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AutomatedSoftumreAnalysis ~of NuclearCoreDischargeData

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AUTOMATEDSOFIWAREANALYSISOFNUCLEARCOREDISCHARGEDATA

Td W.LarsowJamesK.HalM&JoAnnHowcuGeorgeW.EcckstcwandShirleyF.Kbterbm

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ABSTRACT

MonitdngtheMing pmccsso anon-loadnuclearmactaris afull-timejobfmnuckarsaf

-Fak==mmi@rs(CDM$)can~tor’s fiding d tyfw later, e dew bya safcguds-~. A@mNhivCdyfi thiscdkctcddatadptQbe a greatasset to inspectorsbecausemoreinformationcan beCxtrmedtklmthedataandtb analysistimecanbereducedamsider-

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usingactualdatacollectedfkoma CDMW-at m ~~ Iemor kilityo Coktivdy, theseautamatedq~titative analysisprograms

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Nuckarpowerstatiomintheu s C I q w c ( b af a c usuallythetq, fuelcanonlybeacmssedwhatthcIemoris shutdown.b safe-guardsadvan~ tothistypeofIclKtaris thatitis dativelyeasyfmanuclearsafeguardingagmcytomonitorthefuelingp!Uccs&An“mpcctork a st@uding agencycanbesenttothesiteto~ thefuelingpmccdlmOn-badnuclearIemlx’sarcdifferentfim u s l it o pmayobtainaccessto theComfrombothCn@andtheycanbeCatthouslyfidulwithoutshuttingtimtlCbwms a ● o fx _ i p !a~

i challengefhnna safeguardsfmthedivcmiond nuclearmatcddCh40ad

Iemorsmeudl-suitedfocjmdodngplutoniumfiotntheirstadad fuelbtuxik Sabgwd&gamon-badIewmr- MP@ - affid asit ispushedtbugh * lwwtocwhata fieshfitdInlndkis pushedinme SkkOftb feactw,a spcntfid htndkisshbmudy ~sacdktion mcchdsm on thealter$i&eU*g thisfbdittgscheme,● typicalon-loadmmorwill

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discharge55to65fuelbundkperweekl%gurc1showsaamccptualdiagramofthisfuelingcyck.Becausethisis anongoingpraess, it is notsimplefm a safeguardingagencyto continuouslymonitorfuding;itisratherlak L%cnsivctohaveasafeguardsinspmtoron-siteallofthetime.

‘herearcseveralapproachestotheprobkmof monitoringtheconstantfuelingprocessof on-kadreactors.Obviously,sometypeof amcasummntsystemfmprovidingoontimmus,unattdedmonitoringof fuelingis themostattractivealternativebecauseit is thekastMor-intensive.Coredischargemonitors(CDMS)lprovideaconvenientsystemfmsafeguardingon-loadrcacmrsbecausetheycanbeinstalledasanindepcnckn~tamper-msistantpackage.ACDMusesradiationdetectorstomonitorb movementof fid betweenthereactorcoreandthefuelstoragearea.Coredischargemonitoringcanbeperformedbyanekctronicspackagedkd GRAND(m myandneutrondetector)andassdatd detectorsdevelopedattheLosAlamosNationalLaboratory(LosAlamos).A typicalnxasurement statimconsistsof aGRAND,whichis thedata~quisitionekctronkqandfm demctorsmountedinoneenclosure.BwhGRANDcontinuouslyColkctsdatafmmitsassoci-ateddetectoratdiscmtctimeintervalsandtransmitsthedatatoanMS-DOScompatibkmmputcrfa moding. ‘Ilwfm detectorsineachcnclostueincludetwoncutmdetectors(f~sionc~)andtwogamma-raydetectors(ionchambers),oneshieldedandoneunshielded.Becauseof kw-enriohcdf a lowexposureof thespentf datively fewneutronsarcemittedbythespentfud ‘mencutmn&@ctorsarcencasedina containerof heavywater.IntIMidealsitua~ theneutrondetmorsaresensitivetoneutronsmatedby(’y@)mactknsinb deutaiumsumudqg“ thedetecmrs.To producea tmmm in the(y,n)rewtionrequiresa gamma-rayenergythresholdof22 McV.Tormnitorcare~ wemountedf- GRANDsaroundthenuckarcorGtwooneachremorface.Rx Im==of ~ Fig.1showsan cxampkofhowa typkalon-kmd

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rcactormightbelaidoutwiththedetectorsandGRANDsinstwvd.h F16a~a),thecomis nxntntcdinMCbuildingsucht t a facesarcI’”1tk castandwxtsidt Mth:rearer.~ fuelingtakesplacefromcasttowest.orwesttoeast.Becausethecoreis nunmte“ .hisfashion,each~-mentstationisdesignatedbyitslocauoninrelationshiptotk **em,eith~‘hex@teast(SE~north-=wt(NE),southwest(SW),ornarthwcst(NW)corner.

