Creating Groups Interactively and Automatically using the Model Pane

In this tutorial, we review how to automatically skin models, identify and group zone faces, and interactively select and group zones and zone faces. This tutorial also illustrates using the Model Pane to interactively add a shell structural element along a tunnel.

FLAC3D 7.0 Structured Mesh Tutorial

A tutorial showing how to create a structured mesh in FLAC3D 7.0 using the extruder pane.

FLAC3D 6.0 Built-in Model Generation Tools and Workflow

Building Blocks works seamlessly with the FLAC3D 6.0 extruder tool and new Model Pane. Building Blocks includes a library of model primates and users can also add and load their own building block sets.

Tunneldrivning i heterogena förhållanden

InledningProblem: Brist på erfarenhet av tunneldrivning i heterogena förhållanden med konventionell uttagsteknik (borrning och sprängning).

Mål: Fördjupa kunskapen och förståelse av brott och deformationsmönster vid dessa förhållanden.

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.

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.

  • ITASCA Strengthens North American Delivery of Integrated Geomechanics and Hydrogeology Solutions Drawing on decades of geomechanical and hydrogeological expertise, ITASCA has announced the formation of ITASCA...
  • Itasca has announced the release of FLAC2D v9 Itasca has announced the release of FLAC2D v9, revolutionizing the way we analyze and predict...
  • 6th Itasca Symposium on Applied Numerical Modeling The next Itasca Symposium will take place June 3 - 6, 2024, in Toronto, Canada....