An inspection of thermal conductivity of CuO-SWCNTs hybrid nanofluid versus temperature and concentration using experimental data, ANN modeling and new correlation

Rostamian, Seyed Hadi; Biglari, Mojtaba; Saedodin, Seyfolah; Esfe, Mohammad Hemmat

doi:10.1016/j.molliq.2017.02.015

Cited by 219 publications

(45 citation statements)

References 31 publications

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“…AR E % is by Eq. (18): Based on Figs. 4 and 5, one can see that ARE% of main parts of data points are lower than 4 % which is acceptable for engineering purposes.…”

Section: Resultsmentioning

confidence: 99%

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A New Correlation Method for Estimating Thermal Conductivity of Carbon Dioxide in Liquid, Vapor and Supercritical Phases

Rostamian

Lotfollahi

2019

Period. Polytech. Chem. Eng.

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In this study, a new correlation for estimating thermal conductivity (TC) of carbon dioxide was developed based on 2319 data points. The data points were at the temperature ranging from 250 to 1100 K, pressure ranging from 1 to 3000 bar and density ranging from 0.3 to 1400 Kg.m-3 in different phases of liquid, vapor and supercritical. The statistical parameters including average absolute deviation (AAD%), average percent relative error (ARE%), sum of absolute residual (SAR) and the coefficient of determination (R2) have been calculated to evaluate the accuracy of present correlation. The obtained values of AAD%, ARE%, SAR and R2 were 1.98, −0.64, 3510.1 and 0.995, respectively. The predictions of proposed correlation were also compared with three widely used correlations. The results showed that the proposed correlation is able to accurately calculate thermal conductivity of carbon dioxide. In addition, the proposed model is superior to all the existing empirical models considered.

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“…AR E % is by Eq. (18): Based on Figs. 4 and 5, one can see that ARE% of main parts of data points are lower than 4 % which is acceptable for engineering purposes.…”

Section: Resultsmentioning

confidence: 99%

“…In recent decades, many modeling investigations have been proposed to calculate TC of CO 2 and other fluids [12,17,18]. In 2014, Jarrahian and Heidaryan [19] also developed a new correlation for natural gas thermal conductivity as a function of temperature, pressure and composition.…”

Section: Introductionmentioning

confidence: 99%

A New Correlation Method for Estimating Thermal Conductivity of Carbon Dioxide in Liquid, Vapor and Supercritical Phases

Rostamian

Lotfollahi

2019

Period. Polytech. Chem. Eng.

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“…The Root Mean Square Error (RMSE) value achieved from the ANN developed using tentative data is 0.0432 and the R value is 0.9933, which is interpreted as an indication that the ANN can calculate the viscosity values of the nanofluids with ideal accuracy. Rostamian et al 28 established an ANN using tentative data to foretell the thermal conductivity of the CuO‐single walled carbon nanotube (SWCNT)/EG established on volumetric concentration and temperature. Hybrid nanofluid used in the study was prepared using equal volume (50:50) SWCNT and CuO nanoparticles (40 nm) at volumetric concentrations ranging from 0.02% to 0.75%.…”

Section: Introductionmentioning

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

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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.

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“…According to the results obtained from ANN outputs, it was concluded that ANN could better estimate the thermal conductivity of the nanofluid than the theoretical model. Rostamian et al 39 experimentally measured the thermal conductivity values of the CuO‐SWCNT/EG water nanofluid at 20°C to 50°C with nanoparticles of 40 nm in size, ranging from 0.02% to 0.75%. An ANN was developed using experimental data, and thermal conductivity values were estimated using this ANN, using the experimental data, stating that there is no proposed mathematical for calculating the thermal conductivity of the CuO‐SWCNT/EG water nanofluid.…”

Section: Introductionmentioning

confidence: 99%

Experimental study for thermal conductivity of water‐based zirconium oxide nanofluid: Developing optimal artificial neural network and proposing new correlation

Çolak

2020

Int J Energy Res

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Summary In this study, five different water based ZrO2 nanofluids were prepared at volumetric concentrations of 0.0125%, 0.025%, 0.05%, 0.1%, and 0.2%. In the preparation of nanofluids, two‐step method was preferred, magnetic stirrer and ultrasonic homogenizer were used. Their thermal conductivity was measured experimentally in the temperature range of 10°C to 65°C. Using the obtained experimental data, a multi‐layer perceptron feed‐forward back‐propagation artificial neural network was developed. In addition, a new correlation was proposed for the calculation of the thermal conductivity values of the ZrO2/Water nanofluid. The results showed that the ZrO2/Water nanofluid had higher thermal conductivity compared to the base fluid and the thermal conductivity increases with the increase in temperature and concentration. While the artificial neural network developed with experimental data predicted the thermal conductivity of ZrO2/Water nanofluid with an average error of −0.41%, the new correlation developed predicted it with an average error of −0.02%. These values were an indication that the results obtained from the developed artificial neural network and the correlation are in perfect agreement with the experimental data.

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An inspection of thermal conductivity of CuO-SWCNTs hybrid nanofluid versus temperature and concentration using experimental data, ANN modeling and new correlation

Cited by 219 publications

References 31 publications

A New Correlation Method for Estimating Thermal Conductivity of Carbon Dioxide in Liquid, Vapor and Supercritical Phases

A New Correlation Method for Estimating Thermal Conductivity of Carbon Dioxide in Liquid, Vapor and Supercritical Phases

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

Experimental study for thermal conductivity of water‐based zirconium oxide nanofluid: Developing optimal artificial neural network and proposing new correlation

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