11,214 research outputs found
Dirichlet-Neumann and Neumann-Neumann Waveform Relaxation for the Wave Equation
We present a Waveform Relaxation (WR) version of the Dirichlet-Neumann and
Neumann-Neumann algorithms for the wave equation in space time. Each method is
based on a non-overlapping spatial domain decomposition, and the iteration
involves subdomain solves in space time with corresponding interface condition,
followed by a correction step. Using a Laplace transform argument, for a
particular relaxation parameter, we prove convergence of both algorithms in a
finite number of steps for finite time intervals. The number of steps depends
on the size of the subdomains and the time window length on which the
algorithms are employed. We illustrate the performance of the algorithms with
numerical results, and also show a comparison with classical and optimized
Schwarz WR methods.Comment: 8 pages, 6 figures, presented in 22nd International conference on
Domain Decomposition Methods, to appear in Domain Decomposition in Science
and Engineering XXII, LNCSE, Springer-Verlag 201
Modified HLLC-VOF solver for incompressible two-phase fluid flows
A modified HLLC-type contact preserving Riemann solver for incompressible
two-phase flows using the artificial compressibility formulation is presented.
Here, the density is omitted from the pressure evolution equation. Also, while
calculating the eigenvalues and eigenvectors, the variations of the volume
fraction is taken into account. Hence, the equations for the intermediate
states and the intermediate wave speed are different from the previous HLLC-VOF
formulation [Bhat S P and Mandal J C, J. Comput. Phys. 379 (2019), pp.
173-191]. Additionally, an interface compression algorithm is used in tandem to
ensure sharp interfaces. The modified Riemann solver is found to be robust
compared to the previous HLLC-VOF solver, and the results produced are superior
compared to non-contact preserving solver. Several test problems in two- and
three-dimensions are solved to evaluate the efficacy of the solver on
structured and unstructured meshes
Distinguishing Posed and Spontaneous Smiles by Facial Dynamics
Smile is one of the key elements in identifying emotions and present state of
mind of an individual. In this work, we propose a cluster of approaches to
classify posed and spontaneous smiles using deep convolutional neural network
(CNN) face features, local phase quantization (LPQ), dense optical flow and
histogram of gradient (HOG). Eulerian Video Magnification (EVM) is used for
micro-expression smile amplification along with three normalization procedures
for distinguishing posed and spontaneous smiles. Although the deep CNN face
model is trained with large number of face images, HOG features outperforms
this model for overall face smile classification task. Using EVM to amplify
micro-expressions did not have a significant impact on classification accuracy,
while the normalizing facial features improved classification accuracy. Unlike
many manual or semi-automatic methodologies, our approach aims to automatically
classify all smiles into either `spontaneous' or `posed' categories, by using
support vector machines (SVM). Experimental results on large UvA-NEMO smile
database show promising results as compared to other relevant methods.Comment: 16 pages, 8 figures, ACCV 2016, Second Workshop on Spontaneous Facial
Behavior Analysi
Chiral Properties of QCD Vacuum in Magnetars- A Nambu-Jona-Lasinio Model with Semi-Classical Approximation
The breaking of chiral symmetry of light quarks at zero temperature in
presence of strong quantizing magnetic fiels is studied using
Nambu-Jona-Lasinio (NJL) model with Thomas-Fermi type semi-classical formalism.
It is found that the dynamically generated light quark mass can never become
zero if the Landau levels are populated and the mass increases with the
increase of magnetic field strength.Comment: REVTEX 11 Pages, One .eps figure (included
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