Dear colleagues,
I would like to draw your attention to a recent work we have been doing to set up a library of examples of discrete one-sided contact problems with Coulomb friction.
The collection of problems can be found here https://github.com/FrictionalContactLibrary/fclib-library and the C API for writing and listing these files here:
Over the past years, UGent-MMS has developed a stand-alone topology optimization code for additive manufacturing applications. The topology optimization software can deal with thermo-mechanical coupled problems, combined with multi-material selection. Such features are not available in any commercial topology optimization code.
The code can also deal with large industrial optimization problems, with millions degrees of freedom in three-dimensional finite element models.
Chapters to be covered in this book includes (but not
limited to) the following themes:
AI, IoT and BDA powered CE models to evaluate and optimize performance of waste management systems.
Applications of deep learning and unsupervised feature
learning for prediction of sustainable feature smart transportation system
Application of DT and CE technologies for Sustainable design of distributed energy supply systems
Digital Technology-enabled circular economy for sustainable production and consumption
Frameworks to Evaluate the Influence of DT and CE-based
In linear elasticity, universal displacements for a given symmetry class are those displacements that can be maintained by only applying boundary tractions (no body forces) and for arbitrary elastic constants in the symmetry class. In a previous work, we showed that the larger the symmetry group, the larger the space of universal displacements. Here, we generalize these ideas to the case of anelasticity. In linear anelasticity, the total strain is additively decomposed into elastic strain and anelastic strain, often referred to as an eigenstrain.
The LS2P laboratory (from its French name, Laboratoire des Systèmes Portés par la Personne ) develops non-invasive devices for measuring physiological parameters such as heart rate, oxygen saturation or blood pressure. A strong integration is a key factor from the design stage of the sensors. This allows them to be integrated into bracelets, patches or headbands to make them compatible with their daily use.
Authors: Avinash Gupta and Kunnath Ranjith
Abstract:
