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Predicting brittle fracture surface shape from a versatile database
Author(s) -
Huang Yuhang,
Yu Yonghang,
Kanai Takashi
Publication year - 2018
Publication title -
computer animation and virtual worlds
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.225
H-Index - 49
eISSN - 1546-427X
pISSN - 1546-4261
DOI - 10.1002/cav.1865
Subject(s) - computer science , boundary element method , frame (networking) , feature (linguistics) , boundary (topology) , displacement (psychology) , support vector machine , fracture (geology) , linear elasticity , algorithm , artificial intelligence , finite element method , structural engineering , geology , mathematics , mathematical analysis , engineering , psychotherapist , geotechnical engineering , psychology , telecommunications , linguistics , philosophy
In this paper, we propose a novel data‐driven method that uses a machine learning scheme for formulating fracture simulation with the boundary element method (BEM) as a regression problem. With this method, the crack opening displacement (COD) of every correlation node is predicted at the next frame. In our naive prediction, we design a feature vector directly exploiting stress intensities and toughness at the current frame so that our method predicts the COD at the next frame more reliably. Thus, there is no need to solve the original linear BEM system to calculate displacements. This enables us to propagate crack fronts using the estimated stress intensities. There are existing works that use the machine learning approach to accelerate the speed of traditional physics‐based simulations like smoke and fluid, but our work is the first to incorporate the machine learning scheme into BEM‐based fracture simulations. Our implementation accelerates the acquisition of displacements in linear time over the number of crack fronts at each time step compared with the conventional solution whose time complexity grows exponentially based on the BEM linear system. The databases generated by our method are versatile and can be applied to general situations and different models.