Modeling and simulation of thermal power plants with thermosyspro a theoretical introduction and a p

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Theoretical

Guide Baligh El Hefni

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Baligh El Hefni · Daniel Bouskela

Modeling and Simulation of Thermal Power Plants with

Chatou,France

Chatou,France

ISBN978-3-030-05104-4ISBN978-3-030-05105-1(eBook) https://doi.org/10.1007/978-3-030-05105-1

LibraryofCongressControlNumber:2018962771

© SpringerNatureSwitzerlandAG2019

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Preface

Modellingandsimulationisbecominganessentialtooltoassessthebehaviorof largecomplexenergysystemsagainstevermorestringentsafety,availability, environmental,economicandsocietalconstraintspromptedbytheongoingenergy transition.Indeed,thelargenumberofrequirementstobeconsideredandthe complexphysicalinteractionsbetweensystemsandtheirenvironmentcallfor efficientmeansforquantitativeandqualitativeanalysisofthesystemsphysicaland functionalbehavior.

Systemmodelling,alsocalled0D/1Dmodelling,isthedisciplineatthecrossroadsbetweendetailed3Dphysicalmodellingsuchascomputational fluid dynamicsandfunctionalmodellingsuchascontrolsystemdesign.Itaimsatrepresentingthephysicalbehaviorofthewholesystemusing firstprinciplephysical laws.Theselawsareaveragedinspaceandareclosedwithempiricalcorrelationsin ordertocomputethequantitiesofinteresttotheengineerwhileavoidingunrealistic assumptionsandminimizingcomputationaltime.Physicalmodellingismostoften usedforsimulationwhichconsistsinpredictingthesystem’sbehaviorfromgiven initialconditionsoveragiventimeperiod.Thanksto0D/1Dmodelling,thetime periodscanextendoverseveraltimescales(fromsecondstoyears),andsimulation canusuallybeperformedmuchfasterthanrealtimeonordinarylaptops.This convenienceisespeciallyneededforsimulationoverlongtimeperiods.0D/1D modellingisalsousedforassessingandmonitoringthesystemcurrentstatein combinationwithothertechniquessuchasdataassimilationthataimatusingthe knowledgeembeddedinthemodelstoimprovedataquality.

0D/1Dmodellingcancoverthewholesystemengineeringlifecycle,frompreliminarydesigntocommissioning,operationandmaintenance.Itcanbeusedfor diversetaskssuchastheoptimalsizingofarefuelingcavity,theoptimalplant startupthatconsistsinminimizingstartupdelaywhilemeetingoperationalconstraints,theassessmentofsteamgeneratorcloggingwhiletheplantisinoperation, themonitoringanddiagnosticsofefficiencydegradationduetothermallosses, operatorstraining,etc.

Thisbookisaboutthescienceandartofphysicalsystemmodellingappliedto thermalpowerplantswithalibraryofcomponentmodelscalledThermoSysPro whichisusedatEDF(andalsootherorganizations)fortheengineeringofpower plantsatthedesignandoperationphases.Theambitionistoshowhowtomake powerplantmodelsthatprovideconvincingsimulationresults.Tothatend,it containsEDF’slongstandingexperienceinpowerplantmodellingandsimulation. Theequationsusedinthecomponentmodelsarepresentedindetailwiththeir validitydomainsusingmathematicalnotationinatool-independentway.Theyare justi fiedwithrespecttofundamentalknowledgeinthermodynamicsandheat transferusinganalyticalderivationsorproofswhennecessary.Foreachcomponent model,asmalltest-casewithsimulationresultsisgiven.Modelsofthermalpower plants(fossilfuel firedandsolar)arepresentedwithresultsofnumericalsimulation andpracticalhintsonhowtobuildthemwiththelibrary.Inaddition,comparison withrealmanufacturerdataisprovidedinthecaseofacombinedcyclepower plant.Someinsightisalsogivenontheinternalstructureofthelibraryforthe interestedreader.

Thewholespaceofthebookisdedicatedtothephysicalandmathematical aspectsofpowerplantmodelling.Althoughtheyareimportant,thenumerical aspectsarenotconsidered.ThisismadepossiblethankstotheModelicatechnology thatemergedattheturnofthetwenty-firstcenturyandthatisnowfullyoperational inseveralcommercialandopensourcetools.Itallowstotranslateautomatically modelsequationsintoefficientsimulationcode.Therefore,althoughthisbookrelies onModelicatoproducenumericalresults,itisnotanintroductiontomodelingand simulationwithModelica,soitdoesnotpresentthelanguagenordoesitmention theassociatedtechniques.Moreover,thelessonslearnedfromthisbookcanbeused withanykindoftool,notonlyModelicatools.

Thebookisintendedtostudentsandconfirmedpractitionersinpowerplant modellingandsimulation.Allmodelspresentedinthebookcanbefoundinthe ThermoSysProlibrarywhichisreleasedunderopensourcelicenseandfreely availabletothepublic.

Chatou,FranceDanielBouskela October2018BalighElHefni

Acknowledgements

FirstthankstothePRISMEdepartmentofEDFLabfortheirsupporttothe accomplishmentofthisbook.

ManythankstoAudreyJardinforfruitfulcommentsandtoBenoîtBridefor providingcombustioncomponentmodels.

ThankstoallThermoSysProenthusiasticusersfortheircommentsandfeedback onthelibrary.

ThankstotheModelicacommunitywhodevelopedtheModelicalanguagethat madeThermoSysPropossible.

ThankstoPeterFritzsonandtheOpenModelicateamfortheirsupportof ThermoSysProintheOpenModelicaopensourceplatform.

Finally,thankstotheEurekaITEAclusterprogramanddedicatedteamfor providingtheinnovativeframeworkthatboostedthedevelopmentofModelicaand ThermoSysPro.

