TY - JOUR
T1 - Inverse identification of graphite damage properties under complex stress states
AU - Liu, Guangyan
AU - Wang, Lu
AU - Yi, Yanan
AU - Sun, Libin
AU - Shi, Li
AU - Ma, Shaopeng
N1 - Publisher Copyright:
© 2019 The Authors
PY - 2019/12/5
Y1 - 2019/12/5
N2 - As a key material in high-temperature gas-cooled reactors (HTGR), nuclear graphite is a type of quasi-brittle material with complex damage mechanisms. However, it is difficult to characterise the damage properties of nuclear graphite under complex stress states using conventional testing approaches or inverse methods. In this study, a hybrid identification method based on 8-node quadrilateral element digital image correlation (Q8-DIC), a double iterative finite element model updating (FEMU) technique, and artificial neural networks (ANNs) is presented to characterise the damage properties of IG11 graphite material under complex stress states. First, this method is verified using simulated tests on a ring under diametrical compression, then applied to actual mechanical testing of graphite material to evaluate its damage properties. Finally, factors affecting the evolution of damage are discussed. The results indicate that the first principal strain has the most significant effect on the damage evolution of the graphite material.
AB - As a key material in high-temperature gas-cooled reactors (HTGR), nuclear graphite is a type of quasi-brittle material with complex damage mechanisms. However, it is difficult to characterise the damage properties of nuclear graphite under complex stress states using conventional testing approaches or inverse methods. In this study, a hybrid identification method based on 8-node quadrilateral element digital image correlation (Q8-DIC), a double iterative finite element model updating (FEMU) technique, and artificial neural networks (ANNs) is presented to characterise the damage properties of IG11 graphite material under complex stress states. First, this method is verified using simulated tests on a ring under diametrical compression, then applied to actual mechanical testing of graphite material to evaluate its damage properties. Finally, factors affecting the evolution of damage are discussed. The results indicate that the first principal strain has the most significant effect on the damage evolution of the graphite material.
KW - Artificial neural networks
KW - Complex stress states
KW - Damage
KW - Inverse identification
KW - Nuclear graphite
UR - http://www.scopus.com/inward/record.url?scp=85071574836&partnerID=8YFLogxK
U2 - 10.1016/j.matdes.2019.108135
DO - 10.1016/j.matdes.2019.108135
M3 - Article
AN - SCOPUS:85071574836
SN - 0264-1275
VL - 183
JO - Materials and Design
JF - Materials and Design
M1 - 108135
ER -