An experimental study on optimum concentration of silver-water microfluid for enhancing heat transfer performance of a plate heat exchanger

Pourhoseini, S.H.; Naghizadeh, N.

doi:10.1016/j.jtice.2017.03.002

Cited by 14 publications

(6 citation statements)

References 45 publications

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“…After the calculation of the outlet temperatures of the heat exchanger, the inlet and outlet steady temperatures and the flow rates of the hot fluid (Fe 3 O 4 -water magnetic nanofluid) and the cold fluid (distilled water) were used in thermodynamic calculation. Thus, the heat transfer rates of hot flow (Q nf  ) and cold flow (Q w  ) were calculated by the following equations [25,46]:…”

Section: Methodsmentioning

confidence: 99%

“…Addition of nanomaterials to base fluids, which results in nanofluids, is a new and exciting method for producing higher heat transfer rate and thermal efficiency [8][9][10][11][12][13][14][15]. Therefore, in the past decade, many studies were done on thermophysical properties of nanofluids and uses of nanofluids in enhancement of heat transfer performance-especially thermal conductivity-of conventional heat transfer fluids such as water [16][17][18][19][20][21][22][23][24][25][26]. Magnetic nanofluids are a particular group of nanofluids containing magnetized nanoparticles typically called magnetite [27].…”

Section: Introductionmentioning

confidence: 99%

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FHD and MHD effects of Fe₃O₄-water magnetic nanofluid on the enhancement of overall heat transfer coefficient of a heat exchanger

2020

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The electrically conducting magnetic nanofluid of Fe3O4-water was made to flow in a heat exchanger. Afterwards, the ferrohydrodynamics (FHD) and magnetohydrodynamics (MHD) effects of the nanofluid on the enhancement of the overall heat transfer coefficient of the heat exchanger were studies at different nanofluid concentrations, inlet temperatures, and different constant magnetic field intensities. The study dealt with both laminar and turbulent nanofluid flows. The electrical conductivity of the magnetic nanofluid was determined by a Jenway 4510 conductivity meter. By using the experimental data, a Sprint CFD code identified the hydrodynamic and thermal behavior of the nanofluid flow in the presence of constant magnetic fields of intensities of 0, 0.05, 0.1 and 1 Tesla. The results showed that an increase in the concentration of Fe3O4-water nanofluid enhanced its electrical conductivity so considerably that the nanofluid, at the volume fraction of 1%, was observed to be 12.5 times more electrically conductive than distilled water. Also, it was shown that increase in magnetic field intensity had an enhancing effect on the overall heat transfer coefficient, and the effect was stronger at higher magnetic nanofluid concentrations. Furthermore, due to generation of chainlike structures by the FHD effect of the Fe3O4 magnetic nanoparticles, the nanoparticles increased the effective thermal conductivity of the nanofluid and, consequently, the overall heat transfer coefficient in both the laminar and turbulent regimes. Finally, unlike the turbulent flow, the laminar regime allowed for the MHD effect having only a negligible effect on the enhancement of the overall heat transfer coefficient. This was due to velocities in the laminar regime being small.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

FHD and MHD effects of Fe₃O₄-water magnetic nanofluid on the enhancement of overall heat transfer coefficient of a heat exchanger

2020

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“…Silver nanoparticles were produced by a chemical reduction method. , In this method, AgNO 3 (17.0 mg) was dissolved in 100 mL of water in a 250 mL trineck flask. The solution was heated to boiling point with a hemisphere heating mantle under vigorous magnetic stirring.…”

Section: Methodsmentioning

confidence: 99%

Effect of Nanosilver/Water-in-Kerosene Emulsion on NOx Reduction and Enhancement of Thermal Characteristics of a Liquid Fuel Burner

Pourhoseini

Esmaeeli

2017

Energy Fuels

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This study investigated the effect of nanosilver/water-in-kerosene emulsion on thermal and radiative characteristics and pollutant emissions of a liquid burner. The results were compared with the corresponding values resulted from neat kerosene. Light microscopic imaging was used to study microexplosion of the fuel droplets. Also, combining chemiluminescence technique and measurement of pollutant concentrations, a qualitative study was carried out to find out the effect of CO, CO2, water vapor, and intermediate soot particles on the flame emissivity coefficient. The results showed that the Brownian motion of silver nanoparticles and localized convection between nanoparticles and the adjacent fluids increased the evaporation rate of nanoemulsion droplets and the rate of their mixing with oxidizing air. Besides the nanoparticles effect, the secondary injection of nanoemulsion due to microexplosion of water droplets enhanced the mixing rate of nanoemulsion fuel and oxidizing air. Although absorption of heat by water content of nanoemulsion fuel decreased the flame temperature, the nanoemulsion fuel, compared with neat kerosene, enhanced the average radiation of flame by 23%. Further, the results indicated that although CO and CO2 did not change significantly in amount, NOx emission reduced as much as 23.7% when the nanosilver/water-in-kerosene emulsion, instead of neat kerosene, was used.