5.2.2ComputingtheVolumeSpeci

6.2TypicalUsageofPowerPlantModels

6.3TheThermoSysProLibrary

6.4HowtoDevelopaPowerPlantModel

6.4.1UsingtheThermoSysProLibrary

6.4.2ConceptualSteps

ficulty:FindingtheSteady-State

6.4.4PracticalSteps

6.4.5PracticalHints

6.5DynamicModelofaRealCombinedCyclePowerPlant

6.5.1DescriptionofaCombinedCyclePowerPlant

6.5.2DescriptionofthePhuMyCombinedCyclePower

6.5.3DescriptionoftheModel

6.6DynamicModelofaOnce-ThroughSupercriticalCoal-Fired

6.6.1DescriptionofaOnce-ThroughSupercritical Coal-FiredPowerPlant

6.6.2DescriptionoftheModel

6.7DynamicModelofa1MWeConcentratedSolarPowerPlant (CSP)withaPTSC ................................

6.7.1DescriptionoftheConcentratedSolar

7.2.2Nomenclature

7.2.3GoverningEquations

7.2.4ModelicaComponentModel:

7.2.5Test-Case

8CombustionChamberModeling

8.1CombustionChamberforaGasTurbine

8.1.1ModelingPrinciples

8.1.2Nomenclature

8.1.3GoverningEquations

8.1.4ModelicaComponentModel: GTCombustionChamber

8.1.5Test-Case ................................

8.2CombustionChamberforBoilerFurnace

8.2.1ModelingPrinciples

8.2.2Nomenclature

8.2.3GoverningEquations

9.2.1Nomenclature

9.2.2Assumptions

9.2.3OverallHeatTransferCoef

9.2.4ConvectiveHeatTransferCoef

9.2.5TheLogMeanTemperatureDifferenceMethod

9.2.6TheEffectivenessandtheNumberofTransfer UnitsMethod(NTU)

9.2.7TheUAMethod

9.2.8TheEffi

9.2.9TakingintoAccountPhaseTransitions

9.2.10SimpleHeatExchangers

9.4.2TubeBundleHeatExchanger

9.4.3Shell-SideHeatExchangerModeling

9.5.1Introduction

9.5.2DynamicModelingofaWaterHeater

9.5.3DynamicModelingofaSimpleCondenser

13.8.4ModelicaComponentModel:

17.1.3VolumeComponentsandFlowComponents

17.1.4ConnectingVolumeComponentstoFlow

17.1.5StructureoftheConnectors

17.2.1CurrentSituationwithThermoSysProV3.x

17.2.2FutureDirectionsforThermoSysProV4.x

AbouttheAuthors

Dr.-Ing.BalighElHefni has35yearsofresearch,industryandteachingexperience.Hespecialisedinphysicalmodellingofvarioustypesofpowerplants. In2002,hejoinedEDFResearch&Developmentdivision(R&D).Hecontributed tothedevelopmentofpowerplantsmodelingactivities,withintheEDFandits subsidiaries,thankstotheuseofThermoSysProlibrary(developedbytheauthors). Thishasallowedhimtocollaboratewithmajorindustrialplayers,researchand academicsintheframeworkofmanyEuropeanprojects(DLR,Dassault-Systèmes, Dassault-Aviation,Siemens,IFP,OpenModelica …),inwhichEDF’scontribution wasrecognized.Themainobjectivesoftheseprojectsare:toimprovethemodeling languages,thetools,themethods(statestimation),andtoextendthemodeling domains(multi-modesystems,propertymodeling).Theexperienceinthepower plantmodelinghasallowedhimtodevelopsolidcompetenciesandaspeci fic know-howthathasallowedhimtoexpandouractivitytowardsthedevelopmentof solarpowerplants,andcollaboratewithCENER(Spain),ChineseAcademyof Sciences(China),CEA(France)andotherpartners.Heisinchargeoftraining coursesofthemodellingofpowerplantsandenergysystems,forEDFstaffand partners.HewasalsoinchargeofthesametrainingatEcoleCentraledeParis “EngineeringTrainingSchool”

DanielBouskela graduatedfromEcoleCentraledeParis(France).Heiscurrently SeniorResearcheratEDFResearchandDevelopmentDivisionwherehismain interestsareinthedomainofthemodelingandsimulationofenergysystems.

DanielBouskelaisstronglyinvolvedinEuropeanandFrenchcooperative projects.HewasinparticulartheleaderoftheEuropeanMODRIOproject.Heis theco-authoroftheopensourceThermoSysProModelicalibraryforthemodeling andsimulationofpowerplants.

Chapter1 IntroductiontoModelingandSimulation

Abstract Powerplantmodelingplaysakeyroleinmanypurposes,likeprocess designassessment,theassessment,andpredictionofplantperformance,operating procedureevaluation,controlsystemdesign,andsystemprognosisanddiagnosis. Thepresentchapterintroducesthedisciplineof0D/1Dmodelingappliedtothermal hydraulicsandtheirmainapplicationstoreal-lifesystems:how0D/1Dmodeling relatestothe3Dphysicalequations,whatarethefundamentalassumptions underlying0D/1Dphysicalmodelsandthemainlimitationsofthenumericalsolverscommonlyusedforsuchmodels,whatistherationalefora0D/1Dcomponent modelslibraryandwhatkindsofreal-lifesystemscanbemodeledandsimulated fordifferentpurposes(plantsizing,control,operationandmaintenance,prognosis, diagnosisandmonitoring).Also,inthischapter,manyquestionsareanswered: whatisasystem,whatisamodelandmodeling,whatissimulationandwhyis modelingimportant?

1.1Systems,ComplexSystems,andCyber-Physical Systems

Ausualsystemsengineeringdefinitionofasystemisthatitis “asetofinterrelated partsthatworktogethertoaccomplishacommonpurposeormission” (Cloutier etal. 2015).

