1-11 – Computational and Data Driven Homogenization for Nonlinear Composites
Elio Sacco (University of Naples Federico II), Djimédo Kondo (Université Pierre et Marie Curie), Felix Fritzen (University of Stuttgart),
Sonia Marfia (University Roma Tre)
The use of composite materials has become very important in different fields of engineering due to their high performance properties such as light weight and high resistance. The mechanical response of parts and components made of composites is significantly influenced by the heterogeneous microstructure and by the nonlinear phenomena occurring in the constituents.
Heterogeneous materials are often characterized by different types of nonlinearities depending on the nature of the constituents; in fact, they can be subjected to damage, fracture, plastic, viscous phenomena, phase transformation, etc. that have to be properly modeled in order to reproduce the mechanical behavior of the material.
Computational homogenization approaches aim to determine the effective behavior of complex and highly heterogeneous materials, taking into account the nonlinear processes occurring at the microstructural level.
The mini-symposium focuses on the developments and applications of computational homogenization methods and data-driven multiscale approaches with emphasis on non-linear materials. The goal is to provide a forum for communications and interactions on (but not limited to) the following topics:
Multiscale material modeling including (optional) coupled multi-physics behavior (e.g.phase change, nonlinear thermo-mechanics, ...);
Computational homogenization of heterogeneous, linear, nonlinear and time-dependent materials;
Reduction approaches aimed to limit the computational costs in the framework of multiscale analysis;
Computational homogenization including higher-order continuum models;
Multiscale analysis for micro-macro scale-separation;
Multiscale approaches in case of lack of scale separation, e. g. for the transition from homogenization to localization in crack formation;
Data-driven modeling and simulation including machine learning and artificial intelligence;
Data analytics for multiscale methods including microstructure characterization.