Application GMDH artificial neural network for modeling of Al2O3/water and Al2O3/Ethylene glycol thermal conductivity

Application GMDH artificial neural network for modeling of Al2O3/water and Al2O3/Ethylene glycol thermal conductivity

Mohammad H. AhmadiFatemeh Hajizadeh Mohammad Rahimzadeh Mohammad B. Shafii Ali J. Chamkha Giulio Lorenzini Roghayeh Ghasempour 

Faculty of Mechanical Engineering, Shahrood University of Technology, Shahrood, Iran

Faculty of New Sciences and Technologies, University of Tehran, Tehran 1961733114, Iran

Department of Mechanical Engineering, Golestan University, Gorgan, Iran

Mechanical Engineering, Sharif University of Technology, Tehran 11365-11155, Iran

Mech Eng Dept, Endow. Energy and Env., Prince M B Fahd Univ, Al-Khobar 31952, Saudi Arabia

RAK Res., American Univ of Ras Al Khaimah, P.O. Box 10021, United Arab Emirates

Università degli Studi di Parma, Dipartimento di Ingegneria e Architettura, Parma 43124, Italy

Corresponding Author Email: 
mohammadhosein.ahmadi@gmail.com
Page: 
773-782
|
DOI: 
https://doi.org/10.18280/ijht.360301
Received: 
19 Febraury 2018
| |
Accepted: 
2 June 2018
| | Citation

OPEN ACCESS

Abstract: 

Thermal conductivity of nanofluids depends on several parameters including temperature, concentration, and size of nanoparticles. Most of the proposed models utilized concentration and temperature as influential factors in their modeling. In this study, group method of data handling (GMDH) artificial neural networks is applied in order to model the dependency of thermal conductivity on the mentioned factors. Firstly, temperature and concentration considered as inputs and a model is represented. Afterwards, the size of nanoparticles is added to the input variables and the results are compared. Based on obtained results, GMDH is an appropriate method to predict thermal conductivity of the nanofluids. In addition, it is necessary to consider size of nanoparticles in order to have a more precise model.

Keywords: 

nanofluid, thermal conductivity, GMDH, artificial

1. Introduction
2. Method
3. Results and Discussion
4. Conclusion
Appendix
  References

[1] Narei H, Ghasempour R, Noorollahi Y. (2016). The effect of employing nanofluid on reducing the bore length of a vertical ground-source heat pump. Energy Convers. Manag. 123:  581-591. https://doi.org/10.1016/j.enconman.2016.06.079

[2] Jiang S, Zhou D, Zhang L, Ouyang J, Yu X, Cui X, Han B. (2018). Comparison of compressive strength and electrical resistivity of cementitious composites with different nano- and micro-fillers. Arch. Civ. Mech. Eng. 18: 60–68. https://doi.org/10.1016/j.acme.2017.05.010

[3] Solanki JN, Murthy ZVP. (2011). Preparation of silver nanofluids with high electrical conductivity preparation of silver nanofluids with high electrical conductivity. Journal of Dispersion Science and Technology 32(5): 724–730. https://doi.org/10.1080/01932691.2010.480863

[4] Mohammadi M, Mohammadi M, Ghahremani AR, Shafii MB, Mohammadi N. (2014). Experimental investigation of thermal resistance of a ferrofluidic closed-loop pulsating heat pipe. Heat Transf. Eng. 35: 25–33. https://doi.org/10.1080/01457632.2013.810086

[5] Gandomkar A, Saidi MH, Shafii MB, Vandadi M, Kalan K. (2017). Visualization and comparative investigations of pulsating ferro-fluid heat pipe. Appl. Therm. Eng. 116: 56–65. https://doi.org/10.1016/j.applthermaleng.2017.01.068

[6] Aybar HŞ, Sharifpur M, Azizian MR, Mehrabi M, Meyer JP. (2015). A review of thermal conductivity models for nanofluids. Heat Transf. Eng. 36: 1085–1110. https://doi.org/10.1080/01457632.2015.987586

