research

https://computationalengin.blogspot.com/2020/11/stage-edfminesafh-paris-region-advanced_3.html

Dear iMechanicians. I hope that the following paper is of interest to you. We develop the first phase field formulation for fracture (and fatigue) in Shape Memory Alloys. Its potential is demonstrated by solving a variety of paradigmatic 2D and 3D boundary value problems, from R-curves to fatigue cracking of a NiTi biomedical stent.

M. Simoes, E. Martínez-Pañeda. Phase field modelling of fracture and fatigue in Shape Memory Alloys. Computer Methods in Applied Mechanics and Engineering 373 (2021) 113504

EML Webinar on 4 November 2020 will be given by  Zhigang Suo, Harvard University. Discussion leader: Jimmy Hsia, Nanyang Technological University

Title: Topological adhesion, wet and elastic

Dear colleagues, we have our recent paper, Flower Inspiration: Broad-angle Structural Color through Tunable Hierarchical Wrinkles in Thin Film Multilayers, published on Advanced Functional Materials. The cuticular nano-ridges on certain flower petals can selectively display broad-angle iridescence to en

Title:

Enhancing the bonding of CFRP adhesive bonding through laser-based surface preperation sterategies.

Abstract:

A finite element formulation is presented for a direct approach to model elastoplastic deformation in slender bodies using the special Cosserat rod theory. The direct theory has additional plastic strain and hardening variables, which are functions of just the rod's arc-length, to account for plastic deformation of the rod. Furthermore, the theory assumes the existence of an effective yield function in terms of stress resultants, i.e., force and moment in the cross-section and cross-section averaged hardening parameters.

EML Webinar on 28 October 2020 will be given by Prof. Xiaojing Zheng, Lanzhou University. Discussion leader: Prof. Yujie Wei, Institute of Mechanics, Chinese Academy of Sciences.

Title: Multi-scale and Trans-scale Analysis in Particle-laden Turbulence

https://doi.org/10.1016/j.actamat.2020.10.042

Abstract

Dear iMechanical Community

This paper describes a data-driven approach to predict mechanical properties of auxetic kirigami metamaterials with randomly oriented cuts. The finite element method (FEM)was used to generate datasets, the convolutional neural network (CNN) was introduced to train these data, and an implicit mapping between the input orientations of cuts and the output Young’s modulus and Poisson’s ratio of the kirigami sheets was established. With this input–output relationship in hand, a quick estimation of auxetic behavior of kirigami metamaterials is straightforward.

Dear Colleagues,

This is the preprint of an article on the design of elastomer networks for optimal electromechanical response that will appear in JMPS. We explore how various structural properties of an elastomer network (e.g. density of cross-links, fraction of loose-end monomers, orientation density of chains, etc.) affects both its bulk elastic and dielectric properties, and its performance as an actuator. (https://doi.org/10.1016/j.jmps.2020.104171).

Dear US Academic Researchers,

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Toughening Transparent Ceramics with Bio-inspired Architectures

Zhen Yin (1, 2)

1 McGill University, Canada

2 Max Planck Institute for Intelligent Systems, Germany

My account

A phase-field model based on a modified form of the regularized formulation of Griffith’s fracture theory is presented to investigate intergranular and transgranular crack propagations in polycrystalline brittle materials. Grains and grain boundaries are incorporated in the crack initiation and propagation model based on a phase-field model for grain growth, in which the elastic anisotropy varies based on the grain orientation angle, and the grain boundary energy is related to the misorientation angle of the adjacent grains.

Title: Direct coupling predicting the strength and life of fiberous composite laminates

Speaker: Dr. Ken L Reifsnider

Time: 10/8, 11AM-12PM EST.

Please go to https://www.purdue.edu/cmsc/events/2020-webinars/ to register for this talk.

There is a new paper in predicting battery aging without testing based empirical models.  "Entropy generation model to estimate battery aging"

https://www.sciencedirect.com/science/article/abs/pii/S2352152X20315772?...

https://www.linkedin.com/feed/update/urn:li:activity:6713844072907321344/