Skip to main content

research

Role of Interface on the Toughening of Thermoplastic-Based Nanocomposites Reinforced with Nanofibrillated Rubber

Submitted by Mahdi Zeidi on

Optimizing toughening efficiency of nanofibrillated rubber embedded in thermoplastic polymers has always been a challenge. In our work published in Nanoscale journal, by a combined MD-QM method, we demonstrate the roles of interface & interfiber interactions on the toughness and failure mechanisms of rubber-toughened polypropylene nanocomposites.

 

https://doi.org/10.1039/D1NR07363J

 

 

 

Accretion and Ablation in Deformable Solids with an Eulerian Description: Examples using the Method of Characteristics

Submitted by Kiana Naghibzadeh on

Dear colleagues,

We invite you to see the preprint of our new paper "Accretion and Ablation in Deformable Solids with an Eulerian Description: Examples using the Method of Characteristics" which will appear in Mathematics and Mechanics of Solids. Recent work has proposed an Eulerian approach to the surface growth problem, enabling the side-stepping of the issue of constructing the reference configuration. However, this raises the complementary challenge of determining the stress response of the solid. To resolve this, the approach introduced the elastic deformation as an additional kinematic descriptor of the added material, and its evolution has been shown to be governed by a transport equation. Here, we applied the method of characteristics to solve concrete simplified problems motivated by surface growth in biomechanics and manufacturing (https://journals.sagepub.com/doi/10.1177/10812865211054573)

Global Composites Expert Webinar by Dr. Philippe Boisse

Submitted by Wenbin Yu on

cdmHUB invites you to attend the Global Composites Experts Webinar Series. 

Title: Fibrous Shell Approach and 3D Second Gradient Modeling for Textile Composite Draping 

Speaker: Dr.  Philippe Boisse

Time: 12/9, 11AM-12PM EST.

Please go to https://bit.ly/3l6LUgT to register for this webinar.

A generalised phase field model for fatigue crack growth in elastic–plastic solids with an efficient monolithic solver

Submitted by Zeyad I Khalil on

Dear iMechanica Community, I hope that you find the below work of interest to you - We present a generalised phase field formulation for metallic fatigue, where cyclic deformation is modelled by means of a combined non-linear kinematic/isotropic hardening law. You can check it here: https://doi.org/10.1016/j.cma.2021.114286

Participate in the DVC Challenge 2.0

Submitted by jyang526834 on

Hi all,

We at iDICs (International Digital Image Correlation Society) are thrilled to invite you to join the most recent DVC (Digital Volume Correlation) challenge! The DVC Challenge 2.0 has two sub-challenges. The first part is an interlaboratory study on the temporal stability of laboratory X-ray Computed Tomography systems. The second part is to identify available experimental and synthetic images and develop a standardized set of high-quality and other challenging datasets from different imaging modalities, which will be further used for DVC code assessment.

Predicting high cycle fatigue life with unified mechanics theory

Submitted by Cemal Basaran on

50 days' free access to the article. Anyone clicking on this link before December 26, 2021 will be taken directly to the final version of the article on ScienceDirect, which they are welcome to read or download. No signup, registration or fees are required

Effective stiffness as a concave function of inhomogeneity properties

Submitted by ibiomechanic on

Hi everyone, I am a postdoc at Yale University. I am posting on some of my previous work on fiber networks and heterogeneous materials. We reported that effective stiffness is a concave function of inhomogeneity properties, and effective stiffness becomes smaller with increasing heterogeneity as an example consequence. 

 

Journal Club for November 2021: Machine Learning Potential for Atomistic Simulation

Submitted by Wei Gao on

 

Wei Gao

Department of Mechanical Engineering, University of Texas at San Antonio

 

In this journal club, we provide a brief summary on the concept, recent progress and tools of machine learning (ML) potential for atomistic materials modelling. We hope that it could benefit to the readers who are new to this filed and plan to develop their own or use others ML potentials. Comments and disscussions are welcomed.