Systemsaredecomposedintosubsystemsandobjectsatthelowestlevel.They aredynamicallystructuredusingabstractconceptssuchasmodes,states,events, andtrajectories.Modesrefertothelogicalorfunctionalstatesofthesystem(e.g. started,stopped,closed,open,dysfunctional,undermaintenance),whereasstates refertothephysicalstatesofthesystem(e.g.temperature,mass flowrate,angular velocity).Eventscauseswitchingbetweenmodes.Trajectoriesaretheevolutionin timeofthestates.Systemsinteractwiththeirenvironmentviainputsandoutputs. Theinputsrepresenttheactionoftheenvironmentonthesystem,whereasthe outputsrepresenttheinfluenceofthesystemontheenvironment.

© SpringerNatureSwitzerlandAG2019

B.ElHefniandD.Bouskela, ModelingandSimulationofThermalPowerPlants withThermoSysPro, https://doi.org/10.1007/978-3-030-05105-1_1

Forinstance,acoolingsystemwhosemissionistocoolmachinescanbe decomposedintothreesubsystems:apumpingsystemcomposedofpumpsthat circulateswateraroundtheequipmenttobecooled,afeedwatersystemcomposed ofatankandswitchvalvesthatensuressufficientwaterpressureatthepumping systeminlet,andagroupofheatexchangersthattransfersheattotheenvironment. Agivenpumpcanbeinvariousnormalordysfunctionalmodes:started,stopped, cavitating,broken,etc.Thepumphydraulicstateismostfrequentlydescribedby thepumphead(thevariationofpressurethroughthepump)andthepumpvolumetric flowrate(theamountofliquidvolumethatgoesthroughthepumpcasing pertimeunit).Themechanicalstateofthepumpcanbegivenbytheangular velocityandthetorqueoftheshaft.However,iftheshaftisbrokenintotwoparts, thenthemechanicalstateinvolvestheangularvelocitiesandthetorquesofeachend ofthebrokenshaft.Therefore,thestateofabrokenshafthastwiceasmanystate variablesasanormalone.Thisshowsthatmodeswitchingcancauseacomplete structuralchangeinthesystemdescription.Thetemperatureoftheenvironmentis aninputofthesystem(insuchcaseitisassumedthatthesystemdoesnotchange thetemperatureoftheenvironment),andtheheatreleasedtotheenvironmentisan outputofthesystem.

Althoughthereisnowidelyaccepteddefi nitionofacomplexsystem,wewill considerascomplexsystemsthesystemscomposedofnumeroustightlyinteracting subsystems.Cyber-physicalsystemsarecomplexsystemshavingsoftwareand physicalsubsystemsintightinteractionordeeplyintertwined.Goodexamplesof cyber-physicalsystemsarepowerplants,cars,planes,powergrids,etc. Cyber-physicalsystemsexhibit emerging behaviorsthatarenotnecessarilyforeseenatdesigntimeandthatappearatoperationtimeduetothemultipleinteractions (thewholeismorethanthesumofitsparts).Oneofthemainchallengesofphysical modelingandsimulationistobeabletopredictemergingbehaviors.However,the objectiveofthisbookisnottoshowhowtodothat,buttoprovidethefundamental knowledgeintermsofphysicalequationsforthethermalhydraulicpartsofthe systemsthatarenecessaryforthisgoalinparticular,andmoregenerallyforany otherpurposerequiringtheunderstandingofthephysicalbehaviorofthesystem.

1.2WhatisSystemModeling?

Generallyspeaking,modelingistheprocessofrepresentingaparticularconcept, physicalphenomenon,orreal-worldobjectusingabstractnotationsincludingbut notlimitedtomathematicalsymbols.Inthisbook,modelingisreferredtoas derivingfromphysicallawsavalidsetofmathematicalequationsthatdescribethe system physicalbehavior inordertoassessquantitativelyhowthesystemperforms itsdutiesaccordingtosomeprescribedmission,e.g.,toverifywhetherapower plantcomplieswithoperatingrulesduringstart-uporshutdown.Otherwaysof modelingcomplexsystemssuchasstatediagramsorotherkindsofschemas,in particularforthepurposeofexpressingrequirements,assumptions,or logical

behavior,arenotconsideredhere.However,suchmodelsarenecessaryforthe designofcontrolsystemsandcanbeconsideredastheenvironmentofthephysical system(i.e.,theyinteractwiththephysicalsystemviainputsandoutputs).Also, stochasticmodelsarenotexplicitlydealtwith,butrandomnesscanbeintroduced intophysicalmodelsbyreplacingscalarvariableswithdistributionsinthephysical equationsandusingMonteCarlosimulationstocomputetheresponseofthesystem touncertainties.

Physicalmodelingisnotlimitedtoassessingthedynamicbehaviorofthesystem.Itcanalsobeusedtocomputeisolatedoperatingpoints.Thisiscalled static modeling,asopposedto dynamicmodeling thataimsatcomputingsystemstrajectories.Staticmodelingismainlyusedforsystemsizingandoptimizationat designtime,whiledynamicmodelingisoftenusedforsystemcontroldesignand optimizationatoperationtime.Systemdiagnosismayusestaticordynamicmodelingdependingonthephenomenatobeexplored.

1.3WhatisSimulation?

Simulationisanexperimentconductedonamodel.Asmathematicalmodelsare consideredhere,simulationsarenumericalexperimentsconductedwitha computer-executableversionofthemodel,whichisusuallyobtainedbycompiling withacompilerthemodelexpressedinacomputerlanguageintoamachineexecutablecode.Thecomputerlanguageusedformodelingiscalledamodeling language.Thechallengefortheuseristhentowritethemodel’sequationsinthe modelinglanguage.

