Stephane Bordas

Abstract:

We review in this presentation 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 homogenisation-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.[1] O. Goury et al. (2013) A partitioned model order reduction approach to rationalise com- putational expenses in nonlinear fracture mechanics Comp. Meth. App. Mech. Engng.. 256, 169-188.<br />[2] L.A.A.Beex et al.(2014)Quasicontinuum-basedmultiscaleapproachesforplate-likebeam lattices experiencing in-plane and out-of-plane deformation Comp. Meth. App. Mech. En- gng. . 279, 348-378.<br />[3] A. Akbari (2015) Error Controlled Adaptive Multiscale Method For Fracture Modelling in Polycrystalline materials Philosophical Magazine. in press