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Development of the cellular automata framework dedicated for metallic materials microstructure evolution models

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The main goal of the paper is to design and implement a framework based on the cellular automata (CA) method, which is dedicated to numerical simulations of microstructure evolution in metallic materials under thermal and mechanical processing. Major assumptions and implementation details of the proposed solution involving classes containing dedicated fields and methods are discussed. Finally, the cellular automata framework (CAF) is tested for selected case studies supported by the Windows Workflow Foundation (WWF) approach. Particular attention is put on modelling simple grain growth, static recrystallization and phase transformation phenomena occurring at the microstructure level. Obtained results of simulations as well as performance characteristics are also presented in the paper. As a result, the CA framework, which supports design of complex algorithms with flexible data flow and reusable components is proposed.
Rocznik
Strony
48--61
Opis fizyczny
Bibliogr. 33 poz., rys., tab., wykr.
Twórcy
autor
  • AGH University of Science and Technology, Department of Applied Computer Science and Modelling, al. Mickiewicza 30, 30-059 Kraków, Poland
autor
  • AGH University of Science and Technology, Department of Applied Computer Science and Modelling, al. Mickiewicza 30, 30-059 Kraków, Poland
  • AGH University of Science and Technology, Department of Applied Computer Science and Modelling, al. Mickiewicza 30, 30-059 Kraków, Poland
autor
  • AGH University of Science and Technology, Department of Applied Computer Science and Modelling, al. Mickiewicza 30, 30-059 Kraków, Poland
Bibliografia
  • [1] (a) F. Grosman, Application of the flow stress function in programmes for computer simulation of plastic working processes, Journal of Materials Processing Technology 64 (1997) 169–180; (b) Y. Estrin, H.A. Mecking, Unified phenomenological description of work hardening and creep based on one- parameter models, Acta Metallurgica 32 (1984) 57–70.
  • [2] L. Madej, P.D. Hodgson, M. Pietrzyk, Development of the multi-scale analysis model to simulate strain localization occurring during material processing, Archives of Computational Methods in Engineering 16 (2009) 287–318.
  • [3] J. Von Neumann, Theory of Self-reproducing Automata, Bamk A.W., University of Illinois, Urbana, 1966.
  • [4] J. Gawad, M. Pietrzyk, Application of CAFE coupled model to description of microstructure development during dynamic recrystallization, Archives of Metallurgy and Materials 52 (2007) 257–266.
  • [5] D.S. Svyetlichnyy, Modelling of the microstructure: from classical cellular automata approach to the frontal one, Computational Materials Science (2010), http://dx.doi.org/ 10.1016/j.commatsci.2010.07.011.
  • [6] R.L. Goetz, V. Seetharaman, Modeling dynamic recrystalization using cellular automata, Scripta Materialia 38 (1998) 405–413.
  • [7] C.H.J. Davies, Growth of nuclei in a cellular automaton simulation of recrystalization, Scripta Materialia 36 (1997) 35–40.
  • [8] A. Burbelko, Modelling of primary and eutectic solidification by using CAFD method, Computer Methods in Materials Science 11 (2011) 128–134.
  • [9] C.A. Gandin, M. Rappaz, A coupled finite element – cellular automaton model for the prediction of dendritic grain structures in solidification processes, Acta Metallurgica 42 (1994) 2233–2246.
  • [10] Y.J. Lan, D.Z. Li, Y.Y. Li, Modelling austenite decomposition into ferrite at different cooling rate in low-carbon steel with cellular automaton method, Acta Materialia 52 (2004) 1721–1729.
  • [11] M. Pietrzyk, L. Madej, L. Rauch, R. Golab, Multiscale modeling of microstructure evolution during laminar cooling of hot rolled DP steels, Archives of Civil and Mechanical Engineering 10 (2010) 57–67.
  • [12] L. Madej, Development of the Modeling Strategy for the Strain Localization Simulation based on the Digital Material Representation, AGH University Press, Krakow, 2010.
  • [13] L. Madej, A. Mrozek, W. Kus, T. Burczynski, M. Pietrzyk, Concurrent and upscaling methods in multi scale modelling – case studies, Computer Methods in Materials Science 8 (2008) 1–15.
  • [14] M. Cannataro, S. Di Gregorio, R. Rongo, W. Spataro, G. Spezzano, D. Talia, A parallel cellular automata environment on multicomputers for computational science, Parallel Computing 21 (1995) 803–823.
  • [15] G. Spezzano, D. Talia, Programming cellular automata algorithms on parallel computers, Future Generation Computer Systems 16 (1999) 203–216.
  • [16] G. Folino, G. Mendicino, A. Senatore, G. Spezzano, S. Straface, A model based on cellular automata for the parallel simulation of 3D unsaturated flow, Parallel Computing 32 (2006) 357–376.
  • [17] (a) A.R. Brodtkorb, C. Dyken, T.R. Hagen, J.M. Hjelmervik, O.O. Storaasli, State-of-the-art in heterogeneous computing, Scientific Programming 18 (2010) 1–33; (b) D. Shuai, Y. Dong, Q. Shuai, A new data clustering approach: generalized cellular automata, Information Systems 32 (2007) 968–977.
  • [18] (a) http://www.parilya.berlios.de; (b) F. Seredynski, P. Bouvry, A.Y. Zomaya, Cellular automata computations and secret key cryptography, Parallel Computing 30 (2004) 753–766.
  • [19] (a) http://www.cs.sjsu.edu/_pearce/modules/lectures/abs/ as/ca.htm; (b) W. Yuan, K.H. Tan, An evacuation model using cellular automata, Physica A 384 (2007) 549–566.
  • [20] http://www.fourmilab.ch/cellab/.
  • [21] http://www.ddlab.com.
  • [22] http://www.cellumat3d.sourceforge.net/.
  • [23] S. Maerivoet, B. De Moor, Cellular automata models of road traffic, Physics Reports 419 (2005) 1–64.
  • [24] http://www.khronos.org/opencl/.
  • [25] K. Kaspersky, Code Optimization: Effective Memory Usage, Bpb Publications, 2004.
  • [26] http://www.msm.agh.edu.pl/other/framework.tar.gz.
  • [27] F.W. Taylor, The Principles of Scientific Management, 1911, Published in Norton Library, 1967.
  • [28] E. Elmroth, F. Hernández, J. Tordsson, Three fundamental dimensions of scientific workflow interoperability: model of computation, language, and execution environment, Future Generation Computer Systems 26 (2010) 245–256.
  • [29] L. Sieradzki, L. Madej, A perceptive comparison of the cellular automata and Monte Carlo techniques in application to static recrystallization modeling in polycrystalline materials, Computational Materials Science 67 (2013) 156–173.
  • [30] R. Golab, D. Bachniak, K. Bzowski, L. Madej, Sensivity analysis of the cellular automata model for austenite–ferrite phase transformation in steels, Applied Mathematics (2013) 1531–1536.
  • [31] L. Madej, L. Rauch, C. Yang, Strain distribution analysis based on the digital material representation, Archives of Metallurgy and Materials 54 (2009) 499–507.
  • [32] M. Sitko, L. Madej, Development of dynamic recrystallization model based on cellular automata approach, Key Engineering Materials (2014).
  • [33] L. Rauch, K. Bzowski, A. Rodzaj, OpenCL implementation of cellular automata finite element (CAFE) method, Lecture Notes in Computer Science 7204 (2012) 381–390.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3eec9722-d070-4235-9a9b-e93e7f424317
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