Designing an artificial neural network using radial basis function to model exergetic efficiency of nanofluids in mini double pipe heat exchanger
The present study aims at predicting and optimizing exergetic efficiency of TiO 2 -Al 2 O 3 /water nanofluid at different Reynolds numbers, volume fractions and twisted ratios using Artificial Neural Networks (ANN) and experimental data. Central Composite Design (CCD) and cascade Radial Basis Functi...
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Published in | Heat and mass transfer Vol. 54; no. 6; pp. 1707 - 1719 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.06.2018
Springer Nature B.V |
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Abstract | The present study aims at predicting and optimizing exergetic efficiency of TiO
2
-Al
2
O
3
/water nanofluid at different Reynolds numbers, volume fractions and twisted ratios using Artificial Neural Networks (ANN) and experimental data. Central Composite Design (CCD) and cascade Radial Basis Function (RBF) were used to display the significant levels of the analyzed factors on the exergetic efficiency. The size of TiO
2
-Al
2
O
3
/water nanocomposite was 20–70 nm. The parameters of ANN model were adapted by a training algorithm of radial basis function (RBF) with a wide range of experimental data set. Total mean square error and correlation coefficient were used to evaluate the results which the best result was obtained from double layer perceptron neural network with 30 neurons in which total Mean Square Error(MSE) and correlation coefficient (R
2
) were equal to 0.002 and 0.999, respectively. This indicated successful prediction of the network. Moreover, the proposed equation for predicting exergetic efficiency was extremely successful. According to the optimal curves, the optimum designing parameters of double pipe heat exchanger with inner twisted tape and nanofluid under the constrains of exergetic efficiency 0.937 are found to be Reynolds number 2500, twisted ratio 2.5 and volume fraction(
v
/v%) 0.05. |
---|---|
AbstractList | The present study aims at predicting and optimizing exergetic efficiency of TiO2-Al2O3/water nanofluid at different Reynolds numbers, volume fractions and twisted ratios using Artificial Neural Networks (ANN) and experimental data. Central Composite Design (CCD) and cascade Radial Basis Function (RBF) were used to display the significant levels of the analyzed factors on the exergetic efficiency. The size of TiO2-Al2O3/water nanocomposite was 20–70 nm. The parameters of ANN model were adapted by a training algorithm of radial basis function (RBF) with a wide range of experimental data set. Total mean square error and correlation coefficient were used to evaluate the results which the best result was obtained from double layer perceptron neural network with 30 neurons in which total Mean Square Error(MSE) and correlation coefficient (R2) were equal to 0.002 and 0.999, respectively. This indicated successful prediction of the network. Moreover, the proposed equation for predicting exergetic efficiency was extremely successful. According to the optimal curves, the optimum designing parameters of double pipe heat exchanger with inner twisted tape and nanofluid under the constrains of exergetic efficiency 0.937 are found to be Reynolds number 2500, twisted ratio 2.5 and volume fraction(v/v%) 0.05. The present study aims at predicting and optimizing exergetic efficiency of TiO 2 -Al 2 O 3 /water nanofluid at different Reynolds numbers, volume fractions and twisted ratios using Artificial Neural Networks (ANN) and experimental data. Central Composite Design (CCD) and cascade Radial Basis Function (RBF) were used to display the significant levels of the analyzed factors on the exergetic efficiency. The size of TiO 2 -Al 2 O 3 /water nanocomposite was 20–70 nm. The parameters of ANN model were adapted by a training algorithm of radial basis function (RBF) with a wide range of experimental data set. Total mean square error and correlation coefficient were used to evaluate the results which the best result was obtained from double layer perceptron neural network with 30 neurons in which total Mean Square Error(MSE) and correlation coefficient (R 2 ) were equal to 0.002 and 0.999, respectively. This indicated successful prediction of the network. Moreover, the proposed equation for predicting exergetic efficiency was extremely successful. According to the optimal curves, the optimum designing parameters of double pipe heat exchanger with inner twisted tape and nanofluid under the constrains of exergetic efficiency 0.937 are found to be Reynolds number 2500, twisted ratio 2.5 and volume fraction( v /v%) 0.05. |
Author | Ghasemi, Nahid Aghayari, Reza Maddah, Heydar |
Author_xml | – sequence: 1 givenname: Nahid surname: Ghasemi fullname: Ghasemi, Nahid email: n-ghasemi@iau-arak.ac.ir, anahid3@gmail.com organization: Department of Chemistry, Arak Branch, Islamic Azad University – sequence: 2 givenname: Reza surname: Aghayari fullname: Aghayari, Reza organization: Department of Chemistry, Payame Noor University (PNU) – sequence: 3 givenname: Heydar surname: Maddah fullname: Maddah, Heydar organization: Department of Chemistry, Payame Noor University (PNU) |
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Cites_doi | 10.1016/j.conengprac.2013.01.007 10.1016/j.ijheatmasstransfer.2010.06.016 10.1016/j.ijheatfluidflow.2008.04.009 10.1615/HeatTransRes.2012004077 10.1016/j.supflu.2013.09.013 10.1111/j.1745-4549.1999.tb00389.x 10.1162/neco.1990.2.2.210 10.1016/S1164-0235(01)00034-6 10.1093/biomet/62.2.347 10.1016/j.petrol.2006.11.008 10.1016/S0017-9310(00)00139-3 10.1109/72.80341 10.1016/j.asoc.2013.04.021 10.1016/j.icheatmasstransfer.2010.11.016 10.1115/1.4031073 10.1007/s00231-017-2068-6 10.1115/IMECE2014-40354 10.1016/j.icheatmasstransfer.2006.05.001 |
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Snippet | The present study aims at predicting and optimizing exergetic efficiency of TiO
2
-Al
2
O
3
/water nanofluid at different Reynolds numbers, volume fractions... The present study aims at predicting and optimizing exergetic efficiency of TiO2-Al2O3/water nanofluid at different Reynolds numbers, volume fractions and... |
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SubjectTerms | Aluminum oxide Artificial neural networks Basis functions Correlation coefficients Efficiency Engineering Engineering Thermodynamics Exergy Fluid flow Heat and Mass Transfer Heat exchangers Industrial Chemistry/Chemical Engineering Mathematical models Nanocomposites Nanofluids Neural networks Optimization Original Parameters Pipes Predictions Radial basis function Reynolds number Thermodynamics Titanium dioxide |
Title | Designing an artificial neural network using radial basis function to model exergetic efficiency of nanofluids in mini double pipe heat exchanger |
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