[7] Nazari MA, Ghasempour R, Ahmadi MH, Heydarian G, Shafii MB. (2018). Experimental investigation of graphene oxide nanofluid on heat transfer enhancement of pulsating heat pipe. Int. Commun. Heat Mass Transf. 91: 90–94. https://doi.org/10.1016/j.icheatmasstransfer.2017.12.006

[8] Amin TE, Roghayeh G, Fatemeh R, Fatollah P. (2015). Evaluation of nanoparticle shape effect on a nanofluid based flat-plate solar collector efficiency. Energy Explor. Exploit. 33: 659–676. https://doi.org/10.1260/0144-5987.33.5.659

[9] Ali F, Arif M, Khan I, Sheikh NA, Saqib M. (2018) Natural convection in polyethylene glycol based molybdenum disulfide nanofluid with thermal radiation, chemical reaction and ramped wall temperature. Int. J. Heat Technol. 36: 619–631. https://doi.org/10.18280/ijht.360227

[10] Tawfik MM. (2017). Experimental studies of nanofluid thermal conductivity enhancement and applications: A review. Renew. Sustain. Energy Rev. 75: 1239-1253. https://doi.org/10.1016/j.rser.2016.11.111

[11] Ponmani S, William JKM, Samuel R, Nagarajan R, Sangwai JS. (2014). Formation and characterization of thermal and electrical properties of CuO and ZnO nanofluids in xanthan gum, colloids surfaces a physicochem. Eng. Asp. 443: 97-43. https://doi.org/10.1016/j.colsurfa.2013.10.048

[12] Alawi OA, Sidik NAC, Hong WX, Kean TH, Kazi SN. (2018). Thermal conductivity and viscosity models of metallic oxides nanofluids. Int. J. Heat Mass Transf. 116: 1314-1325. https://doi.org/10.1016/J.IJHEATMASSTRANSFER.2017.09.133

[13] Cui W, Shen Z, Yang J, Wu S. (2015). Molecular dynamics simulation on flow behaviors of nanofluids confined in nanochannel. Case Stud. Therm. Eng. 5: 114–121. https://doi.org/10.1016/j.csite.2015.03.007

[14] Hatami M. (2018). Different shapes of Fe3O4/nanoparticles on the free convection and entropy generation in a wavy-wall square cavity filled by power-law non-Newtonian nanofluid. Int. J. Heat Technol. 36: 509–524. https://doi.org/10.18280/ijht.360215

[15] Sivakumar A, Alagumurthi N, Senthilvelan T. (2016). Experimental investigation of forced convective heat transfer performance in nanofluids of Al2O3/water and CuO/water in a serpentine shaped micro channel heat sink. Heat Mass Transf. Und Stoffuebertragung 52: 1265–1274. https://doi.org/10.1007/s00231-015-1649-5

[16] Akilu S, Baheta AT, Sharma KV, US PT, Mol. Liq. J. (2017). https://doi.org/10.1016/j.molliq.2017.09.017

[17] Shanbedi M, Zeinali Heris S, Amiri A, Baniadam M, Heris S.Z., Amiri A., Baniadam M. (2014). Improvement in heat transfer of a two-phased closed thermosyphon using silver-decorated MWCNT/water. J. Dispers. Sci. Technol. 35(8): 1086-1096. https://doi.org/10.1080/01932691.2013.833101

[18] Kouloulias K, Sergis A, Hardalupas Y, Barrett TR. (2017). Measurement of flow velocity during turbulent natural convection in nanofluids. Fusion Eng. Des. 123: 72-76. https://doi.org/10.1016/J.FUSENGDES.2017.05.120

[19] Bahiraei M. (2014). A comprehensive review on different numerical approaches for simulation in nanofluids: Traditional and novel techniques. J. Dispers. Sci. Technol. 35: 984-996. https://doi.org/10.1080/01932691.2013.825210

[20] Aramesh M, Pourfayaz F, Kasaeian A. (2017). Numerical investigation of the nanofluid effects on the heat extraction process of solar ponds in the transient step. Sol. Energy 157: 869-879. https://doi.org/10.1016/J.SOLENER.2017.09.011

[21] Kahani M, Heris SZ, Mousavi SM. (2013). Effects of curvature ratio and coil pitch spacing on heat transfer performance of Al2O3 /water nanofluid laminar flow through helical coils. J. Dispers. Sci. Technol. 34: 1704-1712. https://doi.org/10.1080/01932691.2013.764485

