Modeling of thermal conductivity and density of alumina/silica in water hybrid nanocolloid by the application of Artificial Neural Networks

Kumar, K. Sathish; Boobalan, Chitra; Nagarajan, Fedal Castro; Sivaraman, Srinivas

doi:10.1016/j.cjche.2018.07.018

Cited by 30 publications

(3 citation statements)

References 20 publications

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“…The results revealed that the structure with 10 neurons in each layer contributed to the highest accuracy. Kannaiyan et al [130] proposed a MLP-ANN model for thermal conductivity of Al 2 O…”

Section: Prediction Of Thermal Conductivity Of Hybrid Nanofluidsmentioning

confidence: 99%

A comprehensive review on the application of nanofluid in heat pipe based on the machine learning: Theory, application and prediction

Luo

Xiang

et al. 2021

Renewable and Sustainable Energy Reviews

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Section: Prediction Of Thermal Conductivity Of Hybrid Nanofluidsmentioning

confidence: 99%

A comprehensive review on the application of nanofluid in heat pipe based on the machine learning: Theory, application and prediction

Luo

Xiang

et al. 2021

Renewable and Sustainable Energy Reviews

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“…The uid exhibits non-Newtonian behaviour with a declining viscosity trend in the rising shear Multivariate linear regression (MLR) and Multivariate linear regression with interaction (MLRI) models are used to statistically analyze the experimental data. The thermal conductivity of an alumina-silica hybrid nano uid was predicted using the ANN approach by Boobalan et al [17].…”

Section: Introductionmentioning

confidence: 99%

Predictive ANN modelling of Thermorheological properties of Iron-Oxide yield stress nanofluid

DHAR

Hassan

2022

Preprint

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The intent of the research is to find the dependency of the volume fraction of nanoparticle (φ) and the temperature on the absolute viscosity (µnf) of Fe3O4 nanoparticles in Carbopol polymer gel. Rheological and stability analysis of the solution is identified. A total of 48 viscosity values has been calculated from experiments using two different base fluid concentrations and two different nanofluid concentrations at eight different temperatures. The data gathered are used for the training of an ANN (Artificial Neural Network) to observe results in a predefined range of two input criteria. It uses a feed-forward perceptron ANN with a temperature input, a volume concentration input, and a viscosity output. The topology was established by trial and error, and the two-layer model having ten neurons in the hidden layer that used the tansig function produced the best results. Ten training functions were utilized to analyze the best result for nf prediction, and the trainbr algorithm was found to be the best ANN. Due to the trained ANN, the anticipated value of viscosity is obtained from each temperature and volume concentration combination. The best results were witnessed with trainlm algorithm with an MSE value of 5.92e-4 and a R2 value of 0.9988 for forecasting of viscosity. Nanoparticle volume concentration increases with viscosity, while temperature increases cause viscosity to decrease. As the temperature rises from 15°C to 50°C, the shear stress value drops with a corresponding shear rate. The shear stress value of the associated shear rate decreases as the nanoparticle concentration rises.

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“…They stated that the most optimal model of the ANN developed with 6, 8, 10, and 12 neurons in the hidden layer is the model with 12 neurons and the margin of deviation is calculated as 0.8%. Kannaiyan et al 37 experimentally investigated the thermal conductivities of water‐based nanofluids prepared using Al 2 O 3 and SiO 2 nanoparticles at a temperature range of 20°C to 60°C in volumetric concentrations of 0.05%, 0.1%, and 0.2%. With the experimental data obtained, they developed an ANN to estimate thermal conductivity based on volumetric concentration and temperature.…”

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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Modeling of thermal conductivity and density of alumina/silica in water hybrid nanocolloid by the application of Artificial Neural Networks

Cited by 30 publications

References 20 publications

A comprehensive review on the application of nanofluid in heat pipe based on the machine learning: Theory, application and prediction

A comprehensive review on the application of nanofluid in heat pipe based on the machine learning: Theory, application and prediction

Predictive ANN modelling of Thermorheological properties of Iron-Oxide yield stress nanofluid

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

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