Computational Materials Science: The Simulation of Materials, Microstructures and PropertiesModeling and simulation play an ever increasing role in the development and optimization of materials. Computational Materials Science presents the most important approaches in this new interdisciplinary field of materials science and engineering. The reader will learn to assess which numerical method is appropriate for performing simulations at the various microstructural levels and how they can be coupled. This book addresses graduate students and professionals in materials science and engineering as well as materials-oriented physicists and mechanical engineers. |
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Page xii
... Cellular Automata 199 201 11.1 Introduction and Fundamentals 11.2 Versatility of Cellular Automata in Materials Science 11.3 Formal Description of Cellular Automata 11.4 Probabilistic Cellular Automata . 201 204 205 208 • 11.5 Lattice Gas ...
... Cellular Automata 199 201 11.1 Introduction and Fundamentals 11.2 Versatility of Cellular Automata in Materials Science 11.3 Formal Description of Cellular Automata 11.4 Probabilistic Cellular Automata . 201 204 205 208 • 11.5 Lattice Gas ...
Page 204
... Cellular automata reveal a certain similarity to kinetic Monte Carlo integration ap- proaches ( Chapters 6 and 12 ) . While ... Cellular Automata in Materials Science Formal Description of Cellular Automata Probabilistic Cellular Automata.
... Cellular automata reveal a certain similarity to kinetic Monte Carlo integration ap- proaches ( Chapters 6 and 12 ) . While ... Cellular Automata in Materials Science Formal Description of Cellular Automata Probabilistic Cellular Automata.
Page 209
... cellular automata can use both totalistic and discrete transformation rules . Although probabilistic cellular automata reveal a certain resemblance to the Metropo- lis Monte Carlo algorithm , two main differences occur . First , in ...
... cellular automata can use both totalistic and discrete transformation rules . Although probabilistic cellular automata reveal a certain resemblance to the Metropo- lis Monte Carlo algorithm , two main differences occur . First , in ...
Contents
Material Constants | 1 |
Molecular Dynamics | 7 |
GinzburgLandauType Phase Field Kinetic Models | 10 |
Copyright | |
17 other sections not shown
Common terms and phrases
according allows amounts applications approach approximate assumed atoms average boundary calculated cell cellular Chapter classical components concentration concept considered constant coordinates crystal defects defined density depends derivatives described differential equations diffusion discrete dislocation displacement distribution elastic electron energy ensemble equilibrium et al evolution examples expression field Figure finite difference finite element force formulation function given grain grain boundary growth independent instance integral interaction interface introduced kinetic lattice macroscopic materials science matrix means mechanics Metall method microstructure models molecular dynamics Monte Carlo motion obtained orientation original parameters particle phase physical plasticity position possible potential predictions problem properties Raabe random recrystallization referred represents rules sampling scale segments simple simulations solution solving space spatial statistical step strain stress structure techniques tensor theory transformation typically usually values variables various vector volume written