Observation of the Kibble–Zurek Mechanism in Microscopic Acoustic Crackling Noises

Abstract

Characterizing the fast evolution of microstructural defects is key to understanding “crackling” phenomena during the deformation of solid materials. For example, it has been proposed using atomistic simulations of crack propagation in elastic materials that the formation of a nonlinear hyperelastic or plastic zone around moving crack tips controls crack velocity. To date, progress in understanding the physics of this critical zone has been limited due to the lack of data describing the complex physical processes that operate near microscopic crack tips. We show, by analyzing many acoustic emission events during rock deformation experiments, that the signature of this nonlinear zone maps directly to crackling noises. In particular, we characterize a weakening zone that forms near the moving crack tips using functional networks, and we determine the scaling law between the formation of damages (defects) and the traversal rate across the critical point of transition. Moreover, we show that the correlation length near the transition remains effectively frozen. This is the main underlying hypothesis behind the Kibble-Zurek mechanism (KZM) and the obtained power-law scaling verifies the main prediction of KZM.

Defect production in non-equilibrium phase transitions: experimental investigation of the Kibble–Zurek mechanism in a two-qubit quantum simulator

“…Linear fits of the simulated and experimental data sets yield the parameters listed in table 1. These results agree well with the theoretical predictions of equation (19) and 1  a = ( ), as well as with the LZ-formula, which predicts α = 2 p [34,35].…”

Microscopic Evolution of Laboratory Volcanic Hybrid Earthquakes

“…The first intriguing result of the analysis is that we find that ultrasound excitations possess patterns of temporal evolution of network parameters that are universal among recorded events22232425. The appearance of universal patterns in any measure of excited signals shows the robustness of the collective process in the source(s) against the much faster scattering processes.…”

“…From this perspective, the system relaxes in multiple stages and time scales rather than single time scale. In general, the aforementioned phases are typical for dry events and simple friction tests conducted under dry conditions2223242526 (Fig. S2).…”

“…To evaluate reordered multiple acoustic emission (multiple time series for an occurred event), we use a previously algorithm on waveforms from our reordered acoustic emissions2225 which is the class of functional connectivity networks from multivariate time series data. The main steps of the algorithm are as follows:

The waveforms recorded at each acoustic sensor are normalized to the maximum value of the amplitude in that station.

Each time series is divided according to maximum segmentation, in a way that each segment includes only one data point.

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“…When the introduced acoustic-networks mapped into a binary spin-like system , the linked nodes form a surface with the same sign as the evolution rate of each node’s degree. This can be interpreted as the surface of constant phase (or same family of rays) in analogy with wavefronts, and defects of this surface manifested in binary network structure as the flipping the node (spin) leading to formation of a kink (or domain wall22) in K-chain (see Fig. S14).…”