Heat pipe layout optimization method of power supply heat dissipation module and heat dissipation system

Abstract

The invention discloses a heat pipe layout optimization method for a power supply heat dissipation module and a heat dissipation system, and the method comprises the steps: 1, building a mathematical model for the cooperative influence of roughness parameters on the heat dissipation performance, and defining the key indexes of the heat dissipation performance; 2, through three-dimensional flow thermal coupling numerical simulation, verifying the mathematical model, which is established in the step 1 and has the cooperative influence on the heat dissipation performance, of the roughness parameters; step 3, performing optimization solution based on a multi-objective optimization algorithm; 4, according to an optimization solution result obtained in the step 3, a manufacturing process is matched, target roughness is achieved by adjusting process parameters, and a theoretical optimization result is converted into an actual product; 5, experimental verification and iterative optimization are carried out; the method has the beneficial effects that by establishing the mathematical model for cooperatively influencing the heat dissipation performance through the roughness parameters, the synergistic effect between the surface roughness and convective heat exchange and radiation heat exchange can be systematically considered, and the limitation of depending on experience or single performance index design in the prior art is overcome.

Description

Technical Field

[0001] This invention relates to the field of optimizing the heat dissipation performance of power supply casings, specifically to a method and system for optimizing the heat pipe layout of a power supply heat dissipation module. Background Technology

[0002] In high-power-density power supply equipment, heat dissipation performance is a key factor in ensuring its stable operation. As an important carrier of heat transfer, the surface roughness of the power supply heat sink casing has a significant impact on the heat dissipation effect. In the existing technology, the design of the surface roughness of the power supply heat sink casing mostly relies on experience or adjustment of a single performance index, failing to systematically consider the synergistic effect between surface roughness and convective heat transfer and radiative heat transfer. This results in either low heat dissipation efficiency due to unreasonable roughness, or excessively increasing the roughness in pursuit of high heat dissipation performance, which increases manufacturing costs and fluid resistance, making it impossible to achieve the optimal match between surface roughness and heat dissipation performance. Summary of the Invention

[0003] The purpose of this invention is to solve the problems mentioned above, and therefore proposes a method for optimizing the heat pipe layout of a power supply heat dissipation module.

[0004] The objective of this invention can be achieved through the following technical solution: a heat pipe layout optimization method for a power supply heat dissipation module, comprising:

[0005] Step 1: Establish a mathematical model of the synergistic effect of roughness parameters on heat dissipation performance, and define key indicators of heat dissipation performance, including convective heat transfer coefficient, emissivity of radiative heat transfer, thermal resistance and pressure drop;

[0006] Step 2: Verify the mathematical model of the synergistic influence of roughness parameters on heat dissipation performance established in Step 1 through three-dimensional fluid-thermal coupling numerical simulation;

[0007] Step 3: Optimize the solution based on a multi-objective optimization algorithm. With the determined heat dissipation performance indicators thermal resistance and pressure drop as optimization objectives, and combined with manufacturing process and structural strength constraints, use an improved multi-objective particle swarm optimization algorithm to solve for the optimal roughness parameters, providing parameter basis for actual manufacturing.

[0008] Step 4: Based on the optimization results obtained in Step 3, match the manufacturing process and achieve the target roughness by adjusting the process parameters, thus transforming the theoretical optimization results into actual products;

[0009] Step 5: Experimental verification and iterative optimization. The heat dissipation performance of the optimized shell is measured by building a test system. The experimental data is used to train a BP neural network to establish the mapping relationship between roughness, thermal resistance and voltage drop, and then combined with a genetic algorithm for secondary optimization.

[0010] Furthermore, the mathematical model for the synergistic influence of roughness parameters on heat dissipation performance includes the construction of quantitative relationships, the construction of roughness spectral density function formulas, heat flow path analysis, and the setting of synergistic mechanisms.

