A Novel Parameter Identification Toolbox for the Selection of Hyperelastic Constitutive Models from Experimental Data

Date

2017-10-13

Author

Dal, Hüsnü
Açıkgöz, Kemal

Metadata

Show full item record

Item Usage Stats

177
views

0
downloads

This paper presents a novel parameter identification toolbox based on various multi-objective optimization strategies for the selection of the best constitutive models from a given set of homogeneous experiments. The toolbox aims at providing an objective model selection procedure along with the material parameters for the rubber compound at hand. To this end, we utilize the multi-objective optimization using genetic algorithm of MATLAB. For the validation purposes, we use 10 constitutive laws.

URI

http://dx.doi.org/10.18419/opus-9334
https://hdl.handle.net/11511/72684
https://www.gacm2017.uni-stuttgart.de/registration/Upload/ExtendedAbstracts/ExtendedAbstract_0174.pdf

Conference Name

Proceedings of the 7th GACM Colloquium on Computational Mechanics for Young Scientists from Academia and Industry (October 11-13, 2017)

Collections

Department of Civil Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

A Comparative Study on Two Different Direct Parallel Solution Strategies for Large-Scale Problems Bahcecioglu, T.; Ozmen, S.; Kurç, Özgür (2009-04-08) This paper presents a comparative study on two different direct parallel solution strategies for the linear solution of large scale actual finite element models: global and domain-by-domain. The global solution strategy was examined by utilizing the parallel multi-frontal equation solver, MUMPS [1], together with a finite element program. In a similar manner a substructure based parallel solution framework [2] was utilized for investigating the domain-by-domain strategy. Various large-scale structural model...
New design methods for polyhedral linkages Kiper, Gökhan; Söylemez, Eres; Department of Mechanical Engineering (2006) This thesis analyses the existing types of polyhedral linkages and presents new linkage types for resizing polyhedral shapes. First, the transformation characteristics, most specifically, magnification performances of existing polyhedral linkages are given. Then, methods for synthesizing single degree-of-freedom planar polygonal linkages are described. The polygonal linkages synthesized are used as faces of polyhedral linkages. Next, the derivation of some of the existing linkages using the method given is ...
A Novel Alternating Cell Directions Implicit Method for the Solution of Incompressible Navier Stokes Equations on Unstructured Grids Bas, O.; ÇETE, ALİ RUHŞEN; Mengi, S.; Tuncer, İsmail Hakkı; Kaynak, U. (2017-01-01) In this paper, A Novel Alternating Cell Direction Implicit Method (ACDI) is researched which allows implementation of fast line implicit methods on quadrilateral unstructured meshes. In ACDI method, designated alternating cell directions are taken along a series of contiguous cells within the unstructured grid domain and used as implicit lines similar to Line Gauss Seidel Method (LGS). ACDI method applied earlier for the solution of potential flows is extended for the solution of the incompressible Navier-S...
A parylene based double-channel micro-electrophoresis system for rapid mutation detection Külah, Haluk; Erson Bensan, Ayşe Elif; Sert, Cüneyt (null; 2007-10-08) This paper presents the design and implementation of a double-channel micro-electrophoresis system for rapid mutation detection using heteroduplex analysis (HDA) method. The reported system implements two identical parylene channels located side-by-side, and allows HDA to be performed within a short separation length and time.
A Trie-structured Bayesian Model for Unsupervised Morphological Segmentation Kurfalı, Murathan; Ustun, Ahmet; CAN BUĞLALILAR, BURCU (2017-04-23) In this paper, we introduce a trie-structured Bayesian model for unsupervised morphological segmentation. We adopt prior information from different sources in the model. We use neural word embeddings to discover words that are morphologically derived from each other and thereby that are semantically similar. We use letter successor variety counts obtained from tries that are built by neural word embeddings. Our results show that using different information sources such as neural word embeddings and letter s...

Citation Formats

H. Dal and K. Açıkgöz, “A Novel Parameter Identification Toolbox for the Selection of Hyperelastic Constitutive Models from Experimental Data,” presented at the Proceedings of the 7th GACM Colloquium on Computational Mechanics for Young Scientists from Academia and Industry (October 11-13, 2017), Stuttgart, Germany, 2017, Accessed: 00, 2021. [Online]. Available: http://dx.doi.org/10.18419/opus-9334.