UvA-DARE is a service provided by the library of the University of Amsterdam (http://dare.uva.nl)

UvA-DARE (Digital Academic Repository)

Modelling flow-induced vibrations of gates in hydraulic structures

Erdbrink, C.D.

Link to publication

Citation for published version (APA):Erdbrink, C. D. (2014). Modelling flow-induced vibrations of gates in hydraulic structures.

General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s),other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons).

Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, statingyour reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Askthe Library: https://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam,The Netherlands. You will be contacted as soon as possible.

Download date: 27 Jul 2020

7

1 Introduction

1.1 Motivation

Flooddefencesystemsareofparamount importance for thesafetyofvulnerable low‐lyingareas. In coastal and riverine regions civil engineering structures are built to control thewaterlevelsandtoprotectthehinterland.TheriverdeltaofTheNetherlandsisanexampleofadenselypopulatedandeconomicallysignificantareawheremanyhydraulicstructureswereconstructedforthispurpose.Gatesaremovableelementsofbarriersand formessentialpartsof flooddefencesystems,becausetheyregulatethedischargebetweenbodiesofwater.Managementofinlandwaterlevelsisunthinkablewithoutgatedweirsinriversandchannels.Coastalsluicestructuresusegatestoprovideoutflowtotheseaandprotectionfromhighsealevelsatthesametime.Inbusynavigationchannels large floatingsectorgatesarestandbytobeclosedduringstormsurge events. These structures are for instance found near Rotterdam and in SaintPetersburg, Russia, where a large damwithmultiple gate sections protects the city fromfloods. Smaller gates are found in pipes and in culverts of navigation locks. Reliableoperationofgatesofcivilengineeringstructuresandpreventionofgate failurearecrucialforthereliabilityandsafetyofflooddefences.Hydraulicengineeringisthebranchofcivilengineeringthatisconcernedwiththedesignofstructures related to the flow and transportation of fluids: hydraulic structures. This fieldcombinesstructuralengineeringwithhydrodynamics.Thegatesofhydraulicstructuresarecalled hydraulic structure gates. The adjective ‘hydraulic’ in this context can always betraced back to the hydraulic engineering discipline and should not be confused withhydraulicdevicesusedtooperateallsortsofmechanicalappliances,suchas–indeed–gates.Hydraulic structure gates are found inmany shapes and sizes as part of various types ofstructures. Their primary function is in all cases the regulation of flow discharge, waterpressureorwaterlevels.Dependingontheapplication,gatesinhydraulicstructuresmaybecompletely opened, closed, or partly opened for long periods of time. This defines thechallengeofdesignandoperation:theyneedtohavethestrengthtoendureextremeforces,andtheymustbeopenedorclosedatexactlytherightmoment.Thefactthathydraulicgatesareindirectcontactwithpowerfulflowsmakesthejudgementandquantificationofforcesonthegatesdemanding.Theinteractionofhydraulicgateswithpassingcurrentscanleadtoflow‐inducedvibrations(FIV).Undercertaincircumstancesthesevibrations can grow in size and attain the form of regular oscillations with considerableamplitudes. Such manifestation of FIV poses a threat to hydraulic gate operation. Theassociated exposure to impermissible dynamic forces can lead to unexpected downtime,untimelymaintenance,andevenfailureduringemergencysituations. Inextremecases,thestabilityofthestructureasawholeisthreatened.