EachGRANDcolkctsntdcturadidondtxafiotntnedetu. Tenck ~, .dtcfiP.timestampsi$ andttmpmily storesi T d aa t hfa mtheco%ctionXnpu“ruponIeqtx%tformampumancntstorage.Aalatertime,data@oheofHoadcdfromtheccWctmncomputerfa off-linerwkw.Thedetectmdatafedfhm tk G- consistof fiveC d I ‘ T C

n a h a fohvx f- C dxrA,fissioudmnt)cr~,fissionC c i chmthtv~,andionchamber2.FksionC hALmq)ondstothefirstnmtrondctecto.: .nedw ‘*.wedo-SuleoFissionchamberBis anotherviewofthcfirstneutrondetcctw,whichcanbeusd fa possi~tamperdetectk Thesecondwutrondetectorineachdctecmrenclosureis labClfdoI*tksionoham-bcrc T n c& ti st i ~ m &‘ t t ‘ 0t o pf=. I%rcxampktheNEfissionchamberCis wiredintothelb.‘YGRAND,andtheNWfissionC hC is wiredintotk NECiRANDoThisprovidestk O tll ~@elllwithabackup,incasetheGRW fa we of thedetectorsfail~‘lldscrosswiringis showninFig.2{a)astk spliceboxbetweenthem GRANDSoneachsideof thereactorm. Fhlally,thetwog&rlma-raydetectmmmspondtotheionchamber1and2 Channckrespectively.Figure2(b)shokst%layoutof a detectorenclusim. Anin-depthdiscussionof tk detectorassemblimandtheGRANDekctmnl“CSpackagecanbef- in“TheDesignandInstallationof a CwcDischargeN&mitocfmCANDu-typeReac#Xs,”byLK Halbig,etal?

ThedatacolkctioncomputerssampktheGRANDsata pm-deter.inedinterval,wally wk~10or11seconds.‘Ihctotalnumberofdatapoin,~coilectedbyme detectorM oneGRANDis lim-idto7855 pointsperday.W all20detq thisworksoutto ●s about157100d p p

d a0 713~ ~month. Tostoreallthedatap f~one moi+ f a 1 816000bytcsdstoragc.Itis impmctidtoanalpethisamountofdata.BecatMof~ ~ ~age ~~:$ “

~* G~ U@OY ad-=we* tC&iWC tOdUCCti SUIKWda ~- ~lOW ~v@. ~ ~RA~ -S ~Y aIWC=~VC 2%of he homhg datatotk c tc o m p uThisreducesthedataW*considerably.Becausetk dataaretimes~”.mi>:d,?* -gap donotpresenta probleminansdysisordew of tk data.ShowninFig.3 areg of&t@fiUSDt ( bduring- -k dily.

A qualitativeanalysisof thedatabya safeguardsinspectorcanyielda considerableamottntofinformationonthefuelingactivityatthereactor.Eachlargespikeontk graphcomqmds toapawof fuelbundlesbeingdischargedfromthereactor.Smallerspikesordecaycurvesorbothnnthe@’a@!lltlycorrespondtootheractivitiessuchasthcrotationofthefuelingmachine,ortheradioac-tivedecay,calledcooling,ofthespentfbelfissionproductsbeingheldinthefbcling~hine duringa shufflingoperation.Thereactorpowerlevelcanalsobedctcmiwi fibmthe&la becausetheamountOfbackgroundthedetemrsamsensingcomspds tothecurrentpowerlevelofthereactor.Tk backgroundinthiscontextisconsideredtobetheamountof rdiationthercactorcdtsduringnormaloperationwhennofuelis presentoutsideof thecore.Currently,a s h

m qualitativejudgmentsaboutreactoractivityby visuallyeXaminingthegraphsof detectoractivityonbothftwcsOfthc~. we- aninspectmcancountb numberof spikesonthegraphand&ermineth8tmalnumbemffuelpushesthereactormadeinaparticularday.Thetotalnumberof fitclpashcscountedcouldthehbe compamdto f&Mtydeclarationsf= safeguards

ly, reviewingall the informationcollectedby all the detectorsis avdkation. Unf~

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IAUTOMATEDSOFTWAREANALYSIS

Analysiso Ming datafklmanOn40adfcwtol i acompkxtk Asa~ wcdmignedtbeammatcdanalysissystemtoinvestigatethefeadbilityof8cvcralo&cdve%d -W -diffidtoimpkmcnLTbcscobjectivesamasf-:

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4. COmlatcaneventf= one&tcctorchannelwithallother(ktcctmchannels.ThiscouldbeusedtodetectpossibletamperingandtoCnsumthatauthechannelsamoperatingCm’ectlyo

s Idcmi&thefuelingchannel-which thespentfuelwasdischarged.

Qti~uPof* spentfudbutuues.

Ed o theseO@jcctksCxtrwtsf af t e MO fd T f ct h a safbgudinginspectorassessallthedatacolkctcdfw a prtkuhr on-badreactor.Tofdlitatc theseobjectives,apmotypcanalysistoolwasdevelopedcalledCDManalysis.The(DManalysispackageaccomplishes*tivcs otwthroughf- reasonablywell.Wedevelopedthistooltoexplorethepossibilityof developinga pmdmion-gradeanalysistool.objectivesfiveandsixwereattemptedusinga neuralnetworkmodelingparadigmdescribedlater.TodevelopandtestCDManalysisascompletelyaspossiblew neededaconsiderableamountof data.Unfatwwtdy,onlyabout30daysofdatawasavaiJabkfmanalysis.WeconcludedthatalthoughthetotalamountOfdatausedfa testingtheanalysissoftwarewas~ thesoftwarestillperformedveryWcIL

IDENTIFICATIONOFAREASOFUWERESTANDFUELDISCHARGEEVENTS “

A s i g n ipmhktnwithanautomatedanaIysisof CDMdatais idcntifjdngareasinthedatathata safcguantsinspectorwouldb intcrcstodincxamining.Identifyingareasof highactivitycan.condcmNyreducetheamountof- the_ l t p t d Atsuchan~y sta@

dcveAoptncntit is impatantthatinspcmm~ti7. . do notuscthissystemas a substitutefaamtnatmof allthedaa butmthcrasanaidinthereview_ whenthcmactotisrun-

ningataconstantpowerkve~a baselinecanbecsmNisMasanawrageof thebackgroundnoisecollectedbythe&tccmrs.~~t ~~ -- ~ ti~ = b c~w ~ ~ * oftheaveragesig@ aboveorbelowthebaschtmThisis ale techniquew usedtoidentifyareasof●

(!DManalysismakestwopassesovertheCDMdataduringitsseamhfmareasofintemstInthefirst- itslidesana~alongthc signalbokingfwsignifkantchanges.whenthesbpcoftb osignaljumpsabovew belowtheslidingaveragebymomthan10%,thedatapointsamflaggedasscmmhingtolookintolater.Inthefirst~ a hugeamountOfdatapointsmaybeflaggedasintcr-cstin~Toreducetheclutter,asecondpassis ma&ovcrjttsttheareasthatw flaggedAreasneareachOthcrinthetimesales aleClusteredtoguhcrwith* maximumdatapointbeingmarkedasthemiddleof theevent.Fmmtheresultinglistof areasof_ ●rcpxtcanbegcncmtdtoalertthesafeguardsinvestigatortospccifkareasofthcdataDuringthis&vclopmuwCDManalysiswasnotexpandedtoexplaintotheinvestigatorwhatis actuallyOccuuingintheUnda’IyingsystemItcur-rwulytells* inv “Cmgatortocheckoutcertainareasfixauivity.Inthef-a dctaikdanalysisofanwtualreactorinOpcmtkmalongwiththeColbctddatacouklbeusedtodevelopa systemthatcouldgcnwatcsuchareport.Finding- therefuelingspikesis aseasyasfindingallothercvca*CDManalysisjustneedsto bemomddddng, If tbetltmhoklill9ctveryhiglhatW%*two-passtechniquewillyieldaso ua t f wS ph t g d a ‘ f