Thereareroughlytwokindsofmodelinglanguages:imperativelanguagesand equationallanguages.ImperativelanguagessuchasFortran,C,C++,Java,Python, etc.areusedforimperativeprogramming,whichconsistsinwritingexplicitlythe algorithmsthatcomputethemodel’sequations.Thisrequiresasigni ficanteffort fromtheuserwhomusttranslatemanuallytheequationsthatexpressmathematical relationsintosequenceofcomputinginstructionsthatcomputesthenumerical solutionoftheequations.Itismoreconvenienttoperformthistedioustaskautomaticallybyusinganequationallanguagethatletstheuserexpressthemodel’s equationsdirectlyinequationalform,hence,withverylittletransformationofthe originalequationsaswrittenonpaper.Modelicaisanequationallanguage. Modelicacompilerstranslateequationalmodelsintoimperativeprograms,which areinturncompiledwithregularcompilers(C,C++,Fortran,etc.)toproduce executablecode.Modelicahasbeenusedinthisbooktowriteandverifymodels equations.

Experimentswiththesamemodeldifferaccordingtothenumericalvalues providedtotheinputsofthemodelandtotheinitialvaluesofthestatevariables, whicharealsocalledinputsinthesequel.Thosevaluesmustbephysicallyconsistentinordertoprovidecorrectresults.Consistencycannotbeobtainedusingthe model’sequationsbecausetheunknownsarecomputedusingtheinputsasknown

variables.Inotherwords,theknownvariablesarenotconstrainedbythemodel’s equations.Soalthough,fromanumericalpointofview,anyinputcanproduce numericalresults,anyinputcannotproduce valid numericalresults.Therefore, consistencyoftheinputsmustbeachievedbyothermeanssuchasdataassimilation,forinstance,whichisthescienceofproducingthebestestimateoftheinitial stateofasystembycombininginformationfromobservationsofthatsystem(e.g. viasensors)withanappropriatemodelofthesystem(i.e.,themodelathandtobe initialized),seeSwinbanketal.(2003).Thistechniquewhichusescontinuous optimizationalgorithmsissuccessfullyusedinmeteorologyandcanbeappliedto anyphysicalsystemprovidedithasonlycontinuousinputstobeassimilated(this excludestheassimilationoflogicalinputssuchastheon–offpositionofaswitch). Anothertechnique,whichisusedinthisbook,istocomputetheinputsfromthe knowledgeofthenominaloperatingpointusinginversecomputationonsquare systemsofequations(i.e.,havingasmanyunknownsasequations).Thedrawback ofthistechniqueisthatonehastomakeachoicebetweenredundantinformationin ordertoobtainasquaresystem(e.g.,iftwovalvepositionsinfluenceasinglestate, onehastomakeachoicebetweenthetwovalvepositions).Thistechniqueisused inthisbookasitismorereadilyavailablewithexistingmodelingandsimulation toolsthanoptimizationtechniques.

Tosummarize,asimulationrunconsistsessentiallyinsolvinganinitialvalue problem,i.e.,adifferential-algebraicequationwithcorrectinitialvaluesforthestate variablesandcorrectvaluesfortheinputs.Inputswith fixedvaluesallalonga simulationrunareoftencalledparameters.Thiswillbelookedatinmoredetailin thesequel.

1.4Whatis0D/1DModeling?

Physicalequationsarefunctionsofspaceandtime.3Dmodelsinvolvethethree spacecoordinates.However,whendealingwithspaceandtime,itisoftendesirable toreducethenumberofspacecoordinatestospeed-upthecomputationoftrajectoriesasthefullmodel’sequationsmustbecomputedateachtimestep.Reducing thedimensionalityoftheproblembygoingfromthreespacecoordinatesdownto oneorevenzerospacecoordinatesiscalled0D/1Dmodelingasopposedto3D modeling.Thisisobtainedbyexploitingthegeometricalpropertiesofthemodel suchasthecylindricalsymmetryofapipe.Inthesequel,thisdiscussionisrestricted tothermalhydraulicsystemswhicharethescopeofthisbook.

Thermalhydraulicsistheapplicationof fluiddynamicsforheatandmass transferinenergysystemssuchaspowerplants.Phenomenastudiedincludeconvection,conduction,radiation,phasechange,single-phase(liquidorvapor), two-phase(liquidandvapor),andmulti-phase flows(forexamplewater/steamwith air).Themostcommon fluidsusedinpowerplantsarewater/steamand fluegases, butother fluidscanbeusedaswellsuchasmoltensalt.

1.4Whatis0D/1DModeling?5

Thedynamicphysicalbehaviorofthermalhydraulicsystemsisdescribedwith partialderivativeequations(PDEs)thatexpressthethreefundamentalconservation lawsofmass(1.1),momentum(1.2),andenergy(1.3).

whereD=Dt standsforthematerialderivative(thattakesintoaccountthe fluid motion), V isthe fluidvolume, q isthe fluiddensity, ~ v isthe fluidvelocity, ~ f arethe externalvolumeandsurfaceforcesactinguponthe fluid(suchaspressureand friction), u isthe fluidinternalenergy, Q and W are,respectively,theamountof heatandworkreceivedbythe fluidperunittime.

Theseequationsareclosedbyclosurelaws(fluidcorrelations)thatcompute unknownquantitiesfoundin ~ f suchaspressurelossorheatexchangecoefficientsas functionsofthepressure P andthetemperature T ofthe fluid.Stateequationsare usedtocompute q and u withrespectto P and T.

The0D/1Dmodelingapproachconsistsinaveragingphysicalquantitiesoverthe cross-sectionalarea A perpendiculartothemain flowdirection x,thenalongthe main flowdirection x:

where Dx isalengthincrementand V ¼ A Dx. Inpractice,thismethodconsistsin:

1.Dividingthesystemintocontrolvolumes V ¼ A Dx alongthemain flow direction;

2.Averagingthephysicalquantitiesusing(1.4)forallindividualcontrolvolumes;

3.Connectingthecontrolvolumesalongthemain flowdirectiontoaccountforthe variationofthephysicalquantitiesalongthatdirectioninstepscorrespondingto thelengths Dx ofthecontrolvolumes.

