Heat transfer, and friction factor of Fe3O4-SiO2/Water hybrid nanofluids in a plate heat exchanger: Experimental and ANN predictions

被引:28
|
作者
Alklaibi, A. M. [1 ]
Mouli, Kotturu V. V. Chandra [1 ]
Sundar, L. Syam [2 ]
机构
[1] Majmaah Univ, Coll Engn, Dept Mech & Ind Engn, Al Majmaah 11952, Saudi Arabia
[2] Prince Mohammad Bin Fahd Univ, Coll Engn, Dept Mech Engn, Al Khobar 31952, Saudi Arabia
关键词
Friction factor; Heat transfer coefficient; Thermal performance factor; Fe 3 O 4-SiO 2 hybrid nanofluid; ANN approach; THERMAL PERFORMANCE; PRESSURE-DROP; THERMOPHYSICAL PROPERTIES; TIO2/WATER NANOFLUID; FE3O4; NANOFLUID; CHEVRON ANGLE; FLUID-FLOW; AL2O3/WATER; COOLANT; TUBE;
D O I
10.1016/j.ijthermalsci.2023.108608
中图分类号
O414.1 [热力学];
学科分类号
摘要
The study aims to investigate experimentally the heat transfer, friction factor, and thermal performance of plate heat exchanger when Fe3O4-SiO2/water hybrid nanofluids works as a coolant. The Fe3O4-SiO2 nanoparticles were synthesized with the method of chemical coprecipitation and in-situ growth technique and characterized with x-ray diffraction, vibrating sample magnetometer, and Fourier transform infrared methods. The water-based Fe3O4-SiO2 hybrid nanofluids were prepared and the thermophysical properties were estimated experimentally. The heat transfer and friction factor experiments were conducted at mass flow rates ranging from 0.05 kg/s (Reynolds number of 245) to 0.1166 kg/s (Reynolds number of 936), and volume concentration range from 0.2% to 1.0%. By using 1.0 vol% of Fe3O4-SiO2/water hybrid nanofluids, the study found, in comparison with the base fluid that, the heat transfer coefficient is increased by 21.30%, the Nusselt number increased by 25.85%, and the friction factor increased by 37.59%, respectively, resulting in an increase of 18.1% in thermal performance factor. The Bayesian regularization-ANN analysis accurately predicts the heat transfer coefficient, friction factor and thermal performance factor using the experimental data. The multi-linear regression fit is used to develop the Nusselt number and friction factor based on the experimental data by using the ANN technique.
引用
收藏
页数:17
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