[22] Rashidi S, Farzin F, Amiri A, Shanbedi M. (2015). Heat transfer coefficient prediction of metal oxides based water nanofluids under laminar flow regime using adaptive neuro-fuzzy inference system. Journal of Dispersion Science & Technology 37(9): 1277-2691. https://doi.org/10.1080/01932691.2015.1090318

[23] Taylor P, Tabari ZT, Heris SZ. (2015). Heat transfer performance of milk pasteurization plate heat exchangers using MWCNT/water nanofluid heat transfer performance of milk pasteurization plate heat exchangers using MWCNT/water nanofluid. Journal of Dispersion Science and Technology 36(2): 37-41. https://doi.org/10.1080/01932691.2014.894917

[24] Salimpour MR, Abdollahi A, Afrand M. (2017). An experimental study on deposited surfaces due to nanofluid pool boiling: Comparison between rough and smooth surfaces. Exp. Therm. Fluid Sci. 88: 288-300. https://doi.org/10.1016/J.EXPTHERMFLUSCI.2017.06.007

[25] Fang X, Chen Y, Zhang H, Chen W, Dong A, Wang R. (2016). Heat transfer and critical heat flux of nanofluid boiling: A comprehensive review. Renew. Sustain. Energy Rev. 62: 924–940. https://doi.org/10.1016/J.RSER.2016.05.047

[26] Minakov AV, Pryazhnikov MI, Guzei DV, Zeer GM, Rudyak VY. (2017). The experimental study of nanofluids boiling crisis on cylindrical heaters. Int. J. Therm. Sci. 116: 214–223. https://doi.org/10.1016/J.IJTHERMALSCI.2017.02.019

[27] Dadjoo M, Etesami N, Esfahany MN. (2017). Influence of orientation and roughness of heater surface on critical heat flux and pool boiling heat transfer coefficient of nanofluid. Appl. Therm. Eng. 124: 353–361. https://doi.org/10.1016/J.APPLTHERMALENG.2017.06.025

[28] Hong TK, Yang HS, Choi CJ. (2005). Study of the enhanced thermal conductivity of Fe nanofluids. J. Appl. Phys. 97(6): 280-441. https://doi.org/10.1063/1.1861145

[29] Garg J, Poudel B, Chiesa M, Gordon JB, Ma JJ, Wang JB, Ren ZF. (2008). Enhanced thermal conductivity and viscosity of copper nanoparticles in ethylene glycol nanofluid Enhanced thermal conductivity and viscosity of copper nanoparticles in ethylene glycol nanofluid. Journal of Applied Physics 103: 074301. https://doi.org/10.1063/1.2902483

[30] Zhang H, Wu Q, Lin J, Chen J, Xu Z, Zhang H, Wu Q, Lin J, Chen J, Xu Z. (2010). Thermal conductivity of polyethylene glycol nanofluids containing carbon coated metal nanoparticles Thermal conductivity of polyethylene glycol nanofluids containing carbon coated metal nanoparticles. Journal of Applied Physics 108(12): 124306. https://doi.org/10.1063/1.3486488

[31] Kannaiyan S, Boobalan C, Umasankaran A, Ravirajan A, Sathyan S, Thomas T. (2017). Comparison of experimental and calculated thermophysical properties of alumina/cupric oxide hybrid nanofluids. J. Mol. Liq. 244. https://doi.org/10.1016/j.molliq.2017.09.035

[32] Taylor P, Yu W, Xie H, Wang X, Yu W, Xie H, Wang X. (2011). Enhanced thermal conductivity of liquid paraffin based nanofluids containing copper nanoparticles. Journal of Dispersion Science & Technology 32(7): 948–951. https://doi.org/10.1080/01932691.2010.488503

[33] Sadri R, Kamali KZ, Hosseini M, Zubir N, Kazi SN, Ahmadi G, Dahari M, Huang NM, Golsheikh AM, Sadri R, Kamali KZ, Hosseini M, Zubir N, Kazi SN. (2017). Experimental study on thermo-physical and rheological properties of stable and green reduced graphene oxide nanofluids : Hydrothermal assisted technique. J. Dispers. Sci. Technol. 38: 1302–1310. https://doi.org/10.1080/01932691.2016.1234387