Dx isadaptedtothestudyathand.It,therefore,canbesmallorlargewithout limitation. Dx isequaltozeroforcomponentsconsideredasasingularitiessuchas valves.Itislargeforlongpipesorlargevesselswhennoinformationisneeded regardingthedistributionofphysicalquantitiesalongthecomponentlength.One

mustnotethatthechoiceof Dx doesnotinduceanyapproximationinitselfas computedquantitiesareconsideredasaveragedquantitiesover V ¼ A Dx,butthe larger Dx,thelowertheresolutionofthecomputationinspace.

0D/1Dmodelinggivestheabilitytochoosethespaceresolutionofrealistic modelsdescribedfrom fi rstprinciplephysics.It,therefore,allowstoadjustthe spaceresolutioninordertocomputelargetransientsforcomplexsystemsfor engineeringstudiesthatoftenrequiresimulationspeedordersofmagnitudefaster thanrealtime.Therefore,themainbenefitofthismethodistoallowtherealistic modelingandsimulationofcomplexsystemsoverlargetransients.

Astheonlydifferentialvariableleftin0D/1Dmodelsisdt ,0D/1Dmodelsare setsofdifferential-algebraicequations(DAEs):

where C isacoefficientmatrix, x isthestatevectorofthesystem, _ x isthetime derivativeof x (nottobeconfoundedwiththelengthincrement Dx above), p are fixedparameters(suchas fi xedboundaryconditions),and u areinputsofthesystem (suchasvariableboundaryconditions).Notethat x maycontaintimederivatives.

If C isinvertible,then(1.5)canbetransformedintoanordinarydifferential equation(ODE)andintegratedwithstandardODEnumericalsolvers:

If C isnotinvertible,then(1.5)isatrueDAEthatcannotbetransformedintoan ODEanditsresolutionismoreproblematic.

If C isnotinvertiblebecauseitcontainsrowsequaltozero,then(1.5)canbe writtenasthefollowingDAE:

where a arethealgebraicvariables,i.e.,thevariablesfrom x in(1.5)withzero coefficientsfor _ x,coefficientmatrix D iscoefficientmatrix C withouttherowsand columnscorrespondingtothealgebraicvariables a,and x aretheremainingdifferentialvariables,i.e.,thevariablesfrom x in(1.5)withnonzerocoefficientsfor x. Equation(1.7b)isafrequentcasethatappearswhendynamicsareneglected. ItcanbesolvedwithnumericalsolversthatcombinetheresolutionofODEswith algebraicequations.Ifthesizeof x isequaltozero,then(1.7a,b)boilsdownto (1.7b)andthemodelispurelystatic.Thiscaseisfrequentlyencounteredinsizing problems.Off-the-shelfModelicatoolssolve(1.7a,b)althoughtheyallowto expresstheproblemas(1.5).If C isnotinvertible,thenadivisionbyzerooccursat simulationtime.

If C containspredicates(i.e.,Booleanconditions)thatdependonelementsof x, andifthepredicatesaresuchthat C isnotinvertibleatsomeinstants t,thenthe systemmaybeconsideredasaseriesofcommutingDAEssuchas(1.7a,b)with

1.4Whatis0D/1DModeling?7

varyingstructure(i.e.,varyingsizesfor x, a, p, and u)fromoneDAEtotheother. Suchsystemsarecalledmulti-modesystems.Thereiscurrentlynoindustrialtool abletosolvesuchsystemsalthoughaprototypewasdevelopedintheframeworkof theITEA2MODRIOproject(2012–2016);(Elmqvistetal. 2014;Bouskela 2016), andthedevelopmentofanindustrialtoolisongoingintheframeworkoftheFUI ModeliScaleproject(startedin2018).

1.5Whatisa0D/1DThermalHydraulicComponent ModelsLibrary?

WhenusingDAEssuchas(1.5)torepresentthefundamentalequationsofthermal hydraulics,integrationof(1.1)–(1.3)mustbeperformedoverthevariouscomponentvolumesconsideredinthesystemmodel(pipes,valves,pumps,heat exchangers,turbines,etc.).Thevariouswaysofchoosingtheappropriateclosure lawsandofperformingtheintegrationoverthevariouscomponentvolumes commonlyfoundinthesystemstobemodeledresultinthedifferent0D/1D componentmodelsthatpopulatethelibrary.

Therefore,alibrarycomponentmodelisaDAEsuchas(1.5)thatdependson inputs u andparameters p.Theparametersaresetaccordingtotheproblemathand. Theyusuallyrepresentquantitiesthataregivenasdesigners’ assumptionsoras measuredquantitiesonthesystemoronitsenvironment.Theinputsaregivenas testscenariosorasoutputsfromneighboringcomponents.Thelattercaseisknown as connecting themodelcomponenttoitsneighboringcomponents.Thewayto performsuchconnectionshasastronginfluenceonthestructureofthecomponent models.Thewaytoorganizethecomponentmodelsinthelibraryinordertobe abletocomposeafullmodelbyinterconnectingthemisreferredtoasthe structure ofthelibrary inthesequel.

Inorderforlibrarycomponentmodelstobefullyreusable,i.e.,tobeusedinany modelwithoutmodi fication,theyshouldexhibitthefollowinggoodproperties: 1.Beacausal; 2.Beproperlyparameterized.

Beingacausalmeansthatwhenwrittenas(1.7a,b),theDAEmaybesolvedin anyofthevariables x, _ x, a, p, or u.Thisisneededbecausetheoutputsofone componentmodelaretheinputsofitsconnectedones,andtherefore,theknownor unknownstatusofthevariablesdependsonthewaythecomponentmodelsare

connectedtogethertoformthefullmodel.Theprocessofassigningthisstatustoall variablesinthemodelisknownas causalityanalysis1 andisperformedautomaticallybyModelicatools.

Beproperlyparameterizedmeansthatapropersetofparametersshouldbe definedinordertoaccountformostpossibleusagesofthecomponentmodel,while keepingthesizeofthesetassmallaspossible.

