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Vibrational properties of nanographene

100%
Singh S. K., Peeters F. M.
Nanoscale Systems: Mathematical Modeling, Theory and Applications
|
2013
|
tom 2
10-29
EN
The eigenmodes and the vibrational density of states of the ground state configuration of graphene clusters are calculated using atomistic simulations. The modified Brenner potential is used to describe the carbon-carbon interaction and carbon-hydrogen interaction in case of H-passivated edges. For a given configuration of the C-atoms the eigenvectors and eigenfrequencies of the normal modes are obtained after diagonalisation of the dynamical matrix whose elements are the second derivative of the potential energy. The compressional and shear properties are obtained from the divergence and rotation of the velocity field. For symmetric and defective clusters with pentagon arrangement on the edge, the highest frequency modes are shear modes. The specific heat of the clusters is also calculated within the harmonic approximation and the convergence to the result for bulk graphene is investigated.
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Influence sampling of trailing variables of dynamical systems

100%
Krause P.
Mathematics of Climate and Weather Forecasting
|
2017
|
tom 3
|
nr 1
51-63
EN
For dealing with dynamical instability in predictions, numerical models should be provided with accurate initial values on the attractor of the dynamical system they generate. A discrete control scheme is presented to this end for trailing variables of an evolutive system of ordinary differential equations. The Influence Sampling (IS) scheme adapts sample values of the trailing variables to input values of the determining variables in the attractor. The optimal IS scheme has affordable cost for large systems. In discrete data assimilation runs conducted with the Lorenz 1963 equations and a nonautonomous perturbation of the Lorenz equations whose dynamics shows on-off intermittency the optimal IS was compared to the straightforward insertion method and the Ensemble Kalman Filter (EnKF). With these unstable systems the optimal IS increases by one order of magnitude the maximum spacing between insertion times that the insertion method can handle and performs comparably to the EnKF when the EnKF converges. While the EnKF converges for sample sizes greater than or equal to 10, the optimal IS scheme does so fromsample size 1. This occurs because the optimal IS scheme stabilizes the individual paths of the Lorenz 1963 equations within data assimilation processes.
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