基于人工神经网络湍流模型预测纳米流体的压力降(IJITCS-V5-N11-2)

I.J.InformationTechnologyandComputerScience,2013,11,13-20PublishedOnlineOctober2013inMECS()DOI:10.5815/ijitcs.2013.11.02Copyright©2013MECSI.J.InformationTechnologyandComputerScience,2013,11,13-20ArtificialNeuralNetworkTurbulentModelingforPredictingthePressureDropofNanofluidM.S.YoussefMechanicalEngineeringDepartment,FacultyofEngineering,TaifUniversity,Al-Haweiah,P.O.Box888,SaudiArabiaE-mail:[e-mail]:MechanicalEngineeringDepartment,FacultyofEngineering,AssiutUniversity,AssiutP.O.Box71516,EgyptAbstract—AnArtificialNeuralNetwork(ANN)modelwasdevelopedtopredictthepressuredropoftitaniumdioxide-water(TiO2-water).Themodelwasdevelopedbasedonexperimentallymeasureddata.Experimentalmeasurementsoffullydevelopedturbulentflowinpipeatdifferentparticlevolumetricconcentrations,nanoparticlediameters,nanofluidtemperatureandReynoldsnumberwereusedtoconstructtheproposedmodel.TheANNmodelwasvalidatedbycomparingthepredictedresultswiththeexperimentalmeasureddataatdifferentexperimentalconditions.Itwasshownthat,thepresentANNmodelperformedwellinpredictingthepressuredropofTiO2-waternanofluidunderdifferentflowconditionswithahighdegreeofaccuracy.IndexTerms—Nanofluids,ParticleConcentration,TurbulentFlow,PressureDrop,ArtificialNeuralNetworkI.IntroductionNanofluidisthenameconceivedbyArgonneNationalLaboratorytodescribeafluidinwhichnanometer-sizedsolidparticles,fibers,ortubesaresuspendedinliquidssuchaswater,engineoil,andethyleneglycol(EG).Manyindustrialprocessesinvolvethetransferofheatbymeansofaflowingfluidineitherthelaminarorturbulentregimeaswellasflowingorstagnantboilingfluids.Manyoftheseprocesseswouldbenefitfromadecreaseinthethermalresistanceoftheheattransferfluid.Correspondingly,smallerheattransfersystemswithlowercapitalcostandimprovedthermalefficiencieswouldresult.Nanofluidshavethepotentialtoreducesuchthermalresistancesandcanbeusedindifferentindustrialapplicationssuchaselectronics,transportation,medical,food,andmanufacturingindustryofmanytypes(Yuetal.,2007).Whilethermalpropertiesareimportantforheattransferapplications,theviscosityisalsoimportantindesigningnanofluidsforflowandheattransferapplicationsbecausethepressuredropandtheresultingpumpingpowerdependontheviscosity.Manyexperimentalinvestigationsontheheattransferperformanceandpressuredropofdifferentnanofluidswithvariousnanoparticlevolumeconcentrations,inbothlaminarandturbulentflowregimes,havebeenreported(Koetal.,2007;Heetal.,2007;Pengetal.,2009;DuangthongsukandWongwises,2009;DuangthongsukandWongwises,2010;FotukainandEsfahany,2010;Vajjhaetal.,2010;Tengetal.,2011;andSajadiandKazemi2011).TheresultsofDuangthongsukandWongwises(2010)showedthatthepressuredropofnanofluidswasslightlyhigherthanthebasefluidandincreaseswithincreasingthevolumeconcentrations.Also,theresultsofDuangthongsukandWongwises(2009)andHeetal.(2007)disclosedthatthepressuredropofthenanofluidswasveryclosetothatofthebasefluid.Koetal.(2007)experimentallymeasuredthepressuredropofnanofluidscontainingcarbonnanotubesflowingthroughahorizontaltubeunderlaminarandturbulentflowconditions.Theirresultsrevealedsignificantincreaseinpressuredroponnanofluidsunderlaminarflowcondition,while,thepressuredropofnanofluidspresentedsimilarvaluestothoseofthebasefluidattheturbulentflowconditions.InanotherarticlepublishedbyTengetal.(2011)resultsshowthattheenhancementofpressuredropforTiO2nanofluidwaslowerunderturbulentflowconditionsinacircularpipebuthigherunderlaminarflowconditions.Recently,turbulentheattransferbehavioroftitaniumdioxide/waternanofluidinacircularpipeunderfully-developedturbulentregimeforvariousvolumetricconcentrationswasinvestigatedexperimentallybySajadiandKazemi(2011).Theirmeasurementsshowedthatthepressuredropofnanofluidwasslightlyhigherthanthatofthebasefluidandincreasedwithincreasingthevolumeconcentration.Incontrast,theresultsofFotukianandEsfahany(2010)indicatedthatthemaximumincreaseinpressuredropwasabout20%fornanofluid.Inthesametrend,theexperimentalresultsofPengetal.(2009)showedthatthefrictionalpressure14ArtificialNeuralNetworkTurbulentModelingforPredictingthePressureDropofNanofluidCopyright©2013MECSI.J.InformationTechnologyandComputerScience,2013,11,13-20dropofrefrigerant-basednanofluidincreaseswiththeincreaseofthemassfractionofnanoparticles,andthemaximumenhancementoffrictionalpressuredropwas20.8%undertheirexperimentalconditions.Moreover,Vajjhaetal.(2010)reportedthatthepressurelossofnanofluidsincreasedwithanincreaseinparticlevolumeconcentrationsandtheincreaseofpressurelossfora10%Al2O3nanofluidwasabout4.7timesthatofthebasefluid.Summarizingwhatisreviewedintheexperimentalstudies,onecouldeasilyconcludethat,thevariationintheexperimentaldataofthepressuredropofnanofluidsisattributedtothedifficultiesoftheexperimentalmeasurements.Thesedifficultiesariseduetolackinunderstandingthedetailsandmechanismsofheattransferphenomenonthatchangethethermalconductivityandpressuredropinnanofluids(Kondarajuetal.,2010).Variousanalyticalandnumericalmodelswereproposedtostudythemechanismandpredictthethermalc