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Using Abaqus CZM, How can I get crack length for each increment?

Submitted by ebarbero on
research
#ABAQUS
CZM
2D crack problem

When using CZM for crack propagation, for each CZM element, once the separation delta > delta_c, the adhesive is completely damaged, cannot transmit stress. delta_c is the separation on the right side of the bilinear or trapezoidal models. How can I get the length of the crack for each increment of applied displacement or load? I can see that the totally damage element disappear from the Visualization but I need a better way that gives me crack length, not just a picture that shows the crack longer for each increment.

EML Webinar (Season 2) by Michael Sheetz, on 12 May 2021: Mechanical Stresses Kill Tumor Cells

Submitted by Teng Li on
research
EML
EML Webinar

EML Webinar (Season 2) on 11 May 2021 will be given by Michael Sheetz, University of Texas Medical Branch. Mechanical Stresses Kill Tumor Cells Discussion Leaders: Taher Saif, University of Illinois at Urbana-Champaign and Guy Genin, Washington University, St Louis

Time: 10 am Boston, 3 pm London, 10 pm Beijing on 12 May 2021

Zoom Link: https://ter.ps/EMLWebinarS2

Exact imposition of boundary conditions in physics-informed neural networks

Submitted by N. Sukumar on
research
deep learning
PINN
meshfree methods

We recently proposed a method that uses distance fields to exactly impose boundary conditions in physics-informed neural networks (PINN).  This contribution is available as an arXiv preprint.

Quantitative prediction of rapid solidification by integrated atomistic and phase-field modeling

Submitted by mohsenzaeem on
research
Rapid solidification; additive manufacturing; molecular dynamics; phase field modeling

Dear iMechanica colleagues, I am pleased to share with you our newest paper on qauntitative prediction of rapid solidification. S. Kavousi, B. Novak, D. Moldovan, and M. Asle Zaeem. Quantitative prediction of rapid solidification by integrated atomistic and phase-field modeling. Acta Materialia 211 (2021) 116885 (12 pages).

Abstarct

Postdoctoral Associate Position in the area of machine learning for solid-state batteries

Submitted by Juner Zhu on
research
Postdoc Position

Our team led by Professor Tomasz Wierzbicki at MIT Mechanical Engineering is looking for a highly motivated Postdoctoral Associate in the area of machine learning for solid-state batteries. The candidate is expected to develop machine-learning-based computational tools for the characterization of the interfacial failure in Li-metal all-solid-state batteries. Candidates who have experience in physics-informed machine learning, computational and solid mechanics, multiphysics modeling, and all-solid-state batteries are encouraged to apply by sending a CV to Dr.

Webinar: Efficient High-Fidelity Design and Optimization of Composite Blades/Wings Using VABS

Submitted by Wenbin Yu on
research
VABS;Compsites

On April 27th we will present a webinar hosted by our partner @Altair where we will share efficient high-fidelity design and optimization of composite blades using VABS. Register now at https://bit.ly/3gnQJAN

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