Evaluating the effect of temperature and concentration on the thermal conductivity of ZnO-TiO2/EG hybrid nanofluid using artificial neural network and curve fitting on experimental data

Safaei, Mohammad Reza; Hajizadeh, Ahmad; Afrand, Masoud; Qi, Cong; Yarmand, Hooman; Zulkifli, Norhayah

doi:10.1016/j.physa.2018.12.010

Cited by 150 publications

(40 citation statements)

References 52 publications

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“…Its viscosity is calculated via Equation (6) [45]. Further, Equation 7calculates the thermal conduction of water, while its density and specific heat are evaluated by Equations (8) and (9), respectively [1].…”

Section: Nanofluid Propertiesmentioning

confidence: 99%

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Configuration and Optimization of a Minichannel Using Water–Alumina Nanofluid by Non-Dominated Sorting Genetic Algorithm and Response Surface Method

et al. 2020

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Nanofluids in minichannels with various configurations are applied as cooling and heating fluids. Therefore, it is essential to have an optimal design of minichannels. For this purpose, a square channel with a cylinder in the center connected to wavy fins at various concentrations of an Al2O3 nanofluid is simulated using the finite volume method (FVM). Moreover, central composite design (CCD) is used as a method of design of experiment (DOE) to study the effects of three input variables, namely the cylinder diameter, channel width, and fin radius on the convective heat transfer and pumping power. The impacts of the linear term, together with those of the square and interactive on the response variables are determined using Pareto and main effects plots by an ANOVA. The non-dominated sorting genetic algorithm-II (NSGA-II), along with the response surface methodology (RSM) is applied to achieve the optimal configuration and nanofluid concentration. The results indicate that the effect of the channel width and cylinder diameter enhances about 21% and 18% by increasing the concentration from 0% to 5%. On the other hand, the pumping power response is not sensitive to the nanofluid concentration. Besides, the channel width has the highest and lowest effect on the heat transfer coefficient (HTC) and pumping power, respectively. The optimization for a concentration of 3% indicates that in Re = 500 when the geometry is optimized, the HTC enhances by almost 9%, while the pumping power increases by about 18%. In contrast, by increasing the concentration from 1% to 3%, merely an 8% enhancement in HTC is obtained, while the pumping power intensifies around 60%.

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Section: Nanofluid Propertiesmentioning

confidence: 99%

“…Furthermore, research has been conducted to predict [7][8][9] and optimize [10][11][12] the desired performance of nanofluids in multiple conditions. It should be noted that a few studies have been conducted on the optimization of configuration to obtain the conditions with the expected thermohydraulic attributes.…”

Section: Introductionmentioning

confidence: 99%

Configuration and Optimization of a Minichannel Using Water–Alumina Nanofluid by Non-Dominated Sorting Genetic Algorithm and Response Surface Method

et al. 2020

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“…Several methods and approaches are suggested for estimating the thermophysical properties of the nanofluids [30][31][32][33]. Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), and correlation obtained by curve fitting are among the most applicable ones used in recent years [34][35][36][37]. Wu et al [38] utilized curve fitting for the TC modeling of ZnO-multi walled carbon nano tube (MWCNT)/engine oil nanofluid by using the solid phase fraction and temperature as the inputs and observed that the proposed correlation was able to predict the TC of the nanofluid with maximum deviation lower than 1%.…”

Section: Introductionmentioning

confidence: 99%

Thermal Conductivity Modeling of Nanofluids Contain MgO Particles by Employing Different Approaches

et al. 2020

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The existence of solid-phase nanoparticles remarkably improves the thermal conductivity of the fluids. The enhancement in this property of the nanofluids is affected by different items such as the solid-phase volume fraction and dimensions, temperature, etc. In the current paper, three different mathematical models, including polynomial correlation, Multivariate Adaptive Regression Spline (MARS), and Group Method of Data Handling (GMDH), are applied to forecast the thermal conductivity of nanofluids containing MgO particles. The inputs of the model are the base fluid thermal conductivity, volume concentration, and average dimension of solid-phase, and nanofluids’ temperature. Comparing the proposed models revealed higher confidence of GMDH in estimating the thermal conductivity, which is attributed to its complicated structure and more appropriate consideration of the input’s interaction. The values of R-squared for the correlation, MARS, and GMDH are 0.9949, 0.9952, and 0.9991, respectively. In addition, based on the sensitivity analysis, the effect of thermal conductivity of the base fluid on the overall thermal conductivity of nanofluids is more remarkable compared with the other inputs such as volume fraction, temperature, and dimensions of the particles which are used as the inputs of the models.

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“…Zinc Oxide (ZnO) has also been considered as a potential material for EOR because of its excellent dielectric properties and high dielectric loss in the presence of EM field [17]. To increase the activity of nanoparticles, electromagnetic waves of a specific frequency creates resonance in the nanoparticles changes the oil viscosity and compels to move inside the porous medium [18,19]. Various theoretical models were also proposed by researchers to describe the mechanisms involved in the chain-like formation, such as water-bridge model, electric double layer model and polarization model [20,21].…”

Section: Introductionmentioning

confidence: 99%

Absorption of electromagnetic waves in sandstone saturated with brine and nanofluids for application in enhanced oil recovery

Ali

Soleimani

Yahya

et al. 2020

Journal of Taibah University for Science

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In this study, scattering parameters of sandstone saturated with brine and nanofluids are evaluated experimentally and numerically for the application in enhanced oil recovery (EOR). Zinc Oxide (ZnO) and Bismuth ferrite BiFeO 3 (BFO) nanoparticles were synthesized via facile sol-gel method followed by nanofluid preparation. Sandstone samples were saturated with brine and nanofluids for 48 h. Electromagnetic properties of the saturated sandstones were measured experimentally using the vector network analyzer, and the scattering parameters of the samples were studied numerically by finite element method. BFO displayed higher permeability value of 1.52 and 1.30, as well as superior dielectric permittivity value 11.55 and 6.59 for real and imaginary parts, respectively. In addition, the sandstone saturated with BFO showed an impressive reflection loss (RL) value of −9.77 dB at high frequency. Conclusively, BiFeO 3 nanofluids showed the best potential to enhance oil recovery which can be accredited to the superior electromagnetic properties of BFO. ARTICLE HISTORY

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Evaluating the effect of temperature and concentration on the thermal conductivity of ZnO-TiO2/EG hybrid nanofluid using artificial neural network and curve fitting on experimental data

Cited by 150 publications

References 52 publications

Configuration and Optimization of a Minichannel Using Water–Alumina Nanofluid by Non-Dominated Sorting Genetic Algorithm and Response Surface Method

Configuration and Optimization of a Minichannel Using Water–Alumina Nanofluid by Non-Dominated Sorting Genetic Algorithm and Response Surface Method

Thermal Conductivity Modeling of Nanofluids Contain MgO Particles by Employing Different Approaches

Absorption of electromagnetic waves in sandstone saturated with brine and nanofluids for application in enhanced oil recovery

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