Summary of Work Leading to ASTM E900-15, and a New Transition Temperature Shift Equation for PWR- & BWR-type RPVs

Mark Kirk (presented by Matt Gordon)* USNRC Office of Nuclear Regulatory Research Component Integrity Branch mark.kirk@nrc.gov & matthew.gordon@nrc.gov

International Workshop on RPV Embrittlement and Surveillance Programs Prague, Czech Republic 13th to 15th October 2015

1

* The opinions expressed here are those of these authors. They do not represent an official position of the NRC.

Revision of Standard Guide E900

• ASTM Standards – Reviewed & balloted for re-affirmation, revision, or withdraw once

every five years

• Transition Temperature Shift (TTS) equation in E900-02 & -07 – Adopted in 2002 (based on USA data only) – Re-affirmed (with the rest of E900) in 2007

• This effort – Initiated in 2011 – Objective: Evaluate TTS equations developed since 2002 &

identify equation for use in next revision of E900.

2

Presentation Overview

• TTS equations assessed

• Data used

• Assessment process – Comparison of trend curves

to data – Calibration of trend curves

to data

• Balloting

• Documentation

• Current status & future plans

3

4

TTS Equation Timeline

Many equations developed in the last decade+ • Different data sets • Different approaches to fitting • Different transition-temperature metrics All nine equations evaluated in this effort

1970 1980 1990 2000 2010 2020

USA(RG1.99)

Japan(JEAC-4201)

France

USA(10CFR50.61a)

ASTME900-02

Kirk:WR-C(5)

Erickson:Fit6

Chaouadi:RADAMO

Debarberis

(FIM/FIS)(edf)

(FFI)

YearofAdoptionorPublication

RADAMO

Fit6

WR-C(5)R1

USA/NRC(10CFR50.61a)

ASTM(E900-02)

France

Japan(JEAC-4201)

USA/NRC(Reg.Guide1.99)

RR/UCSB

nls

nls

nls

nls

0.5

0.1

0.25

0.4

0.075

MaxCuTestReactor

LWRSurveillance

USA

all

USA

USA

France

Japan

USA

DCharpy

cve

DHardness

DYield

41J

41J

41J

56J

41J

41J(R2)(R1)

DataSource TTSMetric

*nls =nolimitstated

USA&France

~440

535

2,561

855

736

427

371

177

~400

#Data

Copper [weight %]

16

17

18

19

20

0 0.1 0.2 0.3 0.4

Copper [wt%]

Log {Fluence [n/cm2]}PWR

BWR

MTR

N/A

N/A

N/A

N/A

N/A

JEAC4201-2007(PVP13)

N/A

N/A

N/A

Log

()

[n

/cm

2]

16

17

18

19

20

0 0.1 0.2 0.3 0.4

Copper [wt%]

Log {Fluence [n/cm2]}PWR

BWR

MTR

N/A

N/A

N/A

N/A

N/A

JEAC4201-2007(PVP13)

N/A

N/A

N/A

• Data from – National surveillance

programs (PWRs & BWRs), – Material test reactors (MTRs)

collated, QA checked, & stored in a general purpose spreadsheet (“PLOTTER”)

• E10.02 Subcommittee agreed to use – Only PWR & BWR data – Only data quantified using

T41J

to evaluate ETCs for the next revision of E900 – “BASELINE” data

• MTR data retained for possible future use

Data Collected

PWR

BWR

MTR

PWR

BWR

MTR

n=4,459

6

[Graphic courtesy of P. Todeschini, EDF]

Belgium [32]

Brazil [6]

France [133]

Germany [101]

Holland [6]

Italy [23]

Japan [353]

Mexico [6]

South Korea [124]

Sweden [23]

Switzerland [6]

Taiwan [32]

United States [1040]

Undesignated [21]

