Mimicking natural structures allows the exploitation of proven design concepts for advanced material solutions. Here, our inspiration comes from the anisotropic closed cell structure of wood. The bubbles in our fiber reinforced foam are elongated using temperature dependent viscosity of methylcellulose and constricted drying. The oriented structures lead to high yield stress in the primary direction; 64 times larger than compared to the cross direction. The closed cells of the foam also result in excellent thermal insulation. The proposed novel foam manufacturing process is trivial to up-scale from the laboratory trial scale towards production volumes on industrial scales.

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Aqueous foams are viscoelastic yield stress fluids. Due to their complex rheology, foam flow around an obstacle embedded in a 2D Hele-Shaw cell has been widely studied. Typically, in such geometry in the moving flow reference frame the flow field of viscoelastic fluids exhibit a quadrupolar structure characterized by a negative wake. Here, we introduce a measuring geometry, new in this context, whereby instead of flowing the foam around the obstacle, we move the obstacle as an intruder inside the foam. The proposed setup makes it possible to independently control the driving velocity and the liquid foam properties, such as the gas fraction and polydispersity. We show that the liquid foam velocity field around the intruder is similar to the one observed in viscoelastic fluids, in particular the emergence of a negative wake, e.g. a velocity overshoot downstream side of the obstacle. However, surprisingly, the intensity of this velocity overshoot decreases with the number of intruder passes, probably related to the evolution of the local disordered structure of the liquid foam.

Finding out when cracks become unstable is at the heart of fracture mechanics. Cracks often grow by avalanches and when a sample fails depends on its past avalanche history. We study the prediction of sample failure in creep fracture under a constant applied stress and induced by initial flaws. Individual samples exhibit fluctuations around a typical rheological response or creep curve. Predictions using the acoustic emission from the intermittent crack growth are not feasible until well beyond the sample-dependent minimum strain rate. Using an optical speckle analysis technique, we show that predictability is possible later because of the growth of the fracture process zone.

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.