Buoyancy Driven Heat Transfer Behavior of Zinc Oxide (ZnO)–Water Nanofluids

Paul, Titan C.; Morshed, A. K. M. M.; McCants, Dale A.; Khan, Jamil A.

doi:10.1115/ht2013-17483

Cited by 3 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The viscosity values of the ZnONPs/DW nanofluid obtained at temperature values of 303 and 343K were 1.21 and 0.6 cp respectively at ZnO concentration of 1%. These results are in agreement with the research finding in the paper [25].…”

Section: Resultssupporting

confidence: 94%

Effect of ZnO Nanoparticles on the Thermo-Physical Properties and Heat Transfer of Nano-Fluid Flows

Dhiaa¹,

Salih²,

Al-Yousefi³

2020

IJHT

View full text Add to dashboard Cite

In this research, an improvement in alternative heat transfer and thermal properties of ZnO nanoparticles (NPs)/ distilled water, DW nanofluid are experimentally studied and investigated. The two-step approach is utilized to prepare the ZnO NPs/water nanofluid using different concentrations and different inlet temperatures. Scanning Electron Microscopy (SEM) and UV–visible spectrophotometer is employed to characterize the nanofluid. Varying flow rates and the temperatures are examined in the heat exchanger's horizontial tube and shell pipe as well as the impacts of ZnO nanoparticles and the temperatures are studied on nanofluid's thermal conductivity and viscosity. The findings show improvements in the thermal conductivity of ZnO NPs / DW nanofluid. A maximum value of 6.67% of the thermal conductivity is achieved at a temperature of 343K using 1 % ZnO NPs. Nusselt Number also shown an improvement of 38% at a temperature of 343K using 0.2% wt of ZnO NPs under the turbulent condition (Reynolds Number range of 8000 to 20000). Experimental results are compared with previous correlations and an acceptable agreement is observed.

show abstract

Section: Resultssupporting

confidence: 94%

Effect of ZnO Nanoparticles on the Thermo-Physical Properties and Heat Transfer of Nano-Fluid Flows

Dhiaa¹,

Salih²,

Al-Yousefi³

2020

IJHT

View full text Add to dashboard Cite

show abstract

“…The database includes experimental data-sets collected from various works of literature. In this study, 523 and 302 experimental data samples (Abdollahi et al, 2018;Agarwal et al, 2017;Azmi et al, 2012;Barbés et al, 2013;Buonomo et al, 2015;Colla et al, 2015;Ferrouillat et al, 2013;Ghodsinezhad et al, 2016;Ho et al, 2010;Kim et al, 2007;Kumar et al, 2016;Madhesh et al, 2016;McCants et al, 2009;Pak and Cho, 2007;Pantzali et al, 2009;Pastoriza-Gallego et al, 2009;Patil et al, 2018;Paul et al, 2013;Ramalingam et al, 2016;Saxena et al, 2016;Suresh et al, 2016;Tso and Chao, 2015;Zhu et al, 2011) were taken for the model development of viscosity and thermal conductivity of nanofluids, respectively, which have been extracted from the database. Data includes various nanofluids namely Copper Oxide/water, Aluminium oxide/water, Titanium dioxide/water, Zinc oxide/water, Silver/water, Silicon dioxide/water, and Cerium oxide/water.…”

Section: Data-setmentioning

confidence: 99%

Modelling of Viscosity and Thermal Conductivity of Water-Based Nanofluids using Machine-Learning Techniques

Ganga,

Uddin,

Asthana

et al. 2023

Int. j. math. eng. manag. sci.

View full text Add to dashboard Cite

In this study, a variety of machine-learning algorithms are used to predict the viscosity and thermal conductivity of several water-based nanofluids. Machine learning algorithms, namely decision tree, random forest, extra tree, KNN, and polynomial regression, have been used, and their performances have been compared. The input parameters for the prediction of the thermal conductivity of nanofluids include temperature, concentration, and the thermal conductivity of nanoparticles. A three-input and a two-input model were utilized in modelling the viscosity of nanofluid. Both models considered temperature and concentration as input parameters, and additionally, the type of nanoparticle was considered for the three-input model. The order of importance of the most influential parameters in predicting both viscosity and thermal conductivity was studied. A wider range of input parameters have been considered in an open-access database. With the existing experimental data, all of the developed machine learning models exhibit reasonable agreement. Extra trees were found to provide the best results for estimating thermal conductivity, with a value of 0.9403. In predicting viscosity using a three-input model, extra trees were found to provide the best result with a value of 0.9771, and decision trees were found to provide the best results for estimating the viscosity using a two-input model with a value of 0.9678. In order to study heat transport phenomena through mathematical modelling, it is important to have an explicit mathematical expression. Therefore, the formulation of mathematical expressions for predicting viscosity and thermal conductivity has been carried out. Additionally, a comparison with the Xue and Maxwell thermal conductivity models is made to validate the results of this study, and the results are observed to be reliable.

show abstract

Enhancement of natural convection heat transfer in a square cavity using MWCNT/Water nanofluid: an experimental study

Joshi

Pattamatta

2017

Heat Mass Transfer

View full text Add to dashboard Cite

Buoyancy Driven Heat Transfer Behavior of Zinc Oxide (ZnO)–Water Nanofluids

Cited by 3 publications

References 0 publications

Effect of ZnO Nanoparticles on the Thermo-Physical Properties and Heat Transfer of Nano-Fluid Flows

Effect of ZnO Nanoparticles on the Thermo-Physical Properties and Heat Transfer of Nano-Fluid Flows

Modelling of Viscosity and Thermal Conductivity of Water-Based Nanofluids using Machine-Learning Techniques

Enhancement of natural convection heat transfer in a square cavity using MWCNT/Water nanofluid: an experimental study

Contact Info

Product

Resources

About