BASELINE Data Countries of Origin & Range of Variables

Assessment Process Overview

• PLOTTER spreadsheet developed to enable virtually any assessment – Bias – Scatter – Accuracy of variable trends

relative to any definition of a data subset, or the data as a whole

7

-100

-50

0

50

100

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45

Copper [wt%]

Predicted - Measured T41J [oC]ETC over-prediction

ETC under-predictionPre

dic

tio

n–M

eas

ure

d

T 41J

[C

]

X1

data

Desirable • Bias0 • Smaller scatter • Negligible trend relative to regressor variables

TTS

TTS

-30

20

70

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45

Copper [wt%]

Predicted - Measured T41J [oC]

ETC over-prediction

ETC under-predictionPre

dic

tio

n–M

eas

ure

d

T 41J

[C

]

X1

data

Assessment Process Overview

8

Undesirable • Biased prediction • Larger scatter • Clear trend relative to regressor variables

• PLOTTER spreadsheet developed to enable virtually any assessment – Bias – Scatter – Accuracy of variable trends

relative to any definition of a data subset, or the data as a whole

TTS

TTS

• Finalized on – Primary focus: low bias & small scatter

– Secondary check: small residual trends

• Several members used log-Likelihood, or ln(L), to assess bias & scatter together

n.b.: MAX{ ln(L) } is a least-squares criterion

• Problem: “best equation” of those evaluated not the same for: – All baseline data, &

– Various data subsets

9

Assessment Process

[Result from E. Lucon, before re-calibration]

Best Eqn.

• From initial assessments 4 equations identified as: – Unrestricted in their

copper range, and – Often being ranked as

“among the best” of those evaluated by various members’ assessments

• These four equations re-calibrated to all BASELINE data – E900-02 – 10 CFR 50.61a (“EONY”) – E-Fit 6 – WR-C(5)R1

• Re-calibration process

– Maximize ln(L) of each equation relative to the BASELINE data • Functional forms remain

fixed • Investigated alternative

scatter characterization

– Select equation with

highest ln(L) on the BASELINE, but also with consideration of • Other fit metrics • Data subsets

10

TTS Equation Re-Calibration

11

Re-Calibration Results BASELINE Data w/ Original SD Terms

Assessment of bias, scatter, ln(L)

• Re-calibration – Lowers bias – Lowers scatter – Increases ln(L)

of all TTS equations

• WR-C(5)R1 shows best goodness-of-fit relative to all statistical metrics

Assessment of residual trends • Use Student’s T-statistic to

test for trends in residuals versus regressor variables (e.g., Cu, Ni, …) – Quantitative assessment of

zero-slope on residuals plot – t < 2 indicates no significant

effect

• Radar plot summarizes result

for all variables – Ideal result: a small polygon

near the origin

Re-Calibration Results BASELINE Data w/ Original SD Terms

Cu

Ni

Mn

PLog(F)

Log(f)

T

E900-02

E900 Recalibrated

E900

t=10

t=8

t=6

t=4

Cu

Ni

Mn

PLog(F)

Log(f)

T

Original

10CFR50.61a Recalibrated

Log()

Log( )

10CFR50.61a

t=10

t=8

t=6

t=4Log()

Log( )

Cu

Ni

Mn

PLog(F)

Log(f)

T

E-Fit6

E-Fit6 Recalibrated

E-Fit6

t=10

t=8

t=6

t=4Log()

Log( )

Cu

Ni

Mn

PLog(F)

Log(f)

T

WRC5R1

WRC5R1 Recalibrated

WR-C(5)R1

t=10

t=8

t=6

t=4Log()

Log( )

As Published

Re Calibrated

Cu

Ni

Mn

PLog(F)

Log(f)

T

E900-02

E900 Recalibrated

E900

t=10

t=8

t=6

t=4

Cu

Ni

Mn

PLog(F)

Log(f)

T

Original

10CFR50.61a Recalibrated

Log()

Log( )

10CFR50.61a

t=10

t=8

t=6

t=4Log()

Log( )

