Esfe, Mohammad Hemmat and Saedodin, Seyfolah and Naderi, Ali and Alirezaie, Ali and Karimipour, Arash and Wongwises, Somchai and Goodarzi, Marjan and Dahari, Mahidzal (2015) Modeling of thermal conductivity of ZnO-EG using experimental data and ANN methods. International Communications in Heat and Mass Transfer, 63. pp. 35-40. ISSN 0735-1933, DOI https://doi.org/10.1016/j.icheatmasstransfer.2015.01.001.
![[img]](http://eprints.um.edu.my/style/images/fileicons/application_pdf.png)
|
PDF (Modeling of thermal conductivity of ZnO-EG using experimental data and ANN methods)
Modeling_of_thermal_conductivity_of_ZnO-EG_using_experimental_data.pdf - Published Version Download (908kB) |
Abstract
In the present study, the thermal conductivity of the ZnO-EG nanofluid has been investigated experimentally. For this purpose, zinc oxide nanoparticles with nominal diameters of 18 nm have been dispersed in ethylene glychol at different volume fractions (0.000625, 0.00125, 0.005, 0.01, 0.015, 0.02, 0.03, 0.04, and 0.05) and temperatures (24-50 degrees C). The two-step method is used to disperse nanoparticles in the base fluid. Based on the experimental data, an experimental model has been proposed as a function of solid concentration and temperature. Then, the feedforward multilayer perceptron neural network has been employed for modeling thermal conductivity of ZnO-EG nanofluid. Out of 40 measured data obtained from experiments, 28 data were selected for network training, while the remaining 12 data were used for network testing and validating. The results indicate that both model and ANN outputs are in good agreement with the experimental data. (C) 2015 Elsevier Ltd. All rights reserved.
| Item Type: | Article |
|---|---|
| Funders: | High Impact Research Grant: UM.C/HIR/MOHE/ENG/23, National Science and Technology Development Agency Thailand Research Fund IRG5780005 |
| Additional Information: | ISI Document Delivery No.: CH0VW Times Cited: 0 Cited Reference Count: 27 Cited References: Arani AAA, 2013, EXP THERM FLUID SCI, V44, P520, DOI 10.1016/j.expthermflusci.2012.08.014 Choi S.U.S., 1995, ASME FED, V231, P99 Duangthongsuk W., EXP THERM FLUID SCI, V33 Esfe MH, 2014, EXP THERM FLUID SCI, V55, P1, DOI 10.1016/j.expthermflusci.2014.02.011 Esfe MH, 2014, J THERM ANAL CALORIM, V117, P675, DOI 10.1007/s10973-014-3771-x Esfe MH, 2014, INT J HEAT MASS TRAN, V73, P186, DOI 10.1016/j.ijheatmasstransfer.2014.01.069 Esfe MH, 2014, J THERM ANAL CALORIM, V118, P287, DOI 10.1007/s10973-014-4002-1 Ghadimi A, 2011, INT J HEAT MASS TRAN, V54, P4051, DOI 10.1016/j.ijheatmasstransfer.2011.04.014 Esfe MH, 2014, INT COMMUN HEAT MASS, V58, P138, DOI 10.1016/j.icheatmasstransfer.2014.08.035 Esfe MH, 2014, INT COMMUN HEAT MASS, V58, P176, DOI 10.1016/j.icheatmasstransfer.2014.08.037 Hojjat M, 2011, INT J HEAT MASS TRAN, V54, P1017, DOI 10.1016/j.ijheatmasstransfer.2010.11.039 Indhuja A, 2013, J TAIWAN INST CHEM E, V44, P474, DOI 10.1016/j.jtice.2012.11.015 Jiang HF, 2014, THERMOCHIM ACTA, V579, P27, DOI 10.1016/j.tca.2014.01.012 Khaleduzzaman SS, 2013, INT COMMUN HEAT MASS, V49, P110, DOI 10.1016/j.icheatmasstransfer.2013.10.010 Kole M, 2011, INT J THERM SCI, V50, P1741, DOI 10.1016/j.ijthermalsci.2011.03.027 Mahbubul IM, 2012, INT J HEAT MASS TRAN, V55, P874, DOI 10.1016/j.ijheatmasstransfer.2011.10.021 Mena JB, 2013, APPL THERM ENG, V51, P1092, DOI 10.1016/j.applthermaleng.2012.11.002 Nasiri A, 2012, INT J HEAT MASS TRAN, V55, P1529, DOI 10.1016/j.ijheatmasstransfer.2011.11.004 Pastoriza-Gallego MJ, 2011, FLUID PHASE EQUILIBR, V300, P188, DOI 10.1016/j.fluid.2010.10.015 Phuoc TX, 2011, INT J THERM SCI, V50, P12, DOI 10.1016/j.ijthermalsci.2010.09.008 Sajadi A.R., 2013, INT COMMUN HEAT MASS, V38, P1474 Sarkar J, 2011, RENEW SUST ENERG REV, V15, P3271, DOI 10.1016/j.rser.2011.04.025 Sundar LS, 2013, RENEW SUST ENERG REV, V25, P670, DOI 10.1016/j.rser.2013.04.003 Teng TP, 2013, ENERG CONVERS MANAGE, V67, P369, DOI 10.1016/j.enconman.2012.12.004 Timofeeva EV, 2007, PHYS REV E, V76, DOI 10.1103/PhysRevE.76.061203 Wang W., 2012, COMPREHENSIVE MODEL, P5 Yiamsawas T, 2013, APPL ENERG, V111, P40, DOI 10.1016/j.apenergy.2013.04.068 Esfe, Mohammad Hemmat Saedodin, Seyfolah Naderi, Ali Alirezaie, Ali Karimipour, Arash Wongwises, Somchai Goodarzi, Marjan bin Dahari, Mahidzal Engineering, Faculty /I-7935-2015 Engineering, Faculty /0000-0002-4848-7052 High Impact Research Grant UM.C/HIR/MOHE/ENG/23; Faculty of Engineering, University of Malaya, Malaysia; National Science and Technology Development Agency; Thailand Research Fund IRG5780005; National Research University Project The authors gratefully acknowledge High Impact Research Grant UM.C/HIR/MOHE/ENG/23 and Faculty of Engineering, University of Malaya, Malaysia for their support in conducting this research work. The sixth author would like to thank the "Research Chair Grant" from the National Science and Technology Development Agency, the Thailand Research Fund (IRG5780005) and the National Research University Project for the support. 0 PERGAMON-ELSEVIER SCIENCE LTD OXFORD INT COMMUN HEAT MASS |
| Uncontrolled Keywords: | Thermal conductivity; MgO-EG; Artificial neural network; Nanofluid |
| Subjects: | T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TJ Mechanical engineering and machinery T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering |
| Depositing User: | Mr Jenal S |
| Date Deposited: | 09 Mar 2016 02:44 |
| Last Modified: | 18 Oct 2018 04:23 |
| URI: | http://eprints.um.edu.my/id/eprint/15692 |
Actions (login required)
 |
View Item |
