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Related Experiment Videos

Crack surface roughness in three-dimensional random fuse networks.

Phani Kumar V V Nukala1, Stefano Zapperi, Srdan Simunović

  • 1Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6164, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|October 10, 2006
PubMed
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Damage in the 3D random fuse model spreads diffusively until peak load, then localizes abruptly. Crack width scaling aligns with this localization, forming a log-normal distribution.

Area of Science:

  • Physics
  • Materials Science
  • Computational Modeling

Background:

  • The behavior of cracks in materials is crucial for understanding material failure.
  • Scaling properties and damage accumulation in fracture mechanics are complex phenomena.
  • The random fuse model provides a framework for studying fracture in disordered systems.

Purpose of the Study:

  • To analyze the scaling properties of crack roughness and damage in the 3D random fuse model.
  • To investigate the transition from diffusive damage accumulation to localized failure.
  • To characterize the crack width distribution and its scaling behavior.

Main Methods:

  • Utilizing large system sizes and extensive statistical sampling.
  • Analyzing damage profiles and crack roughness.

Related Experiment Videos

  • Applying data collapse techniques for postpeak regime analysis.

    • Main Results:

    • Damage accumulation follows a diffusive pattern up to peak load, followed by abrupt localization.
    • Global crack width scales as W ~ L(0.5), consistent with localization length scaling (xi ~ L(0.5)).
    • Crack width distributions collapse for various system sizes, exhibiting a log-normal distribution.

    Conclusions:

    • Postpeak damage is primarily driven by localization due to catastrophic failure, leading to crack formation.
    • The observed scaling relationships support a unified understanding of crack growth and material failure.
    • The log-normal distribution of crack widths offers insights into the statistical nature of fracture events.