FASTIGUE: A computationally efficient approach for simulating discrete fatigue crack growth in large-scale structures

Abstract

The renaissance of digital twin technology heralded by recent advancements in machine learning raises the demand for structural analysis tools for real time predictions of the structural performance and ultimately the remaining lifetime. This paper proposes a novel approach – FASTIGUE - for computationally super-efficient discrete fatigue crack growth analysis of large structures particularly for bondlines with high aspect ratios. The computational speed is considerably increased by outsourcing the finite element analysis into a pre-processing step in which the numerical model is solved for a comparatively small number of crack stages; providing the Stress Intensity Factors (SIFs) for a set of auxiliary crack tip location permutations. 3D surface fitting of the auxiliary data provides the SIF ranges as continuous functions of the crack lengths. The fatigue crack growth simulation is performed independently from finite element analysis by utilising the SIF-functions within an explicit growth prediction scheme. The method is applied to a trailing edge crack in a 14.3 m wind turbine blade model and further validated against an analytical solution. It is demonstrated that the computation speed outperforms conventional fatigue analysis approaches relying on update-and-rerun schemes.