Thermal Performance Analysis and Structural Optimization of Main Functional Components of Computers
Source abstract
Abstract
In today’s data-driven age, the thermal properties of computer transistors play an important role. In this research, finite element simulation is employed to construct the structural model of the primary components within a computer chassis, and the thermal performance is evaluated based on ambient temperature, thermal conductivity, and heat dissipation rate. By combining the particle swarm optimization algorithm with numerical simulation for joint simulation and structural optimization, the component layout was optimized to reduce the working temperature. The results show that when the background temperature, that is, the ambient temperature, rises from −20 °C to 60 °C, the maximum operating temperature of the computer is approximately 88 °C. The maximum temperature is mainly in the transistor core and the minimum temperature is in the intake grille, and the operating temperature of the optimized structure decreases by approximately 10 °C. The research shows that the operating temperature is most sensitive to the change of background temperature, and the transistor core is the main heating source. The maximum temperature can be reduced by rationally adjusting the position of the components. This study provides a reference for analyzing the thermal performance of computers and optimizing structures.
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Research context
OpenAlex topics: Heat Transfer and Optimization · Advancements in Semiconductor Devices and Circuit Design · Silicon Carbide Semiconductor Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Field Engineering College, Army Engineering University of PLA, Nanjing 210007, China
Engineering Training Center, Nanjing Vocational University of Industry Technology, Nanjing 210023, China
MIIT Key Laboratory of Multifunctional Lightweight Materials and Structures (MLMS), Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
Funding
National Natural Science Foundation of China · Jiangsu Qing Lan Project · Natural Science Foundation of Jiangsu Province · Natural Science Foundation of Hunan province · China Postdoctoral Science Foundation
References (39)
Show source-provided references
1. Mochi: A Case Study in Translational Computer Science for High–Performance Computing Data Management10.1109/MCSE.2023.3326436 ↗
2. da Silva, E.C., Sato, L.M., and Midorikawa, E.T. (2023). Distributed File System to Leverage Data Locality for Large–File Processing. Electronics, 13.10.3390/electronics13010106 ↗
3. Kwon, S., Ji, M., Kim, M., Leung, J.Y., and Min, B. (2024). Determination of Sequential Well Placements Using a Multi–Modal Convolutional Neural Network Integrated with Evolutionary Optimization. Mathematics, 13.10.3390/math13010036 ↗
4. Lin, Z., Amano, H., Takigahira, M., Terakado, N., Itoyama, K., Gulzar, H., and Nakadai, K. (2024). Advancing Applications of Robot Audition Systems: Efficient HARK Deployment with GPU and FPGA Implementations. Chips, 4.10.3390/chips4010002 ↗
5. Mohapatra, S., Sahoo, P.K., and Mohapatra, S.K. (2023). Healthcare Big Data Analysis with Artificial Neural Network for Cardiac Disease Prediction. Electronics, 13.10.3390/electronics13010163 ↗
6. Performance optimization of cascaded and non–cascaded thermoelectric devices for cooling computer chips10.1016/j.enconman.2019.04.028 ↗
7. Xia, G., Liu, J., Hong, Q., Zhu, P., Xu, P., and Zhu, Z. (2024). An Efficient Frequency Encoding Scheme for Optical Convolution Accelerator. Photonics, 12.10.3390/photonics12010026 ↗
8. Performance of a multi–cooling sources cooling system with photovoltaics and waste heat recovery in data center10.1016/j.enconman.2024.119319 ↗
9. Bearing Capacity of Eccentrically Loaded Footings on Rock Masses Using Soft Computing Techniques
10. Soft Computing Techniques for Predicting Penetration and Uplift Resistances of Dual Pipelines in Cohesive Soils
11. Jilte, R., Ahmadi, M.H., Kumar, R., Kalamkar, V., and Mosavi, A. (2020). Cooling Performance of a Novel Circulatory Flow Concentric Multi–Channel Heat Sink with Nanofluids. Nanomaterials, 10.10.3390/nano10040647 ↗
12. Asim, M., and Siddiqui, F.R. (2022). Hybrid Nanofluids—Next–Generation Fluids for Spray–Cooling–Based Thermal Management of High–Heat–Flux Devices. Nanomaterials, 12.10.3390/nano12030507 ↗
