Comparison of Experimental Measurements of Thermal Conductivity of Fe2O3 Nanofluids Against Standard Theoretical Models and Artificial Neural Network Approach

Agarwal, Ravi; Verma, Kamalesh; Agrawal, Narendra Kumar; Singh, Ramvir

doi:10.1007/s11665-019-04202-z

Cited by 14 publications

(8 citation statements)

References 28 publications

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“…An increase in the thermal conductivity with the temperature was also noticed by Agarwal et al [ 105 ] and the effect was higher by increasing the concentration. They also showed that, at higher concentrations, thermal conductivity enhancement with temperature has been more salient compared to NFs of lower concentrations.…”

Section: Characteristics and Conditions Of Nfs That Influence Thermal...supporting

confidence: 64%

“…Enhancements of 16.45% and 19.76% on the thermal conductivity was observed for Fe 2 O 3 /water and Fe 2 O 3 /EG NFs of 2 vol.% at 70 °C compared to water and EG base fluids at 10 °C, respectively. At last, according the experimental results of [ 105 ], the thermal conductivity enhancement rate was higher for EG base NFs compared to water base NFs, for the same concentration of Fe 2 O 3 NPs.…”

Section: Characteristics and Conditions Of Nfs That Influence Thermal...mentioning

confidence: 86%

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A Review of the Advances and Challenges in Measuring the Thermal Conductivity of Nanofluids

et al. 2022

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Fluids containing colloidal suspensions of nanometer-sized particles (nanofluids) have been extensively investigated in recent decades with promising results. Driven by the increase in the thermal conductivity of these new thermofluids, this topic has been growing in order to improve the thermal capacity of a series of applications in the thermal area. However, when it comes to measure nanofluids (NFs) thermal conductivity, experimental results need to be carefully analyzed. Hence, in this review work, the main traditional and new techniques used to measure thermal conductivity of the NFs are presented and analyzed. Moreover, the fundamental parameters that affect the measurements of the NFs’ thermal conductivity, such as, temperature, concentration, preparation of NFs, characteristics and thermophysical properties of nanoparticles, are also discussed. In this review, the experimental methods are compared with the theoretical methods and, also, a comparison between experimental methods are made. Finally, it is expected that this review will provide a guidance to researchers interested in implementing and developing the most appropriate experimental protocol, with the aim of increasing the level of reliability of the equipment used to measure the NFs thermal conductivity.

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Section: Characteristics and Conditions Of Nfs That Influence Thermal...supporting

confidence: 64%

Section: Characteristics and Conditions Of Nfs That Influence Thermal...mentioning

confidence: 86%

A Review of the Advances and Challenges in Measuring the Thermal Conductivity of Nanofluids

et al. 2022

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“…In the solar energy area, the applications of nanofluids can be found, for example, in photovoltaics thermal systems and thermal energy storage [99]; solar collectors, solar stills, and hydrogen conversion (fuel cells) [100]. Several researchers have shown that the thermal conductivity of nanofluids increased as a function of nanoparticles volume concentration and temperature [101][102][103][104]. Regarding the increase in thermal conductivity with temperature, this characteristic of nanofluids can positively benefit the cooling of the modules.…”

Section: Solar Panels and Collectorsmentioning

confidence: 99%

Recent Developments on the Thermal Properties, Stability and Applications of Nanofluids in Machining, Solar Energy and Biomedicine

et al. 2022

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In this review work, the recent progress made in the use of nanofluids (NFs) applied in three specific areas will be presented: machining, solar energy, and biomedical engineering. Within this context, the discussions will be guided by emphasizing the thermal and stability properties of these fluids. In machining, NFs play a prominent role in the processes of turning, milling, drilling, and grinding, being responsible for their optimization as well as improving the useful life of the tools and reducing costs. In the solar energy field, NFs have been used in the thermal management of the panels, controlling and homogenizing the operating temperature of these systems. In the biomedical area, the advantages of using NFs come from the treatment of cancer cells, the development of vaccines before the improvement of diagnostic imaging, and many others. In all lines of research mentioned in this study, the main parameters that have limited or encouraged the use of these fluids are also identified and debated. Finally, the discussions presented in this review will inspire and guide researchers in developing new techniques to improve the applications of NFs in several fields.

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“…Agarwal et al [52] investigated the performance of nanofluids containing Fe 2 O 3 for the use in heat transfer systems. The performance of nanofluids was investigated in terms of the effect on the thermal conductivity by increasing the concentration of Fe 2 O 3 nanoparticles in water-based liquids and ethylene glycol at 10, 20, 30, 40, 50, 60 and 70 °C.…”

Section: Introductionmentioning

confidence: 99%

A hybrid genetic–BP algorithm approach for thermal conductivity modeling of nanofluid containing silver nanoparticles coated with PVP

Paknezhad

Vakili

Bozorgi

et al. 2020

J Therm Anal Calorim

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Since nanoparticles play a significant role in increasing the thermal conductivity of fluids, the present study aims to predict the thermal conductivity of silver nanofluid coated with polyvinylpyrrolidone (PVP) by the combinational model of multilayer perceptron artificial neural network and genetic algorithm. For modeling, the results of experimental measurements have been used for thermal conductivity of nanofluid containing PVP-coated silver nanoparticle-based deionized water at 25-55 °C in volume fraction of 250 ppm, 500 ppm and 1000 ppm. Henceforth, genetic algorithm is applied to improve learning process in the artificial neural network. It is in this way that the masses were chosen for each neuron's communications as well as their bias happens according to optimization performed by the genetic algorithm. To evaluate the accuracy of the model in predicting thermal conductivity of nanofluid, mean absolute percentage error, root mean square error, coefficient of determination (R 2 ) and mean bias error have exerted indices which are 1.202, 0.345, 0.989 and − 0.016, respectively. The results of the indices and predictions, compared to the experimental results, show high accuracy and reliable combinational model of artificial neural network and genetic algorithm.

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Comparison of Experimental Measurements of Thermal Conductivity of Fe2O3 Nanofluids Against Standard Theoretical Models and Artificial Neural Network Approach

Cited by 14 publications

References 28 publications

A Review of the Advances and Challenges in Measuring the Thermal Conductivity of Nanofluids

A Review of the Advances and Challenges in Measuring the Thermal Conductivity of Nanofluids

Recent Developments on the Thermal Properties, Stability and Applications of Nanofluids in Machining, Solar Energy and Biomedicine

A hybrid genetic–BP algorithm approach for thermal conductivity modeling of nanofluid containing silver nanoparticles coated with PVP

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