soteria platform - (safe long-term operation of light ... · toughness prediction : objective the...

$: SOTERIA platform - (Safe long-term operation of light ... · Toughness prediction : objective The aim of this case study is to compute the fracture toughness of a material, at a given$
SOTERIA platform - (Safe long-term operation oflight water reactors based on improved

understanding of radiation effects in nuclearstructural materials)

Marc BERVEILLER(1), Felix LATOURTE(1), Gilles ADJANOR(1),Julien VIDAL(1), Amel GOSSET(2)

(1) EDF, (2) Phimeca

2018 April - Prague

Marc BERVEILLER (EDF) SOTERIA platform - Workshop 2018 April - Prague 1 / 60

Outline

1 Overview

2 Case Studies

3 Charpy test

4 Toughness prediction

5 Next steps

6 SOTERIA consortium


Overview of the SOTERIA platform

The SOTERIA platform is based on the platform that was developed

during the European project Perfect (FP6) and Perform60 (FP7)

(http://perform60.lgi-consulting.eu/).

The aim of this platform is to model the effect of irradiation on RPV and

Internal materials by using a multiscale approach from nanofeatures

models up to mechanical models.


Overview of the SOTERIA platform : RPV part

For RPV material, the different scales that are taken into account in the

platform are:

Nanofeatures modelling : provide the long-term evolution of

point-defect clusters

Prediction of the hardening due to irradiation at the microscopic

level.

Evaluation of the macroscopic behaviour of RPV steel

Evaluation of the Fracture Toughness due to the increase of

hardening


Outline

1 Overview

2 Case Studies

3 Charpy test


5 Next steps



Definition of implemented case studies

Feedback from Users : difficult to use and to choose the models to chain.

RPV:effective dpa

prediction of hardening due to irradiation

toughness prediction

Charpy impact test

It is now possible to load experimental tensile curve as input of finite

element models.

INTERNALS:Primary pH prediction

microstructure modelling

IASCC prediction with INITEAC code


Outline

1 Overview

2 Case Studies

3 Charpy test


5 Next steps



Charpy test: objective

The aim of this case study is to compute the energy (Charpy energy)

absorbed by a standard notched specimen while breaking under an impact

loading

Charpy test shows whether a metal can be classified as being brittle or

ductile

For ferritic steels, it enables to demonstrate a ductile to brittle transition

(DBTT) with decreasing temperature


Charpy test: general idea of the experiment

Experimentaly one of the most commonly performed test to determine

impact toughness of engineering materials

Involves striking a standard notched specimen (KCV or KCU) supported

at its two ends on an anvil with a controlled weight pendulum swung from

a set height

The height of the swing of the pendulum is a measure of the amount of

energy absorbed in fracturing the specimen


Charpy test: general idea of the experiment

Quantities monitored during the tests:

applied force F (kN)

Displacement of the pendulum D

(mm)

Charpy energy (J)

Quantities monitored after the tests:

Lateral expansion (mm)

Cristallinity (%)


Charpy test: DBTT curve and irradiation effect

Carbon and low alloy steels

exhibit a change in fracture

behavior as the T falls with the

failure mode changing from

ductile to brittle

Temperature shift on the DBT

curve for irradiated material

(ISP)


Charpy test: Modelling approach

+Elasto-viscoplastic behavior for

brittle fracture:

T ∈ −120...− 80◦C

Viscoplastic damage behavior for

ductile fracture: T ∈ −70...0◦C

+

Beremin model in post-processing

⇓Marc BERVEILLER (EDF) SOTERIA platform - Workshop 2018 April - Prague 12 / 60

Charpy test: Modelling approach

Probability of failure Pf vs. t for

different T

Simulated Charpy transition

curve


Charpy test: chaining of modules


Charpy test: material definition

Behavior was identified for 16MND5 based on ...Marc BERVEILLER (EDF) SOTERIA platform - Workshop 2018 April - Prague 15 / 60

Charpy test: Charpy test calculation


Charpy test: Beremin post-processing


Charpy test: execution procedure


Charpy test: output data


Charpy test: Load-displacement curve


Charpy test: Lateral expansion-Fracture energy


Charpy test: Failure probability curve


Charpy calculation: Run the study

To launch this study, you have to click on this button:

If it doesn’t work, launch the classical interface and load the following

study: /share/Study cases/Charpy.prf


Charpy test: perspectives for 2018

1 Calculation with viscoplastic damage behavior for ductile fracture

2 Construction of the full transition curve

3 Possibility to load own material behavior based on the tensile

experimental curve (in Flow Behavior module)


Outline

1 Overview

2 Case Studies

3 Charpy test


5 Next steps



Toughness prediction : objective

The aim of this case study is to compute the fracture toughness of a

material, at a given temperature.

In the context of brittle failure, the toughness, for a single specimen, is

provided as the critical stress intensity factor KJc for which a crack

initiates and propagates.

In the safety cases, one needs to demonstrate that during loadings, the

fracture toughness KJc remains greater than the applied load KI on

postulated defects.


