简介：Inthispaper,weproposeanovelapproachtoachievespectrumprediction,parameterfitting,inversedesign,andperformanceoptimizationfortheplasmonicwaveguide-coupledwithcavitiesstructure(PWCCS)basedonartificialneuralnetworks(ANNs).TheFanoresonanceandplasmon-inducedtransparencyeffectoriginatedfromthePWCCShavebeenselectedasillustrationstoverifytheeffectivenessofANNs.WeusethegeneticalgorithmtodesignthenetworkarchitectureandselectthehyperparametersforANNs.OnceANNsaretrainedbyusingasmallsamplingofthedatageneratedbytheMonteCarlomethod,thetransmissionspectrapredictedbytheANNsarequiteapproximatetothesimulatedresults.Thephysicalmechanismsbehindthephenomenaarediscussedtheoretically,andtheuncertainparametersinthetheoreticalmodelsarefittedbyutilizingthetrainedANNs.Moreimportantly,ourresultsdemonstratethatthismodel-drivenmethodnotonlyrealizestheinversedesignofthePWCCSwithhighprecisionbutalsooptimizessomecriticalperformancemetricsforthetransmissionspectrum.Comparedwithpreviousworks,weconstructanovelmodel-drivenanalysismethodforthePWCCSthatisexpectedtohavesignificantapplicationsinthedevicedesign,performanceoptimization,variabilityanalysis,defectdetection,theoreticalmodeling,opticalinterconnects,andsoon.

简介：Wedevelopatheoreticalmodelforpredictingtheultrasonicattenuationintheliquid-solidsystemcontainingmixedparticles.Theultrasonicattenuationcoefficientisobtainedbycountingthenumberofphononsthatreachthereceiver.UsingtheMonteCarlomethod(MCM),numericalsimulationswereperformedtopredicttheultrasonicattenuationswithnotonlyasingleparticletypebutalsomonodisperseandpolydispersemixedparticles.Thesimulationresultsforthesystemswithasingleparticletypewerecomparedwithvariousstandardmodels.Theresultsshowthattheyagreewellatrelativelylowparticlevolumeconcentrations(within10%).Forsystemswithmixedparticles,theparticlevolumeconcentrationwasfoundtoincreasetoaround10%,andthevariationoftheultrasonicattenuationagainstthemixingratioyieldsanonlineartrend.Moreover,theultrasonicattenuationissignificantlyaffectedbyparticleproperties.Thenumericalresultsalsoshowthatboththeparticletypeandparticlesizedistributionshouldbecarefullytakenintoaccountinthedispersionswithpolydispersemixedparticles,wheretheMCMcangiveamoredirectdescriptionofthephysicsofsoundpropagationcomparedwiththeconventionalmodels.

简介：Thereliabilityandaccuracyofnumericalresultsofmicroparticlefluidizationinaconicalbed,affectedsimultaneouslybymeshrefinement,thegridconfigurationandthewallboundarycondition(BC),areanalyzed.Specifically,pressuregradientsandvelocityprofilesoftitaniapowderarestudiedforaconicalbed.TheGidaspowdragcorrelationanddifferentwallBCsareconsideredusingaEulerian-Euleriantwo-fluidmodel.Predictionsofthepressurefluctuation,powerspectraofthecorrespondingpressurefluctuations,bedpressuredrop,minimumfluidizationvelocity,axialsolidvelocity,bedexpansionratio,andparticlesizedistributionarecomparedwithexperimentaldata.Meshsensitivityanalysisusinghexahedralandtetrahedralcellswithauniformmeshandnear-wallmeshrefinemenrisconductedtoinvestigatetheeffectsofmeshconfigurationsinestimatingparticleflowpatterns.Simulationsshowthatsignificantsavingsintermsofcomputationaltimearerealizedbychoosingauniformmeshwhilethehexahedralstructure,thenear-wallmeshrefinement,andthefree-slipBCgivetheclosestfittotheexperimentaldata.