[34] Pal B, Mallick SS. (2014). Anisotropic CuO nanostructures of different size and shape exhibit thermal conductivity superior than typical bulk powder, colloids surfaces a physicochem. Eng. Asp. 459: 282–289. https://doi.org/10.1016/j.colsurfa.2014.07.017

[35] Sheikholeslami M, Ganji DD. (2017). Numerical modeling of magnetohydrodynamic CuO—Water transportation inside a porous cavity considering shape factor effect, colloids surfaces a physicochem. Eng. Asp. 529: 705–714. https://doi.org/10.1016/j.colsurfa.2017.06.046

[36] Timofeeva EV, Routbort JL, Singh D, Timofeeva EV, Routbort JL, Singh D. (2013). Particle shape effects on thermophysical properties of alumina nanofluids Particle shape effects on thermophysical properties of alumina nanofluids. Journal of Applied Physics 106: 014304. https://doi.org/10.1063/1.3155999

[37] Shaikh S, Lafdi K, Ponnappan R. (2007). Thermal conductivity improvement in carbon nanoparticle doped PAO oil : An experimental study Thermal conductivity improvement in carbon nanoparticle doped PAO oil: An experimental study. Journal of Applied Physics 101(6): 36. https://doi.org/10.1063/1.2710337

[38] Esfe MH, Hajmohammad MH. (2017). Thermal conductivity and viscosity optimization of nanodiamond-Co3O 4 / EG ( 40 : 60 ) aqueous nano fl uid using NSGA-II coupled with RSM. J. Mol. Liq. 238: 545–552. https://doi.org/10.1016/j.molliq.2017.04.056

[39] Taylor P, Kahani M, Heris SZ, Mousavi SM. (2013). Effects of curvature ratio and coil pitch spacing on heat transfer performance of Al2O3 / Water nanofluid laminar flow through helical coils effects of curvature ratio and coil pitch spacing on heat transfer. Journal of Dispersion Science and Technology 37–41. https://doi.org/10.1080/01932691.2013.764485

[40] Abdullah AA, Althobaiti SA, Lindsay KA. (2018). Marangoni convection in water–alumina nanofluids: Dependence on the nanoparticle size. Eur. J. Mech. - B/Fluids. 67: 259–268. https://doi.org/10.1016/J.EUROMECHFLU.2017.09.015

[41] Akhavan-Zanjani H, Saffar-Avval M, Mansourkiaei M, Ahadi M, Sharif F. (2014). Turbulent convective heat transfer and pressure drop of graphene–water nanofluid flowing inside a horizontal circular tube. J. Dispers. Sci. Technol. 35: 1230–1240. https://doi.org/10.1080/01932691.2013.834423

[42] Heris SZ, Shokrgozar M, Poorpharhang S, Shanbedi M., Noie SH. (2014). Experimental study of heat transfer of a car radiator with CuO/ethylene glycol-water as a coolant. J. Dispers. Sci. Technol. 35: 677–684. https://doi.org/10.1080/01932691.2013.805301

[43] Mikkola V, Puupponen S, Granbohm H, Saari K, Ala-Nissila T, Seppälä A. (2018). Influence of particle properties on convective heat transfer of nanofluids. Int. J. Therm. Sci. 124: 187–195. https://doi.org/10.1016/j.ijthermalsci.2017.10.015

[44] Hemmat Esfe M, Rostamian H, Reza Sarlak M, Rejvani M, Alirezaie A. (2017). Rheological behavior characteristics of TiO2-MWCNT/10w40 hybrid nano-oil affected by temperature, concentration and shear rate: An experimental study and a neural network simulating. Phys. E Low-Dimensional Syst. Nanostructures 94: 231–240. https://doi.org/10.1016/J.PHYSE.2017.07.012

