Crack Growth

Parametric Symbolic Regression for Discovering Unified Crack Growth Models from Diverse Experiments
A parametric symbolic regression framework is proposed to discover unified crack growth models from multi-source experimental data.
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Setting Adaptive Inspection Intervals in Helicopter Components, Based on a Digital Twin
The proposed strategy utilizes two connected probabilistic processes, which conduct the diagnosis/prognosis and calculate the inspection intervals, respectively, to adaptively set the inspection intervals according to the updating of the digital twin model.
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Setting Adaptive Inspection Intervals in Helicopter Components, Based on a Digital Twin
Real-Time Prediction of Probabilistic Crack Growth with a Helicopter Component Digital Twin
In this paper, a reduced-order simulation approach is developed by leveraging the symmetric Galerkin boundary element method–finite element method (SGBEM-FEM) coupling method and machine learning methods to realize a real-time prediction of probabilistic crack growth in complex structures with minimum computational burden.
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Real-Time Prediction of Probabilistic Crack Growth with a Helicopter Component Digital Twin