Predicting and following T1 events in dry foams from geometric features

Abstract

Machine learning techniques have been recently applied in predicting deformation in amorphous materials. In this study, we extract structural features around liquid film vertices from images of flowing 2D foam and apply a multilayer perceptron to predict local yielding. We evaluate their importance in the description of the T1 events and show that a high level of predictability may be achieved using well-chosen combinations of features as the prediction data. The most relevant features are extracted by performing the predictions separately for isolated sets of features, and these findings are verified using principal component analysis. Using this approach, we determine which properties of the images are most important with regard to the physics of the processes. Our findings indicate that film lengths and angles between the liquid films joining at the vertex are the most important features that predict the local yield events. These two features describe 83% of the yield events. As an application, we extract the statistics of event waiting times from the experiment.

Liquid Fraction Effect on Foam Flow through a Local Obstacle

“…Previously [ 13 , 24 ], the value of was obtained based on manual measurements “by eye”, which is a time-consuming process responsible for large errors in calculations. Several methods are used to detect T1 events, including the use of neural networks [ 25 , 26 ]. Here, we propose a simple algorithm to obtain based on a purely statistical approach.…”

Dynamic Length Scale and Weakest Link Behavior in Crystal Plasticity

“…These events are also present in bulk materials as shown by studies investigating acoustic signals emitted by these avalanches [6][7][8][9][10]. In amorphous solids and foams deformation is characterized by similar fluctuations and the irreversible units of plasticity are shear transformation zones [11,12], and T1 events [13][14][15], respectively. Based on this accumulated knowledge one may conclude that all these heterogeneous materials exhibit substantially analogous, stochastic plastic behavior.…”

Relaxation dynamics transition near the dry-wet transition in foams

“…When the liquid fraction is low, foams are referred to as being in a dry state. In a dry state, the bubble shape is polyhedral and local bubble rearrangement is induced by T1 events [25][26][27]. It has also been revealed that the local mobility of bubbles changes sharply around φ = 0.045 in quasi-two-dimensional foams confined between two plates [15].…”