research

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.

In this paper, we formulate a theory for the coupling of accretion mechanics and thermoelasticity. We present an analytical formulation of the thermoelastic accretion of an infinite cylinder and of a two-dimensional block.

Fracture mechanics is one of the key research topics in our field (of mechanics) and has a rather rich history of innovation and applications. From earth-quakes to air-planes---the mechanics underpinning the phenomenon of materials falling apart has been essential in the development of technology.  Griffith’s work on fracture was published about a 100 years ago and is often widely regarded as the start of modern fracture mechanics. The Century Fracture Mechanics Summit (CFMS), was held in Singapore on Apr.

Engineering ceramic/nanocomposite interfaces may lead to the development of ultra-tough ceramic nanocomposites. The novel processing method, as well as micromechanical interpretation of the above result, can be found here: https://doi.org/10.1016/j.carbon.2020.02.075

Christos E. Athanasiou

S. Thomas, A. Kulangara Madam, and M. Asle Zaeem. Stone-Wales defect induced performance improvement of BC3 monolayer for high capacity lithium-ion rechargeable battery anode applications. The Journal of Physical Chemistry C (2020) (10 pages). 

http://dx.doi.org/10.1103/PhysRevMaterials.4.033602

Abstract

Dear iMechanicians,

https://pubs.acs.org/doi/abs/10.1021/acsbiomaterials.9b01983

Beta-sheet protein structures and domains are widely found in biological materials such as silk. These assemblies play amajor role in the extraordinary strength and unique properties of biomaterials.  In our new work, we employ simple Langevin-based models to investigate the behavior and the collapse of these structures.

This is the preprint of an article that will appear in Computer Methods in Applied Mechanics and Engineering (https://doi.org/10.1016/j.cma.2020.112946).

Effective Response of Heterogeneous Materials using the Recursive Projection Method

Xiaoyao Peng (Carnegie Mellon University), Dhriti Nepal (Air Force Research Laboratory), Kaushik Dayal (Carnegie Mellon University)

Cyclic Plasticity of the As-Built EOS Maraging Steel: Preliminary Experimental and Computational Results

https://www.mdpi.com/2076-3417/10/4/1232/htm