[0011] Furthermore, the formula for constructing the quantification relationship is as follows:

[0012]

[0013] Where h represents the convective heat transfer coefficient, k reflects the fluid's ability to conduct heat, D is the characteristic length, which is the actually selected geometric dimension, Nu is the Nusselt number, a dimensionless number in fluid mechanics and heat transfer, representing the dimensionless temperature gradient at the wall, used to characterize the intensity of convective heat transfer. In convective heat transfer, a larger Nusselt number indicates more intense convective heat transfer between the fluid and the wall. C, n, m, α, and β are empirical constants determined through experiments or numerical simulations. Re is the Reynolds number, a dimensionless number that characterizes the relative magnitude of inertial and viscous forces in fluid flow to determine the fluid's flow state. Where ρ is the fluid density, v is the fluid velocity, μ is the fluid dynamic viscosity, and Pr reflects the relative magnitude of the fluid's momentum diffusion capacity and heat diffusion capacity, as expressed in the mathematical expression. Let's look at c. p denoted as , where μ is the specific heat capacity at constant pressure of the fluid, k is the dynamic viscosity, and Ra is the thermal conductivity. Ra is used to describe the intensity of natural convection.

[0014] Furthermore, the roughness spectral density function formula is expressed as:

[0015] S(f) = S0·f (-2D+1) ;

[0016] Where D is the fractal dimension, S0 is the reference spectral density coefficient, and f represents the frequency. This formula can express the multi-scale characteristics of surface roughness in mathematical form.

[0017] Furthermore, the heat flow path analysis specifically includes:

[0018] The finite element method is used to create a three-dimensional model of the power supply heat sink casing. The surface roughness features of the casing are incorporated into the model as geometric morphology parameters. Different thermal load boundary conditions are set in the model to simulate the heat transfer process under actual working conditions. Through computational fluid dynamics analysis, the conduction, convection and radiation paths of heat flow on different roughness surfaces are tracked, and the contribution ratio of different paths to the total heat dissipation is quantified.

[0019] Furthermore, the test system is constructed as follows: the heat dissipation shell of the power supply to be tested is installed in a constant temperature test environment chamber to ensure stable ambient temperature and no external airflow interference. Multiple temperature sensors are evenly arranged on the surface of the shell to accurately collect surface temperature data. Pressure sensors are connected to the inlet and outlet positions of the fluid channel inside the shell to monitor the pressure changes of the fluid passing through the shell. At the same time, an infrared thermal imager is used to perform real-time thermal imaging monitoring of the entire surface of the shell at a fixed observation distance to capture its temperature distribution. The data from the temperature sensors, pressure sensors, and thermal image data acquired by the infrared thermal imager are transmitted to the computer data processing center through a data acquisition card to achieve synchronous acquisition and analysis of multi-source data.

[0020] Furthermore, step two specifically involves:

[0021] The model from step one is transformed into an actual simulation, a three-dimensional model is established, and the fractal dimension and roughness spectral density function are transformed into geometric morphology parameters.

[0022] Set material properties and boundary conditions, and use a mesh generation strategy to capture roughness-related data;

[0023] Data is obtained through CFD simulation to verify and optimize the model from step one.

[0024] Furthermore, the parameters in step three include surface roughness height parameters, spacing parameters, and shape parameters.

[0025] Furthermore, the conditions for matching the manufacturing process include processing accuracy, material properties, production cost, and production efficiency.

[0026] The present invention also provides a heat dissipation system for a power supply heat dissipation module, which consists of a heat sink, a fan, heat pipes, a thermal interface, a heat dissipation duct, a housing, and a temperature sensor. The heat dissipation system is analyzed and optimized by a heat pipe layout optimization method for the power supply heat dissipation module.

[0027] Compared with the prior art, the beneficial effects of the present invention are:

[0028] This invention establishes a mathematical model of the synergistic influence of surface roughness parameters on heat dissipation performance, which can systematically consider the synergistic effect between surface roughness and convective heat transfer and radiative heat transfer, overcoming the limitations of existing technologies that rely on experience or single performance indicators for design.

[0029] By utilizing three-dimensional fluid-thermal coupling numerical simulation and multi-objective optimization algorithms, the optimal roughness parameters can be accurately found, achieving multi-objective optimization of minimum thermal resistance, minimum pressure drop, and minimum mass. This reduces manufacturing costs and fluid resistance while ensuring good heat dissipation.

