Inverse analysis for heterogeneous materials and its application to viscoelastic curing polymers

Klinge, Sandra; Steinmann, Paul

doi:10.1007/s00466-015-1126-5

Cited by 9 publications

(4 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, their utilization has continuously been decreasing in the last 10 years. For example, in the case of soft materials (a rather complicated subject) just a few studies using Levenberg-Marquardt or Sequential Quadratic Programming techniques (see, for example, [8,9,10,11,12,13,14,15,16]) have earned at least one or two citations per year.…”

Section: Introduction and Theoretical Backgroundmentioning

confidence: 99%

Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization

2019

View full text Add to dashboard Cite

This study presents a hybrid framework for mechanical identification of materials and structures. The inverse problem is solved by combining experimental measurements performed by optical methods and non-linear optimization using metaheuristic algorithms. In particular, we develop three advanced formulations of Simulated Annealing (SA), Harmony Search (HS) and Big Bang-Big Crunch (BBBC) including enhanced approximate line search and computationally cheap gradient evaluation strategies. The rationale behind the new algorithms—denoted as Hybrid Fast Simulated Annealing (HFSA), Hybrid Fast Harmony Search (HFHS) and Hybrid Fast Big Bang-Big Crunch (HFBBBC)—is to generate high quality trial designs lying on a properly selected set of descent directions. Besides hybridizing SA/HS/BBBC metaheuristic search engines with gradient information and approximate line search, HS and BBBC are also hybridized with an enhanced 1-D probabilistic search derived from SA. The results obtained in three inverse problems regarding composite and transversely isotropic hyperelastic materials/structures with up to 17 unknown properties clearly demonstrate the validity of the proposed approach, which allows to significantly reduce the number of structural analyses with respect to previous SA/HS/BBBC formulations and improves robustness of metaheuristic search engines.

show abstract

Section: Introduction and Theoretical Backgroundmentioning

confidence: 99%

Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization

2019

View full text Add to dashboard Cite

show abstract

“…A similar effort is necessary when a geometrical structure has to be represented. This is the case when a complete geometry of an assembly is investigated [6,7] or if a microstructure is examined, for example, for composites [8][9][10] or foams [11].…”

Section: Introductionmentioning

confidence: 99%

Identifying Elastic and Viscoelastic Material Parameters by Means of a Tikhonov Regularization

Diebels

Scheffer

Schuster

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

For studying the interaction of displacements, stresses, and acting forces for elastic and viscoelastic materials, it is of utmost importance to have a decent mathematical model available. Usually such a model consists of a coupled set of nonlinear differential equations together with appropriate boundary conditions. However, since the different material classes vary significantly with respect to their physical and mechanical behavior, the parameters which appear in these equations are unknown and therefore have to be determined before the equations can be used for further investigations or simulations. It is this very step which is addressed in this article where we consider elastic as well as viscoelastic material behavior. The idea is to compute the parameters as solutions of a minimization problem for Tikhonov functionals. Tikhonov regularization is a well-established solution technique for tackling inverse problems. On the one hand, it assures a computation that is stable with respect to noisy input data, and on the other hand, it involves desired a priori information on the solution. In this article we develop problem adapted Tikhonov functionals and prove that a Tikhonov regularization improves the accuracy especially when the underlying system is ill-conditioned.

show abstract

“…Typically, however, micro-mechanical parameter identification still relies on macroscopic measurements (of displacements and/or forces), that need to be linked to micro-mechanical properties using, e.g., computational homogenization (FE 2 ). This has been theoretically studied by Burczyński and Kuś (2009), Klinge (2012a,b), Klinge and Steinmann (2015), Schmidt et al (2015Schmidt et al ( , 2016, or Beluch and Hatlas (2017). Typically, virtual tests were employed, generating macroscopic measurements by forward evaluation of the multiscale model, assuming validity of (i) the multiscale model (i.e., the homogenization technique) used, and (ii) separation of scales.…”

Section: Introductionmentioning

confidence: 96%

Integrated digital image correlation for micro-mechanical parameter identification in multiscale experiments

Rokoš

Peerlings

Hoefnagels

et al. 2023

International Journal of Solids and Structures

View full text Add to dashboard Cite

Inverse analysis for heterogeneous materials and its application to viscoelastic curing polymers

Cited by 9 publications

References 47 publications

Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization

Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization

Identifying Elastic and Viscoelastic Material Parameters by Means of a Tikhonov Regularization

Integrated digital image correlation for micro-mechanical parameter identification in multiscale experiments

Contact Info

Product

Resources

About