Structural Engineering and Mechanics

  • 제83권3호
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  • Pages.353-361
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  • 2022
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

An efficient adaptive finite element method based on EBE-PCG iterative solver for LEFM analysis

  • Hearunyakij, Manat (Department of Mechanical Engineering Technology, College of Industrial Technology, King Mongkut's University of Technology North Bangkok) ;
  • Phongthanapanich, Sutthisak (Department of Mechanical Engineering Technology, College of Industrial Technology, King Mongkut's University of Technology North Bangkok)
  • 투고 : 2020.03.28
  • 심사 : 2022.05.15
  • 발행 : 2022.08.10
⟨ 이전 논문 다음 논문 ⟩

초록

Linear Elastic Fracture Mechanics (LEFM) has been developed by applying stress analysis to determine the stress intensity factor (SIF, K). The finite element method (FEM) is widely used as a standard tool for evaluating the SIF for various crack configurations. The prediction accuracy can be achieved by applying an adaptive Delaunay triangulation combined with a FEM. The solution can be solved using either direct or iterative solvers. This work adopts the element-by-element preconditioned conjugate gradient (EBE-PCG) iterative solver into an adaptive FEM to solve the solution to heal problem size constraints that exist when direct solution techniques are applied. It can avoid the formation of a global stiffness matrix of a finite element model. Several numerical experiments reveal that the present method is simple, fast, and efficient compared to conventional sparse direct solvers. The optimum convergence criterion for two-dimensional LEFM analysis is studied. In this paper, four sample problems of a two-edge cracked plate, a center cracked plate, a single-edge cracked plate, and a compact tension specimen is used to evaluate the accuracy of the prediction of the SIF values. Finally, the efficiency of the present iterative solver is summarized by comparing the computational time for all cases.

키워드

과제정보

The second author is pleased to acknowledge the College of Industrial Technology, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand, for funding this research work (Grant No. Res-CIT0286/2022).

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