[45] Hemmat Esfe M, Hassani Ahangar MR, Rejvani M, Toghraie D, Hajmohammad MH. (2016). Designing an artificial neural network to predict dynamic viscosity of aqueous nanofluid of TiO2 using experimental data. Int. Commun. Heat Mass Transf. 75: 192–196. https://doi.org/10.1016/J.ICHEATMASSTRANSFER.2016.04.002

[46] Hemmat Esfe M, Bahiraei M, Hajmohammad MH, Afrand M. (2017). Rheological characteristics of MgO/oil nanolubricants: Experimental study and neural network modeling. Int. Commun. Heat Mass Transf. 86: 245–252. https://doi.org/10.1016/J.ICHEATMASSTRANSFER.2017.05.017

[47] Ahmadi Nadooshan A, Hemmat Esfe M, Afrand M. (2017). Prediction of rheological behavior of SiO2-MWCNTs/10W40 hybrid nanolubricant by designing neural network. J. Therm. Anal. Calorim. 1–8. https://doi.org/10.1007/s10973-017-6688-3

[48] Alirezaie A, Saedodin S, Esfe MH, Rostamian SH. (2017). Investigation of rheological behavior of MWCNT (COOH-functionalized)/MgO - engine oil hybrid nanofluids and modelling the results with artificial neural networks. J. Mol. Liq. 241: 173–181. https://doi.org/10.1016/J.MOLLIQ.2017.05.121

[49] Hemmat Esfe M, Tatar A, Ahangar MRH, Rostamian H. (2018). A comparison of performance of several artificial intelligence methods for predicting the dynamic viscosity of TiO2/SAE 50 nano-lubricant. Phys. E Low-Dimensional Syst. Nanostructures 96: 85–93. https://doi.org/10.1016/J.PHYSE.2017.08.019

[50] Esfe MH, Esfande S, Rostamian SH. (2017). Experimental evaluation, new correlation proposing and ANN modeling of thermal conductivity of ZnO-DWCNT/EG hybrid nanofluid for internal combustion engines applications. Appl. Therm. Eng. https://doi.org/10.1016/j.applthermaleng.2017.11.131

[51] Hemmat Esfe M, Esfandeh S, Saedodin S, Rostamian H. (2017). Experimental evaluation, sensitivity analyzation and ANN modeling of thermal conductivity of ZnO-MWCNT/EG-water hybrid nanofluid for engineering applications. Appl. Therm. Eng. 125: 673–685. https://doi.org/10.1016/J.APPLTHERMALENG.2017.06.077

[52] Afrand M, Hemmat Esfe M, Abedini E, Teimouri H. (2017). Predicting the effects of magnesium oxide nanoparticles and temperature on the thermal conductivity of water using artificial neural network and experimental data. Phys. E Low-Dimensional Syst. Nanostructures 87: 242–247. https://doi.org/10.1016/j.physe.2016.10.020

[53] Esfe MH, Rejvani M, Karimpour R, Abbasian Arani AA. (2017). Estimation of thermal conductivity of ethylene glycol-based nanofluid with hybrid suspensions of SWCNT–Al2O3 nanoparticles by correlation and ANN methods using experimental data. J. Therm. Anal. Calorim. 128: 1359–1371. https://doi.org/10.1007/s10973-016-6002-9

[54] Rostamian SH, Biglari M, Saedodin S, Hemmat Esfe M, (2017). An inspection of thermal conductivity of CuO-SWCNTs hybrid nanofluid versus temperature and concentration using experimental data. ANN modeling and new correlation. J. Mol. Liq. 231: 364–369. https://doi.org/10.1016/J.MOLLIQ.2017.02.015

[55] Kasaeian A, Ghalamchi M, Ahmadi MH, Ghalamchi M. (2017). GMDH algorithm for modeling the outlet temperatures of a solar chimney based on the ambient temperature. Mech. Ind. 18: 216. https://doi.org/10.1051/meca/2016034

[56] Ahmadi MH, Ahmadi MA, Mehrpooya M, Rosen MA. (2015). Using GMDH neural networks to model the power and torque of a stirling engine. Sustain 72243–2255. https://doi.org/10.3390/su7022243

