity of PCM, heat retention is easy to form under steady-state conditions, and although fluid cooling has superior heat exchange capacity, it needs a complex channel network and a pump control system, which is difficult to be flexibly embedded in a compact structure. The existing design method lacks the ability of cooperative layout and optimization of PCM and single / multi-phase flow channels, and the functional partition often relies on experience setting, resulting in insufficient coupling of the heat path, limited overall heat transfer performance, poor manufacturing process adaptability, and weak assembly, which limits its engineering landing and large-scale application. At the same time, the existing structure optimization strategy is mostly parameterized design, which is difficult to balance multiple performance and design efficiency, and in the topology optimization design, only the fluid cooling is considered, and the coupled design of the PCM characteristics is lacked.

[0025] Therefore, it is urgent to develop a design method of an integrated coupled topology structure based on phase change material and fluid cooling, which can realize the synchronous optimization and configuration of the solid phase change domain and the fluid cooling domain based on the full consideration of the multi-physical field coupling characteristics, the thermal management function demand and the space integration degree, and further improve the overall performance and practicability of the thermal control system. This method is of great significance to promote the development of high-power-density device thermal management technology and improve the thermal design level in the fields of aviation, aerospace and electronic packaging.

[0026] The technical solutions provided by the embodiments of the present application will be described in detail below with reference to the drawings.

[0027] Figure 1 A flow chart of a design method for a phase change material and fluid cooling integrated coupling topology in the application, specifically comprising the following steps: S101, input the non-uniform heat flux density distribution of the chip to the neural network prediction model to obtain the initial topology cooling structure distribution of the phase change material and the fluid on the chip; the training sample set of the neural network prediction model is calculated according to the heat transfer control equation of the phase change material and fluid coupling; the heat transfer control equation of the phase change material and fluid coupling is constructed by introducing the phase change energy storage function into the fluid momentum conservation equation, which is used to realize the embedded collaborative layout of the phase change material region and the fluid cooling channel; the phase change energy storage function represents the energy storage and release characteristics of the phase change material.

[0028] For some high-power-density chips, there is a significant non-uniform heat flow distribution on the surface, with a heat flux peak of 250 kW / m2, gradually changing to 50 kW / m2 towards the edge area, and a local hot spot size of about 2 mm × 2 mm, with an overall chip size of 20 mm × 20 mm × 1 mm, as shown in Figure 2 The conventional liquid cooling structure has limited instantaneous cooling effect in the hot spot area, and it is difficult to achieve efficient uniform temperature control. The topology optimization coupling thermal management unit designed in the application embeds phase change material and fluid cooling channels, uses a pseudo-3D model, and through structural collaborative optimization, considers the heat storage capacity of the phase change material and the dynamic heat exchange capacity of the fluid cooling, improves the thermal regulation performance. Optionally, the heat transfer control equation of the phase change material and fluid coupling is:

[0029] (1); (2); (3); (4); (5); (6); (7); (8); (9); (10); (11); (12); (13); (14); (15); (16); (17); in, is the Hamiltonian operator, is the velocity field vector, in m / s, is the fluid density in kg / m 3 , is the spatial gradient of velocity, is the velocity field vector About time The derivative of The unit is s, For pressure The gradient, The unit is Pa, is the dynamic viscosity of the fluid, in Pa∙s, is the Darcy number of the fluid, is the thickness of the channel layer, is the design variable, represents the phase change material domain, represents the fluid domain, is the interpolation coefficient of α, is the temperature of the fluid in K, Temperature The time derivative, is the spatial gradient of temperature, is the chip temperature in K, is the phase change interpolation function, is the heat flux density of the phase change material, in W / m 2 , is the phase change energy storage function, unit is W, is the solid density of the phase change material, is the fluid density of the phase change material, in kg / m 3 , is the melting point of the phase change material, in K, is the specific heat capacity of the phase change material in solid state, in J / (kg∙K), is the constant-pressure specific heat capacity of the phase change material when it changes to liquid state, in J / (kg∙K), is the solid density in kg / m 3 , is the specific heat capacity of the solid, is the thermal conductivity of the solid, in W / (m∙K), is the thermal conductivity of the fluid, with the unit of W / (m·K), is the ratio of the thermal conductivity of the fluid and the thermal conductivity of the solid, is the convective heat transfer coefficient between the fluid and the chip layer in the Pseudo model, is the convective heat transfer coefficient between the solid and the chip layer in the Pseudo model, , are the interpolation coefficients of the temperature distribution of the chip layer, is the spatial gradient of the temperature of the chip layer, is the time rate of change of the temperature of the chip layer, is the heat flux, is the thickness of the chip layer.

