Our paper on the phase-field mixture theory of tumor growth is published in JMPS. The continuum model simulates significant mechano-chemo-biological features of avascular tumor growth in the various microenvironment, i.e., nutrient concentration and mechanical stress.

Faghihi, Feng, Lima, Oden, and Yankeelov (2020). A Coupled Mass Transport and Deformation Theory of Multi-constituent Tumor Growth. Journal of the Mechanics and Physics of Solids, 103936.

PhD in Data-driven modelling of non-linear heterogenous materials

We are looking for an enthusiastic candidate with a sound background in micromechanics/solid mechanics, and interest in data-driven multiscale modelling of heterogeneous materials to work on a project 'Heterogeneous Materials in Extreme Environments' within the Centre for Doctoral Training HetSys at the University of Warwick (see https://warwick.ac.uk/fac/sci/hetsys/).

Entry requirements

Journal Club for April 2020: Curvature-Affected Instabilities in Membranes and Surfaces

Fan Xu, Fudan University

 In J. Mech. Phys. Solids

See Rajat Arora's blog entry for abstract and preprint

Theory of physical aging from polymer science is, for the first time, introduced to understand ACL injury and its prevention. By analogy to physical aging of amorphous polymer materials, we think physical aging of two bundles of ACL will largely increase risk of ACL injury. Besides, physical aging will also build a heterogeneous stress and strain in ACL due to its natural anatomic structure, which is a large risk for athletes. The specific designed prevention programs for ACL injury such as plyometrics, strengthening and other neuromuscular training exercises [1] are believed to erase physical aging of ACL. ACL with less physical aging is less likely to get injured in sport activities. In this article, a virtual physical aging simulation is built to validate current hypothesis. Erasing physical aging of ACL may provide an accurate and quantitative way to prevent ACL injury.

By Alexa S. Kuenstler, Yuzhen Chen, Phuong Bui, Hyunki Kim, Antonio DeSimone, Lihua Jin, Ryan C. Hayward

The Laboratory for Soft Machines & Electronics (www.caogroup.org) at the MSU has one postdoc associate opening in the areas of soft materials and machines. The research work is expected to be highly multi-disciplinary, and the specific topics includes: Smart materials and structures, Soft robotics, Artificial skins; Energy harvesters, Wearable Electronics, Machine Learning, etc. This position is available immediately.

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.