research

Abstract

Abstract

Dear fellow iMechanicians,

Here is our recent paper published in EML on novel anti-icing materials based on propylene-glycol (PG) gels. This work was performed in collaboration with Xi Yao, Baohong Chen and myself while working in Zhigang Suo's lab at Harvard, and provides new solutions for anti-icing purposes (i.e. throug blankets design) without large and costly release of PG in the environment.

Anti-icing propylene glycol materials

Xi Yao, Baohong Chen, Xavier P. Morelle and Zhigang Suo*

Details:

Full description & contact: see attached document
University & laboratory: University of Lyon, Laboratoire de Tribologie et Dynamique des Systèmes (LTDS), Ecole Nationale des Travaux Publics de l'Etat (ENTPE), Géomatériaux et Constructions Durables (GCD).
Location: Lyon, France
Period: 4-year China Scholarship Council scholarship application

Context:

The effects of applied load on the formation and reorientation of deltahydrides in alpha-zirconium matrix are studied by a multiphase field model. Simulations with only one hydride seed indicate that the hydride reorientation occurs only when a tensile or compressive strain of ~0.02 is applied along [112-0] (hoop or circumferential direction) or [0001] (radial direction aligned with the basal pole direction), respectively.

Dear iMechanicians, I would like to share our recent work on the poking and bulging of elastic thin sheets that were inspired by the classical indentation test and bulge test. Under clamped boundaries, there have been well-established theories and well-controlled experiments in this field.

Despite being commonly credited with initiating the field of cavitation in elastomers, the famed poker-chip experiments of Gent and Lindley (1959) have yet to be fully explained. One likely reason for their elusiveness is that it had long been presumed that cavitation in elastomers was a phenomenon that could be explained solely on the basis of the elasticity of the elastomer at hand.

Computational Mechanics and Modeling of Composites,  fracture, large deformation, machine learning, heat transfer in polymers