Among the most commonly applied statistical descriptor functions are n-point probability functions, surface correlation functions, pore-size functions, cluster functions, nearest neighbor functions, linear path functions and others (Matheron 1975; Torquato 2002).
We focus on the construction of SSRVEs based on the volume fraction, lineal-path function and spectral density and check the performance in virtual tests.
The morphological descriptors considered are the void-phase two-point probability function and lineal path function computed with or without the application of periodic boundary conditions (PBC).
So as to examine the properties of the lineal path function during computationally exhaustive compression and reconstruction processes, we start with an acceleration of the lineal path evaluation, namely by porting part of its code to a graphics processing unit using the CUDA (Compute Unified Device Architecture) programming environment.
Definitions of atmospheric transmittance, path function, path radiance, equilibrium radiance, contrast, and contrast transmittance are introduced.
The chord length distribution and the lineal path function are two common measures that contain information on particle (or grain) shape that is independent from spatial distribution.
In this paper, we focus on a lineal path function designed to capture short-range effects and phase connectedness, which can hardly be handled by the commonly used two-point probability function.
TAZ to TAZ, TAZ to Station, and Station to TAZ travel time and distance matrices are generated using GIS shortest path function prior to the mode choice process.
Moreover, this accelerated version of the lineal path function is then compared to the two-point probability function during the compression and reconstruction of two-phase morphologies.
