In order to obtain insight into a complex vector field, it is often necessary to construct a hierarchical representation of the field. One way to construct such a hierarchy is based on grouping vectors together using certain similarity criteria. In this paper, we present a study of a 2D vector field clustering technique that is based on piecewise linear vector field approximations and an extension of a data clustering method called Normalized Cut (NC). Specifically, two steps are taken to implement the extended NC method. First, a similarity measurement for vector data is defined. Second, an eigenproblem solver is used to find the eigenvector used for partitioning. After the constructio n of first-level clusters, we can obtain a finer-level clustering by recursively applying the same procedure to intermediate clusters. The resulting clusters capture the features around the critical points.

To better understand fluid flow through shale we performed direct numerical simulation (DNS) of flow in a pore geometry obtained from Marcellus shale image data [1].

In this paper, we present a fourth-order algorithm to solve Poisson's equation in two and three dimensions. We use a Cartesian grid, embedded boundary method to resolve complex boundaries. We use a weighted least squares algorithm to solve for our stencils. We use convergence tests to demonstrate accuracy and we show the eigenvalues of the operator to demonstrate stability. We compare accuracy and performance with an established second-order algorithm. We also discuss in depth strategies for retaining higher-order accuracy in the presence of nonsmooth geometries.

In the context of vector field data visualization, it is often desirable to construct a hierarchical data representation. One possibility to construct a hierarchy is based on clustering vectors using cerain similarity criteria. We combine two fundemental approaches to cluster vectors and construct hierarchical vector field representations. For clustering, a locally constructed linear least-squares approxiamation is incorporated into a similarity measure that considers both Euclidean distance between point pairs (for which dependent vector data are given) and difference in vector values. A modified normalized cut (NC) method is used to obtain a near-optimal clustering of a given discrete vector field data set. To obtain a hierarchical representation, the NC method is applied recursively after the construction of coarse-level clusters. We have applied our NC-based segmentation method to simple, analytically defined vector fields as well as discrete vector field data generated by turbulent flow simulation. Our test results indicate that our proposed adaptation of the original NC method is promising method as it leads to segmentation results that capture the qualitative and topological nature of vector field data.

In order to obtain insight into a complex vector field, it is often necessary to construct a hierarchical representation of the field. One way to construct such a hierarchy is based on grouping vectors together using certain similarity criteria. In this paper, we present a study of a 2D vector field clustering technique that is based on piecewise linear vector field approximations and an extension of a data clustering method called Normalized Cut (NC). Specifically, two steps are taken to implement the extended NC method. First, a similarity measurement for vector data is defined. Second, an eigenproblem solver is used to find the eigenvector used for partitioning. After the construction of first-level clusters, we can obtain a finer-level clustering by recursively applying the same procedure to intermediate clusters. The resulting clusters capture the features around the critical points.

We describe a system supporting the interactive exploration of threedimensional scientific data sets in a virtual reality (VR) environment. This system aids a scientist in understanding a data set by interactively placing and manipulating visualization primitives,e. g., isosurfaces or streamlines, and thereby finding features in the data and understanding its overall structure. We discuss how the requirement of interactivity influences the architecture of the visualization system, and how to adapt standard visualization techniques to work under real-time interaction constraints. Though we have implemented our visualization system to work with multiple types of data sets structures -cartesian, tetrahedral, curvilinear-hexahedral and adaptive mesh refinement (AMR)- we will focus on AMR grids and show how their inherent multiresolution structure is useful for interactive visualization.

Summary. We describe a system supporting the interactive exploration of threedimensional scientific data sets in a virtual reality (VR) environment. This system aids a scientist in understanding a data set by interactively placing and manipulating visualization primitives, e. g., isosurfaces or streamlines, and thereby finding features in the data and understanding its overall structure. We discuss how the requirement of interactivity influences the architecture of the visualization system, and how to adapt standard visualization techniques to work under real-time interaction constraints. Though we have implemented our visualization system to work with multiple types of data sets structures - cartesian, tetrahedral, curvilinear-hexahedral and adaptive mesh refinement (AMR) - we will focus on AMR grids and show how their inherent multiresolution structure is useful for interactive visualization.