1.6Whatare0D/1DModelsUsefulfor?

Thepurposeof0D/1Dmodelsisnottodiscoverorstudynewphysicalphenomena, buttounderstandthephysicalbehaviorofsystemsusingthestandardlawsof physicscomplementedwithphysicalcorrelationsforvariousengineeringpurposes atdesign,commissioning,oroperationtime.Tothatend,itisonlynecessaryto monitorasmallnumberofsignificantvariablescalledthe variablesofinterest.This iswhythespaceaveragingoperationstoreducethedimensionalityoftheproblem from3to1or0areacceptable,providedthatuncertaintymarginsarecorrectly computedtotakeintoaccountrequirementsrelatedtosafetylimitsforinstance. Thisallowsfastcomputationofthesystembehaviorallalongitstrajectoryintime. Intheveryearlyphasesofsystemdesign,oneisgenerallyconcernedwiththe logicalbehaviorofthesysteminordertoverifythatthesystemwillcorrectly performitsmissionsfromafunctionalstandpoint.Thephysicalaspectsarenotvery importantatthisstage.However,atthedetaileddesignphase,whenfunctionsmust beimplementedintophysicalpiecesofequipment,itbecomesimportanttoevaluate differentimplementationalternativesquantitativelyinordertomakesurethatthe system’srequirements,inparticularthoseinvolvingreal-timephysicalconstraints suchassafety,aresatis fiedwhileavoidingoversizing(aslackofprecisequantitativeassessmentmostoftenresultsinexcessiveoperationalmargins),oversizing leadinginturntodelaysandovercosts.Thiscanbeachievedusing0D/1Dmodels, inparticularstaticmodelsforthesizingofnominaloperatingpoints,anddynamic modelsforthedesign,verifi cation,andvalidationofcontrolsystems. Atcommissiontime,0D/1Dmodelscanbeusedtopreparetheacceptancetests.

1Theword causality in causalityanalysis shouldnotbeconfoundedwiththeword causality in physicalcausality whichmeansthatcausesalwaysprecedetheireffects.However,thereisa relationshipbetweenthetwonotions.Theobjectiveofcausalityanalysisistoassigneach unknownvariabletoauniqueequationthatcomputesthisvariableandviceversa.Statederivatives areassignedinthemostobviouswaytoequationssuchas(1.7a).Suchassignmentsareconformantwithphysicalcausalityasstatederivatives(predictors)arethuscomputedfromthestatepast values.However,algebraicvariablesareassignedtoequationssuchas(1.7b)whosephysical causalitiesarelostasalgebraicequationsareobtainedbyneglectingthedynamicsofthesystem thatforcethephysicalcausalities.Theresultoftheanalysismay,thus,notreflectthephysical causalityoftherealsystemforthealgebraicvariables.Thisiswhyalgebraicvariablesshouldnot beusedinamodelwhencausalitiesareimportant,suchasthefeedbackloopofacontrolsystem.

1.6Whatare0D/1DModelsUsefulfor?9

Fig.1.1 Using0D/1Dmodelsfromsystemdesigntosystemoperation.(Source MODRIOproject,withpermissionfromtheauthor:AudreyJardin)

Atoperationtime,0D/1Dmodelsareusefultopredicttheshort-termbehaviorof thesystemtomaketherightoperationdecision,forinstance,tooptimizeplant start-upswhilecomplyingwithequipmentoperationalconstraints.Operatorscanbe trainedfortheconductofdifficulttransients(i.e.,transientsthatarerarelyperformedandaresubjecttotightsafetyconstraints)using0D/1Dmodels.0D/1D modelscanalsobeusedincombinationwithplantonsitemeasurementstomonitor andassesstheplantperformancedegradationssuchaswearorclogginginorderto providekeyeconomicperformanceindicatorsfortheplantandanticipateon maintenanceactionsinordertoreduceplantshutdownformaintenanceandcorrelativelyincreaseplantavailability.

Beyondindividualpowerplants,thereisagrowingneedtoassessthecollective behaviorofenergynetworkswhensubmittedtoperturbationssuchaschangesin regulatory,economic,orweatherconditions,andhowwellthepowersystemcan adapttodynamicandchangingconditions,see,e.g.,EPRI(2016).Thegrowthin variablegenerationsuchassolar(photovoltaicandthermodynamic)andwindisa strongdriverfortheuseof0D/1Dforlarge-scaleenergysystems.Thisnewneed promptedthelaunchoftheModeliScaleprojectthataimsatupscalingModelicato verylargemulti-modephysicalsystems.

Figure 1.1 presentsthedifferentstagesofthesystemslifecycle,fromdesignto operation,where0D/1Dmodelsareuseful.

References

BouskelaD(2016)Multi-modephysicalmodellingofadrumboiler,complexadaptivesystems. ProcComputSci95:516–523

CloutierR,BaldwinC,BoneMA(2015)Systemsengineeringsimplified.CRCPress,Taylor& Francis

ElmqvistH,MattssonSE,OtterM(2014)Modelicaextensionsformulti-modeDAEsystems.In: Proceedingsofthe10thinternationalModelicaconference

EPRI(2016)ElectricPowerSystemFlexibility,challengesandopportunity.Availablefrom https://www.epri.com/#/pages/product/3002007374/?lang=en MODRIOproject(2012–1016),ITEA211004MODRIO.Availablefrom https://github.com/ modelica/modrio and https://www.modelica.org/external-projects/modrio

SwinbankR,ShutyaevV,LahozWA(2003)Dataassimilationfortheearthsystem.In:SeriesIV: earthendenvironmentalsciences,vol26.Kluwer

