Numerical study on CNT nanofluids behavior in laminar pipe flow

Minea, Alina Adriana; Estellé, Patrice

doi:10.1016/j.molliq.2018.08.161

Cited by 40 publications

(6 citation statements)

References 24 publications

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“…90'lı yılların ortalarında nanometre boyutundaki katı partiküllerin bir baz akışkan içerisindeki süspansiyonu Choi ve Eastman [3] tarafından nanoakışkan olarak adlandırılmıştır. 20. yüzyılın ikinci yarısından itibaren ısı transferinin iyileştirilmesinde konvansiyonel iş akışkanlarına alternatif olabileceği için nanoakışkanlar, bilim insanlarının ilgisini çekmiş ve popüler araştırma konuları arasındaki yerini almıştır [4]. Nanoakışkan kavramının ortaya atılmasından itibaren en genel halde nanoakışkanların hazırlanması, termofiziksel ve reolojik özelliklerinin belirlenmesi, çeşitli mühendislik uygulamalarında konvansiyonel iş akışkanlarına göre performanslarının kıyaslanması ve kararlılıkları konularında sayısal ve deneysel çalışmalar yapılagelmektedir [5].…”

Section: Gi̇ri̇ş (Introduction)unclassified

Hibrit nanoakışkanların özgül ısılarının yapay sinir ağları ile tahmin edilmesi

Subasi

Erdem

2021

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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 Prediction of specific heat of hybrid nanofluids with ANN  Optimization of hyperparameters with Bayesian optimization  Development of a specific heat estimator alternative to correlations Figure A. Schematic representation of the network structure used in the present study. Purpose:The aim of this study is to develop an Artificial Neural Networks (ANN) based estimator that can be used to predict the specific heat of deionized water-based CuO + MWCNT, MgO + MWCNT, and SnO2 + MWCNT hybrid nanofluids and to investigate the usability of the ANN-based estimators instead of the commonly used correlations in the literature. Theory and Methods:Experimentally obtained data found in the literature on the specific heat of deionized water-based CuO + MWCNT, MgO + MWCNT and SnO2 + MWCNT hybrid nanofluids measured for various temperature T (25 -50℃ ), volume ratio φ (0.25% -1.50%), and particle diameter dp (20 -50 nm) were used in the present study. The training algorithm's and the network's hyperparameters such as neuron number, hidden layer number, transfer function, epoch number, and learning rate, and the best training algorithm for the problem addressed among various training algorithms were determined by employing the Bayes optimization. K-fold cross-validation was applied as a precaution against overfitting. Results:It has been determined as a result of the optimization that the LM training algorithm gives the best result with the hyperparameter combination where the hidden layer number is 1, the number of neurons is 24, the epoch number is 498, the learning rate is 0.15542 and the transfer function is logsig. The determination coefficient (R 2 ) of the optimized network structure for training, validation, test data, and the whole data was found to be 0.998415, 0.995497, 0.995023, and 0.997504, respectively. Conclusion:It was concluded that the ANN-based estimator obtained in the present study can be used to predict the specific heat of deionized water-based CuO + MWCNT, MgO + MWCNT, and SnO2 + MWCNT hybrid nanofluids with high accuracy. It has been determined that the ANN-based estimator reveals better performance to predict the specific heat of the hybrid nanofluids than the classical correlation. Therefore, it has been concluded that more precise and realistic calculations can be made by using the ANN-based estimator in determining the specific heat of nanofluids required in experimental and numerical studies.

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Section: Gi̇ri̇ş (Introduction)unclassified

Hibrit nanoakışkanların özgül ısılarının yapay sinir ağları ile tahmin edilmesi

Subasi

Erdem

2021

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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“…They founded that the Nusselt number of nanofluids is decreased with the increase of the volume fraction of nanofluid, which is related to non-Newtonian behavior of nanofluids. Minea and Estellé [23] carried out a numerical study of the heat transfer behavior of carbon nanotubes nanofluids under laminar forced flow in horizontal pipe. They evidenced that the convective heat transfer coefficient of nanofluid is better for low Re number and higher nanoparticles volume fraction.…”