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CDManalysiscangenerategraphsof collecteddata.Figure4 is m exampkof anoutputgraphfromCDManalysis.Twochannelsare_ whichamtypicaiof alleventsoccmringonbothfacesof the~. Theverticallinesrepresentmarkedfueldischargeeventsorpowerlevelexcur-Sionsoftheremarorbath.The&l dischargeeventsandthepowerkvelexcursionslu’cailtnarkedindifferentcolorsonthecomputersawn tomakeiteasytodifferentiatebetweenthem.Thethmh-oldvaluetncntiomdearlieris markedbyahorizontalline.inthiscasethetbrcsholdis hightolocateonlyf &chargeandpowerl e vCORRELATIONOFEVENTS

Onceallthef dischargeeventsami&ntifiedandclustered,eacheventis thenlocatedonthedatafromalltheotherchannels.Toaccomplishthiscorrelation,wchadtoovcmotnetheproblemthattheclockson theGRANDsarenotsynchronizeCorrelationis difficultbecauseaneventmarkedwithone timestampon one GRANDmaynotbe foundat thesametimeon anothaGRAND.FindingeventsonGRANDsthatarconthesamereactorf= is nottoodiffiicukCDManalysissimplyfd thespikeheightmaximumIwamsttothetimeatwhichtheeventwasrecoded.Theproblemof findingeventsthatoccurredontheoppositef= inoneparticularGRAND’s~canberesolvedbyusingthecrosswiringof theCftin chambers.CDManalysisfd be eventon fissionchamberC andusesthespiketo performa pseudo-synchronizationof theGRAND’sclock7MsallowsCDManalysistofindwherethepeakshouldbeona&tectorviewinganeventon~ -g *C*

MONITORINGREACTORPOWERLEVEL .

o thea o famkkmifi~ powerleveludtoring israthersimple.whenm Cvattsamoccwrin~backgnntndmdiatknissensedbydwktwtm Theaveaagcofthcbdcgmnd canbe

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w i t h*1 outsidethecore.Theavengevaluerecordedbyeachdetectoris usedasthebaseline.Thisbasdineis markedon thegraphin Fig.4 bya Iutrizontalline.If theaveragevalueof thebackgroundmovesfromthisbaseline,thenthepowerkvel is changing.‘Ilwdatahassknvnthatm powerchangesoccurredinastcpwisefashion.CDManalysiscvaluaeesthepowerexcursknsinthefollowingmanner.Ifthelvactor_ ~ ~ ~ = ti ~ Ofthcaverage~~startstobecomeverysteep.Thisis markedasthebginningof apowercxcumimwhcnthisslqmflattensoutagain,theendof thepowerexcursionis mwkcdTIMnewvalueatwhkhtheaveragebackgroundC Ot i “c ot n p l o r T a b

a a pcmcntageof thepwldincd baselineis thepaccntageof fullpoweratwhichthereactorismnning.(hmmly, C!DManalysis&es notexaminem than~ channelonme &tcctorwhenmakingitspowakvc!computations.Inaproduction-kvelanalysispackage,thispcmcattagcshouldbeanavcxagcof allthepemcntagcscomputdfiwmallchannelsonalldetectors.Bytakingpmwrlevelmcaswmcntsb all sidesof thereactorcomandaveragingtlwm,a momaccumtcpowerlevelreadingcouldbeobtained.Eventhoughcxammin“ g justonechannelgivesa ftiy accumtcreading,within5%,examiningall channelsis a muchbetterstrategybecauseit provides●redundancycheckFigure5 is anexampkof thepowerlevelof a reactorbeingraisedfiunnmicX9-powcrto ~~. NoticethattheC Xo i m s CDManalysisis alsocapableofprintinga reportthat&tailseachstepof thepowerlevelchangeandwhatpowerkvel therewtormovedto.AnexampkofaxepottforthedatagraphedinFig.Sis showninFig.6

I . STATISTICALPHENOMENAOFCDMDATA “: . . .

