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Identification of Multi-inclusion Statistically Similar Representative Volume Element for Advanced High Strength Steels by Using Data Farming Approach
- Source :
- ICCS
- Publication Year :
- 2015
- Publisher :
- Elsevier BV, 2015.
-
Abstract
- Statistically Similar Representative Volume Element (SSRVE) is used to simplify computational domain for microstructure representation of material in multiscale modelling. The procedure of SSRVE creation is based on optimization loop which allows to find the highest similarity between SSRVE and an original material microstructure. The objective function in this optimization is built upon computationally intensive numerical methods, including simulations of virtual material deformation, which is very time consuming. To avoid such long lasting calcu- lations we propose to use the data farming approach to identification of SSRVE for Advanced High Strength Steels (AHSS) characterized by multiphase microstructure. The optimization method is based on a nature inspired approach which facilitates distribution and parallelization. The concept of SSRVE creation as well as the software architecture of the proposed solution is described in the paper in details. It is followed by examples of the results obtained for the identification of SSRVE parameters for DP steels which are widely exploited in modern automotive industry. Possible directions for further development as well as possible industrial applications are described in the conclusions.
- Subjects :
- AHSS
Similarity (geometry)
Computer science
Numerical analysis
Microstructure
Computational science
Domain (software engineering)
Multiscale modelling
Identification (information)
HPC
Representative elementary volume
General Earth and Planetary Sciences
Material Deformation
Representation (mathematics)
SSRVE
Simulation
Data farming
General Environmental Science
Subjects
Details
- ISSN :
- 18770509
- Volume :
- 51
- Database :
- OpenAIRE
- Journal :
- Procedia Computer Science
- Accession number :
- edsair.doi.dedup.....934ea74bbe3aebcd2eaecd89817c7b77