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On-line Access: 2022-06-24
Received: 2021-08-01
Revision Accepted: 2022-01-12
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Citations: Bibtex RefMan EndNote GB/T7714
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,in press.Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/jzus.A2100369 @article{title="Identification of ductile fracture model parameters for three ASTM structural steels using particle swarm optimization", %0 Journal Article TY - JOUR
基于粒子群算法的ASTM结构钢延性断裂模型参数识别研究机构: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。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
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