A Novel Notion of Local and Nonlocal Deformation-Gamuts to Model Elastoplastic Deformation

Localization and nonlocalization are characterized as a measure of degrees of separation between two material points in material’s discrete framework and as a measure of unshared and shared information, respectively, manifested as physical quantities between them, in the material’s continuous domain. A novel equation of motion to model the deformation dynamics of material is proposed. The shared information between two localizations is quantified as nonlocalization via a novel multiscale notion of Local and Nonlocal Deformation-Gamuts or DG Localization and Nonlocalization. Its applicability in continuum mechanics to model elastoplastic deformation is demonstrated. It is shown that the stress–strain curves obtained using local and nonlocal deformation-gamuts are found to be in good agreement with the Ramberg–Osgood equation for the material considered. It is also demonstrated that the cyclic strain hardening exponent and cyclic stress–strain coefficient computed using local and nonlocal deformation-gamuts are comparable with the experimental results as well as the theoretical estimations published in the open literature.

Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning

Systematically characterizing slip behaviours on active faults is key to unraveling the physics of tectonic faulting and the interplay between slow and fast earthquakes. Interferometric Synthetic Aperture Radar (InSAR), by enabling measurement of ground deformation at a global scale every few days, may hold the key to those interactions. However, atmospheric propagation delays often exceed ground deformation of interest despite state-of-the art processing, and thus InSAR analysis requires expert interpretation and a priori knowledge of fault systems, precluding global investigations of deformation dynamics. Here, we show that a deep auto-encoder architecture tailored to untangle ground deformation from noise in InSAR time series autonomously extracts deformation signals, without prior knowledge of a fault’s location or slip behaviour. Applied to InSAR data over the North Anatolian Fault, our method reaches 2 mm detection, revealing a slow earthquake twice as extensive as previously recognized. We further explore the generalization of our approach to inflation/deflation-induced deformation, applying the same methodology to the geothermal field of Coso, California.

A MODEL OF THE WOVEN FABRIC ELONGATION AND BREAKING WITH INFLUENCE OF WARP AND WEFT THREADS UNEVENNESS AND INTERACTION

The article presents the results of development of the woven fabric rectangular sample stretching and tearing computer simulation model. The model allows to set the sample size, elastic modulus and tensile breaking strength of the threads, random variations of these indicators for the warp and weft threads along their length. The modelling algorithm provides for the breakage of the warp threads, the redistribution of stress and strain between the sections of the threads and allows to get a detailed picture of the fabric sample deformation dynamics before it breaks. Examples of modelling results, the influence of the interaction degree between two systems of threads on the features of the strain distribution over the sections of the warp threads are given. The similarity of the simulation results according to the developed algorithm and the finite element method is shown, and the advantages of the proposed algorithm are noted.

A Concise and Geometrically Exact Planar Beam Model for Arbitrarily Large Elastic Deformation Dynamics

The potential of large elastic deformations in control applications, e.g., robotic manipulation, is not yet fully exploited, especially in dynamic contexts. Mainly because essential geometrically exact continuum models are necessary to express these arbitrarily large deformation dynamics, they typically result in a set of nonlinear, coupled, partial differential equations that are unsuited for control applications. Due to this lack of appropriate models, current approaches that try to exploit elastic properties are limited to either small deflection assumptions or quasistatic considerations only. To promote further exploration of this exciting research field of large elastic deflection control, we propose a geometrically exact, but yet concise a beam model for a planar, shear-, and torsion-free case without elongation. The model is derived by reducing the general geometrically exact the 3D Simo–Reissner beam model to this special case, where the assumption of inextensibility allows expressing the couple of planar Cartesian parameters in terms of the curve tangent angle of the beam center line alone. We further elaborate on how the necessary coupling between position-related boundary conditions (i.e., clamped and hinged ends) and the tangent angle parametrization of the beam model can be incorporated in a finite element method formulation and verify all derived expressions by comparison to analytic initial value solutions and an energy analysis of a dynamic simulation result. The presented beam model opens the possibility of designing online feedback control structures for accessing the full potential that elasticity in planar beam dynamics has to offer.

Deep Learning for Autonomous Extraction of Millimeter-scale Deformation in InSAR Time Series

<p>Systematically characterizing slip behaviours on active faults is key to unraveling the physics of tectonic faulting and the interplay between slow and fast earthquakes. Interferometric Synthetic Aperture Radar (InSAR), by enabling measurement of ground deformation at a global scale every few days, may hold the key to those interactions. <br>However, atmospheric propagation delays often exceed ground deformation of interest despite state-of-the art processing, and thus InSAR analysis requires expert interpretation and a priori knowledge of fault systems, precluding global investigations of deformation dynamics. <br>We show that a deep auto-encoder architecture tailored to untangle ground deformation from noise in InSAR time series autonomously extracts deformation signals, without prior knowledge of a fault's location or slip behaviour.<br>Applied to InSAR data over the North Anatolian Fault, our method reaches  2 mm detection, revealing a slow earthquake twice as extensive as previously recognized.<br>We further explore the generalization of our approach to inflation/deflation-induced deformation, applying the same methodology to the geothermal field of Coso, California. </p>