Fusion Reactor Steel

Sonny Martin Tevis Jacobs

Yucheng Zhang Jiawen Chen

• Fusion

• Neural networks

• Models

• Predictions and conclusions

Fusion

• Neutron damage: 100-150 dpa• Transmutation helium• Up to 700ºC

Design parameters

Irradiation Effects

• Irradiation hardening

• Irradiation creep

• Activation

• Swelling

Irradiation Hardening

• Dislocation loops– Condensation of defects

• Helium bubbles

Why model?

• Suitable reactor does not exist

• Experiment would be costly

• Help design ITER

• Because we’re doing a modelling course!

It cost an estimated £8 million to produce the current data

(¥116 million, $14 million,€11 million)

from FISSION reactor.

• Fusion

• Neural networks

• Models

• Predictions and conclusions

Neuron

Biological Neuron

Digital Neuron

Activation Function

(tanh)

input outputinput output

Input Layer

Hidden Layer

Output Layer

f

f

f

X

X

Y

fi = tanh ( Σwij(1)xj + θi

(1) )

y = Σwi(2)fi + θ(2)

‘OR’ logic

Value

Input Output

X1 X2 Y

0 0 0

0 1 1

1 0 1

1 1 1

Train by adjusting weightsY = X1 W1+ X2 W2+W3

a

d

b

c

Wi

WiWi

Wi

W1=1, W2=1, W3=0

y

x

• Fusion

• Neural networks

• Models

• Predictions and conclusions

Total Elongation

00.5

11.5

22.5

33.5

4C N Cr Ni

Mo

Mn Ti Si B Co

Cu

Nb P S Ta Fe

Dam

age_

dpa

sqrt_

dpa

He_

appm

Irrad

iatio

n_Te

mp_

C

Test

_Tem

p_C

HFI

R R2

OR

RH

FRE

BR

_II

Inputs

Sig

nif

ican

ce

Adding known science• He/dpa

• exp[-1/(Irradiation T)]

• exp[-1/(Test T)]

• 1/L

1/L y 2 = y

2, dislocations + y

2, bubbles

Nc = (5.36*1012) exp

Tk

eV

B

15.1

NG = atoms of HeC

HE

N

N

PG = rnbr

Tkn

Gv

bG

2

3

4 3

req = 3/13264232

3/23264232

)2(576242

)2(576242

GbGvGv

GbGvGvGb

Tnknbnb

TnknbnbTnk

0

5

10

15

20

25

30

35

40

45

50

0 50 100 150

Data Point / Number

Tot

al E

long

atio

n / %

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0 50 100

Data Point / NumberL

n(to

tal e

long

atio

n) /

%

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 2 4 6 8

Input

Out

put

-4

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0

0.5

0 2 4 6 8

Input

ln(o

utp

ut)

Yield Strength

0

100

200

300

400

500

600

700

800

900

1000

0 200 400 600 800 1000

Measured / MPa

Pre

dic

ted

/ M

Pa

• Fusion

• Neural networks

• Models

• Predictions and conclusions

Yield Strength Predictions

10 appm He/dpa, 523K

0

200

400

600

800

1000

0 50 100 150

Damage / dpa

Ye

ild S

tre

ss

/ M

Pa

Uniform Elongation

0

5

10

15

20

25

30

35

0 5 10 15 20 25 30 35

Measured / %

Pre

dic

ted

/ %

Uniform Elongation Predictions

20 appm He/dpa, 523K

0102030405060708090

100

0 20 40 60 80 100

Damage / dpa

Elo

ng

atio

n /

%

Measured Uniform Elongation Data

We were not able to create a reasonable model for

uniform elongation

•For the first time, predictions have been made of suitability of austenitic steel for fusion

Total Elongation Predictions

10appm He/dpa, 523K

0

10

20

30

0 50 100 150

Damage / dpa

Elo

ng

ati

on

/ %

Measured Total Elongation Data

• For austenitic stainless steels irradiated to doses consistent with fusion, ductility is likely to be unacceptably small

10appm He/dpa, 523K

0

10

20

30

0 50 100 150

Damage / dpa

Elo

ng

ati

on

/ %

Yield Strength Predictions

10 appm He/dpa, 523K

0

500

1000

1500

0 50 100 150

Damage / dpa

Yie

ld S

tre

ss

/ M

Pa

•Irradiation hardening makes the steel brittle

10 appm He/dpa, 523K

0

500

1000

1500

0 50 100 150

Damage / dpa

Yie

ld S

tre

ss

/ M

Pa

•BCC steel might be better

Future work• Verify experimentally (in 15 years)

• Investigate discrepancy in uniform ductility

• Record comprehensive data when doing experiment

Acknowledgements• Professor Harry Bhadeshia

• Richard Kemp

• The noodle lady in Market Square