Section: Introductionmentioning

confidence: 99%

Numerical simulation of three-dimensional thermo-solutal convection of micropolar multi-walled carbon nanotubes water nanofluid stabilized by lignin and sodium polycarboxylate

Manaa

Abidi

Estellé

et al. 2021

J Therm Anal Calorim

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A computational analysis has been performed in this study to solve three-dimensional thermo-solutal natural convection in a differentially heated cubical enclosure filled with micropolar CNT-water nanofluid stabilized by two types of surfactants lignin and sodium polycarboxylate. The work is carried out for different pertinent parameters as Rayleigh number (10 4 Ra 10 6 ), micropolar parameter (0 K 10), buoyancy ratio (-1 N 0) and nanoparticles's volume fraction (0.0055% 0.557%).It is observed that the heat and mass transfer rates are lower for a micropolar nanofluid model when compared to the pure nanofluid model. In fact, the enhancement of micropolar parameter results a decrease of average Nusselt and Sherwood numbers. The use of lignin as a surfactant ameliorates heat and mass transfer rate and nanofluid flow better than the use of sodium polycarboxylate as a surfactant. The nanoparticles volume fraction can be used as a control element for heat rate and fluid flow. Thus, for a nanoparticles volume concentration less than the critical value, the flow intensity is ameliorated and is deteriorated when it exceed this value.

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“…It was observed that the CFD can predict the convection features using the nanofluid as heat transfer fluid. Many different particles can be used in applications, but in recent years the use of highly conducive carbon nanotubes (CNTs) is growing [46][47][48][49][50]. They provide significant heat transfer enhancements [51][52][53].…”

Section: Introductionmentioning

confidence: 99%

Pulsating Flow of CNT–Water Nanofluid Mixed Convection in a Vented Trapezoidal Cavity with an Inner Conductive T-Shaped Object and Magnetic Field Effects

2020

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Mixed convection of carbon-nanotube/water nanofluid in a vented cavity with an inner conductive T-shaped object was examined under pulsating flow conditions under magnetic field effects with finite element method. Effects of different parameters such as Richardson number (between 0.05 and 50), Hartmann number (between 0 and 30), cavity wall inclination (between 0 ∘ and 10 ∘ ), size (between 0.1 H and 0.4 H) and orientation (between −90 ∘ and 90 ∘ ) of the T-shaped object, and amplitude (between 0.5 and 0.9) and frequency (Strouhal number between 0.25 and 5) of pulsating flow on the convective flow features were studied. It was observed that the average Nusselt number enhanced with the rise of strength of magnetic field, solid nanoparticle volume fraction, and amplitude of the pulsation, while the effect was opposite for higher values of Ri number and cavity wall inclination angle. The presence of the T-shaped object and adjusting its size and orientation had significant impact on the main flow stream from inlet to outlet and recirculations around the T-shaped object and in the vicinity of hot wall of the cavity along with the magnetic field strength. Pulsating flow resulted in heat transfer enhancement as compared to steady flow case for all configurations. However, the amount of increment was different depending on the variation of the parameters of interest. Heat transfer enhancements were 41.85% and 20.81% when the size of the T-shaped object was increased from 0.1 H to 0.4 H. The T-shaped object can be utilized in the vented cavity as an excellent tool for convective heat transfer control. As highly conductive CNT particles were used in water, significant enhancements in the average Nusselt number between 97% and 108% were obtained both in steady flow and in pulsating flow cases when magnetic field was absent or present.

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Numerical study on CNT nanofluids behavior in laminar pipe flow

Cited by 40 publications

References 24 publications

Hibrit nanoakışkanların özgül ısılarının yapay sinir ağları ile tahmin edilmesi

Hibrit nanoakışkanların özgül ısılarının yapay sinir ağları ile tahmin edilmesi

Numerical simulation of three-dimensional thermo-solutal convection of micropolar multi-walled carbon nanotubes water nanofluid stabilized by lignin and sodium polycarboxylate

Pulsating Flow of CNT–Water Nanofluid Mixed Convection in a Vented Trapezoidal Cavity with an Inner Conductive T-Shaped Object and Magnetic Field Effects

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