DOI: 10.57760/sciencedb.31248
title: Analysis and Optimization of 3D-Printed SiC Ceramic Microchannel Heat Sink
title: 基于3D打印的SiC微通道分析与优化

description: Four microchannel structures were designed, with the same heat source power of 720W applied. The inlet flow rate gradually increased from 2.5L/min to 5L/min, and the temperature and pressure drop of each structure were obtained for each flow rate. Draw temperature and velocity cloud maps based on the results obtained at 4L/min. In order to better verify the actual heat dissipation capability of the designed MCHS-SF, SiC ceramic 3D printing was used to manufacture the actual MCHS-SF. To meet the heating power requirements, an experimental structure was designed, including a heating copper block and a nylon shell. A heating rod was placed in the copper block with a total power of 720W, and the nylon shell was used to fix the copper block and microchannel and for insulation. Adding indium sheets between copper blocks and microchannels to reduce contact thermal resistance. The temperature measurement point is set in the copper block. In the experiment, we adopted the same inlet flow rates as the simulation: 2.5L/min, 3L/min, 3.5L/min, 4L/min, 4.5L/min, 5L/min, and controlled the cooling fluid temperature at 8 ℃. We observed and recorded the temperature values of temperature measurement points T1, T2, and T3 at different flow rates, and compared the measured values with the simulated temperature values. The measured values were closely related to the simulated values, with a maximum error of 3.49% and a minimum error of 1.80%. Considering the method of measuring pressure drop in the experiment, it is necessary to include the local pressure loss caused by the geometric structure of the three-way joint, threaded quick twist joint, and microchannel inlet and outlet in the simulation. In experimental measurements, the local pressure loss of two three-way joints can be measured by connecting a small section of pipeline without load. Therefore, in simulation, only the pressure loss of the threaded quick twist joint at the inlet and outlet needs to be considered. It can be seen that the error between the experimental test pressure drop and the simulation is the highest at 7.13% and the lowest at 0.48%. In order to further improve the comprehensive heat dissipation performance of MCHS-SF, multi-objective optimization is carried out on MCHS-SF. Firstly, the design variables and objective function are determined. In this study, minimizing the average surface temperature and pressure drop of the MCHS-SF heat source is set as the optimization objective. The design variables are taken as needle wing diameter, top distance, and needle wing spacing, and parameterized. The Box Behnken experimental design is used to extract sample data. Then, a response surface regression model was constructed using the samples. Finally, the Pareto frontier optimal solution was obtained using NSGA-II, and the optimal design parameters under design constraints were determined. The population size was set to 100, the crossover ratio was 0.9, the mutation rate was 0.3, and the iteration times were 250. 100 Pareto optimal solutions were optimized. 
description: Four microchannel structures were designed, with the same heat source power of 720W applied. The inlet flow rate gradually increased from 2.5L/min to 5L/min, and the temperature and pressure drop of each structure were obtained for each flow rate. Draw temperature and velocity cloud maps based on the results obtained at 4L/min. In order to better verify the actual heat dissipation capability of the designed MCHS-SF, SiC ceramic 3D printing was used to manufacture the actual MCHS-SF. To meet the heating power requirements, an experimental structure was designed, including a heating copper block and a nylon shell. A heating rod was placed in the copper block with a total power of 720W, and the nylon shell was used to fix the copper block and microchannel and for insulation. Adding indium sheets between copper blocks and microchannels to reduce contact thermal resistance. The temperature measurement point is set in the copper block. In the experiment, we adopted the same inlet flow rates as the simulation: 2.5L/min, 3L/min, 3.5L/min, 4L/min, 4.5L/min, 5L/min, and controlled the cooling fluid temperature at 8 ℃. We observed and recorded the temperature values of temperature measurement points T1, T2, and T3 at different flow rates, and compared the measured values with the simulated temperature values. The measured values were closely related to the simulated values, with a maximum error of 3.49% and a minimum error of 1.80%. Considering the method of measuring pressure drop in the experiment, it is necessary to include the local pressure loss caused by the geometric structure of the three-way joint, threaded quick twist joint, and microchannel inlet and outlet in the simulation. In experimental measurements, the local pressure loss of two three-way joints can be measured by connecting a small section of pipeline without load. Therefore, in simulation, only the pressure loss of the threaded quick twist joint at the inlet and outlet needs to be considered. It can be seen that the error between the experimental test pressure drop and the simulation is the highest at 7.13% and the lowest at 0.48%. In order to further improve the comprehensive heat dissipation performance of MCHS-SF, multi-objective optimization is carried out on MCHS-SF. Firstly, the design variables and objective function are determined. In this study, minimizing the average surface temperature and pressure drop of the MCHS-SF heat source is set as the optimization objective. The design variables are taken as needle wing diameter, top distance, and needle wing spacing, and parameterized. The Box Behnken experimental design is used to extract sample data. Then, a response surface regression model was constructed using the samples. Finally, the Pareto frontier optimal solution was obtained using NSGA-II, and the optimal design parameters under design constraints were determined. The population size was set to 100, the crossover ratio was 0.9, the mutation rate was 0.3, and the iteration times were 250. 100 Pareto optimal solutions were optimized.