**FULL TITLE** ASP Conference Series, Vol. **VOLUME**, **YEAR OF PUBLICATION** **NAMES OF EDITORS** Visualization of Scalar Adaptive Mesh Refinement Data

Abstract. Adaptive Mesh Refinement (AMR) is a highly effective computation method for simulations that span a large range of spatiotemporal scales, such as astrophysical simulations, which must accommodate ranges from interstellar to sub-planetary. Most mainstream visualization tools still lack support for AMR grids as a first class data type and AMR code teams use custom built applications for AMR visualization. The Department of Energy's (DOE's) Science Discovery through Advanced Computing (SciDAC) Visualization and Analytics Center for Enabling Technologies (VACET) is currently working on extending VisIt, which is an open source visualization tool that accommodates AMR as a first-class data type. These efforts will bridge the gap between generalpurpose visualization applications and highly specialized AMR visual analysis applications. Here, we give an overview of the state of the art in AMR scalar data visualization research

Visualization of Scalar Adaptive Mesh Refinement Data

Adaptive Mesh Refinement (AMR) is a highly effective computation method for simulations that span a large range of spatiotemporal scales, such as astrophysical simulations, which must accommodate ranges from interstellar to sub-planetary. Most mainstream visualization tools still lack support for AMR grids as a first class data type and AMR code teams use custom built applications for AMR visualization. The Department of Energy's (DOE's) Science Discovery through Advanced Computing (SciDAC) Visualization and Analytics Center for Enabling Technologies (VACET) is currently working on extending VisIt, which is an open source visualization tool that accommodates AMR as a first-class data type. These efforts will bridge the gap between general-purpose visualization applications and highly specialized AMR visual analysis applications. Here, we give an overview of the state of the art in AMR scalar data visualization research

Interactive interrogation of computational mixing data in a virtual environment

Mixing processes are essential in the chemical process industries, including food processors, consumer products corporations, and pharmaceutical manufacturers. The increased use of computational fluid dynamics (CFD) during the design and analysis of static and stirred mixers has provided increased insight into mixing processes. However, the velocities, temperatures, and pressures are insufficient to completely quantify a mixing process. A more complete understanding of mixing processes is given by the material spatial distribution of massless particles as they move through the flow field. This research seeks to combine surround-screen virtual reality and particle tracing of massless particles into an interactive virtual environment to explore the benefits these tools bring to engineers seeking to understand the behavior of fluids in mixing processes. Surround-screen virtual reality (VR) provides a means to immerse users into the mixing data where they can collaboratively investigate the flow features as displayed on a large scale stereo-projection system. This work integrates the particle tracing computation power of the HyperTrace[Superscript TM] commercial software application with new data interrogation techniques made possible by the use of virtual reality technology. Parallel processing to facilitate interactive placement of particles in the flow, volume data selection using a convex hull approach, cutting plane generation, and the integration of voice control and a tablet PC will be presented. Both a stirred mixing vessel and flow through a duct will be used as examples. Finally, the benefits of VR applied to mixing analysis are presented, along with some suggestions for future work in this area