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“…Besides the above approaches, nanofluids are also attractive solutions to improve the efficiencies of heat exchangers. Because of the growing importance of nanofluids, many researchers explored the performances of PHEs employing nanofluids. , In general, researchers studied the pressure drop and heat transfer characteristics. , Many scholars numerically investigated the applications of nanofluids in the PHEs. − Various nanoparticles like SiO, , TiO 2 , CNT, , Al 2 O 3 , − Ag, − CuO, ZnO, and hybrid nanoparticles − have been studied to improve the performance of the PHEs. Kumar et al investigated the effect of nanofluid flow on the performance of the square channel.…”

Section: Machine Learning For Rementioning

confidence: 99%

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Sharma¹,

Said

Kumar³

et al. 2022

Energy Fuels

222

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Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity of the working fluid has a huge impact on the efficiency of the renewable energy system. The addition of a small amount of high thermal conductivity solid nanoparticles to a base fluid improves heat transfer. Even though a large amount of research data is available in the literature, some results are contradictory. Many influencing factors, as well as nonlinearity and refutations, make nanofluid research highly challenging and obstruct its potentially valuable uses. On the other hand, data-driven machine learning techniques would be very useful in nanofluid research for forecasting thermophysical features and heat transfer rate, identifying the most influential factors, and assessing the efficiencies of different renewable energy systems. The primary aim of this review study is to look at the features and applications of different machine learning techniques employed in the nanofluid-based renewable energy system, as well as to reveal new developments in machine learning research. A variety of modern machine learning algorithms for nanofluid-based heat transfer studies in renewable and sustainable energy systems are examined, along with their advantages and disadvantages. Artificial neural networks-based model prediction using contemporary commercial software is simple to develop and the most popular. The prognostic capacity may be further improved by combining a marine predator algorithm, genetic algorithm, swarm intelligence optimization, and other intelligent optimization approaches. In addition to the well-known neural networks and fuzzy- and gene-based machine learning techniques, newer ensemble machine learning techniques such as Boosted regression techniques, K-means, K-nearest neighbor (KNN), CatBoost, and XGBoost are gaining popularity due to their improved architectures and adaptabilities to diverse data types. The regularly used neural networks and fuzzy-based algorithms are mostly black-box methods, with the user having little or no understanding of how they function. This is the reason for concern, and ethical artificial intelligence is required.

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An experimental study on optimum concentration of silver-water microfluid for enhancing heat transfer performance of a plate heat exchanger

Cited by 14 publications

References 45 publications

FHD and MHD effects of Fe₃O₄-water magnetic nanofluid on the enhancement of overall heat transfer coefficient of a heat exchanger

FHD and MHD effects of Fe₃O₄-water magnetic nanofluid on the enhancement of overall heat transfer coefficient of a heat exchanger

Effect of Nanosilver/Water-in-Kerosene Emulsion on NOx Reduction and Enhancement of Thermal Characteristics of a Liquid Fuel Burner

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

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An experimental study on optimum concentration of silver-water microfluid for enhancing heat transfer performance of a plate heat exchanger

Cited by 14 publications

References 45 publications

FHD and MHD effects of Fe3O4-water magnetic nanofluid on the enhancement of overall heat transfer coefficient of a heat exchanger

FHD and MHD effects of Fe3O4-water magnetic nanofluid on the enhancement of overall heat transfer coefficient of a heat exchanger

Effect of Nanosilver/Water-in-Kerosene Emulsion on NOx Reduction and Enhancement of Thermal Characteristics of a Liquid Fuel Burner

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Contact Info

Product

Resources

About

FHD and MHD effects of Fe₃O₄-water magnetic nanofluid on the enhancement of overall heat transfer coefficient of a heat exchanger

FHD and MHD effects of Fe₃O₄-water magnetic nanofluid on the enhancement of overall heat transfer coefficient of a heat exchanger