Avalanche mixing and the simultaneous collapse of two media under uniaxial stress

Salje, Ekhard K. H.; Liu, Hanlong; Xiao, Yang; Jin, Linsen; Planes, Antoni; Vives, Eduard; Xie, Kainan; Jiang, Xiang

doi:10.1103/physreve.99.023002

Cited by 13 publications

(8 citation statements)

References 46 publications

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“…The ML curve increases to a maximum value with an upper bound of ε 1 and then asymptotically decays to the value ε 2 . Such mixing curves have been found experimentally in complex metals, [ 29 ] mixtures of coals and sandstone, [ 26 ] creep avalanches, [ 30 ] etc. The mixing depends crucially on the ratio x between the two energy contributions.…”

Section: Introductionmentioning

confidence: 60%

“…The first step is to realize that avalanche spectra might contain signals of two (or more) separate avalanche systems hidden in the details of the probability distributions functions of the avalanche energy or amplitude. [25,26] Let us first focus on the analysis of the probability density g(E) that accounts for the distribution of energies of avalanche events. Similar analyses can be performed for distributions of other avalanche properties.…”

Section: The Avalanche Superposition In Maximum Likelihood Analysismentioning

confidence: 99%

“…The mixing depends crucially on the ratio x between the two energy contributions. [ 26 ] If x is small, it becomes impossible to observe mixing by this method, so that the nonobservance of mixing using this method is no evidence for its absence. A typical situation is encountered when a large number of dislocations interact with the nucleation of a phase transition inside the same material.…”

Section: Introductionmentioning

confidence: 99%

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Multiple Avalanche Processes in Acoustic Emission Spectroscopy: Multibranching of the Energy−Amplitude Scaling

Chen

Gou

Yuan

et al. 2021

Physica Status Solidi (b)

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Several physical processes can conspire to generate avalanches in materials. Such processes include avalanche mechanisms like dislocation movements, friction processes by pinning magnetic domain walls, moving dislocation tangles, hole collapse in porous materials, collisions of ferroelectric and ferroelastic domain boundaries, kinks in interfaces, and many more. Known methods to distinguish between these species which allow the physical identification of multiavalanche processes are reviewed. A new approach where the scaling relationship between the avalanche energies E and amplitudes A is considered is then described. Avalanches with single mechanisms scale experimentally as E = SiAi2. The energy E reflects the duration D of the avalanche and A(t), the temporal amplitude. The scaling prefactor S depends explicitly on the duration of the avalanche and on details of the avalanche profiles. It is reported that S is not a universal constant but assumes different values depending on the avalanche mechanism. If avalanches coincide, they can still show multivalued scaling between E and A with different S‐values for each branch. Examples for this multibranching effect in low‐Ni 316L stainless steel, 316L stainless steel, polycrystalline Ni, TC21 titanium alloy, and a Fe40Mn40Co10Cr10 high‐entropy alloy are shown.

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Section: Introductionmentioning

confidence: 60%

Section: The Avalanche Superposition In Maximum Likelihood Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multiple Avalanche Processes in Acoustic Emission Spectroscopy: Multibranching of the Energy−Amplitude Scaling

Chen

Gou

Yuan

et al. 2021

Physica Status Solidi (b)

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“…In order to confirm the mixing mechanism for different avalanches during a collapse process, Salje and his coauthors designed a simple and instructive experimental arrangement. They used two materials with different strengths and superimposed them in the same stress device (Figure 6) [71]. The stress was identical for both materials, but the avalanche dynamics were different.…”

Section: Avalanche Mixingmentioning

confidence: 99%

Acoustic Emission Spectroscopy: Applications in Geomaterials and Related Materials

et al. 2021

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As a non-destructive testing technology with fast response and high resolution, acoustic emission is widely used in material monitoring. The material deforms under stress and releases elastic waves. The wave signals are received by piezoelectric sensors and converted into electrical signals for rapid storage and analysis. Although the acoustic emission signal is not the original stress signal inside the material, the typical statistical distributions of acoustic emission energy and waiting time between signals are not affected by signal conversion. In this review, we first introduce acoustic emission technology and its main parameters. Then, the relationship between the exponents of power law distributed AE signals and material failure state is reviewed. The change of distribution exponent reflects the transition of the material’s internal failure from a random and uncorrelated state to an interrelated state, and this change can act as an early warning of material failure. The failure process of materials is often not a single mechanism, and the interaction of multiple mechanisms can be reflected in the probability density distribution of the AE energy. A large number of examples, including acoustic emission analysis of biocemented geological materials, hydroxyapatite (human teeth), sandstone creep, granite, and sugar lumps are introduced. Finally, some supplementary discussions are made on the applicability of Båth’s law.

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“…The diameter of the cavities varies greatly and the solid matrix is amorphous in diffraction experiments; i.e., it forms a glass. The collapse mechanism is understood to be related to the local collapse of a cavity which reduces the local specific volume and triggers the collapse of other cavities via the emitted strain waves [39]. The stochastic nature of the distribution of cavities and their strain interactions gives rise to the formation of avalanches.…”

Section: Introductionmentioning

confidence: 99%

Energy exponents of avalanches and Hausdorff dimensions of collapse patterns

Casals

Salje

2021

Phys. Rev. E

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A simple numerical model to simulate athermal avalanches is presented. The model is inspired by the "porous collapse" process where the compression of porous materials generates collapse cascades, leading to power law distributed avalanches. The energy (E ), amplitude (A max ), and size (S) exponents are derived by computer simulation in two approximations. Time-dependent "jerk" spectra are calculated in a single avalanche model where each avalanche is simulated separately from other avalanches. The average avalanche profile is parabolic, the scaling between energy and amplitude follows E ∼ A 2 max , and the energy exponent is ε = 1.33. Adding a general noise term in a continuous event model generates infinite avalanche sequences which allow the evaluation of waiting time distributions and pattern formation. We find the validity of the Omori law and the same exponents as in the single avalanche model. We then add spatial correlations by stipulating the ratio G/N between growth processes G (linked to a previous event location) and nucleation processes N (with new, randomly chosen nucleation sites). We found, in good approximation, a power law correlation between the energy exponent ε and the Hausdorff dimension H D of the resulting collapse pattern H D −1 ∼ ε −3 . The evolving patterns depend strongly on G/N with the distribution of collapse sites equally power law distributed. Its exponent ε topo would be linked to the dynamical exponent ε if each collapse carried an energy equivalent to the size of the collapse. A complex correlation between ε, ε topo , and H D emerges, depending strongly on the relative occupancy of the collapse sites in the simulation box.

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Avalanche mixing and the simultaneous collapse of two media under uniaxial stress

Cited by 13 publications

References 46 publications

Multiple Avalanche Processes in Acoustic Emission Spectroscopy: Multibranching of the Energy−Amplitude Scaling

Multiple Avalanche Processes in Acoustic Emission Spectroscopy: Multibranching of the Energy−Amplitude Scaling

Acoustic Emission Spectroscopy: Applications in Geomaterials and Related Materials

Energy exponents of avalanches and Hausdorff dimensions of collapse patterns

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