[0030] As shown in Table 1, part of the symbols in the above formula have the meanings shown in Table 1.

[0031] Table 1 Parameter variable setting In the variable symbols of the above formula, the subscript channel represents the channel layer (as shown in 1 in the foregoing), the subscript chip represents the lower chip layer in the pseudo-3D model (as shown in 2 in the foregoing), and the subscript PC represents the phase change material (PCM). The design variable θ∈[0,1] controls the material distribution, 0 represents the solid domain, and 1 represents the fluid domain. Figure 2 Figure 2 With the most uniform temperature distribution and the fastest thermal response as the objective function, the effective heat capacity method with phase change behavior is introduced, and the nonlinear coupling of fluid heat exchange, heat conduction and material is considered to determine the objective function of the heat transfer control equation of the phase change material and the fluid coupling. The objective function of the heat transfer control equation of the phase change material and the fluid coupling is:

[0032] (18); wherein, and are weight coefficients, max( ) is the highest temperature, Var( T ) is the temperature time change amount, which represents the temperature change when a local hot spot appears, T is the integral maximum value of the design variable, that is, the volume constraint of the solid domain and the fluid domain. X Overall, by introducing the phase change energy storage function, as shown in

[0033] Figure 3 Figure 3 ​​​​A schematic diagram of the topology-optimized topology cooling structure distribution through the heat transfer control equation, specifically including: the topology-optimized fluid channel 3, the topology-optimized solid layer 4, and the topology-optimized phase change material 5; the structure distribution realizes the collaborative optimization arrangement of the fluid channel (such as shown in 3) and the PCM material (such as shown in 5) under the topology optimization framework, and the solid layer between the two plays a role of isolation, sealing, and certain support (such as shown in 4), and it should be noted that the above formula only represents single-phase fluid cooling coupling, and the present application also includes but is not limited to multi-phase fluid, phase change fluid, and other fluid types and PCM material coupling design. Figure 3 Figure 3 Figure 3

[0034] Optionally, in order to improve the manufacturing feasibility and assembly reliability of the embedded cooling structure in actual engineering, the present application also provides a manufacturing and assembly constraint optimization method for process-function balance, specifically, the constraint conditions of the heat transfer control equation of the phase change material and fluid coupling in the solving process include minimum radius constraint, structure boundary constraint, equivalent diameter constraint, connectivity constraint, phase change material packaging constraint, and thermal stress stability constraint.

[0035] wherein the minimum radius constraint is: (19); wherein, is the minimum radius, is the field after the minimum radius constraint.

[0036] The double-threshold projection strategy of the Heaviside function is introduced in the variable density projection stage, which ensures that the optimization result boundary is clear and has actual processability, therefore, the structure boundary constraint is: (20); wherein, is the projected field, is a threshold parameter, controlling the minimum feature size, controls the projection steepness.

[0037] The minimum feature size filter (Minimum length scale filter) is applied in cooperation with the convolution filter, and the filter radius value is set to rmin = 0.5 mm, which corresponds to the process resolution of micro-milling or lithography in the manufacturing process.

[0038] The equivalent diameter constraint is: (21); wherein, is the equivalent diameter of the local liquid region,​​​ is the local cross-sectional area, is the perimeter, is the minimum equivalent diameter, is the minimum hydraulic diameter; can be taken as 0.5 mm, avoiding the formation of a small blocked path, ensuring that the fluid can effectively pass through the structure.

[0039] In the design of fluid channels, combined with connectivity analysis and minimum hydraulic diameter constraints, avoid dead zones and heat accumulation. The connectivity constraint is: i

[0040] (22); wherein, is the connectivity function, is the number of outlets, is the total volumetric flow rate of the inlets, is the volumetric flow rate of the i-th outlet, if there is an isolated dead zone in the structure, Q i → 0, resulting in an increase in the connectivity constraint term, and the connectivity constraint is the fluid channel connectivity penalty function.

[0041] In the design of phase change material regions, considering the packaging requirements and thermal-mechanical boundary coupling characteristics in actual applications, an automatic shell construction function module is introduced in the topology optimization process, and a PCM encapsulation region automatic identification mechanism is introduced. Define the PCM region indicator function χPC(x)ϵ[0, 1], when the region is inside the PCM and the distance between the PCM and the fluid channel interface is less than dshell = 0.5 mm, the region is set to solid material, thereby ensuring the sealing of the PCM material and constructing the encapsulation shell layer. Therefore, the phase change material packaging constraint is: when the region is a phase change material and the distance between the phase change material and the fluid channel interface is less than the boundary threshold (0.5 mm), determine the region as a solid material.

[0042] For the thermal stress concentration region that may appear in the structure, a local thickening and support feature generation strategy is introduced, and the thermal stress stability constraint is: (23); wherein, is the thermal stress estimation function, is the Young's modulus of the phase change material, is the linear expansion coefficient of the phase change material, is the given thickness threshold value. When , a thickening layer is constructed to enhance the overall mechanical stability and thermal cycle resistance of the structure, ensuring that the final structure has excellent thermal management performance and meets the actual manufacturing and assembly constraint conditions. As shown in Figure 4 Figure 4 ​A manufacturing and assembly constraint optimized topological cooling structure distribution schematic diagram is provided, and specifically comprises: a manufacturing and assembly constraint optimized fluid channel 6, a manufacturing and assembly constraint optimized solid layer 7 and a manufacturing and assembly constraint optimized phase change material 8.

[0043] In view of the problems of high calculation cost and slow convergence speed of topological optimization in high-dimensional complex space, the application further provides an intelligent optimization and rapid generation mechanism of coupled cooling structure by fusing artificial intelligence. First, a plurality of sample non-uniform heat flux density distributions and corresponding sample topological cooling structure distributions are obtained. A neural network prediction model is trained based on the plurality of sample non-uniform heat flux density distributions and the corresponding sample topological cooling structure distributions. That is, the training sample set of the neural network prediction model includes a plurality of sample non-uniform heat flux density distributions and corresponding sample topological cooling structure distributions of a sample chip. The process of obtaining the training sample set of the neural network prediction model includes: for any sample non-uniform heat flux density distribution, the sample non-uniform heat flux density distribution is used to iteratively solve the heat transfer control equation of the phase change material and the fluid coupling, so as to obtain the sample topological cooling structure distribution, and the constraint condition is used to constrain the iterative solving process of the heat transfer control equation.

[0044] The plurality of sample non-uniform heat flux density distributions are a plurality of typical heat flux density distribution samples.

[0045] The sample non-uniform heat flux density distribution is used to iteratively solve the heat transfer control equation of the phase change material and the fluid coupling, so as to obtain the sample topological cooling structure distribution, including: in each iteration process, the sample non-uniform heat flux density distribution is used to solve the heat transfer control equation of the phase change material and the fluid coupling, to obtain the design variable field, then the design variable field is corrected according to the constraint condition, and the objective function value of the heat transfer control equation of the phase change material and the fluid coupling is calculated according to the design variable field after correction, and the design variable field is optimized according to the objective function value, and the next iteration is performed until the iteration convergence condition is met.

[0046] The iteration convergence condition can include that the number of iterations reaches a preset maximum number of iterations.

[0047] The neural network prediction model adopts the encoding-decoding architecture of U-Net, realizes the rapid prediction from the non-uniform heat flux input to the cooling structure initial value, greatly shortens the initialization stage required by the traditional topological optimization, and the training sample set is: (24); Wherein, The number of samples, The sample non-uniform heat flux density distribution of the first The sample topology cooling structure distribution of the first The sample non-uniform heat flux density distribution of the first The sample topology cooling structure distribution of the first. The input of the neural network prediction model is a two-dimensional non-uniform heat flux density distribution q x , and the output is an initial cooling structure distribution θ init x

[0048] In one embodiment, the loss function used in the training process of the neural network prediction model is: (25); Wherein, is the loss value, indicates the summation calculation of all points in the two-dimensional space, indicates the position coordinates in the two-dimensional space, is the predicted topology cooling structure distribution, is the true topology cooling structure distribution, is the weight factor, is the gradient of the predicted topology cooling structure distribution.

