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Response Surface Methodology and Artificial Neural Network modellings on hydraulic and thermal performances of a disk-shaped heat sink with tree-like microchannels

Li Zhejiang Business Technology Institute, Zhejiang, Ningbo, 315012, China|
Ahmed M. (57224766838) Department of Mechanical Engineering, College of Engineering in Wadi Alddawasir, Prince Sattam Bin Abdulaziz University, Saudi Arabia| Phuoc Quy (57673819700); Galal Patet Research Group, Ho Chi Minh City University of Transport, Ho Chi Minh City, Viet Nam| Makatar (54581708300); Phong Nguyen Department of Mechanical and Mechatronics Engineering, Faculty of Engineering, Prince of Songkla University, Hatyai, Songkhla, 90110, Thailand| Van Nhanh (57578184700); Wae-Hayee Institute of Engineering, Hutech University, Ho Chi Minh City, Viet Nam| Abeer D. (57204971671); Nguyen Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia| Azher M. (57716714900); Algarni Air Conditioning and Refrigeration Techniques Engineering Department, Al-Mustaqbal University College, Babylon, 51001, Iraq| Pradeep Kumar (57220983883); Abed Department of Mechanical Engineering, Institute of Engineering and Technology, Gla University, U.P., Mathura, 281406, India| Yasser (57211803236); Singh Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt| T. Ch. Anil (57694378300); Elmasry Department of Mathematics, Faculty of Science, King Khalid University, P.O. Box 9004, Abha, 61466, Saudi Arabia| Fangyuan (57437384100); Kumar Department of Mechanical Engineering, Vignan's Foundation for Science Technology and Research, Vadlamudi, India|

Case Studies in Thermal Engineering Số , năm 2022 (Tập 40, trang -)

ISSN: 2214157X

ISSN: 2214157X

DOI:

Tài liệu thuộc danh mục:

Article

English

Từ khóa: Forestry; Heat transfer performance; Microchannels; Neural networks; Surface properties; Fractal-shaped microchannel heat sink; Heat transfer performance; Heat transfer performance increment; Hydraulic performance; Hydraulic performance increment; Micro channel heat sinks; Neural response; Optimisations; Optimum designs; Response-surface methodology; Heat sinks
Tóm tắt tiếng anh
To cope with the increasing heat generation resulting from increased power in micro-scale devices, an optimum design of a tree-like microchannel heat sink is suggested utilizing Artificial Neural Networks (ANN) and Response Surface Methodology (RSM). Based on the numerical finite volume model, both ANN and RSM models were trained, and their performances were assessed using four distinct performance criteria. The results showed that both of the models are reliable for the purpose of this study, showing a significantly high adjusted R2 for both performance control variables pressure drop (ΔP) and Nusselt number (Nu). The adjusted R2 values observed for the RSM were 0.987 and 0.997, respectively, for the Nu and ΔP. These values were calculated as high as 0.997 for Nu prediction and 0.997 for ΔP prediction using the ANN. Finally, utilizing the calibrated RSM and ANN models, three optimum design configurations for three different purposes given the priority requirements of the heat sink implementations were suggested. The minimum ΔP configuration is suitable for the longevity of the heat sink, and the maximum Nu configuration is suitable for applications where heat transfer performance is more critical. For a balanced optimum design, the efficiency index was maximized. © 2022 Published by Elsevier Ltd.

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