13. Evaluation of the waste heat utilization from a hot–water–cooled high performance computer via a heat pump10.1016/j.egyr.2021.09.038 ↗
14. Lin, X., Wu, H., Liu, Z., Ying, B., Ye, C., Zhang, Y., and Li, Z. (2020). Design and Analysis of the IGBT Heat Dissipation Structure Based on Computational Continuum Mechanics. Entropy, 22.10.3390/e22080816 ↗
15. Application of nanofluids in computer cooling systems (heat transfer performance of nanofluids)10.1016/j.applthermaleng.2012.03.028 ↗
16. Improving the cooling performance of the straight finned heat sink (SHS) for computer processor using an inorganic PCM10.1016/j.matpr.2022.07.156 ↗
17. Wang, H., Zhu, C., Jin, W., Tang, J., Wu, Z., Chen, K., and Hong, H. (2022). A Linear–Power–Regulated Wireless Power Transfer Method for Decreasing the Heat Dissipation of Fully Implantable Microsystems. Sensors, 22.10.3390/s22228765 ↗
18. Lv, H., Zhang, S., Han, W., Liu, Y., Liu, S., Chu, Y., and Zhang, L. (2020). Design and Realization of an Aviation Computer Micro System Based on SiP. Electronics, 9.10.3390/electronics9050766 ↗
19. Zhao, T., Sun, R., Hou, X., Huang, J., Geng, W., and Jiang, J. (2023). Simulation Study of Influencing Factors of Immersion Phase–Change Cooling Technology for Data Center Servers. Energies, 16.10.3390/en16124640 ↗
20. Embedded Copper Foam Effect on the Nanofluids Thermal Cooling Performance of the Electric Vehicle Battery Pack
21. Dielectric Properties and Thermal Conductivity of Polyvinylidene Fluoride Synergistically Enhanced with Silica@Multi-walled Carbon Nanotubes and Boron Nitride
22. Xue, R., Lin, X., Zhang, B., Zhou, H., Lai, T., and Hou, Y. (2022). CFD and Energy Loss Model Analysis of High-Speed Centrifugal Pump with Low Specific Speed. Appl. Sci., 12.10.3390/app12157435 ↗
23. Di Vito, D., Kanerva, M., Järveläinen, J., Laitinen, A., Pärnänen, T., Saari, K., Kukko, K., Hämmäinen, H., and Vuorinen, V. (2020). Safe and Sustainable Design of Composite Smart Poles for Wireless Technologies. Appl. Sci., 10.10.3390/app10217594 ↗
24. Chen, J., Xuan, D., Wang, B., and Jiang, R. (2021). Structure Optimization of Battery Thermal Management Systems Using Sensitivity Analysis and Stud Genetic Algorithms. Appl. Sci., 11.10.3390/app11167440 ↗
25. Performance analysis and structural optimization of a finned liquid–cooling radiator for chip heat dissipation10.1016/j.apenergy.2022.120048 ↗
26. Multi–objective optimization of thermal modules in high heat flux laptops10.1016/j.applthermaleng.2023.122105 ↗
27. Heat dissipation models by convection and radiation during the real–time operation of office equipment: A case study of computers10.1016/j.enbuild.2023.113592 ↗
28. Numerical investigation of the influence of heat–generating components on the heat dissipation in a tower server10.1016/j.applthermaleng.2024.124313 ↗
29. Martinez, A., and Barker, J. (2020). Quantum Transport in a Silicon Nanowire FET Transistor: Hot Electrons and Local Power Dissipation. Materials, 13.10.3390/ma13153326 ↗
30. A comprehensive review of the de Bruijn graph and its interdisciplinary applications in computing
31. Blevins, R.D. (1984). Applied Fluid Dynamics Handbook, Van Nostrand Reinhold Co.
32. Crawford, B., Cisternas-Caneo, F., Soto, R., Mac-lean, C.P.T., Lara Arce, J., Solís-Piñones, F., Astorga, G., and Giachetti, G. (2025). Binary Secretary Bird Optimization Algorithm for the Set Covering Problem. Mathematics, 13.10.3390/math13152482 ↗
33. Arithmetic and parent–centric headless chicken crossover operators for dynamic particle swarm optimization algorithms10.1007/s00500-017-2917-8 ↗
34. Research on Large–Scale Bi–Level Particle Swarm Optimization Algorithm10.1109/ACCESS.2021.3072199 ↗
35. Optimization of five–parameter BRDF model based on hybrid GA–PSO algorithm10.1016/j.ijleo.2020.164978 ↗
36. van Zyl, J.-P., and Engelbrecht, A.P. (2023). Set–Based Particle Swarm Optimisation: A Review. Mathematics, 11.10.3390/math11132980 ↗
37. A multi–threaded particle swarm optimization–kmeans algorithm based on MapReduce
38. Dual sub–swarm interaction QPSO algorithm based on different correlation coefficients10.1080/00051144.2018.1454732 ↗
39. Quantum particle swarm optimization algorithm with the truncated mean stabilization strategy10.1007/s11128-021-03380-x ↗