[0049] S102, the initial topology cooling structure distribution is taken as the initial population of the multi-objective genetic algorithm, and the initial cooling structure is iteratively optimized by the multi-objective genetic algorithm according to the pre-constructed multi-objective function, to obtain the optimal solution; the multi-objective function is constructed by comprehensively considering the thermal management performance, the structure manufacturability and the fluid dynamics performance.

[0050] The initial topology cooling structure distribution is taken as the initial population of the multi-objective genetic algorithm, the iteration time of the multi-objective genetic algorithm is shortened, the initial population can provide a high-quality search starting point for subsequent multi-objective evolutionary optimization, and the global search efficiency and the convergence quality of the solution are improved.

[0051] In the evolutionary optimization stage, the multi-objective genetic algorithm (MOGA) is introduced, the thermal management performance (temperature peak, heat flux uniformity), the structure manufacturability (machinability, integrity, sealing performance) and the fluid dynamics performance (flow resistance) are comprehensively considered, and the multi-objective collaborative optimization is realized.

[0052] ​​​Specifically, each optimization process of the multi-objective genetic algorithm on the initial cooling structure specifically includes: performing fluid dynamics simulation through the initial cooling structure to obtain a temperature field and a pressure field corresponding to the initial cooling structure; calculating a multi-objective function value according to the temperature field and the pressure field corresponding to the initial cooling structure, and calculating a fitness value according to the multi-objective function value; updating the initial cooling structure through the fitness value, and taking the updated initial cooling structure as the initial cooling structure of the next iteration.

[0053] In one embodiment, the multi-objective function is: (26); wherein, , and represent the first objective function, the second objective function and the third objective function respectively, is a temperature field, is a temperature field gradient, is a pressure field gradient.

[0054] The values of the first objective function, the second objective function and the third objective function can be weighted, and then the weighted values are taken as the fitness value. It should be noted that the weights of the objective functions can be set according to actual needs.

[0055] The multi-objective genetic algorithm calculates each generation first through fluid mechanics CFD simulation to obtain T ( x ), Δ P , q ( x )}. The multi-objective selection retains the Pareto frontier, and the crossover and mutation generate the next generation of design. In the optimization process, a "design-simulation-feedback" closed-loop mechanism is constructed, the optimization effect is fed back in real time based on CFD thermal-flow analysis, the design variables are dynamically adjusted, and iteration update and evolution convergence under the driving of structure performance are realized. This mechanism has good universality and adaptability in multi-heat source, complex boundary condition and multi-material coupled system, significantly improves the intelligent generation ability and design efficiency of the coupled cooling structure under multiple working conditions, and provides effective support for the rapid development of the new generation of high-integration and high-performance thermal management system.

[0056] S103, determining the optimal solution as a target topology cooling structure distribution of the phase change material and the fluid on the chip.

[0057] In one embodiment, as Figure 5As shown, the application also provides a design method of a phase change material and fluid cooling integrated coupling topology, which first initializes a pseudo-3D model, and then performs phase change material-fluid integrated cooling structure topology optimization, mainly by introducing a phase change energy storage function into the N-S equation, and then iteratively solving the temperature uniformity and transient temperature heat response as the objective function to obtain the topology optimized cooling structure, and then performing cooling structure constraint optimization based on the process-function balance, the constraints mainly include: structure boundary constraint, resolution constraint, connectivity constraint, PCM packaging constraint, thermal stress stability constraint, to obtain a training sample set, and then through the intelligent topology optimization and fast generation mechanism of the phase change material-fluid coupling cooling structure: the U-net neural network prediction model and the multi-objective genetic algorithm, the target topology cooling structure distribution of the chip is obtained.

[0058] The design method of the phase change material and fluid cooling integrated coupling topology provided by the application realizes the embedded collaborative layout of the phase change material region and the single-phase / multi-phase fluid channel under the topology optimization framework in two-dimensional / three-dimensional space, introduces typical manufacturing process constraints such as minimum structure size, support connectivity, material processability, and takes into account space utilization efficiency, efficient heat dissipation, and thermal management performance, ensures that the designed structure has manufacturability and assembly, constructs a highly integrated thermal management unit with optimized coupled thermal paths, and finally combines artificial intelligence technologies such as initial layout prediction based on deep learning and multi-objective optimization strategy based on evolutionary algorithms, significantly improves optimization efficiency and solution diversity, accelerates the rapid generation and iteration of high-performance thermal management structures, and provides intelligent support for thermal design in complex application scenarios.

