fusion reactor steel sonny martin tevis jacobs yucheng zhangjiawen chen
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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
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