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fracture

Revisiting Nucleation in the Phase-Field Approach to Brittle Fracture

Submitted by Oscar Lopez-Pamies on

Twenty years in since their introduction, it is now plain that the regularized formulations dubbed as phase-field of the variational theory of brittle fracture of Francfort and Marigo (1998) provide a powerful macroscopic theory to describe and predict the propagation of cracks in linear elastic brittle materials under arbitrary quasistatic loading conditions. Over the past ten years, the ability of the phase-field approach to also possibly describe and predict crack nucleation has been under intense investigation.

Computational Mechanics Postdoctoral Research Scientist Position at Columbia University

Submitted by WaiChing Sun on

Dear colleagues, 

There is a new opening for one postdoc position, to be filled immediately, in my research group in the Department of Civil Engineering and Engineering Mechanics at Columbia University. We are looking for postdocs in the broad area of computational mechanics. Candidates should have expertise in modeling dynamic responses of path-dependent materials. Our project is specifically focused on applications of machine learning (reinforcement learning, graph embedding) for computational plasticity and damage. 

Intrinsic-to-extrinsic transition in fracture toughness through structural design: A lesson from nature

Submitted by Bin Liu on

Published in Extreme Mechanics Letters: https://doi.org/10.1016/j.eml.2020.100685. Catastrophic failure of materials and structures due to unstable crack growth could be prevented if fracture toughness could be enhanced at will through structural design, but how can this be possible if fracture toughness is a material constant related to energy dissipation in the vicinity of a propagating crack tip.

Machine Learning for Fracture Mechanics

Submitted by christos_athanasiou on

Safer batteries, more efficient gas-turbine engines and solar cells, all require better-engineered nanocomposite materials. There is a limitation though -- how to investigate the fracture mechanics of these materials? Machine learning can help us overcome this limitation. Read more in our just-published paper: https://lnkd.in/e7dPBtx

Characterizing fracture stress of defective graphene samples using shallow and deep artificial neural networks

Submitted by Nuwan Dewapriya on

Abstract: Advanced machine learning methods could be useful to obtain novel insights into some challenging nanomechanical problems. In this work, we employed artificial neural networks to predict the fracture stress of defective graphene samples. First, shallow neural networks were used to predict the fracture stress, which depends on the temperature, vacancy concentration, strain rate, and loading direction.

Mode II fracture of an elastic-plastic sandwich layer

Submitted by Emilio Martíne… on

Dear iMechanicians,

Please find below a paper, co-authored with Ivan Cuesta and Norman Fleck, on the mode II fracture of elastic-plastic sandwich layers. The paper is part of the special issue in the Journal of Applied Mechanics dedicated to John Hutchinson's 80th anniversary and the Century Fracture Mechanics Summit.