[0059] The application provides a design method of a phase change material and fluid cooling integrated coupling topology, which aims to realize efficient heat dissipation and precise thermal management of high heat flux devices in a compact space, and specifically includes the following three core points: 1. Embedded collaborative layout strategy of phase change material and fluid cooling channel Under the topology optimization framework, the application integrates the phase change material functional domain and the single-phase or multi-phase fluid cooling channel region. Unlike traditional filling or surface attachment structures, the application realizes collaborative regulation of material distribution and flow path in two-dimensional / three-dimensional space, constructs a thermal management unit with high integration of heat capacity energy storage and active heat exchange functions, and significantly improves space utilization and heat exchange efficiency.

[0060] 2. Cooling structure manufacturing and assembly constraint optimization method based on process-function balance The application introduces typical manufacturing process constraints, including minimum construction size, structural connectivity, processing accessibility and assembly adaptability, to ensure that the design result has actual manufacturability and engineering realizability. Compared with the traditional optimization method only aiming at structural strength or heat dissipation efficiency, the application further introduces the sealing constraint of the cooling path on the basis of replacing the solid domain with the phase change material function, focusing on solving the risk of channel interruption and structural leakage caused by embedded design. Through the coordinated control of the phase change material embedding ratio, distribution area and boundary transition, the continuity and thermodynamic sealing of the fluid channel are maintained while ensuring the heat buffering and energy storage capacity. Finally, the comprehensive influence of the thermal conductivity, phase change characteristics and structural mechanical stability of the phase change material is also considered in the optimization process, to achieve a comprehensive balance between manufacturing process, sealing reliability and thermal coupling performance.

[0061] 3. Intelligent topology optimization and rapid generation mechanism for phase change-fluid coupled cooling structure In view of the problems of complex design space, long solving time and the like caused by the embedded collaborative layout of the phase change material and the fluid channel in the application, an efficient structure generation mechanism integrating artificial intelligence is proposed. The mechanism first uses a deep learning model to quickly predict the initial layout of the cooling structure, effectively shortening the calculation time in the early stage of traditional topology optimization; then, combined with intelligent multi-objective optimization strategies such as evolutionary algorithm, the multiple constraint conditions such as thermal management performance, structural manufacturability and fluid sealing are considered to realize efficient search and rapid convergence.

[0062] 1. Embedded collaborative structure design of phase change-fluid (1) Innovatively realize the embedded collaborative layout of the phase change material region and the single-phase or multi-phase cooling channel based on the topology optimization method in two-dimensional / three-dimensional space, consider the heat storage capacity of the phase change material and the dynamic heat exchange capacity of the fluid cooling in the design process, realize the collaborative integration of multiple thermal management mechanisms, and effectively improve the thermal coupling efficiency and space utilization.

[0063] 2. Process-function constraint feasibility optimization (1) Introduce typical manufacturing process constraints such as minimum construction size, support connectivity and material processability in the topology optimization process to ensure that the structure has actual manufacturability.

[0064] (2) When the topology solid domain is replaced by the phase change material region, consider the fluid sealing, structural strength and heat flux path comprehensively, and balance the functionality and process realizability.

[0065] 3. Rapid generation mechanism integrating artificial intelligence (1) Use a deep learning model to accurately predict the initial layout of the phase change material and the fluid channel in the topology optimization, significantly shorten the calculation time, and reduce the difficulty of solving complex coupled problems.

[0066] (2) Combined with evolutionary strategies such as genetic algorithms, intelligent optimization is performed for multiple objectives in thermal management (such as thermal efficiency, structural strength, sealing effect, manufacturing constraints, etc.), achieving a balance between the diversity of design schemes and high performance, and improving the iterative update efficiency and application adaptability of embedded cooling structures.

[0067] The present invention realizes the embedded collaborative layout of phase change material areas and single-phase / multi-phase fluid channels under the framework of topology optimization, efficiently allocates cooling resources in two-dimensional / three-dimensional space, effectively improves the spatial utilization efficiency and heat flux regulation capability of the structure, and significantly improves the heat flow path and heat diffusion uniformity, thereby enhancing the steady-state and transient temperature control performance of the system under complex thermal loads.

