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 ORCID:

Ya-zhi ZHU

https://orcid.org/0000-0002-9783-2780

Shi-ping HUANG

https://orcid.org/0000-0002-0092-1753

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

http://doi.org/10.1631/jzus.A2100369


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.

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Abstract: 
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.

基于粒子群算法的ASTM结构钢延性断裂模型参数识别研究

作者:朱亚智1,黄仕平2,3,洪浩4
机构:1同济大学,建筑工程系,中国上海,200092;2华南理工大学,土木与交通学院,中国广州,510640;3中新国际联合研究院,中国广州,510700;4上海市政工程设计研究总院(集团)有限公司,中国上海,200092
目的:准确预测延性断裂需要确定材料参数(包括本构参数和延性断裂模型参数),以反映真实的材料响应。传统的材料参数标定方法往往依赖于试错法,需手动调整参数,直到相应的有限元模型得到与实验结果相匹配的材料力学响应。参数估计的过程通常是主观的。为了解决这一问题,本文将材料断裂参数辨识问题转化为优化问题,并引入粒子群优化(PSO)算法作为优化方法。
创新点:1.基于粒子群优化算法,给出了自动识别钢材应变硬化参数的方法;2.建立了ASTM结构钢材Gurson-Tvergaard-Needleman(GTN)损伤模型的参数识别方法。
方法:1.通过圆形缺口杆件的拉伸试验,以试验和有限元模拟的载荷-位移曲线差值为目标方程,采用PSO优化算法及参数自动校准程序,以最小化目标方程确定应变硬化准则和非耦合断裂模型的参数;2.基于文献调研的结果,确定GTN模型各参数的合理取值范围,以此确定PSO算法中参数的取值,从而能够高效、准确地确定GTN参数。
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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