Evaluation of surfactant on stability and thermal performance of Al2O3-ethylene glycol (EG) nanofluids

Zhai, Yuling; Li, Long; Wang, Jiang; Li, Zhouhang

doi:10.1016/j.powtec.2018.11.051

Cited by 89 publications

(30 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The cumulative uncertainty of the values obtained as a result of the experimental studies was calculated by the following equation constructed on the uncertainty of individually apparatus used in the experimental measurements 44 :…”

Section: Validation Of Experimentsmentioning

confidence: 99%

An experimental study on the comparative analysis of the effect of the number of data on the error rates of artificial neural networks

Çolak

2020

Int J Energy Res

107

View full text Add to dashboard Cite

In this study, the effect of the amount of data used in the design of artificial neural networks (ANNs) on the predictive accuracy of ANNs was investigated. Five different ANNs were designed using the experimentally measured specific heat data of the Al 2 O 3 /water nanofluid prepared at volumetric concentrations of 0.0125, 0.025, 0.05, 0.1 and 0.2 using the Al 2 O 3 nanoparticle. The developed ANN is a multi-layer perceptron, feedforward and backpropagation model. In each ANN with 15 neurons in the hidden layer, the volumetric concentration (φ) and temperature (T) values were nominated as input layer factors and the specific heat value was estimated as the output value. With the aim of survey the effect of the amount of data on the predicted results of the ANN, a different amount of datasets were used in each developed ANN. In this context, in total 260 data were used in the Model 1 ANN. Subsequently, the total amount of data was reduced by 20% in each developed neural network and 55 data were used in the ANN named Model#5. The results obtained show that ANNs are highly talented of predicting the specific heat values of Al 2 O 3 /water nanofluid. However, in the comparisons, it was evaluated that the amount of data used had a share on the prediction performance of the ANN and that the decrease in the amount of data with the prediction performance of the ANN decreased.

show abstract

Section: Validation Of Experimentsmentioning

confidence: 99%

An experimental study on the comparative analysis of the effect of the number of data on the error rates of artificial neural networks

Çolak

2020

Int J Energy Res

107

View full text Add to dashboard Cite

show abstract

“…The dispersion results of the previous studies show that low-frequency ultrasonic was more effective than high-frequency ultrasonic in disaggregate powder and dispersion, and 20 kHz ultrasonic probe was found to be very effective in breaking large agglomerations [44][45][46][47][48][49][50]. Heretofore, understanding the effect of frequency on the dispersion stability of nanoparticles in lube oils remains not discussed.…”

Section: Days 21 Daysmentioning

confidence: 99%

Effect of Mixing Temperature, Ultrasonication Duration and Nanoparticles/Surfactant Concentration on the Dispersion Performance of Al2O3 Nanolubricants

Liu¹,

Hou²,

Li³

et al. 2020

Preprint

View full text Add to dashboard Cite

The main goal of this study is to improve the dispersion stability of Al2O3 nanoparticles in polyalphaolefin oil to overcome the sedimentation problem of nanoparticles using the addition of oleic acid as a surfactant. This work investigates the effect of the settling time, temperature, ultrasonic duration and nanoparticles/surfactant (oleic acid) concentration on the dispersion stability of Al2O3 nanoparticles in oil-based solutions. Herein, the visual observation, UV–Vis spectroscopy, dynamic light scattering (DLS) analysis, and transmission electron microscopy (TEM) images were used to evaluate the dispersion stability of the Al2O3 nanoparticles. The results reveal that the thermal method during the synthesis of nanofluids using 50 °C temperature improves the dispersion of nanoparticles. The results also exhibited that increasing the ultrasonic amplitude and prolonging the ultrasonic time during the synthesis of nanofluids influences the dispersion stability. The results showed that the nanolubricant with 0.8 wt.% oleic acid provides better dispersion behavior. Furthermore, the nanolubricants containing 0.005wt% and 0.01wt% Al2O3 nanoparticles demonstrated outstanding dispersion performance, and the stability time reached more than 160 days.

show abstract

“…The maximum stability time was 93 days with 1.0ml of CTAB [57]. Zhai et al reported that PVP performs better than SDS for stabilize Al2O3/EG nanofluid (stable for exceeding 30 days) [58].…”

Section: Addition Of Surfactants1mentioning

confidence: 99%

A review on thermo-physical properties of bio, non-bio and hybrid nanofluids

Chen¹,

Oumer²,

Aziz³

2019

JMES

View full text Add to dashboard Cite

The pressure drop and thermal performance of various nanofluids can be affected by their thermo-physical properties. However, there are many different parameters that need to be considered when determining the thermo-physical properties of nanofluids. This paper highlights a detail reviews on the thermo-physical properties of nanofluids with different material type and effect of some process parameters (such as material type, temperature and concentration) on the thermo-physical properties of nanofluids. Four thermo-physical properties mainly density, viscosity, thermal conductivity and specific heat capacity from different literatures were summarized, discussed and presented. The lowest viscosity value of nanofluids in literature was mango bark water-based nanofluid (0.81cP). On the other hand, the maximum thermal conductivity value of nanofluids in the literature was GNP-Ag water-based nanofluid (0.69W/mK). The density and specific heat capacity are strongly dependent on the material type. Meanwhile, the viscosity and thermal conductivity are greatly affected by temperature and concentration. The most influential parameters on thermo-physical properties of nanofluids are material type followed by temperature. Most of the literatures confirmed bio nanofluids have low viscosity value and hybrid have high thermal conductivity values.

show abstract

Evaluation of surfactant on stability and thermal performance of Al2O3-ethylene glycol (EG) nanofluids

Cited by 89 publications

References 28 publications

An experimental study on the comparative analysis of the effect of the number of data on the error rates of artificial neural networks

An experimental study on the comparative analysis of the effect of the number of data on the error rates of artificial neural networks

Effect of Mixing Temperature, Ultrasonication Duration and Nanoparticles/Surfactant Concentration on the Dispersion Performance of Al2O3 Nanolubricants

A review on thermo-physical properties of bio, non-bio and hybrid nanofluids

Contact Info

Product

Resources

About