[0068] The present invention addresses the issues of weakened sealing and reduced structural strength that may arise when solid domains in the topological structure are replaced with phase change materials. By integrating key process constraints such as minimum structural scale, machinability, and support connectivity, the present invention systematically balances thermal performance and manufacturing feasibility to ensure that the cooling structure has good machinability and assembly stability while meeting thermal functional design requirements.

[0069] This paper constructs an intelligent optimization process that integrates deep learning initial layout prediction with multi-objective optimization using an evolutionary algorithm. This improves the efficiency of structure generation while enhancing the diversity of the solution space and overall performance. This mechanism supports rapid adaptation to high-integration, complex boundary, and multi-constraint coupling problems, significantly accelerating design iteration cycles and providing intelligent, high-performance structural design solutions for thermal management systems.

[0070] When applying the design method of the integrated coupling topology structure based on phase change material and fluid cooling provided by the present invention, it is not necessary to Figure 1 The steps are executed in the order shown. The specific execution order of the steps can be determined according to needs, and the present invention does not limit this.

[0071] The above is a method for designing an integrated coupled topology structure based on phase change material and fluid cooling provided by one or more embodiments of the present invention. Based on the same idea, the present invention also provides a corresponding device for designing an integrated coupled topology structure based on phase change material and fluid cooling, which device includes: An initialization module is used to input the chip's non-uniform heat flux density distribution into a neural network prediction model to obtain the initial topological cooling structure distribution of the phase change material and fluid on the chip. The training sample set of the neural network prediction model is calculated based on the heat transfer control equation for the coupled phase change material and fluid. The heat transfer control equation for the coupled phase change material and fluid is constructed by introducing the phase change energy storage function into the fluid momentum conservation equation to achieve an embedded collaborative layout of the phase change material area and the fluid cooling channel. The phase change energy storage function represents the energy storage and release characteristics of the phase change material. The optimization module uses the initial topological cooling structure distribution as the initial population for a multi-objective genetic algorithm. Based on a pre-established multi-objective function, the algorithm iteratively optimizes the initial cooling structure to obtain the optimal solution. The multi-objective function is constructed by comprehensively considering thermal management performance, structural manufacturability, and fluid dynamics performance. The determination module is used to determine the optimal solution as the target topological cooling structure distribution of the phase change material and the fluid on the chip.

[0072] Regarding the specific limitations of the device for designing an integrated coupled topology structure based on phase change material and fluid cooling, please refer to the limitations of the method for designing an integrated coupled topology structure based on phase change material and fluid cooling above, and will not be repeated here. The various modules in the aforementioned device for designing an integrated coupled topology structure based on phase change material and fluid cooling can be implemented in whole or in part through software, hardware, or a combination thereof. The aforementioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of the aforementioned modules.

[0073] The present invention also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 A design method for an integrated coupled topology structure based on phase change materials and fluid cooling is provided.

[0074] The present invention also provides Figure 6 The structural diagram of the computer equipment shown in FIG. Figure 6 As shown in the figure, at the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory. Of course, it may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 A design method for an integrated coupled topology structure based on phase change materials and fluid cooling is provided.

[0075] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiments of the methods. In the embodiments of the present application, any reference to memory, storage, database or other medium can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0076] The technical features of the above embodiments can be combined in any way. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, but as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

Claims

1. A design method for an integrated coupling topology structure based on phase change materials and fluid cooling, characterized in that: include: The chip's non-uniform heat flux density distribution is input into a neural network prediction model to determine the initial topological cooling structure distribution of the phase change material and fluid on the chip. The training sample set for the neural network prediction model is calculated based on the heat transfer governing equations for the coupled phase change material and fluid. These heat transfer governing equations are constructed by introducing the phase change energy storage function into the fluid momentum conservation equation to achieve an embedded, coordinated layout of the phase change material area and the fluid cooling channels. Phase change energy storage function represents the energy storage and release characteristics of phase change materials; The initial topological cooling structure distribution is used as the initial population of the multi-objective genetic algorithm. Based on a pre-constructed multi-objective function, the algorithm iteratively optimizes the initial cooling structure to obtain the optimal solution. The multi-objective function is constructed by comprehensively considering thermal management performance, structural manufacturability, and fluid dynamics performance. The optimal solution is determined as the target topological cooling structure distribution of phase change material and fluid on the chip.

