Journal Club for April 2020: Curvature-Affected Instabilities in Membranes and Surfaces
Journal Club for April 2020: Curvature-Affected Instabilities in Membranes and Surfaces
Fan Xu, Fudan University
Theoretical and Numerical Analysis of Anterior Cruciate Ligament Injury and its Prevention
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.
Blueprinting Photothermal Shape‐Morphing of Liquid Crystal Elastomers
By Alexa S. Kuenstler, Yuzhen Chen, Phuong Bui, Hyunki Kim, Antonio DeSimone, Lihua Jin, Ryan C. Hayward
Postdoc Position in Soft Electronics and Robotics at Michigan State University
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.
Characterizing fracture stress of defective graphene samples using shallow and deep artificial neural networks
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.
Nonlinear Mechanics of Thermoelastic Accretion
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.
Eight fully funded Master positions in Computational Mechanics and Materials Science at CEMEF (Mines ParisTech & PSL University)
Eight fully funded positions open for the "Digital Materials and Advanced Processes Modelling (MAPMOD)" Master.
MAPMOD is a one-year full-time postgraduated programme in line with industrial needs for high level competences in high added value components or equipment manufacturing in fields such as aeronautics, energy, automotive.
The funding covers tuition fees and partial support of living expenses.
Mini-symposium on “Data-Enabled Predictive Modeling, Machine Learning, and Uncertainty Quantification in Computational Mechanics” at IMECE 2020 conference
Dear Colleagues,
As part of the IMECE 2020 (November 13-19, 2020, Portland, Oregon), we are organizing a topic on “Data-Enabled Predictive Modeling, Machine Learning, and Uncertainty Quantification in Computational Mechanics.” It is listed in Track 12: Mechanics of Solids, Structures, and Fluids: https://event.asme.org/IMECE/Program/Tracks-Topics.