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Ya-zhi ZHU


Shi-ping HUANG


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Journal of Zhejiang University SCIENCE A 2022 Vol.23 No.6 P.421-442


Identification of ductile fracture model parameters for three ASTM structural steels using particle swarm optimization

Author(s):  Ya-zhi ZHU, Shi-ping HUANG, Hao HONG

Affiliation(s):  Department of Structural Engineering, Tongji University, Shanghai 200092, China; more

Corresponding email(s):   ctasihuang@scut.edu.cn

Key Words:  Parameter calibration, Void growth model (VGM), Gurson-Tvergaard-Needleman (GTN) model, A36 steel, A572 Gr. 50 steel, A992 steel, Particle swarm optimization (PSO)

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Ya-zhi ZHU, Shi-ping HUANG, Hao HONG. Identification of ductile fracture model parameters for three ASTM structural steels using particle swarm optimization[J]. Journal of Zhejiang University Science A, 2022, 23(6): 421-442.

@article{title="Identification of ductile fracture model parameters for three ASTM structural steels using particle swarm optimization",
author="Ya-zhi ZHU, Shi-ping HUANG, Hao HONG",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Identification of ductile fracture model parameters for three ASTM structural steels using particle swarm optimization
%A Ya-zhi ZHU
%A Shi-ping HUANG
%J Journal of Zhejiang University SCIENCE A
%V 23
%N 6
%P 421-442
%@ 1673-565X
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2100369

T1 - Identification of ductile fracture model parameters for three ASTM structural steels using particle swarm optimization
A1 - Ya-zhi ZHU
A1 - Shi-ping HUANG
A1 - Hao HONG
J0 - Journal of Zhejiang University Science A
VL - 23
IS - 6
SP - 421
EP - 442
%@ 1673-565X
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A2100369

Accurate prediction of ductile fracture requires determining the material properties, including the parameters of the constitutive and ductile fracture model, which represent the true material response. Conventional calibration of material parameters often relies on a trial-and-error approach, in which the parameters are manually adjusted until the corresponding finite element model results in a response matching the experimental global response. The parameter estimates are often subjective. To address this issue, in this paper we treat the identification of material parameters as an optimization problem and introduce the particle swarm optimization (PSO) algorithm as the optimization approach. We provide material parameters of two uncoupled ductile fracture models—the Rice and Tracey void growth model (RT-VGM) and the micro-mechanical void growth model (MM-VGM), and a coupled model—the gurson-Tvergaard-Needleman (GTN) model for ASTM A36, A572 Gr. 50, and A992 structural steels using an automated PSO method. By minimizing the difference between the experimental results and finite element simulations of the load-displacement curves for a set of tests of circumferentially notched tensile (CNT) bars, the calibration procedure automatically determines the parameters of the strain hardening law as well as the uncoupled models and the coupled GTN constitutive model. Validation studies show accurate prediction of the load-displacement response and ductile fracture initiation in V-notch specimens, and confirm the PSO algorithm as an effective and robust algorithm for seeking ductile fracture model parameters. PSO has excellent potential for identifying other fracture models (e.‍g., shear modified GTN) with many parameters that can give rise to more accurate predictions of ductile fracture. Limitations of the PSO algorithm and the current calibrated ductile fracture models are also discussed in this paper.


结论:1. PSO算法能够准确地预测V形缺口试件的载荷-位移响应和延性断裂萌发,是一种识别延性断裂模型参数的有效算法;2.PSO在识别其他具有更多参数的断裂模型(如剪切修正GTN模型)方面具有很好的潜力,这些模型可以更准确地预测延性断裂。

关键词:参数校准;孔洞增长模型;GTN模型;A36钢;A572 Gr. 50钢;A992钢;粒子群优化

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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