Advanced Computer Simulation Approaches for Soft Matter by Christian Holm, Kurt Kremer, S. Auer, K. Binder, J.G. Curro,

By Christian Holm, Kurt Kremer, S. Auer, K. Binder, J.G. Curro, D. Frenkel, G.S. Grest, D.R. Heine, P.H. Hünenberger, L.G. MacDowell, M. Müller, P. Virnau

Soft subject technology is these days an acronym for an more and more vital

class of fabrics, which levels from polymers, liquid crystals,

colloids as much as complicated macromolecular assemblies, overlaying sizes from

the nanoscale up the microscale. computing device simulations have confirmed as an

indispensable, if now not the main strong, software to appreciate houses

of those fabrics and hyperlink theoretical versions to experiments. during this

first quantity of a small sequence famous leaders of the sector overview

advanced subject matters and supply serious perception into the cutting-edge

methods and medical questions of this energetic area of soppy

condensed topic research.

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Extra info for Advanced Computer Simulation Approaches for Soft Matter Sciences I

Sample text

The proportions of the kinetic energy that are bulk and relative motion. 25 (Zohdi [212]). 1. 3 illustrate the computational results. The type of motion, characterized by the proportions of bulk and rela- ✐ ✐ ✐ ✐ ✐ ✐ ✐ 34 05 book 2007/5/15 page 34 ✐ Chapter 4. 3. The total kinetic energy in the system per unit mass. 25 (Zohdi [212]). 3). 2, as the near-field strength is increased, the component of the kinetic energy corresponding to the relative motion does not decay and actually becomes dominant with time.

Also, such solvers are highly advantageous, since solutions to previous time steps can be used as the first guess to accelerate the solution procedure. Remark. A recursive iterative scheme of Jacobi type, where the updates are made only after one complete system iteration, was illustrated here only for algebraic simplicity. The Jacobi method is easier to address theoretically, while the Gauss–Seidel method, which involves immediately using the most current values, when they become available, is usually used at the implementation level.

In other words, the calculations for each particle are uncoupled, with the updates only coming afterward. Gauss–Seidel, since it requires the most current updates, couples the particle calculations immediately. However, these methods can be combined to create hybrid approaches whereby the entire particulate flow is partitioned into groups and within each group a Gauss–Seidel method is applied. In other words, for a group, the positions of any particles from outside are initially frozen, as far as calculations involving members of the group are concerned.

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