2. The method according to claim 1, characterized in that The training sample set of the neural network prediction model includes a variety of sample non-uniform heat flux density distributions of sample chips and corresponding sample topological cooling structure distributions. The acquisition process of the training sample set of the neural network prediction model includes: For any sample with non-uniform heat flux density distribution, with the goal of the most uniform temperature distribution and the fastest thermal response, the heat transfer control equation of the phase change material and fluid coupling is iteratively solved through the sample's non-uniform heat flux density distribution to obtain the sample's topological cooling structure distribution.

3. The method according to claim 2, characterized in that The heat transfer governing equation for the coupling of phase change material and fluid is: ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； in, is the Hamiltonian operator, is the velocity field vector, is the fluid density, is the spatial gradient of velocity, is the velocity field vector About time The derivative of For pressure The gradient, is the fluid dynamic viscosity, is the Darcy number of the fluid, is the thickness of the channel layer, is the design variable, represents the phase change material domain, represents the fluid domain, is the interpolation coefficient of α, is the temperature of the fluid, Temperature The time derivative, The spatial gradient of temperature, is the chip temperature, is the phase change interpolation function, is the heat flux density of the phase change material, is the phase change energy storage function, is the solid density of the phase change material, is the fluid density of the phase change material, is the melting point of the phase change material, is the specific heat capacity of the phase change material in solid state, is the constant pressure specific heat capacity when the phase change material changes to liquid state, is the solid density, is the specific heat capacity of the solid, is the solid thermal conductivity, is the thermal conductivity of the fluid, is the ratio of the thermal conductivity of the fluid to the thermal conductivity of the solid, is the convection heat transfer coefficient between the fluid and the chip layer in the Pseudo model, is the convection heat transfer coefficient between the solid and chip layers in the Pseudo model, , They are The interpolation coefficients, is the spatial gradient of chip layer temperature, is the chip layer temperature time change rate, is the heat flux density, is the thickness of the chip layer; The objective function of the heat transfer control equation for the coupling of phase change material and fluid is: ； in, and is the weight coefficient, max( T ) is the maximum temperature, Var( T ) is the temperature-time variation, X is the maximum integral value of the design variable.

4. The method according to claim 3, characterized in that The constraints in solving the heat transfer control equations for the coupled phase change material and fluid include minimum radius constraints, structural boundary constraints, equivalent diameter constraints, connectivity constraints, phase change material encapsulation constraints, and thermal stress stability constraints. The minimum radius constraint is: ； in, is the minimum radius, After the minimum radius constraint field; The structural boundary constraints are: ； in, After projection field, is the threshold parameter, Control the steepness of the projection; The equivalent diameter constraint is: ； in, is the equivalent diameter of the local liquid area, is the local cross-sectional area, is the perimeter, is the minimum equivalent diameter, is the minimum diameter; The connectivity constraints are: ； in, is the connectivity function, is the export quantity, is the total volume flow rate at the inlet, For the The volume flow rate of each outlet; The encapsulation constraints of phase change materials are: If the region is a phase change material and the distance between the phase change material and the interface between the fluid channel is less than a boundary threshold, the region is determined to be a solid material; The thermal stress stability constraint is: ； when , build a thickening layer; in, is the thermal stress estimation function, is the Young's modulus of the phase change material, is the linear expansion coefficient of the phase change material, is the critical value of a given thickness.

5. The method according to claim 2, characterized in that The loss function used in the training process of the neural network prediction model is: ； in, is the loss value, Indicates the summation of all points in the two-dimensional space. represents the position coordinates in two-dimensional space, To predict the topological cooling structure distribution, is the real topological cooling structure distribution, is the weight factor, To predict the gradient of the topological cooling structure distribution.

6. The method according to claim 1, characterized in that Each optimization process of the multi-objective genetic algorithm for the initial cooling structure includes: Perform fluid dynamics simulation on the initial cooling structure to obtain the temperature field and pressure field corresponding to the initial cooling structure; Calculate the multi-objective function value according to the temperature field and pressure field corresponding to the initial cooling structure, and calculate the fitness value according to the multi-objective function value; The initial cooling structure is updated by the fitness value, and the updated initial cooling structure is used as the initial cooling structure for the next iteration.

7. The method according to claim 6, characterized in that The multi-objective function is: ； in, 、 and represent the first objective function, the second objective function and the third objective function respectively, is the temperature field, is the temperature field gradient, is the pressure field gradient.

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