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Title: The second Sandia Fracture Challenge. Predictions of ductile failure under quasi-static and moderate-rate dynamic loading

Ductile failure of structural metals is relevant to a wide range of engineering scenarios. Computational methods are employed to anticipate the critical conditions of failure, yet they sometimes provide inaccurate and misleading predictions. Challenge scenarios, such as the one presented in the current work, provide an opportunity to assess the blind, quantitative predictive ability of simulation methods against a previously unseen failure problem. Instead of evaluating the predictions of a single simulation approach, the Sandia Fracture Challenge relied on numerous volunteer teams with expertise in computational mechanics to apply a broad range of computational methods, numerical algorithms, and constitutive models to the challenge. This exercise is intended to evaluate the state of health of technologies available for failure prediction. In the first Sandia Fracture Challenge, a wide range of issues were raised in ductile failure modeling, including a lack of consistency in failure models, the importance of shear calibration data, and difficulties in quantifying the uncertainty of prediction [see Boyce et al. (Int J Fract 186:5–68, 2014) for details of these observations]. This second Sandia Fracture Challenge investigated the ductile rupture of a Ti–6Al–4V sheet under both quasi-static and modest-rate dynamic loading (failure in ~ 0.1 s). Like the previousmore » challenge, the sheet had an unusual arrangement of notches and holes that added geometric complexity and fostered a competition between tensile- and shear-dominated failure modes. The teams were asked to predict the fracture path and quantitative far-field failure metrics such as the peak force and displacement to cause crack initiation. Fourteen teams contributed blind predictions, and the experimental outcomes were quantified in three independent test labs. In addition, shortcomings were revealed in this second challenge such as inconsistency in the application of appropriate boundary conditions, need for a thermomechanical treatment of the heat generation in the dynamic loading condition, and further difficulties in model calibration based on limited real-world engineering data. As with the prior challenge, this work not only documents the ‘state-of-the-art’ in computational failure prediction of ductile tearing scenarios, but also provides a detailed dataset for non-blind assessment of alternative methods.« less
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  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Northwestern Univ., Evanston, IL (United States)
  3. American Univ., New Cairo (Egypt)
  4. CanmetMaterials, Hamilton, ON (Canada)
  5. GE Global Research Center, Niskayuna, NY (United States)
  6. Regensburg University of Applied Sciences (Germany)
  7. NASA Langley Research Center, Hampton, VA (United States)
  8. Cornell Univ., Ithaca, NY (United States)
  9. Global Engineering and Materials Inc., Princeton, NJ (United States)
  10. Univ. of Paris-Saclay, Chatillon (France)
  11. MINES ParisTech, Paris (France); PSL Research Univ., Paris (France)
  12. RWTC Aachen Univ. (Germany)
  13. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
  14. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
  15. Univ. of Illinois, Chicago, IL (United States)
  16. Univ. of Maribor (Slovenia)
  17. Univ. of Texas, Austin, TX (United States)
  18. Thinkviewer LLC, Sugar Land, TX (United States)
Publication Date:
Report Number(s):
Journal ID: ISSN 0376-9429; PII: 89
Grant/Contract Number:
Accepted Manuscript
Journal Name:
International Journal of Fracture
Additional Journal Information:
Journal Volume: 198; Journal Issue: 1-2; Journal ID: ISSN 0376-9429
Research Org:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org:
USDOE National Nuclear Security Administration (NNSA)
Country of Publication:
United States
36 MATERIALS SCIENCE; fracture; rupture tearing; deformation; plasticity; metal; alloy; simulation; prediction; modeling
OSTI Identifier: