1st TOPICAL DAY ON High-performance multi scale modelling
Link to the flyer to the topical day
Empa, Dübendorf, Überlandstrasse 129
Wednesday, November 18, 2015 8:30 to 17:00
Online registration: www.empa.ch/multiscale
Abstract of keynote by Benoit Coasne
Adsorption and transport in multiscale porous media
Complex adsorption/transport in multiscale media stems from
their broad porosity (~ nm to the macroscopic scale). Existing approaches fail to provide a bottom-up description of adsorption/ transport in these materials as (1) they describe only empirically the adsorption/transport interplay and (2) they do not account for the hydrodynamics breakdown at the nm. I will present a rigorous bottom-up model of adsorption/transport in multiscale media. I will first show how adsorption and permeance can be described using Statistical Mechanics without relying on macroscopic concepts inherent to hydrodynamics. Then, I will present a multiscale model of adsorption/transport in such multiscale materials.
Abstract of keynote by Stéphane Bordas
Multi-scale methods for fracture: model learning across scales, digital twinning and factors of safety
Fracture and material instabilities originate at spatial scales much smaller than the ones of the structure of interest: delamination, debonding, fiber breakage, cell-wall buckling are examples of nano-/micro- or meso-scale mechanisms, which can lead to global failure of the material and structure. Such mechanisms cannot, for computational and practical reasons, be accounted for at the structural scale, so that acceleration methods are necessary. In this presentation we review recently proposed approaches to reduce the computational expense associated with multi-scale modelling of fracture. In light of two particular examples, we show connections between algebraic reduction (model-order reduction and quasi-continuum methods) and homogenization-based reduction. We open the discussion towards suitable approaches for machine-learning and Bayesian statistical based multi-scale model selection. Such approaches could fuel a digital-twin concept enabling models to learn from real-time data acquired during the life of the structure, accounting for “real” environmental conditions during predictions, and, eventually, moving beyond the “factors of safety” era.