Have you ever wondered that you can visually see the stress patterns live in a loaded structure! I am not speaking about the contours that you see in commercial finite element software #fea .
For e.g. see the colorful image attached. The fringe patterns are the live stress field around a crack in a loaded structure.
Find more answers on how to interpret these images using state of the art image processing technology in the newly published book:
Towards the development of reusable space systems, high emissivity coatings on fibrous ceramic substrates with improved thermal resistance are needed. In this study WSi2–MoSi2–Si–SiB6-borosilicate glass coatings were prepared on fibrous ZrO2 by slurry dipping and subsequent high temperature rapid sintering. A coating with 20 wt% WSi2 and 50 wt% MoSi2 presents optimal thermal stability with only 10.06 mg/cm2 mass loss and 4.0% emissivity decrease in the wavelength regime 1.27–1.73 μm after 50 h oxidation at 1773 K. The advantages of double phase metal-silicide coatings combining WSi2 and MoSi2 include improved thermal compatibility with the substrate and an enhanced glass-mediated self-healing ability.
Nearly 50 years ago, Eringen developed a nonlocal theory of elasic solids according to which the Causchy stress tensor "sigma" at a point x depends upon the strain tensor "epsilon" not only at the point x but also at all other points in the body. For homogeneous solids, and a few additional assumptions, he showed that the constitutive relation can be reduced to a differential form that has become popular among people studying deformations of functionally graded materials (FGMs). However, an FGM is inhomogene
Dear colleagues,
We invite you to see the preprint of our new paper "Statistical mechanics of a dielectric polymer chain in the force ensemble" that will appear in Journal of the Mechanics and Physics of Solids. Here we compute the electroelasticity of single polymer chains using both analytical approximations and novel MCMC techniques. Working in the fixed force ensemble facilitates the derivation of the analytical approximations, which are shown to agree well with the MCMC results. This work complements prior work on the statistical mechanics of dielectric polymers chains obtained in a different ensemble. (https://doi.org/10.1016/j.jmps.2021.104658).
Dear Colleagues,
I am writing to share an article titled, "Machine-learned prediction of the electronic fields in a crystal", co-authored by Ying-Shi Teh, Professor Kaushik Bhattacharya and myself. The article is published in the journal Mechanics of Materials. Link to the article: https://doi.org/10.1016/j.mechmat.2021.104070
Second nearest neighbor modified embedded-atom method (2NN-MEAM) interatomic potentials are developed for binary aluminum (Al) alloys applicable from room temperature to the melting point. The binary alloys studied in this work are Al-Cu, Al-Fe and Al-Ni. Sensitivity and uncertainty analyses are performed on potential parameters based on the perturbation approach.
Link to the paper - PINN-Poroelasticity
If you are interested in physics informed neural networks (PINNs) and coupled single and multiphase flow in porous media, please check out our work below:
- We find it challenging to solve coupled poroelasticity relations using PINNs (data-free).
EML Webinar (Season 2) on 13 October 2021 will be given by Alain Goriely on dynamics of active filaments. Discussion leader:Krishna Garikipati, University of Michigan
Time: 10 am Boston, 3 pm London, 10 pm Beijing on 13 October 2021
Zoom Link: https://ter.ps/EMLWebinarS2
Mechanobiology and Mechanomedicine: tuning the tension in the life
Baohua Ji (bhji [at] zju.edu.cn)
Department of Engineering Mechanics, Zhejiang University, Hangzhou 310027
Abstract
Our views on how one can leverage machine learning techniques to help constitutive modeling of composite materials are summarized in this paper
Xin Liu, Su Tian, Fei Tao and Wenbin Yu, A review of artificial neural networks in the constitutive modeling of composite materials, Composites Part B, vol. 224, 2021,109152.
https://www.sciencedirect.com/science/article/pii/S1359836821005321.