[57] Pourkiaei SM, Ahmadi MH, Hasheminejad SM. (2016). Modeling and experimental verification of a 25W fabricated PEM fuel cell by parametric and GMDH-type neural network. Mech. Ind. 17: 105. https://doi.org/10.1051/meca/2015050

[58] Das PK, Islam N, Santra AK, Ganguly R. (2017). Experimental investigation of thermophysical properties of Al2O3–water nanofluid: Role of surfactants, J. Mol. Liq. 237. https://doi.org/10.1016/j.molliq.2017.04.099

[59] Das PK. (2017). A review based on the effect and mechanism of thermal conductivity of normal nanofluids and hybrid nanofluids. J. Mol. Liq. 240. https://doi.org/10.1016/j.molliq.2017.05.071

[60] Ilyas SU, Pendyala R, Narahari M. (2017). Stability and thermal analysis of MWCNT-thermal oil-based nanofluids, colloids surfaces a physicochem. Eng. Asp. 527: 11–22. https://doi.org/10.1016/j.colsurfa.2017.05.004

[61] Agarwal R, Verma K, Agrawal NK, Singh R. (2017). Sensitivity of thermal conductivity for Al2O3 nanofluids. Exp. Therm. Fluid Sci. 8019–26. https://doi.org/10.1016/j.expthermflusci.2016.08.007

[62] Senthilraja S, Vijayakumar K, Gangadevi R. (2015). A comparative study on thermal conductivity of Al2O3 /water, cuo/water and – cuo/water nanofluids. Dig. J. Nanomater. Biostructures. 10(4): 1449–1458. http://www.chalcogen.ro/1449_Senthilraja.pdf, accessed October 16, 2017.

[63] Hemmat Esfe M, Afrand M, Yan WM, Akbari M. (2015). Applicability of artificial neural network and nonlinear regression to predict thermal conductivity modeling of Al2O3–water nanofluids using experimental data. Int. Commun. Heat Mass Transf. 66: 246–249. https://doi.org/10.1016/j.icheatmasstransfer.2015.06.002

[64] Li CH, Peterson GP. (2007). The effect of particle size on the effective thermal conductivity of Al2O3-water nanofluids. J. Appl. Phys. 101: 044312. https://doi.org/10.1063/1.2436472

[65] Wang Y, Wu JM. (2015). Numerical simulation on single bubble behavior during Al2O3 /H2O nanofluids flow boiling using moving particle simi-implicit method. Progress in Nuclear Energy 85: 130-139. https://doi.org/10.1016/j.pnucene.2015.06.017

[66] Esfe MH, Saedodin S, Mahian O, Wongwises S. (2014). Thermal conductivity of Al2O3/water nanofluids: Measurement, correlation, sensitivity analysis, and comparisons with literature reports. J. Therm. Anal. Calorim. 117: 675–681. https://doi.org/10.1007/s10973-014-3771-x

[67] Li CH, Peterson GP. (2006). Experimental investigation of temperature and volume fraction variations on the effective thermal conductivity of nanoparticle suspensions (nanofluids). J. Appl. Phys. 99: 084314. https://doi.org/10.1063/1.2191571

[68] Lee S, Choi SUS, Li S, Eastman JA. (1999). Measuring thermal conductivity of fluids containing oxide nanoparticles. J. Heat Transfer 121: 280. https://doi.org/10.1115/1.2825978

[69] Beck MP, Yuan Y, Warrier P, Teja AS. (2009). The effect of particle size on the thermal conductivity of alumina nanofluids. J. Nanoparticle Res. 11: 1129–1136. https://doi.org/10.1007/s11051-008-9500-2

[70] Hemmat Esfe M, Karimipour A, Yan WM, Akbari M, Safaei MR, Dahari M. (2015). Experimental study on thermal conductivity of ethylene glycol based nanofluids containing Al2O3 nanoparticles. Int. J. Heat Mass Transf. 88: 728–734. https://doi.org/10.1016/j.ijheatmasstransfer.2015.05.010

[71] Pryazhnikov MI, Minakov AV, Rudyak VY, Guzei DV. (2017). Thermal conductivity measurements of nanofluids. Int. J. Heat Mass Transf. 104: 1275–1282. https://doi.org/10.1016/j.ijheatmasstransfer.2016.09.080