Tobcttcrundemadthcmactorf@mll=-~~ yanalyzedtheavailabkdamTheh e iOfthcradkionspikesr qM dischargeeventsc beComlatdtothefuelburnupandthe&cationOfthcf c hf w thefuelisbeingdiscbgd ThebumupContributiontothespikeheightis a mult of fissionproductbddupinthespentbl bundks.Ifoncknowstkspikeheightsfromall20Channdsfmapdcular evcn~oneshouldbeti tocomputethcbcatknandpossiblytheburnup.Dcmnmmn“ “ g burnupis a diffkul~multivariatcpmbkmthatwillbedia-cusscdingfcatcrdetaillater.Doall20channckmakcavalidcodbuth tocomputingthelocatknof theM bundks?Themo6t“~-~ whcthcrarnotdetectmonom * Oftheluac-torseeeventsO ct -- ~ $ Cnoughsothatthcemarinm ismalLF@e 7 demmmmsthatdetcc$mononekc&M smeventsonthe!oppositefs whataneventis occumingm onefwxof thereactor,tbtspkeappmingonthcdetectmOnthcoppmingfaceis insignificant.Thismeansthatoneshouldmat each*of the~ separatelyWh- m ptodeterminethekcationo fuelbundlesbeingdischarged.A comlationdoesexktbttwccnthetuospikeheightsonOppodng&tectmonthcsamcrcactorfaceRxa givenbumpof* fid bundksbeingdisc- andas thesome is closerto one&tcctorandfarthcrtianothcf,thesignalwillbelaqp onthenemer&tcctOrandsmalkmnthcfartltcrdewctoc.ThcgraphOfthcsignalstrengthfkxnmmoppositlgncwon&tectomm * same* is showninF@8.

The@atiodu“pis coneshapedbecausethecrrocindatafium●channel- greateras●

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Unfi)mnatdy,theVadanceintheknchanlbcrdataillvcfypmmmced‘nDcrcam●cUMkbbkmlmbcrOfoutliminthedatawtbwaaseOfthcapmtknOfthcdata~-~-ChambcrdataamintcgmtedOvathcentheSan@ingtim$whereastheionChambcritl8pdntSaInpkOvwabO@SoxnsBccawHtb kmchamberdataamnacintegmedWcrtheCntkdataacqdaitkmk* =@@ dataamnatasqmentab ofti ~ MingewntIntegmknCatssesan- *-to occamovatheattiresampktimeyieldingmuchmommpescntativedate.‘mlcalmslessVaIimcccooccurinthenuluundetectanwmmmcntthantb ionchambermcaslm-mcntWeattmptd tousethema ofthccntimMing eventtominimizedance duringtheanal-* butit didnotyieldbettermsalt&Thisoccurredbecausethehe) spikesdo nothavea well-dcllncdshapeonCithathencuaonortlwgamma-myChaMcl&As●mlallbitwasdMkolttodeciu-mincb timeintervalto integratem. q themam&caycurvesf*g Ming -whichcorrupttheintegralvalue.A fbd intend analysiswastried,butitdidnotmdoceh vati-amxin thedataenoughto besi@fkant.Changingthedatacdkctkn to a highersamplingratewouldmate dM datasetfmtmalysislikethisinthefbtum.

Qometricallyspeaking,* dctmonlfromwhichthesedatawewtakenamsetup on eachreactorfacewiththeSouth-llidc&tcccasbeinghigherthanthenorth.If* dkmluxs%cmte$chfdC kt mh of thetwocktccmnarccompatc4w canddvc an equationfi(mlphysks** - Cm@atcsthcl’atimofthwcdistanmtothcrat&60f&tcctaSpibhcigh&Figaro10showshowthedmctcmm setupandgivessampledabks fmderivinga filnukd fGrmof tbWtancesasafimctkmoftheqikehdgk

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NEURALNETWORKS10R SOLVINGTHEFUELGEOMETRYPROBLEM

Ncud nctwofkrmddsamtnathanatd● lyderivedfmnenfeccll biology.TheyamanalMrtw-tionof a collectionof fidamentalunitsorganizedina hiemrchkalfashion.Inbiologicalsystemseachunitwouldrepresenta ncwecell.Learning,andthusreactingtostimuli,isawomjWd inaM&k 60Xsclmw. The blackboxis moldedwhenpatternsof stimuliandtheircorrespondingmspmsesarepresentedina repetitivefashion.Giventheinputandtheexpectedoutput,theMaclcboxkrns themapping-input tooutput.Thelearninginsidetheblackboxtakesplacebyupdat-ingaseriesof weightedmdmmtwd● fimctionsaftcrcachpattcmpresentation.Thegoalis tomini-mizetheglobalerrorovertheentiretrainingset.Aftertraining,themodelis presentedwithnewstimuli.Ifthemtxklhasbeentrainedtoaminimalamountof error,itshouldgeneralizeandpredicttheappmprktcoutputfa anarbitraryinputpattern.Furtherdetails~gardingtheinternal,technicalWOfti neuralnetworkmodelingparadigmmaybef- inParailelDistributedProcessing,byl)widE.Rumdhart andJamesL McCklland.sTheneuralnctwotksusedinthisproof-of-prin-cipkwerecreatedusingNeuralWorksProfessionalWPlus!acommercialneuralnetworkdevelop-menttoolmanufactmdbyNeuralWare,Inc.A SunSPARCstationcomputerwasusedtoperformtheanalysisanddeveloptheneuralnetwork

