Error of the Network Approximation for Densely Packed Composites with Irregular Geometry

Discrete Network Approximation for Highly-packed Composites with Irregular Geometry in Three Dimensions

We introduce a discrete network approximation to the problem of the effective conductivity of the high contrast, highly packed composites in three dimensions. The inclusions are irregularly (randomly) distributed in a hosting medium, so that a significant fraction of them may not participate in the conducting spanning cluster. For this class of spacial arrays of inclusions we derive a discrete ...

DISCRETE NETWORK APPROXIMATION for HIGHLY-PACKED HIGH CONTRAST COMPOSITES with IRREGULAR GEOMETRY in 3D YULIYA GORB

Combination of Approximation and Simulation Approaches for Distribution Functions in Stochastic Networks

This paper deals with the fundamental problem of estimating the distribution function (df) of the duration of the longest path in the stochastic activity network such as PERT network. First a technique is introduced to reduce variance in Conditional Monte Carlo Sampling (CMCS). Second, based on this technique a new procedure is developed for CMCS. Third, a combined approach of simulation and ap...

A Discrete Singular Convolution Method for the Seepage Analysis in Porous Media with Irregular Geometry

A novel discrete singular convolution (DSC)  formulation  is  presented for the seepage analysis in irregular geometric porous media. The DSC is a new promising numerical approach which has been recently applied to solve several engineering problems. For a medium with regular geometry, realizing of the DSC for the seepage analysis is straight forward. But DSC implementation for a medium with ir...

A Discrete Network Approximation for Effective Conductivity of Non-ohmic High-contrast Composites

We develop a discrete network approximation to effective conductivity of high contrast, highly packed particulate composites with nonlinear constituent relations. The key tool is the perforated medium approach, which provides a simple mathematical justification of the discrete network approximation by variational techniques.