[0030] The optimal roughness parameters were achieved by combining specific manufacturing processes, and the reliability and practicality of the optimization results were ensured through experimental verification and iterative optimization. Attached Figure Description

[0031] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0032] Figure 1 This is a flowchart of the heat pipe layout optimization method for the power supply heat dissipation module of the present invention. Detailed Implementation

[0033] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] Please see Figure 1 As shown, the heat pipe layout optimization method for the power supply cooling module includes:

[0035] Step 1: Establish a mathematical model of the synergistic effect of roughness parameters on heat dissipation performance;

[0036] Identify key indicators affecting heat dissipation performance, including the convective heat transfer coefficient h, the emissivity of radiative heat transfer ε, and the thermal resistance R. th Based on the Nusselt number correlation, a quantitative relationship between roughness parameters and convective heat transfer coefficients is established using the pressure drop ΔP.

[0037] Where h represents the convective heat transfer coefficient, which measures the ability of a fluid to transfer heat between itself and a solid surface. A larger h value indicates higher efficiency in heat transfer under the same conditions, meaning stronger convective heat transfer. k reflects the fluid's ability to conduct heat; a larger k value indicates higher efficiency in heat transfer and stronger thermal conductivity under the same temperature gradient. Conversely, a smaller k value indicates weaker thermal conductivity. D is the characteristic length, a geometric dimension chosen based on the specific problem. Nu is the Nusselt number, a dimensionless number in fluid mechanics and heat transfer, representing the dimensionless temperature at the wall surface. The gradient is used to characterize the intensity of convective heat transfer. In the process of convective heat transfer, the larger the Nusselt number, the more intense the convective heat transfer between the fluid and the wall. C, n, m, α, and β are empirical constants determined through experiments or numerical simulations. Re is the Reynolds number, a dimensionless number that characterizes the relative magnitude of inertial and viscous forces in fluid flow to determine the fluid's flow state. When the Reynolds number is less than 2300, it is defined as relatively small, with viscous forces dominating and the fluid exhibiting a laminar flow state, characterized by smooth and regular flow. When the Reynolds number is greater than 4000, it is defined as relatively large, with inertial forces dominating and the fluid exhibiting a turbulent flow state, characterized by disordered and irregular flow. Where ρ is the fluid density, v is the fluid velocity, μ is the fluid dynamic viscosity, and Pr reflects the relative magnitude of the fluid's momentum diffusion capacity and heat diffusion capacity, as expressed in the mathematical expression. Let's look at c. p Pr is the isobaric specific heat capacity of the fluid, μ is the dynamic viscosity, and k is the thermal conductivity. A larger Pr value means that the fluid has a relatively strong momentum diffusion capacity and a relatively weak heat diffusion capacity; conversely, a smaller Pr value indicates that the heat diffusion capacity is relatively stronger. Ra is used to describe the intensity of natural convection.

[0038] Construct the roughness spectral density function formula S(f)=S0·f (-2D+1) Where D is the fractal dimension, ranging from 1 to 2. The closer it is to 2, the richer the multi-scale characteristics of surface roughness. S0 is the reference spectral density coefficient, reflecting the overall level of surface roughness. f represents the frequency, used to characterize different spatial scales. The higher the frequency, the smaller the scale. This formula can express the multi-scale characteristics of surface roughness in mathematical form, providing a quantitative basis for studying the influence of surface roughness on heat dissipation performance. Combined with heat flow path analysis, the synergistic mechanism of micro-roughness and macro-roughness on heat dissipation performance is determined, and the correlation between roughness parameters and emissivity is established. Micro-roughness refers to the high and low undulation feature size in the micrometer and nanometer scale, while macro-roughness is in the millimeter and centimeter scale.

