A comprehensive statistical approach for determining the effect of two non-ionic surfactants on thermal conductivity and density of Al2O3–water-based nanofluids

Altun, Aycan; Şara, Osman Nuri; Şimşek, Barış

doi:10.1016/j.colsurfa.2021.127099

Cited by 20 publications

(2 citation statements)

References 68 publications

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“…The thermal cooling efficiency of single-phase and multiphase flow via fluted tubes was then examined [29]. Altun [30] showed that combining graphene nano-sheets with Al2O3 water in nanofluids improves heat conductivity over base fluids. Siricharoenpanitch et al [31] studied the influence of relevant parameters such as pulsing flow, nanofluid concentration, corrugated tube morphologies, and heat flux on thermal efficiency with a magnetic field.…”

Section: Introductionmentioning

confidence: 99%

Heat transfer and Flow Analysis for Square Tube with Oscillating Electromagnetic Field with Experimental Data by Artificial Neural Network

Sirijaroenpanitch,

Vengsungnle,

Naphon

et al. 2024

Preprint

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The heat transfer and friction factor of ferrofluid passing through a square tube with an oscillating electromagnetic field were investigated experimentally. The impact of electromagnetic rotating direction, flux, and frequency on heat and flow characteristics has been investigated. The Brownian motion of particles has been shown to considerably influence the direction, power, and frequency of electromagnetic rotation, resulting in higher heat transfer. The interruption of electromagnetic flow raises the friction factor even further. In addition, a three-layer back propagation network model is built, with input, hidden, and output layers numbered 5, 17, and 2, respectively. This ANN model performed well statistically, with correlation coefficients ranging from 0.99939 to 0.9996 and mean square errors (MSE) ranging from 0.0106 to 0.0190. The ANN results match the observed data within ± 5% and ± 10% error ranges for heat and flow characteristics, respectively. As a consequence, this machine learning approach might be used to forecast heat exchanger thermal performance.

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Section: Introductionmentioning

confidence: 99%

Heat transfer and Flow Analysis for Square Tube with Oscillating Electromagnetic Field with Experimental Data by Artificial Neural Network

Sirijaroenpanitch,

Vengsungnle,

Naphon

et al. 2024

Preprint

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“…The results showed that 3 hours of ultrasonic bath procedure with 0.1 wt.% surfactant addition can provide the most stable suspension with the best thermal conductivity for subsequent applications within 1 month. Other than that, Altun et al, [18] investigate the thermal conductivity enhancement of Al2O3/water nanofluid compared to based fluid after measuring the thermal conductivity of the nanofluid with the optimum amount of surfactant. This method also accurately compared thermal conductivity enhancement after adding a surfactant to the nanofluid.…”

Section: Introductionmentioning

confidence: 99%

Thermal Conductivity-Based Optimisation of Surfactant on Hybrid Nanofluid

Harun

Sidik

2022

ARFMTS

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A surfactant is an efficient approach for increasing the stability of nanofluids. However, excessive surfactant degrades the hybrid nanofluid's outstanding thermal conductivity. Additionally, there was only a little research on optimising the amount of surfactant used in nanofluids depending on their thermal conductivity. As a result, it is critical to ensure that the created nanofluid is stable without impairing thermal conductivity. The optimisation was carried out in this study utilising Design Expert 11. Surfactant ratios and the mixing ratio of hybrid nanofluid were employed as variables, while thermal conductivity was used as the response. Additionally, the concentration and temperature of the hybrid nanofluid remained constant at 0.5 vol% and 40 °C, respectively. The results indicate that a 1:10 ratio of surfactant to TiO2 is the optimal proportion for the generated TiO2-GNP hybrid nanofluid. The correct amount of surfactant results in a hybrid nanofluid with high thermal conductivity and good stability.

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Absorbance Ratio Optimization as a Function of TiO2-POE Nanolubricant Spectrophotometric Wavelength Using the Quadratic Design on One Factor at a Time

Nugroho

Mamat

Zhang

et al. 2022

Proceedings of the 2nd Energy Security and Chemical Engineering Congress

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A comprehensive statistical approach for determining the effect of two non-ionic surfactants on thermal conductivity and density of Al2O3–water-based nanofluids

Cited by 20 publications

References 68 publications

Heat transfer and Flow Analysis for Square Tube with Oscillating Electromagnetic Field with Experimental Data by Artificial Neural Network

Heat transfer and Flow Analysis for Square Tube with Oscillating Electromagnetic Field with Experimental Data by Artificial Neural Network

Thermal Conductivity-Based Optimisation of Surfactant on Hybrid Nanofluid

Absorbance Ratio Optimization as a Function of TiO2-POE Nanolubricant Spectrophotometric Wavelength Using the Quadratic Design on One Factor at a Time

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