Neuralnetworksseemwell-suitedfm automationof continuousprocessesbecauseof theircapabilitytogeneralizegivenonlyarepresentativesampleofdata Forexampk,NipponSteelhasutilizedneuralnetwds toachieve_ reliabilityintheircontinmtscastingprocess(Hidetaka1991).SNeuralnetworksarcalsomovingintothenuclearpowerarenabecauseof theirpdctivecapability.RohandCheonJwe developedseveralneuralnetworkmodelsto attempttopedictthermalloadmquimmcntsof a nuclearpowerstationandhaveachievedverypromisingm~~7BartkttandUhrighavealsoappliedneuralnetworkstonuckarpowerbycreatinga mmkltoauto-matestatus~08 w -neural nerwork applkations described inthispaperarecon-sickrablydifferentfrompreviousapplicationstonuckarpowerinthatweareperforming nuckarsafeguardsrathcrthanopdmhingthepowergenemdmcapbilitiesofthe~.

The30daysof availabledataykkkd around170e of f d e Unf&tu-nately,theseeventscmlyused90outof the460availabkfhelchannelsintherectorcore.Asweuallthe170eventsoccurred*• resultofM shufflingopemths duringinitialreactor- achalknscbecausedata* dtmg

startup.ThisOpemtknsarcnot~Y ~ “ Ofnamal

reactorfuelingactivity.R~ wethoughtthatanappropriatelytrainedneuralnetworkmodelcouldc-&l eventsintodifkremregionsonthe~ fm. Thistypeofc&Mkadoncouldbeapotentialb t s b ea i nC thenv t eventagainstfti-itydeclamtkns. Regionsm the~facemust bcusedbccauseof the- Ofthegeometryin theproblem.Itis possibkthatthemare&l C tforwhkhthepmpomonof thedismcesfmneachdctccWis theSam&Figure12showsh pmpdons Ofthcdistanmforautheeventsontheeastbceofthereactur●8sfbeld&char&evenm

TheWdappingpointsrqm?entexamplesof thegcomctnc“ Symmeuyproblem.TldSprobkmisthattheratioof thedimncesfiumthetwoopposingdetectmtoa givenM channelcouldbetheSalmformomthanonechannelon thercwtmface Thisproblemcanbe solvedeitherbyusing-of~ ~k Whkhilwludcsymmaicpointsinb same* w bydiminahgtheSylmmrkpohltsduringcreationOfthemodelandalkowingtheneuralMtwrktoinf’’theregkninwhichtheyk Thelattermethodwasusedinthertmdckpmentedh * ~, - ~ *thatusingsymmetrkregionswouldbeagoodsolutionto tbepmblernaswell.Tbepotedal capa-bility to extraphte the&l dischargeIocab basedononlya fm exampimis oneof thekey

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T f il m n ’ am ad i vh c hI i eightmgkms.ThischannelmapandthedghtregionsamshmuninFig.13.Alamtalltbemgionswmchosenbecauseoftbedstrkbutimoftbepointsbhavaihbk-

Because&tectmm em * & WtreliablyseeeventsontheOppos&gfat%only1 Cf h n tO u t c fw u mt nwal netwaknxxJeLAlthat@the~ c- c-k ~ Hy ~k b b c~~J~t h) %add ~ hhg ~k ~-tocm Tbeionchambers=’@=” ●

m asnoisedufingthe-E m=~ ~ w -the inputvectorsintoappmpme categories.Back-pmpagatioflwasCbosmas tbe mddiagparadgmi%causcof itsabilitytouseleal-valusdinput&9m Iesb,..llgneural- d w- of 10m 2- IayefseachWitb9 nodeS#and3 Oul,!&‘riletlm!eqm ~usedtoperfbrmabinarymsppingoftbe eightpssibie _ Figw. 14showsagmphicd~-SentathOfthee i g h t -m o

