Learning

Software Tutorials

Fluid Flow through Jointed Rock

As well as flow through joints, 3DEC 5.2 is capable of simulating fluid flow through the blocks or the matrix (i.e., between the joints). It is assumed that the blocks represent a saturated, permeable solid, such as soil or fractured rock mass.

FLAC3D 7.0 Geometry Mesh Tutorial

This tutorial demonstrates how to generate a 3D volume mesh from surface geometry imported from DXF or STL files. Both hexahedral-dominant and tetrahedral meshes can be generated automatically using the "zone generate from-geometry ..." command in FLAC3D 7. The results of various keywords are shown.

Using Python in Itasca Software

Python scripting is built into current versions of FLAC3D, 3DEC, and PFC. This video introduces users of Itasca software to working with Python and FLAC3D, 3DEC, and PFC types (zones, blocks, ball, structural elements, and so on). The Itasca Module, a comparison with FISH scripting, and object-oriented and array-oriented interfaces are reviewed and demonstrated.

Technical Papers

Using MINEDWto simulate pore pressure as input for FLAC3Dand 3DEC

It has become common practice to create a three-dimensional (3-D) geomechanical model for the analysis of rock stability.

Blast Movement Simulation Through a Hybrid Approach of Continuum, Discontinuum, and Machine Learning Modeling

This work presents a hybrid modeling approach to efficiently estimate and optimize rock movement during blasting. A small-scale continuum model simulates early-stage, near-field blasting physics and generates synthetic data to train a machine learning (ML) model. Key parameters such as expanded hole diameter, burden velocity, and gas pressure are obtained through the ML model, which then inform a discontinuum model to predict far-field muckpile formation. The approach captures essential blast physics while significantly accelerating blast design optimization.

Connectivity, permeability, and channeling in randomly distributed and kinematically defined discrete fracture network models

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.

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