Data-driven fracture mechanics
The data-driven paradigm is becoming a game changer in several fields of science and engineering. We recently started exploring its potential for computational solid mechanics.
Data-driven fracture mechanics
In this project, in collaboration with our colleagues at external pageCaltech and external pageCentrale Nantes, we present a new data-driven paradigm for variational brittle fracture mechanics. The fracture-related material modeling assumptions are removed and the governing equations stemming from variational principles are combined with a set of discrete data points, leading to a model-free data-driven method of solution. The solution at a given load step is identified as the point within the data set that best satisfies either the Kuhn-Tucker conditions stemming from the variational fracture problem or global minimization of a suitable energy functional, leading to data-driven counterparts of both the local and the global minimization approaches of variational fracture mechanics. Both formulations are tested on different test configurations with and without noise and for Griffith and R-curve type fracture behavior.
Data-driven rate-dependent fracture mechanics
The quasi-static model is also extended to the rate-dependent and sub-critical fatigue cases. The balance governing equations are once again obtained from variational principles and the solution of the crack propagation problem is determined as the point of the material data set that best fulfills a local stability principle. The latter condition is enforced by identifying the point in the material data set whose distance from the energy release rate function is minimum, following a closest-point-projection strategy. The approach is tested on different setups adopting different types of rate-dependent fracture and fatigue models affected or not by white noise.