This code implements a linearized Crack-Front model for the mechanics of adhesive contacts in Python. The model includes:
- linear noncircular perturbation of the energy release rate with respect to the circular JKR solution.
- solving for the equilibrium position of the crack-front on a heterogeneous work of adhesion field. Following resolution algorithms are implemented:
- a modified trust-region Newton-CG minimization algorithm
- the crack-propagation algorithm by Rosso and Krauth, that generates a monotonically increasing (or decreasing) sequence of crack positions
- computation of the work of adhesion heterogeneity equivalent to surface roughness.
The crack-perturbation model for the adhesion of spheres with work of adhesion heterogeneity is described and validated against the boundary-element method in Sanner, Pastewka, JMPS (2023).
The crack-perturbation model for the adhesion of rough spheres is described and validated against the boundary-element method in Sanner, Kumar, Jacobs, Dhinojwala,Pastewka, Science Advances (2024).
The crack perturbation method is based on the first-order perturbation of the stress intensity factor derived by Gao and Rice using weight-function theory.
- Gao, Rice, J.Appl.Mech., 54 (1987)
- J. R. Rice, Weight function theory for three-dimensional elastic crack analysis, Fracture Mechanics: Perspectives and Directions (Twentieth Symposium), R. Wei, R. Gangloff, eds. (American Society for Testing and Materials, Philadelphia, USA, 1989), pp. 29–57.
The crack-propagation algorithm by Rosso and Krauth is described in
This code can make use of GPUs accelaration using pytorch if the hardware is available.
First install the dependencies listed below, then quick install with: python3 -m pip install git+https://github.com/ContactEngineering/CrackFront.git
The package requires :
- numpy - https://www.numpy.org/
- NuMPI - https://github.com/imtek-simulation/numpi
- pytorch - https://pytorch.org/
- Adhesion - https://github.com/ContactEngineering/Adhesion and the dependencies of that package
Development of this project is funded by the Deutsche Forschungsgemeinschaft within EXC 2193 and by the European Research Council within Starting Grant 757343.