Functional gradient effects explain the low transverse shear modulus in spruce – Full-field strain data and a micromechanics model

2009 ◽  
Vol 69 (14) ◽  
pp. 2491-2496 ◽  
Author(s):  
B.I. Hassel ◽  
C.S. Modén ◽  
L.A. Berglund
Keyword(s):  
Shear Modulus ◽  
Transverse Shear ◽  
Field Strain ◽  
Full Field ◽  
Micromechanics Model ◽  
Functional Gradient ◽  
Strain Data ◽  
Gradient Effects

scholarly journals Image processing of full-field strain data and its use in model updating

2011 ◽  
Vol 305 ◽  
pp. 012011 ◽  
Author(s):  
W Wang ◽  
J E Mottershead ◽  
C M Sebastian ◽  
E A Patterson
Keyword(s):  
Image Processing ◽  
Model Updating ◽  
Field Strain ◽  
Full Field ◽  
Strain Data

scholarly journals Shape features and finite element model updating from full-field strain data

2011 ◽  
Vol 48 (11-12) ◽  
pp. 1644-1657 ◽  
Author(s):  
Weizhuo Wang ◽  
John E. Mottershead ◽  
Christopher M. Sebastian ◽  
Eann A. Patterson
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Model Updating ◽  
Element Model ◽  
Field Strain ◽  
Finite Element Model Updating ◽  
Shape Features ◽  
Full Field ◽  
Strain Data

scholarly journals A probabilistic approach with built-in uncertainty quantification for the calibration of a superelastic constitutive model from full-field strain data

2021 ◽  
Vol 192 ◽  
pp. 110357
Author(s):  
Harshad M. Paranjape ◽  
Kenneth I. Aycock ◽  
Craig Bonsignore ◽  
Jason D. Weaver ◽  
Brent A. Craven ◽  
...  
Keyword(s):  
Constitutive Model ◽  
Uncertainty Quantification ◽  
Probabilistic Approach ◽  
Field Strain ◽  
Full Field ◽  
Strain Data

Modeling relationships for field strain data under thermal effects using functional data analysis

Measurement ◽  
2021 ◽  
Vol 177 ◽  
pp. 109279
Author(s):  
Huachen Jiang ◽  
Chunfeng Wan ◽  
Kang Yang ◽  
Youliang Ding ◽  
Songtao Xue
Keyword(s):  
Data Analysis ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Thermal Effects ◽  
Field Strain ◽  
Strain Data

scholarly journals Material parameter identification using finite elements with time-adaptive higher-order time integration and experimental full-field strain information

2021 ◽  
Author(s):  
Stefan Hartmann ◽  
Rose Rogin Gilbert
Keyword(s):  
Experimental Data ◽  
Finite Elements ◽  
Parameter Identification ◽  
Material Parameter ◽  
Measurement Data ◽  
Least Square ◽  
Image Correlation ◽  
Field Strain ◽  
Material Parameter Identification ◽  
Full Field

AbstractIn this article, we follow a thorough matrix presentation of material parameter identification using a least-square approach, where the model is given by non-linear finite elements, and the experimental data is provided by both force data as well as full-field strain measurement data based on digital image correlation. First, the rigorous concept of semi-discretization for the direct problem is chosen, where—in the first step—the spatial discretization yields a large system of differential-algebraic equation (DAE-system). This is solved using a time-adaptive, high-order, singly diagonally-implicit Runge–Kutta method. Second, to study the fully analytical versus fully numerical determination of the sensitivities, required in a gradient-based optimization scheme, the force determination using the Lagrange-multiplier method and the strain computation must be provided explicitly. The consideration of the strains is necessary to circumvent the influence of rigid body motions occurring in the experimental data. This is done by applying an external strain determination tool which is based on the nodal displacements of the finite element program. Third, we apply the concept of local identifiability on the entire parameter identification procedure and show its influence on the choice of the parameters of the rate-type constitutive model. As a test example, a finite strain viscoelasticity model and biaxial tensile tests applied to a rubber-like material are chosen.

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