Abstract
ABSTRACTNanofluids have found extended applications in different industrial and engineering systems nowadays. This study aims to investigate the accurate prediction of SiO2 nanofluid effect on the heat transfer performance, specifically convective heat transfer coefficient (H), of a quadrangular cross-section channel by considering affecting fluid flow specifications factors of Re, Pr, and concentration of nanoparticles (x) in the employing working fluid. An experimental setup is used to prepare a database consisting of 270 data points on the H, of SiO2 nanofluids. These data are then applied to develop predictive models based on three intelligent algorithms, namely multi-layer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS), and least squares support vector machine (LSSVM), respectively. Graphical and statistical error criterions are carried out to evaluate the credibility of the proposed approaches. The LSSVM method had the precise performance regarding the mean squared error (MSE) and the coefficient of determination (R2) of 59.7 and 0.9992, respectively. A sensitivity analysis is also carried out to assess the impact of different parameters on the H demonstrating that the Prandtl number has the highest impact with a relevancy factor (r) of 0.524.
Highlights
Nanofluids can be prepared through dispersion of nanoparticles materials into a base fluid which is typically water or oil (Ahmadi et al, 2018; Gurav et al, 2014; Lenin & Joy, 2017) and have found increasing applications in various industrial and engineering systems (Amin, Roghayeh, Fatemeh, & Fatollah, 2015; Khanjari, Pourfayaz, & Kasaeian, 2016)
An experimental investigation of the Al2O3/H2O and TiO2/H2O nanofluids is performed by Nasiri, Etemad, and Bagheri (2011) who investigated the thermal conductivity in an circular duct while the flow regime was completely turbulent and monitored considerable heat transfer enhancements for both nanofluids
In addition to experimental measurement of nanofluid’s H, the present study aims to provide accurate predictive models which is capable to predict the convection heat transfer coefficient in a quadrangular crosssection channel under different Re, Pr, and x, respectively
Summary
Nanofluids can be prepared through dispersion of nanoparticles materials into a base fluid which is typically water or oil (Ahmadi et al, 2018; Gurav et al, 2014; Lenin & Joy, 2017) and have found increasing applications in various industrial and engineering systems (Amin, Roghayeh, Fatemeh, & Fatollah, 2015; Khanjari, Pourfayaz, & Kasaeian, 2016). Nasrin and Alim (2014) investigated the forced convective heat transfer in a solar collector and presented a semiempirical correlation. They reported a 26% increase in the heat transfer rate when a nanofluid was applied in their study. Saeedinia, Akhavan-Behabadi, and Nasr (2012) investigated the nanofluid’s flow and heat transfer and proposed empirical correlations to predict experimental data points with error values ranging from −0.2 to +0.2. Barbés et al (2013) conducted an empirical study to evaluate the thermal behavior and specific heat capacity of Al2O3ethyleneglycol and Al2O3-water nanofluids at different temperatures and nanoparticles’ concentrations They reported a 26% increase in the heat transfer rate when a nanofluid was applied in their study. Sahin, Gültekin, Manay, and Karagoz (2013) stated the thermal conductivity increasing as a result of increasing Al2O3 nanoparticles’ concentration in water. Saeedinia, Akhavan-Behabadi, and Nasr (2012) investigated the nanofluid’s flow and heat transfer and proposed empirical correlations to predict experimental data points with error values ranging from −0.2 to +0.2. Moghadassi, Masoud Hosseini, Henneke, and Elkamel (2009) Studied the impact of nanofluids on the thermal conductivity and presented a unique predictive approach to forecast the values of effective thermal conductivity for different nanofluids. Barbés et al (2013) conducted an empirical study to evaluate the thermal behavior and specific heat capacity of Al2O3ethyleneglycol and Al2O3-water nanofluids at different temperatures and nanoparticles’ concentrations
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Engineering Applications of Computational Fluid Mechanics
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
