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Research Papers

# Optimal Analysis of Entropy Generation and Heat Transfer in Parabolic Trough Collector Using Green-Synthesized TiO2/Water Nanofluids

[+] Author and Article Information
Eric C. Okonkwo

Department of Energy Systems Engineering,
Faculty of Engineering,
Cyprus International University,
Lefkosa 99258,
Mersin 10, North-Cyprus, Turkey
e-mail: echekwube@ciu.edu.tr

Muhammad Abid, Serkan Abbasoglu, Mustafa Dagbasi

Department of Energy Systems Engineering,
Faculty of Engineering,
Cyprus International University,
Lefkosa 99258,
Mersin 10, North-Cyprus, Turkey

Tahir A. H. Ratlamwala

Department of Engineering Sciences,
National University of Sciences and Technology,

1Corresponding author.

Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING: INCLUDING WIND ENERGY AND BUILDING ENERGY CONSERVATION. Manuscript received June 30, 2018; final manuscript received October 17, 2018; published online November 14, 2018. Assoc. Editor: M. Keith Sharp.

J. Sol. Energy Eng 141(3), 031011 (Nov 14, 2018) (15 pages) Paper No: SOL-18-1291; doi: 10.1115/1.4041847 History: Received June 30, 2018; Revised October 17, 2018

## Abstract

This study presents an experimental nanoparticle synthesis and the numerical analysis of a parabolic trough collector (PTC) operating with olive leaf synthesized TiO2/water nanofluid. The PTC is modeled after the LS-2 collector for various operating conditions. An analysis of the heat transfer and entropy generation in the PTC is carried out based on the first and second laws of thermodynamics for various parameters of nanoparticle volumetric concentration (0 ≤ φ ≤ 8%), mass flow rate (0.1 ≤ $m˙$ ≤ 1.1 kg/s), and inlet temperatures (350–450 K) under turbulent flow regime. The effect of these parameters is evaluated on the Nusselt number, thermal losses, heat convection coefficient, outlet temperature, pressure drop, entropy generation rate, and Bejan number. The results show that the values of the Nusselt number decrease with higher concentrations of the nanoparticles. Also, the addition of nanoparticles increases the heat convection coefficient of the nanofluid compared to water. The thermal efficiency of the system is improved with the use of the new nanofluid by 0.27% at flow rates of 0.1 kg/s. The entropy generation study shows that increasing the concentration of nanoparticles considerably decreases the rate of entropy generation in the system. It is also observed that increasing the volumetric concentration of nanoparticles at low mass flow rates has minimal effect on the rate of entropy generation. Finally, a correlation that provides a value of mass flow rate that minimizes the entropy generation rate is also presented for each values of inlet temperature and nanoparticle volumetric concentration.

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## Figures

Fig. 1

Parabolic trough collector system

Fig. 2

Experimental setup for the OLE-TiO2 nanoparticle: (a) olive leaves after washing and drying, (b) crushed olive leaves, (c) aqueous OLE, (d) ethanolic OLE, (e) homogenizer, (f) liquid OLE-TiO2, and (g) scanning electron microscope image of OLE-TiO2

Fig. 3

Energy-dispersive X-ray spectroscopy analysis of OLE-TiO2 nanoparticle

Fig. 4

Thermal model validation for (a) thermal efficiency and (b) outlet temperature

Fig. 5

Variation of Nusselt number with volumetric fraction of nanofluids at (a) 350 K and (b) 450 K

Fig. 6

Variation of heat convection coefficient with volumetric fraction of nanoparticles at (a) 350 K and (b) 450 K

Fig. 7

Variation of heat loss with volumetric fraction of nanoparticles at (a) 350 K and (b)450K

Fig. 8

Variation of thermal efficiency with volumetric fraction of nanoparticles at (a) 350 K and (b) 450 K

Fig. 9

Variation of outlet temperature with volumetric fraction of nanofluids

Fig. 10

Variation of pressure drop with volumetric fraction of nanoparticles at 350 K

Fig. 11

Variation of entropy generation with volumetric fraction of nanofluids at (a) 350 K and (b) 450 K

Fig. 12

Variation of Bejan number with volumetric fraction of nanoparticles

Fig. 13

Variation of entropy generation with mass flow rate at (a) 350 K and (b) 450 K

Fig. 14

Predicted mass flow rate versus numerical results

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