[0039] The heat flow path analysis process is as follows: The power supply heat dissipation shell is modeled in three dimensions using the finite element method. The micro and macro roughness features of the shell surface are incorporated into the model in the form of geometric morphology parameters. Different thermal load boundary conditions are set in the model to simulate the heat transfer process under actual working conditions. Through computational fluid dynamics analysis, the conduction, convection and radiation paths of heat flow on surfaces with different roughness are tracked, and the contribution ratio of different paths to the total heat dissipation is quantified. For example, micro roughness increases the contact area between the surface and the air, promoting convective heat transfer, while macro roughness changes the flow state of the fluid, affecting the convective heat transfer coefficient and the thermal boundary layer thickness.

[0040] The process of determining the synergistic mechanism is as follows: Through a controlled experimental design, the values ​​of S0 and D in the microscopic high-frequency band and the roughness parameters of S0 and D in the macroscopic low-frequency band are changed, while other variables are kept constant. The changes in heat dissipation performance are monitored. Combined with heat flow path analysis, the coupling relationship between microscopic roughness improving surface emissivity and enhancing radiative heat dissipation, and macroscopic roughness optimizing fluid flow and promoting convective heat dissipation is clarified. Finally, a mathematical model of the synergistic influence of roughness parameters on heat dissipation performance is constructed using multiple regression analysis or machine learning algorithms.

[0041] Step 2: The surface roughness heat dissipation performance coupling model established in Step 1 is verified through three-dimensional fluid-thermal coupling numerical simulation, as shown below:

[0042] When creating a 3D model of the power supply heatsink enclosure using ANSYS Workbench, the fractal dimension D and roughness spectral density function S(f)=S0·f in step one will be used. (-2D+1) The described multi-scale roughness features are transformed into geometric topography parameters of a three-dimensional model;

[0043] When setting material properties and boundary conditions, such as heat source power, ambient temperature and inlet wind speed, combined with the key indicators affecting heat dissipation performance determined in step one, a meshing strategy of densifying tetrahedral meshes in the roughness peak and valley regions and having 5 boundary layer meshes is adopted. This is to more accurately capture the influence of micro-roughness on fluid flow and heat transfer as described in step one. Micro-roughness increases the contact area between the surface and the air, promoting convective heat transfer. By densifying the mesh, the convective heat transfer coefficient h and heat flow distribution in this region can be calculated more accurately, which corresponds to the theory of the influence of micro-roughness on heat dissipation performance in step one.

[0044] The temperature field distribution, convective heat transfer coefficient, emissivity, thermal resistance, and pressure drop under different roughnesses (Ra = 1-20 μm) are obtained through CFD simulation, and a database is established. This process is a practical verification and data supplement to the coupled model established in step one. The convective heat transfer coefficient and emissivity data obtained from the simulation are compared and analyzed with the results calculated in step one through theoretical formulas and correlations. If there are deviations, the empirical constants in the model, such as C, n, m, α, and β, can be further optimized to improve the coupled model of surface roughness heat dissipation performance.

[0045] Step 3: Solve the problem using a multi-objective optimization algorithm;

[0046] Based on the determined heat dissipation performance index, thermal resistance R th With pressure drop ΔP as the optimization objective, and considering manufacturing process and structural strength constraints, the optimal roughness parameter is solved using an improved multi-objective particle swarm optimization algorithm, providing parameter basis for actual manufacturing.

[0047] Step 4: Based on the optimization results obtained in Step 3, match the manufacturing process and achieve the target roughness by adjusting the process parameters, thus transforming the theoretical optimization results into actual products;

[0048] Step 5: Experimental verification and iterative optimization;

[0049] The heat dissipation performance of the optimized shell was measured by building a test system. The experimental data was used to train a BP neural network to establish a mapping relationship between roughness, thermal resistance and voltage drop. A genetic algorithm was then used for secondary optimization. If the experimental results did not meet expectations, the results were fed back to step one or step two to adjust the model or simulation parameters, forming an iterative closed loop to continuously improve technical accuracy.

[0050] Regarding the setup of the test system: First, the heat sink housing of the power supply to be tested is installed in a constant temperature test chamber to ensure a stable ambient temperature and no external airflow interference. Multiple temperature sensors, such as K-type thermocouples, are evenly arranged on the surface of the housing to accurately collect surface temperature data. Pressure sensors are connected to the inlet and outlet of the fluid channel inside the housing to monitor pressure changes as the fluid passes through the housing. Simultaneously, an infrared thermal imager is used to perform real-time thermal imaging monitoring of the entire housing surface at a fixed observation distance to capture its temperature distribution. The data from the temperature and pressure sensors, as well as the thermal image data acquired by the infrared thermal imager, are transmitted to the computer data processing center via a data acquisition card to achieve synchronous acquisition and analysis of multi-source data.