Toughness prediction : general approach

Experimentaly, most of the fracture toughness properties are derived from

Charpy impact testing

Fracture toughness is more directly obtained from tensile tests of

Compact Tension specimens (height H, width B)

P, ULLD

CTOD

Quantities monitored during the tests are

applied load P

load-line displacement ULLD

crack-tip opening displacement CTOD


Toughness prediction : general approach

The plastically corrected loading parameter KJ is estimated from the

applied load P and the load line displacement U following the ASTM

1820 / ASTM E-399 standards:

KI =P

B√Wf( aW

)KJ =

K2I

E′+ηplApl

B


Toughness prediction : experimental data

A conventional approach to represent experimental data is to use the Master

Curve that provides the failure probability Pf as a function of critical load KJc:

Pf (KJc ≤ K) = 1 − exp

[−B

B0

(K −Kmin

K0 −Kmin

)4]K0(T ) = 31 + 77. exp [0.019(T − T0)]

−200 −150 −100 −50 0 50T ( ◦C)

0

100

200

300

400

500

600

700

800

900

KJc(MPa√m

)

Essais CT

Essais CT side grooved

MC 1%

MC 50%

MC 99%

T0 =-97 ◦C

But requires a large number of data to be identified!

Identification of T0

The plastic behaviour and fracture behaviour of the material are not explicitly used!


Toughness prediction : local approach to failure

(Behaviour)

To circumvent the limitations of the Master

Curve approach, the local approach to failure

proposes a chaining of a plastic calculation and

a failure post-treatment

The plastic calculation proposed in the platform

is available in the RPV Toughness Module as

”CTCalculation”

It is a 2D calculation using Code Aster as solver

possible chainings with homogenized crystal

plasticity law to benefit from lower scale plastic

models


Toughness prediction : local approach to failure (Beremin)

∀Kj(ti)fitting a Weibull stress σW on the plastic Volume Vp using the σI field

Compute the failure probability Pf

σW =

(∫Vp

σIm dV

V0

)m−1

Pf = 1− exp

[−(σW

σu

)m]

0 50 100 150 200 250 300 350 400K J(MPa

√m)

0.0

0.2

0.4

0.6

0.8

1.0

PR(K

J)

m=30,σu =2600MPam=30,σu =2450MPam=30,σu =2425MPa Beremin parameters : m, σu, V0


Toughness prediction : chaining of modules

list o

f ava

ilabl

e m

odul

esch

ain


Toughness prediction : material definition

calculation

material parameters

loading parameters


Toughness prediction : CT calculation definition

aster

geometry

loading parameters


Toughness prediction : Beremin calculation definition

Beremin parameters

for aster


Toughness prediction : following execution (BZ)


Toughness prediction : following execution (CT)


Toughness prediction : processing results

list o

f ava

ilabl

e re

sults

click on edit


Toughness prediction : tensile curve


Toughness prediction : CT loading curve


Toughness prediction : failure probability curve


Toughness prediction : output data


Toughness prediction : output data - tensile curve


Toughness prediction : output data - tensile curve

save the tensile curve as an xml file


Toughness prediction : Run the study



study: /share/Study cases/Beremin.prf


Toughness prediction : end-user applications?

Computing toughness from microstructure data and hardening

prediction is sometimes not relevant for end-users

Most users have tensile data on the shelf and want to use it,

but sometimes only Rp0.2 and eventually Rm are available

proposed strategy

When limited information is available : Analytical model for tensile

curve

easy to fit from Rp0.2 and Rm

easy to plug with parametric study (variation of temperature)

When a need to use a tensile curve : Tool to process the

experimental curve

elasticity defined by a function (E = A−BT )


Toughness prediction : focus on experimental tensile curve

typical observations that can be done on experimental data obtained

from tensile experiments:

signal/noise ratio in LVDT or strain gages makes difficult the

determination of elastic properties

sometimes negative strain values during the elastic loading

noise can also affect stress measurement, and stress bursts will be

problematic for calculations especially when a viscoplastic model is

used

several constraints to use a tensile curve for mechanical calculations:

smooth evolution is mandatory

FE Code Aster takes the first point as the yield point, and the

Young’s modulus E is computed from this point (and not the one

provided in the elasticity material parameters


Toughness prediction : experimental tensile curve


CT calculation with experimental tensile curve : Run the

study



study: /share/Study cases/experimental tensile curve.prf


Outline

1 Overview

2 Case Studies

3 Charpy test


5 Next steps



Next steps

add a tool allowing to perform parametric studies.

increase the interactions with experimental data

For RPV:

prediction of the irradiated behavior of the material

For Internals:

Prediction of the irradiated behavior of the material


Conclusions

ConclusionsR&D European project on the modelling of the ageing of RPV and

Internals due to irradiation

Development of industrial-oriented numerical tools

Disseminationearly June 2018 : new version of the platform

2018/06/22 UserGroup meeting

Contact me if you want to integrate the UserGroup


Outline

1 Overview

2 Case Studies

3 Charpy test


5 Next steps



SOTERIA consortium


SOTERIA contacts


soteria platform - (safe long-term operation of light ... · toughness prediction : objective the...

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