Conference PaperPDF Available

Assessing steel pipe toughness using chemical composition and microstructure (Addendum - presentation slides)

Authors:
Assessingsteelpipetoughness
usingchemicalcompositionandmicrostructure
Paper#57
NathanSwitzner,JoelAnderson,MichaelRosenfeld,
RSIPS
JonathanGibbs,PeterVeloo,RamonGonzalez
PacificGasandElectricCompany
NathanSwitzner
Assessingsteelpipetoughnessusingchemicalcompositionandmicrostructure
Toughness
Toughnessisthe
abilityofthematerial
towithstandcrack
growth.
Inservicemetal
productsexhibit
cracksorcracklike
defectsofavarietyof
shapesandsizes. https://www.rosengroup.com/global/solutions/services/pipelinecracks
NathanSwitzner
Assessingsteelpipetoughnessusingchemicalcompositionandmicrostructure
PipeToughness
Toughnesscanbean
importantaspectofmaximum
allowableoperatingpressure
(MAOP)reconfirmationbythe
engineeringcritical
assessment(ECA)process.
Toughnessisusedindefect
analysisandremaininglife
assessments.
NathanSwitzner
Assessingsteelpipetoughnessusingchemicalcompositionandmicrostructure
ToughnessfromaSteelmakingPerspective
Tomeettoughness
targets,steel
manufacturers
optimizechemical
compositionand
microstructurealong
withotherprocessing
parameters.
https://www.steelgrips.com/10news/286successfulremote
commissioningoflevel2automationforcontinuouscastercc21
NathanSwitzner
Assessingsteelpipetoughnessusingchemicalcompositionandmicrostructure
Motivation:ANondestructiveToughnessTest
Thereisnowidelyacceptednondestructivemethodforestimating
toughness.
Operatorsmaycutoutandtestpipeorhottapcoupons.
Astatisticalmetallurgicalmodeltoestimatetoughnessbasedonin
situchemicalanalysisandmicrostructuredatawouldprobablyfall
inthe"othertechnology"category,whichrequiresnotification.
https://epcmholdings.com/ho
ttappingadviceon
successfulpipelinetapping/
NathanSwitzner
Assessingsteelpipetoughnessusingchemicalcompositionandmicrostructure
DataSourcesforToughness,Composition,and
Microstructure
Standarddestructivelaboratorymethodswereusedtodeterminethe
compositionforeachofthepipefeaturesinthisstudy.
Calculationsofuppershelfenergyandtransitiontemperatureforafullsize
CharpyVnotchsample.
“Vendor1” ~1000pipesand
features
CharpyVnotch
ToughnessData
Laboratory
CompositionData
“Vendor2” ~120pipesand
features
CharpyVnotch
ToughnessData
Laboratory
CompositionData
PG&E ~41pipesand
features
CharpyVnotch
Toughnessdata
Laboratory
CompositionData
Microstructural
Data
NathanSwitzner
Assessingsteelpipetoughnessusingchemicalcompositionandmicrostructure
KeyMeasurementsfromtheCVNTest
Sincefracturetoughnessisstrongly
controlledbytemperaturefor
steel,CVNtestsareperformedat
multipletemperatures.
Recordeddatainclude:
Test temperature
Energyabsorbed
Percentshear(ductile)
appearance
Rosenfeld, M., “Procedure improves line pipe Charpy test
interpretation,” Oil and Gas Jrnl, 1997, Apr, pp 40-46.
NathanSwitzner
Assessingsteelpipetoughnessusingchemicalcompositionandmicrostructure
AlternativeCalculationMethodforUpper
ShelfEnergyandTransitionTemperature
Based onwork byRosenfeld,API579Section9F.2.2.c
provides analternativemethod to calculate:
UppershelfenergyandTransitiontemperature
Thecalculationmethodrequires:
Pipewallthickness
Test temperature
Samplesize(forsubsizesamples)
Energyabsorbed
Percentsheararea
Ref:Rosenfeld,M.,“ProcedureimproveslinepipeCharpytestinterpretation,”OilandGasJrnl,1997,Apr,pp4046.