Chapter2 IntroductiontoThermodynamics andHeatTransfer

Abstract Thermodynamicsisthesciencethatdealswiththeexchangeofenergyin theformofheatandworkandwiththedifferentstates(solid,liquid,gas,etc.)and properties(density,viscosity,thermalconductivity,etc.)ofsubstancesthatare relatedtoenergyandtemperature.Thermodynamicsisformalizedintothreebasic laws,the firstlawbeingtheconservationofenergy,andthesecondandthirdlaws beingrelatedtothenotionofentropyandiscompletedbythethreemainlawsfor heattransfer:radiation,convection,andconduction.Inthischapter,weintroduce firstthepropertiesofsubstances(density,pressure,andtemperature),energy, enthalpy,andentropy,thentheconceptofstatevariables,thedifferenttypesof thermodynamicsystems,the firstandsecondthermodynamiclaws,thethermodynamicscycles(idealandactualBraytoncycles,idealandactualRankinecycles), theidealgaslaw,andthethreeheattransferprocesses(radiation,convection,and conduction).Itisshownwhythesedifferentnotionsareessentialinorderto computethecompletethermal-hydraulicstateofthesystem,whichisthemain challengeof0D/1Dmodelingandsimulationforthat field.

2.1WhatAreThermodynamicsandThermalHydraulics?

Thermodynamicscanbedefi nedintwoways:thescienceofheatandthermal machinesorthescienceoflargesystems(i.e.,composedofmanyparticles)in equilibrium.Inthisbook,thetwoaspectswillbeconsideredbecausepowerplants arethermalmachinesthatproducemechanicalenergyusingheatandmasstransfer. Asthermalmachines,theyaresubjectedtothermodynamiccycles(cf.Sect. 2.9), andastheyuse fluidstotransferenergyfromthereactortotheturbine,theyare subjectedtothelawsofthermalhydraulicswhichisthecombinationofhydraulics withthermodynamics.

Thetwomainconceptsinthermodynamicsareheatandtemperature.Thesetwo quantitiesaredefinedandusedintwowaysthatreflectthetwoaspectsofthermodynamics:viatheeffi ciencyofthermalmachinesandviastatistics(averages) overvolumescontaininglargenumbersofparticles.Thesequantitiesaregoverned

© SpringerNatureSwitzerlandAG2019

B.ElHefniandD.Bouskela, ModelingandSimulationofThermalPowerPlants withThermoSysPro, https://doi.org/10.1007/978-3-030-05105-1_2

bythe firstandsecondlawsofthermodynamics.Heatandtemperaturearerelated viatheconceptofentropy,withthefundamentalformula:

wheredS isthevariationofentropyofthesystemthatreceives dQrev amountof heatenergyduringareversibleprocessattemperature T. Thetwoadditionalconceptsusedforhydraulicsaretheconservationofmass andtheconservationofmomentum.

2.2ThermodynamicProcesses

Athermodynamicprocessisachangeinthesystemstatefromaninitialstatein equilibriumtoa fi nalstateinequilibrium.Whentheinitialand finalstatesarethe same,theprocessiscalleda cycle

A reversibleprocess isaprocessinwhichthesystemisinequilibriumateach step.Thiscorrespondstoanidealinfinitelyslowtransformationofthesystem whereeachstepoftheprocessisasystemstate.

An irreversibleprocess isaprocessthatisnotreversible.Thiscorrespondsto realprocesseswherechangesbetweentheinitialand finalstatesoccuroutof equilibrium.

2.3PropertiesofSubstances

Propertiesofsubstancesarequantitiessuchasmass,temperature,volume,and pressure.Propertiesareusedtodefi nethecurrentphysicalstateofasubstance. Thermodynamicpropertiesaredividedintotwogeneralclasses:intensiveand extensiveproperties.

Anintensivepropertyisindependentofthemassofthesubstance.Temperature, pressure,speci ficvolume,anddensityareexamplesofintensiveproperties. Thevalueofanextensivepropertyisdirectlyproportionaltothemassofthe substance.Theinternalenergyortheenthalpyisanexampleofextensiveproperties.Massandvolumearealsoextensiveproperties.

Thus,ifaquantityofmatterinagivenstateisdividedintotwoequalpartsin mass,eachpartwillhavethesamevalueoftheintensivepropertyastheoriginal andhalfthevalueoftheextensiveproperty.

Relationshipsbetweenpropertiesareexpressedintheformequationswhichare calledequationsofstate.Themostfamousstateequationistheidealgaslawthat relatesthepressure,volume,andtemperatureofanidealgas(cf.Sect. 2.10).

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[1444] Seward (04) p. 31.

[1445] Hollick and Jeffrey (09) p 24

[1446] Raciborski (94) A. Pl. . figs. 1, 2; Zeiller (002) p. 98.

[1447] v 5950

[1448] Fontaine (89) Pls. . .

[1449] Solms-Laubach (91) A. p. 141.

[1450] Schimper (69) A p 472

[1451] Kurr (45) Pl. . fig. 1.

[1452] Seward (04) p 30

[1453] Saporta (73) A.

[1454] Schenk (67) A.

[1455] Salfeld (07) p 192

[1456] Seward (04) p. 34, fig. 2, Pl. .

[1457] Salfeld (09).

[1458] Schenk (76) Pl fig 7

[1459] Salfeld (09) p. 34.

[1460] Zigno (56) A

[1461] Solms-Laubach (91) A. p. 114.

[1462] Nathorst (78).

[1463] Saporta (73) A p 352

[1464] Nathorst (78) p. 122.

[1465] Zeiller (03) p 52

[1466] Leckenby (64) A. Pl. . fig. 1; Seward (04) p. 36.

[1467] Schenk (87).

[1468] Zeiller (03) Pls –

[1469] Zigno (56) A. Pls. . .

[1470] Seward (00) p. 170.

[1471] Brongniart (28) A p 49

[1472] Seward (00) p. 171.

[1473] Saporta (73) A p 368

[1474] Krasser (95).

[1475] Saporta (73) A. Pl. .

