Direct identification of nonlinear damage behavior of composite materials using the constitutive equation gap method

Merzouki, Tarek; Nouri, H.; Roger, F.

doi:10.1016/j.ijmecsci.2014.10.002

Cited by 9 publications

(6 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There has also been extensive previous work concerned with the use of empirical data for parameter identification in prespecified material models, or for automating the calibration of the models. For instance, the Errorin-Constitutive-Equations (ECE) method is an inverse method for the identification of material parameters such as the Youngs modulus of an elastic material [13,6,12,7,21,35,5,24,20,23]. While such approaches are efficient and reliable for their intended application, namely, the identification of material parameters, they differ from Data Driven Computing in that, while material identification schemes aim to determine the parameters of a prespecified material law from experimental data, Data Driven Computing dispenses with material models altogether and uses fundamental material data directly in the formulation of initial-boundary-value problems and attendant calculations thereof.…”

Section: Materials Identificationmentioning

confidence: 99%

Data Driven Computing with noisy material data sets

Kirchdoerfer

Ortiz

2017

Computer Methods in Applied Mechanics and Engineering

179

181

View full text Add to dashboard Cite

We formulate a Data Driven Computing paradigm, termed maxent Data Driven Computing, that generalizes distance-minimizing Data Driven Computing and is robust with respect to outliers. Robustness is achieved by means of clustering analysis. Specifically, we assign data points a variable relevance depending on distance to the solution and on maximum-entropy estimation. The resulting scheme consists of the minimization of a suitably-defined free energy over phase space subject to compatibility and equilibrium constraints. Distanceminimizing Data Driven schemes are recovered in the limit of zero temperature. We present selected numerical tests that establish the convergence properties of the max-ent Data Driven solvers and solutions.

show abstract

Section: Materials Identificationmentioning

confidence: 99%

Data Driven Computing with noisy material data sets

Kirchdoerfer

Ortiz

2017

Computer Methods in Applied Mechanics and Engineering

179

181

View full text Add to dashboard Cite

show abstract

“…More recently, Guchhait and Banerjee [40] extended it for anisotropic elasticity. Moreover, it has also been used in the field of plasticity [41] and damage [42].…”

Section: A C C E P T E D Mmentioning

confidence: 99%

Comparison of inverse identification strategies for constitutive mechanical models using full-field measurements

Martins

Andrade‐Campos

Thuillier

2018

International Journal of Mechanical Sciences

View full text Add to dashboard Cite

“…There has also been extensive previous work concerned with the use of empirical data for parameter identification in prespecified material models, or for automating the calibration of the models. For instance, the Errorin-Constitutive-Equations (ECE) method is an inverse method for the identification of material parameters such as the Youngs modulus of an elastic material [17,11,16,12,25,39,8,28,24,26]. While such approaches are efficient and reliable for their intended application, namely, the identification of material parameters, they differ from Data Driven Computing, as understood here, in that, while material identification schemes aim to determine the parameters of a prespecified material law from experimental data, Data Driven Computing dispenses with material models altogether and uses fundamental material data directly in the formulation of initial-boundary-value problems and attendant calculations thereof.…”

Section: Introductionmentioning

confidence: 99%

Data‐driven computing in dynamics

Kirchdoerfer

Ortiz

2017

Numerical Meth Engineering

157

108

View full text Add to dashboard Cite

SUMMARYWe formulate extensions to Data Driven Computing for both distance minimizing and entropy maximizing schemes to incorporate time integration. Previous works focused on formulating both types of solvers in the presence of static equilibrium constraints. Here formulations assign data points a variable relevance depending on distance to the solution and on maximum-entropy weighting, with distance minimizing schemes discussed as a special case. The resulting schemes consist of the minimization of a suitablydefined free energy over phase space subject to compatibility and a time-discretized momentum conservation constraint. We present selected numerical tests that establish the convergence properties of both types of Data Driven solvers and solutions.

show abstract

Direct identification of nonlinear damage behavior of composite materials using the constitutive equation gap method

Cited by 9 publications

References 44 publications

Data Driven Computing with noisy material data sets

Data Driven Computing with noisy material data sets

Comparison of inverse identification strategies for constitutive mechanical models using full-field measurements

Data‐driven computing in dynamics

Contact Info

Product

Resources

About