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A complex-variable finite element method-based inverse methodology to extract constitutive parameters using experimental data

delete2022-05-01
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OA
AI
D
Daniel Ramírez-Tamayo *
A
Ayoub Soulami
V
Varun Gupta
D
David Restrepo
A
Arturo Montoya
E
Ethan K. Nickerson
T
Timothy Roosendaal
K
Kevin L. Simmons
G
Gayaneh Petrossian
H
Harry Millwater
DOI:10.1016/j.ijsolstr.2022.111545delete
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摘要

摘要

En 中文
This paper presents the use of full-field kinematic measurements obtained using the digital image correlation (DIC) procedure and load-displacement data to determine constitutive material properties by solving an inverse finite element optimization problem. A key ingredient in the proposed approach is computing accurate sensitivities with respect to the unknown parameters. These sensitivities were used to solve the optimization problem using an accurate, efficient, gradient-based method, and were computed using the complex-variable finite element method, ZFEM. The use of ZFEM's gradients to inversely determine material properties is demonstrated with two examples. First, the elastic-plastic material properties of DP-590 steel are obtained using a tensile test specimen. Second, the cohesive material parameters of an adhesive are determined using a double cantilever beam test. A significant outcome of this paper is that the use of a weighted residual formulation of the interfacial strain fields and the load-displacement data within the optimization procedure provides better estimates of the constitutive properties than using only the load-displacement data. This technique minimizes the relative error in both the strain fields and the load-displacement curve, which is important to obtain accurate interfacial properties.
Keyword:
Complex-variable finite element method
Inverse methods
Abaqus
UEL
Automatic differentiation
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期刊

International Journal of Solids and Structures 封面图
International Journal of Solids and Structures
IF:
3.8
论文数:
1.2W
被引数:
3.1W

机构

P
Pacific Northwest National Laboratory
学者数:
9.0K
论文数: 6.3K
被引数: 14
U
united states department of energy (doe)
学者数:
11.3W
论文数: 9.6W
被引数: 246
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