NathanSwitzner
Assessingsteelpipetoughnessusingchemicalcompositionandmicrostructure
ElementEffectsonCVNUpperShelfEnergy
NathanSwitzner
Assessingsteelpipetoughnessusingchemicalcompositionandmicrostructure
ElementEffectsComparison
NathanSwitzner
Assessingsteelpipetoughnessusingchemicalcompositionandmicrostructure
ElementEffectsComparison
NathanSwitzner
Assessingsteelpipetoughnessusingchemicalcompositionandmicrostructure
ElementEffectsComparison
NathanSwitzner
Assessingsteelpipetoughnessusingchemicalcompositionandmicrostructure
ModelTypesandIterationstoPredictUpper
ShelfEnergyandTransitionTemperature
PredictUpperShelfEnergy
Basedon10compositional
elements(train/test):
Linearmodel
Randomforest
Basedon4compositional
elements(train/test):
Linearmodel
Randomforest
PredictTransitionTemp.
Randomforestmodels:based
on9elementsandthen4
elements(train/test)
PredictUpperShelfEnergyand
TransitionTemperature:
Linearmodelusingboth
compositionand
microstructure(notrain/test)
NathanSwitzner
Assessingsteelpipetoughnessusingchemicalcompositionandmicrostructure
RandomForestModelforUpperShelfEnergy
Arandomforestmodelisadecisiontreebasedmodel
thatisiteratedhundredsorthousandsoftimeson
randomsubsetsofthedata.
Resultsofallthe“trees”areaveragedtoproducea
singlemodel.
Minimizestheinfluenceofasmallnumberofoutliers.
Thedataweresplitbetweentrainingandtestsets,with
80%and20%ofthedata,respectively.
NathanSwitzner
Assessingsteelpipetoughnessusingchemicalcompositionandmicrostructure
R2value
rf_a =0.84
NathanSwitzner
Assessingsteelpipetoughnessusingchemicalcompositionandmicrostructure
R2value
rf_a =0.84
rf_b =0.80
NathanSwitzner
Assessingsteelpipetoughnessusingchemicalcompositionandmicrostructure
RandomForestModelforTransition
Temperature
NathanSwitzner
Assessingsteelpipetoughnessusingchemicalcompositionandmicrostructure
R2value
rf_a =0.51
NathanSwitzner
Assessingsteelpipetoughnessusingchemicalcompositionandmicrostructure
MicrostructureEffects
Microstructurereferstothegrain
structureandfeaturesinapolished
andetchedsampleusuallyonly
visiblethroughamicroscope.
Thegrainsizeandtheamountof
darkphase(pearlite=ferrite+iron
carbide)aretwoquantifiable
aspectsknowntoaffecttoughness.
light
phase
grain
dark
phase
NathanSwitzner
Assessingsteelpipetoughnessusingchemicalcompositionandmicrostructure
NathanSwitzner
Assessingsteelpipetoughnessusingchemicalcompositionandmicrostructure
R2=0.76
NathanSwitzner
Assessingsteelpipetoughnessusingchemicalcompositionandmicrostructure
ConcludingRemarks
Toughnessisanimportant
materialpropertyfor
inhibitingcrackgrowth.
Therearenowidely
recognizedNDEmethodsto
estimatetoughness.
Uppershelfenergy(USE)and
transitiontemperature(TT)
arekeypropertiesforpipeline
remaininglifecalculations.
USEandTTcanbecalculated
usingempiricalequations
basedondestructiveCVN
data.
DestructiveCVN,composition
andmicrostructuredatawere
usedtobuildlinearand
randomforestmodelsto
estimateUSEandTT.
NathanSwitzner
Assessingsteelpipetoughnessusingchemicalcompositionandmicrostructure
FutureWork
Focusonlowertoughness
andhighertransition
temperaturesteels(atrisk
steels)
Examinethecompositionand
microstructureofoutliers
Expandthecompositionand
microstructuredatabases
BeginvalidationusingNDE
compositionand
microstructuredata
Sensitivityanalysis
FormoreinformationcontactNathan
Switzner:nswitzner@rsips.com
ResearchGate has not been able to resolve any citations for this publication.
ResearchGate has not been able to resolve any references for this publication.