Investigations of quasi-static vortex-structures in 3D sand specimens based on DEM and Helmholtz-Hodge vector field decomposition

The paper presents some three-dimensional simulation results of granular vortex-structures in cohesionless initially dense sand during quasi-static plane strain compression. The sand behaviour was simulated using the discrete element method (DEM). Sand grains were modelled by spheres with contact moments to approximately capture the irregular grain shape. The Helmholtz-Hodge decomposition (HHD) of the displacement vector field from DEM calculations was used. The variational discrete multiscale vector field decomposition allowed for separating a vector field into the sum of three uniquely defined components: curl free, divergence free and harmonic. Vortex-structures were strongly connected to shear localization. They slightly changed along the specimen depth. They localized in locations where shear zones ultimately developed

Decomposição de Helmholtz-Hodge via funções de Green

A Decomposição de Helmholtz-Hodge (HHD) de um campo vetorial permite escrevelo de maneira única como uma soma de três campos vetoriais, um irrotacional, outro solenoidal e um harmônico. Quando o domínio em questão é limitado, a HHD não é definida de maneira única, tradicionalmente, faz-se necessário o uso de condições de fronteira para a obtenção da unicidade, contudo tal imposição pode tornar o resultado da decomposição muito diferente do esperado. Esta dissertação apresenta a Decomposição Natural de Helmholtz-Hodge que é a obtenção da HHD sem imposições de condições de fronteira. Usando funções de Green sobre uma extensão infinita do campo combinada com uma análise de influência que as futuras componentes devem ter, é possível obter uma decomposição única sem exigir condições de fronteira. Eliminando assim, eventuais problemas na decomposição que podem ser gerados pelas imposições de condições

Doctor of Philosophy

dissertationWith modern computational resources rapidly advancing towards exascale, large-scale simulations useful for understanding natural and man-made phenomena are becoming in- creasingly accessible. As a result, the size and complexity of data representing such phenom- ena are also increasing, making the role of data analysis to propel science even more integral. This dissertation presents research on addressing some of the contemporary challenges in the analysis of vector fields--an important type of scientific data useful for representing a multitude of physical phenomena, such as wind flow and ocean currents. In particular, new theories and computational frameworks to enable consistent feature extraction from vector fields are presented. One of the most fundamental challenges in the analysis of vector fields is that their features are defined with respect to reference frames. Unfortunately, there is no single ""correct"" reference frame for analysis, and an unsuitable frame may cause features of interest to remain undetected, thus creating serious physical consequences. This work develops new reference frames that enable extraction of localized features that other techniques and frames fail to detect. As a result, these reference frames objectify the notion of ""correctness"" of features for certain goals by revealing the phenomena of importance from the underlying data. An important consequence of using these local frames is that the analysis of unsteady (time-varying) vector fields can be reduced to the analysis of sequences of steady (time- independent) vector fields, which can be performed using simpler and scalable techniques that allow better data management by accessing the data on a per-time-step basis. Nevertheless, the state-of-the-art analysis of steady vector fields is not robust, as most techniques are numerical in nature. The residing numerical errors can violate consistency with the underlying theory by breaching important fundamental laws, which may lead to serious physical consequences. This dissertation considers consistency as the most fundamental characteristic of computational analysis that must always be preserved, and presents a new discrete theory that uses combinatorial representations and algorithms to provide consistency guarantees during vector field analysis along with the uncertainty visualization of unavoidable discretization errors. Together, the two main contributions of this dissertation address two important concerns regarding feature extraction from scientific data: correctness and precision. The work presented here also opens new avenues for further research by exploring more-general reference frames and more-sophisticated domain discretizations