B s c at p eb d go t C o n& aa U a i t t n nt i ascalingpmbkm.Thenumsfkvaluesibtbe heightscanbeverylargentmherstoM toaneurd~To memomethisproblemtbeinputsmeDmmliadtowithinthe Iqpof”lomo1*QTbeSesultof thisMmndbkm is ●vay tightdustsdnsof tbeMxmatbntrainingPmb&mforthenetWodL-9- WoUklusethe

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. g t t n t c Oit is ~ to uscslightvariationsof thestandadback-propagationalgorithm’sdelta-ink.Insteadof thestandarddeha-ruk,thecumulative,gencrit!-izcddelta-rubwasusutloInsteadof updatingtheweightsaftereachpatternPrcscnq acum@a-tivcweightupdatewastalliedandthenappliedattheendof thetrainingepoch.Thishelpsspeedupthetrainingof thenetworkandovcmomcanyproblemsthatmayexistwithorderinthetrainingdata.Themo&lalsousedabiastoassistinspeedinguptheconvergenceofthcnetwork

A fiwr-mgionneuralnetworkwasalsocreatedwith10inputs,1 hid&nlayercontaining15nodes,and2 outputs.~ internalS s a t t f a t k ruk,amthesameasf= h eight-regionmodel.‘JIMtwooutputsof thef--tegion mo&lalsoperformeda- mappingtothefw regionsal themmorfm. Theregionsin thisCawWmchosenran-domlytobequadrantsonthefaceOfthcreactor.m f--rwlionchannclmaDisshowninFig.15. “ , ‘

h the o bothtb eight”andfw”rcgion- &ary mappin~”of h regionnitmbcrswereusedinsteadof bin-ti outputsbecauseit sccmcdtoimprovethe~ SW of thtmo&LBothmodelswereabk toachieveconvergencein underSO000 trainingepochs,withanepochsizeof 12 Weusedtwohiddenlayersintheeight-regionmodelinthehopethatit wouldimprovethegeneralizationof thenetwork,butwc fti thatbothone-andtwo-layernetworksScunaltogenerab equallywell.RXthisprobkm,thew hidden-layernetwdtstwsemorediffi-cultto trainandrequiteda gruatcrnumberof trainingepochs.Thefu-region nctwoskshownin~& 16wascrcatcdusingdy onehid&nlaycrbccauseofthisfinding.

tNEURALNETWORKSFORFUELBURNUPCOMPUTATION

Computingfuelburnupis themostdifficultof theoriginalobjectivesbecause~ contdnsthe_ numberofvariables.Thef- @ctittg&l UQommndtberadktknlevek(ktededby$hcCDMm asf-:

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Forsafe- itmaybe_ tobeableto&tamincifthcfacilityisdischarginglow-btlr-nupfuelfromthereactor.As in thefuel~ -w thinkthatthefwl burnupproblemcanalsobesolvedusinga neuralnetwotktoclassif$theburnupintodiff&aentcatc~ onebeingb W -h i d t ~ ~ n* fw thebump ofcachimlivkiualW bundkbccauscthespikerecodedbytheCDMis anadditiveV o b b & st aT m ca cf w t b c ois ma&alsodependsontherecentimdiationhistoryof the&l bundksbeingdschqcd Fornamdmctoropmtiom**.W wouldneedtobetakeni nx x oB ct hu s cm so incltdalshuf-flingopcrati~ theimdiationhistorybccomcSimkvant.Figure17showsthedistributionof theknown,a&iitivebumupsfmthe&char@ fuelbuldlcsontheCastIWmorf-* Itis~tolmticcthatthebump fallintome Offmdistimt I’cg&nS

Becausetheavailabledataonlycontainedfd eventsduringtheinitial@==-~~-flingoperation,the&l in therectoronlyoccupiedthelastfm positbs ineachof the90 fbdchannelsfmwhichthemwcmrecodedevents.Thefild bundks “&sChar@fimlthcleactorwmneveratdffcmntpositionsinthefuelingchanncL‘Iltismeansthehkposure-&pcn&nt vadab&shavea negligibleeffecton thebump ~ ~ it is possibktomate a neuralnetworkmodelfw classifyingthebump intooneofthcfmcatcgmb basalupontheCDMdata.

