Szczegóły publikacji
Opis bibliograficzny
Identification of multi-inclusion Statistically Similar Representative Volume Element for advanced high strenght steels by using data farming approach / Łukasz RAUCH, Danuta SZELIGA, Daniel BACHNIAK, Krzysztof BZOWSKI, Renata SŁOTA, Maciej PIETRZYK, Jacek KITOWSKI // Procedia Computer Science [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1877-0509. — 2015 — vol. 51, s. 924–933. — Bibliogr. s. 933, Abstr. — Renata Słota, Jacek Kitowski - druga afiliacja: Department of Computer Science AGH. — ICCS 2015 : International Conference on Computational Science : Computational Science at the Gates of Nature : 1–3 June 2015, Reykjavík, Iceland
Autorzy (7)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 90017 |
|---|---|
| Data dodania do BaDAP | 2015-06-24 |
| Tekst źródłowy | URL |
| DOI | 10.1016/j.procs.2015.05.227 |
| Rok publikacji | 2015 |
| Typ publikacji | referat w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | Procedia Computer Science |
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 calculations 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. © The Authors. Published by Elsevier B.V.
