Journal
AIAA JOURNAL
Volume -, Issue -, Pages -Publisher
AMER INST AERONAUTICS ASTRONAUTICS
DOI: 10.2514/1.J062959
Keywords
Digital Engineering; Reduced Order Modelling; Linear Elastic Fracture Mechanics; Rotorcrafts; Structural Integrity; Structural Analysis; Monte Carlo Simulation; Digital Twins; Crack Growth; Machine Learning
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This study introduces a reduced-order modeling (ROM) method for predicting nonplanar crack growth in structural digital twins. The method represents the entire crack surface morphology using a B-spline surface, which captures its impact on crack growth. The proposed method demonstrates superior fracture mechanics parameter prediction and is three orders of magnitude more efficient than full-order simulation.
Nonplanar crack growth holds a critical role in aeronautical structures, necessitating effective analysis under mixed fatigue loading to assess structural integrity. This study introduces a reduced-order modeling (ROM) method for predicting nonplanar crack growth in structural digital twins. The method's advantage lies in its representation of the entire crack surface morphology using a B-spline surface, which better captures its impact on crack growth. The symmetric Galerkin boundary element method-finite element method coupling method is adopted as a full-order method to generate the crack database. Isoparametric coordinates of the crack surface and stress intensity factor serve as input and output, respectively, for training the ROM, which integrates K-means clustering, principal component analysis, and Gaussian process regression. The proposed approach is demonstrated using a rotorcraft-mast-like component. Results reveal superior fracture mechanics parameter prediction compared to the crack-front-based ROM. Furthermore, the method boasts three orders of magnitude greater efficiency than full-order simulation, enabling its coupling with approaches like Monte Carlo for probabilistic crack growth analysis. Future work entails integrating our method into the probabilistic framework of digital twins.
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