Open Access
Issue
E3S Web of Conf.
Volume 559, 2024
2024 International Conference on Sustainable Technologies in Civil and Environmental Engineering (ICSTCE 2024)
Article Number 02001
Number of page(s) 11
Section Mechanical Engineering
DOI https://doi.org/10.1051/e3sconf/202455902001
Published online 08 August 2024
  1. L. Lin, Y. Chen, X. Zhang, and X. Wang, “Optimization of geometry and flow rate distribution for doublelayer microchannel heat sink,” Int. J. Therm. Sci., vol. 78, pp. 158–168, 2014. DOI: 10.1016/j.ijthermalsci.2013.12.009. [CrossRef] [Google Scholar]
  2. W. Qu and I. Mudawar, “Analysis of three-dimensional heat transfer in micro-channel heat sinks,” Int. J. Heat Mass Transf., vol. 45, no. 19, pp. 3973–3985, 2002. DOI: 10.1016/S0017-9310(02)00101-1. [CrossRef] [Google Scholar]
  3. Q. Cai, S. L. Xu, and Y. H. Wu, “Analysis and optimization of microchannel heat sink based on SQP method and numerical simulation,” Symposium on Electro-Mechanical and Microwave Structural Technology, Nanchang, China, 2014, pp. 97–101. [Google Scholar]
  4. Tuckerman, D. B., & Pease, R. F. W. (1981). High-performance heat sinking for VLSI. IEEE Electron device letters, 2(5), 126–129. [CrossRef] [Google Scholar]
  5. Liu, H. L., Qi, D. H., Shao, X. D., & Wang, W. D. (2019). An experimental and numer-ical investigation of heat transfer enhancement in annular microchannel heat sinks. In-ternational Journal of Thermal Sciences, 142, 106–120. [CrossRef] [Google Scholar]
  6. Kempers, R., Colenbrander, J., Tan, W., Chen, R., & Robinson, A. J., 2020Experi-mental characterization of a hybrid impinging microjet microchannel heat sink fabri-cated using high-volume metal additive manufacturing,” International Journal of Therm [Google Scholar]
  7. Zhang, X., Ji, Z., Wang, J., & Lv, X. (2023). Research progress on structural optimiza-tion design of microchannel heat sinks applied to electronic devices. Applied Thermal Engineering, 121294. [Google Scholar]
  8. Sahar, A. M., Wissink, J., Mahmoud, M. M., Karayiannis, T. G., & Ishak, M. S. A. (2017). Effect of hydraulic diameter and aspect ratio on single phase flow and heat transfer in a rectangular microchannel. Applied Thermal Engineering, 115, 793–814. [CrossRef] [Google Scholar]
  9. Soleimanikutanaei, S., Ghasemisahebi, E., & Lin, C. X. (2018). Numerical study of heat transfer enhancement using transverse microchannels in a heat sink. International Jour-nal of Thermal Sciences, 125, 89–100. [CrossRef] [Google Scholar]
  10. Ghani, I. A., Kamaruzaman, N., & Sidik, N. A. C. (2017). Heat transfer augmentation in a microchannel heat sink with sinusoidal cavities and rectangular ribs. International Journal of Heat and Mass Transfer, 108, 1969–1981. [CrossRef] [Google Scholar]
  11. Cheema, M. S., Dvivedi, A., & Sharma, A. K. (2015). Tool wear studies in fabrication of microchannels in ultrasonic micromachining. Ultrasonics, 57, 57–64. [CrossRef] [PubMed] [Google Scholar]
  12. Ramesh, K. N., Sharma, T. K., & Rao, G. A. P. (2021). Latest advancements in heat transfer enhancement in the micro-channel heat sinks: a review. Archives of Computa-tional Methods in Engineering, 28, 3135–3165. [CrossRef] [Google Scholar]
  13. Mukherjee, S., & Mudawar, I. (2002, May). Smart, low-cost, pumpless loop for micro-channel electronic cooling using flat and enhanced surfaces. In ITherm 2002. Eighth Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (Cat. No. 02CH37258) (pp. 360–370). IEEE. [Google Scholar]
  14. Thakre, S., Pandhare, A., Malwe, P. D., Gupta, N., Kothare, C., Magade, P. B., … & Panchal, H. (2023). Heat transfer and pressure drop analysis of a microchannel heat sink using nanofluids for energy applications. Kerntechnik, 88(5), 543–555. [CrossRef] [Google Scholar]
  15. Sur, A., & Gulia, V. (2022). A comprehensive review on microchannel heat exchangers, heat sink, and polymer heat exchangers: Current state of the art. Frontiers in Heat and Mass Transfer (FHMT), 18. [Google Scholar]
  16. Goldberg, D. E. (1994). Genetic and evolutionary algorithms come of age. Communi-cations of the ACM, 37(3), 113–120. [CrossRef] [Google Scholar]
