A review of stability, thermophysical properties and impact of using nanofluids on the performance of refrigeration systems

Yıldız, Gökhan; Ağbulut, Ümit; Gürel, Ali Etem

doi:10.1016/j.ijrefrig.2021.05.016

Cited by 79 publications

(16 citation statements)

References 134 publications

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“…The use of working fluids with improved thermal characteristics would be the most efficient of these strategies . Nanofluids are a viable alternative for improving heat transmission in heat exchangers due to their outstanding thermophysical characteristics . Because of the relevance of nanofluids, numerous researchers explored their performance and use in various heat exchangers.…”

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

208

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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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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

208

View full text Add to dashboard Cite

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“…They found that the addition of nanoparticles enhanced the thermophysical properties and heat transfer characteristics of the lubricant. The researchers specified that nanolubricants typically provide greater thermal conductivity and viscosity in comparison to pure lubricants [2]. Sanukrishna and Prakash [3] experimentally investigated the thermophysical properties of a nanolubricant containing TiO 2 nanoparticles for volume fractions of 0.07 to 0.8% in a temperature range of 20 to 90 °C.…”

Section: Referencementioning

confidence: 99%

Experimental investigation of usage of POE lubricants with Al₂O₃, graphene or CNT nanoparticles in a refrigeration compressor

Dağıdır,

Bilen

2023

Beilstein J. Nanotechnol.

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In this study, the use of nanolubricants containing Al2O3, graphene, and carbon nanotubes (CNTs) at different mass fractions in a refrigeration compressor was experimentally investigated. The required electrical power of the compressor was measured to determine the effect of the use of nanolubricants. Nanoparticles used in the preparation of nanolubricants were gradually added to the lubricant to determine the optimum nanoparticle mass fraction for each nanoparticle type. Thus, it was found that the compressor operated safely and efficiently with nanolubricants. Furthermore, the optimum mass fractions were determined to be 0.750% for Al2O3, 0.250% for graphene, and 0.250% for CNTs for operating conditions of this study. As a result, the required electrical power of the compressor decreased by 6.26, 6.82, and 5.55% with the addition of Al2O3, graphene, and CNT nanoparticles at optimum mass fractions of 0.750, 0.250, and 0.250% to the lubricant, respectively, compared to the compressor using pure oil. Moreover, density and dynamic viscosity of the nanolubricant samples used in the experiments were also measured, and their kinematic viscosity, which is an important parameter for lubricants, was calculated. It was determined that the kinematic viscosity continuously increased with increasing nanoparticle fraction. In conclusion, nanolubricants containing nanoparticles above the optimum mass fraction increase the required electrical power of the compressor. It is concluded that nanoparticle fractions should not be used above the optimum value in nanolubricant applications.

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“…It should be remembered that at higher temperatures, the enhanced viscosity with the nanoparticles will diminish and become closer to that of the base fluid. Therefore, for such systems, the slight increase in viscosity caused by the addition of nanoparticles may be disregarded [50]. The thermal conductivity of the nanorefrigerant was enhanced with the doping of TiO 2 nanoparticles.…”

Section: Average Absolute Deviation Estimationmentioning

confidence: 99%

Pressure Drop and Heat Transfer Characteristics of TiO2/R1234yf Nanorefrigerant: A Numerical Approach

Bibin,

Gundabattini

2023

Sustainability

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Global warming is one of the most dangerous ecological issues facing the globe. Refrigerants are a major contributor to global warming. This investigation mainly focuses on the analysis of a greener nanorefrigerant. Nanorefrigerant can improve the efficiency of refrigeration and air conditioning systems that use vapor compression. In the present investigation, mathematical and computational methods are used to assess the heat transfer and pressure drop properties of TiO2/R1234yf. In order to analyze the heat transfer characteristics and the transport features of the innovative nanorefrigerant, appropriate mathematical predictive models were adapted from earlier investigations. The models are validated by the experiments using TiO2/POE nanolubricant as a test fluid. The investigation was conducted with a temperature range of 10 °C to 40 °C and a volume percentage of nano-sized TiO2 particles in R1234yf refrigerant ranging from 0.2 to 1%. According to the research, the introduction of nanoparticles increases viscosity, thermal conductivity, and density. However, as the amount of nanoparticles rises, the specific heat capacity of the nano-enhanced refrigerant decreases. The nanorefrigerant’s heat transfer coefficient and pressure drop are improved by 134.03% and 80.77%, respectively. The outcomes observed from the predictive technique and the simulation approach had an average absolute variation of 9.91%.

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A review of stability, thermophysical properties and impact of using nanofluids on the performance of refrigeration systems

Cited by 79 publications

References 134 publications

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

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

Experimental investigation of usage of POE lubricants with Al₂O₃, graphene or CNT nanoparticles in a refrigeration compressor

Pressure Drop and Heat Transfer Characteristics of TiO2/R1234yf Nanorefrigerant: A Numerical Approach

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A review of stability, thermophysical properties and impact of using nanofluids on the performance of refrigeration systems

Cited by 79 publications

References 134 publications

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

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

Experimental investigation of usage of POE lubricants with Al2O3, graphene or CNT nanoparticles in a refrigeration compressor

Pressure Drop and Heat Transfer Characteristics of TiO2/R1234yf Nanorefrigerant: A Numerical Approach

Contact Info

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Experimental investigation of usage of POE lubricants with Al₂O₃, graphene or CNT nanoparticles in a refrigeration compressor