A critical review of specific heat capacity of hybrid nanofluids for thermal energy applications

Adun, Humphrey; Wole‐Osho, Ifeoluwa; Okonkwo, Eric C.; Kavaz, Doğa; Dagbasi, Mustafa

doi:10.1016/j.molliq.2021.116890

Cited by 47 publications

(14 citation statements)

References 114 publications

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“…Moreover, specic heat is another important thermophysical property for enhancing the thermal performance of heat transfer devices. 46,47 In this work, specic heat of mono nanouids and hybrid nanouids is calculated using the existing theoretical equations as shown below and the specic heat values with respect to concentration of nanoparticles are tabulated in Table 2.…”

Section: Specic Heat Of Mono and Hybrid Nanouidsmentioning

confidence: 99%

Synthesis, thermophysical characterization and thermal performance analysis of novel Cu-MXene hybrid nanofluids for efficient coolant applications

Kumar,

Shaik

2023

RSC Adv.

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Section: Specic Heat Of Mono and Hybrid Nanouidsmentioning

confidence: 99%

Synthesis, thermophysical characterization and thermal performance analysis of novel Cu-MXene hybrid nanofluids for efficient coolant applications

Kumar,

Shaik

2023

RSC Adv.

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“…The investigational specific heat capacity

(C_{italicPhnf}

) of the blended nanofluid is calculated using the utterance shown below (1).

C_{italicPhnf} = C_{italicPbf} (1 - \emptyset_{1}) + C_{Pn p_{1 +} n p_{2}} \emptyset_{1,}

where

C_{italichnf}

is the SHC of hybrid/blended nanofluids,

(\emptyset_{1})

are volume fractions, C Pbf is the SHC of the base fluid, and

C_{Pn p_{1 +} n p_{2}}

represents the SHC of nanoparticles (Saleh & Sundar, 2021). Prior research on the specific heat capacity of nanofluids from MLP perspectives has been restricted to comparatively simple nanofluids such as Al 2 N 3 /EG, TiN/EG, Si 3 N 4 /EG, Al 2 O 3 /DW, Graphene nanoparticle/DW, MWCNT/DW, Al 2 O 3 in 40% H 2 O‐60% EG, SnO 2 in 40% H 2 O‐60% EG, ZnO in 40% H2O‐60% EG, and CuO‐ (Adun et al, 2021; Çolak et al, 2020; Gao et al, 2021; Kumar et al, 2021; Mousavi et al, 2019; Saleh & Sundar, 2021; Tiwari et al, 2020; Verma et al, 2018; Zainon & Azmi, 2021). In terms of heat transfer attributes, hybrid/blended nanofluids, that further consolidate the advantageous characteristics of two separate nanoparticles, exceed the preceding nanofluids.…”

Section: Statistical Analysis Of Data‐setmentioning

confidence: 99%

Soft computing approaches for prediction of specific heat capacity of hybrid nanofluids

Mathur,

Gupta,

Panwar

et al. 2023

Expert Systems

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Nanofluids are thermoelectric substance with a greater thermal transmission effectiveness than traditional thermal fluids. Nanofluid's specific heat capacity (SHC) is a crucial thermophysical parameter since it controls the fluid's heat transfer coefficient. Surprisingly few research investigations have been done on the prognostic modelling of the specific heat capacity of fusion nanofluids, despite the fact that numerous experiments have been conducted on the heat transfer capacitance of blended nanofluids. This study focuses on the use of algorithms based on machine learning procedures (MLP) to estimate the SHC of blended nanofluids. Numerous numbers of hybrid nanofluids are investigated in this investigation. Total of 984 samples of hybrid nanofluids for specific heat capacity has been collected from nine experimental research papers. Various MLP techniques were utilized in this analysis, including extreme boost gradient boost regression (XGB), support vector regression augmented with a genetic algorithm (support vector regression [SVR]‐genetic algorithm [GA]), grid search optimized based gradient boost regression algorithm (GBR) (GBR‐GSO), and voting ensemble procedure (VE). The obtained correlation coefficients of the SVR‐GA, XGB, VE and GBR‐GSO models for the testing dataset are 98.43%, 96.29%, 94.4% and 96.55% respectively. The SVR‐GA model showed a better predictive accuracy relative to other ML models. This SVR‐GA anticipated model could be used for quick and reliable prediction of the SHC of blended nanofluids which reduces the burden associated with experimental measurement possible future work includes applying a machine‐learning strategy to the problem of determining the diffusivity of hybrid nanofluids.

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“…Due to traditional fluids limited heat transfer capacity, hybrid nanofluids have become a new and efficient heat exchange fluid. − The synthesis process of these nanofluids is more complicated than that for traditional nanofluids.…”

Section: Predicting the Viscosity Of Nanofluids Using Machine Learnin...mentioning

confidence: 99%

“…This is called traditional nanofluid. Recently, hybrid nanofluids are regarded as a more promising heat transfer fluid, and different solid materials are added to the base fluid to prepare hybrid nanofluids. − Suresh et al synthesized Al 2 O 3 –Cu hybrid particles chemically through hydrogen reduction technology. Then the synthesized nanocomposite powder was dispersed in deionized water to prepare a mixed nanofluid .…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Predicting the Thermophysical Properties of Nanofluids Using Machine Learning Methods

Wang

Chen

2022

Ind. Eng. Chem. Res.

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Nanofluids are often used as heat transfer fluids due to their good thermal and flow properties. Nanofluids are widely used in energy systems such as solar collectors, heat exchangers, and heat pipes. The thermophysical properties of nanofluids can significantly affect their performance in engineering systems. According to current research in this field, machine learning is a very attractive method to predict the thermophysical properties of nanofluids. We believe that, compared with experiments, machine learning methods are more efficient, rapid, accurate, and practical. The accuracy of the model is affected by factors such as the input variables, the data set selected during the modeling process, and the applied algorithm. This is a comprehensive review that describes the use of different machine learning methods to predict the thermophysical properties of nanofluids (conventional and hybrid nanofluids). This is very useful for researchers, scientists, and engineers in the field of nanofluids.

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A critical review of specific heat capacity of hybrid nanofluids for thermal energy applications

Cited by 47 publications

References 114 publications

Synthesis, thermophysical characterization and thermal performance analysis of novel Cu-MXene hybrid nanofluids for efficient coolant applications

Synthesis, thermophysical characterization and thermal performance analysis of novel Cu-MXene hybrid nanofluids for efficient coolant applications

Soft computing approaches for prediction of specific heat capacity of hybrid nanofluids

A Comprehensive Review of Predicting the Thermophysical Properties of Nanofluids Using Machine Learning Methods

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