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https://doi.org/10.1103/physre...
Article . 2016 . Peer-reviewed
License: APS Licenses for Journal Article Re-use
Data sources: Crossref
https://dx.doi.org/10.48550/ar...
Article . 2016
License: arXiv Non-Exclusive Distribution
Data sources: Datacite
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Mesoscopic approach to subcritical fatigue crack growth

Authors: Maycon S. Araújo; André P. Vieira; José S. Andrade; Hans J. Herrmann;

Mesoscopic approach to subcritical fatigue crack growth

Abstract

We investigate a model for fatigue crack growth in which damage accumulation is assumed to follow a power law of the local stress amplitude, a form which can be generically justified on the grounds of the approximately self-similar aspect of microcrack distributions. Our aim is to determine the relation between model ingredients and the Paris exponent governing subcritical crack-growth dynamics at the macroscopic scale, starting from a single small notch propagating along a fixed line. By a series of analytical and numerical calculations, we show that, in the absence of disorder, there is a critical damage-accumulation exponent $γ$, namely $γ_c=2$, separating two distinct regimes of behavior for the Paris exponent $m$. For $γ>γ_c$, the Paris exponent is shown to assume the value $m=γ$, a result which proves robust against the separate introduction of various modifying ingredients. Explicitly, we deal here with (i) the requirement of a minimum stress for damage to occur; (ii) the presence of disorder in local damage thresholds; (iii) the possibility of crack healing. On the other hand, in the regime $γ

To appear in Phys. Rev. E

Keywords

FOS: Physical sciences, Disordered Systems and Neural Networks (cond-mat.dis-nn), Condensed Matter - Disordered Systems and Neural Networks

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
Green