  17. Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization (PSO). In Proc. IEEE international conference on neural networks, Perth, Australia (Vol. 4, No. 1, pp. 1942–1948). [Google Scholar]
  18. Van Laarhoven, P. J., Aarts, E. H., van Laarhoven, P. J., & Aarts, E. H. (1987). Simu-lated annealing (pp. 7–15). Springer Netherlands. [Google Scholar]
  19. Hatamlou, A. (2018). Solving travelling salesman problem using black hole algorithm. Soft Computing, 22(24), 8167–8175. [CrossRef] [Google Scholar]
  20. Gandomi, A. H., Yang, X. S., & Alavi, A. H. (2013). Cuckoo search algorithm: a me-taheuristic approach to solve structural optimization problems. Engineering with com-puters, 29, 17–35. [CrossRef] [Google Scholar]
  21. Huan, T. T., Kulkarni, A. J., Kanesan, J., Huang, C. J., & Abraham, A. (2017). Ideology algorithm: a socio-inspired optimization methodology. Neural Computing and Applica-tions, 28, 845–876. [CrossRef] [Google Scholar]
  22. Kashan, A. H. (2009, December). League championship algorithm: a new algorithm for numerical function optimization. In 2009 international conference of soft computing and pattern recognition (pp. 43–48). IEEE. [Google Scholar]
  23. Moosavian, N. (2015). Soccer league competition algorithm for solving knapsack prob-lems. Swarm and Evolutionary Computation, 20, 14–22. [CrossRef] [Google Scholar]
  24. Rao, R. V., & Kalyankar, V. D. (2011). Parameters optimization of advanced machining processes using TLBO algorithm. EPPM, Singapore, 20(20), 21–31. [Google Scholar]
  25. Kulkarni, A. J., Durugkar, I. P., & Kumar, M. (2013, October). Cohort intelligence: a self supervised learning behavior. In 2013 IEEE international conference on systems, man, and cybernetics (pp. 1396–1400). IEEE. [Google Scholar]
  26. Patankar, N. S., & Kulkarni, A. J. (2018). Variations of cohort intelligence. Soft Com-puting, 22(6), 1731–1747. [CrossRef] [Google Scholar]
  27. Gulia, V., & Nargundkar, A. (2019). Optimization of process parameters of abrasive water jet machining using variations of cohort intelligence (CI). In Applications of Ar-tificial Intelligence Techniques in Engineering: SIGMA 2018, Volume 2 (pp. 467–474). Springer Singapore. [Google Scholar]
  28. Kuo, H. C., & Lin, C. H. (2013). Cultural evolution algorithm for global optimizations and its applications. Journal of applied research and technology, 11(4), 510–522. [CrossRef] [Google Scholar]
  29. Liu, Z. Z., Chu, D. H., Song, C., Xue, X., & Lu, B. Y. (2016). Social learning optimiza-tion (SLO) algorithm paradigm and its application in QoS-aware cloud service compo-sition. Information Sciences, 326, 315–333. [CrossRef] [Google Scholar]
  30. Rao, R., & Patel, V. (2012). An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. international journal of indus-trial engineering computations, 3(4), 535–560. [CrossRef] [Google Scholar]
  31. Ismail, O. A., Ali, A. M., Hassan, M. A., & Gamea, O. (2023). Geometric optimization of pin fins for enhanced cooling in a microchannel heat sink. International Journal of Thermal Sciences, 190, 108321. [CrossRef] [Google Scholar]
  32. Zhao, H., Ma, H., Yan, X., Yu, H., Xiao, Y., Xiao, X., & Liu, H. (2023). Investigation of Hydrothermal Performance in Micro-Channel Heat Sink with Periodic Rectangular Fins. Micromachines, 14(10), 1818 [CrossRef] [PubMed] [Google Scholar]
  33. Husain, A., & Kim, K. Y. (2013). Design optimization of manifold microchannel heat sink through evolutionary algorithm coupled with surrogate model. IEEE Transactions on Components, Packaging and Manufacturing Technology, 3(4), 617–624. [CrossRef] [Google Scholar]
  34. Sahu, B. K., Pati, T. K., Nayak, J. R., Panda, S., & Kar, S. K. (2016). A novel hybrid LUS–TLBO optimized fuzzy-PID controller for load frequency control of multi-source power system. International Journal of Electrical Power & Energy Systems, 74, 58–69. [CrossRef] [Google Scholar]
  35. Jain, M., Saihjpal, V., Singh, N., & Singh, S. B. (2022). An overview of variants and advancements of PSO algorithm. Applied Sciences, 12(17), 8392. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.