[0051] This invention starts with theoretical modeling in step one, verifies the model through numerical simulation in step two, optimizes parameters based on simulation results in step three, guides process implementation with optimization results in step four, and finally verifies through experiments in step five, providing feedback and correction. This completes a technical closed loop from theory to practice and then to theoretical refinement, ensuring that the synergistic optimization of surface roughness and heat dissipation performance has a scientific basis and engineering feasibility.

[0052] Example 1:

[0053] A method for synergistically optimizing the surface roughness and heat dissipation performance of a 500W power module heat dissipation shell, using the method of this invention, comprises the following steps:

[0054] Step 1: Establish a coupled model of surface roughness and heat dissipation performance, and determine relevant parameters and correlations, where α = 0.02 and β = 0.5; Step 2: Use ANSYS Workbench to build a heat dissipation shell model, using aluminum alloy as the material, setting the heat source power to 50W, ambient temperature to 25℃, and inlet wind speed to 2m / s, perform mesh generation and CFD simulation, and obtain performance data under different roughnesses; Step 3: Use an improved multi-objective particle swarm optimization algorithm for optimization, and after 500 iterations, obtain the optimal roughness parameter Ra = 6μm; Step 4: Select precision milling... The machining process uses carbide cutting tools (0.2mm tip radius), a spindle speed of 12000rpm, and a feed rate of 0.05mm / tooth to obtain a heat dissipation shell with Ra=6μm. Anodizing is then performed to form a porous rough layer. Step five: A forced air cooling test system was built for experiments. The highest shell temperature was reduced from 82℃ before optimization to 65℃, thermal resistance decreased by 28%, pressure drop increased by 12%, and mass decreased by 15%, meeting the UL94V-0 fire resistance requirement. Compared with the traditional design, heat dissipation efficiency was improved by 40%, while cost increased by only 8%. Based on the experimental data, a BP neural network was trained and combined with a genetic algorithm for secondary optimization, ultimately determining Ra=5.2μm as the optimal solution.

[0055] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of protection of the claims.

[0056] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing the heat pipe layout of a power supply cooling module, characterized in that, include: Step 1: Establish a mathematical model of the synergistic effect of roughness parameters on heat dissipation performance, and define key indicators of heat dissipation performance, including convective heat transfer coefficient, emissivity of radiative heat transfer, thermal resistance and pressure drop; Step 2: Verify the mathematical model of the synergistic influence of roughness parameters on heat dissipation performance established in Step 1 through three-dimensional fluid-thermal coupling numerical simulation; Step 3: Optimize the solution based on a multi-objective optimization algorithm. With the determined heat dissipation performance indicators thermal resistance and pressure drop as optimization objectives, and combined with manufacturing process and structural strength constraints, use an improved multi-objective particle swarm optimization algorithm to solve for the optimal roughness parameters, providing parameter basis for actual manufacturing. Step 4: Based on the optimization results obtained in Step 3, match the manufacturing process and achieve the target roughness by adjusting the process parameters, thus transforming the theoretical optimization results into actual products; Step 5: Experimental verification and iterative optimization. The heat dissipation performance of the optimized shell is measured by building a test system. The experimental data is used to train a BP neural network to establish the mapping relationship between roughness, thermal resistance and voltage drop, and then combined with a genetic algorithm for secondary optimization.

2. The heat pipe layout optimization method for the power supply heat dissipation module according to claim 1, characterized in that, The mathematical model for the synergistic influence of roughness parameters on heat dissipation performance includes the construction of quantitative relationships, the construction of roughness spectral density function formulas, heat flow path analysis, and the setting of synergistic mechanisms.

