Stochastic Fracture Analysis Using Scaled Boundary Finite Element Methods Accelerated by Proper Orthogonal Decomposition and Radial Basis Functions | Zendy

Xiaowei Shen | Zendy; Haowen Hu | Zendy; Zhongwang Wang | Zendy; Xiuyun Chen | Zendy; Chengbin Du | Zendy

Open Access

Stochastic Fracture Analysis Using Scaled Boundary Finite Element Methods Accelerated by Proper Orthogonal Decomposition and Radial Basis Functions

Author(s) -

Xiaowei Shen,

Haowen Hu,

Zhongwang Wang,

Xiuyun Chen,

Chengbin Du

Publication year - 2021

Publication title -

geofluids

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.44

H-Index - 56

eISSN - 1468-8123

pISSN - 1468-8115

DOI - 10.1155/2021/9181415

Subject(s) - finite element method , radial basis function , boundary (topology) , computation , fracture (geology) , stress intensity factor , monte carlo method , mathematics , basis (linear algebra) , basis function , set (abstract data type) , boundary value problem , proper orthogonal decomposition , computer science , mathematical analysis , structural engineering , algorithm , mechanics , geometry , engineering , physics , artificial neural network , turbulence , statistics , geotechnical engineering , machine learning , programming language

This paper presents a stochastic analysis method for linear elastic fracture mechanics using the Monte Carlo simulations (MCs) and the scaled boundary finite element method (SBFEM) based on proper orthogonal decomposition (POD) and radial basis functions (RBF). The semianalytical solutions obtained by the SBFEM enable us to capture the stress intensity factors (SIFs) easily and accurately. The adoption of POD and RBF significantly reduces the model order and increases computation efficiency, while maintaining the versatility and accuracy of MCs. Numerical examples of cracks in homogeneous and bimaterial plates are provided to demonstrate the effectiveness and reliability of the proposed method, where the crack inclination angles are set as uncertain variables. It is also found that the larger the scale of the problem, the more advantageous the proposed method is.

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