23,476 research outputs found
Aspects of Unstructured Grids and Finite-Volume Solvers for the Euler and Navier-Stokes Equations
One of the major achievements in engineering science has been the development of computer algorithms for solving nonlinear differential equations such as the Navier-Stokes equations. In the past, limited computer resources have motivated the development of efficient numerical schemes in computational fluid dynamics (CFD) utilizing structured meshes. The use of structured meshes greatly simplifies the implementation of CFD algorithms on conventional computers. Unstructured grids on the other hand offer an alternative to modeling complex geometries. Unstructured meshes have irregular connectivity and usually contain combinations of triangles, quadrilaterals, tetrahedra, and hexahedra. The generation and use of unstructured grids poses new challenges in CFD. The purpose of this note is to present recent developments in the unstructured grid generation and flow solution technology
Gossip on Weighted Networks
We investigate how suitable a weighted network is for gossip spreading. The
proposed model is based on the gossip spreading model introduced by Lind et.al.
on unweighted networks. Weight represents "friendship." Potential spreader
prefers not to spread if the victim of gossip is a "close friend". Gossip
spreading is related to the triangles and cascades of triangles. It gives more
insight about the structure of a network.
We analyze gossip spreading on real weighted networks of human interactions.
6 co-occurrence and 7 social pattern networks are investigated. Gossip
propagation is found to be a good parameter to distinguish co-occurrence and
social pattern networks. As a comparison some miscellaneous networks and
computer generated networks based on ER, BA, WS models are also investigated.
They are found to be quite different than the human interaction networks.Comment: 8 pages, 4 figures, 1 tabl
A new measure for community structures through indirect social connections
Based on an expert systems approach, the issue of community detection can be
conceptualized as a clustering model for networks. Building upon this further,
community structure can be measured through a clustering coefficient, which is
generated from the number of existing triangles around the nodes over the
number of triangles that can be hypothetically constructed. This paper provides
a new definition of the clustering coefficient for weighted networks under a
generalized definition of triangles. Specifically, a novel concept of triangles
is introduced, based on the assumption that, should the aggregate weight of two
arcs be strong enough, a link between the uncommon nodes can be induced. Beyond
the intuitive meaning of such generalized triangles in the social context, we
also explore the usefulness of them for gaining insights into the topological
structure of the underlying network. Empirical experiments on the standard
networks of 500 commercial US airports and on the nervous system of the
Caenorhabditis elegans support the theoretical framework and allow a comparison
between our proposal and the standard definition of clustering coefficient
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