3. The heat pipe layout optimization method for the power supply heat dissipation module according to claim 1, characterized in that, The formula for constructing the quantitative relationship is as follows: Where h represents the convective heat transfer coefficient, k reflects the fluid's ability to conduct heat, D is the characteristic length, which is the actually selected geometric dimension, Nu is the Nusselt number, a dimensionless number in fluid mechanics and heat transfer, representing the dimensionless temperature gradient at the wall, used to characterize the intensity of convective heat transfer. In convective heat transfer, a larger Nusselt number indicates more intense convective heat transfer between the fluid and the wall. C, n, m, α, and β are empirical constants determined through experiments or numerical simulations. Re is the Reynolds number, a dimensionless number that characterizes the relative magnitude of inertial and viscous forces in fluid flow to determine the fluid's flow state. Where ρ is the fluid density, v is the fluid velocity, μ is the fluid dynamic viscosity, and Pr reflects the relative magnitude of the fluid's momentum diffusion capacity and heat diffusion capacity, as expressed in the mathematical expression. Let's look at c. p denoted as , where μ is the specific heat capacity at constant pressure of the fluid, k is the dynamic viscosity, and Ra is the thermal conductivity. Ra is used to describe the intensity of natural convection.

4. The heat pipe layout optimization method for the power supply heat dissipation module according to claim 1, characterized in that, The roughness spectral density function formula is expressed as follows: S(f)=S0·f (-2D+1) ; Where D is the fractal dimension, S0 is the reference spectral density coefficient, and f represents the frequency. This formula can express the multi-scale characteristics of surface roughness in mathematical form.

5. The heat pipe layout optimization method for the power supply heat dissipation module according to claim 1, characterized in that, The heat flow path analysis specifically includes: The finite element method is used to create a three-dimensional model of the power supply heat sink casing. The surface roughness features of the casing are incorporated into the model as geometric morphology parameters. Different thermal load boundary conditions are set in the model to simulate the heat transfer process under actual working conditions. Through computational fluid dynamics analysis, the conduction, convection and radiation paths of heat flow on different roughness surfaces are tracked, and the contribution ratio of different paths to the total heat dissipation is quantified.

6. The heat pipe layout optimization method for a power supply heat dissipation module according to claim 1, characterized in that, The test system is set up as follows: the heat dissipation shell of the power supply to be tested is installed in a constant temperature test chamber to ensure a stable ambient temperature and no external airflow interference. Multiple temperature sensors are evenly arranged on the surface of the shell to accurately collect surface temperature data. Pressure sensors are connected to the inlet and outlet of the fluid channel inside the shell to monitor the pressure changes of the fluid passing through the shell. At the same time, an infrared thermal imager is used to perform real-time thermal imaging monitoring of the entire surface of the shell at a fixed observation distance to capture its temperature distribution. The data from the temperature sensors, pressure sensors, and thermal image data acquired by the infrared thermal imager are transmitted to the computer data processing center through a data acquisition card to achieve synchronous acquisition and analysis of multi-source data.

7. The heat pipe layout optimization method for the power supply heat dissipation module according to claim 1, characterized in that, Step two specifically involves: The model from step one is transformed into an actual simulation, a three-dimensional model is established, and the fractal dimension and roughness spectral density function are transformed into geometric morphology parameters. Set material properties and boundary conditions, and use a mesh generation strategy to capture roughness-related data; Data is obtained through CFD simulation to verify and optimize the model from step one.

8. The heat pipe layout optimization method for the power supply heat dissipation module according to claim 1, characterized in that, The parameters for step three include surface roughness height parameters, spacing parameters, and shape parameters.

9. The heat pipe layout optimization method for a power supply heat dissipation module according to claim 1, characterized in that, The conditions for matching the manufacturing process include processing accuracy, material properties, production cost, and production efficiency.

10. A heat dissipation system for a power supply heat dissipation module, applied to the above-mentioned heat pipe layout optimization method, characterized in that, The system consists of a heat sink, fan, heat pipes, thermal interface, heat dissipation duct, housing, and temperature sensor. The heat dissipation system is analyzed and optimized using a heat pipe layout optimization method for the power supply heat dissipation module.

Images