‘rhencufalnctUmkmdd usedfa thebump Cdcdath Cmtaiml1 ~ 1h lw 1 n ta 2 outpuWThetwooutputsm usedas8 binaryqmcn@onofthcfmpo6-+cmg!on&Thcinterndstmtureofthc- Suchastbctransfidbncthand karningntle,-~-=--~b ~-- - ~

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R OFNEURALNETWORKMODELSFORSOLVINGGEOMETRYANDBURNUP

n n fw solvingtilegeomnryproblemw trabd awltestedondatafiumtbccastf- of tilerctwtor,altbougbtbcwestfm couldhavebeenusedjustas* Bccau8cofthclimitedamountof~ afqxescntativcnumberof fiid eventswe chosenastbctrainingsetinanattempttoexuaphtc M eventsoncbanndsthenetworkhadIMvcrseenbcfm Forb eight”* -63-- -~ thet rs o o 8 t M e ~ ~ ~t facethatwereusedasthetes;set.Asarcsul~72eventswereclasdfkdhy into1of the8regionsontheIeXXorftwc.Thismans ~~~~~~~bfa ninecvcn~ andby chssifkdtbefc@on82%of tbctime.Bcca9Setbe@on wascorlectfa9 at &2Spo6siMccx&@atd cvcnt&thenctwotkcmmpold

Thefm-regionmodelpcrf’ormai-=ofti-

slightlybttcrthantheeight-regionmodel,withotdy32pat-mnsintbc trainingsetoutof thetotal88.Themodelchsifkd 68eventsCaroctlyinm1of * 4legionsonthecastreactorfa Althoughtbisis only77%~, itgcndkcd andcxqdatdtbcpositionf 3 eventsoutofapossibleS6.ThismeansitU@a@atdcomedy68%Oftbetime.‘rhisiscoddmbly betterthantheeight-regionmodelWitbonlynilEoarlectUuqoMm&

Theneuralnetworkforcomputbgbump pcrfomd cspdally well.Itis impwtamthatpartofthispcrformanccis explainedbythe* tbatthe~missingflomthecquatkm‘l’bebump neuralnetworkwastrainedoa48_ outofthc88tiindwtestsa ‘1’b_ C bb b c i 1 a 4 c8tc@cs 81times= isanxmcy of92%,witb33jmuns beingemapdatdOatofa Thisism~ ~-mssof82%.

21

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CONCLUSIONS

TheCDManalysistoolmated herewillactas a pmtotypfu studyingandcreatinga momrobustCDMdataanalysistooLThepotentialof thistoolas a bundkcounteranda power-kvelatoidtwhasbeendemonstrated.Neuralnetworkimplementationsfordeterminingthema of thereactorf= frotnwhichthe&l wasdischargedandtheadditiveburttopof thetwofuelbundleshavebeensuccessfulenoughtowarrantfurtherresemh.Wethinkthatwithamorecampktesetof_n@vc datafromanoperadngon-loadreactor,neuralnetworkl couldbecreatedtorenderupto 1(M%accuracyin positionandbump. Thedataneededto achievethiscapabilityshouldinclude&l pushes%Xnall460*ls Ofthereactorandacompletecyckoff~l thtwghall 13positionsinevery*10 TheresultsobtaindinthisMeat’chaleC%tmnelypremisingm“sideringthelimitedatnountof dataavailable.Webelievethattheresultscanonlygetbetterwithmomdata.

Futureworkshouldincludedevisingamoteaccumtetechniquef= dctmhing areasof intelestintheCDMdata,ratherthanaslidingaverage.Pbwerleveltnonitaittgusinganaverageoverall20channelswillalsoyielda mommcuratepowefkvel cakulah. Deficienciesin thecolkctionofquantitativedatashouldbecormctd Weneedmomsamplesof dataperunittimeanda gamma-ChannelreadingmantqresentativeOfthcmmummentPerio&In_ difkmnttypes&neuralnetwoalcmodelsshouldbetrkdonceampmsentativeamountOfdatahasbeenobtainedThepata-bilityofneuralnetwolkmodelstoothcrmactonlOfthcs typeShouIdalsobeinvestigated.Neuralnetworkmodelshold@eatpromisefa futureworkin theamaof comdischargemonitoringanda um dof largevolumesofcondmmly colkmddatafmbcttctnuclearsafegttmds.Wefirdy believethata commemw~ toolfw monitoringpowerandcountingfuelbundlesfmtnCDMdatashouldbeddopcd.

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100 NelmlwaicsPmb&malIvPlusRe&mOCuGuide(Nemlw81e,InC.,~ p~ lgll).. . .

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