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Automating XFEM Modeling Process for Optimal Failure Predictions

Author

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  • A. Y. Elruby
  • Sam Nakhla
  • A. Hussein

Abstract

The eXtended Finite Element Method (XFEM) is a versatile method for solving crack propagation problems. Meanwhile, XFEM predictions for crack onset and propagation rely on the stress field which tends to converge at a slower rate than that of displacements, making it challenging to capture critical load at crack onset accurately. Furthermore, identifying the critical region(s) for XFEM nodal enrichments is user-dependent. The identification process can be straightforward for small-scale test specimen while in other cases such as complex structures it can be unmanageable. In this work a novel approach is proposed with three major objectives; alleviate user-dependency; enhance predictions accuracy; minimize computational effort. An automatic critical region(s) identification based on material selected failure criterion is developed. Moreover, the approach enables the selection of optimized mesh necessary for accurate prediction of failure loads at crack initiation. Also, optimal enrichment zone size determination is automated. The proposed approach was developed as an iterative algorithm and implemented in ABAQUS using Python scripting. The proposed algorithm was validated against our test data of unnotched specimens and relevant test data from the literature. The results of the predicted loads/displacements at failure are in excellent agreement with measurements. Crack onset locations were in very good agreement with observations from testing. Finally, the proposed algorithm has shown a significant enhancement in the overall computational efficiency compared to the conventional XFEM. The proposed algorithm can be easily implemented into user-built or commercial finite element codes.

Suggested Citation

  • A. Y. Elruby & Sam Nakhla & A. Hussein, 2018. "Automating XFEM Modeling Process for Optimal Failure Predictions," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-14, August.
  • Handle: RePEc:hin:jnlmpe:1654751
    DOI: 10.1155/2018/1654751
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