2017
DOI: 10.1111/str.12229
|View full text |Cite
|
Sign up to set email alerts
|

A Fourier‐series‐based virtual fields method for the identification of three‐dimensional stiffness distributions and its application to incompressible materials

Abstract: We present an inverse method to identify the spatially varying stiffness distributions in 3 dimensions. The method is an extension of the classical Virtual Fields Method—a numerical technique that exploits information from full‐field deformation measurements to deduce unknown material properties—in the spatial frequency domain, which we name the Fourier‐series‐based virtual fields method (F‐VFM). Three‐dimensional stiffness distributions, parameterised by a Fourier series expansion, are recovered after a singl… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
5
0

Year Published

2018
2018
2024
2024

Publication Types

Select...
6

Relationship

0
6

Authors

Journals

citations
Cited by 12 publications
(5 citation statements)
references
References 30 publications
0
5
0
Order By: Relevance
“…We observed from both simulations and experiments that the proposed virtual fields performed much better in identifying the shear moduli of incompressible solids than the conventional virtual fields. This reveals that the conventional approach, though prevalent in solving parameter identification problems [20,[27][28][29], leads to errors in the identified shear modulus values. Conversely, the proposed virtual fields are capable of identifying shear moduli with very high precision for incompressible and nonhomogeneous solids.…”
Section: Discussionmentioning
confidence: 99%
See 2 more Smart Citations
“…We observed from both simulations and experiments that the proposed virtual fields performed much better in identifying the shear moduli of incompressible solids than the conventional virtual fields. This reveals that the conventional approach, though prevalent in solving parameter identification problems [20,[27][28][29], leads to errors in the identified shear modulus values. Conversely, the proposed virtual fields are capable of identifying shear moduli with very high precision for incompressible and nonhomogeneous solids.…”
Section: Discussionmentioning
confidence: 99%
“…Thereby, it is necessary to make assumptions about the hydrostatic pressure values but if these assumptions are not satisfied, this will induce significant errors on the estimated mechanical properties of solids. To address this issue, a conventional choice of virtual fields which were previously considered [29,30] was based on null displacements on the boundary 0 on    u (4) where  is the boundary of the problem domain. In this case,…”
Section: (1) Virtual Fields Methods With Its Application To Incompressmentioning
confidence: 99%
See 1 more Smart Citation
“…Elastomeric materials, for example, are often accurately modeled as incompressible, and modeling their dynamical response is of current interest in understanding elastomeric actuators . Biological tissues are also often modeled as incompressible elastic or viscoelastic materials, and propagation of shear elastic waves in such media is of interest in a new medical imaging field called elastography . Therefore, it is of interest to have accurate, efficient, and stable methods to compute elastic wave fields in incompressible and nearly incompressible materials.…”
Section: Introductionmentioning
confidence: 99%
“…[1][2][3] Biological tissues are also often modeled as incompressible elastic or viscoelastic materials, and propagation of shear elastic waves in such media is of interest in a new medical imaging field called elastography. [4][5][6][7][8][9][10][11][12][13][14][15] Therefore, it is of interest to have accurate, efficient, and stable methods to compute elastic wave fields in incompressible and nearly incompressible materials. Unfortunately, classical Galerkin discretization struggles with both incompressibility and wave dispersion.…”
Section: Introductionmentioning
confidence: 99%