8

Thedesignofgatesofhydraulicstructureselementarilyfollowsthesameapproachasotherstructuralelements.There isacompromisebetweenseveral criteriadictatedby thegate’schieffunction.Inaproperdesignprocedure,staticanddynamicloadingcasesareidentifiedandassessed.Forstaticdesign forces,analyticalapproachesprovidereasonableestimates;rulesof thumbbasedonextremestatic loading situations e.g.maximumheaddifference facilitateengineeringdecisions.Fordynamicloadingthisformofquantificationisusuallynotanoption.ThereasonwhyFIVofgatesdeferastraightforwardanalyticalapproach is thatthe combination of structural details and flow conditions leads to complex interplays, thedetailsofwhicharedecisivefortheinitiationandgrowthofvibrationsandrelateddynamicforces.Everystudyinhydraulicengineering–foracademicandconsultancypurposesalike–startswithachoicebetweenphysicalmodelling,numericalmodellingandadeskstudybasedonliteratureandexpertknowledge.Physicalmodellingcanbea laboratorystudyofascaled‐downversionofa partofa structureorfieldmeasurementsoftheactualstructure,calledprototypeorinsitumeasurements.Themostprominentandtraditionalwayofinvestigatinggate vibrations, as reflected in publication numbers, is through physical scale modelexperiments in the laboratory supplementedwithanalytical studies for achievingphysicalinterpretationsofthemeasurementdata e.g.Kolkman,1984;ThangandNaudascher,1986;Jongeling,1988 .Thebulkofexperimentalresearchwasdoneintheperiod1965‐1995,withonlyasmallnumberofstudiesat laterdates e.g.BilleterandStaubli,2000 .Experimentalstudies performed in The Netherlands were partly motivated by the construction ofhydraulicstructuresfortheDeltaworksproject,whichendedin1997withtheconstructionoftheMaeslantbarrier,seeFigure1.1.TheDutchlaboratoryresearchongatevibrationswasdone at the research instituteDeltares formerlyWL|DelftHydraulics . Knowledge on thedynamicbehaviourofhydraulicgatesgainedbyphysicalscalemodelstudiesandfieldtestsiscollectedinKolkmanandJongeling 1996 .

Figure 1.1. TheMaeslant barrier in The Netherlands, a storm surge barrier with two floating gates(picturefromhttps://beeldbank.rws.nl,Rijkswaterstaat).

9

Pastphysicalmodelresearchhasproducedareasonablygoodunderstandingofthephysicalworking of gate vibrations (see Section 2.3). Design manuals nowadays contain shortoverviewswithbeneficialgategeometriesandwarnforundesiredconstructiondetails(e.g.Novak et al., 2007). In spite of this, problems still arise in practice with unpredictablefrequency.Thisisattributedtoone,oracombinationof,thefollowingcauses:‐Thegatedesigncontainssuboptimal characteristicswith respect toFIVbecause theproblemwasunderestimatedoroverlookedcompletely.

‐Physicalmodeltestsweredonetocheckandimprovedynamicresponsepropertiesofthe gate section, but unfavourable conditions nevertheless led to vibrations (e.g.Thang,1990).Despiteawarenessinthedesignstage,thereisstillariskofunknownorunanticipatedfailuremodesoccurringinreallife.

‐Negligenceorignoranceleadingtoimproperoperationaldecisions.‐Achangeofhydraulicconditionsduringthelifetimeofthestructuresuchthatdesignconditionsareexceeded.

‐ New operating procedures are introduced, for example in connectionwith updatedenvironmentalrequirements.

‐Gradualdeteriorationofthestructureorsubstandardmaintenancecancontributetoadifferentdynamicresponse.

This list reflects the fact that gate vibrations constitute a non‐standard point of attentionwithin design and operation of hydraulic structures. Preventive measures are as a rulerelativelyeasytoimplementwhentheissueisrecognisedatanearlystage(i.e.wellbeforefinalisingthedesign),butconsequencescanbecomefar‐reachinglateron.The tremendous increase in available computing power in the last three decades haspromoted the usage of computational fluid dynamics (CFD) in environmental engineeringprojects(Batesetal.,2005).CFDmodelshavebecomehouseholdtoolsforsolvingproblemsinhydrology(SolomatineandOstfeld,2008),coastalmanagement(Roelvinketal.,2009)andriver morphology (Van Rijn 1987, Mosselman and Sloff, 2008). Moreover, they are beingusedinoperationalsystemsinthoseareas.Veryfewexamplesofmodelapplicationsexistinwhich vibrations of barrier gates are simulated, however. Numerical research on FIV andfluid‐structureinteraction(FSI)hasfocusedonmoreprominentfieldsofapplication,suchasaerospace engineering or nuclear power engineering, and fundamental activities, such asbenchmarkingofcomputationalmodels.Asaresult,therearenooff‐the‐shelfcomputationalmodels today that are capable of simulating hydraulic gate dynamics to an extent thatenablespracticalusage.Investigations into the reduction of gate vibrations for the large Saint Petersburg barrierFigure1.8 involvedbothphysicalandnumericalmodelling,seeKlimovichetal. 2006 andLupuleacet al. 2008 .This resulted in anadjusteddesignof thebottompartof themainsector gates. Construction of this barrier finished in 2010. For the large barriers in TheNetherlands,noCFDanalyseswereperformedtosimulateandassesstheFIVloads.FortheMaeslant barrier hydraulic design conditions were determined and physical scale modeltests of the structure design were done specifically for checking FIV properties; designimprovementsweremadeaccordingly.

