Introduction to Python scripting by reviewing key concepts and through demonstrations. Part 2 focuses on classes and objects plus lists and dictionaries.
Introduction to Python scripting by reviewing key concepts and through demonstrations. Part 3 focuses on modules and packages, with a focus on NumPy and Matplotlib.
In this example, a pile of earth is modeled overlying undulating ground. This tutorial demonstrates how a FLAC3D model mesh can be easily created using DXF geometries and the ZONE DENSIFY command. How to differentiate parts of the model into separate GROUPs using DXF geometries and the GEOMETRY-SPACE range logic is also demonstrated.
Study stress situation for potential continued mining towards greater depths; stress calibration against stress measurements using numerical modeling; and use of calibrated model to study stresses at existing infrastructure, study stresses at potential future haulage level locations, and as input to local models.
The realism of Discrete Fracture Network (DFN) models relies on the spatial organization of fractures, which is not issued by purely stochastic DFN models. In this study, we introduce correlations between fractures by enhancing the genetic model (UFM) of Davy et al. [1] based on simplified concepts of nucleation, growth and arrest with hierarchical rules.
A major use of DFN models for industrial applications is to evaluate permeability and flow structure in hardrock aquifers from geological observations of fracture networks. The relationship between the statistical fracture density distributions and permeability has been extensively studied, but there has been little interest in the spatial structure of DFN models, which is generally assumed to be spatially random (i.e., Poisson). In this paper, we compare the predictions of Poisson DFNs to new DFN models where fractures result from a growth process defined by simplified kinematic rules for nucleation, growth, and fracture arrest.