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 12
... mesoscopic level . This trend in finite element simulations points from the macroscopic to the mesoscopic scale . For the Potts model , which has its roots in the stochastic Metropolis Monte Carlo method , the reverse applies . By ...
... mesoscopic level . This trend in finite element simulations points from the macroscopic to the mesoscopic scale . For the Potts model , which has its roots in the stochastic Metropolis Monte Carlo method , the reverse applies . By ...
Page 23
... mesoscopic many - body problems by solving the algebraic , differential , or integral expressions that reflect the ... mesoscopic , microscopic , and nanoscopic models . The term macroscopic refers to the sample geometry , mesoscopic to ...
... mesoscopic many - body problems by solving the algebraic , differential , or integral expressions that reflect the ... mesoscopic , microscopic , and nanoscopic models . The term macroscopic refers to the sample geometry , mesoscopic to ...
Page 307
... mesoscopic level , and for the mesoscopic - macroscopic level . The difference in scale that exists between the upper and lower bounds usually de- termines the storage and performance capabilities required in microstructure simulations ...
... mesoscopic level , and for the mesoscopic - macroscopic level . The difference in scale that exists between the upper and lower bounds usually de- termines the storage and performance capabilities required in microstructure simulations ...
Contents
Material Constants | 1 |
Molecular Dynamics | 7 |
GinzburgLandauType Phase Field Kinetic Models | 10 |
Copyright | |
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Common terms and phrases
Acta Metall algorithm approach approximate atoms automaton Burgers vector Cahn calculated cell cellular automata Chapter Chen classical coefficients components computational materials science coordinates crystal deformation density derivatives described deterministic differential equations diffusion discrete dislocation dynamics dislocation line dislocation segments elastic electron ensemble equations of motion equilibrium evolution Figure finite difference finite difference method finite element method force function gradient tensor grain boundary grain growth homogeneous Houtte independent variables integral interaction interface isotropic Khachaturyan kinetic Kocks Kubin lattice defects linear macroscopic matrix mechanics microstructure simulation molecular dynamics Monte Carlo methods neighboring nodes nucleation orientation parameters partial differential particle phase space phenomenological Phys physical plastic polycrystal potential Potts model predictions problem Raabe recrystallization referred Rollett Rönnpagel sample scale shear slip systems solution solving spatial spin Srolovitz statistical stochastic strain rate stress structure subgrain Taylor techniques texture theory three-dimensional transformation two-dimensional typically values vector velocity vertex models