Lab Home | Phone | Search
Center for Nonlinear Studies  Center for Nonlinear Studies
 Home 
 People 
 Current 
 Affiliates 
 Visitors 
 Students 
 Research 
 ICAM-LANL 
 Publications 
 Conferences 
 Workshops 
 Sponsorship 
 Talks 
 Colloquia 
 Colloquia Archive 
 Seminars 
 Postdoc Seminars Archive 
 Quantum Lunch 
 Quantum Lunch Archive 
 CMS Colloquia 
 Q-Mat Seminars 
 Q-Mat Seminars Archive 
 P/T Colloquia 
 Archive 
 Kac Lectures 
 Kac Fellows 
 Dist. Quant. Lecture 
 Ulam Scholar 
 Colloquia 
 
 Jobs 
 Postdocs 
 CNLS Fellowship Application 
 Students 
 Student Program 
 Visitors 
 Description 
 Past Visitors 
 Services 
 General 
 
 History of CNLS 
 
 Maps, Directions 
 CNLS Office 
 T-Division 
 LANL 
 
Thursday, April 05, 2018
2:00 PM - 3:00 PM
CNLS Conference Room (TA-3, Bldg 1690)

Postdoc Seminar

System Reduction Techniques for Discrete Fracture Networks in Porous Media

Shriram Srinivasan
CNLS/EES-16

Porous media are ubiquitous in various engineering applications. I shall mention some of the applications, and what makes modelling of flow through porous media challenging, first from a theoretical standpoint. After a brief overview of the most important modelling approaches, I shall introduce the most commonly used continuum models for flow through porous media and the problem of computation in highly heterogeneous porous media. Following this, my talk will focus on a particular class of such media, comprised of fractured rock where the fractures form the dominant pathways of flow. The paradigm of Discrete Fracture Networks, which is one way to model these media, will be introduced, along with the concept of topological uncertainty that engenders a daunting computational challenge. However, it is known from experiments that not all fractures in the network participate equally in flow and transport, and there exists a "backbone", which is a subset of the full network that dominates overall behavior. One way to circumvent the computational overhead is then to identify the backbone of the flow. The last part of my talk will be devoted to a discussion of system- reduction techniques based on graph-theory and machine learning that can help identify such "backbones" in these fracture networks.