Evolution of burst distribution in fiber bundle model
I. V. Bezsudnov, A. A. Snarskii

TL;DR
This paper investigates how burst size distributions evolve in a fiber bundle model with equal load sharing, revealing that the power-law exponent remains constant across bundle sizes and varies with loading steps, supported by theoretical and numerical analysis.
Contribution
It introduces a probability-theoretic approach to analyze burst distributions and their evolution in fiber bundle models under load, highlighting the invariance of the distribution exponent with bundle size.
Findings
The burst distribution exponent s does not depend on bundle size.
The exponent s varies with loading step, showing regions of constancy and transition.
Numerical simulations up to N=10^10 confirm the theoretical predictions.
Abstract
We consider a fiber bundle model with a equal load sharing and uniformly distributed breakdown thresholds. A unified probability-theoretic approach was used to describe bundle under continuous and discrete load increase. It was shown that the ratio of distribution D(d) of avalanches of sizes d to the number of bundle load steps exactly corresponds to burst probability of size d. Evolution of s - power law distribution exponent of D(d) was studied as a function of loading step and bundle size. It was shown that s does not depend on bundle size. In the numerical experiment, dependence s on loading step was obtained with fiber bundle size up to N=10^10. The regions of s values constancy and the transition region were found. A distribution of fiber bursts on each loading step was recovered in the numerical simulations. It was shown that a change in the type of this distribution is the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTheoretical and Computational Physics · Complex Network Analysis Techniques