[1476] Brongniart (28) A p 60

[1477] Kidston (012) p. 196.

[1478] Potonié (93) A Pl

[1479] Kidston (89) p. 409.

[1480] Zeiller (06) Pls – ; (002) p 100, fig 73

[1481] Weiss, C. E. (70).

[1482] Zeiller (06) p. 90.

[1483] Lesquereux (80) A p 131; Weiss (70)

[1484] Weiss (69) p. 37.

[1485] Grand’Eury (77) A Pl A

[1486] Renault and Zeiller (88) A. p. 219.

[1487] Stur (84).

[1488] Grand’Eury (77) A Pl

[1489] Grand’Eury (08).

[1490] Renault and Zeiller (88) A Pl

[1491] Weiss, C. E. (69); Goeppert (64) A.; Potonié (93) A, (04); Lesquereux (80) A., p. 124; White (99) p. 125.

[1492] Seward (08) p. 97, Pl. .

[1493] Brongniart, in Murchison, Verneuil, and Keyserling (45) Pl A

[1494] Weiss, C. E. (70) p. 871.

[1495] Brongniart (49) A p 24

[1496] Grand’Eury (06).

[1497] Weiss (69) Pls. . .

[1498] Potonié (93) A Pl figs 1, 2

[1499] Weber and Sterzel (96) p. 99.

[1500] Zeiller (90) p. 84.

[1501] Zeiller (983).

[1502] For figures of this and other species, see Potonié (07).

[1503] For synonymy, see Zeiller (90) p 87 and Potonié (07) p 2

[1504] Schuster (08) Pl. . fig. 7.

[1505] Weiss, C. E. (70).

[1506] Schlotheim (20) A p 406

[1507] Renault and Zeiller (88) A. Pl. XIX.

[1508] White (052) p 388

[1509] Forbes (53) p. 43.

[1510] Baily (59) p 75

[1511] Schimper (69) A. p. 473.

[1512] Dawson (71) A p 48; (82)

[1513] Kidston (912) p. 30, Pl. .; (06) p. 434.

[1514] Baily (75) Pl. .

[1515] Carruthers (722) Pl

[1516] Dawson (71) A.

[1517] Smith and White (05) p 39

[1518] Lesquereux (80) A.

[1519] Crépin (74).

[1520] Nathorst (02)

[1521] Schmalhausen (94).

[1522] Nathorst (04)

[1523] Krasser (00) Pl. . figs. 3–7.

[1524] Zeiller (032) p. 27.

[1525] Stur (75) A Pls

[1526] Kidston (882).

[1527] Grand’Eury (08)

[1528] Brongniart (22) A.

[1529] Kidston (052)

[1530] Renault (76).

[1531] Grand’Eury (08).

[1532] White (99) p 128

[1533] Grand’Eury (77) A. p. 122.

[1534] Zeiller (90) Pl. . fig. 6.

[1535] Potonié (99) p 113

[1536] Renault (82) A. Vol. .; Zeiller (90) p. 139.

[1537] Grand’Eury (77) A p 105

[1538] Brongniart (22) A. Pl. . fig. 6. For synonymy, see Kidston (03) p. 773; Zeiller (88) A. p. 261.

[1539] For synonymy, see Kidston (88) p. 354.

[1540] Scheuchzer (1723) A p 129, Pl fig 3

[1541] Lhywd (1760) A. Pl. . fig. 190.

[1542] Lesquereux (79) A Pl

[1543] Fontaine and White (80) p. 47.

[1544] Bunbury (47) Pl

[1545] Kidston (94) p 357; (03) p 806

[1546] White (99) p. 132.

[1547] Zeiller (88) A p 251

[1548] See Vol. . p. 45.

[1549] Zalessky (07) Pl. . fig. 5.

[1550] Zeiller (88) A p 251

[1551] Renault and Zeiller (88) A. p. 251, Pl. .

[1552] Brongniart (28) A p 51

[1553] Lindley and Hutton (33) A. p. 28.

[1554] Lesquereux (66) A.

[1555] Roehl (69)

[1556] Seward (88).

[1557] Potonié (99) p. 153 (note).

[1558] Gutbier (35)

[1559] Presl, in Sternberg (38) A.

[1560] Grand’Eury (04)

[1561] Zeiller (90) Pl. . fig. 9.

[1562] Zeiller (99) p. 46.

[1563] Bunbury (47) A p 427

[1564] Lyell (45) A. Vol. . p. 202.

[1565] Lesquereux (80) A p 146

[1566] Sternberg (26) A.

[1567] Grand’Eury (04).

[1568] Grand’Eury (90) A

[1569] Zeiller (90) Pl. . fig. 6, A.

[1570] Stur (83).

[1571] Scott (07) p 206; Scott and Maslen (06) p 112

[1572] Grand’Eury (04).

[1573] For synonymy, see Kidston (03) p 772: Zeiller (88) A

[1574] Scheuchzer (1723) A. Pl. . fig. 4.

[1575] Kidston (94) p 245

[1576] For synonymy, see Kidston (94) p. 596; (03) p. 806; White (99) p. 117.

[1577] Grand’Eury (04)

[1578] Kidston (94) p. 245.

[1579] Brongniart (28) A. p. 59.

[1580] page 494

[1581] Kidston (94) p. 596.

[1582] Grand’Eury (05)

[1583] Potonié (922); (93) p. 54.

[1584] For synonymy, see Kidston (88) p. 366.

[1585] Zeiller (90) p 45; Potonié (93) A p 57

[1586] Germar (44) Pls. . .

[1587] Kidston (88) p 366

[1588] Stur (83).

[1589] Renault and Zeiller (88) A. p. 196.

[1590] Potonié (93) A p 48

[1591] Zeiller (002) p. 88.

[1592] page 397

[1593] Renault and Zeiller (88) A. p. 178, Pls. .–. Ante, p. 419.

[1594] Potonié (02).

[1595] Schimper (69) A p 688

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