10

Themotivationforthisthesisfirstofalloriginatesfromfirst‐handexperienceinprojectsatDeltaresintheperiod2009‐2011involvingreal‐lifestructures,amongwhichtheHaringvlietdischargesluices (Figure1.2).Prior toengaging in thePhD,physicalmodelmeasurementswere done in the laboratory of Deltares, reported in Erdbrink (2012). An underlyingmotivation is also to raise the level of knowledgeonhydraulic gates andgenerally on thedynamics of hydraulic structures –knowing that this remains a salient topic indesign andmaintenanceoflargestructuresworldwide–andtoimprovejudgementofvibrationrisks.

Figure1.2.DischargethroughagateoftheHaringvlietbarrier,TheNetherlands(picturebyDeltares). 

1.2 Terminologyofcomputationalmodelling

Because computational models are employed in a variety of ways in this thesis, it isimportant tomake clear from the start how they are built and used. Terminology can bequitedifferentacrossresearchfieldsandthereforeazoomed‐outviewisneeded.Strangely,the distinction between model building and model usage is not always recognised. Thisomissionisasignthataproperviewontherelationbetweenproblemdescriptionandmodelemployment could be lacking, notwithstanding the fact that sometimes for a computerscientistmakingamodel is theultimategoal–and,sometimes, foranengineerapplyingamodelistheonlythingthatmatters.1.2.1 ModelbuildingThe traditional approach to setting up a numerical model the adjectives numerical andcomputational are used interchangeably is from a mathematical model, which is adescriptionof thephysicalprocess in formulae.Thismathematicalmodel isa resultof theevaluationofmeasureddataofthephysicalprocessbyanexpert.SeeFigure1.3.Oftentheevaluation that is used to define the mathematical model is not explicitly based on new

11

measurements,orisskippedaltogether,whenthemathematicalequationsareadoptedfrompreviousstudies.

Figure1.3.Buildinganumericalmodelfromamathematicalmodel.Inthisapproachthemodelstructureconsistsofmathematicalexpressionsthatarederivedfromananalyticalendeavourtocapturethephysicalprocess.Themagicofthemathematicalmodel here is that it acts as a bridge: it provides the numerical relations necessary forcomputationanditcontainscompactphysicaldescriptionsthataremeaningfuloutsideofthenumericalrealm.SeeStellingandBooij(1999)foraguidelinehowthisapproachisusedinhydraulicengineeringtoestablishaconnectionbetweennumericalmodelsandreality.A second, different approach is called system identification (SI), defined in Figure 1.4. Ameasured time signal u acts simultaneously as input for the process (or system) beingstudiedandasinputforthenumericalmodel.Thedifferenceoftheoutputsyand ,theerrorsignal,issubsequentlyusedtoadaptthenumericalmodel;thisimprovesitsperformance.Inmostreal‐lifesituationsthemeasuredprocessoutputisinfluencedbynoise.

Figure1.4.Settingupanumericalmodelbysystemidentification.AfterNelles(2001).Theactofrepeatedlyapplyingtheerrorsignalforimprovementofthemodel i.e.ofreducing| | is called training. Inmachine learning, originally abranchof artificial intelligencebuttodayarguablytooextensiveatopic itself tobeconsideredasingleresearchfield, thistypeoftrainingiscalledsupervisedlearning.Thismeansthatbothinputandoutputsignalsare available for gradually improving the model. Note that these ‘signals’ can have allimaginable shapes: they can contain a physical quantity varying in time, for examplepressurep t ,buttheycanalsobeadiscretevariablexthatindicatesinclusionorexclusionof an object in a set, for example x ∊ blue, red . The model resulting from the trainingbasically gives well‐tweaked rules for a mapping from input to output. The described

