Artificial neural network modeling of nanofluid flow in a microchannel heat sink using experimental data |
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| Affiliation: | 1. Department of Mechanical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran;2. Renewable Energies, Magnetism and Nanotechnology Lab., Faculty of Science, Ferdowsi University of Mashhad, Mashhad, Iran;3. Faculty of New Science & Technologies, University of Tehran, Tehran, Iran;4. Fluid Mechanics, Thermal Engineering and Multiphase Flow Research Lab. (FUTURE), Department of Mechanical Engineering, Faculty of Engineering, King Mongkut''s University of Technology Thonburi, Bangmod, Bangkok 10140, Thailand;5. Heat and Thermodynamics Division, Department of Mechanical Engineering, Yildiz Technical University, Yildiz, Besiktas, Istanbul 34349, Turkey;6. The Academy of Science, The Royal Institute of Thailand, Sanam Suea Pa, Dusit, Bangkok 10300, Thailand;1. Young Researchers and Elite Club, Khomeinishahr Branch, Islamic Azad University, Isfahan, Iran;2. Department of Energy and Refrigerating Air-Conditioning Engineering, National Taipei University of Technology, Taipei 10608, Taiwan, ROC;3. Department of Mechanical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran;4. Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia;1. Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia;2. Malaysia – Japan International Institute of Technology (MJIIT), University Teknologi Malaysia Kuala Lumpur, Jalan Sultan Yahya Petra (Jalan Semarak), 54100 Kuala Lumpur, Malaysia |
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| Abstract: | The present paper deals with the artificial neural network modeling (ANN) of heat transfer coefficient and Nusselt number in TiO2/water nanofluid flow in a microchannel heat sink. The microchannel comprises of 40 channels; each channel has a length of 4 cm, a width of 500 μm, and a height of 800 μm. In the ANN modeling of heat transfer coefficient and Nusselt number 23 and 72 datasets have been used, respectively. The experimental Nusselt number has been calculated based on three different thermal conductivity models, four volume fractions of 0, 0.5, 1, and 2%, two values of Reynolds number i.e. 400 and 1200 and three different heating rates including 50.6, 60.7, and 69.1 W. Therefore, the inputs that are introduced to the neural network are volume fraction of nanoparticles, Reynolds number, heating rate, and model number while the output of network is the Nusselt number. It is elucidated that an appropriately trained network can act as a good alternative for costly and time-consuming experiments on the nanofluid flow in microchannels. The average relative errors in the prediction of Nusselt number and heat transfer coefficients were 0.3% and 0.2%, respectively. |
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