Cu

Ni

Mn

PLog(F)

Log(f)

T

E-Fit6

E-Fit6 Recalibrated

E-Fit6

t=10

t=8

t=6

t=4Log()

Log( )

Cu

Ni

Mn

PLog(F)

Log(f)

T

WRC5R1

WRC5R1 Recalibrated

WR-C(5)R1

t=10

t=8

t=6

t=4Log()

Log( )

As Published

Re Calibrated

-100

-50

0

50

100

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45

Copper [wt%]

Predicted - Measured T41J [oC]ETC over-prediction

ETC under-predictionPre

dic

tio

n–M

eas

ure

d

T 41J

[C

]

X1

data

12

TTS

TTS

Assessment of residual trends

• Re-calibration changes significance of residual trends

• WR-C(5)R1 trend curve shows greatest improvement & smallest residual trends

13

Re-Calibration Results BASELINE Data w/ Original SD Terms

Cu

Ni

Mn

PLog(F)

Log(f)

T

E900-02

E900 Recalibrated

E900

t=10

t=8

t=6

t=4

Cu

Ni

Mn

PLog(F)

Log(f)

T

Original

10CFR50.61a Recalibrated

Log()

Log( )

10CFR50.61a

t=10

t=8

t=6

t=4Log()

Log( )

Cu

Ni

Mn

PLog(F)

Log(f)

T

E-Fit6

E-Fit6 Recalibrated

E-Fit6

t=10

t=8

t=6

t=4Log()

Log( )

Cu

Ni

Mn

PLog(F)

Log(f)

T

WRC5R1

WRC5R1 Recalibrated

WR-C(5)R1

t=10

t=8

t=6

t=4Log()

Log( )

As Published

Re Calibrated

• Better captures trends in data, in particular increase in prediction error at higher fluences

• Increases ln(L) for all equations

• “Best fit” TTS equation remains unchanged

14

Re-Calibration Results BASELINE Data w/ New SD Terms

Assessment of Data Subsets

Re-calibrated WR-C(5) has MAX{ ln(L)} for 30 of 35 subsets (86%), and is within 1.9% (or closer) of equation with MAX{ ln(L)} for the other 5 subsets

ASTM Balloting • June 2014

– Unsuccessful Subcommittee ballot – Most negatives voted non-persuasive – One persuasive negative – wanted a way to

communicate details regarding the conditions to which the TTS equation applies

– Work to resolve negative • BASELINE data well characterizes RPV materials;

application in sparse regions unlikely • TTS prediction error increases somewhat in

sparse revisions, but can’t screen without also indicating conditions where E900-15 predictions are accurate.

• Outcome: Modified language with general considerations added to E900-15

• January 2015 – Successful concurrent Main &

Subcommittee ballot – All negatives either withdrawn or changed

to abstentions

• E900-15 published in February 15

E900-15 TTS Equation & Documentation

16

“Adjunct” (i.e., technical basis document) & PLOTTER (data collection) now available on the ASTM Website.

ASTM E900 A Work of Many Hands …

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ASTM Members Non-ASTM Members Name Organization Country Name Organization Country

R. Chaouadi SCK-CEN Belgium M. Cisternas Eletrobras Brazil

M. Erickson PEAI USA T. Hardin EPRI USA R. Gérard Tractebel Belgium H. Hein AREVA Germany

J.B. Hall Westinghouse USA J. May AREVA Germany

M. Kirk NRC USA T-K. Song KINS South Korea E. Lucon NIST USA D. Parfitt Rolls Royce England

N. Luzginova NRG The Netherlands J. Splett NIST USA

S. Ortner NNL England P. Todeschini EDF France

W. Server ATI USA J-C. Wang Taiwan Power Co. Taiwan N. Soneda CRIEPI Japan M. Gris Laguna Verde NPP Mexico

R. Stoller ORNL USA

K. Wilford Rolls Royce England T. Williams Rolls Royce England