processevaluationu

noise

ymathematical

modelnumerical

model

process

numerical model

u error

noise

y

y

12

approachmakesintensiveuseofthe measured dataandisthereforetermedempiricalordata‐drivenmodelling.Thefirstdescribedapproach(Figure1.3)basedonamathematicalmodelthatdescribesthefundamental principles is referred to as physics‐basedmodelling and providesmaximuminsightsintheprocess.Itisaformofwhite‐boxmodelling.Thesecondapproach(Figure1.4)isanexampleofblackboxmodelling.Thissecondapproachrequiresaminimumofdomainexpertiseandcangiveaptnumericalrepresentationsofcomplicatedsystemsthatarebadlyunderstood.Onthedownside,itsaccuracydependsonthe(qualityof)theavailabledataandextrapolation to rangesoutsideof trainingconditions isprecarious.Needless tosay,manyhybridformsweredevelopedthataresometimescalledgreymodels.1.2.2 ModeluseDepending on the problem type that is to be solved, there are various ways to use thenumericalmodel.Figures5to7schematicallyshowhowcomputationalmodelscanbeused(afterNelles,2001).Inthefigures,u(t)isaninputvalueattimetandy(t)and (t)areoutputvalues fromtheprocessand themodel, respectively.Predictivemodelsusepast inputandoutputsignalsfromamonitoredprocesstocomputeapredictionforoneormorestepsintothefuture.IntheleftschemeofFigure1.5,aone‐stepaheadpredictionisshown.

Figure1.5.Useofnumericalmodels.Left:prediction.Right:simulation.Simulationisdifferent.Here,thenumericalmodelisusedtomimictheprocessoutputusingonlypreceding inputdatapoints.SeeFigure1.5(right).Thenumberof timesteps intothepastk‐1,k‐2,…isratherarbitraryforpredictionandsimulation.Thenumberoffuturesteps,however,hasmoreprofoundconsequencesintermsofmodellingrequirements.Figure1.6.Useofnumericalmodels.Left:optimisation.Right:analysis.

numerical model

y(k)

u(k-2)u(k-1)

...

y(k-2)y(k-1)

...

prediction

numerical model y(k)u(k-2)

u(k-1)

...

simulation

strategy numerical model

u y evaluation

optimisation

process y

numerical model

u

y

u

analysis

13

Veryoften,asisthecaseinthepresentstudy,asimulationmodelisusedforoptimisationoranalysis.Whenamodelisusedinoptimisation(Figure1.6ontheleft),thegoalistofindasetof (strategy) parameters for which a certain desired system performance is reached(formulatedasaminimisationormaximisationofapredefinedobjective function).This isdonebyevaluating themodelledoutput signaland looping theoutcomeback toadapt thestrategy, until the desired performance is found. This is also called reverse modelling,becausethereismoreinformationavailableaboutthedesiredmodeloutputthanaboutthemodelparametersontheinputside.Arathersubtleuseofnumericalmodels isanalysis(Figure1.6ontheright).Here,processandmodelcoexistinparallelandthegoalofrunningthemodelistoscrutinisetheworkingof theprocess.Whatcounts isuncoveringdetailsof theprocess, thatarehard tomeasuredirectly, by looking at the results of the numerical model. This is usually more than justcomparingoutputs and .

Figure1.7.Useofnumericalmodelinacontrolscheme.A numericalmodel can be used inside a control loop to facilitate the design of a suitablecontrol measure. Figure 1.7 shows an example lay‐out of a controlled process. It is theperformance of the controller that is the main concern, the extent to which the modelreplicates the process is of secondary importance. There exist different variants of thescheme in Figure 1.7. A last type ofmodel use is fault detection (not shown in a figure),whereseveralversionsofthemodelruninparallelwiththeprocessinordertodetectfailuremodesinasystem.It remains undiscussed so far which model usages benefit from which type of modelconstruction. This all really depends on the problem type, the problem complexity, thespecific questions thatneed tobe answered, aswell as the availabledata and the stateoftheoretical knowledge. Theways computationalmodels canbeused, as introduced in thissection,isnotsynonymouswiththeapplicationofamodel.Howwespeakaboutapplicationsagainquite stronglydependson the focal lengthof thediscipline.A computer scientist orappliedmathematicianmay consider amovingmesh inCFDas an applicationof a certainalgorithm,whileanoffshoreengineerconsiderscomputationsofavibratingmarineriserasan application of a CFD model containing a moving mesh. Section 3.1 digs deeper intoapplicationaspectsofnumericalmodellinginhydraulicengineering.

process

numerical model

u ycontroller

design

r

control

14

1.3 Thesisaims

StudiesintoFIVofgatesuptonowcanbecategorisedinfourgroups:‐ Physical scalemodelling of gate sections complemented by analytical studywith thegoal of gaining fundamental knowledge on excitation mechanisms and optimal gateshapes.

‐ Elastically‐scaled physical scale modelling for checking and improving the design ofactualstructures.

