期刊
INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER
卷 86, 期 -, 页码 25-31出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.icheatmasstransfer.2017.05.020
关键词
Microchannel; Nanofluids; Nusselt number; Artificial neural network
资金
- National Science and Technology Development Agency (NSTDA)
- Thailand Research Fund (TRF)
- National Research University (NRU)
- King Mongkut's University of Technology Thonburi
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 gm, and a height of 800 gm. 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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据