1,309 research outputs found
Multiscale self-organized criticality and powerful X-ray flares
A combination of spectral and moments analysis of the continuous X-ray flux
data is used to show consistency of statistical properties of the powerful
solar flares with 2D BTW prototype model of self-organized criticality
Logarithmic scaling in the near-dissipation range of turbulence
A logarithmic scaling for structure functions, in the form , where is the Kolmogorov dissipation scale and
are the scaling exponents, is suggested for the statistical
description of the near-dissipation range for which classical power-law scaling
does not apply. From experimental data at moderate Reynolds numbers, it is
shown that the logarithmic scaling, deduced from general considerations for the
near-dissipation range, covers almost the entire range of scales (about two
decades) of structure functions, for both velocity and passive scalar fields.
This new scaling requires two empirical constants, just as the classical
scaling does, and can be considered the basis for extended self-similarity
Multiscale SOC in turbulent convection
Using data obtained in a laboratory thermal convection experiment at high
Rayleigh numbers, it is shown that the multiscaling properties of the observed
mean wind reversals are quantitatively consistent with analogous multiscaling
properties of the Bak-Tang-Wiesenfeld prototype model of self-organized
criticality in two dimensions
Logarithmically modified scaling of temperature structure functions in thermal convection
Using experimental data on thermal convection, obtained at a Rayleigh number
of 1.5 , it is shown that the temperature structure functions
, where is the absolute value of the temperature
increment over a distance , can be well represented in an intermediate range
of scales by , where the are the scaling
exponents appropriate to the passive scalar problem in hydrodynamic turbulence
and the function . Measurements are made in the
midplane of the apparatus near the sidewall, but outside the boundary layer
Cascading failures in spatially-embedded random networks
Cascading failures constitute an important vulnerability of interconnected
systems. Here we focus on the study of such failures on networks in which the
connectivity of nodes is constrained by geographical distance. Specifically, we
use random geometric graphs as representative examples of such spatial
networks, and study the properties of cascading failures on them in the
presence of distributed flow. The key finding of this study is that the process
of cascading failures is non-self-averaging on spatial networks, and thus,
aggregate inferences made from analyzing an ensemble of such networks lead to
incorrect conclusions when applied to a single network, no matter how large the
network is. We demonstrate that this lack of self-averaging disappears with the
introduction of a small fraction of long-range links into the network. We
simulate the well studied preemptive node removal strategy for cascade
mitigation and show that it is largely ineffective in the case of spatial
networks. We introduce an altruistic strategy designed to limit the loss of
network nodes in the event of a cascade triggering failure and show that it
performs better than the preemptive strategy. Finally, we consider a real-world
spatial network viz. a European power transmission network and validate that
our findings from the study of random geometric graphs are also borne out by
simulations of cascading failures on the empirical network.Comment: 13 pages, 15 figure
Failure dynamics of the global risk network
Risks threatening modern societies form an intricately interconnected network
that often underlies crisis situations. Yet, little is known about how risk
materializations in distinct domains influence each other. Here we present an
approach in which expert assessments of risks likelihoods and influence
underlie a quantitative model of the global risk network dynamics. The modeled
risks range from environmental to economic and technological and include
difficult to quantify risks, such as geo-political or social. Using the maximum
likelihood estimation, we find the optimal model parameters and demonstrate
that the model including network effects significantly outperforms the others,
uncovering full value of the expert collected data. We analyze the model
dynamics and study its resilience and stability. Our findings include such risk
properties as contagion potential, persistence, roles in cascades of failures
and the identity of risks most detrimental to system stability. The model
provides quantitative means for measuring the adverse effects of risk
interdependence and the materialization of risks in the network
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