‐Fieldmeasurementsonprototypestructures:monitoringofgatebehaviourforacertaintime period after an incident has happened orwhen there is uncertainty about gateoperationprotocols.

‐CFDsimulationsasapurelyacademicstudyorcomplementarytophysicalmodelling.All four types have proven their value, but are time‐consuming. Given the ongoingdevelopments in computationalmethods and the variety of uses of numericalmodels, anexciting idea is toexaminenewwaystostudygatevibrations.This leadstothreeresearchaimsforthisthesis:Aim1This study sets out to critically evaluate previous research work in the field of gatevibrations, examine new design improvements and measures for prevention andattenuation.Aim2The second aim is to contribute to the development, testing and application of novelcomputational techniques with the purpose of providing the engineer with new tools foraddressing gate vibration issues in design, operation and maintenance. By explicitlydiscussing and interpreting model building and usage, this thesis aims to strengthen thebridgebetweennumericalmodellingandproblemsolving.Aim3Thetopic isablendofresearchareas; themultidisciplinaritycomesontheonehandfromthe combination of hydrodynamics (the flow), structural mechanics and mechanicalvibrations(thestructure),on theotherhand itcomes fromthenumeroussub‐branchesofcomputationalscienceandthelargenumberofavailabletechniques.Thequestionriseshowtodealwithmodelchoiceanduseattheintersectionofdisciplines.The third aim, related to the second, is therefore to explore computationalways to tacklemulti‐disciplinaryproblems that are unsolvable by available conventionalmeans from thedisciplines separately. Looking at it from a reverse angle, the question becomes how toincreasetherelevanceandfind‘killerapplications’foremergingcomputationaltechniques.Beforecommencingtworemarksareinorder.‐Itisnotanaimofthisthesistoassembleandincorporateallpreviousworkandprovideananthologyofthetopic,sincethishasbeendonealreadytosatisfactionintextbooks.Asubstantialamountofaccumulatedknowledge is found inKolkman(1976).Thesameauthor contributed to other, updated overviews, of which Kolkman and Jongeling(1996)isthemostcomplete.ThetwobooksbyNaudascher(1991)andNaudascherand

15

Rockwell(1994)togethercontainawealthofanalyses,usableguidelinesandreferencestofurtherstudies.

‐Theworkdoneforandpresentedinthisthesisisnotrelatedtoanyspecificbarrierinreallife.

Figure1.8.OneofthetwolargesectorgatesoftheSaintPetersburgdam.

1.4 Thesisset‐up

Thechaptersareorganisedaccordingtodifferentusesofmodels.Thefocusfrequentlyshiftsbetweenhydraulicengineeringandappliedcomputerscience.Arun‐throughofthechapters.Chapter 2 introduces the necessary physics background. This makes the computationalchallengemoreclear.Assuchitservesasafoundationfortheapproachesandchoicesmadeinsubsequentchapters.Chapter3goesintonumericalmodelsbasedonphysicsequations.InChapter4,acascadeofphysics‐basedmodelsispresentedforcomputingtheimpactofflowthroughhydraulicgates.ThenChapter5discussesaphysical laboratoryexperimentoftwotypesofunderflowgates.Chapter6proceedswithfundamentalnumericalmodellingofthesame two gates in a finite element model. After this, Chapter 7 introduces a data‐drivensystem for barrier operation that avoids gate vibrations. Chapters 8 and 9 explore recentcomputational techniques in the field of evolutionary computing andmake an attempt toapplythesetothevibrationproblem.Thefinalchapterstatestheconclusionsofthethesis.Table1.1classifieseachchapterforthereader’sconvenience.Shortintermediateevaluationsareaddedwhereapplicable,tokeepaneyeonhowtheresearchfitsintothebiggerpicture.

16

Table1.1.Overviewofchapters. chaptersthesisbackbone 1 10framework/methodology/discussion 2 3 89results fromnumericalexperiments 4 6 7 89resultsfromphysicalexperiments 5

Theintroducednotionsregardingmodeltype(numericalorphysical),buildingofnumericalmodels(data‐drivenorfundamental)andmodeluseenableanotherclassification,asshowninTable1.2.Table1.2.Chaptersaccordingtomodelbuildinganduse.

modeltypemodelbuilding↓

modeluse→ an

alysis

optimisation

control

numericalmodeldata‐driven 9 8,9 7

fundamental 3,4,6 4

physicalmodel 5