Lattice-Boltzmann simulations of cerebral blood flow

Computational haemodynamics play a central role in the understanding of blood behaviour in the cerebral vasculature, increasing our knowledge in the onset of vascular diseases and their progression, improving diagnosis and ultimately providing better patient prognosis. Computer simulations hold the potential of accurately characterising motion of blood and its interaction with the vessel wall, providing the capability to assess surgical treatments with no danger to the patient. These aspects considerably contribute to better understand of blood circulation processes as well as to augment pre-treatment planning. Existing software environments for treatment planning consist of several stages, each requiring significant user interaction and processing time, significantly limiting their use in clinical scenarios. The aim of this PhD is to provide clinicians and researchers with a tool to aid in the understanding of human cerebral haemodynamics. This tool employs a high performance fluid solver based on the lattice-Boltzmann method (coined HemeLB), high performance distributed computing and grid computing, and various advanced software applications useful to efficiently set up and run patient-specific simulations. A graphical tool is used to segment the vasculature from patient-specific CT or MR data and configure boundary conditions with ease, creating models of the vasculature in real time. Blood flow visualisation is done in real time using in situ rendering techniques implemented within the parallel fluid solver and aided by steering capabilities; these programming strategies allows the clinician to interactively display the simulation results on a local workstation. A separate software application is used to numerically compare simulation results carried out at different spatial resolutions, providing a strategy to approach numerical validation. This developed software and supporting computational infrastructure was used to study various patient-specific intracranial aneurysms with the collaborating interventionalists at the National Hospital for Neurology and Neuroscience (London), using three-dimensional rotational angiography data to define the patient-specific vasculature. Blood flow motion was depicted in detail by the visualisation capabilities, clearly showing vortex fluid ow features and stress distribution at the inner surface of the aneurysms and their surrounding vasculature. These investigations permitted the clinicians to rapidly assess the risk associated with the growth and rupture of each aneurysm. The ultimate goal of this work is to aid clinical practice with an efficient easy-to-use toolkit for real-time decision support

Large Eddy Simulations of complex turbulent flows

In this dissertation a solution methodology for complex turbulent flows of industrial interests is developed using a combination of Large Eddy Simulation (LES) and Immersed Boundary Method (IBM) concepts. LES is an intermediate approach to turbulence simulation in which the onus of modeling of “universal” small scales is appropriately transferred to the resolution of “problem-dependent” large scales or eddies. IBM combines the efficiency inherent in using a fixed Cartesian grid to compute the fluid motion, along with the ease of tracking the immersed boundary at a set of moving Lagrangian points. Numerical code developed for this dissertation solves unsteady, filtered Navier-Stokes equations using high-order accurate (fourth order in space) finite difference schemes on a staggered grid with a fractional step approach. Pressure Poisson equation is solved using a direct solver based on a matrix diagonalization technique. Second order accurate Adams-Bashforth scheme is used for temporal integration of equations. Dynamic mixed model (DMM) is used to model subgrid scale (SGS) terms. It can represent large scale anisotropy and back-scatter of energy from small-to-large scale through scale-similar term and maintain the energy drain through eddy viscosity term whose coefficient is allowed to change with in the computational domain. This code is validated for several bench-mark problems and is demonstrated to solve complex moving geometry problem such as stator-rotor interaction. A number of parametric studies on jets-in-crossflow are performed to understand complex fluid dynamics issues pertaining to film-cooling. These studies included effects of variation of hole-aspect ratio, jet injection angle, free-stream turbulence intensity and free-stream turbulence length scales on the coherent structure dynamics for jets-in-crossflow. Fundamental flow physics and heat transfer issues are addressed by extracting coherent structures from time-dependent three dimensional flow fields of film-cooling by inclined jet and studying their influence on the film-cooled surface heat transfer. A direct method to perform heat transfer calculations in periodic geometries is proposed and applied to internal cooling in rotating ribbed duct. Immersed boundary method is used to render complex geometry of trapped vortex combustor on Cartesian grid and fluid mixing inside trapped vortex cavity is studied in detail

Meshless Isosurface Generation from Multiblock Data

We propose a meshless method for the extraction of high-quality continuous isosurfaces from volumetric data represented by multiple grids, also called ''multiblock'' data sets. Multiblock data sets are commonplace in computational mechanics applications. Relatively little research has been performed on contouring multiblock data sets, particularly when the grids overlap one another. Our algorithm proceeds in two steps. In the first step, we determine a continuous interpolant using a set of locally defined radial basis functions (RBFs) in conjunction with a partition of unity method to blend smoothly between these functions. In the second step, we extract isosurface geometry by sampling points on Marching Cubes triangles and projecting these point samples onto the isosurface defined by our interpolant. A surface splatting algorithm is employed for visualizing the resulting point set representing the isosurface. Because of our method's generality, it inherently solves the ''crack problem'' in isosurface generation. Results using a set of synthetic data sets and a discussion of practical considerations are presented. The importance of our method is that it can be applied to arbitrary grid data regardless of mesh layout or orientation