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 66
... described by a random walk . However , advanced spatial Monte Carlo simulations can also describe diffusion processes and percolation problems where the successive events are not uncorrelated and / or where the changes in direction are ...
... described by a random walk . However , advanced spatial Monte Carlo simulations can also describe diffusion processes and percolation problems where the successive events are not uncorrelated and / or where the changes in direction are ...
Page 107
... described using the relaxation of single atoms ( Figure 7.4 ) . The atomic interaction is described in terms of an embedded atom potential . The coupling between both regimes is provided by a transition layer . Similar simulations ...
... described using the relaxation of single atoms ( Figure 7.4 ) . The atomic interaction is described in terms of an embedded atom potential . The coupling between both regimes is provided by a transition layer . Similar simulations ...
Page 148
... described by two unit vectors e and a normal to n . The unit vector m is the angular derivative of t ( Asaro et al . 1973 ; Asaro and Barnett 1974 ) . t = = e cos ( 0 ) + a sin ( 0 ) m = dt de = sin ( 0 ) + a cos ( 0 ) ( 9.131 ) Since m ...
... described by two unit vectors e and a normal to n . The unit vector m is the angular derivative of t ( Asaro et al . 1973 ; Asaro and Barnett 1974 ) . t = = e cos ( 0 ) + a sin ( 0 ) m = dt de = sin ( 0 ) + a cos ( 0 ) ( 9.131 ) Since m ...
Contents
Material Constants | 1 |
Fundamentals and Solution of Differential Equations | 3 |
Molecular Dynamics | 7 |
Copyright | |
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algorithm analytical approach approximate atomistic atoms automaton average boundary conditions calculated cell cellular automata Chapter classical coefficients components computational materials science continuum coordinates crystal plasticity deformation dependent derivatives described deterministic diffusion discrete dislocation dynamics displacement elastic electron ensemble equations of motion equilibrium Euler method Figure finite difference method finite element method formulation free energy gradient grain boundary grain growth Hamiltonian independent variables initial-value integral interaction interface Ising model isotropic Khachaturyan kinetic Kocks Kubin large number lattice defects linear macroscopic matrix mechanics mesoscale mesoscopic Metall Metropolis Monte Carlo microstructure evolution microstructure simulation molecular dynamics Monte Carlo methods nodes nucleation orientation parameters particle phase field phase space phenomenological Phys physical polycrystal polynomial Potts model predictions problem Raabe random numbers recrystallization referred Rönnpagel sampling scale solution solving spatial spin Srolovitz statistical stochastic strain rate stress structure techniques tensor texture theory thermodynamic two-dimensional typically Ui+1 values vector velocity volume