Experimental investigation of specific heat of aqueous graphene oxide Al2O3 hybrid nanofluid

Gao, Yuguo; Xi, Yangyang; Yang, Zhenzhong; Sasmito, Agus P.; Mujumdar, Arun S.; Wang, Lijun

doi:10.2298/tsci190404381g

Cited by 28 publications

(12 citation statements)

References 43 publications

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“…The trend of variations in the SH of the nanofluids in different concentrations was similar to the base fluid since it is the main component of the nanofluid. Gao et al [64] experimentally investigated the SH of GO-Al 2 O 3 /water nanofluid and observed that a higher solid fraction caused more reduction in the SH. The highest reduction ratio in the SH was observed at the lowest temperature (20 • C) and the highest mass fraction of solids (0.15% wt), which was equal to 7%.…”

Section: Specific Heatmentioning

confidence: 99%

Thermophysical Properties of Hybrid Nanofluids and the Proposed Models: An Updated Comprehensive Study

et al. 2021

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Thermal performance of energy conversion systems is one of the most important goals to improve the system’s efficiency. Such thermal performance is strongly dependent on the thermophysical features of the applied fluids used in energy conversion systems. Thermal conductivity, specific heat in addition to dynamic viscosity are the properties that dramatically affect heat transfer characteristics. These features of hybrid nanofluids, as promising heat transfer fluids, are influenced by different constituents, including volume fraction, size of solid parts and temperature. In this article, the mentioned features of the nanofluids with hybrid nanostructures and the proposed models for these properties are reviewed. It is concluded that the increase in the volume fraction of solids causes improvement in thermal conductivity and dynamic viscosity, while the trend of variations in the specific heat depends on the base fluid. In addition, the increase in temperature increases the thermal conductivity while it decreases the dynamic viscosity. Moreover, as stated by the reviewed works, different approaches have applicability for modeling these properties with high accuracy, while intelligent algorithms, including artificial neural networks, are able to reach a higher precision compared with the correlations. In addition to the used method, some other factors, such as the model architecture, influence the reliability and exactness of the proposed models.

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Section: Specific Heatmentioning

confidence: 99%

Thermophysical Properties of Hybrid Nanofluids and the Proposed Models: An Updated Comprehensive Study

et al. 2021

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“…They observed that the maximum reduction in specific heat capacity of nanofluid in comparison with the pure base fluid was around 30.12% in mixture ratio of 2:1. In another research, Gao et al [43] measured the specific heat capacity of a hybrid nanofluid, GO-Al 2 O 3 /water nanofluid for different concentrations and temperatures. They found that by increasing the concentration, the specific heat capacity of the nanofluid was decreased and this reduction was more noticeable at lower temperatures.…”

Section: Specific Heat Capacity Of Nanofluidsmentioning

confidence: 99%

“…Similar correlations have been proposed for hybrid nanofluids. For instance, Gao et al [43] proposed a correlation for specific heat capacity of GO-Al 2 O 3 /water by considering concentration and temperature as the inputs, which is:…”

Section: Specific Heat Capacity Of Nanofluidsmentioning

confidence: 99%

Utilization of Machine Learning Methods in Modeling Specific Heat Capacity of Nanofluids

Assad¹,

Mahariq²,

Ghandour³

et al. 2022

Computers, Materials &Amp; Continua

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Nanofluids are extensively applied in various heat transfer mediums for improving their heat transfer characteristics and hence their performance. Specific heat capacity of nanofluids, as one of the thermophysical properties, performs principal role in heat transfer of thermal mediums utilizing nanofluids. In this regard, different studies have been carried out to investigate the influential factors on nanofluids specific heat. Moreover, several regression models based on correlations or artificial intelligence have been developed for forecasting this property of nanofluids. In the current review paper, influential parameters on the specific heat capacity of nanofluids are introduced. Afterwards, the proposed models for their forecasting and modeling are proposed. According to the reviewed works, concentration and properties of solid structures in addition to temperature affect specific heat capacity to large extent and must be considered as inputs for the models. Moreover, by using other effective factors, the accuracy and comprehensive of the models can be modified. Finally, some suggestions are offered for the upcoming works in the relevant topics.

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“…PV/T systems simultaneouslygenerate both electric andthermal energies using the same surface area.Several researchers have used air [4], water [5][6][7][8] and nanofluids to cool down PVs or reduce their surface temperatures by using a number of ways, including spraying water [9] and running a water film on the back surface of a PV with the help of nozzles [10]. Nanofluids are mixtures of phase-solid nanoparticles and deionized water, which are different as compared toconventional cooling fluids [11]. Nowadays, nanofluids have widespread applications and they have unique heat transfer characteristics, substantial thermal conductivity, and enhanced heat transfer mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Experimental investigation of using graphene nanoplatelets and hybrid nanofluid as coolant in photovoltaic thermal systems

ALSHIKHI

Kayfeci

2022

Therm sci

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It is a common observation that the photovoltaic (PV) panelshows a compromised performance when its temperature rises. To handle the performance reduction, most PV panels are equipped with a thermal absorber for removing the solar cells? excessive heatwith the help of a heat transfer fluid. The mentioned thermal absorber system is termed as PV thermal or simply PV/T. This study aims to experimentally investigate the effectsof a graphene nano-platelets (GNP) nanofluid, distilled water, and hybrid nanofluid (HyNF)as transfer fluids in PV/Tcollectors. A hybrid nanofluid comprises aluminum oxide (Al2O3) and GNP. An outdoor experimental setup was installed and tested under the climatic conditions in Karab?k (Turkey) to measure the inlet as well as outlet PV/Tfluid temperatures, ambient temperature with solar radiation, and surface temperatures of both PV/T collector and the PV panel. The mass percentage of the coolant fluids was 0.5% (by weight) and their flow rate was 0.5L/m. Resultsshow that the (GNP)nanofluid is the most effective fluid because it showed superior thermal efficiency among all the tested fluids. Adding a thermal unit to the PV/Tunit increased the overall energy efficiency by 48.4%, 52%, and 56.1% using distilled water, hybrid nanofluid, and graphene nanofluid, respectively.

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Experimental investigation of specific heat of aqueous graphene oxide Al2O3 hybrid nanofluid

Cited by 28 publications

References 43 publications

Thermophysical Properties of Hybrid Nanofluids and the Proposed Models: An Updated Comprehensive Study

Thermophysical Properties of Hybrid Nanofluids and the Proposed Models: An Updated Comprehensive Study

Utilization of Machine Learning Methods in Modeling Specific Heat Capacity of Nanofluids

Experimental investigation of using graphene nanoplatelets and hybrid nanofluid as coolant in photovoltaic thermal systems

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